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Abstract

A study was carried to assess the effect of traffic noise pollution on the work efficiency of shopkeepers in Indian urban areas. For this, an extensive literature survey was done on previous research done on similar topics. It was found that personal characteristics, noise levels in an area, working conditions of shopkeepers, type of task they are performing are the most significant factors to study effects on work efficiency. Noise monitoring, as well as a questionnaire survey, was done in Surat city to collect desired data. A total of 17 parameters were considered for assessing work efficiency under the influence of traffic noise. It is recommended that not more than 6 parameters should be considered for ANFIS modeling hence, before opting for the ANFIS modeling, most affecting parameters to work efficiency under the influence of traffic noise, was chosen by Structural Equation Model (SEM). As a result of the SEM model, two ANFIS prediction models were developed to predict the effect on work efficiency under the influence of traffic noise. R squared for model 1, for training data was 0.829 and for testing data, it was 0.727 and R squared for model 2 for training data was 0.828 and for testing data, it was 0.728. These two models can be used satisfactorily for predicting work efficiency under traffic noise environment for open shutter shopkeepers in tier II Indian cities.
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Bibliography

1. Aliabadi M., Golmohammadi R., Khotanlou H., Mansoorizadeh M., Salarpour A. (2015), Artificial neural networks and advanced fuzzy techniques for predicting noise level in the industrial embroidery workrooms, Applied Artificial Intelligence, 29(8): 766–785, doi: 10.1080/08839514.2015.1071090.
2. Azadeh A., Saberi M., Rouzbahman M., Valianpour F. (2015), A neuro-fuzzy algorithm for assessment of health, safety, environment and ergonomics in a large petrochemical plant, Journal of Loss Prevention in the Process Industries, 34: 100–114, doi: 10.1016/j.jlp.2015.01.008.
3. Banerjee D. (2012), Research on road traffic noise and human health in India: review of literature from 1991 to current, Noise & Health, 14(58): 113–118, doi: 10.4103/1463-1741.97255.
4. Bell P. (1980), Effects of heat, noise, and provocation on retaliatory evaluative behavior, Journal of Social Psychology, 110(1): 97–100, doi: 10.1080/00224545.1980.9924227.
5. Central Pollution Control Board, New Delhi, India (2002), Ambient Air Quality Standards in Respect of Noise.
6. Eriksson C., Nilsson M.E., Pershagen G. (2013), Environmental noise and health – Current knowledge and research needs, Swedish Environmetal Protection Agency Report 6553, Stockholm.
7. Ghosh S., Biswas S., Sarkar D., Sarkar P.P. (2014), A novel neuro-fuzzy classification technique for data mining, Egyptian Informatics Journal, 15(3): 129–147, doi: 10.1016/j.eij.2014.08.001.
8. Hancock P.A., Vasmatzidis I. (1998), Human occupational and performance limits under stress: The thermal environment as a prototypical example, Ergonomics, 41(8): 1169–1191, doi: 10.1080/001401398186469.
9. Ivoševic J., Bucak T., Andraši P. (2018), Effects of interior aircraft noise on pilot performance, Applied Acoustics, 139: 8–13, doi: 10.1016/j.apacoust.2018.04.006.
10. Khambete A.K., Christian R.A. (2014), Predicting efficiency of treatment plant by multi parameter aggregated index, Journal of Environmental Research and Development, 8(3): 530–539.
11. Liu W., Zhao T., Zhou W., Tang J. (2018), Safety risk factors of metro tunnel construction in China: An integrated study with EFA and SEM, Safety Science, 105: 98–113, doi: 10.1016/j.ssci.2018.01.009.
12. Mallick Z., Kaleel A.H., Siddiqui A.N. (2009), An expert system for predicting the effects of noise pollution on grass trimming task using fuzzy modeling, International Journal of Applied Environmental Sciences, 4(4): 389–403.
13. Norris M., Lecavalier L. (2010), Evaluating the use of exploratory factor analysis in developmental disability psychological research, Journal of Autism and Developmental Disorders, 40(1): 8–20, doi: 10.1007/ s10803-009-0816-2.
14. Pal D., Bhattacharya D. (2012), Effect of road traffic noise pollution on human work efficiency in government offices, private organizations, and commercial business centres in Agartala City using fuzzy expert system: A case study, Advances in Fuzzy Systems, 2012: Article ID 828593, doi: 10.1155/2012/828593.
15. Quartieri J., Mastorakis N.E., Guarnaccia C., Troisi A., D’Ambrosio S., Iannone G. (2009), Road intersections noise impact on urban environment quality, [in:] Recent Advances in Applied and Theoretical Mechanics. Proceedings of the 5th WSEAS International Conference on Applied and Theoretical Mechanics(MECHANICS’09), Puerto de la Cruz, Tenerife, Spain, pp. 162–171), WSEAS Press.
16. Rashid T. (2012), Fuzzy logic and neuro fuzzy models for direct current motors, International Journal of Engineering Inventions, 1(7): 68–75.
17. Recio A., Linares C., Banegas J.R., Díaz J. (2016), Road traffic noise effects on cardiovascular, respiratory, and metabolic health: An integrative model of biological mechanisms, Environmental Research, 146: 359– 370, doi: 10.1016/j.envres.2015.12.036.
18. Singh R., Ho L.J., Tan H.L., Bell P.A. (2007), Attitudes, personal evaluations, cognitive evaluation and interpersonal attraction: On the direct, indirect and reverse-causal effects, British Journal of Social Psychology, 46: 19–42, doi: 10.1348/014466606X104417.
19. Tandel B., Macwan J.E.M. (2017), Road traffic noise exposure and hearing impairment among traffic policemen in Surat, Western India, Journal of The Institution of Engineers (India), Series A, 98: 101–105, doi: 10.1007/s40030-017-0210-6.
20. Thompson B. (2004), Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications, American Psychological Association,Washington DC.
21. Tiwari S., Babbar R., Kaur G. (2018), Performance evaluation of two ANFIS models for predicting water quality index of River Satluj (India), Advances in Civil Engineering, 2018: Article ID 8971079, doi: 10.1155/2018/8971079.
22. Yadav M., Tandel B. (2019), Exposure effect study of traffic noise on roadside shopkeepers in Surat City, Indian Journal of Environmental Protection, 39: 1038– 1045.
23. Yadav M., Tandel B. (2021), Structural equation model-based selection and strength Co-relation of variables for work performance efficiency under traffic noise exposure, Archives of Acoustics, 46(1): 155–166, doi: 10.24425/aoa.2021.136569.
24. Zaheeruddin (2006), Modelling of noise-induced annoyance: a neuro-fuzzy approach, 2006 IEEE International Conference on Industrial Technology, 2006, pp. 2686–2691, doi: 10.1109/ICIT.2006.372676.
25. Zaheeruddin, Garima (2006), A neuro-fuzzy approach for prediction of human work efficiency in noisy environment, Applied Soft Computing, 6(3): 283–294, doi: 10.1016/j.asoc.2005.02.001.
26. Zaheeruddin, Jain V.K. (2008), An expert system for predicting the effects of speech interference due to noise pollution on humans using fuzzy approach, Expert Systems with Applications, 35, 1978–1988, doi: 10.1016/j.eswa.2007.08.104.
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Authors and Affiliations

Manoj Yadav
1
ORCID: ORCID
Bhaven Tandel
1

  1. Civil Engineering Department, S. V. National Institute of Technology, Surat, India
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Abstract

The aim of the paper is to experimentally determine the scattering matrix S of an example reflective muffler of cylindrical geometry for Helmholtz number exceeding the plane wave propagation. Determining the scattering matrix of an acoustic systems is a new and increasingly used approach in the assessment of reduction of noise propagating inside duct-like elements of heating, ventilation and air conditioning systems (HVAC). The scattering matrix of an acoustic system provides all necessary information on the propagation of wave through it. In case of the analysed reflective silencer, considered as a two-port system, the noise reduction was determined by calculating the transmission loss parameter (TL) based on the scattering matrix (S). Measurements were carried out in two planes of the cross-section of pipes connected to the muffler.

The paper presents results of the scattering matrix evaluation for the wave composed of the plane wave (mode (0,0)) and the first radial mode (0,1), each of which was generated separately using the self-designed and constructed single-mode generator. The gain of proceeding measurements for single modes stems from the fact that theoretically, calculation of the S-matrix does not require, as will be presented in the paper, calculation of the measurement data inverse matrix. Moreover, if single mode sound fields are well determined, it ensures error minimization. The presented measurement results refer to an example of a duct like system with a reflective muffler for which the scattering matrix S was determined. The acoustic phenomena inside such a system can be scaled by the parameter ka.
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Bibliography

1. Åbom M. (1991), Measurement of the scatteringmatrix of acoustical two-ports, Mechanical System and Signal Processing, 5(2): 89–104, doi: 10.1016/0888- 3270(91)90017-Y.
2. Åbom M., Karlsson M. (2010), Can acoustic multiport models be used to predict whistling, 16th AIAA/ CEAS Aeroacoustics Conference, 5: 4285–4292.
3. Atig M., Dalmont J.P., Gilbert J. (2004), Termination of open-end cylindrical tubes at high sound pressure level, Computes Rendus Mecanique, 332(4): 299– 304, doi: 10.1016/j.crme.2004.02.008.
4. Auger J.M., Ville J.M. (1990), Measurement of linear impedance based on the determination of duct eigenvalues by a Fourier-Lommel’s transform, The Journal of the Acoustical Society of America, 88(1): 19–22, doi: 10.1121/1.399942.
5. Auregan Y., Farooqui M., Groby J.P. (2016), Low frequency sound attenuation in a flow duct using a thin slow sound material, The Journal of the Acoustical Society of America, 139(5): 149–153, doi: 10.1121/ 1.4951028.
6. Chen X.X., Zhang X., Morfey C.L., Nelson P.A. (2004), A numerical method for computation of sound radiation from an unflanged duct, Journal of Sound and Vibration, 270(3): 573–586, doi: 10.1016/ j.jsv.2003.09.055.
7. Dalmont J.P., Nederveen C.J., Joly N. (2001), Radiation impedance of tubes with different flanges: numerical and experimental investigations, Journal of Sound and Vibration, 244(3): 505–534, doi: 10.1006/ jsvi.2000.3487.
8. Gerges S.N.Y., Jordan R., Thieme F.A., Bento Coelho J.L., Arenas J.P. (2005), Muffler modeling by transfer matrix method and experimental verification, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 27(2): 132–140, doi: 10.1590/S1678-58782005000200005.
9. Hocter S.T. (1999), Exact and approximate directivity patterns of the sound radiated from a cylindrical duct, Journal of Sound and Vibration, 227(2): 397– 407, doi: 10.1006/jsvi.1999.2351.
10. Hocter S.T. (2000), Sound radiated from a cylindrical duct with Keller’s geometrical theory, Journal of Sound and Vibration, 231(5): 1243–1256, doi: 10.1006/jsvi.1999.2739.
11. Joseph P., Morfey C.L. (1999), Multimode radiation from an unflanged, semi-infinite circular duct, The Journal of the Acoustical Society of America, 105(5): 2590–2600, doi: 10.1121/1.426875.
12. Jurkiewicz J., Snakowska A., Gorazd Ł. (2012), Experimental verification of the theoretical model of sound radiation from an unflanged duct with low mean flow, Archives of Acoustics, 37(2): 227–236, doi: 10.2478/v10168-012-0030-7.
13. Lavrentjev J., Abom M., Boden H. (1995), A measurement method for determining the source data of acoustic two-port sources, Journal of Sound and Vibration, 183(3): 517–531, doi: 10.1006/jsvi.1995.0268.
14. Lee J.K., Oh K.S., Lee J.W. (2020), Methods for evaluating in-duct noise attenuation performance in a muffler design problem, Journal of Sound and Vibration, 464: 114982, doi: 10.1016/j.jsv.2019.114982.
15. Lidoine S., Batard H., Troyes S., Delnevo A., Roger M. (2001), Acoustic radiation modelling of aeroengine intake comparison between analytical and numerical methods, 7th AIAA/CEAS Aeroacoustics Conference and Exhibit, Maastricht, doi: 10.2514/ 6.2001-2140.
16. Munjal M.L. (1987), Acoustics of Ducts and Mufflers with Application to Exhaust and Ventilation System Design, New York: John Wiley & Sons.
17. Sack S., Abom M., Efraimsson G. (2016), On acoustic multi-port characterisation including higher order modes, Acta Acustica United with Acustica, 102(5): 834–850, doi: 10.3813/AAA.918998.
18. Selamet A., Dickey N.S., Novak J.M. (1994), The Herschel-Quincke tube: A theoretical, computational, and experimental investigation, The Journal of the Acoustical Society of America, 96(5): 3177–3185, doi: 10.1121/1.411255.
19. Sinayoko S., Joseph P., McAlpine A. (2010), Multimode radiation from an unflanged, semi-infinite circular duct with uniform flow, The Journal of the Acoustical Society of America, 127(4): 2159–2168, doi: 10.1121/1.3327814.
20. Sitel A., Ville J.M., Foucart F. (2006), Multiload procedure to measure the acoustic scattering matrix of a duct discontinuity for higher order mode propagation conditions, The Journal of the Acoustical Society of America, 120(5): 2478–2490, doi: 10.1121/1.2354040.
21. Snakowska A., Gorazd Ł., Jurkiewicz J., Kolber K. (2016), Generation of a single cylindrical duct mode using a mode synthesizer, Applied Acoustics, 114: 56–70, doi: 10.1016/j.apacoust.2016.07.007.
22. Snakowska A., Jurkiewicz J. (2010), Efficiency of energy radiation from an unflanged cylindrical duct in case of multimode excitation, Acta Acustica united with Acustica, 96(3): 416–424, doi: 10.3813/AAA.918294.
23. Snakowska A., Jurkiewicz J. (2021), A new approach to the theory of acoustic multi-port networks with multimode wave and its application to muffler analysis, Journal of Sound and Vibration, 490: 115722, doi: 10.1016/j.jsv.2020.115722.
24. Snakowska A., Jurkiewicz J., Gorazd Ł. (2017), A hybrid method for determination of the acoustic impedance of an unflanged cylindrical duct for multimode wave, Journal of Sound and Vibration, 396: 325–339, doi: 10.1016/j.jsv.2017.02.040.
25. Song B. H., Bolton J.S. (2000), A transfer-matrix approach for estimating the characteristic impedance and wave numbers of limp and rigid porous materials, The Journal of the Acoustical Society of America, 107(3): 1131–1152, doi: 10.1121/1.428404.
26. Su J., Rupp J., Garmory A., Carrotte J.F. (2015), Measurements and computational fluid dynamics predictions of the acoustic impedance of orifices, Journal of Sound and Vibration, 352: 174–191, doi: 10.1016/ j.jsv.2015.05.009.
27. Zorumski W.E. (1973), Generalized radiation impedances and reflection coefficients of circular and annular ducts, The Journal of the Acoustical Society of America, 54(6): 1667–1673, doi: 10.1121/1.1914466.
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Authors and Affiliations

Łukasz Gorazd
1

  1. AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Kraków, Poland
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Abstract

Microphone array with minimum variance (MVDR) beamformer is a commonly used method for ambient noise suppression. Unfortunately, the performance of the MVDR beamformer is poor in a real reverberant room due to multipath wave propagation. To overcome this problem, we propose three improvements. Firstly, we propose end-fire microphone array that has been shown to have a better directivity index than the corresponding broadside microphone array. Secondly, we propose the use of unidirectional microphones instead of omnidirectional ones. Thirdly, we propose an adaptation of its adaptive algorithm during the pause of speech, which improves its robustness against the room reverberation and deviation from the optimal receiving direction. The performance of the proposed microphone array was theoretically analyzed using a diffuse noise model. Simulation analysis was performed for combined diffuse and coherent noise using the image model of the reverberant room. Real room tests were conducted using a four-microphone array placed in a small office room. The theoretical analysis and the real room tests showed that the proposed solution considerably improves speech quality.
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Authors and Affiliations

Zoran Šarić
1
ORCID: ORCID
Miško Subotić
1
Ružica Bilibajkić
1
Marko Barjaktarović
2
Nebojša Zdravković
3

  1. Laboratory of Acoustics, Life Activities Advancement Center, Serbia
  2. Faculty of Electrical Engineering, University of Belgrade, Serbia
  3. Faculty of Medical Sciences, University of Kragujevac, Serbia
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Abstract

It is well known that nonlinear ultrasound is sensitive to some microstructural characteristics in material. This paper investigates the dependence of the nonlinear ultrasonic characteristic on Al-Cu precipitation in heat-treated 2219-T6 aluminum alloy specimens. The specimens were heat-treated at a constant temperature 155℃ for different exposure times up to 1800 min. The nonlinearity parameter and the changes of precipitates phase were measured for each of the artificially aged specimens. The experimental results show fluctuations in the fractional change in nonlinear parameter (Δβ/β0) and the changes of precipitated phase over the aging time, but with an interesting correlation between the fractional change in nonlinear parameter (Δβ/β0) and the change of precipitate phase over the aging time. Through the experimental data results, the fractional change in nonlinear parameter (Δβ/β0) and the change of precipitate phase over the aging time were fitted curve. Microstructural observations confirmed that those fluctuations are due to the formation and evolution of precipitates that occur in a unique precipitation sequence in this alloy. These results suggest that the nonlinear ultrasonic measurement can be useful for monitoring second phase precipitation in the 2219-T6 aluminum alloy.
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Bibliography

1. Balasubramaniam K., Valluri J.S., Prakash R.V. (2011), Creep damage characterization using a low amplitude nonlinear ultrasonic technique, Materials Characterization, 62(3): 275–286, doi: 10.1016/j.matchar.2010.11.007.
2. Benal M.M., Shivanand H.K. (2007), Effects of reinforcements content and ageing durations on wear characteristics of Al (6061) based hybrid composites, Wear, 262(5–6): 759–763, doi: 10.1016/j.wear.2006.08.022.
3. Buha J., C R.N., Crosky A.G., Hono K. (2007), Secondary precipitation in an Al-Mg-Si-Cu alloy, Acta Materialia, 55(9): 3015–3024, doi: 10.1016/j.actamat.2007.01.006.
4. Cantrell J.H., Yost W.T. (1997), Effect of precipitate coherency strains on acoustic harmonic generation, Journal of Applied Physics, 81(7): 2957–2962, doi: 10.1063/1.364327.
5. Cantrell J.H., Yost W.T. (2000), Determination of precipitate nucleation and growth rates from ultrasonic harmonic generation, Applied Physics Letters, 77(13): 1952–1954, doi: 10.1063/1.1311951.
6. Cantrell J.H., Zhang X.G. (1998), Nonlinear acoustic response from precipitate-matrix misfit in a dislocation network, Journal of Applied Physics, 84(10): 5469–5472, doi: 10.1063/1.368309.
7. Dace G.E., Thompson R.B., Brasche L.J.H., Rehbein D.K., Buck O. (1991), Nonlinear acoustics, a technique to determine microstructural changes in materials, [in:] Review of Progress in Quantitative Nondestructive Evaluation, Thompson D.O., Chimenti D.E. [Eds], Vol. 10B, pp. 1685–1692, Springer, Boston, MA, doi: 10.1007/978-1-4615-3742-7_71.
8. Demir H., Gündüz S. (2009), The effects of aging on machinability of 6061 aluminium alloy, Materials & Design, 30(5): 1480–1483, doi: 10.1016/j.matdes.2008.08.007.
9. Edwards G.A., Stiller K., Dunlop G.L., Couper M.J. (1998), The precipitation sequence in Al-Mg-Si alloys, Acta Materialia, 46(11): 3893–3904, doi: 10.1016/S1359-6454(98)00059-7.
10. Fang X., Song M., Li K., Du Y. (2010), Precipitation sequence of an aged Al-Mg-Si alloy, Journal of Mining and Metallurgy B: Metallurgy, 46(2): 171–180, doi: 10.2298/JMMB1002171F.
11. Granato A., Lüke K. (1956), Theory of mechanical damping due to dislocations, Journal of Applied Physics, 27(6): 583–593, doi: 10.1063/1.1722436.
12. Hikata A., Chick B.B., Elbaum C. (1965), Dislocation contribution to the second harmonic generation of ultrasonic waves, Journal of Applied Physics, 36(1): 229–236, doi: 10.1063/1.1713881.
13. Kim C.S., Jhang K.Y. (2012), Fatigue-induced micro-damage characterization of austenitic stainless steel 316 using innovative nonlinear acoustics, Chinese Physics Letters, 29(6): 060702, doi: 10.1088/0256-307x/29/6/060702.
14. Kim J., Jhang K.Y. (2013), Evaluation of ultrasonic nonlinear characteristics in heat-treated aluminum alloy (Al-Mg-Si-Cu), Advances in Materials Science and Engineering, 2013: Article ID 407846, doi: 10.1155/2013/407846.
15. Kim J., Song D.G., Jhang K.Y. (2016), Absolute measurement and relative measurement of ultrasonic nonlinear parameters, Research in Nondestructive Evaluation, 28(4): 211–225 doi: 10.1080/09349847.2016.1174322.
16. Li P., Yost W.T., Cantrell J.H., Salama K. (1985), Dependence of acoustic nonlinearity parameter on second phase precipitates of aluminum alloys, IEEE 1985 Ultrasonics Symposium, pp. 1113–1115, doi: 10.1109/ULTS-YM.1985.198690.
17. Metya A., Ghosh M., Parida N., Sagar S.P. (2008), Higher harmonic analysis of ultrasonic signal for ageing behaviour study of C-250 grade maraging steel, NDT & E International, 41(6): 484–489, doi: 10.1016/j.ndteint.2008.01.008.
18. Miao W.F., Laughlin D.E. (1999), Precipitation hardening in aluminum alloy 6022, Scripta Materialia, 40(7): 873–878, doi: 10.1016/S1359-6462(99)00046-9.
19. Mondal C., Mukhopadhyay A., Sarkar R. (2010), A study on precipitation characteristics induced str- ength variation by nonlinear ultrasonic parameter, Journal of Applied Physics, 108(12): 124910, doi: 10.1063/1.3524526.
20. Ozturk F., Sisman A., Toros S., Kilic S., Picu R.C. (2010), Influence of aging treatment on mechanical properties of 6061 aluminum alloy, Materials & Design, 31(2): 972–975, doi: 10.1016/j.matdes.2009.08.017.
21. Park J., Kim M., Chi B., Jang C. (2013), Corre- lation of metallurgical analysis & higher harmonic ul- trasound response for long term isothermally aged and crept FM steel for USC TPP turbine rotors, NDT & E International, 54: 159–165, doi: 10.1016/j.ndteint.2012.10.008.
22. Rajasekaran S., Udayashankar N.K., Nayak J. (2012), T4 and T6 treatment of 6061 Al-15 Vol.% SiCP composite, ISRN Materials Science, 2012: 1–5, doi: 10.5402/2012/374719.
23. Ren G., Kim J., Jhang K.Y. (2015), Relationship between second- and third-order acoustic nonlinear parameters in relative measurement, Ultrasonics, 56: 539–544, doi: 10.1016/j.ultras.2014.10.009.
24. Siddiqui R.A., Abdullah H.A., Al-Belushi K.R. (2000), Influence of aging parameters on the mechani- cal properties of 6063 aluminium alloy, Journal of Materials Processing Technology, 102(1–3): 234–240, doi: 10.1016/S0924-0136(99)00476-8.
25. Troeger L.P., Starke, Jr E.A. (2000), Microstructural and mechanical characterization of a superplas- tic 6xxx aluminum alloy, Materials Science and Engineering: A, 277(1–2): 102–113, doi: 10.1016/S0921-5093(99)00543-2.
26. Viswanath A., Rao B.P.C., Mahadevan S., Parameswaran P., Jayakumar T., Raj B. (2011), Nondestructive assessment of tensile properties of cold worked AISI type 304 stainless steel using nonlin- ear ultrasonic technique, Journal of Materials Processing Technology, 211(3): 538–544, doi: 10.1016/j.jmatprotec.2010.11.011.
27. Xiang Y., Deng M., Xuan F.Z. (2014), Thermal degradation evaluation of HP40Nb alloy steel after long term service using a nonlinear ultrasonic technique, Journal of Nondestructive Evaluation, 33: 279– 287, doi: 10.1007/s10921-013-0222-8.
28. Yassar R.S., Field D.P., Weiland H. (2011), Transmission electron microscopy and differential scan- ning calorimetry studies on the precipitation sequence in an Al-Mg-Si alloy: AA6022, Journal of Materials Research, 20(10): 2705–2711, doi: 10.1557/JMR.2005.0330.
29. You J., Wu Y.X., Gong H., Ahmad A.S, Lei Y. (2019), Determination of the influence of post – heat treatment on second-phase of Al 2219-T6 alloy using ultrasonic non-linear measurement technique, Insight – Non-Destructive Testing and Condition Monitoring, 61(4): 209–213, doi: 10.1784/insi.2019.61.4.209.
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Authors and Affiliations

Jun You
1 2 3
Yunxin Wu
1 4 2 3
Hai Gong
1 4 2 3

  1. Research Institute of Light Alloys, Central South University, Changsha, 410083, China
  2. Nonferrous Metal Oriented Advanced Structural Material and Manufacturing Cooperative Innovation Center, Central South University, Changsha, 410083, China
  3. State Key Laboratory of High-Performance Complex Manufacturing, Central South University, Changsha, 410083, China
  4. School of Mechanical and Electrical Engineering, Central South University, Changsha, 410083, China
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Abstract

The problem of reducing noise in transportation is an important research field to prevent accidents and to provide a civilised environment for people. A material that has recently attracted attention in research to reduce noise is acoustic metamaterial, and most of the research projects so far have been limited to the case of static media without flow. We have studied the sound transmission properties of the acoustic metamaterials with turbulent flow to develop the acoustic metamaterials that are used in transportation. In this paper, the effects of geometrical structure, convection, and eddy on sound propagation in the acoustic metamaterials with turbulent flow are investigated, and the relationships between them are analysed. The effects of convection and eddy reduce the resonant strength of the sound transmission loss resulting from the unique geometry of the acoustic metamaterials, but move the resonant frequencies to opposite directions. In addition, when the convective effect and the eddy effect of the airflow, as well as the intrinsic interaction effect generated from the unique geometrical structure of the acoustic metamaterials cannot be ignored, they exhibit competition phenomena with each other, resulting in a widening of the resonance peak. As a result, these three effects cause the shift of the resonance frequency of the sound transmission loss and the widening of the resonance peak. The results of this study show that even in the case of turbulent flow, the metamaterials can be used for transportation by properly controlling its geometric size and shape.
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Bibliography

1. Ananthan V., Bernicke P., Akkermans R., Hu T., Liu P. (2020), Effect of porous material on trailing edge sound sources of a lifting airfoil by zonal oversetles, Journal of Sound and Vibration, 480: 115386, doi: 10.1016/j.jsv.2020.115386.
2. Bok E., Park J.J., Choi H., Han C.K., Wright O.B., Lee S.H. (2018), Metasurface for water-to-air sound transmission, Physical Review Letters, 120(4): 044302, doi: 10.1103/PhysRevLett.120.044302.
3. Brookea D.C., Umnova O., Leclaire P., Dupont T. (2020), Acoustic metamaterial for low frequency sound absorption in linear and nonlinear regimes, Journal of Sound and Vibration, 485: 115585, doi: 10.1016/j.jsv.2020.115585.
4. Carpio A.R., Avallone F., Ragni D., Snellen M., van der Zwaag S. (2019), Mechanisms of broadband noise generation on metal foam edges, Physics of Fluids, 31(10): 105110, doi: 10.1063/1.5121248.
5. Chaitanya P., Joseph P., Ayton L.J. (2020), Leading edge profiles for the reduction of airfoil interaction noise, AIAA Journal, 58(3): 1118–1129, doi: 10.2514/1.J058456.
6. Deuse M., Sandberg R.D. (2020), Different noise generation mechanisms of a controlled diffusion aerofoil and their dependence on Mach number, Journal of Sound and Vibration, 476: 115317, doi: 10.1016/j.jsv.2020.115317.
7. Du L., Holmberg A., Karlsson M., Åbom M. (2016), Sound amplification at a rectangular t-junction with merging mean flows, Journal of Sound and Vibration, 367: 69–83, doi: 10.1016/j.jsv.2015.12.042.
8. Fan L., Chen Z., Zhang S., Ding J., Li X., Zhang H. (2015), An acoustic metamaterial composed of multi-layer membrane-coated perforated plates for low-frequency sound insulation, Applied Physics Letters, 106(15): 151908, doi: 10.1063/1.4918374.
9. Gikadi J., Föller S., Sattelmayer T. (2014), Impact of turbulence on the prediction of linear aeroacoustic interactions: Acoustic response of a turbulent shear layer, Journal of Sound and Vibration, 333(24): 6548–6559, doi: 10.1016/j.jsv.2014.06.033.
10. Gu Z., Gao H., Liu T., Li Y., Zhu J. (2020), Dopant-modulated sound transmission with zero index acoustic metamaterials, The Journal of the Acoustical Society of America, 148(3): 1636–1641, doi: 10.1121/10.0001962.
11. Jiang X., Li Y., Zhang L.K. (2017), Thermoviscous effects on sound transmission through a metasurface of hybrid resonances, The Journal of the Acoustical Society of America, 141(4): EL363–EL368, doi: 10.1121/1.4979682.
12. Jung J.W., Kim J.E., Lee J.W. (2018), Acoustic metamaterial panel for both uid passage and broadband soundproofing in the audible frequency range, Applied Physics Letters, 112(4): 041903, doi: 10.1063/1.5004605.
13. Kundu P.K., Cohen I.M., Dowling D. (2012), Fluid mechanics, 5th ed., pp. 564–571, Elsevier, doi: 10.1016/C2009-0-63410-3.
14. Kusano K., Yamada K., Furukawa M. (2020), Aeroacoustic simulation of broadband sound generated from low-Mach-number flows using a lattice Boltzmann method, Journal of Sound and Vibration, 467: 115044, doi: 10.1016/j.jsv.2019.115044.
15. Li Y., Assouar B.M. (2016), Acoustic metasurfacebased perfect absorber with deep subwavelength thickness, Applied Physics Letters, 108(6): 063502, doi: 10.1063/1.4941338.
16. Lu K., Wu J., Guan D., Gao N., Jing L. (2016), A lightweight low-frequency sound insulation membrane- type acoustic metamaterial, AIP Advances, 6(2): 025116, doi: 10.1063/1.4942513.
17. Menter F. (1994), Two-equation eddy-viscosity turbulence models for engineering applications, AIAA Journal, 32(8): 1598–1605, doi: 10.2514/3.12149.
18. Nardini M., Sandberg R.D., Schlanderer S.C. (2020), Computational study of the effect of structural compliance on the noise radiated from an elastic trailing-edge, Journal of Sound and Vibration, 485: 115533, doi: 10.1016/j.jsv.2020.115533.
19. Ostashev V.E., Wilson D.K. (2016), Acoustics in Moving Inhomogeneous Media, 2ed., pp. 27–62, Taylor and Francis, doi: 10.1201/b18922.
20. Park J.J., Park C.M., Lee K.J., Lee S.H. (2015), Acoustic superlens using membrane-based metamaterials, Applied Physics Letters, 106(5): 051901, doi: 10.1063/1.4907634.
21. Pierce A.D. (2019), Acoustics: An Introduction to Its Physical Principles and Applications, 3rd ed., pp. 68– 70, Springer, doi: 10.1007/978-3-030-11214-1.
22. Qu S., Sheng P. (2020), Minimizing indoor sound energy with tunable metamaterial surfaces, Physical Review Applied, 14(3): 034060, doi: 10.1103/PhysRevApplied.14.034060.
23. Romani G., Ye Q.Q., Avallone F., Ragni D., Casalino D. (2020), Numerical analysis of fan noise for the NOVA boundary-layer ingestion configuration, Aerospace Science and Technology, 96: 105532, doi: 10.1016/j.ast.2019.105532.
24. Su H., Zhou X., Xu X., Hu G. (2014), Experimental study on acoustic subwavelength imaging of holeystructured metamaterials by resonant tunnelling, The Journal of the Acoustical Society of America, 135(4): 1686–1691, doi: 10.1121/1.4868395.
25. Sui N., Yan X., Huang T.Y., Xu J., Yuan F.G., Jing Y. (2015), A lightweight yet sound-proof honeycomb acoustic metamaterial, Applied Physics Letters, 106(17): 171905, doi: 10.1063/1.4919235.
26. Szoke M., Fiscaletti D., Azarpeyvand M. (2018), Effect of inclined transverse jets on trailing-edge noise generation, Physics of Fluids, 30(8): 085110, doi: 10.1063/1.5044380.
27. Szoke M., Fiscaletti D., Azarpeyvand M. (2020), Uniform flow injection into a turbulent boundary layer for trailing edge noise reduction, Physics of Fluids, 32(8): 085104, doi: 10.1063/5.0013461.
28. Tang H., Lei Y.L., Li X.Z. (2019), An acoustic source model for applications in low Mach number turbulent flows, such as a large-scale wind turbine blade, Energies, 12(23): 4596, doi: 10.3390/en12234596.
29. Wang X., Zhao H., Luo X., Huang Z. (2016), Membrane-constrained acoustic metamaterials for low frequency sound insulation, Applied Physics Letters, 108(4): 041905, doi: 10.1063/1.4940717.
30. Wang Y., Thompson D., Hu Z. (2019), Effect of wall proximity on the flow over a cube and the implications for the noise emitted, Physics of Fluids, 31(7): 077101, doi: 10.1063/1.5096072.
31. Yang Z.J. et al. (2015), Topological acoustics, Physical Review Letters, 114(11): 114301, doi: 10.1103/Phys RevLett.114.114301.
32. Yao H., Davidson L. (2019), Vibro-acoustics response of a simplified glass window excited by the turbulent wake of a quarter-spherocylinder body, The Journal of the Acoustical Society of America, 145(5): 3163–3176, doi: 10.1121/1.5109548.
33. Zheng M.Y., Park C., Liu X.N., Zhu R., Hu G.K., Kim Y.Y. (2020), Non-resonant metasurface for broadband elastic wave mode splitting, Applied Physics Letters, 116(17): 171903, doi: 10.1063/5.0005408.
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Authors and Affiliations

Myong Chol Pak
1
Kwang-Il Kim
1
Hak Chol Pak
1
Kwon Ryong Hong
2

  1. Department of Physics, Kim Il Sung University, Taesong District, Pyongyang, Democratic People’s Republic of Korea
  2. Institute of Natural Sciences, Kim Il Sung University, Taesong District, Pyongyang, Democratic People’s Republic of Korea
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Abstract

In this paper, we investigate a problem on reflection and transmission of plane-waves at an interface between two dissimilar half-spaces of a transversely isotropic micropolar piezoelectric material. The entire model is assumed to rotate with a uniform angular velocity. The governing equations of rotating and transversely isotropic micropolar piezoelectric medium are specialized in a plane. Plane-wave solutions of two-dimensional coupled governing equations show the possible propagation of three coupled plane-waves. For an incident plane-wave at an interface between two dissimilar half-spaces, three reflected and three transmitted waves propagate with distinct speeds. The connections between the amplitude ratios of reflected and transmitted waves are obtained. The expressions for the energy ratios of reflected and transmitted waves are also obtained. A numerical example of the present model is considered to illustrate the effects of rotation on the speeds and energy ratios graphically.
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Authors and Affiliations

Baljeet Singh
1
Asha Sangwan
2
Jagdish Singh
3

  1. Department of Mathematics, Post Graduate Government College, Sector 11, Chandigarh, 160011, India
  2. Department of Mathematics, Government College, Sampla, Rohtak, 124001, Haryana, India
  3. Department of Mathematics, Maharshi Dayanand University, Rohtak, 124001, Haryana, India
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Abstract

Electric guitar manufacturers have used tropical woods in guitar production for decades claiming it as beneficiary to the quality of the instruments. These claims have often been questioned by guitarists but now, with many voices raising concerns regarding the ecological sustainability of such practices, the topic becomes even more important. Efforts to find alternatives must begin with a greater understanding of how tonewood affects the timbre of an electric guitar. The presented study examined how the sound of a simplified electric guitar changes with the use of various wood species. Multiple sounds were recorded using a specially designed test setup and their analysis showed differences in both spectral envelope and the generated signal level. The differences between the acoustic characteristics of tones produced by the tonewood samples explored in the study were larger than the just noticeable differences reported for the respective characteristics in the literature. To verify these findings an informal listening test was conducted which showed that sounds produced with different tonewoods were distinguishable to the average listener.
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Bibliography

1. Ahvenainen P. (2018), Anatomy and mechanical properties of woods used in electric guitars, IAWA Journal, 40(1): 106–S6, doi: 10.1163/22941932-40190218.
2. Ahmed S.A., Adamopoulos S. (2018), Acoustic properties of modified wood under different humid conditions and their relevance for musical instruments, Applied Acoustics, 140: 92–99, doi: 10.1016/j.apacoust.2018.05.017.
3. Bennett B. (2016), The sound of trees: wood selection in guitars and other chordophones, Economic Botany, 70(1): 49–63, doi: 10.1007/s12231-016-9336-0.
4. Carral S. (2011), Determining the just noticeable difference in timbre through spectral morphing: a trombone example, Acta Acustica united with Acustica, 97(3): 466–476, doi: 10.3813/AAA.918427.
5. Fleischer H., Zwicker T. (1998), Mechanical vibrations of electric guitars, Acta Acustica united with Acustica, 84(4): 758–765.
6. Fletcher N., Rossing T. (1998), The Physics of Musical Instruments, doi: 10.1007/978-0-387-21603-4.
7. Green D.M. (1993), Auditory Intensity Discrimination, Springer Handbook of Auditory Research, Vol. 3, Springer, New York, doi: 10.1007/978-1-4612-2728-1_2.
8. Jansson E.V. (1983), Acoustics for the Guitar Maker, Function, Construction and Quality of the Guitar, Publication No. 38 of the Royal Swedish Academy of Music, Stockholm.
9. Koch M. (2001), Building Electric Guitars: How to Make Solid-Body, Hollow-Body and Semi-Acoustic Electric Guitars and Bass Guitars, Koch Verlag, Gleisdorf.
10. Martinez-Reyes J. (2015), Mahogany intertwined: Enviromateriality between Mexico, Fiji, and the Gibson Les Paul, Journal of Material Culture, 20(3): 313– 329, doi: 10.1177/1359183515594644.
11. Ozimek E. (2002), Sound and its Perception. Physical and Psychoacoustic Aspects [in Polish: Dzwiek i jego percepcja. Aspekty fizyczne i psychoakustyczne], Polish Scientific Publishers PWN, Warsaw.
12. Paté A., Le Carrou J., Fabre B. (2013), Ebony vs. Rosewood: experimental investigation about the influence of the fingerboard on the sound of a solid body electric guitar, [in:] Proceedings of the Stockholm Musical Acoustics Conference (SMAC), Stockholm (Sweden), pp. 182–187.
13. Paté A., Le Carrou J., Navarret B., Dubois D., Fabre B. (2015), Influence of the electric guitar’s fingerboard wood on guitarists’ perception, Acta Acustica united with Acustica, 101(2): 347–359, doi: 10.3813/AAA.918831.
14. Puszynski J. (2014), String-wood feedback in electrics string instruments, Annals of Warsaw University of Life Sciences – SGGW Land Reclamation, 2014(85): 196–199.
15. Puszynski J., Molinski W., Preis A. (2015), The effect of wood on the sound quality of electric string instruments, Acta Physica Polonica, 127(1): 114–116, doi: 10.12693/APhysPolA.127.114.
16. Schubert E., Wolfe J. (2006), Does timbral brightness scale with frequency and spectral centroid?, Acta Acoustica United with Acustica, 92(5): 820–825.
17. Torres J., Boullosa R. (2009), Influence of the bridge on the vibrations of the top plate of a classical guitar, Applied Acoustics, 70(11–12): 1371–1377, doi: 10.1016/j.apacoust.2009.07.002.
18. Torres J., Boullosa R. (2011), Radiation efficiency of a guitar top plate linked with edge or corner modes and intercell cancellation, The Journal of the Acoustical Society of America, 130(1): 546–556, doi: 10.1121/1.3592235.
19. Tzanetakis G., Cook P. (2002), Musical genre classification of audio signals, 2002 IEEE Transactions on Speech and Audio Processing, 10(5): 293–302, doi: 10.1109/TSA.2002.800560.
20. Ulrich R., Vorberg D. (2009), Estimating the difference limen in 2AFC tasks: pitfalls and improved estimators, Attention, Perception, & Psychophysics, 71(6): 1219–1227, doi: 10.3758/app.71.6.1219.
21. Wilkowski J., Michalowski P., Czarniak P., Górski J., Podziewski P., Szymanowski K. (2014), Influence of spruce, wenge and obeche wood used for electric guitar prototype on selected sound properties, Annals of Warsaw University of Life Sciences – SGGW. Forestry and Wood Technology, 85: 235–240.
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Authors and Affiliations

Jan Jasiński
1
Stanisław Oleś
1
Daniel Tokarczyk
1
Marek Pluta
1

  1. Department of Mechanics and Vibroacoustics, AGH University of Science and Technology, Cracow, Poland
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Abstract

Various types of passive sonar systems are used to detect submarines. These activities are complex and demanding. Therefore, computer simulations are most often used at the design stage of these systems. For this reason, it is also necessary to simulate the acoustic ambient noise of the sea. The article proposes a new numerical model of surface and quasi-spherical sea noise and presents its statistical parameters. The results of the application of the developed noise model to analyse the received signals of the DIFAR sonobuoy are also presented.
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Bibliography

1. Barclay D.R., Buckingham M.J. (2014), On the spatial properties of ambient noise in the Tonga Trench, including effects of bathymetric shadowing, The Journal of the Acoustical Society of America, 136(5): 2497–2511, doi: 10.1121/1.4896742.
2. Buckingham M.J. (2012), Cross-correlation in bandlimited ocean ambient noise fields, The Journal of the Acoustical Society of America, 131(4): 2643–2657, doi: 10.1121/1.3688506.
3. Burdick W.S. (1984), Underwater Acoustic System Analysis, Prentice-Hall, Englewood Cliffs, NJ.
4. Cohen J. (1988), Statistical Power Analysis for the Behavioral Sciences, 2nd ed., Lawrence Erlbaum Associates, Publishers.
5. Crocker M.J. (1998), Handbook of Acoustics, John Wiley & Sons.
6. Cron B.F., Sherman C.H. (1962), Spatial-correlation functions for various noise models, The Journal of the Acoustical Society of America, 34(11): 1732–1736, doi: 10.1121/1.1909110.
7. Cron B.F., Sherman C.H. (1965), Addendum: Spatial correlation functions for various noise models [J. Acoust. Soc. Am., 34: 1732–1736 (1962)], The Journal of the Acoustical Society of America, 38(4): 885, doi: 10.1121/1.1909826.
8. Franks L.E. (1981), Signal Theory. Revised Edition, Dowden & Culver, Inc.: Stroudsburg, PA.
9. Grelowska G., Kozaczka E., Kozaczka S., Szymczak W. (2013), Underwater noise generated by small ships in the shallow sea, Archives of Acoustics, 38(3): 351–356, doi: 10.2478/aoa-2013-0041.
10. Jagodzinski Z. (1961), Radionavigation Systems [in Polish], Wydawnictwo MON, Warszawa.
11. Klusek Z., Lisimenka A. (2004), Characteristics of underwater noise generated by single breaking wave, Hydroacoustics, 7: 107–114.
12. Klusek Z., Lisimenka A. (2016), Seasonal and diel variability of the underwater noise in the Baltic Sea, The Journal of the Acoustical Society of America, 139(4): 1537–1547, doi: 10.1121/1.4944875.
13. Kochanska I., Nissen I., Marszal J. (2018), A method for testing the wide-sense stationary uncorrelated scattering assumption fulfillment for an underwater acoustic channel, The Journal of the Acoustical Society of America, 143(2): EL116–EL120, doi: 10.1121/1.5023834.
14. Kozaczka E., Grelowska G. (2011), Shipping low frequency noise and its propagation in shallow water, Acta Physica Polonica A, 119(6A): 1009–1012, doi: 10.12693/APhysPolA.119.1009.
15. Lyons R.G. (2004), Understanding Digital Signal Processing, 2nd ed., Prentice Hall, Inc. 16. Mallet A.L. (1975), Underwater Direction Signal Processing System, US Patent No 3,870,989.
17. Ren C., Huang Y. (2020), A spatial correlation model for broadband surface noise, The Journal of the Acoustical Society of America, 147(2): EL99–EL105, doi: 10.1121/10.0000710.
18. Rudnicki M., Marszal J., Salamon R. (2020), Impact of spatial noise correlation on bearing accuracy in DIFAR systems, Archives of Acoustics, 45(4): 709–720, doi: 10.24425/aoa.2020.135277.
19. Salamon R. (2006), Sonar systems [in Polish], Gdanskie Towarzystwo Naukowe, Gdansk, Poland.
20. Schmidt J.H., Schmidt A., Kochanska I. (2018), Multiple-Input Multiple-Output Technique for Underwater Acoustic Communication System, [In:] Proceedings of 2018 Joint Conference – Acoustics, Ustka, Poland, 2018, IEEE Xplore Digital Library, pp. 280– 283, doi: 10.1109/acoustics.2018.8502439.
21. Urick R.J. (1983), Principles of Underwater Sound, 3rd ed., Peninsula Pub.
22. Urick R.J. (1986), Ambient Noise in the Sea, 2nd ed., Peninsula Pub.
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Authors and Affiliations

Mariusz Rudnicki
1
Roman Salamon
1
Jacek Marszal
1

  1. Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Department of Sonar Systems, Gdansk, Poland

Abstract

The paper contains the abstracts of papers presented during 67th Open Seminar on Acoustics September 14–17, 2021.
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Abstract

In this paper, a four-pole system matrix for evaluating acoustic performance (STL) is derived using a decoupled numerical method. During the optimization process, a simulated annealing (SA) method, which is a robust scheme utilized to search for the global optimum by imitating a physical annealing process, is used. Prior to dealing with a broadband noise, to recheck the SA method’s reliability, the STL’s maximization relative to a one-tone noise (400Hz) is performed. To assure the accuracy of muffler’s mathematical model, a theoretical analysis of one-diffuser muffler is also confirmed by an experimental data. Subsequently, the optimal results of three kinds of mufflers (muffler A: one diffuser; muffler B: two diffusers; muffler C: three diffusers) have also been compared. Results reveal that the acoustical performance of mufflers will increase when the number of diffusers installed at the muffler inlet increases
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Bibliography

1. Bie D.A., Hansen C.H. (1988), Engineering Noise Control: Theory and Practice, Unwin Hyman, London.
2. Chang Y.C., Yeh L.J., Chiu M.C. (2004), Numerical studies on constrained venting system with side inlet/outlet mufflers by GA optimization, Acta Acustica united with Acustica, 90(6): 1159–1169.
3. Chang Y.C., Yeh L.J., Chiu M.C. (2005a), Shape optimization on double-chamber mufflers using genetic algorithm, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219(1): 31–42, doi: 10.1243/095440605X8351.
4. Chang Y.C., Yeh L.J., Chiu M.C., Lai G.J. (2005b), Shape optimization on constrained singlelayer sound absorber by using GA method and mathematical gradient methods, Journal of Sound and Vibration, 1286(4–5): 941–961, doi: 10.1016/j.jsv.2004.10.039.
5. Chiu M.C. (2009a), Optimization of equipment allocation and sound-barriers shape in a multi-noise plant by using simulated annealing, Noise & Vibration Worldwide, 40(7): 23–35, doi: 10.1260/095745609788921857.
6. Chiu M.C. (2009b), Simulated annealing optimization on multi-chamber mufflers hybridized with perforated plug-inlet under space constraints, Archives of Acoustics, 34(3): 305–343.
7. Chiu M.C. (2010a), Numerical optimization of a threechamber muffler hybridized with a side inlet and a perforated tube by SA method, Journal of Marine Science and Technology, 18(4): 484–495, doi: 10.51400/2709-6998.1897.
8. Chiu M.C. (2010b), Optimal design of multi-chamber mufflers hybridized with perforated intruding inlets and resonated tube using simulated annealing, Journal of Vibration and Acoustics, 132(5): Article ID 054503, doi: 10.1115/1.4001514.
9. Chiu M.C. (2012), Noise elimination of a multi-tone broadband noise with hybrid Helmholtz mufflers using a simulated annealing method, Archives of Acoustics, 37(4): 489–498, doi: 10.2478/v10168-012-0061-0.
10. Chiu M.C. (2013), Numerical assessment for a broadband and tuned noise using hybrid mufflers and a simulated annealing method, Journal of Sound and Vibration, 332(12): 2923–2940, doi: 10.1016/j.jsv.2012.12.039.
11. Chiu M.C. (2014a), Acoustical treatment of multi-tone broadband noise with hybrid side-branched mufflers using a simulated annealing method, Journal of Low Frequency Noise Vibration and Active Control, 33(1): 79–112, doi: 10.1260/0263-0923.33.1.79.
12. Chiu M.C. (2014b), Optimal design on one-layer closefitting acoustical hoods using a simulated annealing method, Journal of Marine Science and Technology, 22(2): 211–217, doi: 10.6119/JMST-013-0503-1.
13. Chiu M.C., Chang Y.C. (2014), An assessment of high-order-mode analysis and shape optimization of expansion chamber mufflers, Archives of Acoustics, 39(4): 489–499, doi: 10.2478/aoa-2014-0053.
14. Kirkpatrick S., Gelatt C.D., Vecchi M.P. (1983), Optimization by simulated annealing, Science, 220 (4598): 671–680, doi: 10.1126/science.220.4598.671.
15. Metropolis A., Rosenbluth W., Rosenbluth M.N., Teller H., Teller E. (1953), Equation of static calculations by fast computing machines, The Journal of Chemical Physics, 21(6): 1087–1092, doi: 10.1063/1.1699114.
16. Munjal M.L. (1987), Acoustics of Ducts and Mufflers with Application to Exhaust and Ventilation System Design, John Wiley & Sons, New York.
17. Munjal M.L., Rao K.N., Sahasrabudhe A.D. (1987), Aeroacoustic analysis of perforated muffler components, Journal of Sound and Vibration, 114(2): 173– 188, doi: 10.1016/S0022-460X(87)80146-3.
18. Peat K.S. (1988), A numerical decoupling analysis of perforated pipe silencer elements, Journal of Sound and Vibration, 123(2), 199–212.
19. Sullivan J.W. (1979a), A method of modeling perforated tube muffler components I: theory, The Journal of the Acoustic Society of America, 66(3): 772–778, doi: 10.1121/1.383679.
20. Sullivan J.W. (1979b), A method of modeling perforated tube muffler components II: theory, The Journal of the Acoustic Society of America, 66(3): 779–788, doi: 10.1121/1.383680.
21. Sullivan J.W., Crocker M.J. (1978), Analysis of concentric tube resonators having unpartitioned cavities, The Journal of the Acoustic Society of America, 64(1): 207–215, doi: 10.1121/1.381963.
22. Yeh L.J., Chang Y.C., Chiu M.C., Lai G.J. (2004), GA optimization on multi-segments muffler under space constraints, Applied Acoustics, 65(5): 521–543, doi: 10.1016/j.apacoust.2003.10.010.
23. Yeh L.J., Chang Y.C., Chiu M.C. (2006), Numerical studies on constrained venting system with reactive mufflers by GA optimization, International Journal for Numerical Methods in Engineering, 65(8): 1165–1185, doi: 10.1002/nme.1476.
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Authors and Affiliations

Min-Chie Chiu
1
Ho-Chih Cheng
2

  1. Department of Mechanical and Materials Engineering, Tatung University, Taiwan, R.O.C.
  2. Department of Intelligent Automation Engineering, Chung Chou University of Science and Technology, Taiwan, R.O.C.
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Abstract

Lined ducts with porous materials are found in many industrial applications. To understand and simulate the acoustic behaviour of these kinds of materials, their intrinsic physical parameters must be identified. Recent studies have shown the reliability of the inverse approach for the determination of these parameters. Therefore, in the present paper, two inverse techniques are proposed: the first is the multilevel identification method based on the simplex optimisation algorithm and the second one is based on the genetic algorithm. These methods are used of the physical parameters of a simulated case of a porous material located in a duct by the computation of its acoustic transfer, scattering, and power attenuation. The results obtained by these methods are compared and discussed to choose the more efficient one.
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Bibliography

1. Alba J., delRey R., Ramis J., Arenas J.P. (2011), An inverse method to obtain porosity, fiber diameter and density of fibrous sound absorbing materials, Archives of Acoustics, 36(3): 561–574, doi: 10.2478/v10168-011-0040-x.
2. Allard J.F., Attalla N. (2009), Propagation of Sound in Porous Media, Wiley.
3. Allard J.F., Champoux Y. (1992), New empirical equations for sound propagation in rigid frame fibrous materials, The Journal of the Acoustical Society of America, 91(6): 3346–3353, doi: 10.1121/1.402824.
4. Attalla Y., Panneton R. (2005), Inverse acoustical characterization of open cell porous media using impedance tube measurements, Canadian Acoustics, 33(1): 11–24.
5. Attenborough K. (1983), Acoustical characteristics of rigid porous absorbents and granular materials, The Journal of the Acoustical Society of America, 73(3): 85–99, doi: 10.1121/1.389045.
6. Attenborough K. (1987), On the acoustic slow wave in air-filled granular media, The Journal of the Acoustical Society of America, 81(1): 93–102, doi: 10.1121/1.394938.
7. Benjdidia M., Akrout A., Taktak M., Hammami L., Haddar M. (2014), Thermal effect on the acoustic behavior of an axisymmetric lined duct, Applied Acoustics, 86: 138–145, doi: 10.1016/j.apacoust.2014.03.004.
8. Ben Souf M.A., Kessentini A., Bareille O., Taktak M., Ichchou M.N., Haddar M. (2017), Acoustical scattering identification with local impedance through a spectral approach, Compte Rendus Mécanique, 345(5): 301–316, doi: 10.1016/j.crme.2017.03.006.
9. Bérengier M., Stinson M.R., Daigle G.A., Hamet J.F. (1997), Porous road pavements: acoustical characterization and propagation effects, The Journal of the Acoustical Society of America, 101(1): 155–162, doi: 10.1121/1.417998.
10. Chazot J.D., Zhang E., Antoni J. (2012), Characterization of poroelastic materials with a Bayesian approach, The Journal of the Acoustical Society of America, 131(6): 4584–4595, doi: 10.1121/1.3699236.
11. Delany M.E., Bazley E.N. (1970), Acoustical properieties of fibrous absorbent materials, Applied Acoustics, 3: 105–116, doi: 10.1016/0003-682X(70)90031-9.
12. Dhief R., Makni A., Taktak M., Chaabane M., Haddar M. (2020), Investigation on the effects of acoustic liner variation and geometry discontinuities on the acoustic performance of lined ducts, Archives of Acoustics, 45(1): 49–66, doi: 10.24425/aoa.2020.132481.
13. Garoum M., Simon F. (2005), Characterization of non-consolidated cork crumbs as a basic sound absorber raw material, [in:] 12th International Congress on Sound and Vibration, Lisbon, Portugal.
14. Garoum M., Tajayouti M. (2007), Inverse estimation of non acoustical parameters of absorbing materials using genetic algorithms, [in:] 19th International Congress on Acoustics, Madrid, Spain.
15. Goldberg D. (1989), Genetic Algorithms for Search, Optimization and Machine Learning, Addison-Wesley, Reading. 16. Hamet J.F., Bérengier M. (1993), Acoustical characteristics of porous pavements – a new phenomenological model, [in:] Inter-Noise ‘93, Leuven, Belgium.
17. Hentati T., Bouazizi L., Taktak M., Trabelsi H., Haddar M. (2016), Multi-levels inverse identification of physical parameters of porous materials, Applied Acoustics, 108: 26–30, doi: 10.1016/j.apacoust.2015.09.013.
18. Hess H.M., Attenborough K., Heap N.W. (1990), Ground characterization by short-range propagation measurements, The Journal of the Acoustical Society of America, 87(5): 1975–1986, doi: 10.1121/1.399325.
19. Johnson D.L., Koplik J., Dashen R. (1987), Theory of dynamic permeability and tortuosity in fluidsaturated porous media, Journal of Fluid Mechanics, 176: 379–402, doi: 10.1017/S0022112087000727.
20. Kani M. et al. (2019), Acoustic performance evaluation for ducts containing porous material, Applied Acoustics, 147: 15–22, doi: 10.1016/j.apacoust.2018.08.002.
21. Kessentini A.,Taktak M., Ben Souf M.A., Bareille O., Ichchou M.N., Haddar M. (2016), Computation of the scattering matrix of guided acoustical propagation by the Wave Finite Elements approach, Applied Acoustics, 108: 92–100, doi: 10.1016/j.apacoust.2015.09.004.
22. Lafarge D., Lemarinier P., Allard J.F. (1997), Dynamic compressibility of air in porous structures at audible frequencies, The Journal of the Acoustical Society of America, 102(4): 1995–2006, doi: 10.1121/1.419690.
23. Lagarias J.C., Reeds J.A., Wright M.H., Wright P.E. (1998), Convergence properties of the Nelder- Mead Simplex method in low dimensions, SIAM Journal of optimization, 9(1): 112–147, doi: 10.1137/S1052623496303470.
24. Leclaire P., Kelders L., Lauriks W., Melon M., Brown N., Castagnède B. (1996), Determination of the viscous and thermal characteristic lengths of plastic foams by ultrasonic measurements in helium and air, Journal of Applied Physics, 80(4): 2009–2012, doi: 10.1063/1.363817.
25. Mareze P.H., Lenzi A. (2011), Characterization and optimization of rigid – frame porous material, [in:] 18th International Congress on Sound and Vibration, Rio De Janeiro, Brazil.
26. Masmoudi A., Makni A., Taktak M., Haddar M. (2017), Effect of geometry and impedance variation on the acoustic performance of a porous material lined duct, Journal of Theoretical and Applied Mechanics, 55(2): 679–694, doi: 10.15632/jtam-pl.55.2.679.
27. Miki Y. (1990), Acoustical properties of porous materials – modifications of Delany-Bazley models, Journal of the Acoustical Society of Japan, 11(1): 19–24, doi: 10.1250/ast.11.19.
28. Othmani C., Hentati T., Taktak M., Elnady T., Fakhfakh T., Haddar M. (2015), Effect of liner characteristics on the acoustic performance of duct systems, Archives of Acoustics, 40(1): 117–127, doi: 10.1515/aoa-2015-0014.
29. Panneton R., Olny X. (2006), Acoustical determination of the parameters governing viscous dissipation in porous media, The Journal of the Acoustical Society of America, 119(4): 2027–2040, doi: 10.1121/1.2169923.
30. Sellen N., Galland M.A., Hilberunner O. (2020), Identification of the characteristic parameters of porous media using active control, [in:] 8th AIAA/CEAS Aeroacoustics Conference, USA.
31. Shravage P., Bonfiglio P., Pompoli F. (2008), Hybrid inversion technique for predicting geometrical parameters of porous materials, [in:] Acoustics’ 08, Paris, France, pp. 2545–2549.
32. Taktak M., Ville J.M., Haddar M., Gabard G., Foucart F. (2010), An indirect method for the characterization of locally reacting liners, The Journal of the Acoustical Society of America, 127(6): 3548–3559, doi: 10.1121/1.3365250.
33. Ying H. (2010), Development of passive/active hybrid panels for acoustics [in French: Développement de panneaux hybrides passifs/actifs pour l’acoustique], Phd Thesis, Ecole Centrale de Lyon.
34. Zielinski T.G. (2012), Inverse identification and microscopic estimation of parameters for models of sound absorption in porous ceramics, [in:] International Conference on Noise and Vibration Engineering/International Conference on Uncertainty in Structural Dynamics, 17–19 September, Leuven, Belgium.
35. Zielinski T.G. (2014), A methodology for a robust inverse identification of model parameters for porous sound absorbing materials, [in:] International Conference on Noise and Vibration Engineering/International Conference on Uncertainty in Structural Dynamics, 15–17 September, Leuven, Belgium.
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Authors and Affiliations

Kani Marwa
1 2
Amine Makni
1
Mohamed Taktak
1 2
Mabrouk Chaabane
2
Mohamed Haddar
1

  1. Laboratory of Mechanics, Modeling and Productivity (LA2MP), National School of Engineers of Sfax, University of Sfax, Tunisia
  2. Faculty of Sciences of Sfax, University of Sfax, Tunisia
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Abstract

Numerical models allow structural characteristics to be obtained by solving mathematical formulations. The sound absorption capacity of a material can be acquired by numerically simulating an impedance tube and using the method governed by ISO 10534-2. This study presents a procedure of obtaining sound pressure using two microphones and as outline condition, at one end of the tube, the impedance of fiber samples extracted from the pseudostem of banana plants. The numerical methodology was conducted in the ANSYS® Workbench software. The sound absorption coefficient was obtained in the MATLAB® software using as input data the sound pressure captured in the microphones and applying the mathematical formulations exposed in this study. For the validation of the numerical model, the results were compared with the sound absorption coefficients of the fiber sample collected from an experimental procedure and also with the results of a microperforated panel developed by Maa (1998). According to the results, the methodology presented in this study showed effective results, since the largest absolute and relative errors were 0.001 and 3.162%, respectively.
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Bibliography

1. ASTM E1050:2019, Standard test method for impedance and absorption of acoustical materials using a tube, two microphones and a digital frequency analysis system.
2. ASTM E354:2003, Acoustics – measurement of sound absorption in a reverberation room.
3. Bóden H., Abom M. (1986), Influence of errors on the two-microphone method for measuring acoustic properties in ducts, The Journal of the Acoustical Society of America, 79(2): 541–549, doi: 10.1121/1.393542.
4. Ming-hui G., Qing-quan H., Jin-man W., Haipeng Y. (2010), The modeling and simulation analysis of wooden perforated panel absorption structure, Noise & Vibration Wordwide, 41(10): 72–75, doi: 10.1260/0957-4565.41.10.72.
5. Howard C.Q., Cazzolato B.S. (2014), Acoustic Analyses using MATLAB® and ANSYS®, Boca Raton: CRC Press, Taylor & Francis Group.
6. ISO 10534-1:1996, Acoustic – Determination of sound absorption coefficient and impedance in impedance tubes – Part 1: Method using standing wave ratio.
7. ISO 10534-2:1998, Acoustics – Determination of sound absorption coefficient and impedance in impedance tubes. Part 2: Transfer-function method.
8. ISO 354:2003, Measurement of sound absorption in a reverberant room.
9. Kinsler L.E., Frey A.R., Coppens A.B., Sanders J.V. (2000), Fundamentals of Acoustics, Hoboken: John Wiley & Sons, New York.
10. Lara L.T., Boaventura W.C., Pasqual A.M. (2016), Improving the estimated acoustic absorption curves in impedance tubes by using wavelet-based denoising methods, Congresso Iberoamericano de Acústica, Buenos Aires, Argentina, 22, 1–10.
11. Maa D.Y. (1998), Potential of microperforated panel absorber, The Journal of the Acoustical Society of America, 104(5): 2861–2866, doi: 10.1121/1.423870.
12. Rienstra S.W., Hirschberg A. (2014), An Introduction to Acoustics, Eindhoven University of Technology, Netherlands.
13. Silva G.C.C., Nunes M.A.A., Almeida Jr A.B., Lopes R.V. (2013), Acoustic design and construction of an impedance tube for experimental characterization of sound absorbed materials [in Portuguese: Projeto Acústico e Construção de um Tubo de Impedância para Caracterização Experimental de Materiais com Absorção Sonora], [in:] XVIII Congresso de Iniciação Científica da UnB, Brasília, Brazil.
14. Soriano H.L. (2009), Finite Elements – Formulation and Application in Static and Dynamic Structures [in Portuguese: Elementos Finitos – Formulação e Aplicação na Estática e Dinâmica das Estruturas], Rio de Janeiro: Editora Ciência Moderna Ltda.
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Authors and Affiliations

Cláudia Ohana Borges Mendes
1
Maria Alzira De Araújo Nunes
1

  1. Graduate Program in Engineering Materials Integrity, University of Brasília-UnB, College UnB Gama-FGA Área Especial de Indústria Projeção A, Setor Leste, CEP:72.444-240, Gama, Distrito Federal, Brazil
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Abstract

The paper presents an analysis and practical study of the temperature and pressure influence on a nondispersive infrared (NDIR) sensor for measuring the concentration of carbon dioxide in human breath. This sensor is used for monitoring patients’ carbon dioxide (CO2) in the exhaled air. High precision and accuracy of CO2 concentration measurements are essential in air sampling systems for breath analysers. They, however, require an analysis of the influence of the human exhaled air pressure and temperature on the NDIR CO2 sensor. Therefore, analyses of the changes in concentration were carried out at a pressure from 986 mbar to 1027 mbar and a temperature from 20°C to 36°C. Finally, corresponding correction coefficients were determined which allow to reduce the relative uncertainty of CO2 sensor measurements results from 19% to below 5%.
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Bibliography

[1] Chludzinski, T.,&Kwiatkowski, A. (2020). Exhaled breath analysis by resistive gas sensors. Metrology and Measurement Systems, 27(1), 81–89. http://dx.doi.org/10.24425/mms.2020.131718
[2] Bielecki, Z., Stacewicz, T., Wojtas, J., Mikołajczyk, J., Szabra, D., & Prokopiuk, A. (2018). Selected optoelectronic sensors in medical applications. Opto-Electronics Review, 26(2), 122–133. https://doi.org/10.1016/j.opelre.2018.02.007
[3] Buszewski, B., Kęsy, M., Ligor, T., & Amann, A. (2007). Human exhaled air analytics: biomarkers of diseases. Biomedical Chromatography, 21(6), 553–566. https://doi.org/10.1002/bmc.835
[4] Schubert, J. K., Spittler, K. H., Braun, G., Geiger, K.,& Guttmann, J. (2001). CO2-controlled sampling of alveolar gas in mechanically ventilated patients. Journal of Applied Physiology, 90(2), 486–492. https://doi.org/10.1152/jappl.2001.90.2.486
[5] Levitzky, M. G. (2013). Pulmonary Physiology (8th ed.): McGraw-Hill Education.
[6] Singh, O. P., & Malarvili, M. B. (2018). Review of infrared carbon-dioxide sensors and capnogram features for developing asthma-monitoring device. Journal of Clinical and Diagnostic Research, 12(10). https://doi.org/10.7860/JCDR/2018/35870.12099
[7] Singh, O. P., Howe, T. A., & Malarvili, M. B. (2018). Real-time human respiration carbon dioxide measurement device for cardiorespiratory assessment. Journal of Breath Research, 12(2), 026003. https://doi.org/10.1088/1752-7163/aa8dbd
[8] Chen, H.-Y., & Chen, C. (2019). Development of a Breath Analyzer for O2 and CO2 Measurement. The Open Biomedical Engineering Journal, 13(1), 21–32. https://doi.org/10.2174/1874120701913010021
[9] Mikołajczyk, J., Bielecki, Z., Stacewicz, T., Smulko, J.,Wojtas, J., Szabra, D., Lentka, Ł., Prokopiuk, A., & Magryta, P. (2016). Detection of gaseous compounds with different techniques. Metrology and Measurement Systems, 23(2). https://doi.org/10.1515/mms-2016-0026
[10] Prokopiuk, A. (2017). Optoelectronics sensors of hydrocarbons based on NDIR technique. Proceedings of SPIE – The International Society for Optical Engineering, 10455. https://doi.org/10.1117/12.2282779
[11] Hamamatsu. (2021, September 2). Mid infrared LED L13201-0430M. http://www.hamamatsu.com.cn/UserFiles/upload/file/20190527/l13201_series_kled1069e.pdf
[12] Pike Technologies. (2021). Stainless Steel Short-Path Gas Cells. https://www.piketech.com/product/stainless-steel-short-path-gas-cells/
[13] Elliot Scientific. (2021, September 2). BPF 4260-120 Iridian mid-IR Filter. https://elliotscientific.com/Iridian-BPF-4260-120
[14] Vigo. (2021, September 2). PV-3TE-5. https://vigo.com.pl/produkty/pv-3te/
[15] Richards, P. L. (1994). Bolometers for infrared and millimeter waves. Journal of Applied Physics, 76(1), 1–24. https://doi.org/10.1063/1.357128
[16] American Thoracic Society. (2005). ATS / ERS Recommendations for Standardized Procedures for the Online and Offline Measurement of Exhaled Lower Respiratory Nitric Oxide and Nasal Nitric Oxide, 2005. American Journal of Respiratory and Critical Care Medicine, 171(8), 912–930. https://doi.org/10.1164/rccm.200406-710ST
[17] Mansour, E., Vishinkin, R., Rihet, S., Saliba,W., Fish, F., Sarfati, P., & Haick, H. (2020). Measurement of temperature and relative humidity in exhaled breath. Sensors and Actuators B: Chemical, 304, 127371. https://doi.org/10.1016/j.snb.2019.127371
[18] UTECH Co., Ltd. (2021, September 2). UT100C Handheld Capnograph Vital Signs Monitor. https://www.chinautech.com/ut100c-capnograph-monitor-and-pulse-oximeter-etco2-spo2-pulse-rate-. html
[19] Memmert. (2021, September 2). Universal oven UF30. https://www.memmert.com/products/heatingdrying-ovens/universal-oven/UF30/
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Authors and Affiliations

Artur Prokopiuk
1
Zbigniew Bielecki
1
ORCID: ORCID
Jacek Wojtas
1
ORCID: ORCID

  1. Military University of Technology, Institute of Optoelectronics, 00-908 Warsaw, 2 Gen. Sylwestra Kaliskiego St.
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Abstract

Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.
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Bibliography

[1] Chylinski, M., & M., Szmajda, M. (2018). Statistical methods for analysing deceleration and acceleration capacity of the heart rate. In Hunek, W., & Paszkiel, S. (Eds.). Advances in Intelligent Systems and Computing: Vol. 720. Biomedical Engineering and Neuroscience. (pp. 85–97). Springer. https://doi.org/10.1007/978-3-319-75025-5_9
[2] Siecinski, S.,Kostka, P. S.,&Tkacz, E. J. (2020). Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms on healthy volunteers. Sensors, 20(16), 4522. https://doi.org/ 10.3390/s20164522
[3] Acharya, R. U., Joseph, P. K., Kannathal, N., Choo, M. L., & Suri, J. S. (2006). Heart rate variability: A review. Medical and Biological Engineering and Computing, 44(12), 1031–1051. https://doi.org/10.1007/s11517-006-0119-0
[4] Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258
[5] Goldoozian L. S., Zahedi, E., & Zarzoso, V. (2017). Time-varying assessment of heart rate variability parameters using respiratory information. Computers in Biology and Medicine, 89, 355–367. https://doi.org/10.1016/j.compbiomed.2017.07.022
[6] Boardman, A., Schlindwein, F. S., Rocha, A. P., & Leite, A. (2002). A study on the optimum order of autoregressive models for heart rate variability. Physiological Measurement, 23(2), 325–336. https://doi.org/10.1088/0967-3334/23/2/308
[7] Karim, N., Hasan, J. A., & Ali, S. S. (2011). Heart rate variability – A review. Australian Journal of Basic and Applied Sciences, 7(1), 71–77.
[8] Stein, P. K., & Pu, Y. (2012). Heart rate variability, sleep and sleep disorders. Sleep Medicine Reviews, 16(1), 47–66. https://doi.org/10.1016/j.smrv.2011.02.005
[9] Bernardi, L., Valle, F., Coca, M., Calciati, A., & Sleight, P. (1996). Physical activity influences heart rate variability and very-low-frequency components in Holter electrocardiograms. Cardiovascular Research, 32(2), 234–237. https://doi.org/10.1016/0008-6363(96)00081-8
[10] Aoki, K., Stephens, D. P., & Johnson, J. M. (2001). Diurnal variation in cutaneous vasodilator and vasoconstrictor systems during heat stress. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 281(2), 591–595. https://doi.org/10.1152/ajpregu.2001.281.2.R591
[11] Fleisher, L. A., Frank, S. M., Sessler, D. I., Cheng, C., Matsukawa, T., & Vannier, C. A. (1996). Thermoregulation and heart rate variability. Clinical Science, 90(2), 97–103. https://doi.org/10.1042/cs0900097
[12] Akselrod. S., Gordon, D., Ubel. F. A., Shannon, D. C., Barger, A. C., & Cohen, R. J. (1981). Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science, 213(4504), 220–222. https://doi.org/10.1126/science.6166045
[13] Porter, G. A., Jr., & Rivkees, S. A. (2001). Ontogeny of humoral heart rate regulation in the embryonic mouse. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 281(2), 401–407. https://doi.org/10.1152/ajpregu.2001.281.2.r401
[14] Stampfer, H. G., & Dimmitt, S. B. (2013). Variations in circadian heart rate in psychiatric disorders: Theoretical and practical implications. ChronoPhysiology and Therapy, 3, 41–50. https://doi.org/10.2147/CPT.S43623
[15] Jelinek, H. F., Huang, Z. Q., Khandoker, A. H., Chang, D., & Kiat, H. (2013). Cardiac rehabilitation outcomes following a 6-week program of PCI and CABG patients. Frontiers in Physiology, 4, 302. https://doi.org/10.3389/fphys.2013.00302
[16] Li, H., Kwong, S., Yang, L., Huang, D., & Xiao, D. (2011). Hilbert-Huang transform for analysis of heart rate variability in cardiac health. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6), 1557–1567. https://doi.org/10.1109/TCBB.2011.43
[17] Task Force of the European Society of Cardiology and the North American Society of Pacing Electrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043
[18] Wen, F., & He, F.-T. (2011). An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability analysis. Journal of Zhejiang University Science B, 12, 976–982. https://doi.org/10.1631/jzus.b1000392
[19] Mendez, M. O., Bianchi, A. M., Matteucci, M., Cerutti, S., & Penzel, T. (2009). Sleep apnea screening by autoregressive models from a single ECG lead. IEEE Transactions on Biomedical Engineering, 56(12), 2838–2850. https://doi.org/10.1109/tbme.2009.2029563
[20] Penzel, T., Kantelhardt, J. W., Grote, L., Peter, J. H., & Bunde, A. (2003). Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Transactions on Biomedical Engineering, 50(10), 1143–1151. https://doi.org/10.1109/TBME.2003.817636
[21] Chan, H. L., Chou, W. S., Chen, S. W., Fang, S. C., Liou, C. S., & Hwang, Y. S. (2005). Continuous and online analysis of heart rate variability. Journal of Medical Engineering and Technology, 29(5), 227–234. https://doi.org/10.1080/03091900512331332587
[22] Kudrynski, K., & Strumillo, P. (2015). Real-time estimation of the spectral parameters of heart rate variability. Biocybernetics and Biomedical Engineering, 35(4), 304–316. https://doi.org/10.1016/ j.bbe.2015.05.002
[23] Echeverria, J. C., Crowe, J. A., Woolfson, M. S., & Hayes-Gill, B. R. (2001). Application of empirical mode decomposition to heart rate variability analysis. Medical and Biological Engineering and Computing, 39(4), 471–479. https://doi.org/10.1007/bf02345370
[24] Billman, G. E. (2011). Heart rate variability – A historical perspective. Frontiers in Physiology, 2, 86. https://doi.org/10.3389/fphys.2011.00086
[25] Romano, M., Faiella, G., Clemente, F., Iuppariello, L., Bifulco, P., & Cesarelli, M. (2016). Analysis of foetal heart rate variability components by means of empirical mode decomposition. IFMBE Proceedings, 57, 71–74. https://doi.org/10.1007/978-3-319-32703-7_15
[26] Montano, N., Porta, A., Cogliati, C., Costantino, G., Tobaldini, E., Casali, K. R., & Iellamo, F. (2009). Heart rate variability explored in the frequency domain: A tool to investigate the link between heart and behavior. Neuroscience and Biobehavioral Reviews, 33(2), 71–80. https://doi.org/10.1016/j.neubiorev.2008.07.006
[27] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society of London A, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
[28] Chen, M., He, A., Feng, K., Liu, G., & Wang, Q. (2019). Empirical mode decomposition as a novel approach to study heart rate variability in congestive heart failure assessment. Entropy, 21(12), 1169. https://doi.org/10.3390/e21121169
[29] Balocchi, R., Menicucci, D., Santarcangelo, E., Sebastiani, L., Gemignani, A., Ghelarducci, B., & Varanini, M. (2004). Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition. Chaos, Solitons & Fractals, 20(1), 171–177. https://doi.org/10.1016/S0960-0779(03)00441-7
[30] Ortiz, M. R., Bojorges, E. R., Aguilar, S. D., Echeverria, J. C., Gonzalez-Camarena, R., Carrasco, S., Gaitan, M. J., & Martinez, A. (2005). Analysis of high frequency fetal heart rate variability using empirical mode decomposition. Computers in Cardiology, France, 675–678. https://doi.org/10.1109/ CIC.2005.1588192
[31] Helong, L., Yang, L., & Daren, H. (2008). Application of Hilbert-Huang transform to heart rate variability analysis. 2nd International Conference on Bioinformatics and Biomedical Engineering, China, 648–651. https://doi.org/10.1109/ICBBE.2008.158
[32] Neto, E. P. S., Custaud, M. A., Cejka, J. C., Abry, P., Frutoso, J., Gharib, C., & Flandrin, P. (2004). Assessment of cardiovascular autonomic control by the empirical mode decomposition. Methods of Information in Medicine, 43(1), 60–65. https://doi.org/10.1055/s-0038-1633836
[33] Ihlen, E. A. F. (2009). A comparison of two Hilbert spectral analyses of heart rate variability. Medical & Biological Engineering & Computing, 47(10), 1035–1044. https://doi.org/10.1007/ s11517-009-0500-x
[34] Eleuteri, A., Fisher, A. C., Groves, D., & Dewhurst, C. J. (2012). An efficient time-varying filter for detrending and bandwidth limiting the heart rate variability tachogram without resampling: MATLAB open-source code and internet web-based implementation. Computational and Mathematical Methods in Medicine, 2012, Article 578785. https://doi.org/10.1155/2012/578785
[35] Fisher, A. C., Eleuteri, A., Groves, D., & Dewhurst, C. J. (2012). The Ornstein–Uhlenbeck third-order Gaussian process (OUGP) applied directly to the un-resampled heart rate variability (HRV) tachogram for detrending and low-pass filtering. Medical and Biological Engineering and Computing, 50(7), 737–742. https://doi.org/10.1007/s11517-012-0928-2
[36] Varanini, M., Macerata, A., Emdin, M., & Marchesi, C. (1994). Non linear filtering for the estimation of the respiratory component in heart rate. Computers in Cardiology, USA, 565–568. https://doi.org/10.1109/CIC.1994.470129
[37] Estévez, M., Machado, C., Leisman, G., Estévez-Hernández, T., Arias-Morales, A., Machado, A., & Montes-Brown, J. (2016). Spectral analysis of heart rate variability. International Journal on Disability and Human Development, 15(1), 5–17. https://doi.org/10.1515/ijdhd-2014-0025
[38] McCraty, R., & Shaffer, F. (2015). Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and Medicine, 4(1), 46–61. https://doi.org/10.7453/gahmj.2014.073
[39] Nunan, D., Sandercock, G. R. H., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33(11), 1407–1417. https://doi.org/10.1111/j.1540-8159.2010.02841.x
[40] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., Mietus, J. E., Moody, G. B., Peng, C. K.,&Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), 215–220. https://doi.org/10.1161/01.cir.101.23.e215
[41] Terzano, M. G., Parrino, L., Sherieri, A., Chervin, R., Chokroverty, S., Guilleminault, C., Hirshkowitz, M., Mahowald, M., Moldofsky, H., Rosa, A., Thomas, R., & Walters, A. (2001). Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Medicine, 2(6), 537–553. https://doi.org/10.1016/s1389-9457(01)00149-6
[42] Jun, S., Szmajda, M., Khoma, V., Khoma, Y., Sabodashko, D., Kochan, O., & Wang, J. (2020). Comparison of methods for correcting outliers in ECG-based biometric identification. Metrology and Measurement Systems, 27(3), 387–398. https://doi.org/10.24425/mms.2020.132784
[43] Hsu, M.-K., Sheu, J.-C., & Hsue, C. (2011). Overcoming the negative frequencies: instantaneous frequency and amplitude estimation using osculating circle method. Journal of Marine Science and Technology, 19(5), 514–521. https://doi.org/10.6119/JMST.201110_19(5).0007
[44] Bayly E. J. (1968). Spectral analysis of pulse frequency modulation in the nervous systems. IEEE Transactions on Biomedical Engineering, 15(4), 257–265. https://doi.org/10.1109/TBME.1968.4502576
[45] Mateo, J., & Laguna, P. (1996). New heart rate variability time-domain signal construction from the beat occurrence time and the IPFM model. Computers in Cardiology, USA, 185–188. https://doi.org/10.1109/CIC.1996.542504
[46] de Boer, R. W., Karemaker, J. M., & Strackee, J. (1985). Spectrum of a series of point events, generated by the integral pulse frequency modulation model. Medical and Biological Engineering and Computing, 23(2), 138–142. https://doi.org/10.1007/BF02456750
[47] Nakao, M., Norimatsu, M., Mizutani, Y., & Yamamoto, M. (1997). Spectral distortion properties of the integral pulse frequency modulation model. IEEE Transactions on Biomedical Engineering, 44(5), 419–426. https://doi.org/10.1109/10.568918


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Authors and Affiliations

Krzysztof Adamczyk
1
Adam G. Polak
1

  1. Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, B. Prusa Str. 53/55, 50-317 Wrocław, Poland
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Abstract

In this paper, we present metrology and control methods and techniques for electromagnetically actuated microcantilevers. The electromagnetically actuated cantilevers belong to the micro electro mechanical systems (MEMS), which can be used in high resolution force and mass change investigations. In the described experiments, silicon cantilevers with an integrated Lorentz current loop were investigated. The electromagnetically actuated cantilevers were characterized using a modified optical beam deflection (OBD) system, whose architecture was optimized in order to increase its resolution. The sensitivity of the OBD system was calibrated using a reference cantilever, whose spring constant was determined through thermomechanical noise analysis registered interferometrically. The optimized and calibrated OBD system was used to observe the resonance and bidirectional static deflection of the electromagnetically deflected cantilevers. After theoretical analysis and further experiments, it was possible to obtain setup sensitivity equal to 5.28 mV/nm.
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Bibliography

[1] Binnig, G., Quate, C. F., & Gerber, C. (1986). Atomic force microscope. Physical Review Letters, 56(9), 930. https://doi.org/10.1103/PhysRevLett.56.930
[2] Judy, J. W. (2001). Microelectromechanical systems (MEMS): fabrication, design and applications. Smart Materials and Structures, 10(6), 1115–1134. https://doi.org/10.1088/0964-1726/10/6/301
[3] Algamili, A. S., Khir, M. H. M., Dennis, J. O., Ahmed, A. Y., Alabsi, S. S., Hashwan, S. S. B., & Junaid, M. M. (2021). A review of actuation and sensing mechanisms in MEMS-based sensor devices. Nanoscale Research Letters, 16(1), 1–21. https://doi.org/10.1186/s11671-021-03481-7
[4] Woszczyna, M., Gotszalk, T., Zawierucha, P., Zielony, M., Ivanow, Tzv., Ivanowa, K., Sarov, Y., Nikolov, N., Mielczarski, J., Mielczarska, E., & Rangelow, I. W. (2009). Thermally driven piezoresistive cantilevers for shear-force microscopy. Microelectronic Engineering, 86(4), 1212–1215. https://doi.org/10.1016/j.mee.2009.01.043
[5] Shen, B., Allegretto, W., Hu, M., & Robinson, A. M. (1996). CMOS micromachined cantileverin- cantilever devices with magnetic actuation. IEEE Electron Device Letters, 17(7), 372–374. https://doi.org/10.1109/55.506371
[6] Adhikari, R., Kaundal, R., Sarkar, A., Rana, P., & Das, A. K. (2012). The cantilever beam magnetometer: A simple teaching tool for magnetic characterization. American Journal of Physics, 80(3), 225–231. https://doi.org/10.1119/1.3679840
[7] Hsieh, C. H., Dai, C. L., & Yang, M. Z. (2013). Fabrication and Characterization of CMOS-MEMS Magnetic Microsensors. Sensors, 13(11), 14728–14739. https://doi.org/10.3390/s131114728
[8] Rhoads, J. F., Kumar, V., Shaw, S. W., & Turner, K. L. (2013). The non-linear dynamics of electromagnetically actuated microbeam resonators with purely parametric excitations. International Journal of Non-Linear Mechanics, 55, 79–89. https://doi.org/10.1016/j.ijnonlinmec.2013.04.003
[9] Lee, B., Prater, C. B.,&King,W. P. (2012). Lorentz force actuation of a heated atomic force microscope cantilever. Nanotechnology, 23(5), 055709. https://doi.org/10.1088/0957-4484/23/5/055709
[10] Neuman, K. C., & Nagy, A. (2008). Single-molecule force spectroscopy: optical tweezers, magnetic tweezers and atomic force microscopy. Nature Methods, 5(6), 491–505. https://doi.org/10.1038/nmeth.1218
[11] Hoogenboom, B. W., Frederix, P. L. T. M., Yang, J. L., Martin, S., Pellmont, Y., Steinacher, M., Zäch, S., Langenbach, E., Heimbeck, H.-J., Engel, A., & Hug, H. J. (2005). A Fabry–Perot interferometer for micrometer-sized cantilevers. Applied Physics Letters, 86(7), 074101-1. https://doi.org/10.1063/1.1866229
[12] Meyer, G., & Amer, N. M. (1988). Novel optical approach to atomic force microscopy. Applied Physics Letters, 53(12), 1045–1047. https://doi.org/10.1063/1.100061
[13] Boisen, A., Dohn, S., Keller, S. S., Schmid, S., & Tenje, M. (2011). Cantilever-like micromechanical sensors. Reports on Progress in Physics, 74(3), 036101. https://doi.org/10.1088/0034-4885/74/3/036101
[14] Gimzewski, J. K., Gerber, Ch., Meyer, E., & Schlittler, R. R. (1994). Observation of a chemical reaction using a micromechanical sensor. Chemical Physics Letters, 217(5), 589–594. https://doi.org/ 10.1016/0009-2614(93)E1419-H
[15] Wu, G., Ji, H., Hansen, K., Thundat, T., Datar, R., Cote, R., Hagan, M. F., Chakraborty, A. K., & Majumdar, A. (2001). Origin of nanomechanical cantilever motion generated from biomolecular interactions. Proceedings of the National Academy of Sciences, 98(4), 1560–1564. https://doi.org/10.1073/pnas.98.4.1560 [16] Nieradka, K., Kapczynska, K., Rybka, J., Lipinski, T., Grabiec, P., Skowicki, M.,&Gotszalk, T. (2014). Microcantilever array biosensors for detection and recognition of Gram-negative bacterial endotoxins. Sensors and Actuators B: Chemical, 198, 114–124. https://doi.org/10.1016/j.snb.2014.03.023
[17] Helm, M., Servant, J. J., Saurenbach, F., & Berger, R. (2005). Read-out of micromechanical cantilever sensors by phase shifting interferometry. Applied Physics Letters, 87(6), 064101. https://doi.org/10.1063/1.2008358
[18] Putman, C. A. J., de Grooth, B. G., van Hulst, N. F., & Greve, J. (1992). A theoretical comparison between interferometric and optical beam deflection technique for the measurement of cantilever displacement in AFM. Ultramicroscopy, 42, 1509–1513. https://doi.org/10.1016/0304-3991(92) 90474-X
[19] Putman, C. A. J., de Grooth, B. G., van Hulst, N. F., & Greve, J. (1992). A detailed analysis of the optical beam deflection technique for use in atomic force microscopy. Journal of Applied Physics, 72(1), 6–12. https://doi.org/10.1063/1.352149
[20] Hu, Z., Seeley, T., Kossek, S.,&Thundat, T. (2004). Calibration of optical cantilever deflection readers. Review of Scientific Instruments, 75(2), 400–404. https://doi.org/10.1063/1.1637457
[21] Fukuma, T., Kimura, M., Kobayashi, K., Matsushige, K., & Yamada, H. (2005). Development of low noise cantilever deflection sensor for multienvironment frequency-modulation atomic force microscopy. Review of Scientific Instruments, 76(5), 053704. https://doi.org/10.1063/1.1896938
[22] Nieradka, K., Kopiec, D., Małozi˛ec, G., Kowalska, Z., Grabiec, P., Janus, P., Sierakowski, A., Domanski, K., & Gotszalk, T. (2012). Fabrication and characterization of electromagnetically actuated microcantilevers for biochemical sensing, parallel AFM and nanomanipulation. Microelectronic Engineering, 98, 676–679. https://doi.org/10.1016/j.mee.2012.06.019
[23] Miyatani, T., & Fujihira, M. (1997). Calibration of surface stress measurements with atomic force microscopy. Journal of Applied Physics, 81(11), 7099–7115. https://doi.org/10.1063/1.365306
[24] Mishra, R., Grange, W., & Hegner, M. (2012). Rapid and reliable calibration of laser beam deflection system for microcantilever-based sensor setups. Journal of Sensors, 2021, 617386. https://doi.org/10.1155/2012/617386
[25] Naeem, S., Liu, Y., Nie, H. Y., Lau, W. M., & Yang, J. (2008). Revisiting atomic force microscopy force spectroscopy sensitivity for single molecule studies. Journal of Applied Physics, 104(11), 114504. https://doi.org/10.1063/1.3037206
[26] Lee, J., Beechem, T., Wright, T. L., Nelson, B. A., Graham, S., & King, W. P. (2006). Electrical, thermal, and mechanical characterization of silicon microcantilever heaters. Journal of Microelectromechanical Systems, 15(6), 1644–1655. https://doi.org/10.1109/JMEMS.2006.886020
[27] Skwierczynski, J. M., Małozi˛ec, G., Kopiec, D., Nieradka, K., Radojewski, J., & Gotszalk, T. (2011). Radio frequency modulation of semiconductor laser as an improvement method of noise performance of scanning probe microscopy position sensitive detectors. Optica Applicata, 41(2), 323–331.
[28] Butt, H.-J., & Jaschke, M. (1995). Calculation of thermal noise in atomic force microscopy. Nanotechnology, 6(1), 1. https://doi.org/10.1088/0957-4484/6/1/001
[29] Ohler, B. (2007). Cantilever spring constant calibration using laser Doppler vibrometry. Review of Scientific Instruments, 78(6), 063701. https://doi.org/10.1063/1.2743272
[30] Cleveland, J. P., Manne, S., Bocek, D.,&Hansma, P. K. (1993).Anondestructive method for determining the spring constant of cantilevers for scanning force microscopy. Review of Scientific Instruments, 64(2), 403–405. https://doi.org/10.1063/1.1144209
[31] Lévy, R., & Maaloum, M. (2002). Measuring the spring constant of atomic force microscope cantilevers: thermal fluctuations and other methods. Nanotechnology, 13(1), 33. https://doi.org/10.1088/095-4484/13/1/307
[32] Rast, S.,Wattinger, C., Gysin, U.,&Meyer, E. (2000). The noise of cantilevers. Nanotechnology, 11(3), 169. https://doi.org/10.1088/0957-4484/11/3/306
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Authors and Affiliations

Daniel Kopiec
1
Wojciech Majstrzyk
2
Bartosz Pruchnik
1
Ewelina Gacka
1
Dominik Badura
1
Andrzej Sierakowski
2
Paweł Janus
2
Teodor Gotszalk
1

  1. Wrocław University of Technology, Faculty of Microsystems Electronics and Photonics, Department of Nanometrology, Janiszewskiego 11/17, Wrocław 50-372, Poland
  2. Łukasiewicz Research Network, Institute of Microelectronics and Fotonics, Lotników 32/46, Warsaw 02-668, Poland
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Abstract

This paper proposes a new approach called the Predictive Kalman Filter (PKF) which predicts and compensates model errors of inertial sensors to improve the accuracy of static alignment without the use of external assistance. The uncertain model error is the main problem in the field as the Micro Electro Mechanical System (MEMS) inertial sensors have bias which change over time, and these errors are not all observable. The proposed filter determines an optimal equivalent model error by minimizing a quadratic penalty function without augmenting the system state space. The optimization procedure enables the filter to decrease both model uncertainty and external disturbances. The paper first presents the complete formulation of the proposed filter. Then, a nonlinear alignment model with a large misalignment angle is considered. Experimental results demonstrate that the new method improves the accuracy and rapidness of the alignment process as the convergence time is reduced from 550 s to 50 s, and the azimuth misalignment angle correctness is decreased from 52" 47" to 4" 0:02".
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Bibliography

[1] Britting, K. R. (1971). Inertial navigation systems analysis. Wiley Interscience.
[2] Chang, L., Li, J., & Li, K. (2016). Optimization-based alignment for strapdown inertial navigation system: Comparison and extension. IEEE Transactions on Aerospace and Electronic Systems, 52(4), 1697–1713. https://doi.org/10.1109/TAES.2016.130824
[3] Xue, H., Guo, X., & Zhou, Z. (2016). Parameter identification method for SINS initial alignment under inertial frame. Mathematical Problems in Engineering, 2016, 5301242. https://doi.org/10.1155/2016/5301242
[4] Wang, D., Dong, Y., Li, Q., Wu, J., & Wen, Y. (2018). Estimation of small UAV position and attitude with reliable in-flight initial alignment for MEMSinertial sensors. Metrology and Measurement Systems, 25(3), 603–616. https://doi.org/10.24425/123904
[5] Ghanbarpourasl, H. (2020). A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 234(12), 1913–1925. https://doi.org/10.1177/0954410020920473
[6] Guo, S., Chang, L., Li, Y., & Sun, Y. (2020). Robust fading cubature Kalman filter and its application in initial alignment of SINS. Optik, 202, 163593. https://doi.org/10.1016/j.ijleo.2019.163593
[7] Zhang, T., Wang, J., Jin, B., & Li, Y. (2019). Application of improved fifth-degree cubature Kalman filter in the nonlinear initial alignment of strapdown inertial navigation system. Review of Scientific Instruments, 90(1), 015111. https://doi.org/10.1063/1.5061790
[8] Xing, H., Chen, Z.,Wang, C., Guo, M., & Zhang, R. (2019). Quaternion-based Complementary Filter for Aiding in the Self-Alignment of the MEMS IMU. 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), USA, 1–4. https://doi.org/10.1109/ISISS.2019.8739728
[9] Yang, B., Xu, X., Zhang, T., Sun, J., & Liu, X. (2017). Novel SINS initial alignment method under large misalignment angles and uncertain noise based on nonlinear filter. Mathematical Problems in Engineering, 2017, 5917917. https://doi.org/10.1155/2017/5917917
[10] Sun, J., Xu, X., Liu, Y., Zhang, T., & Li, Y. (2015). Initial alignment of large azimuth misalignment angles in SINS based on adaptive UPF. Sensors, 15(9), 21807–21823. https://doi.org/10.3390/s150921807
[11] Han, H., Wang, J., & Du, M. (2017). A fast SINS initial alignment method based on RTS forward and backward resolution. Journal of Sensors, 2017, 7161858. https://doi.org/10.1155/2017/7161858
[12] Kaygısız, B. H., & Sen, B. (2015). In-motion alignment of a low-cost GPS/INS under large heading error. The Journal of Navigation, 68(2), 355–366. https://doi.org/10.1017/S0373463314000629
[13] Xia, X.,&Sun, Q. (2018). Initial alignment algorithm based on theDMCSmethod in single-axis RSINS with large azimuth misalignment angles for submarines. Sensors, 18(7), 1807–2123. https://doi.org/10.3390/s18072123
[14] Li, J., Gao, W., Zhang, Y., & Wang, Z. (2018). Gradient Descent Optimization-Based Self-Alignment Method for Stationary SINS. IEEE Transactions on Instrumentation and Measurement, 68(9), 3278– 3286. https://doi.org/10.1109/TIM.2018.2878071
[15] Camacho, E. F., Ramírez, D. R., Limón, D., De La Peña, D. M., & Alamo, T. (2010). Model predictive control techniques for hybrid systems. Annual Reviews in Control, 34(1), 21–31. https://doi.org/10.1016/j.arcontrol.2010.02.002
[16] Titterton, D., Weston, J. L., & Weston, J. (2004). Strapdown inertial navigation technology. IET. https://doi.org/10.1049/PBRA017E
[17] Analog Devices. (2018). Tactical Grade Ten Degrees of Freedom Inertial Sensor – ADIS16488A. [Datasheet, Rev. F]. https://www.analog.com/media/en/technical-documentation/data-sheets/ADIS16488A.pdf
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Authors and Affiliations

Hassan Majed Alhassan
1
Nemat Allah Ghahremani
1

  1. Malek Ashtar University of Technology, Faculty of Electrical & Computer Engineering, Tehran 15875-1774, Iran
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Abstract

A new solution to the problem of frequency estimation of a single sinusoid embedded in the white Gaussian noise is presented. It exploits, approximately, only one signal cycle, and is based on the well-known 2nd order autoregressive difference equation into which a downsampling is introduced. The proposed method is a generalization of the linear prediction based Prony method for the case of a single undamped sinusoid. It is shown that, thanks to the proposed downsampling in the linear prediction signal model, the overall variance of the least squares solution of frequency estimation is decreased, when compared to the Prony method, and locally it is even close to the Cramér–Rao Lower Bound, which is a significant improvement. The frequency estimation variance of the proposed solution is comparable with, computationally more complex, the Matrix Pencil and the Steiglitz–McBride methods. It is shown that application of the proposed downsampling to the popular smart DFT frequency estimation method also significantly reduces the method variance and makes it even better than the least squares smart DFT. The noise immunity of the proposed solution is achieved simultaneously with the reduction of computational complexity at the cost of narrowing the range of measured frequencies, i.e. a sinusoidal signal must be sufficiently oversampled to apply the proposed downsampling in the autoregressive model. The case of 64 samples per period with downsampling up to 16, i.e. 1/4th of the cycle, is presented in detail, but other sampling scenarios, from 16 to 512 samples per period, are considered as well.
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Bibliography

[1] Kay, S. M. (1993). Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall
[2] Kay, S. M., & Marple, S. L. (1981). Spectrum analysis – A modern perspective. Proc. IEEE, 69, 1380–1419. https://doi.org/10.1109/PROC.1981.12184
[3] Kay, S. M. (1987). Modern Spectrum Analysis. Prentice-Hall.
[4] Zielinski, T. P., & Duda, K. (2011). Frequency and damping estimation methods - an overview. Metrology and Measurement Systems, 18(3), 505–528. https://doi.org/10.2478/v10178-011-0051-y
[5] Duda, K., & Zielinski, T. P. (2013). Efficacy of the frequency and damping estimation of a real-value sinusoid. IEEE Instrumentation & Measurement Magazine, 16(1), 48–58. https://doi.org/10.1109/ MIM.2013.6495682
[6] Borkowski, J., Kania, D., & Mroczka, J. (2018). Comparison of sine-wave frequency estimation methods in respect of speed and accuracy for a few observed cycles distorted by noise and harmonics. Metrology and Measurement Systems, 25(1), 283–302. https://doi.org/10.24425/119567
[7] Harris, F. J. (1978). On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 66(1), 51–83. https://doi.org/10.1109/PROC.1978.10837
[8] Zygarlicki, J., Zygarlicka, M., Mroczka, J., & Latawiec, K. J. (2010). A reduced Prony’s method in power-quality analysis – parameters selection. IEEE Transactions on Power Delivery, 25(1), 979–986. https://doi.org/10.1109/TPWRD.2009.2034745
[9] Zygarlicki, J., & Mroczka, J. (2014). Prony’s method with reduced sampling – numerical aspects. Metrology and Measurement Systems, 21(2), 521–534. https://doi.org/10.2478/mms-2014-0044
[10] Zygarlicki, J. (2017). Fast second order original Prony’s method for embedded measuring systems. Metrology and Measurement Systems, 24(3), 721–728. https://doi.org/10.1515/mms-2017-0058
[11] Hua, Y., & Sarkar, T. K., (1990). Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoid in noise. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(4), 814–824. https://doi.org/10.1109/29.56027
[12] Steiglitz, K.,&McBride, L. (1965). A technique for identification of linear systems. IEEE Transactions on Automatic Control, 10(3), 461–464. https://doi.org/10.1109/TAC.1965.1098181
[13] McClellan, J. H., & Lee, D. (1991). Exact equivalence of the Steiglitz–McBride iteration and IQML. IEEE Transactions on Signal Processing, 39(1), 509–512. https://doi.org/10.1109/78.80841
[14] Wu, R. C., & Chiang, C. T. (2010). Analysis of the exponential signal by the interpolated DFT algorithm. IEEE Transactions on Instrumentation and Measurement, 59(12), 3306–3317. https://doi.org/10.1109/TIM.2010.2047301
[15] Derviškadic, A., Romano, & P., Paolone, M. (2018). Iterative-Interpolated DFT for Synchrophasor Estimation: A Single Algorithm for P- and M-Class Compliant PMUs. IEEE Transactions on Instrumentation and Measurement, 67(2), 547–558. https://doi.org/10.1109/TIM.2017.2779378
[16] Jacobsen, E., & Kootsookos, P. (2007). Fast, accurate frequency estimators. IEEE Signal Processing Magazine, 24(2), 123–125. https://doi.org/10.1109/MSP.2007.361611
[17] Duda, K., & Barczentewicz, S. (2014). Interpolated DFT for sin α (x) windows. IEEE Transactions on Instrumentation and Measurement, 63(3), 754–760. https://doi.org/10.1109/TIM.2013.2285795
[18] Yang, J. Z., & Liu, C. W. (2000). A precise calculation of power system frequency and phasor. IEEE Transactions on Power Delivery, 15(1), 494–499. https://doi.org/10.1109/61.852974
[19] Yang, J. Z., & Liu, C. W. (2001). A precise calculation of power system frequency. IEEE Transactions on Power Delivery, 16(2), 361–366. https://doi.org/10.1109/61.924811
[20] Xia, Y., He, Y., Wang, K., Pei, W., Blazic, Z., & Mandic, D. P. (2017). A complex least squares enhanced smart DFT technique for power system frequency estimation. IEEE Transactions on Power Delivery, 32(2), 1270–1278. https://doi.org/10.1109/TPWRD.2015.2418778
[21] Li, Z. (2021). A total least squares enhanced smart DFT technique for frequency estimation of unbalanced three-phase power systems. International Journal of Electrical Power & Energy Systems, 128, 106722. https://doi.org/10.1016/j.ijepes.2020.106722
[22] Xu, S., Liu, H., & Bi, T. (2020). A novel frequency estimation method based on complex Bandpass filters for P-class PMUs with short reporting latency. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2020.3038703
[23] Duda, K., & Zielinski, T. P. (2021). P Class and M Class Compliant PMU Based on Discrete- Time Frequency-Gain Transducer. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2021.3076831
[24] IEC, IEEE. (2018). Measuring relays and protection equipment – Part 118–1: Synchrophasor for power systems – Measurements (IEC/IEEE Standard No. 60255-118-1).
[25] Moon, T. K., & Stirling W. C. (1999). Mathematical Methods and Algorithms for Signal Processing. Prentice Hall.

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Authors and Affiliations

Krzysztof Duda
1
Tomasz P. Zieliński
2

  1. AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Measurement and Electronics, al. Mickiewicza 30, 30-059 Kraków, Poland
  2. AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Institute of Telecommunications, al. Mickiewicza 30, 30-059 Kraków, Poland
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Abstract

Reliable measurement uncertainty is a crucial part of the conformance/nonconformance decision-making process in the field of Quality Control in Manufacturing. The conventional GUM-method cannot be applied to CMM measurements primarily because of lack of an analytical relationship between the input quantities and the measurement. This paper presents calibration uncertainty analysis in commercial CMM-based Coordinate Metrology. For the case study, the hole-plate calibrated by the PTB is used as a workpiece. The paper focuses on thermo-mechanical errors which immediately affect the dimensional accuracy of manufactured parts of high-precision manufacturers. Our findings have highlighted some practical issues related to the importance of maintaining thermal equilibrium before the measurement. The authors have concluded that the thermal influence as an uncertainty contributor of CMM measurement result dominates the overall budgets for this example. The improved calibration uncertainty assessment technique considering thermal influence is described in detail for the use of a wide range of CMM users.
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Bibliography

[1] International Organization for Standardization (2009). Geometrical product specifications (GPS) – Acceptance and reverification tests for coordinate measuring machines (CMM) – Part 2: CMMs used for measuring linear dimensions (ISO Standard No. 10360-2:2009). https://www.iso.org/standard/40954.html
[2] International Organization for Standardization (2017). Geometrical product specifications (GPS) – Inspection by measurement of workpieces and measuring equipment – Part 1: Decision rules for proving conformance or non-conformance with specifications (ISO Standard No. 14253-1:2017). https://www.iso.org/standard/70137.html
[3] Mussatayev, M., Huang, M.,&Tang, Zh., (2020). Current issues in uncertainty of dimensional tolerance metrology and the future development in the domain of tolerancing. IOP Conference Series: Materials Science and Engineering, 715(1). https://doi.org/10.1088/1757-899X/715/1/012084
[4] Leach, R., & Smith, S. T. (Eds.). (2018). Basics of Precision Engineering. CRC Press.
[5] David, F., & Hannaford, J. (2012). Good Practice Guide No. 80. National Physical Laboratory.
[6] International Organization for Standardization (2013). Geometrical product specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 1: Overview and metrological characteristics (ISO Standard No. ISO/TS 15530-1). https://www.iso.org/standard/38693.html
[7] Płowucha, W. (2019). Point-straight line distance as model for uncertainty evaluation of coordinate measurement. Measurement, 135, 83–95. https://doi.org/10.1016/j.measurement.2018.11.008
[8] Mussatayev, M., Huang, M., & Beshleyev, S. (2020). Thermal influences as an uncertainty contributor of the coordinate measuring machine (CMM). The International Journal of Advanced Manufacturing Technology, 111, pp. 537–547. https://doi.org/10.1007/s00170-020-06012-3
[9] Sładek, J., & G˛aska, A. (2012). Evaluation of coordinate measurement uncertainty with use of virtual machine model based on Monte Carlo method. Measurement, 45(6), 1564–1575. https://doi.org/10.1016/j.measurement.2012.02.020
[10] Saunders, P., Verma, M., Orchard, N., & Maropoulos, P. (2013). The application of uncertainty evaluating software for the utilisation of machine tool systems for final inspection. 10th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance, LAMDAMAP 2013, 219–228.
[11] International Organization for Standardization (2011). Geometrical product specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 3: Use of calibrated workpieces or measurement standards (ISO Standard No. 15530-3). https://www.iso.org/standard/53627.html
[12] International Organization for Standardization (2004). Geometrical Product Specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 3: Use of calibrated workpieces or standards (ISO Standard No. ISO/TS 15530-3). https://www.iso.org/standard/38695.html
[13] European Cooperation for Accreditation of Laboratories. (1995). Coordinate Measuring Machine Calibration [Publication Reference, EAL-G17].
[14] International Organization for Standardization. (2006). Geometrical product specifications (GPS) – Guidelines for the evaluation of coordinate measuring machine (CMM) test uncertainty (ISO Standard No. ISO/TS 23165). https://www.iso.org/standard/24236.html
[15] Fang, C. Y., Sung, C. K., & Lui, K. W. (2005). Measurement uncertainty analysis of CMM with ISO GUM. ASPE Proceedings, United States, 1758–1761.
[16] Płowucha, W. (2020). Point plane distances model for uncertainty evaluation of coordinate measurement. Metrology and Measurement Systems, 27(4), 625–639. https://doi.org/10.24425/mms.2020.134843
[17] Ruffa, S., Panciani, G. D., Ricci, F., & Vicario, G. (2013). Assessing measurement uncertainty in CMM measurements: comparison of different approaches. International Journal of Metrology and Quality Engineering, 4(3), 163–168. https://doi.org/10.1051/ijmqe/2013057
[18] Cheng Y. B., Chen X. H., & Li Y. R. (2020). Uncertainty Analysis and Evaluation of Form Measurement Task for CMM. Acta Metrologica Sinica, 41(2), 134–138. https://doi.org/10.3969/j.issn.1000-1158.2020.02.02 (in Chinese).
[19] Rost, K., Wendt, K., & Härtig, F. (2016). Evaluating a task-specific measurement uncertainty for gear measuring instruments via Monte Carlo simulation. Precision Engineering, 44, 220–230. https://doi.org/10.1016/j.precisioneng.2016.01.001
[20] Valdez, M. O.,&Morse, E. P. (2017). The role of extrinsic factors in industrial task-specific uncertainty. Precision Engineering, 49, 78–84. https://doi.org/10.1016/j.precisioneng.2017.01.013
[21] Yang, J., Li, G., Wu, B., Gong, J., Wang, J., & Zhang, M. (2015). Efficient methods for evaluating task-specific uncertainty in laser-tracking measurement. MAPAN-Journal Metrology Society of India, 30(2), 105–117. https://doi.org/10.1007/s12647-014-0126-9
[22] Haitjema, H. (2019). Calibration of displacement laser interferometer systems for industrial metrology. Sensors, 19(19), 4100. https://doi.org/10.3390/s19194100
[23] Doytchinov, I., Shore, P., Nicquevert, B., Tonnellier, X., Heather, A., & Modena, M. (2019). Thermal effects compensation and associated uncertainty for large magnet assembly precision alignment. Precision Engineering, 59, 134–149. https://doi.org/10.1016/j.precisioneng.2019.06.005
[24] Van Gestel, N. (2011). Determining measurement uncertainties of feature measurements on CMMs (Bepalen van meetonzekerheden bij het meten van vormelementen met CMMs) [Doctoral dissertation, Katholieke Universiteit Leuven]. Digital repository for KU Leuven Association. https://lirias.kuleuven.be/retrieve/157334 [25] Mussatayev, M., Huang, M., & Rysbayeva, G. (2019). Role of uncertainty calculation in dimensional metrology using Coordinate Measuring Machine. ARCTIC Journal, 72(6).
[26] International Organization for Standardization (2005). Test code for machine tools – Part 9: Estimation of measurement uncertainty for machine tool tests according to series ISO 230, basic equations (ISO Standard No. ISO/TR 230-9:2005). https://www.iso.org/standard/39165.html
[27] International Organization for Standardization (2008). Uncertainty of measurement-Part 3: Guide to the expression of uncertainty in measurement (GUM: 1995). https://www.iso.org/standard/50461.html
[28] Cheng, Y.,Wang, Z., Chen, X., Li, Y., Li, H., Li, H., &Wang, H. (2019). Evaluation and optimization of task-oriented measurement uncertainty for coordinate measuring machines based on geometrical product specifications. Applied Sciences, 9(1), 6. https://doi.org/10.3390/app9010006
[29] Jakubiec W., & Płowucha W. (2013). First Coordinate Measurements Uncertainty Evaluation Software Fully Consistent with the GPS Philosophy. Procedia CIRP, 10, 317–322. https://doi.org/10.1016/j.procir.2013.08.049
[30] International Organization for Standardization. (2013). Geometrical Product Specifications (GPS) – systematic errors and contributions to measurement uncertainty of length measurement due to thermal influences (ISO Standard No. ISO/TR 16015:2003). https://www.iso.org/standard/29436.html
[31] Huang, Z., Zhao, L., Li, K., Wang, H., & Zhou, T. (2019). A sampling method based on improved firefly algorithm for profile measurement of aviation engine blade. Metrology and Measurement Systems, 26(4), 757–771. https://doi.org/10.24425/mms.2019.130565
[32] Ramesh, R., Mannan, M. A., & Poo, A. N. (2000). Error compensation in machine tools. A review: part I: geometric, cutting-force induced and fixture-dependent errors. International Journal of Machine Tools and Manufacture, 40(9), 1235–1256. https://doi.org/10.1016/S0890-6955(00)00009-2
[33] International Organization for Standardization. (2004). Test conditions for numerically controlled turning machines and turning centres – Part 8: Evaluation of thermal distortions (ISO Standard No. ISO 13041-8:2004). https://www.iso.org/standard/34663.html
[34] Doytchinov, I., (2017). Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques. [Doctoral dissertation, Cranfield University]. CERN Document Server. https://cds.cern.ch/record/2299206
[35] Štrbac, B., Radlovacki, V., Spasic-Jokic, V., Delic, M., & Hadžistevic, M. (2017). The difference between GUM and ISO/TC 15530-3 method to evaluate the measurement uncertainty of flatness by a CMM. MAPAN, 32(4), 251–257. https://doi.org/10.1007/s12647-017-0227-3
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Authors and Affiliations

Meirbek Mussatayev
1
Meifa Huang
1
Marat Nurtas
2
Azamat Arynov
3

  1. Guilin University of Electronic Technology, School of Mechanical & Electrical Engineering, 1 Jinji Rd, Guilin, Guangxi, 541004, China
  2. International Information Technology University, Department of Mathematical and Computer Modelling, Kazakhstan
  3. School of Engineering at Warwick University, United Kingdom
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Abstract

The degradation of photovoltaic modules and their subsequent loss of performance has a serious impact on the total energy generation potential. The lack of real-time information on the output power leads to additional losses since the panels may not be operating at their optimal point. To understand the behaviour, numerically simulate the characteristics and identify the optimal operating point of a photovoltaic cell, the parameters of an equivalent electrical circuit must first be identified. The aim of this work is to develop a total least-squares based algorithm which can identify those parameters from the output voltage and current measurements, taking into consideration the uncertainties on both measured quantities. This work presents a comparative study of the Ordinary Least Squares (OLS) and Total Least Squares (TLS) approaches to the estimation of the parameters of a photovoltaic cell.
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Bibliography

[1] Blaabjerg, F., & Ionel, D. M. (2015). Renewable Energy Devices and Systems – State-of-the-Art Technology, Research and Development, Challenges and Future Trends. Electric Power Components and Systems, 43(12), 1319–1328. https://doi.org/10.1080/15325008.2015.1062819
[2] Höök, M., & Tang, X. (2013). Depletion of fossil fuels and anthropogenic climate change – A review. Energy Policy, 52, 797–809. https://doi.org/10.1016/j.enpol.2012.10.046
[3] Gangopadhyay, U., Jana, S., & Das, S. (2013). State of Art of Solar Photovoltaic Technology. Conference Papers in Energy, 2013, 1–9. https://doi.org/10.1155/2013/764132
[4] Mao, M., Cui, L., Zhang, Q., Guo, K., Zhou, L.,&Huang, H. (2020). Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies. Energy Reports, 6, 1312–1327.
[5] Ahmed, M. T., Rashel, M. R., Faisal, F.,&Tlemçani, M. (2020). Non-iterative MPPT Method:AComparative Study. International Journal of Renewable Energy Research (IJRER), 10(2), 549–557.
[6] Azmi, F. F. A., Sahraoui, B., & Muzakir, S. K. (2019). Study of ZnO nanospheres fabricated via thermal evaporation for solar cell application. Makara Journal of Technology, 23(1), 11–15.
[7] Boyd, M. T., Klein, S. A., Reindl, D. T., & Dougherty, B. P. (2011). Evaluation and validation of equivalent circuit photovoltaic solar cell performance models. Journal of Solar Energy Engineering, 133(2). https://doi.org/10.1115/1.4003584
[8] Bader, S., Ma, X., & Oelmann, B. (2020). A Comparison of One- and Two-Diode Model Parameters at Indoor Illumination Levels. IEEE Access, 8, 172057–172064. https://doi.org/10.1016/10.1109/ACCESS.2020.3025146
[9] Ciani, L., Catelani, M., Carnevale, E. A., Donati, L., & Bruzzi, M. (2015). Evaluation of the Aging Process of Dye-Sensitized Solar Cells Under Different Stress Conditions. IEEE Transactions on Instrumentation and Measurement, 64(5), 1179–1187. https://doi.org/10.1109/TIM.2014.2381352
[10] Ndiaye, A., Charki, A., Kobi, A., Kébé, C. M. F., Ndiaye, P. A., & Sambou, V. (2013). Degradations of silicon photovoltaic modules: A literature review. Solar Energy, 96, 140–151. https://doi.org/10.1016/10.1016/j.solener.2013.07.005
[11] Lay-Ekuakille, A., Ciaccioli, A., Griffo, G., Visconti, P., & Andria, G. (2018). Effects of dust on photovoltaic measurements: A comparative study. Measurement, 113, 181–188. http://dx.doi.org/10.1016/10.1016/j.measurement.2017.06.025
[12] Cristaldi, L., Faifer, M., Rossi, M., Toscani, S., Catelani, M., Ciani, L., & Lazzaroni, M. (2014). Simplified method for evaluating the effects of dust and aging on photovoltaic panels. Measurement, 54, 207–214. https://doi.org/10.1016/j.measurement.2014.03.001
[13] Carullo, A., Ferraris, F., Vallan, A., Spertino, F., & Attivissimo, F. (2014). Uncertainty analysis of degradation parameters estimated in long-term monitoring of photovoltaic plants. Measurement, 55, 641–649. https://doi.org/10.1016/j.measurement.2014.06.003
[14] Cubas, J., Pindado, S., & Victoria, M. (2014). On the analytical approach for modeling photovoltaic systems behavior. Journal of Power Sources, 247, 467–474. https://doi.org/10.1016/j.jpowsour.2013.09.008
[15] Batzelis, E. I., & Papathanassiou, S. A. (2016). A Method for the Analytical Extraction of the Single-Diode PV Model Parameters. IEEE Transactions on Sustainable Energy, 7(2), 504–512. https://doi.org/10.1109/TSTE.2015.2503435
[16] Hassan Ali, M., Rabhi, A., Haddad, S., & El Hajjaji, A. (2017). Real-Time Determination of Solar Cell Parameters. Journal of Electronic Materials, 46(11), 6535–6543. https://doi.org/10.1016/10.1007/s11664-017-5697-0
[17] Subudhi, B., & Pradhan, R. (2018). Bacterial Foraging Optimization Approach to Parameter Extraction of a Photovoltaic Module. IEEE Transactions on Sustainable Energy, 9(1), 381–389. https://doi.org/10.1109/TSTE.2017.2736060
[18] Long, W., Cai, S., Jiao, J., Xu, M., & Wu, T. (2020). A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management, 203, 112243. https://doi.org/10.1016/j.enconman.2019.112243
[19] Liao, Z., Chen, Z., & Li, S. (2020). Parameters Extraction of Photovoltaic Models Using Triple- Phase Teaching-Learning-Based Optimization. IEEE Access, 8, 69937–69952. https://doi.org/10.1016/10.1109/ACCESS.2020.2984728
[20] Ibrahim, I. A., Hossain, M. J., Duck, B. C., & Fell, C. J. (2020). An AdaptiveWind-Driven Optimization Algorithm for Extracting the Parameters of a Single-Diode PV Cell Model. IEEE Transactions on Sustainable Energy, 11(2), 1054–1066. https://doi.org/10.1109/TSTE.2019.2917513
[21] Gomes, R. C. M., Vitorino, M. A., Corrêa, M. B. de R., Fernandes, D. A.,&Wang, R. (2017). Shuffled Complex Evolution on Photovoltaic Parameter Extraction:AComparative Analysis. IEEE Transactions on Sustainable Energy, 8(2), 805–815. https://doi.org/10.1109/TSTE.2016.2620941
[22] Dkhichi, F., Oukarfi, B., Fakkar, A., & Belbounaguia, N. (2014). Parameter identification of solar cell model using Levenberg–Marquardt algorithm combined with simulated annealing. Solar Energy, 110, 781–788. https://doi.org/10.1016/j.solener.2014.09.033
[23] Alam, D. F., Yousri, D. A., & Eteiba, M. B. (2015). Flower Pollination Algorithm based solar PV parameter estimation. Energy Conversion and Management, 101, 410–422. https://doi.org/10.1016/j.enconman.2015.05.074
[24] Diab, A. A. Z., Sultan, H. M., Do, T. D., Kamel, O. M., & Mossa, M. A. (2020). Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules. IEEE Access, 8, 111102–111140. https://doi.org/10.1109/ACCESS.2020.3000770
[25] Mesbahi, O., Tlemçani, M., Janeiro, F. M., Hajjaji, A., & Kandoussi, K. (2020). A Modified Nelder– Mead Algorithm for Photovoltaic Parameters Identification. International Journal of Smart Grid – IJSmartGrid, 4(1), 28–37.
[26] Mesbahi, O., Tlemçani, M., Janeiro, F. M., Abdeloawahed, H., & Khalid, K. (2019). Estimation of Photovoltaic Panel Parameters by a Numerical Heuristic Searching Algorithm. In 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 401-406). IEEE. https://doi.org/10.1109/ICRERA47325.2019.8996779
[27] Hutcheson, G. D. (2011). Ordinary least-squares regression. L. Moutinho and G.D. Hutcheson, The SAGE Dictionary of Quantitative Management Research, 224–228.
[28] Hadjdida, A., Bourahla, M., Ertan, H. B., & Bekhti, M. (2018). Analytical Modelling, Simulation and Comparative Study of Multi-Junction Solar Cells Efficiency. International Journal of Renewable Energy Research, 8(4), 1824–1832.
[29] Salmi, T., Bouzguenda, M., Gastli, A.,&Masmoudi, A. (2012).MATLAB / Simulink Based Modelling of Solar Photovoltaic Cell. International Journal of Renewable Energy Research (IJRER), 2(2), 213– 218.
[30] Dimova-Malinovska, D. (2010). The state-of-the-art and future development of the photovoltaic technologies – The route from crystalline to nanostructured and new emerging materials. Journal of Physics: Conference Series, 253(1). https://doi.org/10.1088/1742-6596/253/1/012007
[31] Mahmoud, Y., Xiao, W., & Zeineldin, H. H. (2012). A Simple Approach to Modeling and Simulation of Photovoltaic Modules. IEEE Transactions on Sustainable Energy, 3(1), 185–186. https://doi.org/10.1109/TSTE.2011.2170776
[32] Ishaque, K., Salam, Z., & Taheri, H. (2011). Simple, fast and accurate two-diode model for photovoltaic modules. Solar Energy Materials and Solar Cells, 95(2), 586–594. https://doi.org/10.1016/j.solmat.2010.09.023
[33] Babu, B. C., & Gurjar, S. (2014). A Novel Simplified Two-Diode Model of Photovoltaic (PV) Module. IEEE Journal of Photovoltaics, 4(4), 1156–1161. https://doi.org/10.1109/JPHOTOV.2014.2316371
[34] Nelder, J. A.,&Mead, R. (1965).Asimplex method for function minimization. The Computer Journal, 7(4), 308–313. https://doi.org/10.1093/comjnl/7.4.308
[35] Rashel, M. R., Rifat, J., Gonçalves, T., Tlemcani, M., & Melicio, R. (2017). Sensitivity Analysis Through Error Function of Crystalline-Si Photovoltaic Cell Model Integrated in a Smart Grid. International Journal of Renewable Energy Research, 7(4).
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Authors and Affiliations

Oumaima Mesbahi
1 2
Mouhaydine Tlemçani
1 2
Fernando M. Janeiro
1 2 3
Abdeloawahed Hajjaji
4
Khalid Kandoussi
4

  1. University of Évora, Department of Mechatronics, R. Romão Ramalho 59, 7000-671 Évora, Portugal
  2. Instrumentation and Control Laboratory, Institute of Earth Sciences, Évora, Portugal
  3. Instituto de Telecomunicações, Lisbon, Portugal
  4. University of Chouaib Doukkali, Energy Engineering Laboratory, National School of Applied Sciences, El Jadida, Morocco
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Abstract

The rapidly developing measurement techniques and emerging new physical methods are frequently used in otolaryngological diagnostics. A wide range of applied diagnostic methods constituted the basis for the review study aimed at presenting selected modern diagnostic methods and achieved diagnostic results to a wider group of users. In this part, the methods based on measuring the respiratory parameters of patients were analysed. Respiration is the most important and necessary action to support life and its effective duration. It is an actual gas exchange in the respiratory system consisting of removing CO2 and supplying O2. Gas exchange occurs in the alveoli, and an efficient respiratory tract allows for effective ventilation. The disruption in the work of the respiratory system leads to measurable disturbances in blood saturation and, consequently, hypoxia. Frequent, even short-term, recurrent hypoxia in any part of the body leads to multiple complications. This process is largely related to its duration and the processes that accompany it. The causes of hypoxia resulting from impaired patency of the respiratory tract and/or the absence of neuronal respiratory drive can be divided into the following groups depending on the cause: peripheral, central and/or of mixed origin. Causes of the peripheral form of these disorders are largely due to the impaired patency of the upper and/or lower respiratory tract. Therefore, early diagnosis and location of these disorders can be considered reversible and not a cause of complications. Slow, gradually increasing obstruction of the upper respiratory tract (URT) is not noticeable and becomes a slow killer. Hypoxic individuals in a large percentage of cases have a shorter life expectancy and, above all, deal with the consequences of hypoxia much sooner.
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Bibliography

[1] Anniko, M., Bernal-Sprekelsen, M., Bonkowsky, V., Bradley, P. J., & Iurato, S. (2010). Otorhinolaryngology, Head and Neck Surgery. Berlin: Springer. https://www.doi.org/10.1007/978-3-540-68940-9
[2] Önerci, M., Ferguson, B. (2010). Diagnosis in Otorhinolaryngology. Berlin, Heidelberg: Springer-Verlag. https://www.doi.org/10.1007/978-3-642-11412-0
[3] Guibas, G., & Papadopoulos, N. (2017). Viral Upper Respiratory Tract Infections. Viral Infections in Children, II, 1-25. Springer, Cham. https://www.doi.org/10.1007/978-3-319-54093-1_1
[4] Kumpitsch, C., Koskinen, K., & Schöpf, V. Moissl-Eichinger, C. (2019). The microbiome of the upper respiratory tract in health and disease. BMC Biol, 17(87). https://doi.org/10.1186/s12915-019-0703-z
[5] DeBerry-Borowiecki, B., Kukwa, A., & Blanks, R. H. I. (1988). Cefalometric analysis for diagnosis and treatment of obstructive sleep apnea. BMC Biol, 98(2), 226-234. https://doi.org/10.1288/00005537-198802000-00021
[6] Jarmołowicz-Aniołkowska, N. (2020). Private report.
[7] Rybak, A., Zaj˛ac, A., & Kukwa, A. (2019). Measurement of the upper respiratory tract aerated space volume using the results of computed tomography. Metrology and Measurement Systems, 26(2), 387– 401. https://doi.org/10.24425/mms.2019.128366
[8] Nitkiewicz, Sz., Baranski, R.,Kukwa, A.,&Zaj˛ac, A. (2018). Respiratory disorders, measuring method and equipment. Metrology and Measurement Systems, 25(1), 187–202. https://doi.org/10.24425/118157
[9] Nitkiewicz, Sz. (2018). Wspomaganie diagnostyki wybranych schorzen dróg oddechowych [Doctoral dissertation, Białystok University of Technology]. (in Polish).
[10] Mitchel, C. (2017). Endoscopic Examination of the Upper Respiratory Tract. In L. R. R. Costa, & M. R. Paradis (Eds.) Manual of Clinical Procedures in the Horse (1th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118939956.ch20
[11] Zając, A., Gryko, Ł., & Gilewski, M. (2015). Temperature stabilization of the set of laser diodes working independently. Electrical Review, 91(2), 196–198. https://doi.org/10.15199/48.2015.02.44
[12] Zając, A., Kasprzak, J., Urbanski, Ł., Gryko, Ł., Szymanska, J., & Maciejewska, M. (2016). Swiatło w diagnostyce medycznej. In A. Michalski (Ed.). Metrologia w medycynie – wybrane zagadnienia. (pp. 219-298). WAT. (in Polish)
[13] Polak, A. G., & Hantos, Z. (2019). Simulation of respiratory impedance variations during normal breathing using a morphometric model of the lung. In World Congress on Medical Physics and Biomedical Engineering 2018 (pp. 553–557). Springer, Singapore. https://doi.org/10.1007/978-981-10-9035-6_102
[14] Polak, A. G., & Mroczka, J. (2017, May). Modeling the impact of heterogeneous airway narrowing on the spirometric curve. In Proceedings of the 9th International Conference on Bioinformatics and Biomedical Technology (pp. 70–75). https://doi.org/10.1145/3093293.3093301 (in Polish).
[15] Nyquist, H. (1928). Certain topics in Telegraph Transmission Theory. Transaction of the American Institute of Electrical Engineers. 47(2). https://doi.org/10.1109/T-AIEE.1928.5055024
[16] Bialasiewicz, J. T. (2015, July). Application of wavelet scalogram and coscalogram for analysis of biomedical signals. In Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science (Vol. 333). Spain. https://avestia.com/EECSS2015_Proceedings/files/papers/ ICBES333.pdf
[17] Daubechies, I. (1992). Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611970104
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Authors and Affiliations

Andrzej Kukwa
1
Andrzej Zając
2
Robert Barański
3
Szymon Nitkiewicz
4 5
Wojciech Kukwa
6
Edyta Zomkowska
7
Adam Rybak
8

  1. University of Warmia and Mazury, Olsztyn, Department and Clinic of Otorhinolaryngology, Head and Neck Diseases, Collegium Medicum, Warszawska St. 30, 10-082 Olsztyn, Poland
  2. Military University of Technology, Warsaw, Institute of Optoelectronics, Kaliskiego St., 2, 00-908, Warsaw, Poland
  3. AGH University of Science and Technology in Kraków, Department of Mechanics and Vibroacoustics, Mickiewicza St. 30, 30-059 Kraków, Poland
  4. University of Warmia and Mazury in Olsztyn, Department of Mechatronics, Faculty of Technical Science, Oczapowskiego St. 2, Olsztyn, Poland
  5. University of Warmia and Mazury in Olsztyn, Department of Neurosurgery, School of Medicine, Oczapowskiego St. 2, Olsztyn, Poland
  6. Medical University of Warsaw, Warsaw, Faculty of Dental Medicine, Zwirki i Wigury St. 61, 02-091 Warsaw, Poland
  7. University Hospital in Olsztyn, Clinic of Otorhinolaryngology, Head and Neck Surgery, Warszawska St. 30,10-082 Olsztyn, Poland
  8. LABSOFT Sp. z o. o., Puławska St. 469, 02-844 Warsaw, Poland
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Abstract

The determination of precise emitter location is a very important task in electronic intelligence systems. Its basic requirements include the detection of the emission of electromagnetic sources (emitters), measurement of signal parameters, determining the direction of emitters, signal analysis, and the recognition and identification of their sources. The article presents a new approach and algorithm for calculating the location of electromagnetic emission sources (radars) in a plane based on the bearings in the radio-electronic reconnaissance system. The main assumptions of this method are presented and described i.e. how the final mathematical formulas for calculating the emitter location were determined for any number of direction finders (DFs). As there is an unknown distance from the emitter to the DFs then in the final formulas it is stated how this distance should be calculated in the first iteration. Numerical simulation in MATLAB showed a quick convergence of the proposed algorithm to the fixed value in the fourth/fifth iteration with an accuracy less than 0.1 meter. The computed emitter location converges to the fixed value regardless of the choice of the starting point. It has also been shown that to precisely calculate the emitter position, at least a dozen or so bearings from each DFs should be measured. The obtained simulation results show that the precise emitter location depends on the number of DFs, the distances between the localized emitter and DFs, their mutual deployment, and bearing errors. The research results presented in the article show the usefulness of the tested method for the location of objects in a specific area of interest. The results of simulation calculations can be directly used in radio-electronic reconnaissance systems to select the place of DFs deployment to reduce the emitter location errors in the entire reconnaissance area.
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Bibliography

[1] Willey, R. G. (1985). Electronic Intelligence: The Interception of Radar Signals. Artech House.
[2] Oshman, Y., & Davidson, P. (1999). Optimization of observer trajectories for bearings-only target localization. IEEE Transactions on Aerospace and Electronic Systems, 35(3), 892–902. https://doi.org/10.1109/7.784059
[3] Tehrani, M. A., Laurin, J. J., & Savaria, Y. (2016). Multiple targets direction-of-arrival estimation in frequency scanning array antennas. IET Radar, Sonar & Navigation, 10(3), 624–631. https://doi.org/10.1049/iet-rsn.2015.0401
[4] Rutkowski, A., & Kawalec, A. (2020). Some of Problems of Direction Finding of Ground-Based Radars Using Monopulse Location System Installed on Unmanned Aerial Vehicle. Sensors, 20(18), 5186. https://doi.org/10.3390/s20185186
[5] Wang, Y., Jie, H., & Cheng, L. (2019). A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality forWireless Sensor Networks. Sensors, 19(16), 3638. https://doi.org/10.3390/s19173638
[6] Chow, T. L. (2001). Passive emitter location using digital terrain data [Doctoral dissertation, Binghamton University State University of New York].
[7] Poisel, R. A. (2012). Electronic Warfare Target Location Methods (2nd. ed.). Artech House.
[8] Willey, R. G. (2006). ELINT. The Interception and Analysis of Radar Signals. Horizon House Publications.
[9] Chan, Y. T., & Ho, K. C. (1994). A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 42(8), 1905–1915. https://doi.org/10.1109/78.301830
[10] Bugaj, J., & Górny, K. (2019, March). Analysis of estimation algorithms for electromagnetic source localization. In XII Conference on Reconnaissance and Electronic Warfare Systems (Vol. 11055, p. 110550W). International Society for Optics and Photonics. https://doi.org/10.1117/12.2524927
[11] O’Connor, A., Setlur, P., & Devroye, N. (2015). Single-sensor RF emitter localization based on multipath exploitation. IEEE Transactions on Aerospace and Electronic Systems, 51(3), 1635–1651. https://doi.org/10.1109/TAES.2015.120807
[12] Adamy, D. L. (2001). EW 101. A First Course in Electronic Warfare. Artech House. [13] Adamy, D. L. (2004). EW 102. A Second Course in Electronic Warfare. Horizon House Publications.
[14] Becker, K. (1992). An efficient method of passive emitter location. IEEE Transactions on Aerospace and Electronic Systems, 28(4), 1091–1104. https://doi.org/10.1109/7.165371
[15] Foy, W. H. (1976). Position-location solutions by Taylor-series estimation. IEEE Transactions on Aerospace and Electronic Systems, AES-12(2), 187–194. https://doi.org/10.1109/TAES.1976.308294
[16] Mangel, M. (1981). Three Bearing Method for Passive Triangulation in Systems with Unknown Deterministic Biases. IEEE Transactions on Aerospace and Electronic Systems, AES-17(6), 814–819. https://doi.org/10.1109/TAES.1981.309133
[17] Adamy, D. L. (2005). Emitter Location: Reporting Location Accuracy. The Journal of Electronic Defense, (7).
[18] Kelner, J. M., & Ziółkowski, C. (2020). Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions. Sensors, 20(9), 2575. https://doi.org/10.3390/s20092575
[19] Mahapatra, P. R. (1980). Emitter location independent of systematic errors in direction finders. IEEE Transactions on Aerospace and Electronic Systems, AES-16(6), 851–855. https://doi.org/10.1109/TAES.1980.309009
[20] Matuszewski, J., & Dikta, A. (2017, April). Emitter location errors in electronic recognition system. In XI Conference on Reconnaissance and Electronic Warfare Systems (Vol. 10418, p. 104180C). International Society for Optics and Photonics. https://doi.org/10.1117/12.2269295
[21] Stansfield, R. G. (1947). Statistical theory of DF fixing. Journal of the Institution of Electrical Engineers-Part IIIA: Radiocommunication, 94(14), 762–770. https://doi.org/10.1049/ji-3a-2.1947.0096
[22] Vakin, S. A., Shustov, L. N., Dunwell, R. H. (2001). Fundamentals of Electronic Warfare. Artech House.
[23] Bature, U. I., Tahir, N. M., Yakub, N. A., & Baba, M. A. (2020). Multi-baseline Emitter Location System: A Correlative Interferometer Approach. Nigerian Journal of Engineering, 27(2), 92–98.
[24] Becker, K. (1992). An efficient method of passive emitter location. IEEE Transactions on Aerospace and Electronic Systems, 28(4), 1091–1104. https://doi.org/10.1109/7.165371
[25] Tian, B., Huang, H., & Li, Y. (2009, September). Direction of arrival estimation using nonlinear function of sum and difference beam. In 2009 IEEE Youth Conference on Information, Computing and Telecommunication (pp. 311–314). IEEE. https://doi.org/10.1109/YCICT.2009.5382360
[26] Ghilani, C. D., &Wolf, P. R. (2007). Adjustment Computations: Spatial Data Analysis (4th ed.). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470121498
[27] Gavish, M., & Weiss, A. J. (1992). Performance analysis of bearing-only target location algorithms. IEEE Transactions on Aerospace and Electronic Systems, 28(3), 817–828. https://doi.org/10.1109/7.256302
[28] He, Y., Behnad, A., & Wang, X. (2015). Accuracy analysis of the two-reference-node angle-of-arrival localization system. IEEE Wireless Communications Letters, 4(3), 329–332. https://doi.org/10.1109/LWC.2015.2415788
[29] Kukes, I. S., Starik, M. Ye. (1964). Principles of Radio Direction Finding. Soviet Radio Publishing House. (in Russian)
[30] Paradowski, L. R. (1998, May). Microwave emitter position location: present and future. In 12th International Conference on Microwaves and Radar. MIKON-98. Conference Proceedings (IEEE Cat. No. 98EX195) (pp. 97–116). IEEE. https://doi.org/10.1109/MIKON.1998.738464
[31] Paradowski, L. R. (1997). Uncertainty ellipses and their application to interval estimation of emitter position. IEEE Transactions on Aerospace and Electronic Systems, 33(1), 126–133. https://doi.org/10.1109/7.570715
[32] Rui, L., & Ho, K. C. (2014). Elliptic localization: Performance study and optimum receiver placement. IEEE Transactions on Signal Processing, 62(18), 4673–4688. https://doi.org/10.1109/TSP.2014.2338835
[33] Shirman Ya. D. (Eds.). (1970). Theoretical fundamentals of radiolocation. Sovietskoe Radio. (in Russian)
[34] Tondwalkar, A. V., & Vinayakray-Jani, P. (2015, December). Terrestrial localization by using angle of arrival measurements in wireless sensor network. In 2015 International Conference on Computational Intelligence and CommunicationNetworks (CICN) (pp. 188–191). IEEE. https://doi.org/10.1109/CICN.2015.44
[35] Wang, Z., Luo, J. A.,&Zhang, X. P. (2012).Anovel location-penalized maximum likelihood estimator for bearing-only target localization. IEEE Transactions on Signal Processing, 60(12), 6166–6181. https://doi.org/10.1109/TSP.2012.2218809
[36] Ziółkowski, C., & Kelner, J. M. (2015). The influence of propagation environment on the accuracy of emission source bearing. Metrology and Measurement Systems, 22(4), 591–600.
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Authors and Affiliations

Jan Matuszewski
1
Tomasz Kraszewski
1
ORCID: ORCID

  1. Military University of Technology, Faculty of Electronics, Institute of Radioelectronics, gen. S. Kaliskiego 2, 00–908 Warsaw, Poland
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Abstract

Eye tracking systems are mostly video-based methods which require significant computation to achieve good accuracy. An alternative method with comparable accuracy but less computational expense is 2D microelectromechanical (MEMS) mirror scanning. However, this technology is relatively new and there are not many publications on it. The purpose of this study was to examine how individual parameters of system components can affect the accuracy of pupil position estimation. The study was conducted based on a virtual simulator. It was shown that the optimal detector field of view (FOV) depends on the frequency ratio of the MEMS mirror axis. For a value of 1:13, the smallest errors were at 0.°, 1.65°, 2.3°, and 2.95°. The error for the impact of the signal sampling rate above 3 kHz stabilizes at 0.065° and no longer changes its value regardless of increasing the number of samples. The error for the frequency ratio of the MEMS mirror axis increases linearly in the range of 0.065°–0.1°up to the ratio of 1:230. Above this there is a sudden increase to the average value of 0.3°. The conducted research provides guidance in the selection of parameters for the construction of eye tracking MEMS mirror-based systems.
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Bibliography

[1] Duchowski, A. T., (2017). Eye tracking methodology: Theory and practice. Springer. https://doi.org/10.1007/978-3-319-57883-5
[2] Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009, September). Learning to predict where humans look. IEEE 12th International Conference on Computer Vision (pp. 2106–2113). IEEE. https://doi.org/10.1109/ICCV.2009.5459462
[3] Goldberg, J. H., & Kotval, X. P. (1999). Computer interface evaluation using eye movements: methods and constructs. International Journal of Industrial Ergonomics, 24(6), 631–645. https://doi.org/10.1016/S0169-8141(98)00068-7
[4] Hansen, D. W., & Ji, Q. (2009). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 478–500. https://doi.org/10.1109/TPAMI.2009.30
[5] Carvalho, N., Laurent, E., Noiret, N., Chopard, G., Haffen, E., Bennabi, D., & Vandel, P. (2015). Eye movement in unipolar and bipolar depression: A systematic review of the literature. Frontiers in Psychology, 6, 1809. https://doi.org/10.3389/fpsyg.2015.01809
[6] Bittencourt, J., Velasques, B., Teixeira, S., Basile, L. F., Salles, J. I., Nardi, A. E., Budde, H., Cagy, M., Piedade, R., & Ribeiro, P. (2013). Saccadic eye movement applications for psychiatric disorders. Neuropsychiatric Disease and Treatment, 9, 1393. https://doi.org/10.2147/NDT.S45931
[7] Duchowski, A. T., Medlin, E., Gramopadhye, A., Melloy, B., & Nair, S. (2001, November). Binocular eye tracking in VR for visual inspection training. Proceedings of the ACM symposium on Virtual reality software and technology (pp. 1–8). https://doi.org/10.1145/505008.505010
[8] Blattgerste, J., Renner, P., & Pfeiffer, T. (2018, June). Advantages of eye-gaze over head-gaze-based selection in virtual and augmented reality under varying field of views. Proceedings of the Workshop on Communication by Gaze Interaction (pp. 1–9). https://doi.org/10.1145/3206343.3206349
[9] Pasarica, A., Bozomitu, R. G., Cehan, V., Lupu, R. G., & Rotariu, C. (2015, October). Pupil detection algorithms for eye tracking applications. 2015 IEEE 21st International Symposium for Design and Technology in Electronic Packaging (SIITME) (pp. 161–164). IEEE. https://doi.org/10.1109/SIITME.2015.7342317 [10] Stengel, M., Grogorick, S., Eisemann, M., Eisemann, E., & Magnor, M. A. (2015, October). An affordable solution for binocular eye tracking and calibration in head-mounted displays. Proceedings of the 23rd ACM international conference on Multimedia (pp. 15–24). https://doi.org/10.1145/2733373.2806265
[11] Wen, Q., Bradley, D., Beeler, T., Park, S., Hilliges, O.,Yong, J.,&Xu, F. (2020).Accurate Real-time 3D Gaze Tracking Using a Lightweight Eyeball Calibration. Computer Graphics Forum, 39(2), 475–485. https://doi.org/10.1111/cgf.13945
[12] Lee, G. J., Jang, S. W., & Kim, G. Y. (2020). Pupil detection and gaze tracking using a deformable template. Multimedia Tools and Applications, 79(19), 12939–12958. https://doi.org/10.1007/ s11042-020-08638-7
[13] Gegenfurtner, A., Lehtinen, E., & Säljö, R. (2011). Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23(4), 523–552. https://doi.org/10.1007/s10648-011-9174-7
[14] Sarkar, N., O’Hanlon, B., Rohani, A., Strathearn, D., Lee, G., Olfat, M., & Mansour, R. R. (2017, January). A resonant eye-tracking microsystem for velocity estimation of saccades and foveated rendering. IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS) (pp. 304–307). IEEE. https://doi.org/10.1109/MEMSYS.2017.7863402
[15] Bartuzel, M. M., Wróbel, K., Tamborski, S., Meina, M., Nowakowski, M., Dalasinski, K., Szkulmowska, A. & Szkulmowski, M. (2020). High-resolution, ultrafast, wide-field retinal eye-tracking for enhanced quantification of fixational and saccadic motion. Biomedical Optics Express, 11(6), 3164–3180. https://doi.org/10.1364/BOE.392849
[16] Meyer, J., Schlebusch, T., Fuhl, W., & Kasneci, E. (2020). A novel camera-free eye tracking sensor for augmented reality based on laser scanning. IEEE Sensors Journal, 20(24), 15204–15212. https://doi.org/10.1109/JSEN.2020.3011985
[17] Pomianek, M., Piszczek, M., Maciejewski, M., & Krukowski, P. (2020, October). Pupil Position Estimation Error in an Eye Tracking System Based on the MEMS Mirror Scanning Method. Proceedings of the 3rd International Conference on Microelectronic Devices and Technologies (MicDAT’ 2020) (pp. 28–30). IFSA.
[18] Pengfei, Y., Zhengming, C., Jing, T., & Lina, Q. (2016). Virtual Simulation System of Cutter Suction Dredger Based on Unity3D. Journal of Systems Simulation, 28(9), 2069–2075.
[19] Richards, D., & Taylor, M. (2015). A Comparison of learning gains when using a 2D simulation tool versus a 3D virtual world: An experiment to find the right representation involving the Marginal Value Theorem. Computers & Education, 86, 157–171. https://doi.org/10.1016/j.compedu.2015.03.009
[20] Müller, L. M., Mandon, K., Gliesche, P., Weiß, S., & Heuten, W. (2020, November). Visualization of Eye Tracking Data in Unity3D. 19th International Conference on Mobile and Ubiquitous Multimedia (pp. 343–344). https://doi.org/10.1145/3428361.3431194
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Authors and Affiliations

Mateusz Pomianek
1
Marek Piszczek
1
Marcin Maciejewski
1

  1. Military University of Technology, Institute of Optoelectronics, 2 Kaliskiego St., 00-908 Warsaw, Poland
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Abstract

The aim of this paper is to compare three different methods of analysis of results of lightning impulse breakdown voltage measurements of solid materials such as insulating pressboard. These three methods are the series method, the step method and the up-and-down method which are applied to withstand voltage estimation commonly in high voltage engineering. To obtain the data needed for the analysis a series of experimental studies was carried out. It included studies of mineral oil and natural ester impregnating 1 mm of thick cellulose-based pressboard. In order to show the distribution of breakdown voltage the Weibull distribution was additionally applied in data analysis. The results were also assessed from the viewpoint of dielectric liquid used for impregnation. The studies carried out showed that series and step methods give comparable results opposite to the up-and-down method. The latest overstates the results for mineral oil impregnated pressboard and understates for natural ester impregnated pressboard when juxtaposing them with the rest of the methods applied. In addition, there is lack of possibility to assess the withstand voltage for the up-and-down method directly from the vector of random variable. It is possible only as a result of a specially developed equation which always arouses doubt. From the methods applied it seems that the step method can be a great substitution for the series method as intuitive, fast in application and limiting the number of samples in solid insulation material testing.
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Bibliography

[1] Liu, Q.,Wang, Z. D., & Perrot, F. (2009). Impulse breakdown voltages of ester-based transformer oils determined by using different test methods. IEEE Conference on Electrical Insulation and Dielectric Phenomena, 608–612. https://doi.org/10.1109/CEIDP.2009.5377741
[2] Rozga, P. (2016). Streamer propagation in a non-uniform electric field under lightning impulse in short gaps insulated with natural ester and mineral oil. Bulletin of the Polish Academy of Sciences: Technical Science, 64(1), 171–179. https://doi.org/10.1515/bpasts-2016-0019
[3] Rozga, P. (2016). Using the three-parameter Weibull distribution in assessment of threshold strength of pressboard impregnated by different liquid dielectrics. IET Science, Measurement & Technology, 10(6), 665–670. https://doi.org/10.1049/iet-smt.2016.0061
[4] Aniserowicz, K. (2019). Analytical calculations of surges caused by direct lightning strike to underground intrusion detection system. Bulletin of the Polish Academy of Sciences: Technical Science, 67(2), 263–269. https://doi.org/10.24425/bpas.2019.128118
[5] Mosinski, F. (1995). Metody statystyczne w technice wysokich napięć. Wydawnictwo Politechniki Łódzkiej. (in Polish)
[6] Vibholm, S., & Thyregod, P. (1988). A study of the up-and-down method for non-normal distribution functions. IEEE Transactions on Electrical Insulation, 23(3), 357–364. https://doi.org/10.1109/14.2375
[7] Rozga, P. (2019). Lightning strength of gas, liquid and solid insulation – experience formthe laboratory tests. The International Conference on Power Transformers “Transformer’19”, 199–212.
[8] Khaled, U., & Beroual, A. (2020). Lightning impulse breakdown voltage of synthetic and natural ester liquids-based Fe3O4, Al2O3 and SiO2 nanofluids. Alexandria Engineering Journal, 59(5), 3709–3713. https://doi.org/10.1016/j.aej.2020.06.025
[9] Zhang, Q., You, H., Guo, C., Qin, Y., Ma, J., &Wen, T. (2016) Experimental research of dispersion of SF6 discharge breakdown voltage under lighting impulse. High Voltage Engineering, 42(11), 3415– 3420.
[10] Zhang, Y., Xie, S., Jiang, X., Ye, L., Zhang, Ch., Sun, P., Mu, Z., & Sima, W. (2019). Study on consistency of failure probability characteristics of oil-paper insulation under different impulse voltages. Proceedings of the 21st International Symposium on High Voltage Engineering, 1192–1206. https://doi.org/10.1007/978-3-030-31676-1_111
[11] Cousineau, D. (2009). Fitting the three-parameter Weibull distribution: review and evaluation of existing and new methods. IEEE Transactions on Dielectrics and Electrical Insulation, 16(1), 281– 288. https://doi.org/10.1109/TDEI.2009.4784578
[12] European Standards. (2014). Electric strength of insulating materials – Test methods – Part 3: Additional requirements for 1,2/50 μs impulse tests (IEC 60243-3: 2014).
[13] Witos, F., Opilski, Z., Szerszen, G., & Setkiewicz, M. (2019). The 8AE-PD computer measurement system for registration and analysis of acoustic emission signals generated by partial discharges in oil power transformers. Metrology and Measurement Systems, 26(2), 403–418. https://doi.org/10.24425/mms.2019.128355
[14] Shen, Z., Wang, F., Wang, Z., Li, J. (2021). A critical review of plant-based insulating fluids for transformer: 30 years of development. Renewable and Sustainable Energy Reviews, 41, 110783. https://doi.org/10.1016/j.rser.2021.110783
[15] Liu, Q., & Wang, Z. D. (2013) Breakdown and withstand strengths of ester transformer liquids in a quasi-uniform field under impulse voltages. IEEE Transactions on Dielectrics and Electrical Insulation, 20(2), 571–579. https://doi.org/10.1109/TDEI.2013.6508761
[16] Mohan Rao, U., Fofana, I., Beroual, A., Rozga, P., Pompili, M., Calcara, L., & Rapp, K. J. (2020). A review on pre-breakdown phenomena in ester fluids: Prepared by the international study group of IEEE DEIS liquid dielectrics technical committee. IEEE Transactions on Dielectrics and Electrical Insulation, 27(5), 1546–1560. https://doi.org/10.1109/TDEI.2020.008765
[17] Dixon,W. J. (1965). The Up-and-Down method for small samples. Journal of the American Statistical Association, 60, 967–978.
[18] Malska,W., & Mazur, D. (2017). Analiza wpływu prędkosci wiatru na generację mocy na przykładzie farmy wiatrowej. Przegląd Elektrotechniczny, 93(4), 54–57 https://doi.org/10.15199/48.2017.04.14
[19] Kalbfleisch, J. D., & Prentice, R. L. (2002). The statistical analysis of failure time data (2nd ed.). J. Wiley. https://doi.org/10.1002/9781118032985
[20] De Haan, L., & Ferreira, A. (2007). Extreme value theory: an introduction. Springer Science & Business Media. https://doi.org/10.1007/0-387-34471-3
[21] Chmura, L., Morshuis, P. H. F., Smit, J. J., & Janssen, A. (2015). Life-data analysis for condition assessment of high-voltage assets. IEEE Electrical Insulation Magazine, 31(5), 20–25. https://doi.org/10.1109/MEI.2015.7214443
[22] Cargill. (2018). https://www.cargill.com/bioindustrial/fr3-fluid/fr3-fluid-technical-details [23] Nynas. (20210). Nytro Taurus (IEC 60296) Ed. 5 – Standard Grade. https://www.nynas.com/en/product-areas/transformer-oils/oils/nytro-taurus/
[24] Rozga P., Beroual A., Przybylek P., Jaroszewski M., & Strzelecki K. (2020). A review on synthetic ester liquids for transformer applications. Energies, 13(23), 6429. https://doi.org/10.3390/en13236429
[25] European Standards. (2011). Power transformers – Part 1: General (IEC 60076-1:2011)
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Authors and Affiliations

Artur Klarecki
1 2
Paweł Rózga
1
Filip Stuchała
1

  1. Lodz University of Technology, Institute of Electrical Power Engineering, Stefanowskiego 18/22, 90-924 Lodz, Poland
  2. Lodz University of Technology, Interdisciplinary Doctoral School, Zeromskiego 116, 90-924 Lodz, Poland
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Abstract

Noise is a fundamental metrological characteristic of the instrument in surface topography measurement. Therefore, measurement noise should be thoroughly studied in practical measurement to understand instrument performance and optimize measurement strategy. This paper investigates the measurement noise at different measurement settings using structured illumination microscopy. The investigation shows that the measurement noise may scatter significantly among different measurement settings. Eliminating sample tilt, selecting low vertical scanning interval and high exposure time is helpful to reduce the measurement noise. In order to estimate the influence of noise on the measurement, an approach based on metrological characteristics is proposed. The paper provides a practical guide to understanding measurement noise in a wide range of applications.
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Bibliography

[1] International Organization for Standardization. (2019). Geometrical product specifications (GPS) – Surface texture: Areal – Part 600: Metrological characteristics for areal topography measuring methods (ISO 25178-600:2019). https://www.iso.org/standard/67651.html
[2] de Groot, P., & DiSciacca, J. (2020). Definition and evaluation of topography measurement noise in optical instruments. Optical Engineering, 59(6), 064110. https://doi.org/10.1117/1.OE.59.6.064110
[3] Eifler, M., Hering, J., Seewig, J., Leach, R. K., von Freymann, G., Hu, X., & Dai, G. (2020). Comparison of material measures for areal surface topography measuring instrument calibration. Surface Topography: Metrology and Properties, 8(2), 025019. https://doi.org/10.1088/2051-672X/ab92ae
[4] Vanrusselt, M., Haitjema, H., Leach, R., & de Groot, P. (2021). International comparison of noise in areal surface topography measurements. Surface Topography: Metrology and Properties, 9(2), 025015. https://doi.org/10.1088/2051-672X/abfa29
[5] Giusca, C. L., Leach, R. K., Helary, F., Gutauskas, T., & Nimishakavi, L. (2012). Calibration of the scales of areal surface topography-measuring instruments: Part 1. Measurement noise and residual flatness. Measurement Science and Technology, 23(3), 035008. https://doi.org/10.1088/0957-0233/23/3/035008
[6] Grochalski, K., Wieczorowski, M., Pawlus, P., & H’Roura, J. (2020). Thermal sources of errors in surface texture imaging. Materials, 13(10), 2337. https://doi.org/10.3390/ma13102337
[7] Fu, S., Cheng, F., Tjahjowidodo, T., Zhou, Y., & Butler, D. (2018). A non-contact measuring system for in-situ surface characterization based on laser confocal microscopy. Sensors, 18(8), 2657. https://doi.org/10.3390/s18082657
[8] Barker, A., Syam, W. P., & Leach, R. K. (2016, October). Measurement noise of a coherence scanning interferometer in an industrial environment. Proceedings of the Thirty-First Annual Meeting of the American Society for Precision Engineering (vol. 65, pp. 594–599). http://eprints.nottingham.ac.uk/id/eprint/38454
[9] Gomez, C., Su, R., De Groot, P., & Leach, R. (2020). Noise reduction in coherence scanning interferometry for surface topography measurement. Nanomanufacturing and Metrology, 3, 68–76. https://doi.org/10.1007/s41871-020-00057-4
[10] Leach, R. (Ed.). (2011). Optical Measurement of Surface Topography (Vol. 8). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-12012-1
[11] Maculotti, G., Feng, X., Galetto, M., & Leach, R. (2018). Noise evaluation of a point autofocus surface topography measuring instrument. Measurement Science and Technology, 29(6), 065008. https://doi.org/10.1088/1361-6501/aab528
[12] De Groot, P. J. (2017). The meaning and measure of vertical resolution in optical surface topography measurement. Applied Sciences, 7(1), 54. https://doi.org/10.3390/app7010054
[13] Haitjema, H., & Morel, M. A. A. (2005). Noise bias removal in profile measurements. Measurement, 38(1), 21–29. https://doi.org/10.1016/j.measurement.2005.02.002
[14] Leach, R., Haitjema, H., Su, R.,&Thompson, A. (2020). Metrological characteristics for the calibration of surface topography measuring instruments: a review. Measurement Science and Technology, 32(3), 032001. https://doi.org/10.1088/1361-6501/abb54f
[15] DIN. (2008). Optical measurement and microtopographies – Calibration of interference microscopes and depth measurement standards for roughness measurement (VDI/VDE 2655 Blatt 1.1).
[16] DIN. (2010). Optical measurement of microtopography – Calibration of confocal microscopes and depth setting standards for roughness measurement (VDI/VDE 2655 Blatt 1.2).
[17] de Groot, P., & DiSciacca, J. (2018, August). Surface-height measurement noise in interference microscopy. Interferometry XIX (Vol. 10749, p. 107490Q). International Society for Optics and Photonics. https://doi.org/10.1117/12.2323900
[18] Pawlus, P., Reizer, R., & Wieczorowski, M. (2017). Problem of non-measured points in surface texture measurements. Metrology and Measurement Systems, 24(3), 525–536. https://doi.org/10.1515/mms-2017-0046
[19] International Organization for Standardization. (2012). Geometrical product specifications (GPS) – Surface texture: Areal – Part 3: Specification operators (ISO 25178-3:2012).
[20] Blateyron, F. (2014, May). Good practices for the use of areal filters. Proc. 3rd Seminar on Surface Metrology of the Americas.
[21] Podulka, P. (2020). Proposal of frequency-based decomposition approach for minimization of errors in surface texture parameter calculation. Surface and Interface Analysis, 52(12), 882–889. https://doi.org/10.1002/sia.6840
[22] He, B., Zheng, H., Ding, S.,Yang, R.,& Shi, Z. (2021).Areviewof digital filtering in surface roughness evaluation. Metrology and Measurement Systems, 28(2). https://doi.org/10.24425/mms.2021.136606
[23] Podulka, P. (2020). Comparisons of envelope morphological filtering methods and various regular algorithms for surface texture analysis. Metrology and Measurement Systems, 27(2), 243–263. https://doi.org/10.24425/mms.2020.132772
[24] Podulka, P. (2021). Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements. Materials, 14(2), 333. https://doi.org/10.3390/ma14020333
[25] Todhunter, L., Leach, R., & Blateyron, F. (2020). Mathematical approach to the validation of surface texture filtration software. Surface Topography: Metrology and Properties, 8(4), 045017. https://doi.org/10.1088/2051-672X/abc0fb
[26] Vanrusselt, M., & Haitjema, H. (2020). Reduction of noise bias in 2.5 D surface measurements. In Proceedings of Euspen’s 20th International Conference & Exhbition, 277–281. European Society for Precision Engineering; Nothampton.
[27] Gomez, C., Su, R., Lawes, S., & Leach, R. (2019). Comparison of two noise reduction methods in coherence scanning interferometry for surface measurement. The 14th International Symposium on Measurement Technology and Intelligent Instruments.
[28] Sánchez, Á. R., Thompson, A., Körner, L., Brierley, N., & Leach, R. (2020). Review of the influence of noise in X-ray computed tomography measurement uncertainty. Precision Engineering, 66, 382–391. https://doi.org/10.1016/j.precisioneng.2020.08.004
[29] confovis GmbH. Structured Illumination Microscopy. https://www.confovis.com/en/optical-measurement
[30] International Organization for Standardization. (2012). Geometrical product specifications (GPS) – Surface texture: Areal – Part 2: Terms, definitions and surface texture parameters (ISO 25178-2:2012).
[31] International Organization for Standardization. (2020). Geometrical product specifications (GPS) – Surface texture: Areal – Part 700: Calibration, adjustment and verification of areal topography measuring instruments (ISO/DIS 25178-700:2020).
[32] Leach, R., Haitjema, H., & Giusca, C. (2019). A metrological characteristics approach to uncertainty in surface metrology. Optical Inspection of Microsystems, 73–91. CRC Press.
[33] Haitjema, H. (2015). Uncertainty in measurement of surface topography. Surface Topography: Metrology and Properties, 3(3), 035004. https://doi.org/10.1088/2051-672X/3/3/035004
[34] Yang, Z., Kessel, A., & Häusler, G. (2015). Better 3D Inspection with Structured Illumination: Signal Formation and Precision. Applied Optics, 54(22), 6652–6660. https://doi.org/10.1364/AO.54.006652
[35] Gomez, C., Su, R., Thompson, A., DiSciacca, J., Lawes, S., & Leach, R. K. (2017). Optimization of surface measurement for metal additive manufacturing using coherence scanning interferometry. Optical Engineering, 56(11), 111714. https://doi.org/10.1117/1.OE.56.11.111714
[36] Zhou, Y., Troutman, J., Evans, C., & Davies, A. (2014, June). Using the random ball test to calibrate slope dependent errors in optical profilometry. Optical Fabrication and Testing, OW4B-2. Optical Society of America. https://doi.org/10.1364/OFT.2014.OW4B.2
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Authors and Affiliations

Zhen Li
1
ORCID: ORCID
Sophie Gröger
1

  1. Chemnitz University of Technology, Department of Production Measuring Technology, Reichenhainer Straße 70, 09126 Chemnitz, Germany

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