Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 6
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

In this paper, the stock price-inflation nexus is investigated using the tools of wavelet power spectrum, cross-wavelet power spectrum and cross-wavelet coherency to unravel time and frequency dependent relationships between stock prices and inflation. Our results suggest that for a frequency band between sixteen and thirty two months, there is some evidence of the fisher effect. For rest of the frequencies and time periods however there is no evidence of the fisher effect and it seems stock prices have not played any role as an inflation hedge.

Go to article

Authors and Affiliations

Niyati Bhanja
Arif Billah Dar
Aviral Kumar Tiwari
Olaolu Richard Olayeni
Download PDF Download RIS Download Bibtex

Abstract

Analysis of power consumption presents a very important issue for power distribution system operators. Some power system processes such as planning, demand forecasting, development, etc.., require a complete understanding of behaviour of power consumption for observed area, which requires appropriate techniques for analysis of available data. In this paper, two different time-frequency techniques are applied for analysis of hourly values of active and reactive power consumption from one real power distribution transformer substation in urban part of Sarajevo city. Using the continuous wavelet transform (CWT) with wavelet power spectrum and global wavelet spectrum some properties of analysed time series are determined. Then, empirical mode decomposition (EMD) and Hilbert-Huang Transform (HHT) are applied for the analyses of the same time series and the results showed that both applied approaches can provide very useful information about the behaviour of power consumption for observed time interval and different period (frequency) bands. Also it can be noticed that the results obtained by global wavelet spectrum and marginal Hilbert spectrum are very similar, thus confirming that both approaches could be used for identification of main properties of active and reactive power consumption time series.

Go to article

Authors and Affiliations

Samir Avdakovic
Adnan Bosovic
Download PDF Download RIS Download Bibtex

Abstract

Load profiles of residential consumers are very diverse. This paper proposes the usage of a continuous wavelet transform and wavelet coherence to perform analysis of residential power consumer load profiles. The importance of load profiles in power engineering and common shapes of profiles along with the factors that cause them are described. The continuous wavelet transform and wavelet coherence has been presented. In contrast with other studies, this research has been conducted using detailed (not averaged) load profiles. Presented load profiles were measured separately on working day and weekend during winter in two urban households. Results of applying the continuous wavelet transform for load profiles analysis are presented as coloured scalograms. Moreover, the wavelet coherence was used to detect potential relationships between two consumers in power usage patterns. Results of coherence analysis are also presented in a colourful plots. The conducted studies show that the Morlet wavelet is slightly better suitable for load profiles analysis than the Meyer’s wavelet. Research of this type may be valuable for a power system operator and companies selling electricity in order to match their offer to customers better or for people managing electricity consumption in buildings.
Go to article

Bibliography

  1.  M. Bicego, A. Farinelli, E. Grosso, D. Paolini, and S.D. Ramchurn, “On the distinctiveness of the electricity load profile”, Pattern Recognit. 74, 317‒325 (2018), doi: 10.1016/j.patcog.2017.09.039
  2.  P. Piotrowski, D. Baczyński, S. Robak, M. Kopyt, M. Piekarz, and M. Polewaczyk, “Comprehensive forecast of electromobility mid- term development in Poland and its impacts on power system demand”, Bull. Pol. Ac.: Tech, 68(4), 697‒709 (2020), doi: 10.24425/ bpasts.2020.134180
  3.  M. Sepehr, R. Eghtedaei, A. Toolabimoghadam, Y. Noorollahi, and M. Mohammadi, “Modeling the electrical energy consumption profile for residential buildings in Iran”, Sustain. Cities Soc. 41, 481‒489 (2018), doi: 10.1016/j.scs.2018.05.041
  4.  Z. Ning and D. Kirschen, “Preliminary Analisys of High Resolution Domestic Load Data, Part of Supergen Flexnet Project”, The University of Manchester, 2010. [Online]. https://labs.ece.uw.edu/real/Library/Reports/Preliminary_Analysis_of_High_Resolution_Domestic_Load_ Data_Compact.pdf
  5.  J.L. Ramirez-Mendiola, Ph. Grunewald, and N. Eyre, “Linking intra-day variations in residential electricity demand loads to consumer’s activities: What’s missing ?”, Energy Build. 161, 63‒71 (2018), doi: 10.1016/j.enbuild.2017.12.012
  6.  J.L. Ramirez-Mendiola, Ph. Grunewald, and N. Eyre, “The diversity of residential electricity demand – A comparative analysis of metered and simulated data”, Energy Build. 151, 121‒131 (2017), doi: 10.1016/j.enbuild.2017.06.006
  7.  M. Bartecka, P. Terlikowski, M. Kłos, and Ł. Michalski, „Sizing of prosumer hybrid renewable energy systems in Polnad”, Bull. Pol. Ac.: Tech, 68(4), 721‒731 (2020), doi: 10.24425/bpasts.2020.133125
  8.  D.S. Osipov, A.G. Lyutarevich, R.A. Gapirov, V.N. Gorunkov, and A.A. Bubenchikov, “Applications of Wavelet Transform for Analysis of Electrical Transients in Power Systems: The Review”, Prz. Elektrotechniczny (Electrical Review), 92(4), 162‒165 (2016), doi: 10.15199/48.2016.04.35
  9.  R. Kumar and H.O. Bansal, “Hardware in the loop implementation of wavelet based strategy in shunt active power filter to mitigate power quality issues”, Electr. Power Syst. Res. 169, 92‒104 (2019), doi: 10.1016/j.epsr.2019.01.001
  10.  R. Escudero, J. Noel, J. Elizondo, and J. Kirtley, “Microgrid fault detection based on wavelet transformation and Park’s vector approach”, Electr. Power Syst. Res. 152, 401‒410 (2017), doi: 10.1016/j.epsr.2017.07.028
  11.  M. El-Hendawi and Z. Wang, “An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting”, Electr. Power Syst. Res. 182 (2020), doi: 10.1016/j.epsr.2020.106265
  12.  K. Dowalla, W. Winiecki, R. Łukaszewski, and R. Kowalik, „Electrical appliances identyfication based on wavelet transform of power supply voltage signal”, Prz. Elektrotechniczny (Electrical Review), 94 (11), 43‒46 (2018), doi: 10.15199/48.2018.11.10 [in Polish].
  13.  A. Graps, “An introduction to wavelets”, IEEE Comput. Sci. Eng. 2, 50‒61 (1995), doi: 10.1109/99.388960
  14.  Ch. Chiann and P. A. Morettin, “A wavelet analysis for time series”, J. Nonparametr. Statist. 10(1), 1‒46, (1999), doi: 10.1080/10485259808832752
  15.  P. Sleziak, K. Hlavcova, and J. Szolgay, “Advanatges of a time series analysis using wavelet transform as compared with Fourier analysis”, Slov. J. Civ. Eng. 23(2), 30‒36, (2015), doi: 10.1515/sjce-2015-0010
  16.  S. Avdakovic, A. Nuhanovic, M. Kusljugic, E. Becirovic and E. Turkovic, “Wavelet multiscale analysis of a power system load variance”, Turk. J. Electr. Eng. Comp. Sci. 1035‒1043, (2013), doi: 10.3906/elk-1109-47
  17.  M. Hayn, V. Bertsch, and W. Fichtner, “Electricity load profiles in Europe: The importance of household segmentation”, Energy Res. Soc. Sci. 3, 30–45, (2014), doi: 10.1016/j.erss.2014.07.002
  18.  R. Cruickshank, G. Henze, R. Balaji, H. Br-Mathias, and A. Florita, “Quantifying the Opporturnity Limits of Automatic Residential Electric Load Shaping”, Energies 12, (2019), doi: 10.3390/en12173204
  19.  M. Kott, “The electricity Consumption in Polish Households”, Modern Electr. Power Syst. 2015 – MEPS’15, Wrocław, Poland, July 6‒9, 2015, doi: 10.1109/MEPS.2015.7477166
  20.  O. Elma and U.S. Selamogullar, “A Survey of a Residential Load Profile for Demand Side Managemenet Systems”, The 5th IEEE Internationl Conference on Smart Energy Grid Enegineering, 2017, doi: 10.1109/SEGE.2017.8052781
  21.  P. Kapler, “Utilization of the adaptive potential of individual power consumers in interaction with power system”, Ph.D. Thesis, Warsaw University of Technology, Faculty of Electrical Engineering, (2018), [in Polish].
  22.  A. Grinsted, J.C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical time series”, Nonlinear Process Geophys. European Geosciences Union (EGU), 11(5/6), 561‒566, (2004), doi: 10.5194/npg-11-561-2004
  23.  B. Cazelles, M. Chavez, D. Berteaux, F. Menard, J.O. Vik, S. Jenouvrier, and N. C. Stenseth, “Wavelet analysis of ecological time series”, Oecologia 156, 287‒304 (2008), doi: 10.1007/s00442-008-0993-2
Go to article

Authors and Affiliations

Piotr Kapler
1
ORCID: ORCID

  1. Warsaw University of Technology, Faculty of Electrical Engineering, Power Engineering Institute, ul. Koszykowa 75, 00-662, Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

Time invariant linear operators are the building blocks of signal processing. Weighted circular convolution and signal processing framework in a generalized Fourier domain are introduced by Jorge Martinez. In this paper, we prove that under this new signal processing framework, weighted circular convolution also has a generalized time invariant property. We also give an application of this property to algorithm of continuous wavelet transform (CWT). Specifically, we have previously studied the algorithm of CWT based on generalized Fourier transform with parameter 1. In this paper, we prove that the parameter can take any complex number. Numerical experiments are presented to further demonstrate our analyses.
Go to article

Bibliography

  1.  N. Holighaus, G. Koliander, Z. Průša, and L.D. Abreu, “Characterization of Analytic Wavelet Transforms and a New Phaseless Reconstruction Algorithm,” IEEE Trans. Signal Process., vol. 67, no. 15, pp. 3894–3908, 2019.
  2.  M. Rayeezuddin, B. Krishna Reddy, and D. Sudheer Reddy, “Performance of reconstruction factors for a class of new complex continuous wavelets,” Int. J. Wavelets Multiresolution Inf. Process., vol. 19, no. 02, p. 2050067, 2021, doi: 10.1142/S0219691320500678.
  3.  Y. Guo, B.-Z. Li, and L.-D. Yang, “Novel fractional wavelet transform: Principles, MRA and application,” Digital Signal Process., vol. 110, p. 102937, 2021. [Online]. Available: doi: 10.1016/j.dsp.2020.102937.
  4.  V.K. Patel, S. Singh, and V.K. Singh, “Numerical wavelets scheme to complex partial differential equation arising from Morlet continuous wavelet transform,” Numer. Methods Partial Differ. Equations, vol. 37, no. 2, pp. 1163–1199, mar 2021.
  5.  C.K. Chui, Q. Jiang, L. Li, and J. Lu, “Signal separation based on adaptive continuous wavelet-like transform and analysis,” Appl. Comput. Harmon. Anal., vol. 53, pp. 151‒179, 2021.
  6.  O. Erkaymaz, I.S. Yapici, and R.U. Arslan, “Effects of obesity on time-frequency components of electroretinogram signal using continuous wavelet transform,” Biomed. Signal Process. Control, vol. 66, p. 102398, 2021.
  7.  Z. Yan, P. Chao, J. Ma, D. Cheng, and C. Liu, “Discrete convolution wavelet transform of signal and its application on BEV accident data analysis,” Mech. Syst. Signal Process., vol. 159, 2021.
  8.  R. Bardenet and A. Hardy, “Time-frequency transforms of white noises and Gaussian analytic functions,” Appl. Comput. Harmon. Anal., vol. 50, pp. 73–104, 2021, doi: 10.1016/j.acha.2019.07.003.
  9.  M.X. Cohen, “A better way to define and describe Morlet wavelets for time-frequency analysis,” NeuroImage, vol. 199, pp. 81–86, 2019. doi: 10.1016/j.neuroimage.2019.05.048.
  10.  H. Yi and H. Shu, “The improvement of the Morlet wavelet for multi-period analysis of climate data,” C.R. Geosci., vol. 344, no. 10, pp. 483–497, 2012.
  11.  S.G. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, 2009.
  12.  H. Yi, P. Ouyang, T. Yu, and T. Zhang, “An algorithm for Morlet wavelet transform based on generalized discrete Fourier transform,” Int. J. Wavelets Multiresolution Inf. Process., vol. 17, no. 05, p. 1950030, 2019, doi: 10.1142/S0219691319500309.
  13.  R. Tolimieri, M. An, and C. Lu, Algorithms for Discrete Fourier Transform and Convolution. Springer, 1997.
  14.  J.-M. Attendu and A. Ross, “Method for finding optimal exponential decay coefficient in numerical Laplace transform for application to linear convolution,” Signal Process., vol. 130, pp. 47–56, 2017.
  15.  W. Li and A.M. Peterson, “FIR Filtering by the Modified Fermat Number Transform,” IEEE Trans. Acoust. Speech Signal Process., vol. 38, no. 9, pp. 1641–1645, 1990.
  16.  M.J. Narasimha, “Linear Convolution Using Skew-Cyclic Convolutions,” Signal Process. Lett., vol. 14, no. 3, pp. 173–176, 2007.
  17.  J. Martinez, R. Heusdens, and R.C. Hendriks, “A Generalized Poisson Summation Formula and its Application to Fast Linear Convolution,” IEEE Signal Process Lett., vol. 18, no. 9, pp. 501–504, 2011.
  18.  R.C. Guido, F. Pedroso, A. Furlan, R.C. Contreras, L.G. Caobianco, and J.S. Neto, “CWT×DWT×DTWT×SDTWT: Clarifying terminologies and roles of different types of wavelet transforms,” Int. J. Wavelets Multiresolution Inf. Process., vol. 18, no. 06, p. 2030001, 2020, doi: 10.1142/S0219691320300017.
  19.  P. Kapler, “An application of continuous wavelet transform and wavelet coherence for residential power consumer load profiles analysis,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 1, p. e136216, 2021, doi: 10.24425/bpasts.2020.136216.
  20.  J. Martinez, R. Heusdens, and R.C. Hendriks, “A generalized Fourier domain: Signal processing framework and applications,” Signal Process., vol. 93, no. 5, pp. 1259‒1267, 2013.
  21.  S. Hui and S.H. Żak, “Discrete Fourier transform and permutations,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 6, pp. 995–1005, 2019.
  22.  Z. Babic and D.P. Mandic, “A fast algorithm for linear convolution of discrete time signals,” in 5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service. TELSIKS 2001. Proceedings of Papers (Cat. No.01EX517), vol. 2, 2001, pp. 595–598.
  23.  H. Yi, S. Y. Xin, and J. F. Yin, “A Class of Algorithms for ContinuousWavelet Transform Based on the Circulant Matrix,” Algorithms, vol. 11, no. 3, p. 24, 2018.
  24.  D. Spałek, “Two relations for generalized discrete Fourier transform coefficients,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no. 3, pp. 275– 281, 2018, doi: 10.24425/123433.
Go to article

Authors and Affiliations

Hua Yi
1
ORCID: ORCID
Yu-Le Ru
1
Yin-Yun Dai
1

  1. School of Mathematics and Physics, Jinggangshan University, Ji’an, 343009, P.R. China
Download PDF Download RIS Download Bibtex

Abstract

Purpose: to demonstrate the possibility of finding features reliable for more precise distinguishing between normal and abnormal Pattern Electroretinogram (PERG) recordings, in Continuous Wavelet Transform (CWT) coefficients domain. To determine characteristic features of the PERG and Pattern Visual Evoked Potential (PVEP) waveforms important in the task of precise classification and assessment of these recordings. Material and methods: 60 normal PERG waveforms and 60 PVEPs as well as 47 PERGs and 27 PVEPs obtained in some retinal and optic nerve diseases were studied in the two age groups (<= 50 years, > 50 years). All these signals were recorded in accordance with the guidelines of ISCEV in the Laboratory of Electrophysiology of the Retina and Visual Pathway and Static Perimetry, at the Department and Clinic of Ophthalmology of the Pomeranian Medical University. Continuous Wavelet Transform (CWT) was used for the time-frequency analysis and modelling of the PERG signal. Discriminant analysis and logistic regression were performed in statistical analysis of the PERG and PVEP signals. Obtained mathematical models were optimized using Fisher F(n1; n2) test. For preliminary evaluation of the obtained classification methods and algorithms in clinical practice, 22 PERGs and 55 PVEPs were chosen with respect to especially difficult discrimination problems (“borderline” recordings).

Results: comparison between the method using CWT and standard time-domain based analysis showed that determining the maxima and minima of the PERG waves was achieved with better accuracy. This improvement was especially evident in waveforms with unclear peaks as well as in noisy signals. Predictive, quantitative models for PERGs and PVEPs binary classification were obtained based on characteristic features of the waveform morphology. Simple calculations algorithms for clinical applications were elaborated. They proved effective in distinguishing between normal and abnormal recordings.

Conclusions: CWT based method is efficient in more precise assessment of the latencies of the PERG waveforms, improving separation between normal and abnormal waveforms. Filtering of the PERG signal may be optimized based on the results of the CWT analysis. Classification of the PERG and PVEP waveforms based on statistical methods is useful in preliminary interpretation of the recordings as well as in supporting more accurate assessment of clinical data.

Go to article

Authors and Affiliations

K. Penkala
Download PDF Download RIS Download Bibtex

Abstract

Noise induced hearing loss (NIHL) is a serious occupational related health problem worldwide. The A-wave impulse noise could cause severe hearing loss, and characteristics of such kind of impulse noise in the joint time-frequency (T-F) domain are critical for evaluation of auditory hazard level. This study focuses on the analysis of A-wave impulse noise in the T-F domain using continual wavelet transforms. Three different wavelets, referring to Morlet, Mexican hat, and Meyer wavelets, were investigated and compared based on theoretical analysis and applications to experimental generated A-wave impulse noise signals. The underlying theory of continuous wavelet transform was given and the temporal and spectral resolutions were theoretically analyzed. The main results showed that the Mexican hat wavelet demonstrated significant advantages over the Morlet and Meyer wavelets for the characterization and analysis of the A-wave impulse noise. The results of this study provide useful information for applying wavelet transform on signal processing of the A-wave impulse noise.
Go to article

Authors and Affiliations

Jun Qin
Pengfei Sun

This page uses 'cookies'. Learn more