Search results

Filters

  • Journals
  • Authors
  • Contributor
  • Keywords
  • Date
  • Type

Search results

Number of results: 208
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Industry 4.0 is expected to provide high quality and customized products at lower costs by increasing efficiency, and hence create a competitive advantage in the manufacturing industry. As the emergence of Industry 4.0 is deeply rooted in the past industrial revolutions, Advanced Manufacturing Technologies of Industry 3.0 are the precursors of the latest Industry 4.0 technologies. This study aims to contribute to the understanding of technological evolution of manufacturing industry based on the relationship between the usage levels of Advanced Manufacturing Technologies and Industry 4.0 technologies. To this end, a survey was conducted with Turkish manufacturers to assess and compare their manufacturing technology usage levels. The survey data collected from 424 companies was analyzed by machine learning approach. The results of the study reveal that the implementation level of each Industry 4.0 technology is positively associated with the implementation levels of a set of Advanced Manufacturing Technologies.
Go to article

Authors and Affiliations

Tuğba Sari
1

  1. Konya Food and Agriculture University, Department of Management Information Systems, Turkey
Download PDF Download RIS Download Bibtex

Abstract

Traffic classification is an important tool for network management. It reveals the source of observed network traffic and has many potential applications e.g. in Quality of Service, network security and traffic visualization. In the last decade, traffic classification evolved quickly due to the raise of peer-to-peer traffic. Nowadays, researchers still find new methods in order to withstand the rapid changes of the Internet. In this paper, we review 13 publications on traffic classification and related topics that were published during 2009-2012. We show diversity in recent algorithms and we highlight possible directions for the future research on traffic classification: relevance of multi-level classification, importance of experimental validation, and the need for common traffic datasets.
Go to article

Authors and Affiliations

Paweł Foremski
Download PDF Download RIS Download Bibtex

Abstract

There is a discrepancy between the research exploring e-learning at medical universities in Central/Eastern and Western European countries. The aim of the MeSPeLA study was to explore the understanding, experience and expectations of Polish medical students in terms of e-learning. Questionnaire containing open-ended and closed questions supplemented by focus group discussion was validated and performed among 204 medical students in Poland before COVID-19 pandemia. Several domains: understanding of e-learning definitions; students’ experience, preferences, expectations and perceptions of e-learning usefulness, advantages and disadvantages were addressed. The qualitative data were analyzed using an inductive approach. 46.0% of students chose a communication-oriented definition as the most appropriate. 7.4% claimed not to have any experience with e-learning. 76.8% of respondents indicated they had contact with e-learning. The main reported e-learning advantages were time saving and easier time management. The most common drawback was limited social interactions. The acceptance of the usage of e-learning was high. Medical undergraduates in Poland regardless of the year of studies, gender or choice of future specialization showed positive attitudes towards e-learning. Students with advanced IT skills showed a better understanding of the e-learning definition and perceived e-learning to be a more useful approach. The expectations and perceptions about e-learning in Polish medical schools seems similar to some extent to that in Western European and the United States so we can be more confident about applying some lessons from these research to Poland or other post-communist countries. Such application has been accelerated due to COVID-19 pandemia.
Go to article

Authors and Affiliations

Mirosława Püsküllüoğlu
1
Michał Nowakowski
2
Sebastian Ochenduszko
3
David Hope
4
Helen Cameron
5

  1. Department of Clinical Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Cracow Branch, Kraków, Poland
  2. 2nd Department of General Surgery, Jagiellonian University Medical College, Kraków, Poland
  3. Department of Medical Oncology, Hospital Universitario Dr Peset, Valencia, Spain
  4. Centre for Medical Education, The Chancellor’s Building, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, Scotland
  5. Aston Medical School, Aston University, Birmingham, UK
Download PDF Download RIS Download Bibtex

Abstract

This study introduces a two-step reinforcement learning (RL) strategy tailored for "The Lord of the Rings: The Card Game", a complex multistage strategy card game. The research diverges from conventional RL methods by adopting a phased learning approach, beginning with a foundational learning step in a simplified version of the game and subsequently progressing to the complete, intricate game environment. This methodology notably enhances the AI agent’s adaptability and performance in the face of the unpredictable and challenging nature of the game. The paper also explores a multi-phase system where distinct RL agents are employed for various decision-making phases of the game. This approach has demonstrated remarkable improvement, with the RL agents achieving a winrate of 78.5 % at the highest difficulty level.
Go to article

Authors and Affiliations

Konrad Godlewski
Bartosz Sawicki
Download PDF Download RIS Download Bibtex

Abstract

This paper presents the improved version of the classification system for supporting glaucoma diagnosis in ophthalmology. In this

paper we propose the new segmentation step based on the support vector clustering algorithm which enables better classification performance.

Go to article

Authors and Affiliations

K. Stąpor
Download PDF Download RIS Download Bibtex

Abstract

Background: The skills and attitudes of medical staff affect the quality of the healthcare system, hence the study of academic motivation and quality of life of medical students.
Materials and Methods: The study involved 203 students of the Jagiellonian University Medical College. Academic motivation was assessed using the Academic Motivation Scale and quality of life using the World Health Organization Quality of Life-BREF questionnaire. Academic Motivation Scale is based on the Self-Determination Theory, which distinguishes several dimensions of motivation arranged along self-determination continuum from amotivation, through extrinsic, controllable motivation, to intrinsic, autonomous motivation.
Results: For our students, the main reason for taking up studies was identified regulation, it means that they perceive studying as something important for them, giving more opportunities in the future. Next was intrinsic motivations to know, where gaining knowledge is a value in itself. The third was external regulation, which indicate that the choice of studies was regulated by the dictates of the environment or the desire to obtain a reward. Female students showed a more intrinsically motivational profile than male students. Motivation became less autonomous as the years of study progressed. Most students rated their quality of life as good or very good. There was weak correlation between students’ good quality of life and more self-determined academic motivation.
Conclusions: Our students are mainly intrinsically motivated, most of them positively assess the quality of life. A more autonomous approach to learning coexisted with a positive assessment of quality of life.
Go to article

Authors and Affiliations

Dorota Zawiślak
1
Karolina Skrzypiec
1
Kamila Żur-Wyrozumska
1
Mariusz Habera
1
Grzegorz Cebula
1

  1. Centre for Innovative Medical Education, Jagiellonian University Medical College, Kraków, Poland
Download PDF Download RIS Download Bibtex

Abstract

. Federated learning is an upcoming concept used widely in distributed machine learning. Federated learning (FL) allows a large number of users to learn a single machine learning model together while the training data is stored on individual user devices. Nonetheless, federated learning lessens threats to data privacy. Based on iterative model averaging, our study suggests a feasible technique for the federated learning of deep networks with improved security and privacy. We also undertake a thorough empirical evaluation while taking various FL frameworks and averaging algorithms into consideration. Secure multi party computation, secure aggregation, and differential privacy are implemented to improve the security and privacy in a federated learning environment. In spite of advancements, concerns over privacy remain in FL, as the weights or parameters of a trained model may reveal private information about the data used for training. Our work demonstrates that FL can be prone to label-flipping attack and a novel method to prevent label-flipping attack has been proposed. We compare standard federated model aggregation and optimization methods, FedAvg and FedProx using benchmark data sets. Experiments are implemented in two different FL frameworks – Flower and PySyft and the results are analyzed. Our experiments confirm that classification accuracy increases in FL framework over a centralized model and the model performance is better after adding all the security and privacy algorithms. Our work has proved that deep learning models perform well in FL and also is secure.
Go to article

Authors and Affiliations

R Anusuya
1
ORCID: ORCID
D Karthika Renuka
1
ORCID: ORCID

  1. Department of Information Technology, PSG College of Technology, Coimbatore, TN 641004, India
Download PDF Download RIS Download Bibtex

Abstract

Prof. Małgorzata Kossut of the Nencki Institute of Experimental Biology talks about brain plasticity, the mechanisms of learning, and the mysteries of forgetfulness.

Go to article

Authors and Affiliations

Małgorzata Kossut
Download PDF Download RIS Download Bibtex

Abstract

In recent years, deep learning and especially deep neural networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the convolutional neural networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied.

Go to article

Authors and Affiliations

M. Grochowski
A. Kwasigroch
A. Mikołajczyk
Download PDF Download RIS Download Bibtex

Abstract

Thousands of low-power micro sensors make up Wireless Sensor Networks, and its principal role is to detect and report specified events to a base station. Due to bounded battery power these nodes are having very limited memory and processing capacity. Since battery replacement or recharge in sensor nodes is nearly impossible, power consumption becomes one of the most important design considerations in WSN. So one of the most important requirements in WSN is to increase battery life and network life time. Seeing as data transmission and reception consume the most energy, it’s critical to develop a routing protocol that addresses the WSN’s major problem. When it comes to sending aggregated data to the sink, hierarchical routing is critical. This research concentrates on a cluster head election system that rotates the cluster head role among nodes with greater energy levels than the others.We used a combination of LEACH and deep learning to extend the network life of the WSN in this study. In this proposed method, cluster head selection has been performed by Convolutional Neural Network (CNN). The comparison has been done between the proposed solution and LEACH, which shows the proposed solution increases the network lifetime and throughput.
Go to article

Authors and Affiliations

Hardik K Prajapati
1
Rutvij Joshi
2

  1. Gujarat Technological University, Ahmedabad, Gujarat, India
  2. Parul University, Vadodara, Gujarat, India
Download PDF Download RIS Download Bibtex

Abstract

Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions' images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
Go to article

Authors and Affiliations

Sachin Nayak
1
Shweta Vincent
1
Sumathi K
2
Om Prakash Kumar
3
Sameena Pathan
4

  1. Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
  2. Department of Mathematics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
  3. Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
  4. Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
Download PDF Download RIS Download Bibtex

Abstract

The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.

Go to article

Authors and Affiliations

T. Pajchrowski
P. Siwek
A. Wójcik
ORCID: ORCID
Download PDF Download RIS Download Bibtex

Abstract

Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.
Go to article

Bibliography

  1. K.I. Talbot, P.R. Duley, and M.H. Hyatt, “Specific emitter identification and verification”, Technol. Rev. 2003, 113–133, (2003).
  2. G. Baldini, G. Steri, and R. Giuliani, “Identification of wireless devices from their physical layer radio-frequency fingerprints”, in: Encyclopedia of Information Science and Technology, pp. 6136–6146, 4th Edition, IGI Global, 2018.
  3. A.E. Spezio, “Electronic warfare systems”, IEEE Trans. Microw. Theory Tech. 50(3), 633–644 (2002).
  4. O. Ureten and N. Serinken, “Wireless security through rf fingerprinting”, Can. J. Electr. Comp. Eng. 32(1), 27–33 (2007).
  5. S.U. Rehman, K.W. Sowerby, and C. Coghill, “Radio-frequency fingerprinting for mitigating primary user emulation attack in low-end cognitive radios”, IET Commun. 8(8), 1274–1284 (2014).
  6. V. Brik, S. Banerjee, M. Gruteser, and S. Oh, “Wireless device identification with radiometric signatures”, in: Proceedings of the 14th ACM international Conference on Mobile Computing and Networking, San Francisco, USA: ACM, 2008, pp. 116– 127.
  7. Y. Huang, et al., “Radio frequency fingerprint extraction of radio emitter based on i/q imbalance”, Procedia Computer Science 107, 472–477 (2017).
  8. L.J. Wong, W.C. Headley, and A.J. Michaels, “Specific emitter identification using convolutional neural network-based iq imbalance estimators”, IEEE Access 7, 33544–33555 (2019).
  9. G. López-Risueño, J. Grajal, and A. Sanz-Osorio, “Digital channelized receiver based on time-frequency analysis for signal interception”, IEEE Trans. Aerosp. Electron. Syst. 41(3), 879–898 (2005).
  10. C. Bertoncini, K. Rudd, B. Nousain, and M. Hinders, “Wavelet fingerprinting of radio-frequency identification (rfid) tags”, EEE Trans. Ind. Electron. 59(12), 4843–4850 (2011).
  11. J. Lundén and V. Koivunen, “Automatic radar waveform recognition”, IEEE J. Sel. Top. Signal Process. 1(1), 124–136 (2007).
  12. L. Li, H.B. Ji, and L. Jiang, “Quadratic time–frequency analysis and sequential recognition for specific emitter identification”, IET Signal Process. 5(6), 568–574 (2011).
  13. Y. Yuan, Z. Huang, H. Wu, and X. Wang, “Specific emitter identification based on Hilbert–Huang transform-based time– frequency–energy distribution features”, IET Commun. 8(13), 2404–2412 (2014).
  14. J. Zhang, F. Wang, Z. Zhong, and O. Dobre, “Novel hilbert spectrum-based specific emitter identification for single-hop and relaying scenarios”, in: 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, USA, IEEE, 2015, pp. 1–6.
  15. J. Zhang, F. Wang, O. Dobre, and Z. Zhong, “Specific emitter identification via Hilbert–Huang transform in single-hop and relaying scenarios”, IEEE Trans. Inf. Forensic Secur. 11(6), 1192–1205 (2016).
  16. Z. Tang and S. Li, “Steady signal-based fractal method of specific communications emitter sources identification”, in: Wireless Communications, Networking and Applications, pp. 809– 819, Springer, 2016.
  17. G. Huang, Y. Yuan, X. Wang, and Z. Huang, “Specific emitter identification based on nonlinear dynamical characteristics”, Can. J. Electr. Comp. Eng. 39(1), 34–41 (2016).
  18. Y. Jia, S. Zhu, and L. Gan, “Specific emitter identification based on the natural measure”, Entropy 19(3), 117 (2017).
  19. J. Dudczyk and A. Kawalec, “Specific emitter identification based on graphical representation of the distribution of radar signal parameters”, Bull. Pol. Acad. Sci. Tech. Sci. 63(2), 391–396 (2015).
  20. Y. Zhao, Y. Li, L. Wui, and J. Zhang, “Specific emitter identification using geometric features of frequency drift curve”, Bull. Pol. Acad. Sci. Tech. Sci. 66(1), 99–108 (2018).
  21. L. Rybak and J. Dudczyk, “A geometrical divide of data particle in gravitational classification of moons and circles data sets”, Entropy 22(10), 1088 (2020).
  22. Q. Wu, et al., “Deep learning based rf fingerprinting for device identification and wireless security”, Electron. Lett. 54(24), 1405–1407 (2018).
  23. L. Ding, S. Wang, F. Wang, and W. Zhang, “Specific emitter identification via convolutional neural networks”, IEEE Commun. Lett. 22(12), 2591–2594 (2018).
  24. K. Merchant, S. Revay, G. Stantchev, and B. Nousain, “Deep learning for rf device fingerprinting in cognitive communication networks”, IEEE J. Sel. Top. Signal Process. 12(1), 160–167 (2018).
  25. Y. Pan, S. Yang, H. Peng, T. Li, and W. Wang, “Specific emitter identification based on deep residual networks”, IEEE Access 7, 54425– 54434 (2019).
  26. J. Matuszewski and D. Pietrow, “Recognition of electromagnetic sources with the use of deep neural networks”, in XII Conference on Reconnaissance and Electronic Warfare Systems, 2019, vol. 11055, pp. 100–114, doi: 10.1117/12.2524536.
  27. L.J. Wong, W.C. Headley, S. Andrews, R.M. Gerdes, and A.J. Michaels, “Clustering learned cnn features from raw i/q data for emitteridentification”, in: MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), Los Angeles, USA, 2018, pp. 26–33.
  28. G. Baldini, C. Gentile, R. Giuliani, and G. Steri, “Comparison of techniques for radiometric identification based on deep convolutional neural networks”, Electron. Lett. 55(2), 90–92 (2018).
  29. W. Wang, Z. Sun, S. Piao, B. Zhu, and K. Ren, “Wireless physical-layer identification: Modeling and validation”, IEEE Trans. Inf. Forensic Secur. 11(9), 2091–2106 (2016).
  30. S. Andrews, R.M. Gerdes, and M. Li, “Towards physical layer identification of cognitive radio devices”, IEEE Conference on Communications and Network Security (CNS), Las Vegas, USA, IEEE, 2017, pp. 1–9.
  31. I.F. Akyildiz, W.Y. Lee, M.C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey”, Comput. Netw. 50(13), 2127–2159 (2006).
  32. S.J. Pan and Q. Yang, “A survey on transfer learning”, IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009), doi: 10.1109/ TKDE.2009.191.
  33. Y. Sharaf-Dabbagh and W. Saad, “Transfer learning for device fingerprinting with application to cognitive radio networks”, in: 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Hong Kong, China, 2015, pp. 2138–2142.
  34. M. Wang and W. Deng, “Deep visual domain adaptation: A survey”, Neurocomputing 312, 135–153 (2018). doi: 10.1016/j. neucom.2018.05.083.
  35. Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation”, in: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 2015, pp. 1180–1189.
  36. Y. Ganin, et al., “Domain-adversarial training of neural networks”, J. Mach. Learn. Res. 17(1), 2096–2030 (2016).
  37. G. Wilson and D.J. Cook, “A survey of unsupervised deep domain adaptation”, CoRR, 2018, abs/1812.02849. Available from: http://arxiv. org/abs/1812.02849.
  38. I. Goodfellow, et al., “Generative adversarial nets”, in: Advances in Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672–2680.
  39. U. Satija, N. Trivedi, G. Biswal, and B. Ramkumar, “Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios”, IEEE Trans. Inf. Forensic Secur. 14(3), 581–591 (2018).
  40. E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 2017, pp. 7167–7176.
  41. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition”, in: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016, pp. 770–778.
  42. L. Maaten and G. Hinton, “Visualizing data using t-sne”, J. Mach. Learn. Res. 9, 2579–2605 (2008).
  43. C. Chen, et al., “Progressive feature alignment for unsupervised domain adaptation”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 627–636.
  44. P. Panareda-Busto and J. Gall, “Open set domain adaptation”, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 754–763.
  45. Z. Cao, M. Long, J. Wang, and M.I. Jordan, “Partial transfer learning with selective adversarial networks”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018, pp. 2724–2732.
  46. K. You, M. Long, Z. Cao, J. Wang, and M.I. Jordan, “Universal domain adaptation”, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA,2019.
Go to article

Authors and Affiliations

Keju Huang
1
Junan Yang
1
Hui Liu
1
Pengjiang Hu
1

  1. College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China
Download PDF Download RIS Download Bibtex

Abstract

The paper proposes to apply an algorithm for predicting the minimum level of the state of charge (SoC) of stationary supercapacitor energy storage system operating in a DC traction substation, and for changing it over time. This is done to insure maximum energy recovery for trains while braking. The model of a supercapacitor energy storage system, its algorithms of operation and prediction of the minimum state of charge are described in detail; the main formulae, graphs and results of simulation are also provided. It is proposed to divide the SoC curve into equal periods of time during which the minimum states of charge remain constant. To predict the SoC level for the subsequent period, the learning algorithm based on the neural network could be used. Then, the minimum SoC could be based on two basic types of data: the first one is the time profile of the energy storage load during the previous period with the constant minimum SoC retained, while the second one relies on the trains’ locations and speed values in the previous period. It is proved that the use of variable minimum SoC ensures an increase of the energy volume recovered by approximately 10%. Optimum architecture and activation function of the neural network are also found.

Go to article

Authors and Affiliations

W. Jefimowski
A. Nikitenko
Z. Drążek
M. Wieczorek
Download PDF Download RIS Download Bibtex

Abstract

The methodologies for learning a foreign language, including Italian, are strategies. In recent years, active learning, which places the learner at the center of the process, has become very important. The main objective of this article is to provide an overview of teaching methodologies, with a focus on the Bulgarian situation. In the research, teaching methodologies such as interdisciplinarity, role-play, circle time, cooperative learning, flipped classroom, and peer education will be analyzed.
Go to article

Authors and Affiliations

Radeya Gesheva
1
ORCID: ORCID

  1. Università di Sofia “San Clemente D’ocrida”
Download PDF Download RIS Download Bibtex

Abstract

The term “metalearning”, which was introduced into scientific literature by J. Biggs (1985) is, broadly speaking, an awareness of one’s own learning process and exercising control over it. Metalearning, whose roots lie in the personal, early experiences of the child related to learning, and which is expressed in her or his current concepts – is considered in this article as a basic condition for the acquisition of one of the key competences of 21st century man, namely, the learning competence. Recognizing the importance of colloquial concepts of learning, as well as their uniqueness and contextuality – in the article I will present the main problems associated with learning about the vision and understanding of the personal worlds of the learning of pupils, coming at the end of early education. On the basis of analysis of the scientific literature and previous studies conducted abroad, as well as a number of my own research projects (resulting from the application of quantitative or qualitative approach), I will present questions, doubts and selected emerging difficulties in the application of both the presented research approaches.

Go to article

Authors and Affiliations

Janina Uszyńska-Jarmoc
Download PDF Download RIS Download Bibtex

Abstract

This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.
Go to article

Bibliography

[1] H. Boubertakh, S. Labiod, M. Tadjine and P.Y. Glorennec: Optimization of fuzzy PID controllers using Q-learning algorithm. Archives of Control Sciences, 18(4), (2008), 415–435
[2] I.Carlucho, M. De Paula, S.A. Villar and G.G.Acosta: Incremental Qlearning strategy for adaptive PID control of mobile robots. Expert Systems With Applications, 80, (2017), 183–199, DOI: 10.1016/j.eswa.2017.03.002.
[3] K. Delchev: Simulation-based design of monotonically convergent iterative learning control for nonlinear systems. Archives of Control Sciences, 22(4), (2012), 467–480.
[4] M. Jelali: An overview of control performance assessment technology and industrial applications. Control Eng. Pract., 14(5), (2006), 441–466, DOI: 10.1016/j.conengprac.2005.11.005.
[5] M. Jelali: Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance. Springer-Verlag London, (2013)
[6] H.-K. Lam, Q. Shi, B. Xiao, and S.-H. Tsai: Adaptive PID Controller Based on Q-learning Algorithm. CAAI Transactions on Intelligence Technology, 3(4), (2018), 235–244, DOI: 10.1049/trit.2018.1007.
[7] D. Li, L. Qian, Q. Jin, and T. Tan: Reinforcement learning control with adaptive gain for a Saccharomyces cerevisiae fermentation process. Applied Soft Computing, 11, (2011), 4488–4495, DOI: 10.1016/j.asoc.2011.08.022.
[8] M.M. Noel and B.J. Pandian: Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach. Applied Soft Computing, 23, (2014), 444–451, DOI: 10.1016/j.asoc.2014.06.037.
[9] T. Praczyk: Concepts of learning in assembler encoding. Archives of Control Sciences, 18(3), (2008), 323–337.
[10] M.B. Radac and R.E. Precup: Data-driven model-free slip control of antilock braking systems using reinforcement Q-learning. Neurocomputing, 275, (2017), 317–327, DOI: 10.1016/j.neucom.2017.08.036.
[11] A.K. Sadhu and A. Konar: Improving the speed of convergence of multi-agent Q-learning for cooperative task-planning by a robot-team. Robotics and Autonomous Systems, 92, (2017), 66–80, DOI: 10.1016/j.robot.2017.03.003.
[12] N. Sahebjamnia, R. Tavakkoli-Moghaddam, and N. Ghorbani: Designing a fuzzy Q-learning multi-agent quality control system for a continuous chemical production line – A case study. Computers & Industrial Engineering, 93, (2016), 215–226, DOI: 10.1016/j.cie.2016.01.004.
[13] K. Stebel: Practical aspects for the model-free learning control initialization. in Proc. of 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR), Poland, (2015), DOI: 10.1109/MMAR.2015.7283918.
[14] R.S. Sutton and A.G. Barto: Reinforcement learning: An Introduction, MIT Press, (1998)
[15] S. Syafiie, F. Tadeo, and E. Martinez: Softmax and "-greedy policies applied to process control. IFAC Proceedings, 37, (2004), 729–734, DOI: 10.1016/S1474-6670(16)31556-2.
[16] S. Syafiie, F. Tadeo, and E. Martinez: Model-free learning control of neutralization process using reinforcement learning. Engineering Applications of Artificial Intelligence, 20, (2007), 767–782, DOI: 10.1016/j.engappai.2006.10.009.
[17] S. Syafiie, F. Tadeo, and E. Martinez: Learning to control pH processes at multiple time scales: performance assessment in a laboratory plant. Chemical Product and Process Modeling, 2(1), (2007), DOI: 10.2202/1934- 2659.1024.
[18] S. Syafiie, F. Tadeo, E. Martinez, and T. Alvarez: Model-free control based on reinforcement learning for a wastewater treatment problem. Applied Soft Computing, 11, (2011), 73–82, DOI: 10.1016/j.asoc.2009.10.018.
[19] P. Van Overschee and B. De Moor: RAPID: The End of Heuristic PID Tuning. IFAC Proceedings, 33(4), (2000), 595–600, DOI: 10.1016/S1474- 6670(16)38308-8.
[20] M. Wang, G. Bian, and H. Li: A new fuzzy iterative learning control algorithm for single joint manipulator. Archives of Control Sciences, 26(3), (2016), 297–310. DOI: 10.1515/acsc-2016-0017.
[21] Ch.J.C.H. Watkins and P. Dayan: Technical Note: Q-learning. Machine Learning, 8, (1992), 279–292, DOI: 10.1023/A:1022676722315.
Go to article

Authors and Affiliations

Jakub Musial
1
Krzysztof Stebel
1
ORCID: ORCID
Jacek Czeczot
1

  1. Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science, Department of Automatic Control and Robotics, 44-100 Gliwice, ul. Akademicka 16, Poland
Download PDF Download RIS Download Bibtex

Abstract

In this article I present the main assumptions and discuss issues of pedagogy as a science and the field of education during a special meeting of the Committee of the Academy of Pedagogical Sciences at Adam Mickiewicz University in Poznan. I focus on the institutional leaders in science teaching who are rectors and deans of Faculties of Education in Poland. Moreover, they are co-authors of relevant teaching and research solutions in science teaching. In the age of growing crisis in the academic community we can, as educators, discuss how no to be to be surprised by pathogenic processes and events, but how to be able to counteract them. Furthermore, how to show representatives of other academic disciplines and structures of learning, how to deal with common to us problems.
Go to article

Authors and Affiliations

Bogusław Śliwerski
Download PDF Download RIS Download Bibtex

Abstract

Equilibrium, disequilibrium and adaptation. The inspirations for spatial economics. This paper is a part of author’s long-term research project related to dynamics and evolution of space economy. In the attempts of theoretical reconstruction of these processes the notion of equilibrium plays an important role, as well as related notions: disequilibrium and adaptation. In the analysis of equilibrium the author drew on the concepts elaborated by the neoclassical school of economics. In the analysis of disequilibrium the concept of physics turned out to be fertilizing, namely the concept of dissipative structures and self-organisation. The concept of adaptation is elaborated in depth in biology. These three concepts have been applied in spatial economics long since. Further research is necessary however, to make these application more relevant to spatial economics, and in this way more fruitful.
Go to article

Authors and Affiliations

Ryszard Domański
Download PDF Download RIS Download Bibtex

Abstract

The traditional self organizing map (SOM) is learned by Kohonen learning. The main disadvantage of this approach is in epoch based learning when the radius and rate of learning are decreasing functions of epoch index. The aim of study is to demonstrate advantages of diffusive learning in single epoch learning and other cases for both traditional and anomalous diffusion models. We also discuss the differences between traditional and anomalous learning in models and in quality of obtained SOM. The anomalous diffusion model leads to less accurate SOM which is in accordance to biological assumptions of normal diffusive processes in living nervous system. But the traditional Kohonen learning has been overperformed by novel diffusive learning approaches.

Go to article

Authors and Affiliations

Radek Hrebik
Jaromír Kukal

This page uses 'cookies'. Learn more