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36th International Conference on electrical Drives and Power Electronics, EDPE 2023

作者： Korkosz, Mariusz Kutsyk, Andriy Krzywdzinska-Kornak, Krystyna Suliga, Mateusz Semeniuk, Mykola Rzeszow University of Technology Faculty of Electrical and Computer Engineering Rzeszow Poland Lviv Polytechnic National University Institute of Power Engineering and Control System Lviv Ukraine

ISBN: (纸本)9798350322750

The paper analyzes the characteristics of a dual-channel three-phase induction motor. Motors designed for multi-channel power supply are dedicated to applications requiring greater reliability. Each channel allows a certain degree of increase in the ability to continue operation after the failure states. The use of a dual channel power supply is the most common way to increase drive train redundancy. This paper presents the tests of the behaviour of a dual-channel three-phase induction motor in the event of loss of power to one of the phases (in one or two channels) and single-channel operation. A correctly designed motor allows for continuous operation in single-channel power mode. Numerical and laboratory tests confirmed this assumption. It has been demonstrated that the loss of power to one phase of a channel should result in the channel being turned off. © 2023 IEEE.

关键词： Fault tolerance

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A highly integrated flexible array non-contact ECG sensing electrode based on FPC 8

A highly integrated flexible array non-contact ECG sensing e...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Wu, Gang Wang, Kaikai Feng, Baoliang Shi, Binjun Liu, Zhen Zuo, Guokun Shi, Changcheng Faculty of Electrical Engineering & Computer Science Ningbo University Ningbo China Ningbo Institute of Materials Technology & Engineering Cas Ningbo China Ningbo Cixi Institute of Biomedical Engineering Ningbo China

ISBN: (纸本)9798350302455

The existing array non-contact ECG sensing technology mostly uses rigid electrode or conductive fabric electrode, in which the rigid electrode is too hard to make the electrode difficult to close to the human body well, and the conductive fabric electrode is too soft to cause the electrodes to be wrinkled, both of which will lead to poor ECG signal quality. In order to overcome the problem that it is difficult to obtain high-quality ECG signals due to electrode materials, this study combined the manufacturing process of Flexible Printed Circuit (FPC) with the surface treatment process of Electroless Nickel/Immersion (ENIG) and adopted polyimide (PI) and rolled copper as electrode materials, which makes the array electrode electrodes extremely flexible, tough, and flat. At the same time, in terms of electrode design, the test and verification of the electrode unit also ensured the rationality of the array electrode. Subsequently, the electrode was verified by electrode test and human experiment, and the results showed that the electrode could receive ECG signals well, and compared with the ECG signals obtained by the traditional Ag/AgCl electrodes, the signal-to-noise ratio (SNR) of the signals obtained by the two types of electrodes reached above 38 dB and the waveform characteristics were highly consistent. © 2023 IEEE.

关键词： Electrocardiograms

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ChatGPT the Omniscient? A Guide to Effective Prompting 7

ChatGPT the Omniscient? A Guide to Effective Prompting

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Kovari, Attila Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The invention of artificial intelligence and natural language processing has revolutionised human-machine interaction, and OpenAI's ChatGPT models are at the forefront of this. GPT-3 and GPT-4 models generate human-like text, answer questions and perform all kinds of language-related tasks. ChatGPT relies heavily on the quality of the requests it receives. This paper addresses the issue of efficient prompting in order to maximise its potential. Different prompting techniques are described in the paper: prompt template design, continuous prompts, few-shot learning, chain of thought prompting and metadata expansion. The conclusions show that learning how to write good prompts is crucial for realizing the full potential of ChatGPT in various natural language processing tasks. © 2024 IEEE.

关键词： Zero-shot learning

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Air Quality Monitoring in Ulaanbaatar Using Fog Computing and IoT, Looking for the Correlation Between Air Pollution and Public Health 28

Air Quality Monitoring in Ulaanbaatar Using Fog Computing an...

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28th International Scientific Conference on Transport Means 2024

作者： Patlins, A. Nyamkhuu, B. Kunicina, N. Riga Technical University Faculty of Computer Science Information Technology and Energy Institute of Industrial Electronics Electrical Engineering and Energy 10 Zunda Embankment RigaLV-1048 Latvia

In the research under discussion, the authors put forth a novel approach to air quality monitoring in Ulaanbaatar, Mongolia’s capital, by leveraging the power of fog computing. This innovative method involves the collection of air pollution data in a manner that is both cost-effective and efficient, thereby making it a viable solution for a city where air pollution is a significant concern. The primary objective of this approach is to estimate the impact of air pollution on public health. This is achieved through the application of statistical methods to the collected data, which allows for the understanding of the health risks associated with poor air quality. By doing so, it is hoped that this research will contribute to raising awareness about the dangers posed by air pollution, a problem that is often overlooked but has far-reaching implications for public health. One of the key features of the proposed approach is its focus on expanding the coverage of air quality monitoring. This is particularly relevant in the Ger areas of Ulaanbaatar, where pollution levels tend to spike during the winter months. The inhabitants of these areas, who often rely on coal for heating, are among the most affected by the adverse effects of air pollution. Therefore, by extending the reach of monitoring efforts to these areas, the research aims to provide a more accurate and comprehensive picture of the city’s air quality. In addition to the above, the research also takes into account the role of transportation in contributing to air pollution. In Ulaanbaatar, as in many other urban centers around the world, transportation is a one of major sources of air pollution. This includes not only public transport but also private vehicles, which collectively contribute a significant amount of pollutants to the city’s air. By considering the impact of transportation on air quality, the proposed approach acknowledges the multifaceted nature of the air pollution problem and underscores the need

关键词： Air quality

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Analysis of the Characteristics of a Six-phase Induction Motor 36

Analysis of the Characteristics of a Six-phase Induction Mot...

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36th International Conference on electrical Drives and Power Electronics, EDPE 2023

作者： Korkosz, Mariusz Kutsyk, Andriy Krzywdzinska-Kornak, Krystyna Nowak, Marek Semeniuk, Mykola Rzeszow University of Technology Faculty of Electrical and Computer Engineering Rzeszow Poland Lviv Polytechnic National University Institute of Power Engineering and Control System Lviv Ukraine

ISBN: (纸本)9798350322750

The paper includes a report on the preliminary results of six-phase induction motor tests. Using a power supply with more than three phases requires a slightly different approach in the motor design process. In theory, a six-phase induction motor design should be more resistant to the loss of power to one, two, or three phases. A numerical analyzis of selected operating states of an asymmetrical power supply to an induction motor was carried out. Verification of the numerical results was carried out under laboratory conditions. © 2023 IEEE.

关键词： Induction motors

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Tekele Zemamie Action Recognition in Ethiopia Orthodox Tewahedo Church Using CNN-Bi-LSTM

Tekele Zemamie Action Recognition in Ethiopia Orthodox Tewah...

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2024 International Conference on Information and Communication technology for Development for Africa, ICT4DA 2024

作者： Fikirie, Tenagne Azmeraw, Emiyamrew Tesfay, Wakjira Debasu, Abrham Bahir Dar University Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology P.O.Box 26 Bahir Dar Ethiopia University of Gondar College of Engineering Department of Computer Engineering Gondar Ethiopia Assosa University Department of Computer Engineering Assosa Ethiopia Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology P.O.Box 26 Bahir Dar Ethiopia

ISBN: (纸本)9798331527792

Human action recognition (HAR) is a computer vision technique used to understand the activity of the action performed in the scene. computer vision technology has become popular and is applied in various areas like surveillance systems, robot vision, satellite imaging, etc. However, using computer vision in human activity recognition is still challenging due to the dynamic movement of human motion, noise, occlusion, complex background, variable dressing style, varying illumination, and others. Various research has been conducted to address these problems, yet they remain challenging. In this study, we applied human action recognition techniques to interpret the meaning of Tekele Zemamie's actions in the Ethiopian Orthodox Tewahedo Church. Tekele Zemamie is church choirs' most common activity during stage ceremonies and other events. To carry out the research experimental work we have prepared 900 video clip datasets. The dataset was recorded in a 2.5-meter distance using a 48MP Camera. From the dataset, 80% was used for training and the remaining 20% was used to test the model's performance. We applied skip frame selection techniques to a single video by selecting 30 frames and we applied spatial and temporal feature extraction techniques. To overcome the stated problems and to develop the recognition model experimental works have been examined using two deep-learning models CNN-LSTM and CNN-Bi-LSTM, and we achieved 95% and 97% recognition accuracy respectively. From the proposed models, CNN-Bi-LSTM works better than the other model. © 2024 IEEE.

关键词： Video recording

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Coherence Awareness in Diffractive Neural Networks

Coherence Awareness in Diffractive Neural Networks

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kleiner, Matan Michaeli, Lior Michaeli, Tomer Faculty of Electrical and Computer Engineering Technion Haifa32000 Israel Division of Engineering and Applied Science California Institute of Technology 1200 E. California Avenue PasadenaCA91125 United States

We demonstrate the significant i nfluence of th e il lumination co herence on diffractive networks, and propose a framework for network optimization with any prescribed degree of spatial and temporal coherence. We ana... 详细信息

ISBN: (纸本)9781957171395

关键词： Diffractive optics

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural Computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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