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11th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2024

作者： Lelen, Michal Kulisz, Monika Barszcz, Marcin Jozwik, Jerzy Piesko, Pawel Cybul, Kamil Salamacha, Daria Zawada- Michalowska, Magdalena Lublin University of Technology Faculty of Mechanical Engineering Department of Production Engineering Lublin Poland Lublin University of Technology Management Faculty Department of Organisation of Enterprise Poland Lublin University of Technology Institute of Computer Science Electrical Engineering and Computer Science Faculty Lublin Poland Lublin University of Technology Doctoral School of the Lublin University of Technology Lublin Poland Central Office of Measures Warsaw Poland Lublin University of Technology Doctoral School at the Lublin University of Technology Lublin Poland

ISBN: (纸本)9798350385045

The paper presents the results of temperature measurements after machining samples made of AW-2024 aluminum alloy and S235 steel. The temperature was measured at eight measurement points of examples. The machining process was performed using variable cutting parameters, i.e. wall thickness after machining, cutting depth and cutting speed. The results estimation was made based on the statistical analysis of the obtained measurements. The average values were used to determine the time courses of cooling curves. To perform calculations and numerical simulation, the artificial neural network algorithm of the Matlab 2023b package was used. Thanks to this, the time after which the workpiece can be measured with an inspection probe was determined. As a result, the measurement results showed that the use of artificial neural networks allows for accurate prediction of the start time of the probe measurement, which improves the efficiency of the process and minimizes prediction errors. © 2024 IEEE.

关键词： Neural networks

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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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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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Fractional Order Differential Evolution

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wang, Kaiyu Gao, Shangce Zhou, MengChu Zhan, Zhi-Hui Cheng, Jiujun Faculty of Engineering University of Toyama Toyama-shi Japan Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA College of Artificial Intelligence Nankai University Tianjin China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its performance enhancement. This work proposes Fractional Order Differential Evolution (FODE) to enhance DE performance from two aspects. Firstly, a bi-strategy co-deployment framework is proposed. The population-based and parameter-based strategies are combined to leverage their respective advantages. Secondly, the fractional order calculus is first applied to the differential vector to enhance DE’s exploration ability by using the historical information of populations, and ensures the diversity of population in an evolutionary process. We use the 2017 IEEE Congress on Evolutionary Computation (CEC) test functions, and CEC2011 real-world problems to evaluate FODE’s performance. Its sensitivity to parameter changes is discussed and an ablation study of multi-strategies is systematically performed. Furthermore, the variations of exploration and exploitation in FODE are visualized and analyzed. Experimental results show that FODE is superior to other state-of-the-art DE variants, the winners of CEC competitions, other fractional order calculus-based algorithms, and some powerful variants of classic algorithms. IEEE

关键词： Particle swarm optimization (PSO)

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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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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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Quantum-Coherent X-rays from Crystals

Quantum-Coherent X-rays from Crystals

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Shi, Xihang Wong, Lee Wei Wesley Wong, Liang Jie Kaminer, Ido Solid State Institute Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa32000 Israel School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

We present a quantum analysis of X-ray radiation generated from free electrons interacting with crystalline materials, revealing the role of the electron's quantum-wave nature and of the radiation's quantum-pa... 详细信息

ISBN: (纸本)9781957171258

关键词： Crystals

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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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