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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Tae-Kil Kim Jae-Hak Kim Jung-Hwi Kim Gyeong-Cheol Kim Ju-Hun Lee Chang-Gi Yeo Sang-Yong Jung eP Drivetrain Test Team Hyundai Motor Company Department of Electrical and Computer Engineering Sungkyunkwan University eP Drivetrain Test Team Hyundai Motor Company Hwaseong-si Korea Inverter Engineering Design Team Hyundai Motor Company Hwaseong-si Korea eP System Engineering Design Team Hyundai Motor Company Hwaseong-si Korea Electrified Propulsion Test Group Hyundai Motor Company Hwaseong-si Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon-si Korea

With electric vehicle models becoming diverse for each car manufacturer, the commonality of driving components is becoming highly necessary to enhance productivity and reduce cost. In the case of traction motors, a common method is to diversify motors by varying the stacking length according to the required power. Another method is to assemble different type of rotors with an identical stator. By this method, it can be fabricated as an interior permanent magnet synchronous motor (IPMSM) and induction motor (IM). The second method is convenient and can achieve a high component commonality. However, IMs are known for their low power densities and low efficiencies compared with permanent magnet motors. In this study, the performance of two types of motors with identical stator is compared experimentally. To demonstrate advantages and disadvantages of IM compared with IPMSM, which is widely used as a traction motor for electric vehicles, the test results are described focusing on IM.

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The Pairing Analysis Improvement of Magnetized Structure in Electromagnetic Bulging Process in Case of Tube with Field Shaper

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Journal of Electromagnetic Analysis and Applications 2024年第4期16卷 43-67页

作者： Blaise Kevine Lenz Soronga Zhanqian Zhan Jing Liu Jonas Asamoah Mohamed Bangali Bangura Matala Makiese N’kodia Kambani Chrionie Md Jobayer Hossain Yongdong Chen Program Debugging and Design Department Guangdong Lyric Robot Automation Co. Ltd Huizhou China Electrical and Automation Department China Three Gorges University Yichang China Electronic Department Power Electronic Research Center University of Kent Giles Ln Canterbury UK Program Debugging Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Electrical and Computer Engineering Department South China University of Technology Guangzhou China World Standardization Certification and Testing Group (Shenzhen) Co. Ltd. Shenzhen China Electrical Engineering-Power System Mapon University Kindu Democratic Republic of Congo Department of Economy Managment School of Management Computer and Telecomunication Rabbat Marocco Materials Visual Calibration Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Program Design Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China

In the current practical science, the accuracy in the formability of metal alloys being the goal when using electromagnetic forming (EMF) technology, which is a high-speed processing technology that uses Lorentz forces to achieve plastic deformation of sheet metal;according to the previous analysis, the results have shown that in most cases, the Lorentz force acting on the workpiece (metal) is not uniform, there are uneven axial deformations of the metal plates which prevent the rapid advancement of today’s technology. In this article, we presented some advanced analyzes which will lead us to improve the technical solution for the problems of non-uniform axial deformations of the metals in the traditional tube electromagnetic forming technology (EMF). A field shaper is used as a practical forming tool to influence the magnetic field and magnetic pressure distribution, thereby improving the forming ability and result during the electromagnetic forming (EMF) process and we see that induced eddy current control is realized by changing the structural parameters of the magnetic field shaper;which improves the strength and controllability of the magnetic force that acts on the workpiece;thereby a greater radial magnetic pressure can be achieved with field shaper than the case without it;the field shaper regulates the electromagnetic force, the distribution of the magnetic pressure decreases, and the uniform force area of the tube increases which effectively enhances the uniform range of the pipe electromagnetic bulging and the electromagnetic induction coupling between the coil and the metallic workpiece is generally required to produce the Lorentz forces. Using COMSOL Multiphysics® simulation software helped us to accurately represent the real world, simulating multiple physical effects that happened in this model during the process.

关键词： Electromagnetic Forming Field Shaper Lorentz Force Magnetic Pressure Distribution

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Virtual Reality Technology in Peruvian Higher Education: Influence of University Tenure

Virtual Reality Technology in Peruvian Higher Education: Inf...

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Virtual Campus (JICV), X International Conference on

作者： Diego Vergara-Rodríguez Ronald Melgarejo Solis Álvaro Antón-Sancho Carlos Edmundo Navarro Depaz Pablo Fernández-Arias Agariadne Dwinggo Samala Technology Instruction and Design in Engineering and Education Research Group (TiDEE.rg) Catholic University of Ávila Ávila Spain Department of Virtual Education Web Digital School University Lima Penú Dept. System Engineering and Computer Science San Marcos University Lima Perú Facuity of Engineering Universitas Negeri Padang Padang West Sumatera Indonesia

ISBN: (数字)9798350343441

ISBN: (纸本)9798350343458

The use of virtual reality (VR) technologies in higher education has been growing in recent years due to their didactic potential in the most diverse areas of knowledge. However, the implementation of VR in universities is affected by several factors, including the level of digitization of the country or the tenure, private or public, of the universities. In this paper, a quantitative, descriptive and correlational research is carried out on the assessments of a group of professors in Peru about the use of VR in their lectures and on the differences in these perceptions according to the tenure of the university of the participants. For this purpose, a validated questionnaire was used, and its responses were statistically analyzed. The results reveal that professors express high ratings for the use of VR but show insufficient levels of digital skills for its use. In addition, professors from private universities give higher ratings of the didactic and usability aspects of VR. It is suggested that VR equipment and faculty training should be strengthened.

关键词： Training Virtual reality Usability

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A Detailed Assessment of Model Form Uncertainty in a Load-Carrying Truss Structure 38th

A Detailed Assessment of Model Form Uncertainty in a Load-Ca...

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38th IMAC, A Conference and Exposition on Structural Dynamics, 2020

作者： Feldmann, Robert Gehb, Christopher M. Schäffner, Maximilian Matei, Alexander Lenz, Jonathan Kersting, Sebastian Weber, Moritz System Reliability Adaptive Structures and Machine Acoustics SAM Technische Universität Darmstadt Darmstadt Germany Department of Mathematics Research Group Optimization Technische Universität Darmstadt Darmstadt Germany Department of Mathematics Research Group Probability and Statistics Technische Universität Darmstadt Darmstadt Germany Department of Computer Integrated Design Technische Universität Darmstadt Darmstadt Germany

ISBN: (纸本)9783030487782

In structural engineering, different assumptions and simplifications during the mathematical modeling process may lead to a set of competing mathematical models with different complexity and functional relationships. The quantification of the resulting model form uncertainty for competing mathematical models may be used to select the model that predicts the experimental measurements of a system most adequately for given requirements, e.g. highest possible accuracy or low computational costs. In this paper, a review and application of four selected approaches to detect and quantify model form uncertainty using experimental measurements and simulation data are conducted: detection of model form uncertainty by (1) parameter estimation with optimal design of experiments as well as quantification of model form uncertainty by (2) the area validation metric for comparing the cumulative density function of a numerical simulation with measurements, (3) a non-parametric regression approach to describe the model error and (4) a Gaussian process based quantification of model form uncertainty. As an exemplary system, an experimental load-carrying truss structure is considered with its system output being the maximum axial tensions in selected truss members due to static and dynamic loads. In two competing mathematical models, the truss structure is either assembled with rigidly connected beams or pin jointed rods. The proposed approaches (1) to (4) detect and quantify model form uncertainty in the two competing models of the load-carrying truss structure, which are subsequently compared and evaluated in terms of their simulation accuracy. Depending on the model requirements, the adequate truss structure model with quantified model form uncertainty may then be selected for further investigations. © 2020, The Society for Experimental Mechanics, Inc.

关键词： Parameter estimation

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Methodology for creation of labeled image datasets of entrained air voids and aggregates in concrete surfaces using confocal laser scanning microscopy

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Advanced Engineering Informatics 2025年 67卷

作者： Viktor Kostic Qadeer Khan Daniel Cremers Jithender Timothy Thomas Kränkel Christoph Gehlen TUM School of Engineering and Design Department of Materials Engineering Chair of Materials Science and Testing Centre for Building Materials (cbm) Technical University of Munich Munich 81245 BY Germany TUM School of Computation Information and Technology Computer Vision Group Technical University of Munich Munich 81245 BY Germany Munich Center for Machine Learning Munich 81245 BY Germany

Several strategies using neural network algorithms have been established to automate the process of evaluating parameters regarding the freeze–thaw resistance of hardened concrete from image data. However, the performance of such models is dependent on the quality of the datasets used for training. Hand-labeling concrete image data is challenging, where especially small micro air voids (<300 μ m) can be overlooked. This potentially leads to algorithms that are unable to accurately detect the micro air void content, despite being required by current standards (e.g. DIN EN 480-11). To account for this, a method was developed using confocal laser scanning microscopy and the “Segment Anything” model. Acquiring surface height-data through confocal laser scanning microscopy expendites the labeling process and creates more reliable training data due to the detectability of air voids through height differences. Furthermore, by combining the height-data with RGB-images it was possible to accurately distinguish between air voids, aggregates and cement paste with high repeatability between repeated labeling iterations. Keeping high magnification levels (120-480x) enabled the detection of air voids down to 5 μ m. The framework and dataset presented in this study are fundamental for further development of new neural network based algorithms, specialized for the characterization and evaluation of the freeze–thaw resistance of concrete.

关键词： Concrete Freeze–thaw resistance Air voids Labeling Confocal laser scanning microscopy Segment anything model

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Cross-domain intelligent cooperative spectrum sensing algorithm based on Federated Learning and Swin-Transformer neural network

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Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Lingwei Xu Zhihe Gao Yufang Li T. Aaron Gulliver Department of Information Science and Technology Qingdao University of Science & Technology Qingdao 266061 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 Hubei China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi Minzu University) Nanning 530006 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 2Y2 Canada

The deployment of 6th Generation (6G) mobile networks will bring revolutionary changes to organizations and users through higher speeds, lower latency, and more intelligent network management. This will require more efficient spectrum sharing to improve network performance. Cognitive Radio (CR) can be employed to help achieve this goal. However, data collection at the fusion center for cooperative spectrum sensing (CSS) creates a risk of sensing user (SU) data leakage. In this paper, an intelligent CSS algorithm for mobile communication is proposed based on Federated Learning with a Swin-Transformer. First, to achieve the cross-domain process, a Continuous Wavelet Transform (CWT) is used with the normalized time series data to obtain a time-frequency spectrum map with joint time-frequency features. Second, Federated Learning is employed to realize distributed CSS to reduce sensing overhead, improve the security of sensing data, and eliminate the SU data isolation with traditional distributed CSS. Then, a new Swin-Transformer spectrum sensing is proposed for fusion learning of the time-frequency spectrum map. The fusion of Federated Learning and Swin-Transformer neural network provides cooperation with multiple SUs to achieve efficient spectrum sensing using the K -rank fusion criterion. Compared with the Convolutional Neural Network (CNN), ViT-Transformer, Graph Neural Network (GNN) and EfficientNet algorithms, the detection probability of the proposed algorithm has increased by 45 %, and the false alarm probability has decreased by 13 %.

关键词： Cross domain processing Federated learning Mobile cooperative spectrum sensing Swin-transformer

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Regularity-based evolutionary multi-objective optimization review

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Swarm and Evolutionary computation 2025年 97卷

作者： Yulan Lu Xueyi Guo Jinggai Geng Shuai Wang Zhenkun Wang Aimin Zhou Jianyong Sun Xinhui Si Xin Sun Hu Zhang School of Mathematics and Physics University of Science and Technology Beijing Beijing 100083 China School of Materials Science & Engineering Changzhou University Changzhou Jiangsu 213164 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Shanghai Institute of AI Education East China Normal University Shanghai 200062 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an 710049 China CASIC Research Institute of Intelligent Decision Engineering Beijing 100074 China

Under mild conditions, the Pareto optimal solutions of a continuous m -dimensional multi-objective optimization problem (MOP) have been proved to form a piecewise ( m -1)-dimensional manifold structure in the search space, a characteristic known as the regularity property. As a domain knowledge of MOP, since the first proposal in 2008, this regularity property has demonstrated significant potential for enhancing the performance of multiobjective evolutionary algorithms (MOEAs). However, there has yet to be a systematic survey of the regularity property within the design of MOEAs. This article aims to address this gap by providing a comprehensive review of regularity-based evolutionary multi-objective optimization (REMO) approaches. We hope that this survey will help EMO researchers to have a comprehensive understanding of REMO.

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2021年 1237 AISC卷 v-vi页

作者： Dong, Yucheng Herrera-Viedma, Enrique Matsui, Kenji Omatsu, Shigeru Briones, Alfonso González González, Sara Rodríguez Business School Sichuan University Chengdu China Dept. of System Design Osaka Institute of Technology Osaka Japan GRASIA Research Group Facultad de Informática Universidad Complutense de Madrid Madrid Spain University of Granada Granada Spain Hiroshima University Osaka Japan IoT European Digital Innovation Hub Bioinformatics Intelligent Systems and Educational Technology Research Group Department of Computer Science Faculty of Science University of Salamanca Salamanca Spain

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems computational modeling

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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