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Comparative Study on Electromagnetic Performance of Slotted and Slotless PM Synchronous Motors According to Aspect Ratios

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IEEE Transactions on Magnetics 2025年第6期61卷

作者： Hong, Hyeonbin Lee, Jae-Kwang Jung, Dong-Hoon Lee, Ju Kim, Hyunwoo Hanyang University Department of Electrical Engineering Seoul04763 Korea Republic of Korea Electronics Technology Institute Intelligent Mechatronics Research Center Bucheon14502 Korea Republic of Andong National University Department of Electrical and Materials Engineering Andong36729 Korea Republic of Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN47907 United States

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high torque density. PMSM are generally classified into slotted and slotless types, each offering distinct advantages. A key design parameter of PMSM is the aspect ratio, defined as the ratio of the stator outer diameter to the stack length, which significantly affects electromagnetic performance and application suitability. In this article, a comparative study of 1.35 kW slotted and slotless PMSM is proposed based on aspect ratios under the same volume, using a finite element analysis (FEAs). Although the volume of PMSM primarily determines electromagnetic performance, such as electromagnetic torque and loss, the aspect ratio is also crucial because of its impact on magnetic leakage flux. Constraints, including the volume of PMSM, magnet thickness, and current density, are selected to compare electromagnetic performances based on aspect ratio. Optimal models of slotted and slotless PMSM are designed, and their electromagnetic performances are compared. The optimal slotless PMSM achieves a torque ripple approximately 31.08%p lower than that of the optimal slotted PMSM. Furthermore, the total harmonic distortion (THD) of the induced voltage is reduced by approximately 20.16%p. To verify this study, the 1.35 kW optimal slotless PMSM is manufactured and experimentally tested, showing errors of less than 5% compared to FEA results. © 1965-2012 IEEE.

关键词： Aspect ratio

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Improving the Efficacy of Binary Communication Security in Communication Networks of Smart Cities

Improving the Efficacy of Binary Communication Security in C...

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3rd International Conference on Internet of Things, Communication and intelligent Technology, IoTCIT 2024

作者： Salama, Ramiz Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Mersin 10 Nicosia Turkey Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey

ISBN: (纸本)9789819627660

Recently, smart cities have become more and more popular as a way to enhance society and the standard of living for its people. These are the results of the increased social and economic prospects that the advancement of information and communication technology (ICT) has brought about. But there are certain issues with our presumptions about privacy and security. The usage of cellphones and other technologies has already enhanced people’s engagement and sense of connection. Gadgets, smart energy meters, and security appliances are commonplace these days. The Internet of Things will soon be fully connected to homes, public spaces, healthcare systems, and other social institutions. In this work, we improve the security performance of communication networks in smart cities by implementing a novel framework and scheme to guarantee strong data secrecy and authentication. The smart city system can set up shared session key procedures and mutual authentication to secure a two-way communication channel between the control center and smart meters. Our comprehensive simulation was used to test and evaluate the security performance of the smart city communication network both with and without our suggested technique. Throughput, latency, load, and traffic received packets per second were all measured. The resources available in our suggested solution were further limited by the fact that we used a network intrusion detection system (NIDS) and a man-in-the-middle (MITM) attack to evaluate and validate the security criteria. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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The intelligent biped robot AIRO: System overview and omnidirectional walking method design*

The intelligent biped robot AIRO: System overview and omnidi...

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IEEE International Conference on robotics and Biomimetics

作者： Yun Liu Anhuan Xie Shiqiang Zhu Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical Engineering Dalhousie University Halifax Nova Scotia Canada

ISBN: (纸本)9781665481106

In order to verify the omnidirectional walking theory and to meet the requirements of rapid prototyping and economy, a bipedal robot system AIRO based on commercial servo motor-driven joints is designed and implemented. Compared with other motor-driven biped robots of the same size, the structure body of AIRO is lighter, and the leg mass distribution is more in line with the principle of the linear inverted pendulum model. Firstly, the overall system composition of the robot is introduced, including the mechanical structure part and the control system part; Then, based on the linear inverted pendulum model principle and the input speed command of the system, the omnidirectional walking planning and control of the robot is realized in combination with the current state of the robot; Finally, a successful omnidirectional walking experiment was carried out on AIRO to verify the effectiveness of the biped robot motion system.

关键词： Legged locomotion Robot motion Atmospheric modeling Three-dimensional printing Rapid prototyping Topology Plastics

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Robust triboelectric information-mat enhanced by multi-modality deep learning for smart home

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InfoMat 2023年第1期5卷 139-160页

作者： Yanqin Yang Qiongfeng Shi Zixuan Zhang Xuechuan Shan Budiman Salam Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingaporeSingapore National University of Singapore Suzhou Research Institute(NUSRI) SuzhouPeople's Republic of China Singapore Institute of Manufacturing Technology and National University of Singapore(SIMTech-NUS)Joint Lab on Large-area Flexible Hybrid Electronics National University of SingaporeSingaporeSingapore Printed Intelligent Device Group Singapore Institute of Manufacturing TechnologyAgency for ScienceTechnology and Research(A*STAR)SingaporeSingapore NUS Graduate School-Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingaporeSingapore

In metaverse,a digital-twin smart home is a vital platform for immersive communication between the physical and virtual *** nanogenerators(TENGs)sensors contribute substantially to providing smart-home ***,TENG deployment is hindered by its unstable out-put under environment ***,we develop a digital-twin smart home using a robust all-TENG based information mat(InfoMat),which consists of an in-home mat array and an entry *** interdigital electrodes design allows environment-insensitive ratiometric readout from the mat array to can-cel the commonly experienced environmental *** position sensing is also achieved because of the interval arrangement of the mat ***,the two-channel entry mat generates multi-modality informa-tion to aid the 10-user identification accuracy to increase from 93% to 99% compared to the one-channel ***,a digital-twin smart home is visualized by real-time projecting the information in smart home to virtual reality,including access authorization,position,walking trajectory,dynamic activities/sports,and so on.

关键词： digital twin environment-insensitive multi-modality deep learning scalability smart home triboelectric information-mat

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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Attention in Attention for PET-CT Modality Consensus Lung Tumor Segmentation

Attention in Attention for PET-CT Modality Consensus Lung Tu...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuzhou Zhao Xinyu Zhou Haijing Guo Qianyu Guo Yan Zuo Shaoli Song Shuyong Gao Wenqiang Zhang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Department of Nuclear Medicine Fudan University Shanghai Cancer Center China Shanghai Engineering Research Center of AI Robotics Academy for Engineering and Technology Fudan University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Combination of multi-modal PET-CT imaging for lung tumor segmentation is significant for clinical treatment. Existing methods have not fully considered the impact of noise in PET-CT on the multi-modal interaction. To address this, we propose a novel Attention in Attention Network (AiANet). AiANet can mutually learn multi-modal characteristics for segmentation through its cross-learning modules. Within the cross-learning module, we introduce two nested-attention blocks, namely Attention in Self-Attention (AiSA) and Attention in Cross-Attention (AiCA), for multi-scale feature enhancement and multi-modal feature interaction. Importantly, since traditional attention weights calculated solely or unilaterally based on PET or CT can be vulnerable to the inevitable noisy information, we embed a novel Attention in Attention (AiA) module into AiSA and AiCA. The AiA module can seek cross-modal consensus for attention weights to alleviate their noise. Experimental results on clinical PET-CT data of lung cancer demonstrate the superiority of our method.

关键词： Image segmentation Computed tomography Aggregates Noise Lung cancer Imaging Noise measurement Tumors

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A Study on Reduction of AC Copper Loss According to Pole/Slot Combination of EV Traction Motor

A Study on Reduction of AC Copper Loss According to Pole/Slo...

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IEEE Conference on Electromagnetic Field Computation

作者： Jin-Uk Choi Ki-Deok Lee Jae-Kwang Lee Jeong-Jong Lee Myung-Hwan Yoon Dong-Hoon Jeong Ju Lee Intelligent Mechatronics Research Center Korea Electronics Technology Institude Bucheon South Korea Department of Mechanical and Automotive Robotics Halla University Wonju South Korea Department of Electrical Engineering Hanyang University Seoul South Korea

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

This paper is about reducing AC copper loss of an EV traction motor with a maximum speed of 21000 rpm. Recently, as a high-power density design method for traction motors for EVs, the advantages of high slot fill factor and short end turns of hairpin windings have emerged. However, when using hairpin windings, AC copper loss increases at higher speeds, greatly reducing efficiency and power density, so measures to reduce this loss are necessary. AC copper loss is largely divided into skin effect, proximity effect, and eddy current loss due to leakage magnetic flux, which all increase in proportion to the frequency of use. In this paper, the characteristics according to pole/slot combination were analyzed by comparing the AC copper loss and output characteristics at the rated and maximum speed operating points of the 8p/48s model and the 6p/36s model.

关键词： Stator cores Magnetic flux density Density measurement Magnetic cores Windings Traction motors Loss measurement

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A Multi-stage Precision Landing Method for Autonomous eVTOL Based on Multi-marker Joint Localization

A Multi-stage Precision Landing Method for Autonomous eVTOL ...

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IEEE International Conference on robotics and Biomimetics

作者： Senwei Xiang Minxiang Ye Shiqiang Zhu Jason Gu Anhuan Xie Zehua Men Zhejiang Lab Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Electric vertical takeoff and landing aircraft (eVTOL) has drawn more and more attention from home and abroad in recent years. It is believed autonomous eVTOL will create a new era of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM). To autonomous eVTOL, precision landing is an extremely critical operation for it directly affects flight safety. In this paper, we analyze the special issues an eVTOL will encounter when it lands in the UAM applications. A multi-stage precision landing method based on multi-marker joint localization is proposed accordingly. Our method contains three key elements: a compatible vertiport with multiple visual markers; an accurate, fast detection and localization algorithm for the vertiport and a multi-stage landing strategy. We implement our method on an eVTOL prototype named ZJ-Copter developed by Zhejiang Lab. A series of real-world experiments have been conducted to validate the effectiveness and accuracy of the proposed method. Experiment results show that our method works well in real-world scenarios for autonomous eVTOL without GPS signal during landing process. The positioning accuracy is less than 0.1m (altitude $< \mathbf{10}\mathbf{m}$ ), while the landing accuracy is less than 0.5m.

关键词： Location awareness Visualization Robot kinematics Helicopters Prototypes Feature extraction Safety

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Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences

arXiv

引用

arXiv 2023年

作者： Pataki, Zador Altillawi, Mohammad Kanakis, Menelaos Pautrat, Rémi Shen, Fengyi Liu, Ziyuan Van Gool, Luc Pollefeys, Marc The Computer Vision and Geometry Lab Department of Computer Science ETH Zurich Switzerland The Computer Vision Center CVC-Barcelona The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Computer Vision Lab Department Electrical Engineering ETH Zurich Switzerland The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Center for Processing Speech and Images KU Leuven The Computer Vision Lab ETH Zurich Switzerland

Modern learning-based visual feature extraction networks perform well in intra-domain localization, however, their performance significantly declines when image pairs are captured across long-term visual domain variations, such as different seasonal and daytime variations. In this paper, our first contribution is a benchmark to investigate the performance impact of long-term variations on visual localization. We conduct a thorough analysis of the performance of current state-of-the-art feature extraction networks under various domain changes and find a significant performance gap between intra- and cross-domain localization. We investigate different methods to close this gap by improving the supervision of modern feature extractor networks. We propose a novel data-centric method, Implicit Cross-Domain Correspondences (iCDC). iCDC represents the same environment with multiple Neural Radiance Fields, each fitting the scene under individual visual domains. It utilizes the underlying 3D representations to generate accurate correspondences across different long-term visual conditions. Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap. When evaluated on popular long-term localization benchmarks, our trained networks consistently outperform existing methods. This work serves as a substantial stride toward more robust visual localization pipelines for long-term deployments, and opens up research avenues in the development of long-term invariant descriptors. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

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computers, Materials & Continua 2021年第12期69卷 2983-2995页

作者： El-Sayed M.El-kenawy Hattan F.Abutarboush Ali Wagdy Mohamed Abdelhameed Ibrahim Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina 42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza 12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design *** is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data *** advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture *** algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole *** proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and *** offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

关键词： Antenna optimization machine learning artificial intelligence multilayer perceptron sine cosine algorithm grey wolf optimizer

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