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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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A Flexible and Extensible Framework for Multiple Answer Modes Question Answering 33

A Flexible and Extensible Framework for Multiple Answer Mode...

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33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021

作者： Fan, Cheng-Chung Luo, Shang-Bao Chang, Kuang-Yu Wu, Meng-Tse Wu, Tzu-Man Liang, Chao-Chun Chen, Kuan-Yu Su, Keh-Yih Kuo, Chia-Chih Liao, Pei-Jun Hsu, Chiao-Wei Tsai, Shih-Hong Smolka, Aleksandra Wang, Hsin-Min Tsao, Yu Institute of Information Science Academia Sinica Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taiwan

ISBN: (纸本)9789869576949

This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms;however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4%. © 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Statistical tests

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HPCNet: A Hybrid Progressive Coupled Network for Image Deraining

HPCNet: A Hybrid Progressive Coupled Network for Image Derai...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Qiong Wang Kui Jiang Jinyi Lai Zheng Wang Jianhui Zhang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Huawei Technologies Cloud BU College of Computer Science and Technology Hangzhou Dianzi University

Convolutional neural networks (CNNs) and Transformers have significantly succeeded in low-level vision tasks. Although prominent complementary characteristics exist regarding the larger receptive field and better convergence, only some efforts have compacted them efficiently due to their individual and nonnegligible weakness. In this paper, we propose a hybrid progressive coupled network (HPCNet) for rain perturbation removal, which integrates the advantages of these two architectures while maintaining both effectiveness and efficiency. In particular, we achieve the progressive decomposition and association of rain-free and rain features, designed as an asymmetrical dual-path mutual representation network to alleviate the computational cost. Meanwhile, we equip the network with high-efficiency convolutions and resolution rescaling strategy to trade off the computational complexity. Extensive experiments show that our method outperforms MPRNet on average while saving 87.2% and 61.1% of the computational cost and parameters.

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ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification

arXiv

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arXiv 2024年

作者： Wang, Xianlong Hu, Shengshan Zhang, Yechao Zhou, Ziqi Zhang, Leo Yu Xu, Peng Wan, Wei Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Information and Communication Technology Griffith University SouthportQLD4215 Australia

Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victim models. Recently, defense mechanisms such as adversarial training, image transformation techniques, and image purification have been proposed. However, these schemes are either susceptible to adaptive attacks, built on unrealistic assumptions, or only effective against specific poison types, limiting their universal applicability. In this research, we propose a more universally effective, practical, and robust defense scheme called ECLIPSE. We first investigate the impact of Gaussian noise on the poisons and theoretically prove that any kind of poison will be largely assimilated when imposing sufficient random noise. In light of this, we assume the victim has access to an extremely limited number of clean images (a more practical scene) and subsequently enlarge this sparse set for training a denoising probabilistic model (a universal denoising tool). We then introduce Gaussian noise to absorb the poisons and apply the model for denoising, resulting in a roughly purified dataset. Finally, to address the trade-off of the inconsistency in the assimilation sensitivity of different poisons by Gaussian noise, we propose a lightweight corruption compensation module to effectively eliminate residual poisons, providing a more universal defense approach. Extensive experiments demonstrate that our defense approach outperforms 10 state-of-the-art defenses. We also propose an adaptive attack against ECLIPSE and verify the robustness of our defense scheme. Our code is available at https://***/CGCL-codes/ECLIPSE. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

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International Conference on computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles

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Applied Soft Computing 2023年 143卷

作者： Abbasi, Shirin Rahmani, Amir Masoud Balador, Ali Sahafi, Amir Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Mälardalen University Västerås72123 Sweden Faculty of Computer Engineering Department South Tehran Branch Islamic Azad University Tehran Iran

Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm's objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities;in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm's performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services. © 2023 Elsevier B.V.

关键词： Genetic algorithms

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices for Indoor Positioning

NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices f...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Tong Wu Han Li Bo Gao Ke Xiong Pingyi Fan Khaled Ben Letaief Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Ultra-wideband (UWB) is a popular technology for indoor positioning. However, non-line-of-sight (NLOS) propagation caused by human occlusion dramatically affects the accuracy of UWB-based indoor positioning. Most of the existing works focused on mitigating NLOS errors in the scenarios with building structure occlusion, but the NLOS mitigation under human occlusion has not been well addressed. To this end, this paper aims to mitigate the NLOS impact of human occlusion, and then proposes a method of NLOS mitigation to reduce NLOS ranging errors under human occlusion with low-cost UWB devices (i.e., NM-LUD). It takes a set of signal features extracted from low-cost devices as input, calculates their weights and mitigates the ranging error based on the features' weights. Experiment results on the two data sets collected by LinkTrack S and UML1 UWB devices show that the method reduces ranging error from 0.9009 m to 0.1435 m (about by 84.07%) and from 0.7466 m to 0.0792 m (about by 89.39%) respectively, which greatly mitigates the influence of NLOS caused by human occlusion.

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Dynamic Reliability Analysis Model for Fault-tolerant Network Routing

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Chinese Journal of Electronics 2023年第3期21卷 500-504页

作者： Bin WANG Chunming WU Qiang YANG Yaguan QIAN Xiaonan WANG Computer Science College Zhejiang University Hangzhou China National Digital Switching System Engineering and Technological Research Center Zhengzhou China College of Electrical Engineering Zhejiang University Hangzhou China College of Science Zhejiang University of Science and Technology Hangzhou China

The appropriate selection of redundant routing paths to promote fault-tolerant capability is a key issue for enhancing reliability of routing algorithms. In this paper it is a dynamic reliability analysis model that presented for fault-tolerant routing algorithms. Through theoretical analysis, the direct relationship between routing and its dynamic reliability is established. Through this model it can effectively assess the performance of existing fault-tolerant routing algorithms which provides an efficient model for the routing algorithm design.

关键词： Fault tolerance Analytical models Heuristic algorithms Fault tolerant systems Reliability theory Routing Performance analysis

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Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection

arXiv

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arXiv 2024年

作者： Gan, Yuhang Xuan, Wenjie Luo, Zhiming Fang, Lei Wang, Zengmao Liu, Juhua Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China Information Technology Co. Ltd. Ningbo China

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations. To this end, this paper explores incorporating semantic priors to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the common knowledge of the visual foundation models (i.e., FastSAM) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes, which brings further increments. Specifically, we construct pseudo-change detection data from public single-temporal remote sensing segmentation datasets for large-scale pre-training, where an extra branch is also introduced for the proxy semantic segmentation task. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing stateof- the-art methods. The code is available at SA-CD. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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