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A New Resilient Configuration of Wound Rotor Resolver in Mechanical Fault Conditions

IEEE Sensors Journal 2025年第11期25卷 19069-19076页

作者： Zare, F. Tootoonchian, F. Nasiri-Gheidari, Z. Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran Iran University of Science and Technology Electrical Engineering Department Tehran Iran Sharif University of Technology Electrical Engineering Department Tehran Iran

Resolvers are widely employed as position sensors to detect both rotational and linear displacement. Among the various types, wound rotor resolvers offer superior accuracy in absolute measurements, especially under mechanical fault conditions, but they present significant manufacturing challenges. This paper introduces a novel winding arrangement designed to simplify the manufacturing process of wound rotor resolvers and enhance the accuracy of the sensor under mechanical fault conditions. The performance of the resolver with this new winding configuration is analyzed, with a particular focus on its resilience to eccentricity faults. To validate the high fault tolerance of the proposed design, a prototype of the optimized configuration was developed and tested. The performance of the prototype under these conditions was comprehensively evaluated through experimental measurements. © 2001-2012 IEEE.

关键词： Failure (mechanical)

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Measurement of acceleration and angular velocity of particles during a 3D silo discharge

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Particuology 2024年第9期92卷 61-71页

作者： Tongtong Mu Yiming Li Quan Chen Ran Li Hui Yang College of Medical Instrument Shanghai University of Medicine and Health SciencesShanghai201318China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China

Accurate measurement of the three-dimensional(3D)movement of discrete particles is crucial for comprehending complex granular rheology in *** this paper,the acceleration and angular velocity of particles in 3D silos are measured by using a spherical detector based on inertial technology and magnetic positioning *** acceleration of particles is the largest in the center of silos,which suggest that the resistance generated by friction and extrusion is the ***,the angular velocity distribution follows lognormal function except for particles near the *** correlation between acceleration and angular velocity is opposite in different flow *** reveals for the first time that the extent to which the resultant force on the particles affects their rotational motion is related to the flow *** results have practical significance for regulating the granular flow pattern and optimizing the structural design of silos.

关键词： Granularflow Silo Angular velocity Acceleration

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Bifurcation analysis and control study of improved full-speed differential model in connected vehicle environment

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Chinese Physics B 2024年第7期33卷 245-266页

作者：艾文欢雷正清李丹洋方栋梁刘大为 College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China College of Electrical Engineering Lanzhou Institute of TechnologyLanzhou 730050China

In recent years, the traffic congestion problem has become more and more serious, and the research on traffic system control has become a new hot spot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable pivots can alleviate the traffic congestion problem from a new perspective. In this work, the full-speed differential model considering the vehicle network environment is improved in order to adjust the traffic flow from the perspective of bifurcation control, the existence conditions of Hopf bifurcation and saddle-node bifurcation in the model are proved theoretically, and the stability mutation point for the stability of the transportation system is found. For the unstable bifurcation point, a nonlinear system feedback controller is designed by using Chebyshev polynomial approximation and stochastic feedback control method. The advancement, postponement, and elimination of Hopf bifurcation are achieved without changing the system equilibrium point, and the mutation behavior of the transportation system is controlled so as to alleviate the traffic congestion. The changes in the stability of complex traffic systems are explained through the bifurcation analysis, which can better capture the characteristics of the traffic flow. By adjusting the control parameters in the feedback controllers, the influence of the boundary conditions on the stability of the traffic system is adequately described, and the effects of the unstable focuses and saddle points on the system are suppressed to slow down the traffic flow. In addition, the unstable bifurcation points can be eliminated and the Hopf bifurcation can be controlled to advance, delay, and disappear,so as to realize the control of the stability behavior of the traffic system, which can help to alleviate the traffic congestion and describe the actual traffic phenomena as well.

关键词： bifurcation analysis vehicle queuing bifurcation control Hopf bifurcation

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Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

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IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

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science China(Information sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

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An End-to-End Two-Branch Network Towards Robust Video Fingerprinting

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2371-2384页

作者： Xu, Yingying Zhou, Yuanding Li, Xinran Zhao, Gejian Qin, Chuan University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

With the increasing number of edited videos, many robust video fingerprinting schemes have been proposed to solve the problem of video content authentication. However, most of them either deal with the temporal and spatial features symmetrically or insufficiently consider the temporal information. In this work, an end-to-end two-branch network toward robust video fingerprinting (RVFNet) is proposed, where the two branches focus on the temporal and spatial information, respectively. The temporal branch aims to comprehensively capture complex motion patterns by combining subtle motion changes with the overall motion trend. The spatial branch exploits the pixel-level information obtained by multiple receptive fields while preserving significant structural features. Deep metric learning is employed in the training process, and we adopt hard triplet loss to constrain the generation of fingerprints. Furthermore, we construct a large-scale and complex dataset for the robust video fingerprinting task based on multiple video content-preserving manipulations in actual scenarios. The size of our dataset exceeds most datasets adopted in the current robust video fingerprinting schemes. Based on the proposed dataset, experimental results demonstrate that our scheme achieves outstanding performance improvements compared with the state of the art. © 2020 IEEE.

关键词： Convolution

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Unsupervised anomaly detection and localization via bidirectional knowledge distillation

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Neural Computing and Applications 2024年第29期36卷 18499-18514页

作者： Wang, Xiaoming Wang, Yongxiong Pan, Zhiqun Wang, Guangpeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model provides evidence for anomaly detection and localization. However, the teacher model is pretrained for classification, while the anomaly scores in the distillation-based anomaly detection method are indirectly derived from the classification scores. The mismatch between the two tasks can hinder the optimization of the model. To tackle this issue, we propose an innovative bidirectional knowledge distillation model. In this approach, forward knowledge distillation is pivotal in bolstering the model’s capacity for generalization. Simultaneously, backward knowledge distillation promotes diversity in representing anomalies. This reciprocal knowledge exchange effectively wards off potential performance declines due to target inconsistency. Through bidirectional knowledge distillation, we establish a more encompassing and resilient framework for knowledge transfer. Additionally, we introduce a novel data augmentation strategy to simulate anomalies and effectively eliminate unnecessary noise. In experiments on the MVTec AD, the proposed model achieves competitive results compared to state-of-the-art methods, 97.47% on image-level AUC, 98.23% on pixel-level AUC, and 94.77% on instance-level PRO. These results demonstrate the practicality of our approach in anomaly detection and localization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Robust federated contrastive recommender system against targeted model poisoning attack

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science China(Information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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