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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Leveraging SAM for Automatic Prostate Segmentation on Micro-Ultrasound Images

Leveraging SAM for Automatic Prostate Segmentation on Micro-...

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Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Radcliffe, Olivia Lawford-Wickham, Imogen A. Wilson, Paul F.R. Abolmaesumi, Purang Mousavi, Parvin Medical Informatics Laboratory School of Computing Queen's University Kingston Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (纸本)9781510686021

PURPOSE: Deep learning is promising for enabling accurate and automatic prostate segmentation. Existing deep learning segmentation model approaches often rely on large training datasets for good generalization. We aim to adapt pre-trained foundation models to enable accurate, data-efficient, and robust prostate segmentation. METHODS: We adapt the promptable foundation model SAM1 for prostate segmentation on rotational micro-ultrasound scans2 of the prostate. We design novel prompting strategies to provide the model with enhanced 3D context awareness to improve segmentation. RESULTS: Our model, which we call SliceTrack-SAM, outperforms prior state-of-the-art in micro-ultrasound prostate segmentation. We achieve a Dice coefficient of 94.0% and Hausdorff distance 1.76 mm on the MicroSegNet dataset. Qualitative analysis and ablation studies further validate the success of our approach. CONCLUSION: Transfer learning from pre-trained foundation models can alleviate the challenge of data scarcity and improve the generalization of medical imaging deep learning systems. Prompts provide a flexible and effective way to leverage auxiliary information, such as 3D context, when using these models. © 2025 SPIE.

关键词： Image segmentation

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Ultra-Fast 4H-SiC LGAD With Etched Termination and Field Plate

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 845-847页

作者： Yang, Tao Sekely, Ben J. Satapathy, Yashas Allion, Greg Atar, Gil Barletta, Philip Haber, Carl Holland, Steve Muth, John F. Pavlidis, Spyridon Stucci, Stefania Tishelman-Charny, Abraham Lawrence Berkeley National Laboratory Physics Division BerkeleyCA94720 United States North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States Brookhaven National Laboratory UptonNY11973 United States

Silicon carbide Low Gain Avalanche Detectors (4H-SiC LGADs), exhibiting an ultra-fast time response and excellent time resolution, are reported. Via TCAD simulations, the use of field plates is proposed to suppress the high electric field caused by the negative bevel-etched angle. Experimental measurements confirm that the field plate significantly increases the breakdown voltage. Gain and time resolution are measured by using the ultraviolet transient current technique (UV-TCT), showing that 4H-SiC LGADs possess excellent timing performance, with a time resolution better than 35 ps in response to an injected laser signal tuned to represent a single minimum ionizing particle (MIP) at room temperature. Additionally, the gain suppression effect is observed in the 4H-SiC LGAD for the first time. © 1980-2012 IEEE.

关键词： Injection lasers

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Iterative Reconstruction for Multimodal Neutron Tomography

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IEEE Transactions on Nuclear Science 2025年第5期72卷 1686-1697页

作者： Gregor, Jens Heath, Matthew R. Deller, Timothy Blackston, Matthew A. Hausladen, Paul The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Leidos Oak RidgeTN37830 United States

We describe a unified framework for model-based iterative 3-D reconstruction of multimodal neutron transmission, hydrogen-scatter, and induced-fission images from low resolution data recorded using 14.1-MeV neutrons and the associated-particle imaging (API) technique. The framework, which was developed to facilitate use in challenging field-deployment scenarios, is centered around physics-based system models and a total variation (TV) constrained implementation of the simultaneous iterative reconstruction technique (SIRT). Modified to solve a statistically weighted least squares (WLS) problem, the SIRT algorithm is accelerated using ordered subsets and Nesterov’s momentum for which we derive a near-optimal value of the governing Lipschitz constant. The approach enables the reconstruction of images that are high resolution compared to the acquired data and is robust to both limited statistics and a limited number of projection angles. Moreover, the framework is fast enough to be practical. Example images are provided that demonstrate both the ability to perform fast-neutron imaging of high-atomic-number materials with low radiation dose and the benefit of multimodal neutron imaging to identify key materials. © 1963-2012 IEEE.

关键词： Constrained optimization

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T-SNVAE: Deep probabilistic learning with local and global structure for industrial process monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第06期6卷 1603-1613页

作者： Huang, Jian Liu, Zizhuo Yang, Xu Liu, Yupeng Lv, Zhaomin Peng, Kaixiang Ersoy, Okan K. University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering China University of Science and Technology Beijing Shunde Innovation School Foshan China Shanghai University of Engineering Science School of Urban Railway Transportation China Purdue University Electrical and Computer Engineering West Lafayette United States

Variational autoencoder is a generative deep learning model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t- SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN Embedding. Through minimizing the Kullback-Leibler divergence of joint probabilities between the original data and the reconstructed data, variational autoencoder learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process. © 2020 IEEE.

关键词： Embeddings

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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Reviewing metal supported solid oxide fuel cells for efficient electricity generation with biofuels for mobility

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Journal of Energy Chemistry 2025年第04期103卷 106-153页

作者： Fábio C.Antunes Jo?o P.J.de Oliveira Ricardo S.de Abreu Thiago Dias Bruno B.N.S.Brand?o Josué M.Gon?alves Josimar Ribeiro Julian Hunt Hudson Zanin Gustavo Doubek School of Electrical and Computer Engineering University of Campinas School of Food Engineering University of Campinas School of Chemical Engineering University of Campinas Mackenzie Institute for Research in Graphene and Nanotechnologies (Mack Graphe) Mackenzie Presbyterian Institute Electrochemistry Research and Development Laboratory (LPDE) Department of Chemistry Center of Exact Sciences Federal University of Espírito Santo Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology

Metal-Supported Solid Oxide Fuel Cells（MS-SOFCs） hold significant potential for driving the energy *** electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell（SOFCs） and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells（PEMFCs） due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication *** review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future *** such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged *** concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries（TPBs）.This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available ***,the End-of-Life（EoL）,recycling,and life-cycle assessments（LCAs） of the Fuel Cell Hybrid Electric Vehicles（FCHEVs） were *** comparing Fuel Cell Electric Vehicles（FCEVs）equipped with（PEMFCs） and FCHEVs equipped with MS-SOFCs,both powered with hydrogen（H2） generated by different routes were *** review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.

关键词： Biofuel Fuel cell Hybrid electric vehicle Metal-supported solid oxide fuel cell Powertrain systems Reform

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Shorter ZK-SNARKs from square span programs over ideal lattices

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Cybersecurity 2025年第1期8卷 208-226页

作者： Xi Lin Heyang Cao Feng‑Hao Liu Zhedong Wang Mingsheng Wang Key Laboratory of Cyberspace Security Defense Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina School of Electrical Engineering&Computer Science Washington State UniversityPullmanWAUSA School of Cyber Science and Engineering Shanghai Jiaotong UniversityShanghaiChina

Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment *** with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space *** tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)*** this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic *** with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK ***,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×*** constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge *** develop concretely small constructions by using module-switching and key-switching procedures in a novel way.

关键词： Zk-SNARKs Post-quantum Succinct argument

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