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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Yi, Zheyuan Sun, Kai Li, Qiang Liu, Zengyang Tsinghua University State Key Laboratory of Power System Department of Electrical Engineering Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China University Blacksburg Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State BlacksburgVA United States

ISBN: (纸本)9798331516116

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process. © 2025 IEEE.

关键词： Thermal modeling

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Optimal Information Provision for Strategic Hybrid Workers

arXiv

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arXiv 2022年

作者： Shah, Sohil Amin, Saurabh Jaillet, Patrick The Laboratory for Information and Decision Systems The Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Operations Research Center CambridgeMA United States

We study the problem of information provision by a strategic central planner who can publicly signal about an uncertain infectious risk parameter. Signalling leads to an updated public belief over the parameter, and agents then make equilibrium choices on whether to work remotely or in-person. The planner maintains a set of desirable outcomes for each realization of the uncertain parameter and seeks to maximize the probability that agents choose an acceptable outcome for the true parameter. We distinguish between stateless and stateful objectives. In the former, the set of desirable outcomes does not change as a function of the risk parameter, whereas in the latter it does. For stateless objectives, we reduce the problem to maximizing the probability of inducing mean beliefs that lie in intervals computable from the set of desirable outcomes. We derive the optimal signalling mechanism and show that it partitions the parameter domain into at most two intervals with the signals generated according to an interval-specific distribution. For the stateful case, we consider a practically relevant situation in which the planner can enforce in-person work capacity limits that progressively get more stringent as the risk parameter increases. We show that the optimal signalling mechanism for this case can be obtained by solving a linear program. We numerically verify the improvement in achieving desirable outcomes using our information design relative to no information and full information benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

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On the generalization of stochastic gradient descent with momentum

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 1132-1187页

作者： Ali Ramezani-Kebrya Kimon Antonakopoulos Volkan Cevher Ashish Khisti Ben Liang Department of Informatics University of Oslo and Visual Intelligence Centre Integreat Norwegian Centre for Knowledge-driven Machine Learning Oslo Norway Laboratory for Information and Inference Systems (LIONS) EPFL EPFL STI IEL LIONS Lausanne Switzerland Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that there exists a convex loss function for which the stability gap for multiple epochs of SGD with standard heavy-ball momentum (SGDM) becomes unbounded. Then, for smooth Lipschitz loss functions, we analyze a profiles momentum-based update rule, i.e., SGD with early momentum (SGDEM) under a broad range of step-sizes, and show that it can train machine learning models for multiple epochs with a guarantee for generalization. Finally, for the special case of strongly convex loss functions, we find a range of momentum such that multiple epochs of standard SGDM, as a special form of SGDEM, also generalizes. Extending our results on generalization, we also develop an upper bound on the expected true risk, in terms of the number of training steps, sample size, and momentum. Our experimental evaluations verify the consistency between the numerical results and our theoretical bounds. SGDEM improves the generalization error of SGDM when training ResNet-18 on ImageNet in practical distributed settings.

关键词： uniform stability generalization error heavy-ball momentum stochastic gradient descent non-convex

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Realizing Intelligent Spectrum Management for Integrated Satellite and Terrestrial Networks

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Journal of Communications and Information Networks 2021年第1期6卷 32-43页

作者： Ying-Chang Liang Junjie Tan Haonan Jia Jintao Zhang Lian Zhao Center for Intelligent Networking and Communications University of Electronic Science and Technology of ChinaChengdu 611731China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical Computerand Biomedical EngineeringRyerson UniversityToronto M2J4A6ONCanada

Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput,lower latency,and wider ***,spectrum scarcity places obstacles in the sustainable *** accommodate the expanding network within a limited spectrum,spectrum sharing is deemed as a promising ***,cognitive radio(CR)has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum ***,the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited *** this paper,we propose an intelligent spectrum management framework based on software defined network(SDN)and artificial intelligence(AI).Specifically,SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network(ISTN)with reconfigurability and *** is further used to make predictive environment perception and to configure the network for optimal resource ***,the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.

关键词： cognitive radio(CR) spectrum management satellite communications integrated satellite and terrestrial networks(ISTN)

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Work-in-Progress: A Hybrid Collaborative Multi Agent Decision Making Algorithm With Factored-Value Max-Plus

Work-in-Progress: A Hybrid Collaborative Multi Agent Decisio...

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International Black Sea Conference on Communications and networking (BlackSeaCom)

作者： Paul Cotae Nii Emil Alexander Reindorf Myong Kang Alexander Velazquez Department of Electrical and Computer Engineering The University of the District of Columbia Washington DC USA U. S. Naval Research Laboratory Center for High Assurance Computer Systems Washington DC USA

We focus on a real-time multi agent decision-making algorithm that combines a centralized algorithm and a distributed algorithm. A network segmentation is unavoidable in a dynamic environment. In such cases, it is necessary for each agent to continually make the most urgent real-time decisions in both centralized and decentralized ways. In this paper, we present a Hybrid Factored-Value Max-Plus algorithm with cost which has online, anytime, and scalable properties despite network segmentation. We also study the performance of centralized and distributed algorithms to understand the performance characteristics of a hybrid algorithm.

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Optimal scheduling of ESS in distribution system with multiple DG 13

Optimal scheduling of ESS in distribution system with multip...

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13th International Conference on electrical and Electronics engineering, ELECO 2021

作者： Tunçel, Suleyman Seker, Ayse Aybike Oskouei, Morteza Zare Gozel, Tuba Mohammadi-Ivatloo, Behnam Abapour, Mehdi Gebze Technical University Department of Electronics Engineering Kocaeli41400 Turkey Department of Electrical and Electronics Engineering Iskenderun Technical University Ratay 31200 Turkey Smart Energy Systems Laboratory University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9786050114379

Increasing tendency to highly penetrate distribution networks with renewables due to environmental concerns, has lead distribution network operators to establish energy storage systems (ESS) for obtaining required flexibility. In this paper, the role of a widely used ESS, namely, compressed-air energy storage (CAES) is evaluated considering hosting distributed generation capacity of network. A deterministic scheduling strategy is proposed to minimize cost of operation and maintenance of network in presence of PV and wind generation as renewable sources. By utilization of CAES, renewable energy curtailment has been reduced while total electricity cost injected from the grid is minimized. Optimal charge and discharge scheduling of CAES has allowed excess renewable energy to be stored and then injected to the network on peak hours in order to reduce cost by decreasing power grid dependency on peak hours. © 2021 Chamber of Turkish electrical Engineers.

关键词： Compressed air

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Resilience-oriented Operation of Power Distribution Networks with Line Hardening and Comprehensive Reconfiguration Measures

Resilience-oriented Operation of Power Distribution Networks...

引用

IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Zhengmao Li Zhao Shi Guangchun Ruan Yuzhang Lin Jin Zhao School of Electrical Engineering Aalto University Espoo Finland School of Electrical and Electronic Engineering Nanyang Technological University Singapore Laboratory for Information & Decision Systems Massachusetts Institute of Technology Boston The USA Department of Electrical and Computer Engineering New York University New York the USA Department of Electronic and Electrical Engineering Trinity College Dublin Dublin Ireland

With the frequent occurrence of extreme events like natural disasters and man-made attacks, the resilience concept is attracting worldwide research attention. Thus, this paper proposes a resilient operation model for the power distribution network (PDN) to recover load and limit economic loss to the greatest possible extent. First, the line hardening measure is applied to strengthen the PDN to avoid the serious breakdown of distribution lines. In addition, a comprehensive single commodity flow-based reconfiguration approach is used which not only sets the buses connecting generation sources but also those at the end of broken lines as the slack buses. Then, the radiality of PDNs can still be guaranteed after reconfiguration. The resilience-oriented operation problem can be originally formulated as a nonlinear programming one while efficient linearization methods are applied to reduce computational burdens. Hence, the operation problem is reformulated as a mixed-integer linear programming one which will enjoy favorable solution performances. Finally, case studies on an IEEE 33 PDN including comparison cases with traditional benchmarks are done to show the effectiveness of our method.

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A More Accurate FEA Model for Machines with Segmented Stators Manufactured using Oriented Steel

A More Accurate FEA Model for Machines with Segmented Stator...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Anmol Aggarwal Bhuvan Khoshoo John Agapiou Shanelle N. Foster General Motors Pontiac MI USA Department of Electrical & Computer Engineering Michigan State University East Lansing MI USA Retired Manufacturing Systems Research Laboratory General Motors Warren MI USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Using oriented steel for segmented stator construction in AC electric machine can improve the efficiency of machine by decreasing core losses compared to a machine designed with non-oriented steel. Therefore, analysis of the oriented steel stator in AC electric machines in finite analysis is of great interest. Several attempts were made to accurately simulate oriented steel in segmented stators; however, the insight into the flux distribution and consequently the perceived properties of the oriented steel is at-large missing. In this work, a physics-based approach to model oriented steel for radial flux AC machines is developed to accurately capture the magnetic properties by considering the direction of flux. The determined magnetic properties are used to model an equivalent isotropic model in FEA. The accuracy of developed FEA model is validated experimentally.

关键词： Stator cores Accuracy Correlation AC machines Core loss Energy conversion Finite element analysis Steel Magnetic properties

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Learnable Residual-based Latent Denoising in Semantic Communication

arXiv

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arXiv 2025年

作者： Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Cyber and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images. Copyright © 2025, The Authors. All rights reserved.

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FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method

FAD-SAR: A Novel Fishing Activity Detection System via Synth...

引用

IEEE International Symposium on Intelligent Signal Processing and Communication systems (ISPACS)

作者： Yanbing Bai Siao Li Rui-Yang Ju Zihao Yang Jinze Yu Jen-Shiun Chiang Center for Applied Statistics School of Statistics Renmin University of China Beijing China Graduate Institute of Networking and Multimedia National Taiwan University Taipei City Taiwan Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical and Computer Engineering Tamkang University New Taipei City Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216.

关键词： Deep learning Training Radar cross-sections Object detection Signal processing Radar polarimetry Data mining Synthetic aperture radar Remote sensing Monitoring

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