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Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Mostefa Kermadi Zayed Hossain Saad Mekhilef Marizan Mubin Hazlie Mokhlis School of Engineering University of Leicester Leicester United Kingdom Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory (PEARL) Universiti Malaya Kuala Lumpur Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn Australia Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia

Silicon Carbide (SiC) switching devices offer significant advantages compared to Silicon (Si) switching devices, including lower power losses, and higher switching speed. This paper proposes a half-bridge evaluation board to facilitate the testing and development of various power converter topologies for R&D purposes. The half-bridge module can be configured to test a wide range of DC/DC converter topologies, and multiple half-bridge modules can be stacked in series or parallel to form different DC/AC power converter topologies. The board is flexible for evaluating various SiC MOSFETs, IGBTs, and Schottky diodes in the TO-247 package. It incorporates isolated power supplies and highly integrated smart gate drivers with numerous features to prevent damage to the power switching devices under abnormal operating conditions. Additionally, the board includes featured indicators to notify the user of faults. This paper provides a comprehensive description of the board’s features, utilization, and design considerations, along with testing results to showcase its performance. The evaluation board serves as a valuable tool for researchers and engineers involved in the development and evaluation of power converters utilizing SiC technology.

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Modular Multilevel DC-DC Converter with Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

Modular Multilevel DC-DC Converter with Inherent Bipolar Ope...

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作者： Zhao, Shanshan Chen, Yu Cui, Shenghui Hu, Jingxin State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Seoul National University Department of Electrical and Computer Engineering Korea Republic of Institute for Power Generation and Storage Systems E. on Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-Tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype. © 2017 CPSS.

关键词： Poles

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Flexible Intelligent Metasurfaces for Enhancing MIMO Communications

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IEEE Transactions on Communications 2025年

作者： An, Jiancheng Han, Zhu Niyato, Dusit Debbah, Merouane Yuen, Chau Hanzo, Lajos Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to 'morph' its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups. © 1972-2012 IEEE.

关键词： Covariance matrix

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Learning the continuous-time optimal decision law from discrete-time rewards

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National science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua Xie Kan Xie Frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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Silicon Nitride Integrated Photonics with Intrinsic Quantum Emitters

Silicon Nitride Integrated Photonics with Intrinsic Quantum ...

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Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Senichev, Alexander Martin, Zachariah O. Peana, Samuel Yesilyurt, Omer Matthiessen, Owen M. Sychev, Demid Lawrie, Benjamin Lagutchev, Alexei S. Boltasseva, Alexandra Shalaev, Vladimir M. Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center Department of Energy a National Quantum Information Science Research Center of the U.S. Oak RidgeTN37931 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We create intrinsic quantum emitters in silicon nitride, study their structure and temperature-dependent optical properties, and demonstrate monolithic integration with photonic waveguides to evaluate the potential of... 详细信息

ISBN: (纸本)9781957171272

关键词： Integrated photonics Optical properties Photonic integrated circuits Quantum information Quantum light sources Silicon nitride

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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service 19

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Le...

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19th IEEE International Conference on e-science, e-science 2023

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

ISBN: (纸本)9798350322231

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** © 2023 IEEE.

关键词： Privacy-preserving techniques

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User cooperation for IRS-aided secure MIMO systems

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Intelligent and Converged Networks 2022年第1期3卷 86-102页

作者： Gui Zhou Cunhua Pan Hong Ren Kezhi Wang Kok Keong Chai Kai-Kit Wong School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China. Department of Computer and Information Sciences Northumbria UniversityNewcastle upon TyneNE18STUK. Department of Electronic and Electrical Engineering University College LondonLondonWC1E 6BTUK.

An intelligent reflecting surface(IRS)is proposed to enhance the physical layer security in the Rician fading channel wherein the angular direction of the eavesdropper(ED)is aligned with a legitimate user.A two-phase communication system under active attacks and passive eavesdropping is considered in this *** base station avoids direct transmission to the attacked user in the first phase,whereas other users cooperate in forwarding signals to the attacked user in the second phase,with the help of IRS and energy harvesting *** the occurrence of active attacks,an outage-constrained beamforming design problem is investigated under the statistical cascaded channel error model,which is solved by using the Bernstein-type *** average secrecy rate maximization problem for the passive eavesdropping is formulated,which is then addressed by a low-complexity *** numerical results of this study reveal that the negative effect of the ED’s channel error is larger than that of the legitimate user.

关键词： intelligent reflecting surface(IRS) reconfigurable intelligent surface(RIS) robust design energy harvesting physical layer security

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SecBCS: a secure and privacy-preserving blockchain-based crowdsourcing system

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science China(Information sciences) 2020年第3期63卷 20-33页

作者： Chao LIN Debiao HE Sherali ZEADALLY Neeraj KUMAR Kim-Kwang Raymond CHOO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and Engineering Wuhan University State Key Laboratory of Cryptology College of Communication and Information University of Kentucky Department of Computer Science and Engineering Thapar University Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering University of Texas at San Antonio

A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd management, conventional solutions, such as Upwork and Amazon Mechanical Turk, generally depend on a trusted third-party platform. There exist several potential security concerns(e.g., sensitive leakage, single point of failure and unfair judgment) in such a centralized paradigm. Hence, a recent trend in crowdsourcing is to leverage blockchain(a decentralized ledger technology) to address some of the existing limitations. A small number of blockchain-based crowdsourcing systems(BCSs) with incentive mechanisms have been proposed in the literature, but they are generally not designed with security in mind. Thus, we study the security and privacy requirements of a secure BCS and propose a concrete solution(i.e., SecBCS)with a prototype implementation based on JUICE.

关键词： crowdsourcing blockchain secure privacy-preserving smart contract

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Model authentication hashing: Identifying pirated neural network models

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Expert Systems with Applications 2025年 283卷

作者： Xiong, Cheng Feng, Guorui Sun, Yunlong Qian, Zhenxing Yao, Heng Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Department of Tourism Fudan university Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of China Ministry of Culture and Tourism Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China

Nowadays, a great number of neural network models have been trained and applied in many fields. At the same time, the emergence of vast network models has already highlighted the security problems of intellectual property protection. High-value pre-trained models of original inventors may be pirated and used by illegal users without authorization. In order to solve this problem, we first give the definition of model differentiation, and then propose an effective robust hashing method based on model parameters for piracy identification in this paper. Through our model hash generation network, the parameters of original model are transformed into a one-way hash sequence with the fixed length, and the robustness and discrimination of the model hashes can be utilized to authenticate model parameters. Experimental results show that the accuracy rates of similar model detection and different model discrimination under different cases are almost all greater than 96.5%, and the comprehensive performance of proposed method is also the best one compared with other 3 SOTA methods. These results mean that the proposed method can be applied for identifying illegal model piracy effectively. © 2025 Elsevier Ltd

关键词： Terrorism

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

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