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作者： Wu, Mingzhe Wang, Kui Yang, Kehu Konstantinou, Georgios Li, Yun Wei Li, Yongdong Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing100084 China School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing100083 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

A unified selective harmonic elimination (SHE) control for four-level hybrid-clamped (4L-HC) inverters is presented in this article. With this unified strategy, all four-level switching patterns and the corresponding switching angles can be obtained simultaneously by solving one group of unified four-level SHE equations. Therefore, the optimal switching pattern of each modulation index with the designed optimization goal can be evaluated, and the best overall output performance is achieved. In order to ensure the proper operation of the 4L-HC inverter, the voltages across the three-phase flying capacitors and three dc-link capacitors must be controlled and balanced at one-third of the dc-link voltage. The proposed voltage control method is based on redundant switching states and introducing a slight variation to the precalculated switching angles, which extends or limits the conduction time of capacitors depending on voltage deviation and current direction. The effect of switching angle variations on the harmonic performance is also studied. Simulation and experimental results are presented to confirm the validity of the unified model and the proposed voltage-balancing strategy. Comparisons with phase-shifted pulsewidth modulation (PWM) are also presented to demonstrate that the implemented SHE-PWM could significantly reduce the switching frequency while keeping the capacitor voltages well balanced within the expected band limits. © 1986-2012 IEEE.

关键词： Harmonic analysis

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Integrated coordination of production scheduling, preventive maintenance, and energy conservation in flexible manufacturing environments

引用

Journal of Cleaner Production 2025年 518卷

作者： Wang, Yu Xia, Tangbin Li, Yaping Gao, Shuo Wang, Dong Xi, Lifeng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Special Environment Digital Manufacturing Equipment Technology Innovation Center Mianyang China Department of Management Science and Engineering College of Economics and Management Nanjing Forestry University Nanjing210037 China

The focus of this study is on the integration and coordination of three critical tasks - production scheduling, preventive maintenance, and energy conservation - within flexible manufacturing environments. Following the investigation, the current research predominantly emphasizes the simplistic joint optimization of only two tasks while neglecting important considerations: 1) potential incompatibilities between production and maintenance activities on the same machine;and 2) the necessity of ensuring that machines are in an off-state before conducting maintenance activities. This study addresses these gaps by comprehensively considering the inherent interdependencies among the three tasks when formulating the mathematical model. The model incorporates factors such as variable job processing times due to machine degradation and maintenance activities, unpredictability in maintenance requirements arising from diverse job assignments, and energy consumption involving machine on/off control strategies. Unlike traditional decomposed approaches, this study proposes a synchronous optimization approach that effectively handles conflicts arising from the concurrent execution of all three tasks on any machine. To optimize the solution, an improved multi-objective optimization algorithm, which integrates discrete particle swarm optimization with simulated annealing (IMDPSSA), is devised to obtain Pareto approximate optimal solutions that efficiently balance economic, reliability, and sustainability objectives. Ultimately, an illustrative example is provided to demonstrate the applicability of the proposed methodology, which thoroughly examines job assignments, maintenance activities, and machine on/off procedures. Extensive experiments are further conducted to showcase the superior performance of the presented methodology and algorithm in terms of solution quality and objective values through rigorous comparisons with existing research. © 2025 Elsevier Ltd

关键词： Multiobjective optimization

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High-coherence parallelization in integrated photonics

arXiv

引用

arXiv 2023年

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Tao, Zihan Jin, Ming Chen, Ruixuan Ge, Zhangfeng Fang, Zhou Zhang, Ning Liu, Yadong Cai, Pengfei Hu, Weiwei Shu, Haowen Pan, Dong Bowers, John E. Wang, Xingjun Chang, Lin State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA United States Frontiers Science Center for Nano-optoelectronics Peking University Beijing China SiFotonics Technologies Co. Ltd. Beijing China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, building coherent systems in integrated photonics previously came at great expense in hardware integration and energy efficiency: the lack of a power-efficient way to generate highly coherent light necessitates bulky lasers and amplifiers, while frequency and phase recovery schemes require huge digital signal processing resources. In this work, we demonstrate a high-coherence parallelization strategy that facilitates advanced integrated coherent systems at a minimum price. Using a self-injection locked microcomb to injection lock a distributed feedback laser array, we boost the microcomb power by a record high gain of up to 60 dB on chip with no degradation in coherence. This strategy enables tens of highly coherent channels with an intrinsic linewidth down to the 10 Hz level and power of more than 20 dBm. The overall electrical to optical wall-plug efficiency reaches 19%, comparable with that of the state-of-the-art semiconductor lasers. Driven by this parallel source, we demonstrate a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s. Importantly, the high coherence we achieve reduces the coherent-related DSP consumption by 99.999% compared with the traditional III-V laser pump scheme. This work paves a way to realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Advancements in Superconducting Microwave Cavities and Qubits for Quantum Information systems

arXiv

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arXiv 2023年

作者： Krasnok, Alex Dhakal, Pashupati Fedorov, Arkady Frigola, Pedro Kelly, Michael Kutsaev, Sergey Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States ARC Centre for Engineered Quantum System School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia RadiaBeam LLC Santa MonicaCA90403 United States Argonne National Laboratory LemontIL60439 United States

Superconducting microwave cavities with ultra-high Q-factors are revolutionizing the field of quantum computing, offering long coherence times exceeding 1 ms, which is critical for realizing scalable multi-qubit quantum systems with low error rates. In this work, we provide an in-depth analysis of recent advances in ultra-high Q-factor cavities, integration of Josephson junction-based qubits, and bosonic-encoded qubits in 3D cavities. We carefully examine the sources of quantum state dephasing caused by damping and noise mechanisms in cavities and qubits, highlighting the critical challenges that need to be addressed to achieve even higher coherence times. We critically survey the latest progress made in implementing single 3D qubits using superconducting materials, normal metals, and multi-qubit and multi-state quantum systems. Our work sheds light on the promising future of this research area, including novel materials for cavities and qubits, modes with nontrivial topological properties, error correction techniques for bosonic qubits, and new light-matter interaction effects. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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Double JPEG Compression Detection Based on Markov Model 18th

Double JPEG Compression Detection Based on Markov Model

引用

18th International Workshop on digital Forensics and Watermarking, IWDW 2019

作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yung-Qing Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030435745

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Feedback LSTM Network Based on Attention for Image Description Generator

引用

computers, Materials & Continua 2019年第5期59卷 575-589页

作者： Zhaowei Qu Bingyu Cao Xiaoru Wang Fu Li Peirong Xu Luhan Zhang Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering Portland States UniversityPortlandOR 97207-0751USA

Images are complex multimedia data which contain rich semantic *** of current image description generator algorithms only generate plain description,with the lack of distinction between primary and secondary object,leading to insufficient high-level semantic and accuracy under public evaluation *** major issue is the lack of effective network on high-level semantic sentences generation,which contains detailed description for motion and state of the principal *** address the issue,this paper proposes the Attention-based Feedback Long Short-Term Memory Network(AFLN).Based on existing codec framework,there are two independent sub tasks in our method:attention-based feedback LSTM network during decoding and the Convolutional Block Attention Module(CBAM)in the coding ***,we propose an attentionbased network to feedback the features corresponding to the generated word from the previous LSTM decoding *** implement feedback guidance through the related field mapping algorithm,which quantifies the correlation between previous word and latter word,so that the main object can be tracked with highlighted detailed ***,we exploit the attention idea and apply a lightweight and general module called CBAM after the last layer of VGG 16 pretraining network,which can enhance the expression of image coding features by combining channel and spatial dimension attention maps with negligible *** experiments on COCO dataset validate the superiority of our network over the state-of-the-art *** scores and actual effects are *** BLEU 4 score increases from 0.291 to 0.301 while the CIDEr score rising from 0.912 to 0.952.

关键词： Image description generator feedback LSTM network attention CBAM

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Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

arXiv

引用

arXiv 2022年

作者： Li, Yang Wei, Xinhao Li, Yuanzheng Dong, Zhaoyang Shahidehpour, Mohammad The Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China The School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The Electrical and Computer Engineering Department Illinois Institute of Technology ChicagoIL60616 United States The ECE Department The King Abdulaziz University Saudi Arabia

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified. Copyright © 2022, The Authors. All rights reserved.

关键词： Smart power grids

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

引用

15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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