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arXiv 2021年

作者： Zhang, Yichen Cui, Hantao Liu, Jianzhe Qiu, Feng Hong, Tianqi Yao, Rui Li, Fangxing Fran Energy System Division Argonne National Laboratory LemontIL60439 United States School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Department of Eecs University of Tennessee KnoxvilleTN37909 United States

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments. Copyright © 2021, The Authors. All rights reserved.

关键词： Frequency response

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Robust model predictive control for linear systems with polytopic uncertainties under weighted mef-tod protocol

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IEEE Transactions on systems, Man, and Cybernetics: systems 2019年第7期49卷 1470-1481页

作者： Song, Yan Wang, Zidong Ding, Derui Wei, Guoliang Department of Control Science and Engineering Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China Department of Computer Science Brunel University London Uxbridge21589 United Kingdom School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia

This paper is concerned with the robust {-Grant-info-{2}/H-\infty } model predictive control problem for a class of linear systems with polytopic uncertainties under weighted maximum-error-first and try-once-discard (MEF-TOD) protocol. To prevent data from collision, the weighted MEF-TOD protocol is employed during the data transmission from sensors to controller, where only one sensor is allowed to transmit the measurement at every time instant. A switched linear system is established with respect to the switching signals generated by the adopted weighted MEF-TOD protocol. By taking the influence of the exogenous disturbance and the weighted MEF-TOD protocol into consideration, an online optimization problem with nonconvex conditions is formulated. Then, to deal with the couplings of unknown variables, singular value decomposition technique is utilized to transform the nonconvex conditions into solvable ones. Subsequently, a set of dynamic output-feedback controllers is designed to guarantee the stability of the closed-loop system with guaranteed robust { H-{2}/H-\infty } performance. Finally, a direct current motor example is used to illustrate the validity and effectiveness of the proposed methods. © 2013 IEEE.

关键词： Model predictive control

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Single Recording Without Heterodyning in Optical Scanning Holography

SSRN

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SSRN 2022年

作者： Liu, Jung-Ping Lee, Chieh-Cheng Zhang, Yaping Yao, Yongwei Poon, Ting-Chung Department of Photonics Feng Chia University 100 Wenhwa Rd. Seatwen Taichung40724 Taiwan Digital Optics Center Feng Chia University 100 Wenhwa Rd. Seatwen Taichung40724 Taiwan Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and Technology Yunnan Kunming650500 China Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

We have built a system for off-axis optical scanning holography (OSH) and for the first time experimentally demonstrated the recording of a hologram without heterodyning or a spatial light modulator for temporal frequency shifting. To solve the problem of phase fluctuation, a phase monitoring module is included in the system. Single sideband filtering together with phase compensation is performed for the reconstruction without the zeroth-order light and the twin image. Properties and practical issues in the coherent-mode and incoherent-mode of off-axis OSH are also discussed. © 2022, The Authors. All rights reserved.

关键词： Scanning

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Multi-View Unsupervised Feature Selection With Soft Label Learning and Tensor Low Rank Approximation

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Electronics Letters 2025年第1期61卷

作者： Jian Zhao Yitong Wu Mingyu Wu Eileen Lee Ming Su William Holderbaum Chenguang Yang Jiaxing Key Laboratory of Industrial Intelligence and Digital Twin Jiaxing Vocational and Technical College Jiaxing Zhejiang China Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Thanh Dong University Hai Duong City Hai Duong Province Vietnam Department of Engineering Manchester Metropolitan University Manchester UK Department of Computer Science University of Liverpool Liverpool U K

Advancements in information and storage technologies have led to the proliferation of high-dimensional multi-view data, necessitating robust feature selection methods. However, existing approaches often disregard data fuzziness and employ simplistic multi-view fusion strategies, thereby failing to simultaneously account for view diversity and consistency. To address these limitations, we introduce an unsupervised multi-view feature selection method, MESA, which integrates soft label learning and tensor low-rank approximation. Specifically, we first leverage the Fuzzy C-Means algorithm to construct an initial soft label matrix by measuring distances between data points and cluster prototypes. Next, we form a third-order tensor from the soft label matrices across multiple views and impose a tensor nuclear norm constraint to capture both view consistency and diversity. To achieve a unified framework for soft label learning and feature selection, we employ a sparse regression model. Additionally, we develop an efficient optimisation algorithm based on the alternating direction method of multipliers for iterative variable updates. Extensive experiments validate the effectiveness of our proposed approach, demonstrating notable performance improvements.

关键词： feature selection learning (artificial intelligence) tensors

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Scaling QAM Technique for LED-based Visible Light Communication system with Signal-related Noise

Scaling QAM Technique for LED-based Visible Light Communicat...

引用

2019 IEEE International Conference on Electrical engineering and Photonics, EExPolytech 2019

作者： Zheng, Di Zhang, Hongming Pan, Changyong Song, Jian Department of Electronic Engineering Beijing China Research Institute of Tsinghua University in Shenzhen Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Shenzhen China

We propose a scaling QAM technique considering the signal-related noise in a practical VLC system. Experiments are performed and the results indicate that the proposed scaling QAM can improve the performance of VLC sy... 详细信息

ISBN: (纸本)9781728144399

关键词： Quadrature amplitude modulation

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Sparse Large-Scale Fading Decoding in Cell-Free Massive MIMO systems

arXiv

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tugfe Ai, Bo School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Department of Computer Science KTH Royal Institute of Technology KistaSE-16440 Sweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems are characterized by having many more access points (APs) than user equipments (UEs). A key challenge is to determine which APs should serve which UEs. Previous work has tackled this combinatorial problem heuristically. This paper proposes a sparse large-scale fading decoding (LSFD) design for CF mMIMO to jointly optimize the association and LSFD. We formulate a group sparsity problem and then solve it using a proximal algorithm with block-coordinate descent. Numerical results show that sparse LSFD achieves almost the same spectral efficiency as optimal LSFD, thus achieving a higher energy efficiency since the processing and signaling are reduced. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

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Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

arXiv

引用

arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Razzak, Farid Wen, Ji-Rong Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The New York University Columbia University New York United States The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

引用

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Dasom Kim Jin Hou Geon Lee Ayush Agrawal Sunghwan Kim Hao Zhang Di Bao Andrey Baydin Wenjing WU Fuyang Tay Shengxi Huang Daivd Hagenmüller Elbert E. M. Chia Dai-Sik Kim Minah Seo Aditya D. Mohite Junichiro Kono Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Physics and Astronomy Seoul National University Seoul Republic of Korea Department of Chemical and Biomolecular Engineering Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Smalley-Curl institute Rice University Houston TX USA CESQ-ISIS (UMR 7006) Universite de Strasbourg and CNRS Strasbourg France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Department of Physics and Astronomy Rice University Houston TX USA

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Couplings Phonons Laser modes Perovskites Electro-optical waveguides Photonics

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Retraction Note: Using hardware counter-based performance model to diagnose scaling issues of HPC applications

引用

Neural Computing and Applications 2024年第23期36卷 14615-14615页

作者： Ding, Nan Xu, Shiming Song, Zhenya Zhang, Baoquan Li, Jingmei Zheng, Zhigao Department of Computer Science and Technology Tsinghua University Beijing China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology Qingdao China Department of Earth System Science Tsinghua University Beijing China First Institute of Oceanography State Oceanic Administration Qingdao China Department of Computer Science and Technology University of Minnesota Minneapolis USA Department of Computer Science and Technology Harbin Engineering University Harbin China Department of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

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