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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Yingzhu Mallada, Enrique Li, Zhongkui You, Pengcheng Peking University College of Engineering Beijing China Peking University State Key Laboratory for Turbulence and Complex Systems Beijing China Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States

ISBN: (纸本)9798350316339

Minimax problems have attracted much attention due to various applications in constrained optimization problems and zero-sum games. Identifying saddle points within these problems is crucial, and saddle flow dynamics offer a straightforward yet useful approach. This study focuses on a class of bilinearly coupled minimax problems with strongly convex-linear objective functions. We design an accelerated algorithm based on saddle flow dynamics, achieving a convergence rate beyond the stereotype limit (the strong convexity constant). The algorithm is derived from a sequential two-step transformation of a given objective function. First, a change of variables is applied to render the objective function better-conditioned, introducing strong concavity (from linearity) while preserving strong convexity. Second, proximal regularization, when staggered with the first step, further enhances the strong convexity of the objective function by shifting some of the obtained strong concavity. After these transformations, saddle flow dynamics based on the new objective function can be tuned for accelerated exponential convergence. Besides, such an approach can be extended to weakly convex-weakly concave functions and still guarantees exponential convergence to one stationary point. The theory is verified by a numerical test on an affine equality-constrained convex optimization problem. © 2024 IEEE.

关键词： Constrained optimization

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Data-driven Power Flow Method Based on Exact Linear Regression Equations

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Journal of Modern Power systems and Clean Energy 2022年第3期10卷 800-804页

作者： Yanbo Chen Chao Wu Junjian Qi State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical&Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Power flow(PF)is one of the most important calculations in power *** widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)*** smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to *** address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing *** the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data *** online computing stage,the nonlinear iterative calculation is not *** results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.

关键词： Data driven exact linear regression equation Fast-decoupled power flow Newton-Raphson method

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Circumstance Evaluation Using Extreme Value Analysis on Charging Station Data: The Case of DEI Blue in Greece 14th

Circumstance Evaluation Using Extreme Value Analysis on Cha...

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14th International Conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Panagoulias, Dimitrios P. Sarmas, Elissaios Marinakis, Vangelis Tsihrintzis, George A. Department of Informatics University of Piraeus Piraeus18534 Greece Decision Support Systems Lab School of Electrical and Computer Engineering National Technical University of Athens Zografou-Athens15773 Greece

ISBN: (纸本)9783031674259

We apply Extreme Value Analysis on sets of Electric Vehicle (EV) charging data with the goal to extract indications in the data of future trends in the demand for EV charging. The datasets have been provided by DEI blue, a service designed to provide integrated e-mobility solutions to individuals, companies and public administration throughout Greece. Eventhough the data are limited as they have been collected during a period of only twelve months, our analysis still reveals trends in the data highlighting a probable seasonality. The seasonality is observed during periods of extreme heat, regional population mobility and incoming visitors. These occurrences impact technical aspects of battery efficiency, grid capacity and a shift of behavioural usage patterns. Our findings cannot be ignored by policy makers as they are expected to intensify in the years to come due to the fast-rising number of EVs in use. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electrification

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CRD-CGAN:category-consistent and relativistic constraints for diverse text-to-image generation

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Frontiers of computer Science 2024年第1期18卷 61-75页

作者： Tao HU Chengjiang LONG Chunxia XIAO College of Intelligent Systems Science and Engineering Hubei Minzu UniversityEnshi 445000China School of Computer Science Wuhan UniversityWuhan 430072China Key Laboratory of Performing Art Equipment&System Technology Ministry of Culture and TourismBeijing 100007China Meta Reality Labs BurlingameCA94010USA

Generating photo-realistic images from a text description is a challenging problem in computer *** works have shown promising performance to generate synthetic images conditional on text by Generative Adversarial Networks(GANs).In this paper,we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic *** on those constraints,a category-consistent and relativistic diverse conditional GAN(CRD-CGAN)is proposed to synthesize K photo-realistic images *** use the attention loss and diversity loss to improve the sensitivity of the GAN to word attention and ***,we employ the relativistic conditional loss to estimate the probability of relatively real or fake for synthetic images,which can improve the performance of basic conditional ***,we introduce a category-consistent loss to alleviate the over-category issues between K synthetic *** evaluate our approach using the Caltech-UCSD Birds-200-2011,Oxford 102 flower and MS COCO 2014 datasets,and the extensive experiments demonstrate superiority of the proposed method in comparison with state-of-the-art methods in terms of photorealistic and diversity of the generated synthetic images.

关键词： text-to-image diverse conditional GAN relativi-stic category-consistent

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Modelling Weave effect in PCBs using 2D cross-sectional analysis

Modelling Weave effect in PCBs using 2D cross-sectional anal...

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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Khilkevich, Victor Hinaga, Scott Missouri University of Science and Technology Dept. of Electrical and Computer Engineering Electromagnetic Compatibility Laboratory RollaMO United States Cisco Systems Inc San JoseCA United States

ISBN: (纸本)9798350360394

Printed circuit board dielectric substrates are composite materials produced by embedding fiber glass fabrics into epoxy resin. Because of this the medium in the PCB transmission lines is inhomogeneous which often leads to degradation of the signal integrity performance of the lines, particularly due to the differential skew. The detrimental effect of the fiber weave can be modeled relatively accurately using full-wave analysis, but at a high computational cost. Alternative modelling techniques are less demanding, but often lack accuracy. This article investigates a possibility of using the 2D cross-sectional analysis for the fiber weave effect modeling, which considerably decreases the computational cost of modeling while retaining the accuracy inherent to the field solvers. © 2024 IEEE.

关键词： System-on-chip

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

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China Communications 2024年第4期21卷 10-22页

作者： Zhao Jiwei Chen Jiacheng Sun Zeyu Shi Yuhang Zhou Haibo Xuemin(Sherman)Shen School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310058China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518000China Department of Electrical and Computer Engineering University of WaterlooWaterlooOntarioN2L 3G1Canada

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network ***,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the *** paper proposes a novel transmission scheme without relying on physical layer channel ***,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user *** comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input ***,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified *** also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss *** evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

关键词： beamforming complex neural networks deep learning FD-RAN

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement 63

Distributed Optimization with Finite Bit Adaptive Quantizati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Artificial Intelligence Thrust of the Information Hub Guangzhou China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Kth Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. © 2024 IEEE.

关键词： Bandwidth

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Neural Architecture Search for Image Dehazing

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1337-1347页

作者： Mandal, Murari Meedimale, Yashwanth Reddy Satish Kumar Reddy, M. Vipparthi, Santosh Kumar Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Bhubaneswar751024 India Walmart Global Tech Hyderabad500081 India Texas A&m University Computer Science and Engineering Department College StationTX77843 United States Indian Institute of Technology Ropar Cvpr Laboratory at Department of Electrical Engineering Rupnagar140001 India

Manual design of deep networks require numerous trials and parameter tuning, resulting in inefficient utilization of time, energy, and resources. In this article, we present a neural architecture search (NAS) algorithm - AutoDehaze, to automatically discover effective neural network for single image dehazing. The proposed AutoDehaze algorithm is built on the gradient-based search strategy and hierarchical network-level optimization. We construct a set of search space layouts to reduce memory consumption, avoid the NAS collapse issue, and considerably accelerate the search speed. We propose four search spaces AutoDehaze B, AutoDehaze U1, AutoDehaze U2, and AutoDehaze L, which are inspired by the boat-shaped, U-shaped, and lateral connection-based designs. To the best of authors knowledge, this is a first attempt to present an NAS method for dehazing with a variety of network search strategies. We conduct a comprehensive set of experiments on Reside-Standard (SOTS), Reside-β (SOTS) and Reside-β (HSTS), D-Hazy, and HazeRD datasets. The architectures discovered by the proposed AutoDehaze quantitatively and qualitatively outperform the existing state-of-the-art approaches. The experiments also show that our models have considerably fewer parameters and runs at a faster inference speed in both CPU and GPU devices. © 2020 IEEE.

关键词： Network architecture

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