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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Ghiasvand, Sajjad Reisizadeh, Amirhossein Alizadeh, Mahnoosh Pedarsani, Ramtin University of California Department of Electrical and Computer Engineering Santa BarbaraCA93106 United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States

ISBN: (纸本)9798331541033

As distributed learning applications like Federated Learning, the Internet of Things (IoT), and Edge Computing expand, addressing their limitations becomes crucial. We approach decentralized learning across a network of communicating clients or nodes, focusing on two primary challenges: data heterogeneity and adversarial robustness. To address these, we introduce a decentralized minimax optimization method incorporating two key components: local updates and gradient tracking. Minimax optimization serves as a fundamental tool for adversarial training, ensuring robustness. Local updates are vital in Federated Learning (FL) to alleviate the communication bottleneck, while gradient tracking is necessary to demonstrate convergence amid data heterogeneity. Our analysis of the proposed algorithm, Dec-Fed Track, in nonconvex-strongly-concave minimax optimization demonstrates its convergence to a stationary point. Additionally, numerical experiments support our theoretical results. © 2024 IEEE.

关键词： Federated learning

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Transi-Net: An Explainable Deep Learning Model Ensemble For Prostate's Transition Zone Segmentation 23

Transi-Net: An Explainable Deep Learning Model Ensemble For ...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos S. Kalantzopoulos, Charalampos Marias, Kostas Tsiknakis, Manolis Koutsouris, Dimitrios D. Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical & Computer Engineering Biomedical Research Institute National Technical University of Athens FORTH Athens Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute Unit of Medical Technology and Intelligent Information Systems FORTH University of Ioannina Ioannina Greece

ISBN: (纸本)9798350393118

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Vibration Signal Feature Extraction of Wu’s Shallow Acupuncture Based on CV-SVM

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2322-2340页

作者： Fang, Weidong Chen, Zebin Pan, Jeng-Shyang School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Chaoyang University of Technology Taichung413310 Taiwan

Wu Binghuang shallow acupuncture technique was selected as the sixth batch of intangible heritage items in Fujian Province in 2019, and Wu Binghuang shallow acupuncture technique has good effect on treating insomnia in clinical trials. The shallow acupuncture technique has three kinds of techniques: " drainage method", " tonic method", and " flat tonic and flat drainage", which can be used for different treatment purposes, and the three techniques have high operational similarity. In the development of the shallow needle instrument using modern electronic technology to simulate the shallow needle technique of Bing-Huang Wu, it is necessary to extract and distinguish the vibration signals of the three modes. To address the problem of difficulty in differentiating Wu’s shallow acupuncture techniques, a feature extraction method based on EMD sample entropy, energy occupation ratio after Pyramid decomposition and CV-SVM is proposed in this paper. The vibration signal is noise reduced by using wavelet noise reduction, firstly, the EMD decomposition is performed on the noise reduced data, the correlation coefficient between individual IMF and the original signal is calculated, the IMF with the correlation coefficient greater than 0.1 is selected as the effective component, the sample entropy of the effective component is calculated, then the Pyramid decomposition of the noise reduced vibration signal is divided into 9 layers, the relative energy of each layer is calculated, and the sample entropy of the effective component and the relative energy of each layer are calculated. The sample entropy of the effective component and the relative energy of each layer are formed into a Govett collection. The CV-SVM is then employed to identify the signal patterns, resulting in an average recognition rate of 76% that possesses engineering application value. The vibration data of Prof. Wu Binghuang’s treatment with shallow needles were analyzed, and the practical application of the p

关键词： Noise abatement

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Indoor Corridor Channel Measurements and Characterization in the Upper Mid-Band

Indoor Corridor Channel Measurements and Characterization in...

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European Conference on Antennas and Propagation, EuCAP

作者： Nektarios Moraitis Alexandros Rogaris Ileana Popescu Konstantina S. Nikita School of Electrical and Computer Engineering National Technical University of Athens and Institute of Communication and Computer Systems Athens Greece

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper presents a preliminary characterization of the indoor channel in the upper mid-band. Narrowband measurements are launched in corridors taking into consideration line-of-sight (LOS) and non-line-of-sight (NLOS) propagation scenarios at diverse frequencies between 7 and 20 GHz. The path loss, the shadow fading, and the K-factor properties of the channel are measured and assessed. According to the results, the measured path loss can be accurately forecasted by the Close-In (CI) empirical model. Simple linear relationships are delivered, tailored to characterize the path loss exponent, the shadow fading and the K-factor variation versus frequency. The latter parameter degrades linearly versus frequency exhibiting negative values in NLOS scenarios and at frequencies above 8 GHz.

关键词： Measurement uncertainty Line-of-sight propagation Europe Rayleigh channels Predictive models Antennas and propagation Loss measurement Frequency diversity Frequency measurement Narrowband

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A Comprehensive Power Consumption Model for GPU Data Centers with Time-VAE

A Comprehensive Power Consumption Model for GPU Data Centers...

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International Conference of electrical, Electronic and Networked Energy systems, EENES 2024

作者： Wang, Senyao Wu, Jianhong Zhang, Ruozhu The School of Electrical and Computer Engineering The University of Sydney SydneyNSW2008 Australia The School of Electrical Engineering Shanghai University of Electric Power Shanghai200090 China State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819618675

In this study, we propose and evaluate a comprehensive power consumption model for GPU-based data centers that integrates the energy consumption of various task types, including language processing, speech recognition, and image generation, along with the power consumption of other components such as the CPU, memory, network, and storage. Using real GPU power consumption data, we measured the server energy consumption. To compensate for the limited experimental data and ensure accurate evaluation of average power consumption per unit time after model convergence, we utilize the TimeVAE model to generate supplementary data. The PCA and t-SNE visualizations validate the effectiveness of the TimeVAE model in preserving data distribution and structure. Our comprehensive model provides valuable insights into the energy consumption patterns in GPU-based data centers and offers practical guidance for optimizing energy usage to improve overall efficiency and sustainability. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Data centers

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Accelerated Saddle Flow Dynamics for Bilinearly Coupled Minimax Problems 63

Accelerated Saddle Flow Dynamics for Bilinearly Coupled Mini...

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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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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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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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