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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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A Novel Approach to Smart Grid Energy Management Using the Osprey Optimization Algorithm 4

A Novel Approach to Smart Grid Energy Management Using the O...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rudrapogu, Nissisolomon Hemalatha, N. Warkad, Sanjay B Jayanthi, A. Vinoth, K. Jayaram, B. Lecturer in Eee Nissisolomon Rudrapogu Mbts Government Polytechnic College Department of Electrical and Electronics Engineering Section Andhra Pradesh Guntur522005 India Bharath Institute of Higher Education and Research Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India P. R. Pote Patil College of Engineering & Management Department of Electrical Engineering Maharashtra Amravati444602 India Jeppiaar Institute of Technology Department of Physics Tamil Nadu Sriperumbudur631502 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798331523923

Smart Grid Energy Management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization. © 2025 IEEE.

关键词： Clean energy

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Independent Near-Field Monitoring: A Novel Approach to Improve Active Safety in Autonomous Vehicles

Independent Near-Field Monitoring: A Novel Approach to Impro...

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IEEE Symposium on Intelligent Vehicle

作者： Junnan Pan Prodromos Sotiriadis Ferdinand Englberger Department of Electrical and Computer Engineering Institute of Embedded Systems University of the Bundeswehr Munich Munich Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper introduces an innovative near-field monitoring system designed to enhance the active safety features of autonomous vehicles during low-speed driving. The proposed system has dedicated Light Detecting and Ranging (LiDAR) components. It can independently detect and respond to potential hazards, even without the high-level system’s intervention, and apply the emergency brake promptly. Unlike traditional systems that depend on a comprehensive mapping of the entire surrounding environment, our approach focuses on immediate decision-making within the keep-out area (KOA) upon detecting obstacles. The monitoring system utilizes information from the planned driving route to delineate specific monitoring boundaries. This paper explains different monitoring strategies that are suitable for various driving conditions. These strategies incorporate the use of 2-dimensional layouts, including rectangles and parallelograms, to cover the driving path area and fulfill the monitoring requirements of the autonomous vehicle in diverse scenarios.

关键词： Laser radar Layout Decision making Hazards Distance measurement Brakes Monitoring

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Data Completeness-aware Transmission Control for Large Spatio-Temporal Data Retention

Data Completeness-aware Transmission Control for Large Spati...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Kaneyasu, Hotaka Nobayashi, Daiki Tsukamoto, Kazuya Ikenaga, Takeshi Lee, Myung Graduate School of Engineering Kyushu Institute of Technology Fukuoka Japan Kyushu Institute of Technology Faculty of Engineering Department of Electrical and Electronics Engineering Fukuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Department of Computer Science and Networks Fukuoka Japan City College of New York Department of Electrical and Computer Engineering New York United States

ISBN: (纸本)9781665441544

With the development of IoT technology, various kinds of data are generated by IoT devices. Some of this data contains information on geographical location and time. We refer to such data as spatio-temporal data (STD). Since the 'local production and consumption' of STD is effective for location-dependent applications, we have proposed an STD retention system that utilizes vehicles as data sources. STD sources ranges from simple sensory data to large video data. However, since previous methods assumed to retain an STD consisting of only one packet, retention of large STD sources may suffer from frequent packet collisions due to the increase in the number of packet transmissions. In this paper, we propose a data completeness-aware transmission control mechanism for large STD retention. Our simulation results showed that the proposed scheme reduces channel collision by suppressing the forwarding of incomplete data, and achieved a nearly 100% coverage rate. © 2022 IEEE.

关键词： Internet of things

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Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning

arXiv

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arXiv 2024年

作者： Abbas, Momin Kar, Koushik Chen, Tianyi Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

Deep neural networks (DNNs) have made significant strides in tackling challenging tasks in wireless systems, especially when an accurate wireless model is not available. However, when available data is limited, traditional DNNs often yield subpar results due to underfitting. At the same time, large language models (LLMs) exemplified by GPT-3, have remarkably showcased their capabilities across a broad range of natural language processing tasks. But how LLMs can benefit challenging non-language tasks in wireless systems is not fully unexplored. In this work, we propose to leverage the in-context learning ability (a.k.a. prompting) of LLMs to solve wireless tasks in the low data regime without any training or fine-tuning, unlike DNNs which require training. We further demonstrate that the performance of LLMs varies significantly when employed with different prompt templates. To solve this issue, we employ the latest LLM calibration methods. Our results reveal that using LLMs via ICL methods generally outperforms traditional DNNs on the symbol demodulation task and yields highly confident predictions when coupled with calibration techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Calibration

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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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Modeling the Effect of Gate-Drain Parasitic Capacitance of a SiC MOSFET in a Half-Bridge During the Soft Turn-Off and Hard Turn-On Transition

Modeling the Effect of Gate-Drain Parasitic Capacitance of a...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Ayodhya Somiruwan Gamwari Ayooluwa Ajiboye Rakesh Resalayyan Alireza Khaligh Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College Park USA

A novel frequency-domain approach to quantify the effect of the gate-drain parasitic capacitance of a SiC MOSFET is presented in this paper. The effect of this parasitic capacitance during the soft turn-off and hard turn-on transition of a MOSFET half bridge can cause an undesired spurious turn-on in the soft turned-off switch. The proposed model based on Miller’s theorem accurately quantifies the effect and the behavior of the gate loop irrespective of the usage of spurious turn-on mitigating active Miller clamp. The insights drawn in this paper can be used in designing the gate loop of the power MOSFET to avoid undesired spurious turn-on of the MOSFET due to crosstalk. Moreover, the analytical model is validated with hardware experimental results.

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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