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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： N. Vijayalakshmi Yuvaraj S V. Vijaya Baskar R. Krishnaswamy K. Sasikala C. Srinivasan Department of Electrical and Electronics Engineering Government Polytechnic college Deputed from Annamalai University Coimbatore Tamilnadu India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering University College of Engineering Ariyalur Tamil Nadu India Department of Electrical and Electronics Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Microgrid (MG) systems have become a good way to make power generation and distribution more efficient, especially when Distributed Energy Resources (DERs) are introduced. The inverter is a critical part of MGs since it regulates the voltage output to the grid. A unique method is proposed to tune the settings of a Proportional-Integral-Derivative (PID) controller for the inverter using Genetic Algorithm (GA). The main aim is to use GAs to improve the inverter control in an MG that is monitored by the Internet of Things (IoT). GA's are very useful for resolving difficult optimization issues. Optimization of voltage regulation, stability, and power transmission to the grid may be achieved by using GA-based tuning of PID controller settings. From the simulation results, the GA-based methodology provides better control performance than conventional tuning approaches, providing exact voltage regulation and improving the MG's stability and dependability.

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Verification of autonomous vehicles: Scenario generation based on real world accidents

Verification of autonomous vehicles: Scenario generation bas...

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2020 IEEE SoutheastCon, SoutheastCon 2020

作者： Holland, James C. Sargolzaei, Arman Department of Computer Science Advanced Mobility Institute Florida Polytechnic University LakelandFL United States Department of Electrical and Computer Engineering Advanced Mobility Institute Florida Polytechnic University LakelandFL United States

ISBN: (纸本)9781728168616

With the advent of technologies that allow for self-driving and autonomous vehicles, it is of the utmost importance to ensure the safety of occupants, pedestrians, and other motorists. Investigating when, why, and how these vehicles fail is integral to preventing future recurrences of these kinds of incidents. By gathering these accidents, their associated data, and simulating them, it is possible to identify the major reasons for the accident as well as allowing for the testing and verification of autonomous vehicles (AVs), before they encounter the real world. This knowledge can then be further refined for the purposes of establishing a set of standards for AV safety and capabilities. This paper proposes a novel system for crash scenario generation which works based on actual AV crashes. In addition, there does not yet exist a cloud based database for housing AV crashes, locations, and descriptions. Furthermore, there also does not exist an automated system that creates simulated testing scenarios based on real AV crashes. Our proposed algorithm checks the AV crash location and designs virtual roads from map data about the crash location, retrieved from Open Street Maps. Then it is planned to use natural language processing (NLP) to generate the crash scenario based on the crash description. The virtual scenario, which includes real features of roads - such as curvature, number of lanes, and speed limits - will be used to test AVs. Another portion of the proposed system will allow for important features to be extracted automatically from these crashes, which will be used for improving the road designs capable of addressing AV's needs. © 2020 IEEE

关键词： Accidents

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Precision Atmospheric Wind Measurements with a Frequency Comb Calibrated Laser Heterodyne Radiometer

Precision Atmospheric Wind Measurements with a Frequency Com...

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2023 Optics and Photonics for Sensing the Environment, ES 2023 in Optica Sensing Congress - Part of Optical Sensors and Sensing Congress 2023

作者： Cole, Ryan K. Fredrick, Connor Nguyen, Newton H. Frankenberg, Christian Diddams, Scott A. Time and Frequency Division National Institute of Standards and Technology BoulderCO United States Department of Physics University of Colorado Boulder BoulderCO United States Division of Geological and Planetary Science California Institute of Technology PasadenaCA United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Dept. of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States

We report atmospheric spectroscopy of CO2 using a frequency comb calibrated laser heterodyne radiometer. The measured spectra exhibit Doppler shifts due to atmospheric wind, and we track the wind-induced shifts with ~... 详细信息

ISBN: (纸本)9781957171241

关键词： Radiometers

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Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China Guilin University of Electronic Technology Department of Information and Telecommunication Guilin541004 China Northwestern State University of Louisiana Department of Engineering Technology NatchitochesLA71497 United States Communications Research Centre Canada OttawaONK2H 8S2 Canada

ISBN: (纸本)9798350321524

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users' preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user's on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems. © 2023 IEEE.

关键词： Recommender systems

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Doping Shortens the Metal/Metal Distance and Promotes OH Coverage in Non-Noble Acidic Oxygen Evolution Reaction Catalysts

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Journal of the American Chemical Society 2023年第14期145卷 7829-7836页

作者： Wang, Ning Ou, Pengfei Miao, Rui Kai Chang, Yuxin Wang, Ziyun Hung, Sung-Fu Abed, Jehad Ozden, Adnan Chen, Hsuan-Yu Wu, Heng-Liang Huang, Jianan Erick Zhou, Daojin Ni, Weiyan Fan, Lizhou Yan, Yu Peng, Tao Sinton, David Liu, Yongchang Liang, Hongyan Sargent, Edward H. Department of Electrical and Computer Engineering University of Toronto 35 St George Street TorontoONM5S 1A4 Canada School of Materials Science and Engineering Key Laboratory of Efficient Utilization of Low and Medium Grade Energy Ministry of Education Tianjin University Tianjin300350 China Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada School of Chemical Sciences The University of Auckl Auckland1010 New Zealand Department of Applied Chemistry National Yang Ming Chiao Tung University Hsinchu300 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei10617 Taiwan Center of Atomic Initiative for New Materials National Taiwan University Taipei10617 Taiwan State Key Lab of Hydraulic Engineering Simulation and Safety School of Materials Science & Engineering Tianjin University Tianjin300354 China

Acidic water electrolysis enables the production of hydrogen for use as a chemical and as a fuel. The acidic environment hinders water electrolysis on non-noble catalysts, a result of the sluggish kinetics associated with the adsorbate evolution mechanism, reliant as it is on four concerted proton-electron transfer steps. Enabling a faster mechanism with non-noble catalysts will help to further advance acidic water electrolysis. Here, we report evidence that doping Ba cations into a Co3O4 framework to form Co3-xBaxO4 promotes the oxide path mechanism and simultaneously improves activity in acidic electrolytes. Co3-xBaxO4 catalysts reported herein exhibit an overpotential of 278 mV at 10 mA/cm2 in 0.5 M H2SO4 electrolyte and are stable over 110 h of continuous water oxidation operation. We find that the incorporation of Ba cations shortens the Co-Co distance and promotes OH adsorption, findings we link to improved water oxidation in acidic electrolyte. © 2023 American Chemical Society.

关键词： Catalysts

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Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

arXiv

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks

arXiv

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arXiv 2023年

作者： Zhang, Songge Wu, Wen Hu, Penghui Li, Shaofeng Zhang, Ning Peking University Shenzhen Graduate School Peking University Shenzhen China Frontier Research Center Peng Cheng Laboratory Shenzhen China School of Electronic Science and Engineering Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Windsor Windsor Canada

In this paper, we propose a novel distributed learning scheme, named group-based split federated learning (GSFL), to speed up artificial intelligence (AI) model training. Specifically, the GSFL operates in a split-then-federated manner, which consists of three steps: 1) Model distribution, in which the access point (AP) splits the AI models and distributes the client-side models to clients;2) Model training, in which each client executes forward propagation and transmit the smashed data to the edge server. The edge server executes forward and backward propagation and then returns the gradient to the clients for updating local client-side models;and 3) Model aggregation, in which edge servers aggregate the server-side and client-side models. Simulation results show that the GSFL outperforms vanilla split learning and federated learning schemes in terms of overall training latency while achieving satisfactory accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Wireless networks

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Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks

arXiv

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arXiv 2024年

作者： Cang, Yihan Chen, Ming Pan, Yijin Yang, Zhaohui Hu, Ye Sun, Haijian Chen, Mingzhe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211100 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The School of Electrical and Computer Engineering The University of Georgia AthensGA30602 United States The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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Detection of Detonators in Baggage Using Deep CNN architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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Distributed Coordinated Optimal Control for Synchronous Operation of Virtually Coupled Train Set

Distributed Coordinated Optimal Control for Synchronous Oper...

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International Conference on Intelligent Transportation

作者： Xiaolin Luo Dongming Wang Wei Wang Yindong Ji Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of California Riverside CA USA Traffic Control Technology Co. Ltd Beijing China Department of Automation Tsinghua University Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Virtual coupling technologies have been widely studied in recent years, due to their merits of flexibility and efficiency. However, to operate a virtually coupled train set (VCTS) as a single train, the synchronous operation is still a critical problem to be addressed, especially in arriving. To this end, we propose a distributed coordinated optimal control (DCOC) approach integrated with distributed optimization and model predictive control. First, we formulate a globally coordinated optimal control problem for VCTS, where the states of units in VCTS are coupled in the cost and constraints. Then, we design a distributed algorithm to solve the global problem, where local problems are decoupled into ones with fewer dimensions. Next, we employ distributed model predictive control to guarantee the satisfaction of safety constraints before the convergence of the distributed algorithm. Finally, experimental results demonstrate that our approach can achieve stable and synchronous operation for VCTS and save by 90% computation time than existing centralized approaches.

关键词： Optimal control Robustness Safety Topology Synchronization Distributed algorithms Optimization Intelligent transportation systems Predictive control Convergence

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