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International Conference on electrical systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)

作者： Daniel Zakzewski Rakesh Resalayyan Apurv Kumar Yadav Alireza Khaligh Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory The Institute for Systems Research University of Maryland College Park United States Department of Electrical Engineering Indian Institute of Technology Roorkee Uttrakhand India

This paper presents a closed form analytical model for semiconductor and capacitor currents in a 5-Level Active Neutral Point Clamped Converter (5L-ANPC) topology. This model enables device selection, performance prediction, and design optimization of the topology. Models for the switch and FC currents are derived based on the topology's switching states. A model for input capacitor current is derived by analyzing the interaction of three phase legs of the topology. The model is verified using conventional time-domain switching simulation of the topology.

关键词： Semiconductor device modeling Analytical models Capacitors Transportation Stars Switches Predictive models

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Rule-Based Fuzzy Controller for Solid State Transfer Switch Towards Fast Sensitive Loads Transfer

Rule-Based Fuzzy Controller for Solid State Transfer Switch ...

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作者： Arsad, A.Z. Hannan, M.A. Lipu, M. S. Hossain Rahman, M. S. Abd. Ker, Pin Jern Mansor, Muhamad Muttaqi, Kashem M. Uddin, Mohammad Nasir UniversitiTenaga Nasional Kajang Malaysia Department of Electrical Power Engineering Universiti Tenaga Nasional Kajang Malaysia Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Bangi Malaysia Universiti Tenaga Nasional Kajang Malaysia Institute of Sustainable Energy College of Engineering Universiti Tenaga Nasional Kajang Malaysia Department of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia

This article deals with the development of an intelligent fuzzy logic controller for solid-state transfer switch toward fast fault detection and power transfer to the alternative feeder. A set of fuzzy rules are developed for the controller which in turn shift the loads from the preferred feeder to the alternate feeder with quality power resolving fault interruption or voltage sags. The voltage and the current control loop generate a signal to the fuzzy control block to provide an on or off pulse for the switches upon activate fuzzy rules in the interference engine. In this article, a compilation of 35%, 50%, 70%, and 100% faults are created to demonstrate the effectiveness of the proposed fuzzy controller toward detecting fault levels and transferring power to the desired feeder. It was discovered that the proposed fuzzy logic controller detects faults in the range of 0.054 to 0.061 milliseconds and transfers power in the range of 1.742 to 3.107 ms, which is significantly faster and more reliable than the existing literature. © 1972-2012 IEEE.

关键词： Fuzzy rules

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Do algorithms and barriers for sparse principal component analysis extend to other structured settings?

arXiv

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arXiv 2023年

作者： Wang, Guanyi Lou, Mengqi Pananjady, Ashwin Department of Industrial Systems Engineering and Management National University of Singapore Singapore Schools of Industrial and Systems Engineering United States Electrical and Computer Engineering Georgia Institute of Technology United States

We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general class includes vanilla sparse PCA as well as its variants with graph sparsity. With the goal of studying these problems under a unified statistical and computational lens, we establish fundamental limits that depend on the geometry of the problem instance, and show that a natural projected power method exhibits local convergence to the statistically near-optimal neighborhood of the solution. We complement these results with end-to-end analyses of two important special cases given by path and tree sparsity in a general basis, showing initialization methods and matching evidence of computational hardness. Overall, our results indicate that several of the phenomena observed for vanilla sparse PCA extend in a natural fashion to its structured counterparts. Copyright © 2023, The Authors. All rights reserved.

关键词： Principal component analysis

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Efficient Model Training in Decentralized systems with Federated Learning 2

Efficient Model Training in Decentralized Systems with Feder...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rao, M.S. Srinivasa Saraswathy, S. Neela, V. Arun, M.R. Gupta, Sandeep Taqui, Syed Noeman Vignana Jyothi Institute of Engineering & Technology Vnr Department of Mechanical Engineering Telangana Hyderabad India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India School of Computing and Information Technology Reva University Karnataka Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Department of Electrical Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

In the fast-changing field of machine learning, data privacy and model training efficiency are crucial. Federated Learning (FL) is a breakthrough method for training models on several decentralized devices or servers without centralizing raw data. This study examines Federated Learning's challenges in optimizing model training in dispersed systems. The first stage is to examine the Federated Learning (FL) framework and its merits, particularly in protecting data privacy and reducing server dependence. The next section will discuss communication overhead, stragglers, and non-IID (independent and identically distributed) data issues in a decentralized training model. We developed novel optimization methodologies and adaptive algorithms to improve Federated Learning (FL) efficiency. This includes updating asynchronous models, adaptability-based communication frequency, and non-IID data management. We also present a resilient aggregation method that mitigates Byzantine failures in participating nodes. We demonstrate that our methods improve convergence rate, communication costs, and model performance trained with Federated Learning (FL) by conducting a series of comprehensive experiments and analyzing real-world case studies. We show that these enhancements do not compromise data privacy, preserving Federated Learning (FL) principles. In conclusion, this study advances Federated Learning in decentralized systems. In an age of data sensitivity, this approach could help implement efficient, scalable, and privacy-preserving machine-learning methods. © 2023 IEEE.

关键词： Byzantine Failures Communication Efficiency Data Privacy Decentralized systems Federated Learning Model Training Non-IID Data

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Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management

arXiv

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arXiv 2024年

作者： Meer, Irshad A. Besser, Karl-Ludwig Ozger, Mustafa Schupke, Dominic Poor, H. Vincent Cavdar, Cicek School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering Princeton University United States Airbus Central Research and Technology Taufkirchen82024 Germany Department of Electronic Systems Aalborg University Copenhagen2450 Denmark

Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

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computers, Materials & Continua 2021年第5期67卷 2449-2462页

作者： Nabeela Awan Salman Khan Mohammad Khalid Imam Rahmani Muhammad Tahir Nur Alam MD Ryan Alturki Ihsan Ullah Department of Computer Science Government Girls Degree College MardanKPK23200Pakistan Department of Electrical Energy System UETPeshawar25000Pakistan Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityKSA Department of Smart Computing Kyungdong UniversityGlobal CampusGosung24764South Korea Department of Information Sciences College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Robotics Engineering Daegu Gyeongbuk Institute of Science and TechnologySouth Korea

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy *** smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart *** complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research *** challenges can be resolved through various machine learning techniques and data *** this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart *** proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution *** the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm *** simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

关键词： PSO IBR machine learning IoT smart cities CDBR

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FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field∗

arXiv

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arXiv 2024年

作者： Stathoulopoulos, Nikolaos Lindqvist, Björn Koval, Anton Agha-Mohammadi, Ali-Akbar Nikolakopoulos, George Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA911 09 United States

In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the GICP point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Multi-Layer Network Formation Through HAPS Base Station and Transmissive RIS-Equipped UAV

Multi-Layer Network Formation Through HAPS Base Station and ...

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IEEE Conference on Wireless Communications and Networking

作者： Faical Khennoufa Khelil Abdellatif Halim Yanikomeroglu Metin Ozturk Taissir Elganimi Ferdi Kara Khaled Rabie Department of Electrical Engineering LGEERE Lab El-Oued University El-Oued Algeria Department of Systems and Computer Engineering NTN Lab Carleton University Ottawa Canada Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Türkiye Department of Electrical and Electronic Engineering University of Tripoli Libya Division of Communications Systems KTH Royal Institute of Technology Sweden Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Türkiye Department of Computer Engineering & Center for Communication Systems and Sensing King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels.

关键词： NOMA System performance Reconfigurable intelligent surfaces Benchmark testing Performance gain Autonomous aerial vehicles Hardware Internet of Things MISO Relays

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Analysis of Air-Gap Field Modulation in Asymmetric Stator Tooth Bilateral-Excitation Flux Modulation Machine

Analysis of Air-Gap Field Modulation in Asymmetric Stator To...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Fei Zhao Kuang Yang Ruixiang Liu Jincheng Yu S M Sajjad Hossain Rafin Qasim Ali School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University USA Department of Electrical Engineering Sukkur IBA University Pakistan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

In view of the complexity of the flux density harmonic components of bilateral-excitation flux modulation (BFM) machines, it is not easy to analyze the role of air-gap flux density harmonics on the electromagnetic torque of BFM machines. In this digest, Taking the novel asymmetric stator tooth (AST) BFM machine as the research object, the flux modulation phenomena of the PM magneto motive-force (MMF) and armature winding MMF are analyzed in detail. Meanwhile, based on the air-gap field modulation theory and Maxwell stress tensor equation, the contribution of air-gap flux density harmonics to the electromagnetic torque of the BFM machine is obtained.

关键词： Magnetic flux Torque Tensors Magnetomechanical effects Air gaps Windings Modulation

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Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment

Deep Attention Driven Reinforcement Learning (DAD-RL) for Au...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jayabrata Chowdhury Venkataramanan Shivaraman Sumit Dangi Suresh Sundaram P B Sujit Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bangalore Department of Aerospace Engineering Indian Institute of Science Bangalore Department of Data Science & Engineering and Electrical Engineering & Computer Sci-ence Indian Institute of Science Education and Research Bhopal

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Autonomous Vehicle (AV) decision-making in ur-ban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV/ego must understand the weightage of various spatiotemporal interactions in a scene. Contemporary works use colos-sal transformer architectures to encode interactions mainly for trajectory prediction, resulting in increased computational complexity. To address this issue without compromising spatiotemporal understanding and performance, we propose the simple Deep Attention Driven Reinforcement Learning (DAD-RL) framework, which dynamically assigns and incorporates the significance of surrounding vehicles into the ego's RL-driven decision-making process. We introduce an AV-centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles. To understand map and route context, we employ a context encoder to extract features from context maps. The spatiotemporal representations combined with contextual encoding provide a comprehensive state representation. The resulting model is trained using the Soft-Actor Critic (SAC) algorithm. We evaluate the proposed framework on the SMARTS urban benchmarking scenarios without traffic signals to demonstrate that DAD-RL outperforms recent state-of-the-art methods. Furthermore, an ablation study underscores the importance of the context-encoder and spatiotemporal attention encoder in achieving superior performance.

关键词： Heuristic algorithms Decision making Reinforcement learning Transformers Feature extraction Encoding Spatiotemporal phenomena Trajectory Safety Vehicle dynamics

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