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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yan, Xinyi Li, Jiaojiao Zhang, Zhiyuan Xu, Haitao Zheng, Tie Du, Qian Xidian University Isn Xi'an China Nssc Key Laboratory of Eitss Beijing China Mississippi State University Department of Electrical and Computer Engineering Starkville United States

ISBN: (纸本)9798331515669

Cross-domain few-shot learning (CDFSL) hyper-spectral image classification (HSIC) techniques have significantly enhanced the performance of HSIC under a few annotations. Due to the scattered spatial distribution of HSI and limited labels, the spatial information of the HSI sample is restricted, which means the spectral information may contain more discriminative category features than spatial information. However, most existing CDFSL HSIC methods lack targeted spectral discriminative feature extraction and struggle to capture spectral subtleties. To address this, a Human Eye-like Dual-Branch Network (HEDB-Net) is proposed, comprising a central branch and a peripheral branch. The central branch focuses on extracting core features from complete spectral dimension HSI patches. Simultaneously, these patches are input into the peripheral branch via random sampling and reintegration of spectral bands, enabling the model to perceive initially dispersed spectral information. These two branches represent the model's central and peripheral vision for the spectrum, allowing it to focus on different levels of feature information and significantly boosting spectral perception capabilities. To fully leverage the advantages of synergistic vision, a consistency constraint is applied to align their category probability prediction distributions, which helps ensure consistency in classification decisions between the two branches. Experimental results on the PaviaU dataset demonstrate that HEDBNet outperforms other advanced algorithms in terms of overall accuracy(OA), average accuracy(AA), and Kappa coefficient. This proves that HEDBNet can simultaneously focus on primary and secondary spectral features, thus significantly boosting classification accuracy. © 2024 IEEE.

关键词： Image annotation

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer Science 2024年第5期5卷 1-11页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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A High-Frequency Transformer Design with Leakage Integration for Auxiliary Power Supply in Railway Application 11

A High-Frequency Transformer Design with Leakage Integration...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Li, Zheqing Jin, Feng Zhao, Chunyang Hsieh, Eric Li, Qiang Virginia Polytechnic and State University Center for Power Electronic Systems Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9788957083505

DC-DC converters are a critical component for the auxiliary power supplies in railway applications to regulate wide range input voltage. With the power consumption increasing, the momentum toward high-power high-density continues unbated. The key to shrink the converter size is high-frequency operation. The bottleneck is the magnetic component, especially for the high-power converters. With wide-band gap (WBG) devices, frequency can be hundreds of kilohertz level, which can reduce the transformer volume significantly. By adopting the shorting control for gain boost, only a small resonant inductance is required to handle the wide input voltage. The low inductance provides the opportunity to integrate the resonant inductor into the transformer to further reduce the size of magnetic components. In this paper, a novel transformer is proposed with the integration of additional leakage inductor for resonance with evenly distributed core loss, and no sacrifice the winding loss and. A 30-kW 100-kHz prototype with 2.4 kW/L power density is built to verify the feasibility of the proposed transformer, whose peak efficiency reaches 99.2%. © 2023 The Korean Institute of Power Electronics.

关键词： Energy gap

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Distributed Consensus Multi-Agent System Control of AC Microgrid Under Various Communication Disturbances

Distributed Consensus Multi-Agent System Control of AC Micro...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ibtissam Kharchouf Mahmoud S. Abdelrahman Hossam M. Hussein S M Sajjad Hossain Rafin Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

The resilience of AC microgrids (MGs) in the face of communication disturbances, including variable delays, packet loss, packet corruption (noise), and limited bandwidth, is studied. A Controller Hardware-in-the-Loop (CHIL) platform is used to investigate the ramifications of these disturbances comprehensively. The MG cyber-physical system (CPS) integrates a real-time islanded AC microgrid deployed on RT-LAB, distributed secondary consensus controllers on single-board computers, and a real communication network infrastructure. Through meticulous analysis and experimentation, this research highlights the vulnerabilities of AC MGs to communication disturbances and their detrimental effects on the system resiliency, stability, and efficiency. The experiments emphasize the need for designing robust and adaptive control strategies that can effectively mitigate the effects of communication disruptions and enhance the overall reliability and performance of AC microgrid systems.

关键词： Noise Packet loss Microgrids Bandwidth Cyber-physical systems Stability analysis Real-time systems Delays Resilience Multi-agent systems

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Robust Synergetic Observer based Fault-Tolerant Control for Electric Vehicle Applications 50

Robust Synergetic Observer based Fault-Tolerant Control for ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Boumegouas, Mohammed Kabir Billa Kouzi, Katia Trabelsi, Mohamed Iqbal, Atif Birame, Mhamed Shadmand, Mohammad B. Laboratory of Semiconductors and Functional Materials Amar Telidji University of Laghouat Algeria Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait Department of Electrical Engineering Qatar University Doha Qatar Department of Electrical & Computer Engineering University of Illinois ChicagoIL United States

ISBN: (纸本)9781665464543

Speed sensor faults are very common in electric vehicle (EV) applications, often disrupting system performance due to the reliance on speed sensor data by the electric drives. Consequently, fault-tolerant control approaches require robust observer structures capable of functioning effectively during faults, swiftly identifying and reconfiguring failures. Thus, this paper presents a novel fault-tolerant control design leveraging a synergetic observer (SO) to substitute the speed sensor in the event of fault. Comparative analysis with a super-twisting sliding mode observer (ST-SMO) highlights the proposed observer's superior performance in terms of tracking accuracy and ripple reduction. The presented simulation results demonstrate promising performance of the proposed observer for fault-tolerant control in EV applications. © 2024 IEEE.

关键词： Permanent magnets

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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Strategies for Optimizing Energy Efficiency in Residential Buildings: An Integrated Framework Approach 3

Strategies for Optimizing Energy Efficiency in Residential B...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Kourougianni, Fanourios Olympios, Andreas V. Arsalis, Alexandros Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (纸本)9798350375923

Sustainable design and nearly zero-energy buildings (NZEBs) are essential for managing energy consumption and reducing carbon emissions in the building sector while maintaining occupant comfort. This study focuses on developing an advanced modeling framework for optimizing energy efficiency in residential buildings, integrating computational design, building performance simulation, and multi-objective optimization (MOO). Applied to a typical archetype in Cyprus, the framework evaluates diverse energy-efficient strategies, including innovative measures such as thermochromic glazing, natural ventilation, automated dimming of artificial lighting, and rule-based control electrochromic glazing systems, among other conventional energy efficiency measures (EEMs). MOO considers energy needs and investment costs. Results demonstrate significant energy savings potential, with optimal combinations of EEMs achieving a 77% reduction in cooling demand and 73% in heating demand compared to the baseline. This tool proves valuable for decision-making in both new construction and retrofitting, identifying cost-effective pathways towards NZEBs. © 2024 IEEE.

关键词： Green buildings

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EMS-Based HESS Using an Adaptive Filter Approach with SoC Balancing for DCMG

EMS-Based HESS Using an Adaptive Filter Approach with SoC Ba...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Hossam M. Hussein Mahmoud S. Abdelrahman Ibtissam Kharchouf S M Sajjad Hossain Rafin Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Globally, microgrid-based renewable energy systems are increasingly adopted as part of the transition to sustainable energy sources. However, their intermittent nature and low inertia necessitate energy storage systems to maintain system stability. A highly effective solution involves employing hybrid energy storage systems combining batteries and supercapacitors. This combination leverages the unique characteristics of each component, particularly their energy and power densities, to effectively mitigate fluctuations in the DC-bus voltage under varying power conditions. To address these challenges, this paper proposes a novel energy management system-based hybrid energy storage system and a modified filter-based control technique to enhance the stability of islanded DC microgrids. Further, balance the state of charge of each storage device without breaching the stability state of the system. The effectiveness of the proposed system was validated through various study cases conducted in the MATLAB/SIMULINK environment. Additionally, this paper explores the topology of hybrid battery/supercapacitor systems, the dynamics of pulsed power loads, and the capability of the energy management system (EMS) to manage available resources across different operating modes to minimize their impact on DC-bus voltage stability.

关键词： Renewable energy sources Uncertainty Adaptive filters Microgrids Stability analysis Topology State of charge Energy management systems MATLAB Voltage control

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Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling

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IEEE Transactions on Control systems Technology 2024年第1期32卷 214-224页

作者： Hosseinzadeh, Mehdi Sinopoli, Bruno Bobick, Aaron F. Washington State University School of Mechanical and Materials Engineering PullmanWA99164 United States Washington University in St. Louis Department of Electrical and Systems Engineering St. LouisMO63130 United States Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States

This article introduces the notion of danger awareness in the context of human-robot interaction, which decodes whether a human is aware of the existence of the robot, and illuminates whether the human is willing to engage in ensuring safety. This article also quantifies the notion as a single binary variable, called danger awareness coefficient, and provides a game-theoretic interpretation for that. Employing an online Bayesian learning method to update the robot's belief about the human's danger awareness by observing their actions, it is shown how the robot can build a predictive human model to anticipate the human's future actions. To enrich robot's observations, and thus to improve safety and efficiency of the robot, the robot is equipped with a danger signaling system to generate awareness in the human. Finally, a planning scheme is proposed to provide an efficient and probabilistically safe plan for the robot. The effectiveness of the proposed scheme is demonstrated through simulation studies on an interaction between a self-driven car and a pedestrian. © 1993-2012 IEEE.

关键词： Human robot interaction

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Collection: The Aegean Ro-Boat Race 2023

IEEE Data Descriptions

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IEEE Data Descriptions 2024年 1卷 87-94页

作者： Alexandros Troupiotis-Kapeliaris Nikolaos Sapountzis Giannis Spiliopoulos Thomas Kogias Piyabhum Chaysri Bernardo AntÓnio Correia Gabriel Miguel Filipe Santos Silva Elias Xidias Konstantinos Blekas João Tasso de Figueiredo Borges de Sousa Dimitris Zissis Department of Product and Systems Engineering University of the Aegean Syros Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece Department of Electrical and Computer Engineering of the Faculty of Engineering University of Porto Porto Portugal

In this article, we introduce a publicly available real-world dataset collected during the Aegean Ro-Boat Race 2023, which took place at the University of the Aegean in Syros, Greece. The Aegean Ro-Boat Race represents an international competition at the university level, challenging teams to innovate and develop autonomous marine robotic systems capable of performing in unknown dynamic maritime environments under real-world conditions. The 2023 competition featured three primary mission tasks, each designed to test different aspects of the robotic systems: 1) high-speed performance for evaluating the speed and agility of the autonomous vessels; 2) collision avoidance for assessing the systems’ ability to detect and avoid obstacles in real-time; and 3) endurance for testing the operational longevity and efficiency of the robotic systems over extended periods. In total, seven teams registered for the competition, with five of them being from Greece and two from the countries of Portugal and Latvia. Due to several technical difficulties, three vessels were able to complete all races, and data were recorded during their entire participation. The spatiotemporal data for the “Aegean Ro-Boat Race” was gathered through an onboard data logging system that continuously monitored various sensors, including global positioning system (GPS), for all vessels during the entire competition. The dataset includes positional reports from the vessels during all three races (totaling over 6500 records), the positions of the external track and obstacle buoys, together with a file regarding the weather conditions during the race day. IEEE SOCIETY/COUNCIL computer Society (CS), Aerospace and Electronic systems Society (AESS), Signal Processing Society (SPS), Oceanic engineering Society (OES), Intelligent Transportation systems Society (ITSS) DATA TYPE/LOCATION CSV; Syros, Greece DATA DOI/PID 10.5281/zenodo.13318421

关键词： Robots Collision avoidance Boats Navigation Trajectory Testing Spatiotemporal phenomena Drones Technological innovation Sea measurements

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