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IEEE ANDESCON

作者： José de Arimatéia O. Filho Paulo Fernando Silva Luis De Oro Arenas Tiago Davi Curi Busarello Helmo Kelis Morales Paredes Institute of Science and Technology of Sorocaba São Paulo State University (Unesp) Sorocaba Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina (UFSC) Blumenau Brazil

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

This paper proposes the design of a repetitive controller along with capacitor voltage feedforward applied to an LCL filter active power filter (APF). The repetitive controller can operate in systems with disturbances exhibiting high harmonic content and the feedforward action can reshape the output impedance of the APF. The conservative power theory (CPT) is proposed to obtain reference signals for the purpose of total mitigation of unwanted currents. The proposed technique ensures that only the active current flows through the electrical grid, while maintaining the stability of the APF-grid system through passivity theory. Finally, the proposed control strategy is validated using the Matlab/Simulink® software under several conditions of grid imbalances and harmonic distortions.

关键词： Power filters Stability criteria Capacitors Power system stability Active filters Filtering theory Impedance Voltage control Feedforward systems Power harmonic filters

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on Computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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An Intelligent Battery Management System for an Electric Vehicle Powered by Solar PV Array

An Intelligent Battery Management System for an Electric Veh...

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International Universities Power engineering Conference (UPEC)

作者： Ali Falih Challoob Nur Azzammudin Bin Rahmat Vigna K. Ramachandaramurthy Nagham Saeed Amjad Jaleel Humaidi Institute of Power Engineering Universiti Tenaga Nasional Strategic Planning Centre Malaysia School of Computing and Engineering University of West London London United Kingdom Control and Systems Eng. Dept. University of Technology Baghdad Iraq

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

Implementing a Battery Management System (BMS) in Electric Vehicles (EVs) offers several benefits that enhance the efficiency, security, and durability of the vehicle's battery system. The BMS oversees and controls the performance of each cell in the battery pack, guaranteeing that they function within predetermined voltage and temperature thresholds to maintain safety. In this paper, an intelligent BMS for an EV model powered by a solar PV array was proposed using a fuzzy logic (FL) controller. The proposed method was applied to EVs consisting of four Lithium-ion batteries. The simulation results are achieved using MATLAB/Simulink software. The estimation of the state of charge (SOC) for each battery was used as input to the FL which controls the discharging/ charging mode for each battery via power semiconductor switches. Three different scenarios are used to verify the suggested method. A full control FL operation was obtained, and the batteries were protected successfully against overcharging and over-discharging.

关键词： Fuzzy logic Lithium-ion batteries Battery management systems Electric vehicles Threshold voltage Software Temperature control State of charge MATLAB Voltage control

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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Computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods

Classification of Attention Performance Post-Longitudinal tD...

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International Conference for Convergence of Technology (I2CT)

作者： Akash K Rao Vishnu K Menon Arnav Bhavsar Shubhajit Roy Chowdhury Ramsingh Negi Varun Dutt Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Cognitive Control and Machine Learning Group Institute of Nuclear Medicine and Allied Sciences Delhi India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention.

关键词： Measurement Machine learning algorithms Filtering Machine learning Brain modeling Electroencephalography Task analysis

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Classification of attention performance post-longitudinal tDCS via functional connectivity and machine learning methods

arXiv

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arXiv 2024年

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Cognitive Control and Machine learning group Institute of Nuclear Medicine and Allied Sciences Delhi India

关键词： Electroencephalography

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Development of an Adaptive Technology for Distance Learning of Microprocessor Technology Based on Immunological Homeostasis

Development of an Adaptive Technology for Distance Learning ...

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Samigulina, Galina Samigulina, Zarina Porubov, Dmitriy Institute of Information and Computing Technologies Lab. Intellectual Control Systems and Forecasting Almaty Kazakhstan Kazakh - British Technical University School of Information Technologies and Engineering Almaty Kazakhstan East Kazakhstan Technical University Named after D. Serikbayev Competence and Technology Transfer Center in the Field of Automation and Mechatronics Ust-Kamenogorsk Kazakhstan

ISBN: (纸本)9781665475952

At present, with the development of modern IT technologies and artificial intelligence methods, it is relevant to develop adaptive distance education systems for students of technical specialties capable of providing a personalized cognitive approach to the process of obtaining knowledge and timely adjustment of the learning trajectory. Particular difficulties arise when teaching the principles of operation of complex industrial equipment based on microprocessor technology, the basics of programming and the development of automatic control systems based on them. The problems associated with the complexity of adapting technical training courses for specific students and the peculiarities of information perception in the absence of direct contact with the teacher can be successfully solved with the introduction of algorithms based on a unified artificial immune system (UAIS). An innovative e-learning technology is proposed for students of engineering specialties in order to obtain high-quality technical professional education in a short time on real industrial equipment. When creating the technology and predicting the learning outcomes of students, immunological homeostasis was used to quickly correct the learning process. UAIS was developed on the basis of modified clonal selection, negative selection and immune network algorithms in order to identify the most effective of them in predicting multivariate dynamic data about students and forming an adequate immune response to maintain immunological homeostasis. The main stages and advantages of the proposed technology are given. The simulation results were obtained with the participation of students of the Faculty of Information Technology on the basis of the Kazakh-British Technical University. © 2023 IEEE.

关键词： engineering education

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Fuzzy Filter Design for Affine Systems with Sensor Faults:A Dynamic Event-Triggered Approach

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Journal of Systems Science & Complexity 2022年第5期35卷 1761-1784页

作者： CHENG Jun WU Yuyan WU Zhengguang LI Kezan School of Mathematics and Statistics Center for Applied Mathematics of GuangxiGuangxi Normal Univer-sityGuilin 541006China School of Mathematics and Statistics Guangxi Normal UniversityGuilin 541006China School of Information Science and Engineering Chengdu UniversityChengdu 610106China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China

This study addresses the issue of dynamic event-triggered-based filtering for fuzzy affine *** alleviate the utilization of constraint bandwidth resources and improve the efficiency of the signals exchange,a dynamic event-triggered protocol is forwarded to regulate the trigger instants with objective system ***,the nonhomogeneous Markov process is proposed to characterize the dynamic behaviors of sensor faults,where the time-varying transition probabilities belong to a convex polytope ***,the validity and applicability of devised filter design methodology for fuzzy affine systems are displayed via two practical models.

关键词： Dynamic event-triggered protocol fuzzy affine systems nonhomogeneous Markov process sensor fault

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Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

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Computers, Materials & Continua 2023年第4期75卷 1827-1845页

作者： Abdul Sattar Palli Jafreezal Jaafar Manzoor Ahmed Hashmani Heitor Murilo Gomes Aeshah Alsughayyir Abdul Rehman Gilal Department of Computer and Information Sciences Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610Malaysia Centre for Research in Data Science UTPPerak32610Malaysia High Performance Cloud Computing Centre(HPC3) UTPPerak32610Malaysia School of Engineering and Computer Science Victoria University of WellingtonWellington6012New Zealand AI Institute University of Waikato WellingtonHamilton3240New Zealand Anti-Narcotics Force Ministry of Narcotics ControlIslamabad46000Pakistan College of Computer Science and Engineering Taibah UniversityMadinah42353Saudi Arabia

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

关键词： Classification data streams class imbalance concept drift class imbalance ratio

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Adaptive global sliding mode control for multilateral teleoperation systems with varying-time delay

Adaptive global sliding mode control for multilateral teleop...

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International Conference on Sciences and Techniques of Automatic control and Computer engineering (STA)

作者： Asma Ounissi Neila Mezghani Ben Romdhane Mohamed Boukattaya Laboratory of sciences and technique of automatic control and computing (Lab-STA) National school of engineering of Sfax Sfax Tunisia Laboratory of sciences and technique of automatic control and computing (Lab-STA) Sfax Preparatory Engineering Institute Sfax Tunisia

ISBN: (纸本)9781665482622

In this paper, we suggest an adaptive control for a multilateral teleoperation system (MTS) with an unknown upper limit on the system uncertainty (SU) and varying-time delay (VTD). A novel adaptive global sliding mode (AGSMC) has been addressed, which guarantees the elimination of the reaching phase and forces the tracking error to reach the sliding surface (SS) from the beginning. Moreover, the adaptive tuning law has been addressed in order to deal with the undetermined upper limit of the SU. The convergence and stability analysis of the proposed methods is verified by the Lyapunov stability theory. The simulation is carried out on 1 master, 1 leader, and 3 followers teleoperation system is developed to show the effectiveness of the developed control.

关键词： Uncertainty Simulation Robustness Stability analysis Delays Tuning Sliding mode control

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