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35th International Conference on Information Technologies, InfoTech 2021

作者： Todorov Marinov, Milko University of Ruse Faculty of Electrical Engineering Electronics and Automation Department of Computer Systems and Technologies 8 Studentska Str. Ruse7017 Bulgaria

ISBN: (纸本)9781665403245

MapReduce is a widely used programming model for processing big data. Bloom filters are spatially efficient probabilistic data structures for fast queries that tell whether an element is a member of a set and allow false positive results. With reference to this, this article discusses the main characteristics, realisation techniques and scenarios for using Bloom filters when big data is analysed and processed. In addition to this, the study presents an implementation of a Bloom filter in a distributed MapReduce framework. © 2021 IEEE.

关键词： Data handling

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Fast convergent on-board Li-ion battery state of charge estimation via back-forth moving horizon estimation

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IEEE Transactions on Power Electronics 2025年

作者： Liu, Zhihao Xiao, Yu Yuan, Yuan Xu, Xiaodong Luo, Biao Huang, Tingwen Ministry of Education School of Automation and Key Laboratory of Industrial Intelligence and Systems Central South University Changsha410083 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China

Accurate state of charge (SOC) estimation is crucial for the safe operation of lithium-ion batteries (LIBs), yet existing methods are limited by sensitivity to initial SOC guess or high computational complexity. To address these challenges, this study proposes an onboard back-forth moving horizon estimation (BFMHE) framework for SOC estimation, achieving high precision, robustness, and computational efficiency compatible with microcontroller implementation. An efficient linear equivalent circuit model (ECM) is established, where the open-circuit voltage-SOC relationship is approximated by piecewise linear functions. The proposed framework employs two MHE estimators that iteratively operate in opposite directions over a finite time horizon, which enables rapid convergence. Moreover, this study provides a rigorous proof of the stability of the BFMHE method for the battery system with current input. The proposed approach is validated experimentally under various operating conditions and with different initial SOC guesses. Experimental results show that the proposed onboard BFMHE method achieves a maximum estimation error of less than 1% and quickly converges to the true SOC value, demonstrating strong robustness to initial estimation errors. © 1986-2012 IEEE.

关键词： Lithium-ion batteries

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automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

automation has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： automation machinery intelligence

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EEG Classification Using Contrastive Learning and Riemannian Tangent Space Representations

EEG Classification Using Contrastive Learning and Riemannian...

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Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Ahmed Tibermacine Imad Eddine Tibermacine Meftah Zouai Abdelaziz Rabehi Department of Computer Science University of Biskra Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Zian Achour University of Djelfa Djelfa Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

This paper introduces a novel advanced framework for the classification of Electroencephalography (EEG) signals through the integration of Riemannian geometry and a bespoke contrastive learning framework. Initially, the EEG signals are segmented and converted into covariance matrices, which are regularized to ensure positive definiteness, thus enabling their interpretation as Symmetric Positive Definite (SPD) matrices. These SPD matrices are then projected onto the Riemannian manifold, facilitating the extraction of discriminative features by utilizing Euclidean geometric operations within the tangent space. A specialized neural network architecture, TangentSpaceNet, is devised to map these features into a lower-dimensional space. In this space, a customized contrastive loss function, predicated on Euclidean distance, is implemented to enhance class separability. This approach substantially improves the robustness and accuracy of EEG signal classification. Empirical assessments highlight the effectiveness of the suggested approach, showcasing significant improvements in classifying performance 93% accuracy, 94%precision, 94% recall, and 94% F1-score. The novel fusion of Riemannian geometry and contrastive learning presents a sturdy and adaptable structure for analyzing EEG signals, presenting considerable prospects for utilization in brain-computer interfaces, cognitive neuroscience, and the identification of neurological conditions.

关键词： Geometry Training Accuracy Computational modeling Contrastive learning Brain modeling Feature extraction Electroencephalography Covariance matrices Matrix converters

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Comparative Analysis of SVM and CNN Classifiers for EEG Signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Ahmed Tibermacine Djouher Akrour Rofeida Khamar Imad Eddine Tibermacine Abdelaziz Rabehi Department of Computer Science University of Biskra Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Zian Achour University of Djelfa Djelfa Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants’ favorite music, and Interstellar’s main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli.

关键词： Support vector machines Accuracy Recurrent neural networks Pattern classification Brain modeling Feature extraction Electroencephalography Telecommunications Convolutional neural networks Object recognition

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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An Experimental Research on Temperature Accuracy Algorithm in Fan Coil Thermostat 6

An Experimental Research on Temperature Accuracy Algorithm i...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Ari, Yunus Cücük, Ergün Karateke, Seda Institute of Graduate Studies Istanbul University - Cerrahpaşa Electric and Electronic Engineering Istanbul34320 Turkey Control and Automation Systems Industry and Trade Inc. Department of Research and Development Ges Teknik Air Conditioning Istanbul34852 Turkey Istanbul Topkapi University Faculty of Engineering Department of Computer Engineering Istanbul34087 Turkey

ISBN: (纸本)9798350346671

The efficient operation of HVAC&R systems are based on keeping indoor temperature and air quality at an optimum level without disturbing comfort. Starting from this point, in this experimental research, the factors affecting the measurement accuracy during the room temperature detection of a wall mount fan coil thermostat were examined. Among the external effects, the effect of the device components on the measurement was focused and the solution of the measurement deviations occurred in this extent was investigated. Based on the experiments, the algorithm that must be applied to reach the optimum temperature measurement of a room thermostat developed for Fan Coil systems was proposed. © 2023 IEEE.

关键词： Thermostats

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Stability Analysis of Cyber-Physical systems via Dynamic Feedback Linearization and Linear Matrix Inequalities

Stability Analysis of Cyber-Physical Systems via Dynamic Fee...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Rachid Boutssaid Said Kririm Bensalem Boukili Noureddine Chaibi Laboratory of Materials Signals Systems and Physical Modeling Ibn Zohr University Faculty of Sciences Agadir Agadir Morocco Computer Science Signal Automation and Cognitivism Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Nonlinear control is a challenging aspect in modeling and controlling complex systems, specifically Cyber-Physical systems (CPSs), where the system dynamics are inherently nonlinear. Robotics and autonomous vehicle applications mandate the need for stability and robustness guarantees despite such nonlinearities. This paper addresses these challenges by employing dynamic feedback linearization, which extends classical feedback linearization by incorporating dynamic compensators to transform nonlinear systems into linear systems via appropriate state, feedback, and compensator transformations. The linear system can be altered to incorporate classical control techniques in a simpler form to analyze. Moreover, the stability of the transformed system will be analyzed extensively based on Lyapunov quadratic functions and Linear Matrix Inequalities (LMIs) to come up with a robust and systematic toolbox for stability analysis. Additionally, its applicative effectiveness to an autonomous vehicle model will demonstrate its usefulness for addressing practical CPS scenarios. This work develops novel knowledge regarding the behavior of nonlinear systems in the framework of stability, and this is crucial for the design and deployment of CPSs in complex applications.

关键词： Linear systems Cyber-physical systems Stability analysis Robustness Feedback linearization Linear matrix inequalities Nonlinear dynamical systems Safety Vehicle dynamics Autonomous vehicles

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On Hyper-Stability Theory Based Multivariable Nonlinear Adaptive Control: Experimental Validation on Induction Motors

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IET Electric Power Applications 2025年第1期19卷

作者： Bekhiti, Belkacem Nail, Bachir Tibermacine, Imad Eddine Salim, Ramzi Blida Algeria Faculty of Sciences and Technology University of Ziane Achour Djelfa Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratoire d’Automatique et Analysis des Systems National Polytechnic School Oran Algeria

This paper presents a novel hyperstability-based adaptive control strategy for induction motors, distinguishing itself from conventional model reference adaptive control (MRAC) approaches by integrating enhanced robustness against parametric uncertainties and external disturbances. Unlike traditional adaptive controllers, the proposed Hyper-stable adaptive controller (H-MRAC) ensures improved transient performance and faster convergence rates, validated through both theoretical analysis and experimental verification. Key innovations include the integration of hyper-stability theory into adaptive control design and a comprehensive evaluation of parameter uncertainties, which significantly improves motor performance in variable conditions. Experimental results demonstrate a 42% reduction in integral squared error (ISE), a 37% improvement in integral absolute error (IAE), a 28.7% improvement in integral time absolute error (ITAE) and a 25% faster convergence compared to standard MRAC. A detailed comparison with passivity-based control strategies shows a 26.5% improvement in steady-state performance and 30% faster transient response. Despite these successes, the paper discusses limitations related to computational complexity, real-time implementation challenges, and the impact of sensor noise on control performance. The potential need for DSPs or FPGA-based solutions is also addressed. Finally, the generalisability of the proposed control method across different motor types and power ratings is considered with future directions for broader validation in diverse industrial scenarios. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Model reference adaptive control

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Integral Reinforcement Learning-Based Angular Acceleration Autopilot for High Dynamic Flight Vehicles

SSRN

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SSRN 2023年

作者： Liu, Yingxin Hu, Yuhui Shen, Kai Qiu, Jiatai Neusypin, Konstantin A. School of Automation Beijing Institute of Technology Beijing100081 China Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow105005 Russia

In the synthesis of acceleration autopilot for high dynamic flight vehicle (HDFV), the full perception of system states, such as the angle of attack, is usually unavailable. Autopilots with feedback of angular acceleration (AFAA) have become more perspective for HDFV with stringent requirements on response speed and high maneuverability, compared with autopilot with feedback of angular rate (AFAR). Although classical optimal control methodology has validated its great vitality during the synthesis of autopilots both in theory and practice while this approach requires complete knowledge of the system dynamics. Reinforcement learning (RL) method has now proved to be an effective technique for adaptive optimal control of partially unknown nonlinear systems. In this paper, a novel data-driven integral RL algorithm with feedback of angular acceleration was proposed for the synthesis of HDFV’s autopilot. And numerical simulation was carried out to validate the effectiveness of proposed online integral RL-based angular acceleration autopilot for HDFVs. Besides, the tracking performance under different wave commands, the robustness against parameter uncertainties and the noise attenuation capacity between classical optimal tracking approach and proposed integral RL method were analyzed for AFAR and AFAA, respectively. Simulation results showed, angular acceleration autopilot with proposed integral RL algorithm possessed better tracking performance against various disturbances. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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