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14th International Conference on information Society and Technology, ICIST 2024

作者： Ivačko, Nikola Ćirić, Ivan Dimitrijević, Nikola Mitić, Dimitrije Milošević, Maša Kitić, Ana Krstić, Dušan Department of Mechatronics and Control Faculty of Mechanical Engineering University of Niš Aleksandra Medvedeva 14 Niš18000 Serbia Faculty of Information Technologies Belgrade Metropolitan University Belgrade Serbia Faculty of Electrical Engineering University of Niš Aleksandra Medvedeva 14 Niš18000 Serbia NAIS ROBOTICS D.O.O Aleksandra Medvedeva bb Niš Serbia

ISBN: (纸本)9783031714184

This paper explores the efficacy of two distinct approaches in computer vision, particularly in intelligent sports training systems. Our focus is on the development and comparison of two methodologies for image recognition in a basketball score detection system, named "Koško." The first approach uses a pre-trained model with two combined datasets in the training phase. This method leverages diverse data to enhance the model’s ability to generalize and accurately recognize scores in basketball games. In contrast, the second approach fine-tunes a model initially trained on one dataset with a subsequent second dataset. Rooted in recent transfer learning studies, this method shows that selectively fine-tuning a subset of layers or customizing learning rates can significantly improve robustness to out-of-distribution (OOD) data. By enforcing weight projections, this approach aims to maintain the generalization capabilities of the pre-trained models while adapting to the new dataset. Our research balances precise score detection and pragmatic system development, contributing to intelligent sports training systems. This work highlights the feasibility of combining machine learning and vision-based methodologies to advance sports training systems. Research on basketball score detection using the YOLOv8n model shows the pre-trained model achieved 93.75% accuracy and a 3% false positive rate, outperforming the fine-tuned model’s 88.71% accuracy and 5% false positive rate. By evaluating both networks, this study aims to provide clear strategies for implementing computer vision systems in sports, particularly in the dynamic environment of basketball. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Basketball

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Resilient Event-Triggered Model Predictive control for Adaptive Cruise control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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A Comprehensive Review on Challenges and Possible Solutions of Battery Management Systems in Electric Vehicles

A Comprehensive Review on Challenges and Possible Solutions ...

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Progress in Applied electrical engineering (PAEE)

作者： Joddumahanthi Vijaychandra Łukasz Knypiński Department of Electrical & Electronics Engineering Lendi Institute of Engineering and Technology Srikakulam India Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

Electric vehicle (EV) technology has come up with showing many alternative solutions to the issues mainly caused due to the carbon emissions by the conventional vehicles and hence grabbed a massive attention of the entire world. Furthermore, while electric vehicles (EVs) have their own advantages, they also have a number of drawbacks, such as concerns with heat management, battery management, health degradation, integration of power electronic conversions, charging and discharging issues, etc. The estimation of SOC and RUL is quiet difficult task as these are highly dependent of temperature measurements. Hence, thermal management is the key challenging task that to be considered in EVs. A comprehensive discussion of battery management systems, including their significant attributes has been addressed in this article.

关键词： Thermal management of electronics Temperature measurement Lithium-ion batteries Reviews Battery management systems Estimation Electric vehicles Power electronics State of charge Resistance heating

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet 42

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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42nd Chinese control Conference, CCC 2023

作者： Wang, De-Xiu Meng, Qing-Hao Han, Rui-Xue Li, Hong-Yue Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581543

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electronic nose

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Real-Time Servo control with Gesture Recognition on Nvidia Jetson Using CNNs

Real-Time Servo Control with Gesture Recognition on Nvidia J...

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Cybernetics & Informatics (K&I)

作者： Ladislav Körösi Slavomír Kajan Michal Kováč Jaromír Skirkanč Ivan Sekaj Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

Artificial intelligence (AI) algorithms are increasingly utilized across various domains, including real-time industrial control. This paper presents a real-time servo system control using hand gestures, detected by a convolutional neural network (CNN) implemented on an NVIDIA Jetson Edge device. The recognized gestures are transmitted via a deterministic industrial protocol (Profinet) to a programmable logic controller (PLC), which drives the servo system through motion function blocks (MFBs). This approach demonstrates significant improvements in real-time control for human-machine interaction (HMI) applications compared to direct CNN implementations on PLCs.

关键词： Accuracy Gesture recognition control systems Cameras Real-time systems Stability analysis Convolutional neural networks Reliability Servomotors Artificial intelligence

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Intersection Model control using Machine Learning Methods

Intersection Model Control using Machine Learning Methods

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Cybernetics & Informatics (K&I)

作者： Ivan Sekaj Jaromír Skirkanić Slavomír Kajan Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

This paper describes the design of a simulation model of intersection control using using neuroevolution. The goal is to optimize the traffic flow by maximizing the throughput of the intersection and minimizing the waiting time of any participant in the traffic situation. Our neuroevolution approach uses a feed-forward neural network which is trained by genetic algorithm. The proposed neuroevolution method is compared with a reinforcement learning method, specifically a Q -learning one. The efficiency of these methods is assessed using suitable simulated training and testing scenarios provided via the model’s environment. These results indicate that the neuroevolutiontrained artificial neural network slightly outperforms the reinforcement learning approach in terms of throughput and waiting times at the intersection. These findings highlight the potential of neuroevolution as a perspective machine learning method.

关键词： Training Q-learning Scalability Artificial neural networks Traffic control Throughput Vehicle dynamics Intelligent transportation systems Testing Genetic algorithms

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Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations

arXiv

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arXiv 2023年

作者： Kuwalek, Piotr Institute of Electrical Engineering and Electronics Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan Poland

Voltage fluctuations are common disturbances in power grids. Initially, it is necessary to selectively identify individual sources of voltage fluctuations to take actions to minimize the effects of voltage fluctuations. Selective identification of disturbing loads is possible by using a signal chain consisting of demodulation, decomposition, and assessment of the propagation of component signals. The accuracy of such an approach is closely related to the applied decomposition method. The paper presents a new method for decomposition by approximation with pulse waves. The proposed method allows for an correct identification of selected parameters, that is, the frequency of changes in the operating state of individual sources of voltage fluctuations and the amplitude of voltage changes caused by them. The article presents results from numerical simulation studies and laboratory experimental studies, based on which the estimation errors of the indicated parameters were determined by the proposed decomposition method and other empirical decomposition methods available in the literature. The real states that occur in power grids were recreated in the research. The metrological interpretation of the results obtained from the numerical simulation and experimental research is discussed. © 2023, CC BY.

关键词： Demodulation

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Infradar-Localization: Single-Chip Infrared- and Radar-Based Monte Carlo Localization 19

Infradar-Localization: Single-Chip Infrared- and Radar-Based...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Wang, Dong Masannek, Marco May, Stefan Nüchter, Andreas Nuremberg Institute of Technology Georg Simon Ohm Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg90489 Germany Julius-Maximilians-University Würzburg Department of Informatics Xvii Robotics Würzburg97074 Germany

ISBN: (纸本)9798350320695

This paper proposes a novel approach for indoor robot localization that leverages a fusion of information from single-chip infrared (Time-of-Flight) and radar sensors. The aim of our research is the development of a cost-effective and lightweight system that can achieve high-precision robot localization. Unlike traditional localization methods based on LiDARs or cameras, our proposed system uses single-chip infrared and radar sensors to overcome the limitations of high cost and bulky hardware. Specifically, we employ a Doppler radar-based velocity motion model for the estimation of the robot's ego-motion, eliminating the need for additional sensors such as IMU or wheel encoders. Next, we describe a hybrid sensor model for single-chip infrared and radar sensors that provides robust and accurate environmental perception with dynamic outlier removal. Finally, we integrate these components into a Monte Carlo localization framework to generate accurate real-time estimation of the robot's position and orientation. This is the first time a single-chip infrared and radar fusion-based framework has been applied to robot localization, to the best of our knowledge. Through a comprehensive experimental evaluation, we demonstrate the system's high accuracy and efficiency, achieving an average localization error of 9 cm in diverse indoor environments. This remarkable performance, combined with the low-cost and lightweight nature of our proposed solution, positions it as a highly promising alternative for a wide range of applications, including robotics, smart homes, and autonomous vehicles. The significant advancements of this novel approach offer vast potential to revolutionize the field of localization, enabling more precise and cost-effective navigation systems. © 2023 IEEE.

关键词： Monte Carlo methods

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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