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12th World Conference on Intelligent systems for Industrial Automation, WCIS 2022

作者： Jabbarova, K.I. Huseynov, O.H. Jabbarova, Aynur I. Department of Computer Engineering Azerbaijan State Oil and Industry University Azadlig Ave. 34 BakuAZ1010 Azerbaijan Research Laboratory of Intelligent Control and Decision Making Systems in Industry and Economics Azerbaijan State Oil and Industry University Azadlig Ave. 34 BakuAZ1010 Azerbaijan Department of Mathematics and Statistics Azerbaijan State Economic University Baku Azerbaijan

ISBN: (纸本)9783031515200

Real-world decision-making problems are characterized by a fusion of fuzzy and probabilistic uncertainties. In view of this, Zadeh introduced the concept of Z-number to describe imprecision and partial reliability of decision relevant information. In this paper we proposed an approach to solving Q-learning problem where rewards and constraints over actions are described by using Z-numbers. A typical decision problem is used to illustrate the approach. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

arXiv

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arXiv 2023年

作者： Desai, Tushar Oliva, Federico Ferrari, Riccardo M.G. Carnevale, Daniele Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Rome Italy

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation is vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Dut to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model. © 2023, CC BY.

关键词： Simulink

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Validation of Moment Monitoring Results in Rolling Mill Stand Spindles

Validation of Moment Monitoring Results in Rolling Mill Stan...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Olga A. Gasiyarova Boris M. Loginov Andrey A. Radionov Vadim R. Gasiyarov Vadim R. Khramshin Department of Material Science LLC NPP Uchtekh-Profi Chelyabinsk Russia Magnitogorsk Iron and Steel Works Magnitogorsk Russia Department of Automation and Control Moscow Polytechnic University Moscow Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia

The authors highlight the need for the development and industrial deployment of online monitoring systems for the elastic moment on the rolling mill stand spindles. An example of a 5000 plate mill was used to demonstrate that these systems are made due to the need for a reliable moment assessment in dynamic modes. This problem is relevant for the development of control systems limiting the maximum (amplitude) values of moments when the workpieces enter the stand. The shock loading causes a multifold increase of the dynamic moment over the steady rolling moment. This results in the fatigue breakdown of spindle connections., as well as mill failures and emergency stops. The authors provide information on a telemetric elastic moment monitoring system installed on a 5000 mill. The data were validated in the industrial context. To achieve this, the authors propose a procedure where experimental research is carried out in two stages. During the first stage, the electromagnetic moment of the motor is compared to the spindle moment in the quasi-steady rolling mode when the elastic properties of the spindle are not manifested. During the second stage, the elastic moments of the upper and lower roll motors are assessed during 9 passes of reverse rolling. Some conclusions are made about the proportion of roll motor loads, spindle moment amplitudes, and the relevance of aligning rolling moments through the improvement of electric drive speed control algorithms.

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Intellectual lidar-based object classification for V2V communication technology implementation 22

Intellectual lidar-based object classification for V2V commu...

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Proceedings of the 6th International Conference on Future Networks & Distributed systems

作者： Ansaf Abdulnagimov Ekaterina Lopukhova Gleb Alektorov Nail Klyavlin Department of Automated Control Systems Ufa University of Science and Technology Russian Federation Department of Telecommunication Systems Ufa University of Science and Technology Russian Federation Department of Computational Mathematics and Engineering Cybernetics Ufa University of Science and Technology Russian Federation Department of High Performance Computing Systems and Technology Ufa University of Science and Technology Russian Federation

ISBN: (纸本)9781450399050

The deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. The robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. The features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. The best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.

关键词： PointNet

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Compressive Sensing-Based Reconstruction of Lissajous-Like Nodding Lidar Data

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ASME Letters in Dynamic systems and control 2021年第1期1卷 011009页

作者： Benson, Michael T. Sathishchandra, Harish Clayton, Garrett M. Andersson, Sean B. Department of Mechanical Engineering Villanova University VillanovaPA19085 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Center for Nonlinear Dynamics and Control Department of Mechanical Engineering Villanova University VillanovaPA United States Department of Mechanical Engineering Division of Systems Engineering Boston University BostonMA United States

In this article, a compressive sensing-based reconstruction algorithm is applied to data acquired from a nodding multibeam Lidar system following a Lissajous-like trajectory. Multibeam Lidar systems provide 3D depth information of the environment, but the vertical resolution of these devices may be insufficient in many applications. To mitigate this issue, the Lidar can be nodded to obtain higher vertical resolution at the cost of increased scan time. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally subsample the imaged area. In this article, a compressive sensing-based reconstruction algorithm is applied to the data collected during a relatively fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare the reconstructions to those made using simple nearest-neighbor interpolation. © 2021 ASME Letters in Dynamic systems and control. All rights reserved.

关键词： Compressed sensing

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Sequential fusion estimation for multisensor systems with non-Gaussian noises

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Science China(Information Sciences) 2020年第12期63卷 153-165页

作者： Liping YAN Chenying DI Q.M.Jonathan WU Yuanqing XIA Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Windsor

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.

关键词： state estimation sequential fusion non-Gaussian disturbance heavy-tailed noise multivariate $t$-distribution

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhanced Performance

Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhan...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Departement of Computer Engineering College of Computer Qassim University Buraidh Saudi Ababia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

High node mobility, rapid topology changes provide specific challenges for vehicular ad hoc networks (VANETs), which have an immediate impact on the routing protocols' performance. Traditional approaches, like the Optimized Link State Routing (OLSR) protocol, provide proactive route management but fail to fully account for critical dynamic parameters like link stability, relative vehicle speed, node distance, and bandwidth availability. This work proposes a hybrid method combining OLSR with Q-learning to facilitate real-time adaptive routing. The model leverages dynamic metrics to proactively evaluate links while employing Q-learning to optimize routing decisions based on rewards computed from performance factors like delay, packet loss rate, and link duration. Simulation results demonstrate that our approach significantly outperforms classic OLSR. The improvements include reduced packet loss rates, increased average throughput, lower average latency, and a reduction in control overhead. These findings confirm that integrating dynamic metrics and adaptive learning effectively addresses the challenges posed by VANETs.

关键词： Q-learning Simulation Vehicular ad hoc networks Packet loss Routing Throughput Routing protocols Stability analysis Topology Vehicle dynamics

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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