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arXiv 2021年

作者： Cenedese, Carlo Cucuzzella, Michele Scherpen, Jacquelien M.A. Grammatico, Sergio Cao, Ming Department of Information Technology and Electrical Engineering ETH Zürich Zurich Switzerland Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Netherlands Delft Center for Systems and Control TU Delft Netherlands

—In this paper, we study how to alleviate highway traffic congestion by encouraging plug-in hybrid and electric vehicles to stop at a charging station around peak congestion times. Specifically, we design a pricing policy to make the charging price dynamic and dependent on the traffic congestion, predicted via the cell transmission model, and the availability of charging spots. Furthermore, we develop a novel framework to model how this policy affects the drivers’ decisions by formulating a mixed-integer potential game. Technically, we introduce the concept of "road-to-station" (r2s) and "station-to-road" (s2r) flows, and show that the selfish actions of the drivers converge to charging schedules that are individually optimal in the sense of Nash. In the second part of this work, submitted as a separate paper (Part II: Case Study), we validate the proposed strategy on a simulated highway stretch between The Hague and Rotterdam, in The Netherlands. Copyright © 2021, The Authors. All rights reserved.

关键词： Roads and streets

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Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network

Automated Human Facial Emotion Recognition System Using Dept...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology (IAICT)

作者： Antor Mahamudul Hashan K Al-Saeedi Adnan Adhab Rizu Md Rakib Ul Islam Kumar Avinash Subhankar Dey Department of Intelligent Information Technologies Ural Federal University named after the first President of Russia B. N. Yeltsin Yekaterinburg Russian Federation Department of Automated Control Systems National University of Science and Technology “MISiS” Moscow Russian Federation Department of Information Technology and Management Systems Ural Federal University named after the first President of Russia B. N Yeltsin Yekaterinburg Russian Federation Department of Big Data Analytics And Video Analysis Methods Ural Federal University named after the first President of Russia B. N. Yeltsin Yekaterinburg Russian Federation

The automatic human facial emotion recognition (AHFER) system has its wide significant contribution in several disciplines, such as human-computer collaboration, human-robot interaction, and so on. Multiple research projects have been conducted regarding this topic because it is a challenging and interesting task, especially in the area of computer vision. The purpose of the work is to recognize facial emotions using a depthwise separable convolutional neural network (DS-CNN). Apart from that, a facial emotion dataset has been proposed, and splitting functions, intensity normalization, image cropping, and grayscale conversion have been used in data pre-processing. The AHFER system is capable of recognizing four types of emotions: happy, sad, angry, and neutral. The results of the experiment showed that the AHFER method is 99 percent accurate when training and 93 percent accurate when validating. Additionally, we determined the confusion matrix with precision, recall, and fl-score. A comparison between the DS-CNN and DNN models was performed. The DS-CNN model performed significantly better than the DNN model. The DS-CNN model could be improved in the future by including more facial emotion categories.

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A Survey of Model Predictive control Methods for Traffic Signal control

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IEEE/CAA Journal of automatica Sinica 2019年第3期6卷 623-640页

作者： Bao-Lin Ye Weimin Wu Keyu Ruan Lingxi Li Tehuan Chen Huimin Gao Yaobin Chen IEEE the School of Mechanical and Electrical Engineering Jiaxing University the State Key Laboratory of Industrial Control Instituteof Cyber-Systems and Control Technology Zhejiang University the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis Mechanical Engineering and Mechanics Ningbo University

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

关键词： Autonomous vehicles coordination control mixed integer programming model predictive control system decomposition traffic flow models traffic signal control

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Tutorial on Asymptotic Properties of Regularized Least Squares Estimator for Finite Impulse Response Model

arXiv

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arXiv 2021年

作者： Ju, Yue Chen, Tianshi Mu, Biqiang Ljung, Lennart The School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China The Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

In this paper, we give a tutorial on asymptotic properties of the Least Square (LS) and Regularized Least Squares (RLS) estimators for the finite impulse response model with filtered white noise inputs. We provide three perspectives: the almost sure convergence, the convergence in distribution and the boundedness in probability. On one hand, these properties deepen our understanding of the LS and RLS estimators. On the other hand, we can use them as tools to investigate asymptotic properties of other estimators, such as various hyper-parameter estimators. © 2021, CC BY.

关键词： Probability distributions

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Dynamic Analysis and control of Fractional Order Memristor Hopfield Neural Network

Dynamic Analysis and Control of Fractional Order Memristor H...

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Chinese control Conference (CCC)

作者： Feng Liu Gang Cai Feifan Zhou Wei Wang Hua Wang Xiuqin Yang School of Automation China University of Geosciences Wuhan China Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Department of Mechanical Engineering Boston University Boston MA USA School of Education Minzu University of China Beijing China

In this paper, a novel smooth magnetron is introduced to construct a fractional memristor Hopfield neural network (fractional order M-HNN). The local stability of equilibrium point are analyzed theoretically. Taking the memristor coupling strength coefficient and the fractional order as bifurcation parameters, the phase trajectory diagram, the bifurcation diagram of the system are drawn to analyze the influence on the dynamic behavior of the neural network. When the system parameters are fixed, the hyperchaos phenomenon of the fractional order M-HNN model is revealed. Finally, the PD controller is applied to the model to enhance the stability of the system.

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Kinematics modeling and trajectory planning for the skull reconstruction robot

Kinematics modeling and trajectory planning for the skull re...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Wang, Weiqun Wang, Xiru Hou, Zeng-Guang Fang, Zhijie Jiao, Yuze Luo, Yangyu Gong, Jian State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Beijing100080 China Center for Excellent in Brain Science and Intelligence Technology Beijing100190 China Department of Electronics Polytech Nice Sophia Nice06200 France CASIA UCAS Department of Pediatric Neurosurgery Beijing Tiantan Hospital Capital Medical University Beijing100050 China

ISBN: (纸本)9781665436786

A skull reconstruction robot has been developed to effectively overcome shortcomings of the traditional skull surgery and improve its efficiency and quality. In this study, kinematics modeling and trajectory planning for the robot are carried out. Firstly, the kinematic model of the robot is established based on the D-H method. The positioning points, which are used to determine the cutting plane, are given on the surface of the skull by the surgeon. The contour data for cutting can be obtained using a two dimensional laser scanning sensor, by adjusting the scanning light curtain to coincide with the cutting plane. Then, to ensure that the tools fixed at the robot end can always be perpendicular to the skull surface during the cutting or drawing process, the principal component analysis is introduced to design the normal vectors at the selected key points of the point cloud data for the skull surface. Moreover, the quintic polynomial interpolation method is used to construct the curves between the adjacent key points to make up for the defects of the skull surface and ensure the smoothness of the trajectory. Finally, workspace analysis based on Monte Carlo method is implemented, and the proposed kinematics modeling and trajectory planning methods are validated by simulation. © 2021 IEEE.

关键词： Monte Carlo methods

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Adaptive stabilization of minimum-phase systems under quantized measurements

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IFAC-PapersOnLine 2020年第2期53卷 3761-3766页

作者： Anton Selivanov Alexander Fradkov Department of Automatic Control and Systems Engineering The University of Sheffield UK Saint Petersburg University Russia Institute for Problems of Mechanical Engineering Russian Academy of Sciences Russia

We construct an adaptive controller for a linear minimum-phase system of an arbitrary relative degree with an unknown bounded disturbance and dynamically quantized measurements. The key novelty is the extension of the shunting method (parallel feedforward compensator) to plants with bounded disturbances. This method leads to an augmented system of relative degree one that is stabilized by a passification-based adaptive controller. Moreover, we design a switching procedure for the controller parameters and the quantizer’s zoom that ensures the state convergence from an arbitrary set to an ellipsoid whose size depends on the disturbance bound. The results are demonstrated by an example of an aircraft flight control.

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Distributed Model Predictive control with Asymmetric Adaptive Terminal Sets for the Regulation of Large-scale systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 6899-6904页

作者： Ahmed Aboudonia Annika Eichler John Lygeros Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Switzerland Deutsches Elektronen-Synchroton DESY 22607 Hamburg Germany

In this paper, a novel distributed model predictive control (MPC) scheme with asymmetric adaptive terminal sets is developed for the regulation of large-scale systems with a distributed structure. Similar to typical MPC schemes, a structured Lyapunov matrix and a distributed terminal controller, respecting the distributed structure of the system, are computed offline. However, in this scheme, a distributed positively invariant terminal set is computed online and updated at each time instant taking into consideration the current state of the system. In particular, we consider ellipsoidal terminal sets as they are easy to compute for large-scale systems. The size and center of these terminal sets, together with the predicted state and input trajectories, are considered as decision variables in the online phase. The efficacy of the proposed scheme is illustrated in simulation by comparing it to a recent distributed MPC scheme with adaptive terminal sets.

关键词： Predictive control Invariance Distributed control Large-scale systems Adaptive control

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Tactics for using a Mobile Robot to Recover an Overturned Wheeled Vehicle

Research Square

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Research Square 2021年

作者： Ikeda, Hidetoshi Atoji, Shinya Amemiya, Manami Tajima, Shingo Kitada, Takayoshi Fukai, Kotaro Sato, Keisuke National Institute of Technology Toyama College Department of Mechanical Engineering Toyama939-8630 Japan National Institute of Technology Toyama College Department of Electrical and Control Systems Engineering Toyama939-8630 Japan

This paper describes mobile robot tactics for recovering a wheeled vehicle that has overturned. If such a vehicle were to tip over backward off its wheels and become unable to recover itself, especially in areas where it would be difficult for humans to enter and work, total working efficiency could decline significantly, not only because the vehicle is no only able to perform its job, but because it becomes an obstacle to other work. Herein, we propose a robot-based recovery method that can be used to recover such overturned vehicles and demonstrates its utility. The recovery robot, which uses a mounted manipulator and hand to recover the overturned vehicle, is also equipped with a camera and a personal computer (PC). The ARToolKit software package installed on the robot-mounted PC detects ARmarkers attached to the overturned vehicle and uses the information they provide to orient itself in order to perform recovery operations. To facilitate these ARToolKit-based operations, it is necessary to conduct a theoretical analysis to clarify the distance between the robot manipulator and vehicle, and thus achieve an objective position for grasping the overturned vehicle. The experimental results obtained in this study demonstrate the effectiveness of our proposed system. © 2021, CC BY.

关键词： Mobile robots

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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