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Chinese automation Congress (CAC)

作者： Peng Lan Lele Wang Zhiqiang Pu Dianwei Qian Jianqiang Yi Control and Computer Engineering North China Electric Power University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426480

This paper considers a multi-target coverage problem where agents aim to efficiently cover multiple targets while maintaining connectivity. An improved harmony search algorithm is proposed. In particular, the harmony generation part of the harmony search algorithm is designed to generate all the harmony memory and new harmony vectors, so that all decisions are made at the beginning to ensure connectivity. Moreover, we propose a new strategy to dynamically adjust pitch adjusting rate (PAR) and bandwidth (BW). In the initial stage of the algorithm, small PAR and large BW value not only increase the diversity of understanding vectors, but also improve the global search ability of the algorithm. In the later stage of algorithm optimization, we use large PAR and small BW value, so as to improve the local search ability of the algorithm and make the solution vector converge[2]. Numerical simulation is performed to verify the effectiveness of the algorithm. We find that the performance of the proposed algorithm in this paper is close to the traditional greedy algorithm which has no connectivity restrictions.

关键词： Greedy algorithms automation Heuristic algorithms Bandwidth Maintenance engineering Numerical simulation Optimization

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HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation

arXiv

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arXiv 2023年

作者： Vuong, An Nguyen, Toan Vu, Minh Nhat Huang, Baoru Binh, H.T.T. Vo, Thieu Nguyen, Anh FPT Software AI Center Viet Nam Automation & Control Institute TU Wien Austria Austria Imperial College London United Kingdom Hanoi University of Science and Technology Viet Nam Faculty of Mathematics and Statistics TDTU Viet Nam Department of Computer Science University of Liverpool United Kingdom

Visual navigation, a foundational aspect of Embodied AI (E-AI) and robotics has been extensively studied in the past few years. While many 3D simulators have been introduced for the visual navigation tasks, scarcely works have combined human dynamics, creating the gap between simulation and real-world applications. Furthermore, current 3D simulators incorporating human dynamics have several limitations, particularly in terms of computational efficiency, which is a promise of modern simulators. To overcome these issues, we introduce HabiCrowd, the new standard benchmark for crowd-aware visual navigation that includes a crowd dynamics model with diverse human settings into photorealistic environments. Empirical evaluations demonstrate that our proposed human dynamics model achieves state-of-the-art performance in collision avoidance while exhibiting superior computational efficiency compared to its counterparts. We leverage HabiCrowd to conduct several comprehensive studies on crowd-aware visual navigation tasks and human-robot interactions. The source code and data can be found at https://***/. © 2023, CC BY-NC-SA.

关键词： Human robot interaction

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Asynchronous Distributed Optimization via ADMM with Efficient Communication

Asynchronous Distributed Optimization via ADMM with Efficien...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA US Hong Kong China Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each node is endowed with a convex local cost function, and is able to communicate with its neighbors over a directed communication network. Furthermore, we assume that the communication channels between nodes have limited bandwidth, and each node suffers from processing delays. We present a distributed algorithm which combines the Alternating Direction Method of Multipliers (ADMM) strategy with a finite time quantized averaging algorithm. In our proposed algorithm, nodes exchange quantized valued messages and operate in an asynchronous fashion. More specifically, during every iteration of our algorithm each node (i) solves a local convex optimization problem (for the one of its primal variables), and (ii) utilizes a finite-time quantized averaging algorithm to obtain the value of the second primal variable (since the cost function for the second primal variable is not decomposable). We show that our algorithm converges to the optimal solution at a rate of O (1/ k) (where $k$ is the number of time steps) for the case where the local cost function of every node is convex and not-necessarily differentiable. Finally, we demonstrate the operational advantages of our algorithm against other algorithms from the literature.

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems Science & Engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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Cyber-Physical Systems Based Business Models

Cyber-Physical Systems Based Business Models

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IEEE International Conference on automation, Quality and Testing, Robotics, AQTR

作者： Eugen Pop Daniela Gî fu Mihnea Alexandru Moisescu Automatics and Industrial Informatics Department Faculty of Automatic Control and Computers University "Politehnica" of Bucharest R&#x0026 D Institute for Automation - IPA SA Romania Institute of Computer Science Romanian Academy &#x2013 Ia&#x0219 i branch Faculty of Computer Science "Alexandru Ioan Cuza" University of Ia&#x0219 i Romania Automatics and Industrial Informatics Department Faculty of Automatic Control and Computers University "Politehnica" of Bucharest Romania

Currently, the use of cyber-physical systems is becoming widespread in various fields, primarily, because they enable many innovative functionalities through their networking. These systems include complex computation elements that interacts with the physical environment through sensors or actuators. Their solutions are based on sharing of hardware equipment and software services, targeting to provide added value proposition for various customers. The research and development in this domain are going on not only in the technical direction, but is also targeting the value creation for customers, in a technical and business-oriented manner. This paper presents a cyber-physical systems-based business model realized through services composition. The service composition framework integrates Web services which can be interconnected and can be accessed by mobile clients through smartphones, on mobile and wireless communications channels. The response time for multiuser requests for services access is evaluated, because it is an important quality of service performance criteria of our e-business model service composition framework. The results are promising.

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Rethinking development and major research plans of Industrial Internet in China

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Fundamental Research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural Science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

the paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis; binary classification decision tree; feed-forward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes.

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Live Demo: E2P–Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P–Events to Polarization Reconstruction from P...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Tobi Delbruck Zuowen Wang Haiyang Mei Germain Haessig Damien Joubert Justin Haque Yingkai Chen Moritz B. Milde Viktor Gruev Sensors Group Institute of Neuroinformatics Univ. of Zurich and ETH Zurich Switzerland AIT Austrian Institute of Technology Center for Vision Automation & Control High-Performance Vision Systems Vienna Austria Intl. Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Sydney Australia Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [...

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Research on Target Detection Method Based on Improved YOLOv5 19th

Research on Target Detection Method Based on Improved YOLOv5

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Wang, Rongzhuang Liu, Jun Zhang, Hualiang School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9783031203084

At present, various target detection algorithms are used in detection and classification. It is a problem to improve the accuracy and speed of target detection by using deep learning and neural network model to train the parameters of the data set. Aiming at the problems of insufficient detection, inaccurate detection of complex background and loss of small target feature information in yolov5 target detection, an improved method combining hole convolution, moving exponential average and retraining of positive samples is proposed in this paper. This method effectively solves the problems of complex background, occlusion and incomplete feature extraction, reduces the loss rate of target features, and improves the detection accuracy of each category. The improved yolov5 target detection algorithm in this paper can not only use multi feature fusion and accelerate positive sample matching, but also use cavity convolution to increase receptive field, improve the detection effect of small targets and reduce the occurrence of missed detection and false detection. Experiments show that the improved yolov5 algorithm has better detection rate and lower loss rate than many existing target detection algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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