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Dynamic Performance Prediction in Batch-Based Assembly System with Bernoulli Machines and Changeovers

Complex System Modeling and Simulation 2022年第3期2卷 224-237页

作者： Zunjun Wang Zhiyang Jia Xiuxuan Tian Jingchuan Chen Bei Pan State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Industrial and Manufacturing Systems Engineering University of Hong KongHong Kong 999077China School of Engineering Tokyo University of TechnologyTokyo 192-0982Japan

Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.

关键词： flexible assembly system dynamic performance Bernoulli machines finite buffers changeovers

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Synthesis of Diagnostic Observers for Linear systems Based on Optimal control Methods

Synthesis of Diagnostic Observers for Linear Systems Based o...

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2022 International Conference on control, automation and Diagnosis, ICCAD 2022

作者： Kabanov, Aleksey A. Zuev, Aleksandr V. Zhirabok, Aleksey N. Filaretov, Vladimir F. Department of Informatics and Control in Technical Systems Sevastopol State University Russia Laboratory of Intelligent Information Systems for Marine Robots Institute of Marine Technology Problems FEB RAS Russia Department of Automation and Robotics Far Eastern Federal University Russia Robotics Laboratory Institute of Automation and Control Processes FEB RAS Russia

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

The paper considers the problem of fault identification in dynamic systems described by linear differential equations. To solve this problem, diagnostic observers constructed on the basis of optimal control methods are used. The proposed new method for the synthesis of such observers is based on the idea of introducing a new system with some auxiliary control which plays a role of an unknown vector function describing emerging faults. This control is proposed to be formed in such a way that it provides a minimum for the residual functional. Unlike popular approaches to solving the problem of fault identification based on observers operating in a sliding mode, the proposed method allows us to expand the class of systems for which the identification problem can be solved. It is known that the methods of sliding mode observers design impose certain restrictions on the systems under consideration. The suggested theory is illustrated by an example. The simulation results confirmed the operability of the diagnostic observers synthesized using the method proposed in the work. © 2022 IEEE.

关键词： Linear systems

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Frequency Domain-Based Low-Rank Approximation for Magnetic Anomaly Detection

Frequency Domain-Based Low-Rank Approximation for Magnetic A...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Yumeng Lv Congyu Liao Huan Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China Department of Radiology Stanford University CA USA

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Current magnetic anomaly detection (MAD) methods prioritize signal-to-noise ratio (SNR) over signal features and edge information, leading to signal distortion. To address this issue, a new MAD approach utilizes structured lowrank approximation and block singular value decomposition based on the spatial frequency domain, dubbed FL-BSVD is proposed. First, the low-rankness structure of the magnetic anomaly signal is obtained through 2D Discrete Fourier Transform (2D-DFT) and structured Hankel transformation. Then, block singular value decomposition is applied to the Hankel matrix to reduce noise while preserving more signal edge features and enhancing detection accuracy. Finally, a field experiment comparing FL-BSVD with four commonly used methods is conducted. The experiment confirms that FL-BSVD can effectively recover magnetic anomaly signal features and edge information in a strong noisy environment.

关键词： Image edge detection Frequency-domain analysis Noise reduction Magnetic domains Feature extraction Noise measurement Magnetic anomaly detection Signal to noise ratio Singular value decomposition Periodic structures

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Quantification of valve stiction in control loops using adaptive differential evolution algorithm

Quantification of valve stiction in control loops using adap...

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2024 China automation Congress, CAC 2024

作者： Yang, Dong Lang, Xun Wu, Jiande Yu, Wanke Liu, Songhua Xie, Lei Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies. © 2024 IEEE.

关键词： Adaptive algorithms

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2024 China automation Congress, CAC 2024

作者： Lang, Yumin Wu, Jiande Lang, Xun Yu, Wanke Yang, Chuanyan Su, Hongye Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

Oscillation is a significant indication of the deterioration of control system performance and needs to be carefully monitored to ensure safe and stable process operations. However, industrial processes are often prone to noisy artifacts and nonstationary trends, resulting in low signal-to-noise ratios. In order to tackle this problem, this study introduces a novel framework for enhanced trend removal and noise reduction, which is featured by the following steps:(i) The circulant singular spectrum analysis (CiSSA) is used to decompose oscillation data into several reconstructed components (RCs). (ii) The estimated power spectral density (PSD) ofthese RCs is then smoothed using the Savitzky-Golay filter (SGF) to eliminate noise and identify the RCs corresponding to oscillatory components. (iii) These identified RCs are subsequently reconstructed to produce purified oscillatory data. The effectiveness of the suggested approach is validated using simulations and real-world industrial case studies. The findings indicate that the method notably enhances the quality of oscillatory data. © 2024 IEEE.

关键词： Power spectral density

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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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Modeling the Clustering of Wireless Sensor Networks Using the K-means Method

Modeling the Clustering of Wireless Sensor Networks Using th...

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2021 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", T and QM and IS 2021

作者： Voronova, Lilia I. Voronov, Vyacheslav I. Mohammad, Nawar Moscow Technical University Communications and Informatics 'MTUCI' Department of Intelligent Systems in Control and Automation Moscow Russia

ISBN: (纸本)9781665433020

Currently, the development of information and communication technologies has led to the emergence of the Internet of Things. The technological basis for the implementation of the concept of the Internet of Things is wireless sensor networks. Clustering, a machine learning technique, is an effective way to extend the lifecycle and reduce power consumption of wireless sensor networks (WSNs). The article presents modeling and clustering of flying wireless sensor networks that form when a group of drones moves. The introduction of moving sensors leads to dynamic clustering of the entire system at successive times. complicating the determination of the number of individual clusters and their integration. The clustering mechanism is performed using the K-means algorithm with respect to the location of wireless stationary and flying sensors, described by coordinates x, y, and z in three-dimensional space. © 2021 IEEE.

关键词： Wireless sensor networks

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Non-fragile Joint State and Fault Estimation for Complex Networks with Missing Measurements and Switching Topology 41

Non-fragile Joint State and Fault Estimation for Complex Net...

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第41届中国控制会议

作者： Pingping Gao Jun Hu Chaoqing Jia Zhihui Wu Department of Mathematics Harbin University of Science and Technology School of Automation Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a non-fragile joint state and fault estimation problem is investigated for time-varying complex networks with switching topology and randomly occurring nonlinearities under the missing *** phenomena of the switching topology,randomly occurring nonlinearities and missing measurements are described by three mutually independent Bernoulli random variables,where the uncertain occurrence probabilities are *** addition,the parameter perturbations of the estimator gain matrix are characterized by a set of zero-mean multiplicative noises.A novel time-varying estimation method is designed and an upper bound of estimation error covariance matrix is obtained by solving two recursive matrix ***,the desired estimator gain matrix is parameterized by minimizing the trace of the obtained upper ***,a sufficient condition is given to ensure the boundedness of the upper bound matrix by using the mathematical induction ***,a simulation study is given to verify the feasibility of the proposed estimation scheme.

关键词： Time-varying complex networks switching topology randomly occurring nonlinearities missing measurements uncertain occurrence probabilities joint estimation

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Design and locomotion characteristic analysis of a novel tensegrity hopping robot

Design and locomotion characteristic analysis of a novel ten...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Mo, Jixue Gao, Changqing Fang, Hao Yang, Qingkai Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China Beijing Institute of Technology School of Automation Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9798350325706

In consideration of the poor locomotion ability of most traditional tensegrity robot, a novel tensegrity hopping robot powered by push-pull electromagnets was proposed with better locomotivity. It is able to conduct stable consecutive progressing action and turning action through hopping. This paper covers the structural design, theoretical modeling of the robot's hopping process, as well as its self-righting analysis, simulation and experimental verification. The average moving speed and relative moving speed were measured to be 0.334m/s and 0.641 body length/s respectively, which obviously surpass the general moving ability of most traditional tensegrity robot. In addition, the robot was also validated to have good self-righting characteristic for stable consecutive hopping. Thus this robot can be potentially applied for search, rescue, detection, etc. in outdoor and unstructured environment. © 2023 IEEE.

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EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry

arXiv

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arXiv 2024年

作者： Wu, Xiaoyi Chen, Yushuai Li, Zhan Hong, Ziyang Hu, Liang The Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potential for robust perception in adverse conditions. As the latest generation of FWCW radars, the 4D mmWave radar provides point cloud with range, azimuth, elevation, and Doppler velocity information, despite inherent sparsity and noises in the point cloud. In this paper, we propose EFEAR-4D, an accurate, highly efficient, and learning-free method for large-scale 4D radar odometry estimation. EFEAR-4D exploits Doppler velocity information delicately for robust ego-velocity estimation, resulting in a highly accurate prior guess. EFEAR-4D maintains robustness against point-cloud sparsity and noises across diverse environments through dynamic object removal and effective region-wise feature extraction. Extensive experiments on two publicly available 4D radar datasets demonstrate state-of-the-art reliability and localization accuracy of EFEAR-4D under various conditions. Furthermore, we have collected a dataset following the same route but varying installation heights of the 4D radar, emphasizing the significant impact of radar height on point cloud quality - a crucial consideration for real-world deployments. Our algorithm and dataset will be available soon at https://***/CLASS-Lab/EFEAR-4D. © 2024, CC BY-NC-SA.

关键词： Millimeter waves

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