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arXiv 2022年

作者： Li, Linlin Ding, Steven X. Liang, Ketian Chen, Zhiwen Xue, Ting School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg47057 Germany Key Laboratory of Energy Saving Control and Safety Monitoring for Rail Transportation of Hunan Provincial School of Automation Central South University Changsha410083 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard machine learning method and widely applied for fault (anomaly) detection and classification. In the context of representation learning, the so-called latent (hidden) variable plays an important role towards an optimal fault detection. In ideal case, the latent variable should be a minimal sufficient statistic. The existing autoencoder-based fault detection schemes are mainly application-oriented, and few efforts have been devoted to optimal autoencoder-based fault detection and explainable applications. The main objective of our work is to establish a framework for learning autoencoder-based optimal fault detection in nonlinear dynamic systems. To this aim, a process model form for dynamic systems is firstly introduced with the aid of control theory, which also leads to a clear system interpretation of the latent variable. The major efforts are made on the development of a control theoretic solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced and proven for dynamic systems and fault detection specifications. In particular, the existence conditions for such a latent variable are derived, based on which a loss function and further a learning algorithm are developed. This learning algorithm enables optimally training of autoencoders to achieve an optimal fault detection in nonlinear dynamic systems. A case study on three-tank system is given at the end of this paper to illustrate the capability of the proposed autoencoder-based fault detection and to explain the essential role of the latent variable in the proposed fault detection system. Copyright © 2022, The Authors. All rights reserved.

关键词： Fault detection

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Planning a haemodialysis process by minimum time control of hybrid systems with sliding motion

Planning a haemodialysis process by minimum time control of ...

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IEEE Conference on Decision and control

作者： Radosław Pytlak Damian Suski Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781665436601

The aim of the paper is to provide a computational tool for planning a haemodialysis process. It is shown that optimization methods can be used to obtain the most effective treatment focused on removing both urea and phosphorus during the process. Our approach to the planning process is based on a model which takes into account a rebound phenomenon and that results in a hybrid model in which sliding motion is likely to occur. For such a model we construct an optimization problem and a computational method for solving it. The presented approach to optimal control problems with hybrid systems is different from the others in several aspects. First of all, it is assumed that a hybrid system can exhibit sliding modes. Secondly, the system’s motion on the switching surface is described by index 2 differential–algebraic equations and that guarantees accurate tracking of the sliding motion surface. Thirdly, the gradients of the problem’s functionals are evaluated with the help of adjoint equations. The adjoint equations presented in the paper take into account sliding motion and exhibit jump conditions at transition times. The stated adjoint equations are used in a globally convergent algorithm which generates a sequence of controls whose accumulation points satisfy the weak maximum principle for optimal control problems with hybrid systems. The paper presents numerical results of solving haemodialysis planning problem.

关键词： Tracking Sensitivity analysis Computational modeling Process control Optimal control Optimization methods Switches

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Unsupervised Monocular Depth Estimation with Attention Based Inception Pipe and Overlap Regularized Loss 21

Unsupervised Monocular Depth Estimation with Attention Based...

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Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Xiaoyuan Jiang Xihai Chen Zhao Zhang NARI Technology Co. LtdNARI-TECH Nanjing Control Systems Ltd NARI Group Corporation / State Grid Electric Power Research Institute China

ISBN: (纸本)9781450385893

Unsupervised learning methods that treat depth estimation as image reconstruction has achieved promising results recently. Most existing methods generate disparity maps from multi-layers residual network by training image reconstruction loss for left right view image pairs. Even the smooth regularized loss that allows for the depth discontinuities at object contours has been introduced, depth maps generated by these methods still suffer from border blur or dilation at image gradients. To overcome this problem, we improve the unsupervised depth estimation network by attaching the attention based inception pipe and overlap regularized loss. While using residual layer to generate the disparity maps, we also enforce the global inception pipe to auto-adjust depth changes according to the spatial attention values. In addition, we use the overlap regularized weights in final loss computation to reduce the reconstruction errors at non-overlapping area for the aligned image pairs. Our methods can present more depth details at object contours and boundaries and produce superior performance for depth estimation on KITTI dataset compared to the typical methods.

关键词： Depth estimation Overlap regularized loss Unsupervised Attention based inception pipe

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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Spatial distribution and risk assessments of mercury in topsoils of Central Asia

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Geoscience Frontiers 2023年第4期14卷 270-279页

作者： Zhengzheng Yang Junming Guo Shiwei Sun Dingming Ni Pengfei Chen Dipesh Rupakheti Huhu Kang Sabur F Abdullaev Salamat Abdyzhapar uulu Shichang Kang Key Laboratory of Western China's Environmental Systems(Ministry of Education) College of Earth and Environmental SciencesLanzhou UniversityLanzhou 730000China State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China S.U.Umarov Physical Technical Institute of the National Academy of Sciences of Tajikistan Dushanbe 734063Tajikistan Research Center for Ecology and Environment of Central Asia(Bishkek) 720001Kyrgyzstan Geography Department Geology InstituteNational Academy of Sciences720001Kyrgyzstan

Central Asia is one of the largest arid areas on earth,yet little is known about the concentration levels and risks of mercury(Hg)in the soils of this *** this study,extensive sampling of topsoils(0-10 cm)from representative landscapes was carried out over Central Asia(i.e.,Tajikistan,Uzbekistan,and Kyrgyzstan).The total mercury(THg)concentrations in topsoils varied widely from 1.6 to 908.0 ng/g,with high values observed in samples collected in the capital cities and urban *** THg concentrations among different landscapes showed a decreasing order of urban(79.8±184.0 ng/g)>woodland(27.3±28.9 ng/g)>grassland(20.6±15.9 ng/g)>farmland(18.3±9.5 ng/g)>desert(12.3±8.0 ng/g).High THg concentrations were found in the capital cities/urban clusters,followed by a gradual decrease towards the *** concentrations were found to be negatively correlated with the distance from the sampling sites to their nearest cities,indicating that anthropogenic emissions significantly influenced the spatial distribution of topsoil Hg.A significant correlation between THg concentrations and topsoil total organic carbon(TOC)contents was also observed,suggesting that TOC played an essential role in the spatial distribution of topsoil *** assessments of pollution and potential ecological risk suggested that topsoils in highly densely-populated areas were contaminated by Hg and had higher degrees of potential ecological *** health risk assessment results showed that the exposure risk of topsoil Hg to children was higher than that to ***,there was no unacceptable human health risk of topsoil *** study clarified the spatial distribution and risks of Hg in the Central Asian topsoils,offering new insight into the risk prevention and control of soil Hg.

关键词： Mercury Topsoil Landscape Spatial distribution Risk assessment Central Asia

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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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IoT Based Modular Grow Box System Using the AR

IoT Based Modular Grow Box System Using the AR

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2020 IEEE International Conference on Problems of Infocommunications Science and technology, PIC S and T 2020

作者： Turchenko, Viktor Kit, Ivan Osolinskyi, Oleksandr Zahorodnia, Diana Bykovyy, Pavlo Sachenko, Anatoliy Ternopil National Economic University Dept. for Information Computer Systems and Control Ternopil Ukraine Research Institute for Intelligent Computer Systems Ternopil National Economic University Ternopil Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics Radom Poland

ISBN: (纸本)9781728191775

nowadays, living space, food and water for the rapidly growing urban population are not used quite sparingly, which makes them extremely vulnerable resources. Therefore, the trend has emerged where people in urban areas choose to have a small garden (for example, a balcony garden), and want to grow healthy and environmentally friendly food. To achieve this, a compact plant growing system 'Grow Box' is needed, which gives an optimal use of space and can provide an automated plant growing process. The authors proposed the concept of IoT based smart system using the augmented reality for growing plants in the city. System implementation has a modular structure and can act as an experimental site or a full-fledged tool with some changes for greenhouses. © 2020 IEEE.

关键词： Augmented reality

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Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear Positive Semi-Markov Jump systems with Time-varying Delays

Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear P...

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Chinese control Conference (CCC)

作者： Chao Ma Hang Fu Yidao Ji Wei Wu School of Mechanical Engineering University of Science and Technology Beijing China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper, the $\mathcal{L}_{1}$ -gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design approach. More precisely, the mode-dependent filters are designed in terms of practical observed modes instead of true system modes, such that less conservatism can be achieved. In addition, the effect of time-varying delays is taken into account for more robustness and applicability. By selecting suitable stochastic Lyapunov-Krasovskii functions and applying the linear programming method, sufficient conditions are established to fulfill the desired $\mathcal{L}_{1}$ -gain performance. Eventually, the illustrative simulation is performed to verify the effectiveness of our developed control scheme.

关键词： Maximum likelihood detection Sufficient conditions Filtering Hidden Markov models Stochastic processes Nonlinear filters Linear programming

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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2022 Chinese Automation Congress, CAC 2022

作者： Hong, Weixin Wu, Huaiyu Shen, Zhen Xiong, Gang Hu, Bin Dong, Xisong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781665465335

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available. © 2022 IEEE.

关键词： Reinforcement learning

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Event-Triggered Tracking control of Networked Multi-Agent systems

arXiv

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arXiv 2022年

作者： Ren, Wei Dimarogonas, Dimos V. ICTEAM Institute Université Catholique de Louvain Louvain-la-Neuve 1348 Belgium Division of Decision and Control Systems EECS KTH Royal Institute of Technology StockholmSE-10044 Sweden

This paper studies the tracking control problem of networked multi-agent systems under both multiple networks and event-triggered mechanisms. Multiple networks are to connect multiple agents and reference systems with decentralized controllers to guarantee their information transmission, whereas the event-triggered mechanisms are to reduce the information transmission via the networks. In this paper, each agent has a network to communicate with its controller and reference system, and all networks are independent and asynchronous and have local event-triggered mechanisms, which are based on local measurements and determine whether the local measurements need to be transmitted via the corresponding network. To address this scenario, we first implement the emulation-based approach to develop a novel hybrid model for the tracking control of networked multi-agent systems. Next, sufficient conditions are derived and decentralized event-triggered mechanisms are designed to guarantee the desired tracking performance. Furthermore, the proposed approach is applied to derive novel results for the event-triggered observer design problem of networked multi-agent systems. Finally, two numerical examples are presented to illustrate the validity of the developed results. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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