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International Conference on Information Science and control Engineering (ICISCE)

作者： Ehtisham Lodhi Fenghua Zhu Zeeshan Lodhi Qasim Saleem Gang Xiong FeiYue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

In this modern era, technology has advanced to such an extent to make our daily life more proficient and convenient. This paper presents the design and implementation of vending machine (VM) for students, which operates on radio frequency identification technology (RFID). Hence, it turns the VM into smart shopping booth (SSB). The SSB gives the access only through RFID card and vend different products according to the desire of student. The SSB is equipped with RFID reader and student identity card that holds the RFID tag. When the student identity card is scanned for verification and then after selecting the required products, will be dispensed off if a student has available balance in his/her identity card and SMS will be sent to students about remaining balance in their cards by GSM technology. The programming for controller is done by using proton IDE language. The SSB is the customized idea of vending machine and it attempts to resolve various issues that appeared in coin/note based VM. The student identity card RFID based technology is a breakthrough towards a paperless and cashless environment. Moreover, it is very beneficial in saving the precious time of students due to fast paced life and also user friendly. RFID based SSB also improved the communication and sensing capabilities of VM. Hence, our suggested approach outperforms in all aspects.

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Relation Model of Burden Operation and State Variables of Blast Furnace Based on Low Frequency Feature Extraction*

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IFAC-PapersOnLine 2017年第1期50卷 13796-13801页

作者： Zhang, Kexin Wu, Min An, Jianqi Cao, Weihua Liu, Zhentao Ning, Fulong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

In the blast furnace ironmaking process, the running state of the blast furnace can be directly adjusted by the burden operation. Therefore, the explicit relation between the burden operation and the running state variables is vital for the blast furnace operators. However, the state variables of the blast furnace are affected by many factors and the burden matrix is complicated. In this paper, a support vector regression(SVR) predicting model based on low frequency feature extraction is proposed to solve these problems. First, the low frequency components of state variables time series which are mostly affected by the burden operation are extracted. Then, the dimensions of the burden matrix are reduced by a devised simple expression method. Finally, the relation model based on SVR is proposed to predict the values of the expected state variables according to the simplified burden matrix and the value of initial state variables. The simulation on real factory data indicates that the model reflects the accurate quantitative relation between the burden operation and the state variables. © 2017

关键词： Blast furnaces Blast furnace practice Extraction Feature extraction Support vector regression Blast furnace iron making Blast furnace operators burden operation Initial state variables Low frequency components Relation models State variables Support vector regression (SVR)

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Coalition formation based on a task-oriented collaborative ability vector

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 139-148页

作者： Hao FANG Shao-lei LU Jie CHEN Wen-jie CHEN School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Coalition formation is an important coordination problem in multi-agent systems, and a proper description of collaborative abilities for agents is the basic and key precondition in handling this problem. In this paper, a model of task-oriented collaborative abilities is established, where five task-oriented abilities are extracted to form a collaborative ability vector. A task demand vector is also described. In addition, a method of coalition formation with stochastic mechanism is proposed to reduce excessive competitions. An artificial intelligent algorithm is proposed to compensate for the difference between the expected and actual task requirements, which could improve the cognitive capabilities of agents for human commands. Simulations show the effectiveness of the proposed model and the distributed artificial intelligent algorithm.

关键词： Collaborative vector Task allocation Multi-agent system Coalition formation Artificial intelligence

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Event-Trigger Extended Kalman Filter Design for Distributed Generation Network with Limited Communication 36

Event-Trigger Extended Kalman Filter Design for Distributed ...

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第36届中国控制会议

作者： Xing Xin Luyu Li Sen Li Zhen Li Jian Li Zhen Chen Xiangdong Liu Beijing Institute of Electronic System Engineering School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology

ISBN: (纸本)9781538629185

For distributed generation(DG) network, it is important to estimate the real-time states. The information-centric networking(ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm, which reduces the communication burden in ICN, is to drop the observation of each smart grid in a random way. However, the accuracy of this algorithm recession decays rapidly with the increasing drop rate. To guarantee an appropriate estimation accuracy when the drop rate increases, this paper introduces the event-trigger strategy into the estimation algorithm. An event-trigger extended kalman filter(ET-EKF) is established in this paper to adapt the nonlinearity of DG system. ET-EKF reduces the communication burden and achieves an appropriate estimation accuracy at the same time. Finally, its feasibility and performance are demonstrated using the standard IEEE 39-bus system with phasor measurement units(PMUs).

关键词： Event-trigger extended Kalman filter(ET-EKF) real-time estimation distributed generation network smart grids phasor measurement unit(PMU)

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A NSGA-II based Peak Load Shifting Optimization Method for Customers with Distributed Generators Considering Time-of-use Price

A NSGA-II based Peak Load Shifting Optimization Method for C...

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Chinese control Conference

作者： Hao Zhou Min Ding Bo Hu Qingyi Wang Zhili Tan Danyun Li Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan P. R. China Global Energy Interconnection Development and Cooperation Organization Beijing P. R. China

With the extensive use of distributed generation, the traditional demand response analysis cannot meet the current requirements. This paper proposes a NSGA-II based peak load shifting optimization method for customers with distributed generators considering time-of-use price. Firstly, a fuzzy classification method divides the daily power into three time segments, peak hours, flat hours and valley hours. Secondly, on the basis of the time-of-use price, a peak load shifting optimization model is built with constraints and the objectives of minimizing the peak load, maximizing the valley load, and minimizing the peak-valley difference. Then, a NSGA-II based optimization method solves the optimal model and obtains the optimal electricity prices of different time segments to shift the peak load and the valley load. Finally, the simulation results shows the effectiveness of the proposed method.

关键词： Load management Load modeling Real-time systems Mathematical model Distributed power generation Optimization Heuristic algorithms

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Stochastic stabilization of linear systems under delayed and noisy feedback control 36

Stochastic stabilization of linear systems under delayed and...

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第36届中国控制会议

作者： ZONG Xiaofeng LI Tao ZHANG Ji-Feng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice Department of MathematicsEast China Normal University Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper is to study the stabilizability and stabilization issues of linear dynamical systems based on the delayed and noisy feedback control. For the general linear systems, the necessary conditions and sufficient conditions for mean square and almost sure stabilizability are deduced and the corresponding feedback controls are designed according to the generalized algebraic Riccati equation. It is revealed that the stabilizability is up to the system parameters（delays, noises, and unstable eigenvalues of the original system） and unstable systems can be stabilized in mean square if the feedback control is disturbed by noises with small intensities. It is showed that second-order integrator systems must be stabilizable for any given noise intensity and delay.

关键词： Stabilization Feedback control Noise Delay

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An algorithm for Quadruped Robot’s Inverse Kinematic Problems Based on PSO

An algorithm for Quadruped Robot’s Inverse Kinematic Proble...

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第37届中国控制会议

作者： Wei Shen Min Li Junzheng Wang Changkun Chai Wen Zheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Institute of Software Chinese Academy of Sciences Siemens China LTD

The inverse problem is an essential problem in robot *** order to deal with the inverse kinematic problems of hydraulic quadruped robot,the motion process of a single leg is modeled,simulated and analyzed.A new method based on particle swarm optimization（PSO） is proposed in this paper to overcome low computational efficiency,long calculation time and accumulative *** proposed algorithm is attributed to:1) limit the range of particles initialization;2) select suitable compressibility factors;3) employ linear interpolation method to improve the efficiency of the inverse *** simulation results demonstrate that the developed method can provide enhanced computational efficiency and significantly reduce the calculation time while ensuring the precision requirement,and can also eliminate the accumulative error.

关键词： Inverse kinematics Motion optimization Particle swarm optimization Quadruped robot

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State Estimation and Fault Detection for Nonlinear Dynamic systems

State Estimation and Fault Detection for Nonlinear Dynamic S...

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第37届中国控制会议

作者： Baohua Lu Liping Yan Hongxue Chen Yuanqing Xia Mengyin Fu Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

In practical problems, the dynamic systems are usually nonlinear. In this case, the traditional Kalman filter cannot be used for state estimation or fault detection. The two typical extension based on Kalman filtering framework is the Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF). Theoretically speaking, UKF is better than EKF when estimation accuracy is concerned, especially for high degree nonlinear cases. This paper is concerned with the state estimation and fault detection problem for a class of nonlinear dynamic systems. A novel fault detection and analyse method is presented based on the period residual of EKF and UKF. For different kind of faults, mainly, the system parameter error, the sensor/data error, EKF and UKF are used, and the estimation and fault detection effects are compared and analyzed.

关键词： nonlinear system state estimation fault detection parameter error sensor error

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Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine

Convolutional Neural Network Features Comparison Between Bac...

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Chinese control Conference

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

Recently deep learning based architectures have been widely deployed in many problems of artificial intelligence. Among deep learning models, Convolutional Neural Networks (CNN) have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation (BP) algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine (ELM) to train any convolutional neural network has been proposed. The developed framework (ELM-CNN) is based on the concept of auto-encoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method (BP-CNN) has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.

关键词： Training Convolutional neural networks Feature extraction Computer architecture Convolution Machine learning Encoding

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A smooth double proximal primal-dual algorithm for a class of distributed nonsmooth optimization problem

arXiv

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arXiv 2018年

作者： Wei, Yue Fang, Hao Zeng, Xianlin Chen, Jie Pardalos, Panos M. School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China Industrial and Systems Engineering University of Florida

This technical note studies a class of distributed nonsmooth convex consensus optimization problem. The cost function is a summation of local cost functions which are convex but nonsmooth. Each of the local cost functions consists of a twice differentiable (smooth) convex function and two lower semi-continuous (nonsmooth) convex functions. We call this problem as single-smooth plus double-nonsmooth (SSDN) problem. Under mild conditions, we propose a distributed double proximal primal-dual optimization algorithm. The double proximal operator is designed to deal with the difficulty caused by the unproximable property of the summation of those two nonsmooth functions. Besides, it can also guarantee that the proposed algorithm is locally Lipschitz continuous. An auxiliary variable in the double proximal operator is introduced to estimate the subgradient of the second nonsmooth function. Theoretically, we conduct the convergence analysis by employing Lyapunov stability theory. It shows that the proposed algorithm can make the states achieve consensus at the optimal point. In the end, nontrivial simulations are presented and the results demonstrate the effectiveness of the proposed algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost functions

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