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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Yanjie Duan Yisheng Lv Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences

Traffic flow prediction is very important in the deployment of intelligent transportation system. Based on our previous research on deep learning approach for traffic data prediction, we further evaluates the performance of the SAE model for traffic flow prediction at daytime and *** 250 experimental tasks training a SAE model and evaluating its performance at daytime and nighttime with 3different criteria, we obtain the best combination of hyper parameters for each criterion at different times on weekday and non-weekday, respectively. Experimental results show that the MAE and RMSE at daytime are larger than that at nighttime,while the MRE at daytime are smaller than that at *** different criteria, the hyper parameters of the SAE model should vary accordingly. The results in this paper indicate that in real applications, traffic flow prediction using the deep learning approach can be a combination of multiple SAE models with different parameters suitable for different periods, which is of significance in future research.

关键词： SAE MAE Performance Evaluation of the Deep Learning Approach for Traffic Flow Prediction at Different Times

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Competitive perspective identification via topic based refinement for online documents

Competitive perspective identification via topic based refin...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Junjie Lin Wenji Mao Daniel Zeng State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

ISBN: (纸本)9781509038664

People write online documents from different personal perspectives. The competitive perspectives they hold reflect the conflicts in their fundamental stances and viewpoints. For many security-related applications, it is both beneficial and critical to identify the competitive perspectives implied in online documents. Previous work on competitive perspective identification is based on word features, which did not consider that the word usage for perspective expression varies with topics in documents. Thus topic information can be incorporated and contribute to a more fine-grained treatment of perspective identification. Motivated by this, this paper proposes an approach for competitive perspective identification in online documents via topic based refinement. Our approach refines the basic word feature-based perspective identification model with latent semantic information. In addition, we develop a self-adaptive process to fit the model parameters automatically. Experimental study shows the effectiveness of our approach compared to the related work and the baseline methods.

关键词： Mathematical model Training Support vector machines Semantics Fitting Optimization Testing

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Way-point tracking control for a biomimetic underwater vehicle based on backstepping

Way-point tracking control for a biomimetic underwater vehic...

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Chinese control Conference (CCC)

作者： Rui Wang Shuo Wang Yu Wang Qingping Wei State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing P. R. China

In this paper, a new approach of way-point tracking control is investigated for a biomimetic underwater vehicle (BUV) propelled by undulatory fins. First, the system design and the model of the BUV are presented. Compared with traditional autonomous underwater vehicles (AUVs) propelled by screw propellers, the concept design enhances the maneuverability and stability of AUVs at low speed. Then the way-point tracking problem of the BUV is formulated. Moreover, a way-point tracking control scheme combining a guidance system of low complexity with backstepping technique is proposed to achieve fast and effective convergence to the desired path composed of a series of given way-points for this BUV. In the end, simulations demonstrate the performance of the proposed way-point tracking controller.

关键词： Target tracking Backstepping Force control systems Underwater vehicles Propulsion

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Labelling Topics in Weibo Using Word Embedding and Graph-Based Method

Labelling Topics in Weibo Using Word Embedding and Graph-Bas...

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International Conference on Information systems Engineering (ICISE)

作者： Zhipeng Jin Qiudan Li Can Wang Daniel D. Zeng Lei Wang The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Department of Management Information Systems University of Arizona Tucson Arizona USA

Nowadays, in China, Weibo is becoming an increasingly popular way for people to know what is happening in the world. Labelling topics is of much importance for better understanding the semantics of topics. Existing works mainly focus on deriving candidate labels by exploring the use of external knowledge, which may be more appropriate for well formatted and static documents. Recently, it has been a new trend to generate labels for sparse and dynamic microblogging environment using summarization method. The challenges of labelling topics are how to obtain coherent candidate labels and how to rank the labels. In this paper, based on the latest research work in deep learning, we propose a novel and unified model for labelling topics in Weibo, which firstly adopts word embedding and clustering method to learn dense semantic representation of topic words and mine the coherent candidate topic labels, then, generates interpretable labels using a graph-based model. Experimental results show that topics labels discovered by our model not only have high topic coherence, but also are meaningful and interpretable.

关键词： Labeling Semantics Coherence Machine learning Xenon Modeling Heuristic algorithms

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A General Adaptive Dynamic Programming Approach with Experience Replay

A General Adaptive Dynamic Programming Approach with Experie...

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International Joint Conference on Neural Networks

作者： Bin Wang Dongbin Zhao Jin Cheng Yuan Xu Yueyang Li School of Electrical Engineering University of Jinan The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781509006212

Experience replay is a promising approach to improve the learning efficiency of adaptive dynamic programming. A general model-free adaptive dynamic programming (ADP) approach with the experience replay technology is investigated in this paper to solve the optimal control problems in continuous state and action spaces. Both the critic network and action network are modeled with a feedforward neural network with one hidden layer. During the learning process, a number of recently observed data samples are recorded in a database. When updating the parameters of the neural networks, the data in the sample database are repeatedly used to update the weights of the action network and the critic network. Implementation details of the algorithm are given, and simulation experiments are utilized to verify the learning efficiency of the proposed approach.

关键词： Erbium Dynamic programming Neural networks Databases Optimal control Heuristic algorithms Approximation algorithms

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A fast and compact saliency score regression network based on fully convolutional network

arXiv

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arXiv 2017年

作者： Xi, Xuanyang Luo, Yongkang Li, Fengfu Wang, Peng Qiao, Hong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China LSEC Institute of Applied Mathematics AMSS Chinese Academy of Sciences Beijing100190 China

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified end-to-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time). Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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A novel policy iteration based deterministic Q-learning for discrete-time nonlinear systems

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Science China(Information Sciences) 2015年第12期58卷 147-161页

作者： WEI QingLai LIU DeRong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming Q learning policy iteration neural networks nonlinear systems optimal control

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Performance evaluation of the deep learning approach for traffic flow prediction at different times

Performance evaluation of the deep learning approach for tra...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yanjie Duan Yisheng Lv Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao Shandong China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509029280

Traffic flow prediction is very important in the deployment of intelligent transportation system. Based on our previous research on deep learning approach for traffic data prediction, we further evaluates the performance of the SAE model for traffic flow prediction at daytime and nighttime. Through 250 experimental tasks training a SAE model and evaluating its performance at daytime and nighttime with 3 different criteria, we obtain the best combination of hyper parameters for each criterion at different times on weekday and non-weekday, respectively. Experimental results show that the MAE and RMSE at daytime are larger than that at nighttime, while the MRE at daytime are smaller than that at nighttime. For different criteria, the hyper parameters of the SAE model should vary accordingly. The results in this paper indicate that in real applications, traffic flow prediction using the deep learning approach can be a combination of multiple SAE models with different parameters suitable for different periods, which is of significance in future research.

关键词： Predictive models Machine learning Training Data models Roads Mathematical model Prediction algorithms

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A Process Simulation-Based Quantitative HAZOP Analysis Method

A Process Simulation-Based Quantitative HAZOP Analysis Metho...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Jianlong Chen Xiwei Liu Sifeng Jing Gang Xiong Xiuqin Shang Changjian Cheng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Chemical Engineering and the Environment Beijing Institute of Technology Institute of Smart Education Systems Qingdao Academy of Intelligent Industries

Hazard and operability(HAZOP) methodology is an important Process Hazard Analysis(PHA) technique for ensuring safety production in chemical industry. Due to the lacks of accuracy and reliability in manual HAZOP analysis, the quantitative HAZOP analysis has gained more and more attention. In this work, a process simulation-based HAZOP analysis method and its flow chart are proposed. And the proposed method is applied to a steam generation subsystem in an ethylene pyrolysis unit on a dynamic process simulation platform. All deviation parameters of system can be calculated and described quantitatively. The case study shows the proposed method is useful for the effective risk prevention and control measures in a chemical process.

关键词： Quantitative HAZOP process simulation Process Hazard Analysis

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Improvement of reliable H control for discrete-time fuzzy systems with time-varying delays

Improvement of reliable H control for discrete-time fuzzy sy...

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2016 IEEE International Conference on Fuzzy systems, FUZZ-IEEE 2016

作者： Wang, Shenquan Jiang, Yulian Li, Yuanchun Liu, Derong College of Electrical and Electronic Engineering Changchun University of Technology Changchun130012 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781509006250

This paper is concerned with the reliable H control problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delays and stochastic actuator faults based on a novel summation inequality. A discrete-time homogeneous Markov chain is used to represent the stochastic behavior of actuator faults. By employing a fuzzy basis-dependent Lyapunov functional and the novel summation inequality, an improved sufficient condition is established to ensure that the resultant closedloop system is stochastically stable with an H performance index. Meanwhile, the solvability condition for the reliable H control is also established, by which the reliable H fuzzy controller can be solved from linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the present approach. © 2016 IEEE.

关键词： Linear matrix inequalities

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