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Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM Vehicular Networks

Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM ...

17th International IEEE Conference on intelligent Transportation systems & The Asia-Pacific Council on systems Engineering Conference 2014

作者： Shuangshuang Han Liuqing Yang Xiang Cheng Shichao Chen Fenghua Zhu Zhongdong Yu Haitao Xie Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronics Engineering and Computer Science Peking University Guang Zhou Communication Information Construction Investment and Operation Co.Ltd.

Multiple-Input Multiple-Output- Orthogonal Frequency Division Multiplexing(MIMO-OFDM) is adopted to vehicular networks to increase the capacity,reliability and *** this paper,iterative demodulation and decoding algorithms are studied to approach the capacity of MIMOOFDM vehicular *** analysing the drawbacks of the Gaussian approximation on the interference cancellation,NonGaussian approximation is proposed to enhance the performance of interference cancellation based detectors with large *** results demonstrate that the proposed non-Gaussian algorithm can achieve a significant performance gain over existing ones with high order constellations.

关键词： Gaussian Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM Vehicular Networks

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Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2013年第2期14卷 984-993页

作者： Li, Ye Li, Bo Tian, Bin Yao, Qingming Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China

In urban traffic video monitoring systems, traffic congestion is a common scene that causes vehicle occlusion and is a challenge for current vehicle detection methods. To solve the occlusion problem in congested traffic conditions, we have proposed an effective vehicle detection approach based on an AND-OR graph (AOG) in this paper. Our method includes three steps: constructing an AOG for representing vehicle objects in the congested traffic condition;training parameters in the AOG;and, finally, detecting vehicles using bottom-up inference. In AOG construction, sophisticated vehicle feature selection avoids using the easily occluded vehicle components but takes highly visible components into account. The vehicles are well represented by these selected vehicle features in the presence of a congested condition with serious vehicle occlusion. Furthermore, a hierarchical decomposition of the vehicle representation is proposed during AOG construction to further reduce the impact of vehicle occlusion. After AOG construction, all parameters in the AOG are manually learned from the training images or set and further applied to the bottom-up vehicle inference. There are two innovations of our method, i.e., the usage of the AOG in vehicle detection under congested traffic conditions and the special vehicle feature selection for vehicle representation. To fully test our method, we have done a quantitative experiment under a variety of traffic conditions, a contrast experiment, and several experiments on congested conditions. The experimental results illustrate that our method can effectively deal with various vehicle poses, vehicle shapes, and time-of-day and weather conditions. In particular, our approach performs well in congested traffic conditions with serious vehicle occlusion.

关键词： Active basis model (ABM) AND-OR graph (AOG) bottom-up inference maximally stable extremal region (MSER) vehicle detection

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Electrified Vehicles and the Smart Grid: The ITS Perspective

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2014年第4期15卷 1388-1404页

作者： Cheng, Xiang Hu, Xiaoya Yang, Liuqing Husain, Iqbal Inoue, Koichi Krein, Philip Lefevre, Russell Li, Yaoyu Nishi, Hiroaki Taiber, Joachim G. Wang, Fei-Yue Zha, Yabing Gao, Wen Li, Zhengxi Peking Univ Beijing 100871 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Sch Automat Wuhan 430074 Peoples R China Colorado State Univ Ft Collins CO 80523 USA N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Keio Univ Keio Adv Res Ctr Yokohama Kanagawa 2238522 Japan Keio Univ Dept Syst Design Engn Yokohama Kanagawa 2238522 Japan Univ Illinois Grainger Ctr Elect Machinery & Elect Dept Elect & Urbana IL 61801 USA Univ N Dakota Grand Forks ND 58202 USA Univ Texas Dallas Dept Mech Engn Richardson TX 75083 USA Clemson Univ Clemson SC 29634 USA Chinese Acad Sci Key Lab Complex Syst & Intelligence Sci Beijing 100864 Peoples R China Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Hunan Peoples R China Peking Univ Beijing 100871 Peoples R China North China Univ Technol Beijing 100144 Peoples R China

Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles;2) infrastructure;3) travelers;4) systems, operations, and scenarios;5) communications;and 6) social, economic, and political.

关键词： Energy efficiency intelligent transportation systems smart grid

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HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer

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IEEE SENSORS JOURNAL 2013年第5期13卷 1849-1856页

作者： Tong, Lina Song, Quanjun Ge, Yunjian Liu, Ming Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Hefei 230031 Peoples R China

Falls in the elderly have always been a serious medical and social problem. To detect and predict falls, a hidden Markov model (HMM)-based method using tri-axial accelerations of human body is proposed. A wearable motion detection device using tri-axial accelerometer is designed and realized, which can detect and predict falls based on tri-axial acceleration of human upper trunk. The acceleration time series (ATS) extracted from human motion processes are used to describe human motion features, and the ATS extracted from human fall courses but before the collision are used to train HMM so as to build a random process mathematical model. Thus, the outputs of HMM, which express the marching degrees of input ATS and HMM, can be used to evaluate the risks to fall. The experiment results show that fall events can be predicted 200-400 ms ahead the occurrence of collisions, and distinguished from other daily life activities with an accuracy of 100%.

关键词： Accelerometer acceleration time series (ATS) fall detection fall prediction hidden Markov model (HMM)

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Event-Triggered Reinforcement Learning Approach for Unknown Nonlinear Continuous-Time System

Event-Triggered Reinforcement Learning Approach for Unknown ...

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International Joint Conference on Neural Networks

作者： Xiangnan Zhong Zhen Ni Haibo He Xin Xu Dongbin Zhao Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston USA College of Mechatronics and Automation National University of Defense Technology Changsha China State Key Lab of Management and Control for Complex Systems Chinese Academy of Sciences Beijing P. R. China

This paper provides an adaptive event-triggered method using adaptive dynamic programming (ADP) for the nonlinear continuous-time system. Comparing to the traditional method with fixed sampling period, the event-triggered method samples the state only when an event is triggered and therefore the computational cost is reduced. We demonstrate the theoretical analysis on the stability of the event-triggered method, and integrate it with the ADP approach. The system dynamics are assumed unknown. The corresponding ADP algorithm is given and the neural network techniques are applied to implement this method. The simulation results verify the theoretical analysis and justify the efficiency of the proposed event-triggered technique using the ADP approach.

关键词： Equations Neural networks Stability analysis Performance analysis Approximation algorithms Approximation methods Heuristic algorithms

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一种仿生水下机器人的设计与动力学分析(英文)

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自动化学报 2013年第8期39卷 1330-1338页

作者： WEI Qing-Ping WANG Shuo DONG Xiang SHANG Liu-Ji TAN Min The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Institute of Intelligent Machines Chinese Academy of Sciences

A biomimetic underwater vehicle propelled by two undulating long-fins is introduced in this paper. The concerned vehicle is propelled by two symmetrical undulating long-fins installed on both sides. Ten servo motors are employed to drive the long-fins and cosine wave function is employed for motor control. A real-time control system is designed for controlling the long-fins by adjusting its oscillating frequency and oscillating amplitude. An inertial measurement unit is installed to collect the accelerations and angular velocity. To obtain the relationship between oscillating frequency/amplitude and swimming performance, kinematic analysis and hydromechanic analysis are given. By dividing the long-fin into many small elements and computing the hydrodynamic force acting on each element, the instantaneous thrust generated by the long-fin is obtained. Then the average thrust of the long-fin is obtained by summing up the forces acted on the elements in one undulating period. Then swimming experiments are carried out to validate the vehicle design and kinematic analysis and hydromechanic analysis. And two swimming motion modes including marching and rotating locomotion are chosen. Finally, discussions between the swimming performance and the oscillating parameters are given.

关键词：水下机器人车辆设计动力学分析起伏仿生鳍计算流体动力学运动学分析

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Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

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JOURNAL OF intelligent MANUFACTURING 2013年第6期24卷 1241-1252页

作者： Gu, Nong Cao, Zhiqiang Xie, Liangjun Creighton, Douglas Tan, Min Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Waurn Ponds Vic 3216 Australia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Schlumberger Ltd Houston TX 77073 USA

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

关键词： control charts Concurrent patterns Singular spectrum analysis Learning vector quantization networks Aluminium smelting

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On-road Vehicle Detection Method based on Multi-scale Active Basis Model

On-road Vehicle Detection Method based on Multi-scale Active...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Yao, Yanjie Xiong, Gang Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan Res Inst CASIA Dongguan Peoples R China State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781479903801

This paper introduces a vehicle detection method based on multi-scale active basis model in traffic surveillance systems. Due to the effects of perspective, vehicles which are close to the camera are larger and more detailed than the far ones on individual video frames. Using camera calibration, we get the multi-scale information of vehicles, and then we learn the multi-scale active basis model from the target training sample set by using the shared sketch algorithm. The multi-scale ABM can detect vehicles in various poses, shapes, and sizes in a whole video frame. The experiment results show that this proposed method can fit the changes of vehicle size in images.

关键词： ABM multiscale camera calibration vehicle detection

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A HAZOP Based Model for Safety management Risk Assessment in Petrochemical Plants 11

A HAZOP Based Model for Safety Management Risk Assessment in...

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11th World Congress on intelligent control and Automation

作者： Jing, Sifeng Liu, Xiwei Cheng, Changjian Shang, Xiuqin Xiong, Gang Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan Res Inst CASIA Songshan Lake 523808 Dongguan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781479958252

To identify specific safety management vulnerabilities that lead to deviations and failure of safeguards, a safety management risk assessment model is proposed in this paper. Firstly, accident cases happened in a petrochemical plant from 2000 to 2013 are analyzed. The indirect common causations of accidents are found and chosen as risk analysis factors. Then, the actual process of safety management is investigated, and the main framework of risk assessment is made. Finally, the model is built based on parallel management theory, accident causation theory and classic HAZOP technical. And targeted measures of such vulnerabilities is made. Compared with classic HAZOP technical, the model has advantages in searching for risks from technical and managerial two levels. The method provides a new way for the safety management objectives of changing "accident handling, postmortem prevention" to "intrinsic safety, advance prevention" in petrochemical plant.

关键词： parallel management management risk assessment HAZOP technical accident causation theory petrochemical plant

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Data-based control, optimization, modeling and applications

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期23卷 1839-1842页

作者： Zhao, Dongbin Shen, Yi Wang, Zhanshan Hu, Xiaolin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Minist Educ Image Proc & Intelligent Cont Wuhan 430074 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

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