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第28届中国控制与决策会议

作者： Chengkun Cui Gui-Bin Bian Zeng-Guang Hou Xiao-Liang Xie Liang Peng Dongxu Zhang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781467397155

In this paper,a novel robust nonlinear model is proposed to predict human lower extremity motion based on the multi-channel surface electromyography(sE MG) *** prediction model is established by a data-driven dynamic recurrent neural *** sE MG signals acquired from human lower extremity muscles are used as the inputs of the prediction *** outputs of the model are joint angles of hip,knee and *** from the traditional feedforward network structure,this model has several feedback loops,thus it can take advantage of the output feedback *** validate the effectiveness of the proposed method,five able-bodied people participated in the cycling exercises and relevant data were recorded in real *** performance of the proposed prediction model is compared to those of the feedforward neural network with augmented inputs(FFNNAI) for the motion prediction accuracy and *** results show that the proposed method provides acceptable performance which is clearly better than the FFNNAI-based approach under different experimental schemes.

关键词： sEMG motion prediction dynamic recurrent neural networks human-robot interaction

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Fast Rejecting Mismatches Using Pair-wise Similarity

Fast Rejecting Mismatches Using Pair-wise Similarity

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World Congress on Intelligent control and automation

作者： Deheng Qian Xu Yang Yuren Zhang Hong Qiao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467384155

Point matching problem seeks the optimal correspondences between two sets of points. However, the matching result often includes some mismatches that decrease the matching precision. In this paper, we propose a fast algorithm to reject mismatches using pair-wise similarity. The intuition of our algorithm is that the matches should be similar with their neighboring matches due to local consistency. Our algorithm consists of two steps. In the first step, the algorithm eliminates mismatches at the cost of rejecting some correct matches to obtain a refined matching result with a high precision. In the second step, the algorithm regains the correct matches rejected in the first step to improve the recall of the final matching result. The time complexity of the algorithm is O(n~2), which is asymptotically faster than conventional algorithms that reject mismatches. We demonstrate the effectiveness of the proposed algorithm by multiple experiments over widely used datasets.

关键词： MISMATCH complexity classes algorithms recall Mental Recall

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Travel Time Prediction with LSTM Neural Network

Travel Time Prediction with LSTM Neural Network

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IEEE International Conference on Intelligent Transportation systems

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

ISBN: (纸本)9781509018901

Travel time is one of the key concerns among travelers before starting a trip and also an important indicator of traffic conditions. However, travel time acquisition is time delayed and the pattern of travel time is usually irregular. In this paper, we explore a deep learning model, the LSTM neural network model, for travel time prediction. By employing the travel time data provided by Highways England, we construct 66 series prediction LSTM neural networks for the 66 links in the data set. Through model training and validation, we obtain the optimal structure within the setting range for each link. Then we predict multi-step ahead travel times for each link on the test set. Evaluation results show that the 1-step ahead travel time prediction error is relatively small, the median of mean relative error for the 66 links in the experiments is 7.0% on the test set. Deep learning models considering sequence relation are promising in traffic series data prediction.

关键词： Travel time Travelers relative errors Traffic Model trains Neural network TEST SETS

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Overview of deep learning

Overview of deep learning

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Xuedan Du Yinghao Cai Shuo Wang Leijie Zhang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation CAS Beijing China

ISBN: (纸本)9781509044245

In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make better use of datasets for feature extraction. Because of its practicability, deep learning becomes more and more popular for many researchers to do research works. In this paper, we mainly introduce some advanced neural networks of deep learning and their applications. Besides, we also discuss the limitations and prospects of deep learning.

关键词： Decision support systems Erbium Recurrent neural networks

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Observer-based fault detection for affine nonlinear systems with disturbances and uncertainties 35

Observer-based fault detection for affine nonlinear systems ...

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第35届中国控制会议

作者： HAN Huayun YANG Ying LI Linlin Chen Yu State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University School of Automation and Electrical Engineering University of Science and Technology Beijing the Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Beijing Institute of Astronautics System Engineering

ISBN: (纸本)9781509009107

This paper is concerned with the integrated design schemes of L observer-based fault detection（FD） systems for affine nonlinear processes with disturbances and uncertainties,*** this end,a so called L observer-based FD scheme is studied ***,the integrated design approaches for nonlinear systems with disturbances and uncertainties are addressed,*** the end,examples are given to illustrate the effectiveness of the proposed approaches.

关键词： Robust Fault Detection Observer Design Affine Nonlinear systems

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Design and dynamic analysis for Amoeba-like roboţs turning-mechanism applied with spring and damp system

Design and dynamic analysis for Amoeba-like roboţs turning-...

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2016 International Conference on Advanced Robotics and Mechatronics, ICARM 2016

作者： Dong, Xiang Sun, Henghui Wang, Zengfu Cheng, Long Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781509033645

A new turning-mechanism for Amoeba-like Robot was proposed in this paper, which based on the amoeba-like robot kinematics characteristics of tail contracting and skin flipping. First, one kind of variable-speed node composed of the spring and damp was designed. Because of the existing of the variable-speed nodes, the velocities of the two strip-skins depended not only on the velocities of threads that gone through the variable-speed nodes, but also on the spring and damp it contained. So, although the velocities of threads that induced the velocities of the strip-skins were the same, the velocities of the strip-skins still can be different since the variable-speed nodes existed. That difference in velocities made the turning movement possible. Second, the dynamic model for the turning movement of the Amoeba-like Robot was established and analyzed. The analysis results included the angular velocity and angle curve related to time, which were computed with the dynamic model when shortening speed of threads length was constant. The results achieved in this paper provided an important basic for the research on the behaviors and control strategy of amoeba-like robot. The new turning-mechanism can be popularized to the tracked mobile robot, for it provided a new and available turning method. © 2016 IEEE.

关键词： Velocity

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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 Slope Detection Method Based on 3D LiDAR Suitable for Quadruped Robots

A Slope Detection Method Based on 3D LiDAR Suitable for Quad...

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World Congress on Intelligent control and automation

作者： Xiangrui Meng Zhiqiang Cao Leijie Zhang Shuo Wang Chao Zhou State Key Laboratory of Management and Control for Complex Systems Institute of Automation CAS Beijing 100190 China

ISBN: (纸本)9781467384155

This paper introduces a slope detection method based on point cloud data from 3D LiDAR for quadruped robots in unknown environments. For quadruped robots, they need to adjust their gaits according to different slope angles to avoid some potential dangers. 3D LiDAR is used to gather point cloud data, which is superior to 2D LiDAR in speed and accuracy. In this paper, a slope detection method using bilateral filtering and RANSAC algorithms is discussed. The experiments of slope detection are fulfilled with the consideration of different angles of slopes and different orientations to the slopes, and the results demonstrate that errors of angle estimation is small.

关键词： Laser radar Three-dimensional displays Robot kinematics Robot sensing systems Mobile robots Lidar quadruped robot three-dimensional displays Manipulator sensing systems Robot kinematics Mobile robots bilateral filtering Unknown environments

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Autonomous vehicles testing methods review

Autonomous vehicles testing methods review

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International Conference on Intelligent Transportation

作者： WuLing Huang Kunfeng Wang Yisheng Lv FengHua Zhu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China

ISBN: (纸本)9781509018901

Driving test is critical to the deployment of autonomous vehicles. It is necessary to review the related works since the methodologies summaries are rare, which will help to set up an integrated method for autonomous driving test in different development stages, and help to provide a reliable, quick, safe, low cost and reproducible method and accelerate the development of autonomous vehicle. In this paper, we review the related autonomous driving test works, including autonomous vehicle functional verification, vehicle integrated testing, system validation in different architectures. This review work will be helpful for autonomous vehicle development.

关键词： Testing Autonomous vehicles Sensors Safety Computer architecture Planning

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Analysis and forecasting of urban traffic condition based on categorical data

Analysis and forecasting of urban traffic condition based on...

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

作者： Yuan-yuan Chen Yisheng Lv Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9781509029280

Urban traffic prediction is a critical component in intelligent transportation systems for both citizens and traffic management agencies. It is beneficial to know current and future traffic conditions prior a trip or a route for travelers. And it is also very helpful for proactive traffic management for transportation administrative sectors. In this paper, we apply classification techniques to forecast traffic conditions based on categorical data collected from open web maps. To this end, we first collect traffic condition data from AMAP which is a web map, navigation and location based services provider in China. Then we primarily analyze AMAP data with trend analysis and power spectrum analysis. Finally, we employ random walk, naïve Bayes, decision tree and support vector machine methods to forecast traffic conditions in the future based on historical and current conditions. Experimental results demonstrate that it is feasible to make forecast on traffic conditions with reasonable accuracy.

关键词： Decision support systems Intelligent transportation systems Planning Decision trees Support vector machines Market research

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