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2017 2nd World Symposium on Mechanical and control Engineering（WSMCE 2017）

作者： Xiaolan Xie Tianwei Yuan Xiao Zhou College of Information Science and Engineering Guilin University of Technology Guangxi Universities Key Laboratory of Embedded Technology and Intelligent Information Processing Guilin University of Technology College of Mechanical and Control Engineering Guilin University of Technology

Based on the Spark big data analysis platform, using Yarn management resource scheduling problem, using Hbase as the storage mode of distributed data. Through the mass of car data, dig out the key factors that affect the safety of car performance, and show in the form of visualization. An effective scheme is put forward for the maintenance and fault detection of the user's vehicle. The experimental results show that the analysis method based on Spark platform can quickly, effectively and accurately analyze the key information and play a guiding and analytical role for users.

关键词： Vehicle Spark Hbase Platform

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Deep learning for robust outdoor vehicle visual tracking

Deep learning for robust outdoor vehicle visual tracking

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ji-ng Xin Xing Du Jian Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an P.R.China Faculty of Engineering and Information Technology University of Technology (UTS) Sydney Australia

ISBN: (纸本)9781509060689

Robust visual tracking for outdoor vehicle is still a challenging problem due to large appearance variations caused by illumination variation, occlusion and scale variation, etc. In this paper, a deep-learning-based approach for robust outdoor vehicle tracking is proposed. Firstly, a stacked denoising auto-encoder is pre-trained to learn the feature representation way of images. Then, a k-sparse constraint is added to the stacked denoising auto-encoder and the encoder of k-sparse stacked denoising auto-encoder (kSSDAE) is connected with a classification layer to construct a classification neural network. After fine-tuning, the classification neural network is applied to online tracking under particle filter framework. Extensive tracking experiments are conducted on a challenging single object online tracking evaluation platform benchmark to verify the effectiveness of our tracker. Experiments show that our tracker outperforms most state-of-the-art trackers.

关键词： Robustness Object tracking Machine learning Feature extraction Biological neural networks Visualization

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RESEARCH ON CLOUD PLATFORM OF CASTINGS QUALITY MANAGEMENT BASED ON SPARK

RESEARCH ON CLOUD PLATFORM OF CASTINGS QUALITY MANAGEMENT BA...

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2017 2nd World Symposium on Mechanical and control Engineering（WSMCE 2017）

作者： Xiaolan Xie Xiao Zhou Tianwei Yuan College of Information Science and Engineering Guilin University of Technology Guangxi Universities Key Laboratory of Embedded Technology and Intelligent Information Processing Guilin University of Technology College of Mechanical and Control Engineering Guilin University of Technology

The casting quality management costs too much manpower, material and financial resources and is still lack of scientific status. In view of these conditions, this paper builds the casting quality management cloud platform based on Spark. Build and deploy Spark Standalone on the local computer. Parallel computing and cloud storage mechanism are implemented by Spark. Task management scheduling is implemented by Standalone. The foundry enterprise uses this platform to carry on the big data analysis to the influence factors of casting quality in the casting production. According to the results of the analysis, the scientific guidance and efficient scheduling of the manufacturing process are carried out. The quality management of castings is standardized and scientific. The scrap rate of castings is reduced. Through the large data analysis of various factors affecting the quality of casting, a set of scientific and efficient management scheme is provided.

关键词： Castings quality management Spark Cloud platform Big data

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INTERNET OF VEHICLES DATA SERVICE SYSTEM BASED ON CLOUD PLATFORM

INTERNET OF VEHICLES DATA SERVICE SYSTEM BASED ON CLOUD PLAT...

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2017 2nd World Symposium on Mechanical and control Engineering（WSMCE 2017）

作者： Xiaolan Xie Qiangqing Zheng Zhihong Guo College of Information Science and Engineering Guilin University of Technology Guangxi Universities key Laboratory of Embedded Technology and Intelligent Information Processing (Guilin University of Technology) College of Mechanical and Control Engineering Guilin University of Technology

The technology of Internet of automobile is designed to solve problems in field of transportation about safety, efficiency and environment. The system is based on data supplied by hardware in vehicles, design a mobile client APP and a management system for sellers through cloud computing, mass data distribution technology, which achieve the automobile driving data collection and analysis, fault reminders, track search, vehicle positioning, tips information release and other functions, and also achieve the intelligent analysis of automobile driving data. To some extent, the technology solves the problems such as automobile mileage not clear, hard to locate the automobile location, complicated ownership transfer, unable to self-help troubleshooting, hard to find the nearest repair point and so on.

关键词： Internet of vehicles BDS Automotive hardware Hadoop

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Nonconvex penalties with analytical solutions for one-bit compressive sensing

arXiv

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arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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Association rule analysis for tour route recommendation and application to WCTSNOP

Association rule analysis for tour route recommendation and ...

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ISPRS Geospatial Week 2017

作者： Fang, Hui Chen, Chongcheng Lin, Jiaxiang Liu, Xianfeng Fang, Dong Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Spatial Information Research Centre of Fujian Fuzhou University District Xueyuan Road China Dept. of Computer Science Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou University District Xiyuangong Road China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China Fuzhou Silviscene Information Technology Co. Ltd. China

The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality. © Authors 2017. CC BY 4.0 License.

关键词： Data mining

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Robust Monocular Visual Odometry using Optical Flows for Mobile Robots 35

Robust Monocular Visual Odometry using Optical Flows for Mob...

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第35届中国控制会议

作者： LI Haifeng HU Zunhe CHEN Xinwei College of Computer Science and Technology Civil Aviation University of China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Department of Computer Science Minjiang University

ISBN: (纸本)9781509009107

A robust optical flow-based visual odometry method using a single onboard camera is proposed in this *** improve the quality of the noisy optical flows,a correction method across multiple frames is ***,the optical flows in the plane at infinity are detected and removed as these optical flows have very low signal to noise ratio for robot translation ***,a RANSAC approach for robot ego-motion estimation is *** experiments are carried out and the results show that the proposed method is able to estimate the camera trajectory robustly with reasonable accuracy.

关键词： visual odometry optical flows mobile robot

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controllability of giant connected components in a directed network

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Physical Review E 2017年第4期95卷 042318-042318页

作者： Xueming Liu Linqiang Pan H. Eugene Stanley Jianxi Gao Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Center for Polymer Studies and Department of Physics Boston University Boston Massachusetts 02215 USA School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou 450002 Henan China Center for Complex Network Research and Department of Physics Northeastern University Boston Massachusetts 02115 USA

When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p=pm. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by pm〈k〉. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.

关键词： Network structure

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Fuzzy control for Uncertain Vehicle Active Suspension Systems via Dynamic Sliding-Mode Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2017年第1期47卷 24-32页

作者： Wen, Shiping Chen, Michael Z. Q. Zeng, Zhigang Yu, Xinghuo Huang, Tingwen School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong Platform Technologies Research Institute RMIT University MelbourneVIC3001 Australia Texas A and M University at Qatar Doha23874 Qatar

This paper investigates the fuzzy control issue for uncertain active suspension systems via dynamic sliding-mode method. The Takagi-Sugeno fuzzy approach is adopted on the background of the varying masses to describe the prescribed nonlinear system in order to achieve the design targets via the method of sector nonlinearity. This paper employs the dynamic sliding-mode scheme to control nonlinear active suspension systems. In the proposed sliding-mode control scheme, the sliding surface function is formed linearly with the system states and control inputs. Then, a fuzzy dynamic term is utilized to construct the sliding-mode feedback controller. In existing results, the sliding mode is achieved and maintained with no consideration of the system perturbations. Thus, sufficient conditions are proposed to make the sliding surface reachable with the existence of the system perturbations to make the augmented system stable. Finally, simulation results are presented to verify the effectiveness of the proposed schemes. © 2013 IEEE.

关键词： Robust control

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Transitioning conditional probability to discriminative classifier over inductive reasoning

Transitioning conditional probability to discriminative clas...

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International Conference on Awareness Science and Technology (iCAST)

作者： Peng Mei Fuquan Zhang Lin Xu Hongyong Leng Lei Chen Guo Liu School of Software Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Innovative Information Industry Research Institute Fujian Normal University Fuzhou China

Overcome context-independent probabilities based reasoning, with decomposition of categories and predicates into features as non-stable predicates. With distinction between generative classifier and discriminative classifier, we purpose to use some discriminative classifiers such as dual-form perceptron and kernelized support vector machine to improve to result of reasoning process. With capability of dual-form perceptron and kernelized support vector machine, finding linear or non-linear decision boundary for similarity-like supporting predicate for reasoning process.

关键词： Cognition Classification algorithms Support vector machines Logistics Kernel Training Training data

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