检索结果-内蒙古大学图书馆

第一届亚洲模式识别会议

作者： Zhong Zhang Wen Zhou Chunheng Wang Baihua Xiao Shuang Liu State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Background modeling is a fundamental yet challenging issue in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handling complex scenes. In this paper, we propose a multi-scale framework, which combines both texture and intensity feature, to achieve a robust and accurate solution. Our contributions are three folds: first, we provide a multi-scale analysis for the issue;second, for texture feature we propose a novel texture operator named Scale-invariant Centersymmetric Local Ternary Pattern, and a corresponding Pattern Adaptive Kernel Density Estimation technique for its probability estimation;third, we design a Simplified Gaussian Mixture Models for intensity feature. Our method is tested on several complex real world videos with illumination variation, soft shadows and dynamic backgrounds. The experimental results clearly demonstrate that our method is superior to the previous methods.

关键词： background modeling multi-scale fusion texture intensity

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A New Calibration Method for an Inertial and Visual Sensing System

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International Journal of Automation and computing 2012年第3期9卷 299-305页

作者： Xu, De Wang, Hua-Wei Li, You-Fu Tan, Min State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China East China Research Institute of Electronic Engineering Hefei 230088 China Department of Manufacturing Engineering and Engineering Management City University of Hong Kong Hong Kong China

The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.

关键词： Inertial sensor visual sensor calibration motion estimation robot vision.

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The Smart Street Lighting System Based on NB-IoT

The Smart Street Lighting System Based on NB-IoT

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Chinese Automation Congress (CAC)

作者： Chen, Shichao Xiong, Gang Xu, Jia Han, Shuangshuang Wang, Fei-Yue Wang, Kun Macau Univ Sci & Technol Inst Automat State Key Lab Management & Control Complex Syst Chinese Acad Sci Taipa Macau Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan Peoples R China Beijing Univ Chem Technol Inst Automat State Key Lab Management & Control Complex Syst Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Qingdao Acad Intelligent Ind Qingdao Peoples R China Beijing Univ Chem Technol Beijing Peoples R China

ISBN: (纸本)9781728113128

With the development of communication technology, in order to meet the smart city's demand for intelligent infrastructure, the smart street lighting system based on NB-IoT is an effective method to realize the goal. This paper elaborates on the smart street lighting system's architecture, including the perception and control layer, the transport layer, the platform layer and the application layer. In addition, the actual system is built to verify. This system uses STM32 to build the perception and controller, and relies on China Telecom's NB-IoT communication network for the data transmission, finally using the IoT management platform-Huawei OceanConnect- to implement the connection management, the device management, the data calculation and the remote control functions. This system verifies the feasibility of the smart street lighting system based on NB-IoT technology, which provides the reference for the promotion and application. It is expected to provide intelligent, diversified, efficient and economical services for the smart cities.

关键词： smart street lighting system NB-IoT IoT smart city

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Design of Cascade control Framework for ROV control and Simulation 17

Design of Cascade Control Framework for ROV Control and Simu...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Qiu, Changlin Kong, Shihan Zhou, Chao Yu, Junzhi Institute of Automation Cas State Key Lab Management and Control for Complex Systems Beijing100190 China Naval Research Academy Beijing100072 China

ISBN: (纸本)9781728164151

In this paper, a cascade control framework is proposed a class of remote operated vehicles (ROVs) to enhance the control performance and capacity of interference resistance. Specifically, the control framework is composed of a fuzzy-based controller in primary loop and a PID neural network based controller in secondary loop. In the primary loop, fuzzy controller is implemented to avoid precise establishment of ROV model for calculating the expected velocity of the vehicle. Furthermore, in the secondary loop, a PID neural network with adaptation is adopted to accomplish the capacity of resisting disturbance. Eventually, the control framework is verified by theoretical and simulation analyses. The obtained results indicate that the proposed cascade control framework endows the ROV with the ability to guarantee the control performance and attenuate external disturbance effectively. © 2020 IEEE.

关键词： controllers

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A Modified Line-of-Sight Method for Path Tracking Applied to Robotic Fish 17

A Modified Line-of-Sight Method for Path Tracking Applied to...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Du, Sheng Zhou, Chao Yu, Junzhi Wu, Zhengxing State Key Lab Management and Control for Complex Systems Institute of Automation Cas Beijing100190 China Naval Research Academy Beijing100072 China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164151

This paper proposes an improved line-of-sight method to navigate a two-joint-actuated tuna-like robotic fish. Based on the characteristics of thunniform swimmer, a simplified kinematic and dynamic model is constructed, and the intrinsic body swing is illustrated. To deal with the nonlinear and under-actuated dynamics of the robotic fish, the reference head angle is designed with cross-track error, which substantially simplifies the control of linear path tracking. Furthermore, with the physical constraint, co-simulation using Automatic Dynamic Analysis of Mechanical systems (ADAMS) software and MATlab simulink reveals the effectiveness. The obtained results show that the proposed method is able to make both the cross-track error and the heading error fluctuate in an acceptable range quickly. © 2020 IEEE.

关键词： MATlab

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A k-Nearest Neighbor Locally Weighted Regression Method for Short-Term Traffic Flow Forecasting

A <i>k</i>-Nearest Neighbor Locally Weighted Regression Meth...

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15th International IEEE Conference on intelligent Transportation systems (ITSC)

作者： Li, Shuangshuang Shen, Zhen Xiong, Gang Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781467330633

In this paper, a k-nearest neighbor locally weighted regression method (k-LWR) is proposed to forecast the short-term traffic flow. Inspired by k-nearest neighbor (k-NN) method, the traffic flows which have the same clock time with the current traffic flow are viewed as neighbors. The traffic flows which have the same clock time with the predicted traffic flow are viewed as the outputs of the neighbors. The neighbors most similar to the current traffic flow are viewed as nearest neighbors. It is observed that each nearest neighbor has different similarity with the current traffic flow, and the similarity is relevant to the contribution of the nearest neighbor's output to predicted traffic flow. The greater the similarity is, the greater the contribution is. These contributions of the nearest neighbors' outputs are obtained by the locally weighted regression (LWR) method. In this way, k-LWR uses less data, but uses it more effectively. We use the root mean square error (RMSE) between the actual traffic flow and the predicted traffic flow as the measurement. The proposed method is tested on the actual data from Xingye intersection and Feihu intersection in Jiangsu Province in China. The experimental results show that k-LWR has 20% and 24% improvement over the pattern recognition algorithm (PRA), 26% and 30% improvement over k-NN, for the two intersections, respectively.

关键词： Vectors Forecasting Clocks Databases Pattern recognition Prediction algorithms Equations

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A Multi-Antenna Spectrum Sensing Scheme Based on Main Information Extraction and Genetic Algorithm Clustering

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IEEE ACCESS 2019年 7卷 119620-119630页

作者： Zhuang, Jiawei Wang, Yonghua Zhang, Shunchao Wan, Pin Sun, Chenhao Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China South Cent Univ Nationalities Hubei Key Lab Intelligent Wireless Commun Wuhan 430074 Peoples R China

Spectrum sensing is an indispensable technology for cognitive radio networks, which enables secondary users (SUs) to discover spectrum holes and to opportunistically use under-utilized channels without causing interference to primary users. Aim at improving the sensing performance, a multi-antenna spectrum sensing scheme based on main information extraction and genetic algorithm clustering (MIEGAC) is proposed in this paper. Specifically, in order to reduce the amount of signal that is transferred to the fusion center, an information pre-processing scheme based on principal component analysis (PCA) is presented. Main information from the sensing signal is extracted via PCA, which reduces the cost of the reporting channel and the impact of interfering information on detection result. Furthermore, an information fusion method is described in this paper, which takes the place of complicated matrix decomposition algorithms. Moreover, inspired by machine learning, a clustering scheme based on genetic algorithm is introduced to classify signal features, which implements the spectrum sensing decision and avoids calculating the decision threshold. Simulation results illustrate that the MIEGAC can considerably improve the sensing performance for spectrum sensing. Significantly, this paper provides a novel approach for the design of centralized spectrum sensing algorithms in cognitive radio technologies.

关键词： Spectrum sensing principal component analysis information fusion genetic algorithm clustering

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Analyzing the Segmentation Granularity of RTB Advertising Markets: A Computational Experiment Approach 4th

Analyzing the Segmentation Granularity of RTB Advertising Ma...

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4th National Conference on Social Media Processing (SMP)

作者： Qin, Rui Yuan, Yong Wang, Feiyue Li, Juanjuan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Peoples R China

ISBN: (纸本)9789811000805;9789811000799

Real Time Bidding (RTB) is an emerging business model of online computational advertising with the rise of Internet and big data. It can help advertisers achieve the precision marketing through evolving the traditional business logic from buying ad-impressions to directly buying the matched target audiences. As an important part of RTB markets, Demand Side Platforms (DSPs) play a critical role in matching advertisers with their target audiences via market segmentation, and their segmentation strategies (especially the choice of granularity) have key influences in improving the efficiency of RTB markets. This paper studied DSPs' strategies for market segmentation, and established a selection model of the granularity for segmenting RTB markets. We proposed to validate our model using a computational experiment approach, and the experimental results show that the market segmentation granularity has the potential of improving both the total revenue of all the advertisers and the expected revenue for each advertiser.

关键词： Real time bidding Demand side platforms Market segmentation granularity Computational experiments Precision marketing

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Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

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IEEE ACCESS 2020年 8卷 5777-5786页

作者： Zhang, Shunchao Wang, Yonghua Wan, Pin Zhuang, Jiawei Zhang, Yongwei Li, Yi Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China South Cent Univ Nationalities Hubei Key Lab Intelligent Wireless Commun Wuhan 430074 Peoples R China

In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme.

关键词： Cognitive radio robust cooperative spectrum sensing sensing data fusion K-medoids clustering algorithm Mean-shift clustering algorithm

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Discrete-time Stochastic Iterative Learning control: A Brief Survey

Discrete-time Stochastic Iterative Learning Control: A Brief...

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10th World Congress on intelligent control and Automation (WCICA)

作者： Shen, Dong Xiong, Gang Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467313988

This note gives a brief survey on discrete-time stochastic iterative learning control (SILC) from three aspects, namely, SILC for linear system, nonlinear system and system with other stochastic signal. Two major approaches, stochastic Kalman filtering approach and stochastic approximation approach, for SILC are proposed. Some open questions are also included.

关键词： Discrete-time Stochastic System Iterative Learning control Linear System Nonlinear System

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