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A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem

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Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3722-3733页

作者： Yuan, Weitao Liang, Xiaodan Chen, Hanning Na, Lin Zou, Tao School of Computer Science and Software Engineering Tianjin Polytechnic University Tanjin300387 China Beijing Shenzhou Aerospace Software Technology Company Limited Beijing110000 China Laboratory of Information Service and Intelligent Control Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Robust Principal Component Analysis (RPCA), which is a popular parsimony model, is becoming increasingly important for researchers to do data analysis and prediction. The RPCA formulation is made of two components: sparse penalty and low rank penalty. These two competing terms are balanced with one parameter, which is essential for the effectiveness of RPCA. However, in real-world applications, the lack of data adaptive methods for choosing the right parameter hinders the popularization of RPCA. In this work, RPCA is generalized to a multiobjective optimization problem without any balancing parameter. The new model is named as Multiobjective Robust Principal Component Analysis (MRPCA). We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve RPCA problem, which is a high dimensional convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. To handle this dimensionality problem, we introduce a novel mutation, termed as Alternating Direction Method of Multipliers mutation (ADMM mutation), that works well in high dimensional decision space. Numerical experiments show that this modified NSGA-II, which converges much faster than the standard one, can deal with the curse of dimensionality well. Furthermore, numerical image reconstruction test confirms that the reconstruction performance of our modified NSGA-II is better than the traditional proximal algorithm, which is usually used to solve RPCA problem. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Multiobjective optimization

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Design and Implementation of Tourism Activity Recognition and Discovery System

Design and Implementation of Tourism Activity Recognition an...

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World Congress on intelligent Control and Automation

作者： Yuan Yifan Du Junping Fan Dan JangMyung Lee Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Department of Electronics Engineering Pusan National University

ISBN: (纸本)9781467384155

With the rapid development of technologies, such as cloud computing, big data and so forth, tourism big data has drawn the attention of many scholars. How to make full use of a large amount of raw data from tourism activity, and how to recognize and discover tourism activity quickly, accurately and conveniently from information which is accumulated over a long period have become a new direction of tourism data application. In this paper, text analysis, text classification and other relevant technologies are combined based on text mining. Furthermore, a tourism activity recognition and discovery system is designed and implemented, which has achieved the recognition and discovery of tourism activity from a large amount of information. The research results could contribute to recommending and predicting tourism activity in China.

关键词： tourism Technology Development big data text mining Outcomes Research

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A Real-Time People Number Detection Algorithm of Scenic Spot Based on Density Center Clustering

A Real-Time People Number Detection Algorithm of Scenic Spot...

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World Congress on intelligent Control and Automation

作者： Wen Yaomin Du Junping Liang Meiyu Fan Dan JangMyung Lee Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Department of Electronics Engineering Pusan National University

ISBN: (纸本)9781467384155

In recent years, accidents occur frequently because the people number of scenic spots is lack of control. In this paper, a novel real-time people number detection algorithm of scenic spot based on density center clustering (DPBC) is proposed. Taking account of the complexity of the scenic environment, we use the Gauss mixture model (GMM) to suppress the background interference, extract the feature points from the crowd and cluster these feature points by applying a clustering algorithm based on density center. Then we establish a training set and estimate the people number by using the support vector regression model (SVR). We implement the proposed algorithm based on Opencv and use PETS dataset to validate the effectiveness of our proposed algorithm. Experimental results demonstrate that compared with Aibiol algorithm and Conte algorithm, our proposed algorithm improved the accuracy for estimating the people number in a scenic spot.

关键词： Feature extraction Clustering algorithms Algorithm design and analysis Interference Communications technology Estimation Prediction algorithms

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ABNORMAL EVENT DETECTION BASED ON SOCIAL FORCE MODEL COMBINED WITH CROWD VIOLENT FLOW

ABNORMAL EVENT DETECTION BASED ON SOCIAL FORCE MODEL COMBINE...

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2016 4th IEEE International Conference on Cloud Computing and Intelligence Systems（IEEE CCIS2016）

作者： Yaomin Wen Junping Du Jang Myung Lee Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Department of Electronics Engineering Pusan National University

ISBN: (纸本)9781509012572

In this study, we use the social force model(SFM) combined with the violent flows descriptor(vif) to detect abnormal events. Our method extracts the local density to calculate the parameters of SFM and the vif to represent the crowd stability status. The Latent Dirichlet Allocation(LDA) model is established based on the bag-of-words model combined with the temporal and spatial features of visual words, and then the maximum likelihood function is used to identify abnormal events.

关键词： social force model Violent flow LDA model maximum likelihood

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TOURISM ACTIVITY RECOGNITION AND DISCOVERY BASED ON IMPROVED LDA MODEL

TOURISM ACTIVITY RECOGNITION AND DISCOVERY BASED ON IMPROVED...

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2016 4th IEEE International Conference on Cloud Computing and Intelligence Systems（IEEE CCIS2016）

作者： Yifan Yuan Junping Du Jang Myung Lee Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Department of Electronics Engineering Pusan National University

ISBN: (纸本)9781509012572

LDA(Latent Dirichlet Allocation) model is a kind of unsupervised learning model which can extract the hidden topic from text in recent years. In this paper,we proposed a novel LDA model based on the traditional LDA model, which is integrated into the information of text category(Activity-topic LDA). In this paper, the Activity-topic LDA is proposed to improve the original latent Dirichlet allocation(LDA)model. On the basis of the LDA, the proposed method adds the tourism activity information, and obtains the probability distribution model of the tourism *** on this model, we can identify and discover the theme of tourism activities.

关键词： Latent Dirichlet allocation Text mining Gibbs sampling

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Optimizing collaborative sparse dictionary for compressive light field photography

Optimizing collaborative sparse dictionary for compressive l...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Xiuhuan Zang Yunhui Shi Jin Wang Wenpeng Ding Baocai Yin Beijing Advanced Innovation Center for Future Internet Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology School of Software Engineering Beijing University of Technology Beijing China College of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781509053179

Compressive light field photography has gained a significant research interest during the past years. One important problem in light field images is the effective representation of the structures. In this paper, based on collaborative projection and sparse dictionary, we propose a light field reconstruction optimization scheme for compressive light field photography. By utilizing the special structural feature of high correlations among light field views, an optimized structured dictionary is proposed, which is more memory and time efficient compared with general sparse dictionary. Furthermore, we propose the corresponding structured dictionary learning model and efficient algorithm to train the optimized dictionary with fixed projection. Simulation results on light field data set demonstrate the effectiveness of our optimizing scheme in terms of reconstruction quality compared with general sparse dictionary.

关键词： Dictionaries Image reconstruction Training Collaboration Photography Algorithm design and analysis Compressed sensing

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Efficient Wall-Corner Collision Avoidance using D-Images

Efficient Wall-Corner Collision Avoidance using D-Images

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IEEE International Conference on Mechatronics and Automation

作者： Guohe Fu Xinyu Zhang Shanghai Key Laboratory of Trustworthy Computing MoE Engineering Research Center for Software/Hardware Co-design Intelligent Robot Motion & Vision Laboratory East China Normal University Shanghai China

ISBN: (纸本)9781509023974

When an autonomous mobile robot exploring in an unstructured environment, it may often enter a wall-corner, but be stuck there. We present a wall-corner collision detection and avoidance algorithm using depth images. We first determine if a mobile robot is trapped in a wall-corner using historic alternate turnings. Then we propose a geometric approach to let the robot escape a corner. We demonstrate the efficiency of our algorithm using simulations and experimental tests. Using our algorithm, a robot can quickly detect and escape wall-corners. Moreover, we extend our algorithm to handle a very challenging scenario, Z-shaped Zigzag paths.

关键词： wall-corner collision detection avoidance algorithm mobile robot

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Formally Verifying Navigation Safety for Ground Robots

Formally Verifying Navigation Safety for Ground Robots

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IEEE International Conference on Mechatronics and Automation

作者： Manni Zhang Xinyu Zhang Shanghai Key Laboratory of Trustworthy Computing MoE Engineering Research Center for Software/Hardware Co-design Intelligent Robot Motion & Vision Laboratory East China Normal University Shanghai China

ISBN: (纸本)9781509023974

Robots' continuous physical behaviors are controlled by discrete instructions generated using complicated control algorithms. In such a robotic hybrid system, guaranteeing robot's navigation safety can be more challenging than those discrete controls alone or only the continuous robot motions. For a robotic system operating in the real world, navigation safety is critical in order to avoid potential harm for robots or their surrounding environment. We present safety guarantee and obstacle avoidance control algorithms for mobile ground robots modeled by hybrid programs. Using the verification tool KeYmaera, we formally verify that our algorithms satisfy passive or passive friendly safety properties in an environment consisting of stationary/dynamic obstacles. These algorithms allow us to check the navigation safety at the design stage of a robotic system.

关键词： motion safety collision avoidance formal methods mobile ground robot

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Design of chinese natural language in fuzzy boundary determination algorithm based on big data

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Journal of Information Hiding and Multimedia Signal Processing 2017年第2期8卷 423-434页

作者： Zhang, Fu Quan Mao, Zi Jing Ding, Gang Yi Xu, Lin School of Software Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Via Room A9#301 Juyuan JiyuanFuzhou350008 China Department of Electrical Engineering University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Via Arrow Oaks 126 San AntonioTX78249 United States Beijing Institute of Technology Via Room 611 Software Building 5 South Zhongguancun Street Haidian Beijing100081 China Innovative Information Industry Research Institute Fujian Normail University Fuzhou350300 China Via Fuqing Brunch of Fujian Normal University Campus County 1# Longjiang Road FuqingFuzhou350300 China

In this study, we proposed a new method to determine the fuzzy boundary of natural language based on big data. According to the principle of natural language recognition, the acoustic characteristics of the analysis, natural language acoustic model and statistical model is established, Viterbi decoding algorithm has been applied for natural language decoding, and take this as the basis, we implemented deep learning methods for natural language feature extraction, and used the support vector machine method for classification, according to the characteristics of natural language significant division, in accordance with the constraints, combined with large data analysis method, to determine the natural language fuzzy boundary. Experimental results indicate that using the proposed method, compared with existing methods the recall and accuracy rate have been improved. © 2017 ISSN.

关键词： Big data

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