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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Jie Guo Zhihua Wei Duoqian Miao Ministry of Education Key Laboratory of Embedded System and Service Computing Shanghai China Department of Computer Science and Technology Tongji University

ISBN: (纸本)9781479982622

Lane detection based on computer vision is a key technology of Automatic Drive system for intelligent vehicles. In this paper, we propose a real-time and efficient lane detection algorithm that can detect lanes appearing in urban streets and highway roads under complex background. In order to enhance lane boundary information and to be suitable for various light conditions, we adopt canny algorithm for edge detection to get good feature points. We use the generalized curve lane parameter model, which can describe both straight and curved lanes. We propose an improved random sample consensus (RANSAC) algorithm combined with the least squares technique to estimate lane model parameters based on feature extraction. Experiments are conducted on both real road lane videos captured by Tongji University and Caltech Lane Datasets. The experimental results show that our algorithm is can meet the real time requirement and fit lane boundaries well in various challenging road conditions.

关键词： Feature extraction Roads Computational modeling Image edge detection Algorithm design and analysis Real-time systems Mathematical model

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Research on the Matthews correlation coefficients metrics of personalized recommendation algorithm evaluation

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International Journal of Hybrid Information Technology 2015年第1期8卷 163-172页

作者： Liu, Yingbo Cheng, Jiujun Yan, Chendan Wu, Xiao Chen, Fuzhen Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology Tongji University Shanghai China

The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a successful recommendation system is the performance of recommendation algorithm. When scholars put forward some new recommendation algorithms, they claim that the new algorithms have been improved in some respects, better than previous algorithm. So we need some evaluation metrics to evaluate the algorithm performance. Due to the scholar didn’t fully understand the evaluation mechanism of recommendation algorithms. They mainly emphasized some specific evaluation metrics like Accuracy, Diversity. What’s more, the academia did not establish a complete and unified assessment of recommendation algorithms evaluation system which is credibility to do the work of recommendation evaluation. So how to do this work objective and reasonable is still a challengeable task. In this article, we discussed the present evaluation metrics with its respective advantages and disadvantages. Then, we put forward to use the Matthews Correlation Coefficient to evaluate the recommendation algorithm’s performance. All this based on an open source projects called mahout which provides a rich set of components to construct the classic recommendation algorithm. The results of the experiments show that the applicability of Matthews correlation coefficient in the relative evaluation work of recommendation algorithm. © 2015 SERSC.

关键词： Recommender systems

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An immune approach for detecting the user's behavior of electronic transaction process

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Journal of Computational Information systems 2015年第2期11卷 683-692页

作者： Jiang, Shaoping Yan, Chungang Zhao, Peihai Wang, Mimi Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

In the process of electronic transaction, there are many abnormal conditions such as user's own mistakes, account theft and so on. Inspired by mechanisms of immune homeostasis, immune surveillance and immune update from the biological immune system, an immune approach for detecting the user's behavior of electronic transaction process is proposed to solve these problems and make the user's behaviors credible. By computing age of each log and cleaning up the aging logs, logs which can reflect the user's habit could be gotten and the mechanism of immune homeostasis is realized. With the newly-generated transaction sequence detected to be abnormal or not, the mechanisms of immune surveillance is achieved. Updating related sets is the realization of immune update, and the goal of detecting the user's behavior can be reached. The experimental results show that the true positive rate can reach 92% when the false positive rate is 10.8%. 1553-9105/Copyright © 2015 Binary Information Press

关键词： Physiology

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Tracking Salient Keypoints for Human Action Recognition

Tracking Salient Keypoints for Human Action Recognition

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IEEE International Conference on systems, Man and Cybernetics

作者： Hanli Wang Yun Yi Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Department of Mathematics and Computer Science Gannan Normal University Ganzhou China

ISBN: (纸本)9781479986989

It plays an important role to recognize human actions from realistic videos in multimedia event detection and understanding. To this aim, a novel human tracking approach is proposed in this paper. Firstly, salient key points trajectories are generated to track human actions at multiple spatial scales. Then, camera motion elimination is utilized to further improve the robustness of motion trajectories. To depict human motions accurately and efficiently, the Histogram of Oriented Gradient (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histogram (MBH) are employed with the Fisher vector model being utilized to aggregate these three features. Extensive experimental results on four challenging human action video datasets demonstrate that the proposed approach is able to achieve better recognition performances in a more computationally efficient manner as compared with a number of state-of-the-art approaches.

关键词： Trajectory Videos Tracking Cameras Robustness Feature extraction Optical saturation

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Correlative Filters for Convolutional Neural Networks

Correlative Filters for Convolutional Neural Networks

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IEEE International Conference on systems, Man and Cybernetics

作者： Peiqiu Chen Hanli Wang Jun Wu Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781479986989

This paper introduces a regularization method called Correlative Filter (CF) for Convolutional Neural Network (CNN), which takes advantage of the relevance between the convolutional kernels belonging to the same convolutional layer. During the process of training with the proposed CF method, several pairs of filters are designed in a manner of randomness to contain opposite weights in low-level layers. Regarding higher level layers where synthetical features are processed, the relation between correlative filters is explored as translation of various directions. The proposed CF method attempts to optimize the inner structure of convolutional layers and it can work jointly with other regularization techniques, such as stochastic pooling, Dropout, etc. The experimental results on the competitive image classification benchmark dataset CIFAR-10 demonstrates the performance of the proposed CF method, additionally, it is also verified that the proposed CF method is wonderful to be employed to enhance several state-of-the-art regularization models.

关键词： Feature extraction Neurons Training Stochastic processes Convolution Kernel Biological neural networks

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Null space based discriminant sparse representation large margin for face recognition

Null space based discriminant sparse representation large ma...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ying Wen Lili Hou Lianghua He Department of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781479919611

In this paper, we propose a novel subspace learning algorithm, termed as null space based discriminant sparse representation large margin (NDSLM). There are two contributions in the paper. First, we propose a new expectation to obtain the neighborhood information for large margin subspace learning, i.e., the within-neighborhood scatter and between-neighborhood scatter are modeled by the sparse reconstruction weights of the samples from the same class and different classes, respectively. Since the neighborhood information formed by sparse representation can capture non-linearities in the data, the proposed method possesses more discriminative information than the traditional large margin learning methods with the expectation using Euclidean distance, etc. Second, the large margin information integrated into the model of Fisher criterion makes the discriminating power of NDSLM further boosted. NDSLM addresses the small sample size problem by solving an eigenvalue problem in null space. Experiments on ORL, Yale, AR, Extended Yale B and CMU PIE five face databases are performed to evaluate the proposed algorithm and the results demonstrate the effectiveness of NDSLM.

关键词： Principal component analysis Silicon Pipelines Glass Databases

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An efficient algorithm of information recommendation between groups in social networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2015年第6期43卷 1090-1093页

作者： Huang, Zhen-Hua Zhang, Bo Fang, Qiang Xiang, Yang Department of Computer and Technology Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China

Information recommendation between groups is one of the most important ways for information sharing and transmitting in social networks. However, it needs exponential time cost to achieve the exact optimal recommendation solution. Hence this paper proposed an efficient algorithm EAOORS (Efficient Algorithm for Obtaining Optimal Recommendation Solution) which equivalently reduced this exponential time cost problem to extended steiner tree problem, and fast achieved the approximate optimal recommendation solution in the polynomial time complexity. The experimental result shows that the EAOORS algorithm is more efficient than the existing ones. ©, 2015, Chinese Institute of Electronics. All right reserved.

关键词： Approximation algorithms

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A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification

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Chinese Journal of Electronics 2014年第4期23卷 706-711页

作者： ZHOU Jiarui ZHU Zexuan JI Zhen College of Biomedical Engineering and Instrument Science Zhejiang University Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software EngineeringShenzhen University

A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics,and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.

关键词： Metabolomics Feature weighting Memetic algorithm

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Autonomic Performance and Power Control on Virtualized Servers:Survey, Practices, and Trends

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Journal of computer Science & Technology 2014年第4期29卷 631-645页

作者：周笑波蒋昌俊 IEEE ACM Department of Computer Science University of Colorado Colorado Springs U.S.A. The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University CCF Department of Computer Science and Technology Tongji University

Modern datacenter servers hosting popular Internet services face significant and multi-facet challenges in performance and power control. The user-perceived performance is the result of a complex interaction of complex workloads in a very complex underlying system. Highly dynamic and bursty workloads of Internet services fluctuate over multiple time scales, which has a significant impact on processing and power demands of datacenter servers. High-density servers apply virtualization technology for capacity planning and system manageability. Such virtuMized computer systems are increasingly large and complex. This paper surveys representative approaches to autonomic performance and power control on virtualized servers, which control the quality of service provided by virtualized resources, improve the energy efficiency of the underlying system, and reduce the burden of complex system management from human operators. It then presents three designed self-adaptive resource management techniques based on machine learning and control for percentile-based response time assurance, non-intrusive energy-efficient performance isolation, and joint performance and power guarantee on virtualized servers. The techniques were implemented and evaluated in a testbed of virtualized servers hosting benchmark applications. Finally, two research trends are identified and discussed for sustainable cloud computing in green datacenters.

关键词： autonomic computing joint performance and power control virtualized server Internet application sustainable computing

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Automatic landing for fixed-wing UAV using stereo vision with a single camera and an orientation sensor: A concept

Automatic landing for fixed-wing UAV using stereo vision wit...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Montika Sereewattana Miti Ruchanurucks Panjawee Rakprayoon Supakorn Siddhichai Shoichi Hasegawa ICT for Embedded System Kasetsart University Bangkok Thailand Department of Electrical Engineering Kasetsart University Bangkok Thailand Image Processing Laboratory National Electronics and Computer Technology Center Pathumthani Thailand Precision and Intelligence Laboratory Tokyo Institute of Technology Yokohama Japan

This paper presents concepts and some experiment on UAV automatic landing on any flat area without prior knowledge of (detachable) markers' positions. The process consists of two key notions. First, the UAV approximates 3D positions of four color markers using a single camera stereoscopic scheme. In other words, the aircraft flies and captures images of ground to create depth map of the four markers. Second, it relies on a pose estimation algorithm (here, Homography) to approximate rotational and, especially, translational information between air to ground. What is novel in this concept is z-coordinate of markers on ground can always be normalized based on our kinematic chain. Thus, homography can be used. Additionally, depth estimation using stereo vision with a camera is shown in experimental result. Furthermore, the whole process is also possible without GPS data, which might post problem in some certain situations, e.g. inadequate positioning accuracy for small landing area, or unreliability during warfare.

关键词： Cameras Global Positioning system Aircraft Image color analysis Calibration Transmission line matrix methods Three-dimensional displays

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