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International Joint Conference on Neural Networks

作者： Yinxiang Huang Yun Li Baohua Qiang School of Computer Science & Technology Nanjing University of Posts and Telecommunications China Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology China

ISBN: (纸本)9781509006212

Internet traffic classification is one of the key foundations for research works and traffic engineering in Internet. With the rapid increase of Internet applications and the number of Internet flow, the technique challenges are coupled with development of traffic classification all the time. Currently, the machine learning-based technique has attracted much attention, since it can address the issues that the usage of the dynamic port numbers and the encryption technique at the transport layer in traffic. As we have known, feature selection is one of the key problems in machine learning. In this paper, in order to improve the efficiency of feature selection in dealing with large scale traffic data problem, especially to imbalance classification problem that occur in traffic classification, a Min-Max Ensemble Feature Selection (M2-EFS) is proposed to deal with traffic data, which based on balanced data partition and min-max ensemble strategy. The experimental results demonstrate that the M2-EFS can obtain higher performance in most cases, and it could efficiently deal with imbalanced problems.

关键词： Internet Training Classification algorithms Feature extraction Niobium Partitioning algorithms Algorithm design and analysis

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Dynamic SDN Controller Assignment in Data Center Networks: Stable Matching with Transfers

Dynamic SDN Controller Assignment in Data Center Networks: S...

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Annual IEEE International Conference on Computer Communications

作者： Tao Wang Fangming Liu Jian Guo Hong Xu Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology NetX Lab @ City University of Hong Kong

ISBN: (纸本)9781467399548

Software defined networking is becoming increasingly prevalent in data center networks for its programmability that enables centralized network configuration and management. However, since switches are statically assigned to controllers, traffic dynamics cause load imbalance among the controllers. As a result, some controllers are not fully utilized, while switches connected to overloaded controllers may experience long response times. In this paper, we consider dynamic controller assignment so as to minimize the average response time of the control plane. We formulate this problem as a stable matching problem with transfers, and propose a hierarchically two-phase algorithm that integrates key concepts from both matching theory and coalitional games to solve it efficiently. Theoretical analysis proves that our algorithm converges to a near-optimal Nash stable solution within tens of iterations. Extensive simulations show that our approach reduces response time by about 86%, and achieves better load balancing among controllers compared to static assignment.

关键词： Controllers Reaction Time control surfaces Switches data center unbalanced load programmability

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Robust Adaptive Control of Nonlinear systems with Asymmetric Non-smooth Saturation

Robust Adaptive Control of Nonlinear Systems with Asymmetric...

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第28届中国控制与决策会议

作者： Xuehai Tu Junfeng Lai Kai Zhao Institute of Smart System and Renewable Energy School of AutomationChongqing University The Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing Universit

ISBN: (纸本)9781467397155

In this paper,the robust adaptive control is proposed for a class of parametric uncertain nonhnear system with asymmetric non-smooth input *** introducing a well-defined smooth function,a robust adaptive control algorithms are developed to deal with the problem of asymmetric non-smooth *** most of the existing control schemes for the uncertain nonhnear system,which requires an assumption that the control gain is an unknown constant,the developed controller only needs that the control gain is a bounded and unknown time-varying *** this work,the robust adaptive controller can guarantee global uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis.A simulation results is presented to illustrate the effectiveness of the proposed adaptive control technique.

关键词： Robust Adaptive Control Asymmetric Non-Smooth Saturation Uniformly Bounded

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Novel Clustering-Based Approach for Local Outlier Detection

Novel Clustering-Based Approach for Local Outlier Detection

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IEEE Conference on Computer Communications Workshops

作者： Haizhou Du Shengjie Zhao Daqiang Zhang Jinsong Wu Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Software Engineering Tongji University Department of Electrical Engineering Universidad de Chile

ISBN: (纸本)9781467399562

With the rapid expansion of data scale, big data mining and analysis have attracted increasing attention. Outlier detection as an important task of data mining is widely used in many applications. However, conventional outlier detection methods have difficulty handling large-scale datasets. In addition, most of them typically can only identify global outliers and are over sensitive to parameters variation. In this paper, we propose a novel method for robust local outlier detection with statistical parameters, which incorporates the clustering-based ideas in dealing with big data. Firstly, this method finds some density peaks of dataset by 3σ standard. Secondly, each remaining data object in the dataset is assigned to the same cluster as its nearest neighbor of higher density. Finally, we use Chebyshev's inequality and density peak reachability to identify local outliers of each group. The experimental results demonstrate the efficiency and accuracy of the proposed method in identifying both global and local outliers. Moreover, the method is also proved to be more stability analysis than typical outlier detection methods, such as LOF (Local Outlier Factor) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise).

关键词： Outlier detection Big data Data mining Clustering-based

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A metadata management strategy based on event-classification in intelligent transportation system 1

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Su, Yayun Zhang, Yaying Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

With the explosive growth of data information, the object-oriented storage system has been widely used. This paper proposed a metadata management strategy based on Distributed File system-Ceph in terms of event classification, taking advantage of the characteristics of data in urban traffic system. The large amount of data with a wide variety of sources and data types was first classified by machine learning, and a classification model was established. Then, improvements on load balancing were made to the existing Ceph Load Balancing Strategy of metadata partition. The metadata partitioning is to assign and migrate metadata obtained from the event classification model to the target server chosen by the fuzzy optimum method. Experimental results show that the proposed load balancing strategy based on event classification can not only make the overall load of the metadata servers in a relatively stable state but also make the migration times less than that of other algorithms. The extra overhead of the system is also reduced. © Springer International Publishing Switzerland 2015.

关键词： Metadata

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A novel storing and accessing method of traffic incident video based on spatial-temporal analysis 15th

A novel storing and accessing method of traffic incident vid...

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Zhang, Yaying Zhu, Yinyin Key Laboratory of Embedded System and Service Computing Tongji University Shanghai201804 China

ISBN: (纸本)9783319271217

Hadoop Distributed File system (HDFS) is a reliable and scalable data storage solution. However, it has great weakness in storage of the numerous small files. A merging method of small video files containing traffic incidents is proposed to improve the HDFS storage efficiency of small files. As traffic incident videos can be classified in terms of time and the crossroad where the incident happens, the proposed method merges video files together by time and region (usually adjacent crossroads). Indexing mechanism has been improved in later searching for small video files. The whole HDFS file block related to specific incidents will be read out to local cache. The experimental results show that when accessing for traffic incidents by region in certain period, the average search time and the memory load of HDFS NameNode will be effectively reduced. © Springer International Publishing Switzerland 2015.

关键词： Storage efficiency

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Impact of Growing-Season Meteorology on Japonica Rice Productivity in Northeastern China

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IOP Conference Series: Earth and Environmental Science 2017年第1期86卷

作者： W Yu Y E Zhang Y Wang J J Liu W Chen S S Seng J Y Zhuang L W Gao A-G Ahmed Z M Li G Q Li S W Xu Agricultural Information Institute Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Information Service Technology Ministry of Agriculture/Key Laboratory of Intelligent Agricultural Early Warning Technology and System Chinese Academy of Agricultural Sciences Beijing 100081 China

Meteorological factors have major impact on crop productivity in the world. Many researchers have evaluated the possible impact of climate change on crop yields using models. Here we use a 1980–2015panel dataset from China Agricultural Cost and Return Yearbook to investigate the meteorological impact on Chinese Japonica rice yield growth. We find that average temperature has significantly positive effect on rice yields; while maximum temperature and minimum temperature have negative effect on rice yield. Physicalinputs and time trend (technological progress) into the crop yield have an accurate estimation on crop yields. Means including irrigation or drainage could use during growing season.

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The timing variations and spatial pattern of winter wheat in China 5

The timing variations and spatial pattern of winter wheat in...

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5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016

作者： Jiayu, Zhuang Zhemin, Li Yanni, Li Xi, Yong Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing100081 China Key Laboratory of Agri-information Service Tachnology Ministry of Agriculture Beijing100081 China Science and Technology on Integrated Information System Laboratory Institute of Software Beijing100081 China School of Computer Science Univ. of Chinese Academy of Science Beijing100081 China

ISBN: (纸本)9781509023509

In this paper winter wheat was selected as the research object. This paper focuses on evolving two aspects approach to timing variations and spatial pattern of the historical production of winter wheat, and the spatial and temporal characteristics of winter wheat in-depth analysis. The result presented the production of winter wheat growth slowly in the southwest of China, while growth rapidly in the east. The fluctuation ratios to identify the differences in China's winter wheat of different varieties and districts and the causes of said differences was investigated. It is revealed that higher grain yield can be best explained by adoption of advanced technologies, followed by material inputs, environment, climate and policy. Our research in timing variations and spatial pattern helps to study the intricacies of the dependences between these factors and grain yield. The research which presented will be effectively applied in agricultural monitoring and early warning applications. © 2016 IEEE.

关键词： Geographic information systems

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Novel Detection service Method for Moving Object in IOT

Novel Detection Service Method for Moving Object in IOT

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2016 3rd International Conference on Materials Engineering,Manufacturing technology and Control(ICMEMTC 2016)

作者： Jie Chen Degan Zhang School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education

In embedded Internet of Things(IOT) environment, there are the troubles such as complex background, illumination changes, shadows and other factors for detecting moving object, so we put forward a new detection service method through mixing Gaussian Mixture Model(GMM), edge detection service method and continuous frame difference method in this paper. In time domain, the new method uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection service method which mixes edge detection service method, continuous frame difference method and GMM to get initial contour of moving object, and gets ultimate moving object. This method not only can well adapt to the illumination gradients and background disturbance occurred on scene, but also can well solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional method. As experimental result showing, this method holds better real-time and robustness. It is not only easily implemented, but also can accurately detect moving object.

关键词： GMM embedded IOT moving object detection service

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Shadow lane robust detection by image signal local reconstruction

International Journal of Signal Processing, Image Processing...

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International Journal of Signal Processing, Image Processing and Pattern Recognition 2016年第3期9卷 89-102页

作者： Du, Mingfang Junzheng, Wang Nan, Li Duoyang, Li School of Automation Beijing Union University Beijing China State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China Beijing Key Laboratory of Information Service Engineering

In order to resolve shadow interference and slow image processing speed in the visual navigation of unmanned vehicles on city roads, a new lane detection algorithm based on the Inverse Perspective Mapping(IPM) of vertical sub-image reconstruction using local bands was proposed. The lane’s IPM aerial image was obtained using the projective transformation of three-dimensional images of city roads. The ROI portion of the IPM map was decomposed and analyzed using the sym3 wavelet, and after analyzing and comparing the experimental results the first and second levels of the vertical sub images were selected for the reconstruction, compression, and removal of shadows from the original image. The Canny algorithm was proposed and adopted in order to extract the edge features of the reconstructed images according to real-time road image quality. A modified, polar angle, constraint-based, fast Hough transform was used to locate the candidate lanes. Finally, the main control point, straight-line-fitting algorithm was used to fit the final lanes, which achieved the precise location and recognition of the lane lines. The California Polytechnic lane data set, a public data platform, which is now widely used in road visual recognition fields around the world, was selected for the testing and verification of the algorithm. The results of the experiment and actual operation indicated that, despite having a storage space of approximately one tenth the size of similar algorithms, this detection algorithm effectively solves the issue of shadow removal, meets the real-time requirements of roadway image processing systems for unmanned vehicles with a recognition time of less than 20ms, and is robust. © 2016 SERSC.

关键词： Wavelet transforms

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