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第25届中国控制与决策会议

作者： Sichao Wei Jianyi Guo Zhengtao Yu Peng Chen Yantuan Xian School of Information Engineering and Automation Kunming University of Science and Technology Intelligent Information Processing Key Laboratory Kunming University of Science and Technology

ISBN: (纸本)9781467355339

In order to resolve the comprehension difficulties of theory and implementation about Chinese text classification in " The principle and application of pattern recognition" curriculum for graduate students, this paper introduces the experiment of Chinese text classification into teaching practice. According to the text classification characteristics, we design the experiment scheme about Chinese text classification based on SVM, using word frequency statistics to extract feature and SVM classification algorithm, using vector space model to construct the feature space of text classification. So that readers can deeply understand and master the theoretical knowledge through the open the link, then expand on this basis

关键词： feature selection, support vector machine algorithm, text classification, teaching experiment

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ProvenanceLens: Service provenance management in the cloud

ProvenanceLens: Service provenance management in the cloud

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International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Tao Li Ling Liu Xiaolong Zhang Kai Xu Chao Yang Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Wuhan University of Science and Technology School of Computer Science. Georgia Institute of Technology Atlanta GA USA Computer Science and Information Engineering Hubei University Wuhan China

ISBN: (纸本)9781479953288

Service provenance can be defined as a profile of service execution history. Queries of service provenance data can answer questions such as when and by whom a server is invoked? which services operate on this data? What might be the root cause for the service failure? Most of the organizations today collect and manage their own service provenance in order to trace service execution failures, locate service bottlenecks, guide resource allocation, detect and prevent abnormal behaviors. As services become ubiquitous, there is an increasing demand for proving service provenance management as a service. This paper describes ProvenanceLens, a two-tier service provenance management framework. The top tier is the service provenance capturing and storage subsystem and the next tier provides analysis and inference capabilities of service provenance data, which are value-added functionality for service health diagnosis and remedy. Both tiers are built based on the service provenance data model, an essential and core component of ProvenanceLens, which categorizes all service provenance data into three broad categories: basic provenance, composite provenance and application provenance. In addition, ProvenanceLens provides a suite of basic provenance operations, such as select, trace, aggregate. The basic provenance data is collected through a light-weight service provenance capturing subsystem that monitors service execution workflows, collects service profiling data, encapsulates service invocation dependencies. The composite and application provenance data are aggregated through a selection of provenance operations. We demonstrate the effectiveness of ProvenanceLens using a real world educational service currently in operation for a dozen universities in China.

关键词： Silicon Analytical models Data models Computational modeling

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The Design of Digit Recognition Teaching Experiment Based on BP Neural Network

The Design of Digit Recognition Teaching Experiment Based on...

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第25届中国控制与决策会议

作者： Haitao Yu Jianyi Guo Zhengtao Yu Yantuan Xian Peng Chen School of Information Engineering and Automation Kunming University of Science and Technology Intelligent Information Processing Key Laboratory Kunming University of Science and Technology

ISBN: (纸本)9781467355339

The teaching of pattern recognition course in postgraduates' study is too theoretical to learn, and lack of teaching experiments. To solve these problems, this paper researches and explores on how to design digit recognition experiment in this *** propose an digit recognition teaching plan based on BP neural network. Firstly, we summarize the development and application of digit recognition, as well as the existing problems during the teaching activities at present;then, we use the case teaching method to explain the experimental procedures,and use the method of BP neural network to recognise digit, including the principle of digit recognition, the principle of image preprocessing and the design of neural network, and so on. With the aims of optimizing the original teaching form, stimulating students' study interests, training their abilities of practice and scientific research , we introduce the design of experiment. Finally, help students to solve practical problems..

关键词： The practice of pattern recognition course,BP neural network,Digit recognition

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Multi-level dosing and preact self-adaption correcting automatic batch control model

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Computer Modelling and New Technologies 2014年第4期18卷 226-231页

作者： Liao, Xuechao Liu, Zhenxing College of Computer Science Wuhan University of Science and Technology Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System Wuhan 430081 China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China

The process flow and system structure of automatic batch weighing system are presented. In order to increase production speed and dosing accuracy, the multi-level dosing control model (high/low speed dosing + inching dosing) is designed. Besides, the inching dosing mode is adopted to accurately compensate the weight deviation. In order to solve the problem that the fall of materials in-air cannot be easily controlled and out of tolerance. The multi-level dosing control model and preact will correct after each dosing dynamically with iteration method, moreover, the target value is predicted with second-order estimator, so as to increase the dosing speed with high weighing accuracy. The successful application proves that the control model can realize the rapid and accurate control of batch weighing process and has quite favourable control and reliability.

关键词： Iterative methods

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New logistics distribution route dispatching mode based on genetic algorithm-ant colony algorithm

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Computer Modelling and New Technologies 2014年第10期18卷 313-318页

作者： Yi, Fasheng Li, Xiaoling Yuan, Jimin School of Computer Science and Technology Chengdu University Chengdu China University Key Laboratory of Pattern Recognition and Intelligent information Processing Chengdu China The Department Computer Engineering Chengdu Aeronautic Vocational and Technical College Chengdu China

For multi-target route optimization with constraint conditions, the mathematical model for logistics distribution route optimization is built to accelerate response speed of logistics enterprises to customers, improve service quality, and strengthen the satisfaction of customers, and a new algorithm with the combination of genetic and ant colony algorithms is proposed to solve the selection issues of such logistics route. Initial pheromone is formed with genetic algorithm, based on which the optimal solution is rapidly sought with ant colony algorithm, and complementary advantages are achieved between above two algorithms. Application examples and simulations are available for calculation, and the results show that such algorithm is practical and effective to optimize logistics distribution route.

关键词： Genetic algorithms

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Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

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Stochastic Environmental Research and Risk Assessment 2014年第8期28卷 1957-1972页

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.

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News Credibility Evaluation on Microblog with a Hierarchical Propagation Model

News Credibility Evaluation on Microblog with a Hierarchical...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhiwei Jin Juan Cao Yu-Gang Jiang Yongdong Zhang University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing CAS China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9781479943012

Benefiting from its openness, collaboration and real-time features, Micro blog has become one of the most important news communication media in modern society. However, it is also filled with fake news. Without verification, such information could spread promptly through social network and result in serious consequences. To evaluate news credibility on Micro blog, we propose a hierarchical propagation model. We detect sub-events within a news event to describe its detailed aspects. Thus, for a news event, a three-layer credibility network consisting of event, sub-events and messages can represent it from different scale and reveal vital information for credibility evaluation. After linking these entities with their semantic and social associations, the credibility value of each entity is propagated on this network to achieve the final evaluation result. By formulating this propagation process as a graph optimization problem, we provide a globally optimal solution with an iterative algorithm. Experiments conducted on two real-world datasets show that the proposed model boosts the accuracy by more than 6% and the F-score by more than 16% over a baseline method.

关键词： Optimization Clustering algorithms Vectors Semantics Feature extraction Symmetric matrices Media

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A candidate solutions generator based on mixed strategy for non-rigid object extraction

A candidate solutions generator based on mixed strategy for ...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Min Jiang Xiaozhou Zhou Shijie Yao Zhaohui Gan Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan University of Science and Technology Wuhan P. R. China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan P. R. China

Extracting non-rigid object from images can be used in object recognition, medical image analysis, video monitoring, etc. In order to improve the efficiency and accuracy of visual object extraction, we design a candidate shape generator based on a mixture strategy, called mixture generator, it combines the image data driven method with model parameter driven method, and tends to generate valid shape in area which has a high shape prior density value by exploiting the GPDM model, so the efficiency of search is greatly improved. To prove the accuracy of our mixture generator, we have done experiments under the framework of global optimization algorithm (simulated annealing) on the FGNET face database. Experiments show that, compared with traditional ASM algorithm, our method is not only insensitive to initialization conditions, but also can put up with clutters and realize a more robust object extraction.

关键词： Shape Generators Face Active shape model Data models Simulated annealing

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Incremental tensor principal component analysis for image recognition

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Advanced Materials Research 2013年 710卷 584-588页

作者： Zhao, Wei Dong Liu, Chang Yan, Tao College of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu 610106 China

ISBN: (纸本)9783037857205

Aiming at the disadvantages of the traditional off-line vector-based learning algorithm, this paper proposes a kind of Incremental Tensor Principal Component Analysis (ITPCA) algorithm. It represents an image as a tensor data and processes incremental principal component analysis learning based on update-SVD technique. On the one hand, the proposed algorithm is helpful to preserve the structure information of the image. On the other hand, it solves the training problem for new samples. The experiments on handwritten numeral recognition have demonstrated that the algorithm has achieved better performance than traditional vector-based Incremental Principal Component Analysis (IPCA) and Multi-linear Principal Component Analysis (MPCA) algorithms. © (2013) Trans Tech Publications, Switzerland.

关键词： Principal component analysis

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Incremental tensor discriminant analysis for image detection

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Advanced Materials Research 2013年 710卷 579-583页

作者： Yan, Tao Liu, Chang Zhao, Wei Dong College of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu 610106 China

ISBN: (纸本)9783037857205

In order to settle incremental learning and preserve the space information of images, this paper proposes an incremental tensor discriminant analysis for facial image detection. The proposed algorithm employs tensor representation to preserve the structure information and introduces the incremental learning to solve the on-line learning for new added samples. The experiments have shown that our method achieves better classification performance and reduces the computational cost effectively compared with other algorithms. © (2013) Trans Tech Publications, Switzerland.

关键词： Discriminant analysis

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