检索结果-内蒙古大学图书馆

Optimize diagnosability and sensor placement in discrete event system

Journal of Convergence Information Technology 2011年第7期6卷 219-224页

作者： Jian, Zhao Dantong, Ouyang Xiaoyu, Wang Liming, Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Changchun University Changchun China

Diagnosability indicates that whether the fault can be detected in finite time, which is an important property in model based diagnosis. As diagnosis depends on the sensor placement and the modeling, it is hard to make a choice whether to place more sensors for testing or to compute more diagnosis pathways in practical application. In this paper, a method is proposed to resolve this by defining key point. The key points take priority on testing among the existing sensors. In these key points, the observation can be optimized and be diagnosis tested efficiently. Experimental result indicates that this method achieves efficient faults distinction and identification, and reduces the cost of sensors and the computational complexity of diagnostic in the case of normal behavior.

关键词： Discrete event simulation

来源：评论

学校读者我要写书评

暂无评论

A new feature selection method based on clustering

A new feature selection method based on clustering

引用

2011 8th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural computation, ICNC'11

作者： Liu, Huawen Mo, Yuchang Wang, Jiyi Zhao, Jianmin Department of Computer Science Zhejiang Normal University Jinhua 321004 China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012 China School of Computer Science and Engineering Southeast University Nanjing 211189 China

ISBN: (纸本)9781612841816

Feature selection is an effective technique to put the high dimension of data down, which is prevailing in many application domains, such as text categorization and bio-informatics, and can bring many advantages, such as improving efficiency and avoiding over-fitting, to learning algorithms. Currently, many efforts have been attempted in this field and various feature selection methods have been developed and proved to be very competitive. Unlike other selection methods, in this paper we propose a new method to select important features using a manner of feature clustering. The main character of our method is that it works like data clustering in an agglomerative way. In this method, each feature is considered as a data point clustered with between-cluster and within-cluster distances. As a result, the selected feature subset has minimal redundancy among its members and maximal relevance with the class labels. Our performance evaluations on seven benchmark datasets show that the classification performance achieved by our proposed method is better than other feature selection methods. © 2011 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

A novel method for RNA secondary structure prediction

A novel method for RNA secondary structure prediction

引用

2011 7th International Conference on Natural computation, ICNC 2011

作者： Xing, Chong Ji, Zhaohua Wang, Gang Wang, Yao Shen, Wei Liang, Yanchun Key Laboratory for Symbol Computation and Knowledge Engineering of National Education MinistryCollege of Computer Science and TechnologyJilin University Changchun China College of Information Engineering Guanghua College of Changchun University Changchun China College of Communications Engineering Jilin University Changchun China

ISBN: (纸本)9781424499533

In this paper, we propose PSOfold, a particle swarm optimization for RNA secondary structure prediction. PSOfold is based on the recently published IPSO. We present two strategies to improve the performance of IPSO. Firstly, in order to boost the competence in searching an optimal solution, fuzzy logic control is used to adaptively adjust the parameters in PSO. Accordingly, three fuzzy logic controls are designed by which the inertia weight, learning factors and the number of ants are tuned respectively. Secondly, to further settle the stem permutation problem, we put forward a solution conversion strategy (SCS), which can transform discrete values of stems into an ordered stem combination, thereby supplying an enhanced solution to evaluation of objective function. An evaluation of the performance of PSOfold in terms of prediction accuracy is made via comparison with one dynamic programming algorithm mfold and four metaheuristics, IPSO, ACRNA, RnaPredict, SARNA-Predict and mfold for ten individual known structures. PSOfold is able to predict structures with higher prediction accuracy than the other metaheuristic based methods on certain sequences, and has comparable performance compared with mfold. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Eliminating repeated constraint checks in MaxRPC algorithms

引用

ICIC Express Letters, Part B: Applications 2011年第5期2卷 1189-1194页

作者： Guo, Jinsong Li, Zhanshan Zhang, Liang Geng, Xuena Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University No. 2699 Qianjin Street Changchun 130012 China College of Computer Science and Technology Jilin University No. 2699 Qianjin Street Changchun 130012 China

Filtering techniques are used in Constraint Satisfaction Problems to remove all the local inconsistencies during a processing step or prune the search tree efficiently during search. Local consistencies are used as preprocessing filtering techniques. MaxRPC which can remove more values than arc consistency is not used as widely as arc consistency for its relatively expensive time cost. We discover that, in the algorithms enforcing maxRPC, there are repeated constraint checks which can affect the efficiency of the algorithm negatively. In this paper, we propose a method making use of a data structure of AC4 and the relation "stronger" between local consistencies to eliminate the repeated constraint checks. Since the repeated constraint checks can be eliminated and the time cost of enforcing maxRPC can be reduced, the applicable occasions of maxRPC should be reconsidered. © 2011 ISSN 2185-2766.

关键词： Constraint satisfaction problems

来源：评论

学校读者我要写书评

暂无评论

Translation of SPARQL to SQL based on integrity constraint

引用

Journal of computational Information Systems 2011年第2期7卷 394-402页

作者： Cui, Xianji Ouyang, Dantong Ye, Yuxin Wang, Xiaolong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Computation and Knowledge Engineering Ministry of Education China

Most existing storages of the ontology use relational databases as a backend to manage RDF data. This motivates us to translate SPARQL queries, the proposed standard for RDF querying, into equivalent SQL queries. At the basis of giving the IC-based storage to store the ontology with integrity constraints, we present the corresponding translation of SPARQL to SQL called IC-based translation. With the different structure of the tables in the relational databases, the SPARQL query statements are divided into four types. For the different type of query statements, we give the corresponding translations. Copyright © 2011 Binary Information Press.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

A novel framework for gene selection

引用

International Journal of Advancements in Computing Technology 2011年第3期3卷 184-191页

作者： Zhao, Wei Wang, Gang Wang, Hong-bin Chen, Hui-ling Dong, Hao Zhao, Zheng-dong Network Information Center Changchun University of Science and Technology Changchun 130022 China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Microarray data are highly redundant and noisy, and most genes are believed to be uninformative with respect to studied classes, as only a fraction of genes may present distinct profiles for different classes of samples. This paper proposed a novel hybrid framework (NHF) for the classification of high dimensional microarray data, which combined information gain(IG), F-score, genetic algorithm(GA), particle swarm optimization(PSO) and support vector machines(SVM). In order to identify a subset of informative genes embedded out of a large dataset which is contaminated with high dimensional noise, the proposed method is divided into three stages. In the first stage, IG is used to construct a ranking list of features, and only 10% features of the ranking list are provided for the second stage. In the second stage, PSO performs the feature selection task combining SVM. F-score is considered as a part of the objective function of PSO. The feature subsets are filtered according to the ranking list from the first stage, and then the results of it are supplied to the initialization of GA. Both the SVM parameter optimization and the feature selection are dynamically executed by PSO. In the third stage, GA initializes the individual of population from the results of the second stage, and an optimal result of feature selection is gained using GA integrating SVM. Both the SVM parameter optimization and the feature selection are dynamically performed by GA. The performance of the proposed method was compared with that of the PSO based, GA based, Ant colony optimization (ACO) based and simulated annealing (SA) based methods on five benchmark data sets, leukemia, colon, breast cancer, lung carcinoma and brain cancer. The numerical results and statistical analysis show that the proposed approach is capable of selecting a subset of predictive genes from a large noisy data set, and can capture the correlated structure in the data. In addition, NHF performs significantly better than th

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

A Heuristic Genetic Process Mining Algorithm

A Heuristic Genetic Process Mining Algorithm

引用

International Conference on computational Intelligence and Security

作者： Jiafei Li Jihong OuYang Mingyong Feng Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Jilin University Changchun Jilin CN Industry Applications Department Cattsoft Technology Company Limited Beijing China

The current GPM algorithm needs many iterations to get good process models with high fitness which makes the GPM algorithm usually time-consuming and sometimes the result can not be accepted. To mine higher quality model in shorter time, a heuristic solution by adding log-replay based crossover operator and direct/indirect dependency relation based mutation operator is put forward. Experiment results on 25 benchmark logs show encouraging results.

关键词： Data mining Genetics Heuristic algorithms computational modeling Process control Industries Measurement

来源：评论

学校读者我要写书评

暂无评论

A Loss Assessment Design Method Based on SOA for Insurance Informationization

A Loss Assessment Design Method Based on SOA for Insurance I...

引用

International Conference on Digital Manufacturing and Automation (ICDMA)

作者： Niya Li Jian Zhang Wei Lv Chengye Jin Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Jilin University Changchun Jilin CN College of Computer Science and Technology Jilin University China LeJin Electronics Company Limited Jilin China

The loss assessment is an important operation of claim process in insurance industry. On the growing tide of making the insurance information system the in-depth support to optimizing operation and serving insurant, a methodological framework for the loss assessment is given based on SOA technology, Under the framework, the operation process design, the client design, the service design and the database design are given. These design results have been validated by an actual application system.

关键词： Insurance Databases Companies Information systems Service oriented architecture Design methodology

来源：评论

学校读者我要写书评

暂无评论

A Chinese online document clustering algorithm based on hidden sentiment vector

引用

Journal of computational Information Systems 2011年第6期7卷 1974-1981页

作者： Yang, Hui Wang, Zhe Zhou, Xu Wang, Zuo Zhou, Tong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education China

The core idea of clustering algorithm is the division of data into groups of similar objects. Some clustering algorithms are proven good performance on document clustering, such as k-means and UPGMA etc. However, few document clustering algorithms pay attention to the hidden sentiment which is a very important feature of the documents. This paper presents an improved k-means algorithm (HSK-Means) based on hidden sentiment vector for Chinese document clustering. Chinese dependency grammar rules are used to extract document feature and evaluate hidden sentiment. A new method for selecting initial cluster centroids based on rank mechanism of similarity is also proposed to improve accuracy of clustering algorithm. The experimental results on real online document sets illustrate that the performance of HSK-Means algorithm is better than classic document clustering algorithms. Copyright © 2011 Binary Information Press.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Support vector description of clusters for image annotation

Support vector description of clusters for image annotation

引用

International Workshop on Advanced computational Intelligence and Intelligent Informatics, IWACIII 2011

作者： Sun, Liang Yoshida, Shinichi Ge, Hongwei Liang, Yanchun College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China School of Information Kochi University of Technology Kochi 7828502 Japan Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 116024 China

Image annotation is a challenging problem due to the rapid growing of real world image archives. In this paper, we propose a novel approach to the solving of this problem based on a variant of the support vector clustering (SVC) algorithm, i.e., the support vector description of clusters. The system has two major components, the training process and the annotating process. In the training process, clusters of image manually annotated by descriptive words are used as training instances. Each cluster is described by a one-cluster SVC model. The proposed model can exploit the advantage of SVC for its ability to delineate cluster boundaries of arbitrary shape. Moreover, the training process of the one-cluster SVC model is formulated as the process of building density estimator for underlying distribution of the cluster. In the annotating process, for a test image, the probability of this instance being generated by each model is computed. And then the relevant words are selected based on the obtained probabilities. Simulated experiments were conducted on the Corel60k data set. The results demonstrate the performance of the proposed algorithm, compared with the performance of other algorithms.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：