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Constructing global view with an ontology-based method for information sharing in the virtual organization

Journal of Systems Engineering and Electronics 2005年第3期16卷 566-573页

作者：张英朝张维明肖卫东黄金才沙基昌 Dept .of Management Science and Engineering National Univ .of Defense Technology

The problem of constructing global view of heterogeneous information sources for information sharing is becoming more and more important due to the availability of multiple information sources within the virtual organization. Global view is defined to provide a unified representation of the information in the different sources by analyzing concept schemas associated with them and resolving possible semantic heterogeneity. An ontology-based method for global view construction is proposed. In the method, （ 1 ） Based on the formal ontologies, the concept of semantic affinity is introduced to assess the level of semantic relationship between information classes from different information sources; （2） Information classes are classified by semantic affinity levels using clustering procedures so that their different representations can be analyzed for unification; （3） Global view is constructed starting from selected clusters by unifying representation of their elements. The application example of using the method in the joint-aerial defense organization is illustrated and the result shows that the proposed method is feasible.

关键词： global view ontology information sharing virtual organization clustering algorithm.

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Research on High Resolution Satellite Image Classification algorithm.based on Convolution Neural Network

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International Journal of Technology Management 2016年第9期 53-55页

作者： Gaiping He Xi'an International University Xi'anShaanxi710077 China

Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm.requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm.can effectively classify the image.

关键词： High Resolution Satellite Image Classification Convolution Neural Network clustering algorithm.

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Prediction of malignancy selective neural networks degree in brain glioma using ensemble

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Journal of Shanghai University(English Edition) 2006年第3期10卷 244-246页

作者：刘天羽李国正吴耿锋 School of Computer Engineering and Science Shanghai University Shanghai 200072 P.R. China

A clustering algorithm.based selective neural networks ensemble （CLUSEN） is proposed to predict the degree of malignancy in brain glioma. Since the degree prediction of malignancy is critical before brain surgery, many learning methods are used like rule induction algorithm. single neural networks, support vector machines, etc. Ensemble learning methods can improve the generalization of single learning machine, and are becoming popular in the machine learning and medical data processing communities. The procedure of CLUSEN can efficiently remove redundancy learning individuals and help improve the diversity of ensemble methods. CLUSEN is used to predict the degree of malignancy in brain glioma. Experimental results on a set of brain glioma data show that, compared to support vector machines, rule induction and single neural networks, the classification accuracy of CLUSEN is higher.

关键词： ensemble learning neural networks brain glioma clustering algorithm.

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