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	1. Agent-based Interopration Model（AIM）For Agent-Web Services Combination （AWSC）In Bioinformatics Web Application
	Li Wei,Lv Zhongdogn
	Computer Science and Technology 11 January 2010
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:As a new developing technology model，Agent attracts more attention in Internet，which benefits more the distributed application and integration, especially in the area of Web Service Collaboration(WSC) and Web Service Interoperation (WSI).An Agent-based Interoperation Model (AIM) proposed in this paper achieves the Agent-Web Services combination（AWSC）. The AIM mainly depending on ACL specifications to complete the Agent communication for Web Service composition and excution. Then a bioinformatics Web Service application has been introduced as Agent-Web Services combination case. It demonstrates how the proposed Agent-Web Services combination（AWSC）framework can be used to establish a collaborative environment that provides dynamic Web services integration and interopration.
	TO cite this article:Li Wei,Lv Zhongdogn . Agent-based Interopration Model（AIM）For Agent-Web Services Combination （AWSC）In Bioinformatics Web Application[OL].[11 January 2010] http://en.paper.edu.cn/en_releasepaper/content/38715


	2. A Fault Diagnosis Modeling Method Combined RBF Neural Network with Rough Set Theory
	Zhou Liuyang,Shi Yuwen,Zhang Yunlong
	Computer Science and Technology 25 June 2009
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In order to improve diagnosis precision and decreasing misinformation diagnosis, according to the intelligence complementary strategy, a new complex intelligent fault diagnosis method based on rough sets theory and RBF neural network is presented. Firstly, basis on data pretreatment, the fault diagnosis decision table is formed, and continuous datum are discretized by using hybrid clustering method. Rough sets theory as a new mat hematical tool is used to deal with inexact and uncertain knowledge for pattern recognition. The target is mainly to remove redundant information and seek for reduced decision tables which to obtain the minimum fault feature subset. The neural networks adopted were of the feed-forward variety with one hidden layer. They were trained using back-propagation. The method can reduce the false alarm rate and missing alarm rate of the fault diagnosis system effectively, and can detect the composed faults while keep good robustness.
	TO cite this article:Zhou Liuyang,Shi Yuwen,Zhang Yunlong. A Fault Diagnosis Modeling Method Combined RBF Neural Network with Rough Set Theory[OL].[25 June 2009] http://en.paper.edu.cn/en_releasepaper/content/33405

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