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A web-based monitor and management system architecture for enterprise Virtual Private Network
(2005-01-01)
Chen, RS
;
Chang, CC
;
Yeh, SW
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This research is based on the new network management technologies such as Policy-based Network Management, Mobile Agent, etc., to design a VPN monitor and management system architecture that is suitable for the enterprise use and contains high level management with low Network Traffic load. This system architecture integrates the monitor and management for both VPN devices and general network devices, such the feature makes enterprise can integrate the monitor and manage the VPN and Intranet at the same time and solve the problems caused by using the VPN. The results of this research provide a VPN system architecture for monitoring and managing the enterprises and establish a prototype for the enterprise VPN` monitor and management system.
A heuristic approach to generating file spanning trees for reliability analysis of distributed computing systems
(1997-11-01)
Chen, DJ
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Chen, RS
;
Huang, TH
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資訊科學與工程研究所
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Institute of Computer Science and Engineering
The reliability of Distributed Computing Systems (DCS) in terms of Distributed Program Reliability (DPR) and Distributed System Reliability (DSR) has been studied intensively. Current reliability algorithms available for the analysis of DPR and DSR include MFST, FARE, FST, and FST-SPR. This paper presents a reliability algorithm, called HRFST, that eliminates the need to search a spanning tree during each subgraph generation. The HRFST algorithm reduces both the number of subgraphs (or trees) generated and the actual execution time required for analysis of DPR and DSR. Examination of several sample cases shows that the HRFST algorithm is more efficient than the FST-SPR algorithm.
Reduction of the total execution time to achieve the optimal k-node reliability of distributed computing systems using a novel heuristic algorithm
(2000-01-01)
Chiu, CC
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Yeh, YS
;
Chen, RS
;
資訊科學與工程研究所
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Institute of Computer Science and Engineering
A distributed computing system is a collection of processor-memory pairs connected by communication links. A k-node set is a subset of total nodes in a distributed computing system. A k-node set with capacity constraint is a k-node set that possesses sufficient node capacity. Because computing the reliability of a distributed computing system is generally an NP-hard problem, an adequate k-node set with a given capacity constraint must be determined by an effective algorithm with an approximate reliability. Relatively few investigations, namely an exact method and a k-tree reduction method, have examined k-node reliability optimization with capacity constraint. Such investigations either spent an exponential time or rarely obtained an optimal solution. Therefore, in this work, we present a novel heuristic algorithm to reduce the computational time and deviation from an exact solution. The proposed algorithm has simple independent steps, including selection of k-node sets according to a node's weight or a link's weight. The number of selected k-node sets is either one or two, thereby spending less time to compute the reliability of k-node sets. Computational results demonstrate that the proposed algorithm is more effective and provides a better solution for a large distributed computing system than those in previous investigations. (C) 2000 Elsevier Science B.V. All rights reserved.
A hybrid genetic algorithm approach on multi-objective of assembly planning problem
(2002-09-01)
Chen, RS
;
Lu, KY
;
Yu, SC
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In practice, modeling an assembly system often requires assigning a set of operations to a set of workstations. The aim is to optimize some performance indices of an assembly line. This assignation is usually a tedious design procedure so a significant amount of manpower is required to obtain a good work plan. Poor assembly planning may significantly increase the cost of products and reduce productivity. However, these optimization problems fall into the class of NP-hard problems. Finding an optimal solution in an acceptable time is difficult, even using a powerful computer. This study presents a hybrid genetic algorithm approach to the problems of assembly planning with various objectives, including minimizing cycle time, maximizing workload smoothness, minimizing the frequency of tool change, minimizing the number of tools and machines used, and minimizing the complexity of assembly sequences. A self-tuning method was developed to correct infeasible-chromosomes. Several examples were employed to illustrate the proposed approach. Experimental results indicated that the proposed method can efficiently yield many alternative assembly plans to support the design and operation of a flexible assembly system. (C) 2003 Elsevier Science Ltd. All rights reserved.
Mining fuzzy association rules for classification problems
(2002-09-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
資訊管理與財務金融系註：原資管所+財金所
The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems. (C) 2002 Elsevier Science Ltd. All rights reserved.
Generating learning sequences for decision makers through data mining and competence set expansion
(2002-10-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
資訊管理與財務金融系註：原資管所+財金所
For each decision problem, there is a competence set, proposed by Yu, consisting of ideas, knowledge, information, and skills required for solving the problem. Thus, it is reasonable that we view a set of useful patterns discovered from a relational database by data mining techniques as a needed competence set for solving one problem. Significantly, when decision makers have not acquired the competence set, they may lack confidence in making decisions. In order to effectively acquire a needed competence set to cope with the corresponding problem, it is necessary to find appropriate learning sequences of acquiring those useful patterns, the so-called competence set expansion. This paper thus proposes an effective method consisting of two phases to generate learning sequences. The first phase finds a competence set consisting of useful patterns by using a proposed data mining technique. The other phase expands that competence set with minimum learning cost by the minimum spanning table method proposed by Feng and Yu. From a numerical example, we can see that it is possible to help decision makers to solve the decision problems by use of the data mining technique and the competence set expansion, enabling them to make better decisions.
Grey self-organizing feature maps
(2002-10-01)
Hu, YC
;
Chen, RS
;
Hsu, YT
;
Tzeng, GH
;
科技管理研究所
In each training iteration of the self-organizing feature maps (SOFM), the adjustable output nodes can be determined by the neighborhood size of the winning node. However, it seems that the SOFM ignores some important information, which is the relationships that actually exist between the input training data and each adjustable output node, in the learning rule. By viewing input data and each adjustable node as a reference sequence and a comparative sequence, respectively, the grey relations between these sequences can be seen. This paper thus incorporates the grey relational coefficient into the learning rule of the SOFM, and a grey clustering method, namely the GSOFM, is proposed. From the simulation results, we can see that the best result of the proposed method applied for analysis of the iris data outperforms those of other known unsupervised neural network models. Furthermore, the proposed method can effectively solve the traveling salesman problem. (C) 2002 Elsevier Science B.V. All rights reserved.
Finding fuzzy classification rules using data mining techniques
(2003-01-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
資訊管理與財務金融系註：原資管所+財金所
Data mining techniques can be used to discover useful patterns by exploring and analyzing data, so, it is feasible to incorporate data mining techniques into the classification process to discover useful patterns or classification rules from training samples. This paper thus proposes a data mining technique to discover fuzzy classification rules based on the well-known Apriori algorithm. Significantly, since it is difficult for users to specify the minimum fuzzy support used to determine the frequent fuzzy grids or the minimum fuzzy confidence used to determine the effective classification rules derived from frequent fuzzy grids, therefore the genetic algorithms are incorporated into the proposed method to determine those two thresholds with binary chromosomes. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that the proposed method performs well in comparison with other classification methods. (C) 2002 Elsevier Science B.V. All rights reserved.
A fuzzy data mining algorithm for finding sequential patterns
(2003-04-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
Shieh, JH
;
科技管理研究所
Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered fist of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.
Discovering fuzzy association rules using fuzzy partition methods
(2003-04-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
Fuzzy association rules described by the natural language are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. In this paper, a new algorithm named fuzzy grids based rules mining algorithm (FGBRMA) is proposed to generate fuzzy association rules from a relational database. The proposed algorithm consists of two phases: one to generate the large fuzzy grids, and the other to generate the fuzzy association rules. A numerical example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstrating the effectiveness of the proposed algorithm. (C) 2002 Elsevier Science B.V. All rights reserved.

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