學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

🏛️國立陽明交通大學機構典藏

Search Results

Now showing 1 - 10 of 19

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
;
Yeh, YS
;
Chen, RS
;
資訊科學與工程研究所
;
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.
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.
A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method
(2003-05-01)
Chen, RS
;
Hu, YC
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre-specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily, obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method.
Acquisition of compound skills and learning costs for expanding competence sets
(2003-09-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
Chiu, YJ
;
科技管理研究所
For each decision problem, there is a competence set consisting of ideas, knowledge, information, and skills for solving that problem. When decision makers have not acquired the competence set, it is more difficult for them to make decisions. In order to effectively acquire a needed competence set to cope with the problem they face, finding an appropriate learning sequence for acquiring needed single skills for decision makers, the so-called competence set expansion, is very necessary. A compound skill represents a collection of single skills that might be acquired, and some useful compound skills can be added to the needed competence set to help acquire some single skills. To effectively expand the competence set, effective acquisitions of compound skills and learning costs are both necessary. This paper thus proposes a data mining technique to extract potentially useful compound skills from single skills. Subsequently, an effective method is proposed to obtain the learning cost between any two skills. A computer simulation is employed to further show that it is feasible to use those potentially useful compound skills to facilitate the acquisition of single skills through a known integer programming method for expanding the competence set. (C) 2003 Elsevier Ltd. All rights reserved.
A case study in the design of BTO/CTO shop floor control system
(2003-10-01)
Chen, RS
;
Lu, KY
;
Yu, SC
;
Tzeng, HW
;
Chang, CC
Although computerized information systems are conventionally used to control production, applying a manual approach and improper logistical assignments typically reduces operating efficiency and increases customer service response time. Satisfying the requirements of remaining competitiveness in the current marketplace, such as offering build-to-order (BTO) or configuration-to-order (CTO) services is difficult. This study presents an integrated information system for use in shop floor controlling systems (SFCS) for kitting parts, producing goods, packing finished goods and distributing them, to enhance the performance of the BTO/CTO production system. Several information technologies and devices, such as barcode system, the electronic pick-to-light picking system, electronic Kanban (eKanban), and others are adopted to support the relevant logistics. A personal computer (PC) manufacturer is used to demonstrate the proposed system. The findings of the study reveal that the proposed information system can respond to the status of the production line in real time. This responsiveness can help the production controller to make a suitable decision and quickly meet the requirements of the BTO/CTO production system. (C) 2003 Elsevier Science B.V. All rights reserved.

學術出版;;Publications

Browse

Filters

Settings

Sort By

Results per page

🏛️國立陽明交通大學機構典藏

Search Results