Seung-Gook Hwang

Pattern Analysis of Core Competency Model for Subcontractors of Construction Companies Using Fuzzy TAM Network

TAM(Topographic Attentive Mapping) network is a biologically-motivated neural network. It is composed of input layer, category layer, and output layer. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis on core competency model for subcontractors of construction companies and show its usefulness.

Fuzzy Structure Analysis for Factors of Project Performance

The structure of layer for success factors of company project can be known by FSM which is available to recognize structure of the system. However, the relationship between the layers which belongs to the success factors of company projects can be unknown. From these viewpoints, this study is to analyze the structure of the relation between the layers contained the success factors of company projects using fuzzy system. Therefore, the relationship between lower vs middle layer and middle vs upper layer are obtained by evaluation model of company projects which has possibility and necessity of fuzzy system as fuzzy relation matrix. Using these results, fuzzy relation analysis is done and showed the usefulness.

A Study on Acquisition of Evaluation Rules for Subcontractors of Construction Companies Using Fuzzy ID3

In construction industry, it is very important to secure the eminent subcontractors of construction companies in order to increase the competitive power, these subcontractors ate selected through the total evaluation. Also, the results of evaluation is used management data the subcontractors. Therefore, if we know the evaluation rules of subcontractors previously, it can be used to manage the subcontractors effectively. Thus, using fuzzy ID3 which is obtain the if-then fuzzy rule from the given data, we acquire the rules for evaluation of subcontractors and show its usefulness.

Fuzzy TAM Network Model Using SOM

The fuzzy TAM(Topographical Attentive Mapping) network is a supervised method of pattern analysis which is composed of input layer, category layer, and output layer. But if we don`t know the target value of the pattern, the network can not be trained. In this case, the target value can be replaced by a result induced by using an unsupervised neural network as the SOM (Self-organizing Map). In this paper, we apply the results of SOM to fuzzy TAM network and show its usefulness through the case study.

Development of Project Success Model in Enterprise Using FCM

To increase the chance of the success of project, it is necessary to identify the important factors influencing the performance of project. Cognitive map has been used for analysing by investigating causal links among relevant factors. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. Project performance is influenced by several factors such as input resources, possibility of achievement, concern of others and so on. The object of this paper is to develop the project success model of enterprise using a fuzzy cognitive map approach to investigate the degree of causal relationship between project performance and these factors.

Pattern Analysis of an Evaluation Model on Disaster Management System Using Fuzzy ID3

In this paper, an evaluation model of disaster management system which is developed by using GRI G3 version and BCP process for the sustainability management of small and middle enterprises was proposed. The proposed evaluation model can be used to evaluate the disaster management system directly, and to establish the disaster management system as an index. Also, the rules obtained by pattern analysis using fuzzy ID3 for the proposed evaluation model are expected to use the guideline effectively for self evaluation in the small and middle enterprises.

Development of Classification Model Using Neural Network

In this paper, a model to classify the method using the fuzzy TAM with preprocessing of data was developed. The preprocessing method can be divide the problem using the characteristics in the case of category type factor. In case of continuous type factor, if there was exist factor`s range which is not overlapping by class, the data belong to the range was fixed and eliminated in classification. After these preprocessing of data, classified operation of Fuzzy TAM is performed.