• Journal of Construction Engineering and Project Management
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• v.8 no.4
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• pp.1-24
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• 2018

In the last decade, the use of Building Information Modeling (BIM) as a new technology has been applied with traditional Computer-aided design implementations in an increasing number of architecture, engineering, and construction projects and applications. Its employment alongside construction management, can be a valuable tool in helping move these activities and projects forward in a more efficient and time-effective manner. The traditional stakeholders, i.e., Owner, A/E and the Contractor are involved in this BIM system that is used in almost every activity of construction projects, such as design, cost estimate and scheduling. This article extracts major features of the application of BIM from perspective of participating BIM components, along with the different phrases, and applies to them a logistic analysis using a fuzzy performance tree, quantifying these phrases to judge the effectiveness of the BIM techniques employed. That is to say, these fuzzy performance trees with fuzzy logic concepts can properly translate the linguistic rating into numeric expressions, and are thus employed in evaluating the influence of BIM applications as a mathematical process. The rotational fuzzy models are used to represent the membership functions of the performance values and their corresponding weights. Illustrations of the use of this fuzzy BIM performance tree are presented in the study for the uninitiated users. The results of these processes are an evaluation of BIM project performance as highly positive. The quantification of the performance ratings for the individual factors is a significant contributor to this assessment, capable of parsing vernacular language into numerical data for a more accurate and precise use in performance analysis. It is hoped that fuzzy performance trees and fuzzy set analysis can be used as a tool for the quality and risk analysis for other construction techniques in the future. Baldwin's rotational models are used to represent the membership functions of the fuzzy sets. Three scenarios are presented using fuzzy MEAN, AND and OR gates from the lowest to intermediate levels of the tree, and fuzzy SUM gate to relate the intermediate level to the top component of the tree, i.e., BIM application final performance. The use of fuzzy MEAN for lower levels and fuzzy SUM gates to reach the top level suggests the most realistic and accurate results. The methodology (fuzzy performance tree) described in this paper is appropriate to implement in today's construction industry when limited objective data is presented and it is heavily relied on experts' subjective judgment.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.321-327
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• 2014

Feature selection is the technique to improve the classification performance by using a minimal set by removing features that are not related with each other and characterized by redundancy. This study proposed new feature selection using the distance between the center of gravity of the bounded sum of weighted fuzzy membership functions (BSWFMs) provided by the neural network with weighted fuzzy membership functions (NEWFM) in order to improve the classification performance. The distance-based feature selection selects the minimum features by removing the worst features with the shortest distance between the center of gravity of BSWFMs from the 24 initial features one by one, and then 22 minimum features are selected with the highest performance result. The proposed methodology shows that sensitivity, specificity, and accuracy are 97.7%, 99.7%, and 98.7% with 22 minimum features, respectively.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.687-689
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• 1998

This paper represents fundamental developments in Fuzzy and Neural approaches. The Fuzzy Controller(FC) and plant are cascaded in Adaptive framework. Each of which produces its outputs. The adjustable parameters all pertain to the fuzzy controller is implemented as an Adaptive FC to adjust the environments of the plant. There is an error meaure block which is a difference between the actual state and desired state. We introduce error back propagation algorithm in neural method. To speed up convergence, we follow a steepest decent in the sense that each parameter set update leads to a smaller error measure and is learned by this methodology. Inverted pendulum is a typical testbed to measure the effectiveness of nonlinear control system. finally we simulated the adaptive fuzzy controller to be able to bring back to the upright position of the its angle and angular velocity.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.119-126
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• 2012

Recently, the breakdown of online banking servers and the leakage of customer information give rise to much concern about the security of information systems in financial and banking companies in Korea. The enforcement of security for information system becomes much more important issue than earlier. However, the security reinforcement of information system is restricted by a budget. In addition, the activities' cost to secure information system from threatening are under uncertain circumstances and should be established by a human decision maker who is basically uncertain and vague. Thus, making the budget for information system is exposed to any extent of the risk for these reasons. First, we introduce brief fuzzy set theory and fuzzy AHP (Analytic Hierarchy Process) methodology. Then, the cost elements that comprise yearly budget are presented and the priorities among the cost elements are calculated by fuzzy AHP. The cost elements that are exposed to risk are evaluated from the both perspectives of the risk impact and risk occurrence possibility which are expressed as linguistic terms. To get information on the risk profiles-pessimistic, most likely, and optimistic-for each cost element, the evaluation is accomplished and the result is presented. At last, the budget ranges-minimum, mode, maximum-for each cost element are estimated with the consideration of the risk profiles.

• Journal of Environmental Impact Assessment
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• v.5 no.1
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• pp.47-60
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• 1996

Nitrate contamination problems from groundwater supplies have been reported throughout many countries in the world, including Korea. Nitrate salts can induce methemoglobinemia and possibly human gastric cancer. To reduce human health risk from nitrate in groundwater supplies, several nitrate risk-management strategies can be developed based on the acceptable level of human health risk, the reasonableness of nitrate-control cost, and the technical feasibility of nitrate-control methods. However, due to a lack of available information, assessing risk, cost and technical feasibility contains elements of uncertainty. In the present paper, a nitrate risk-management methodology using fuzzy sets in combination with a multiobjective decision-making (MODM) technique is developed to assist decision makers in evaluating, with uncertain information, various nitrate risk-management strategies in order to decide a proper strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1119-1124
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• 2012

In this paper I introduced a new methodology; Fuzzy-set Qualitative comparative analysis (fsQCA) calibration introduced by Charles C Ragin to measure the degree of technology innovation in OECD countries and non OECD countries and developed a simple technology development pattern based on patents number of traditional technology, information and communication technology and biotechnology which are published by OECD Working group. Through this typology and measurement of technology innovation degree, we can provide appropriate and necessary science and technology policies for each developed or developing country and technology sectors like information and communication technology.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.10
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• pp.1469-1478
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• 2015

In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN.

• The Journal of Information Systems
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• v.19 no.4
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• pp.65-85
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• 2010

The importance of communities of practice(CoP) as an organizational informal unit for fostering knowledge transfer and sharing gains a lot of attention from KM researchers and practitioners. Since most of CoPs are formulated online these days, the credibility or trustworthiness of knowledge contents circulated within a certain CoP should be considered thoroughly for them to be fully utilized safely. Here comes the need for an appropriate trust measuring methodology to determine the true value of knowledge given by unknown people through an online channel. In this paper, an improved trust measuring method is proposed using new trust variables such as level of degrees derived from the relationships among community users. In addition, activeness, relevance, and usefulness of the knowledge contents themselves, which are calculated automatically using a text categorization technique, are also used for trust measurement. The proposed framework incorporates fuzzy set and calculation concepts to help build trust matrices and models, which are used to measure the level of trust involved in specific knowledge artifacts concerned.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.172-174
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• 2002

This paper proposes a new methodology for adequacy evaluation of composite power system considering an ambiguity of overload of transmission lines. Nodal arrival powers under considering an ambiguity of overload of transmission lines can be evaluated using the proposed method. Fuzzy set theory has been used in order to consider the permission level of overload of transmission lines. The problem of adequacy evaluation has been formulated using fuzzy linear programing. The effectiveness of the proposed method considering permissible overload of transmission lines has been demonstrated on the IEEE RTS.