• Journal of Korean Society of Transportation
• /
• v.18 no.4
• /
• pp.87-94
• /
• 2000

본 논문은 교통사업들의 투자우선순위 결정시 적용 가능한 방법을 제시한다. 지금의 평가방법을 부정하는 새로운 방법이라기보다는 기존의 평가방법을 보완하는 기법이다. 특히 특성이 다른 교통사업들간의 평가시 이들 특성들이 평가과정에 감안되지 않을 경우는 평가결과의 대외 설득력이 부족하게 된다. 이를 극복하기 위해 지금까지 각종 정량적 정성적 방법을 동원하였지만 그 범위와 방법에 한계가 존재하였다. 이를 보완하는 대안으로서 본 논문에서는 기존 평가과정에서 고려되기 어려운 사업의 여러 특성들을 전문가들의 경험과 판단을 통해 최대한 반영하는 방법을 제시한다. 이 기법의 기본 이론은 집단의사결정이론(group decision theory)이며, 여기에 퍼지이론이 접목된 퍼지집단 결정이론(fuzzy group decision theory)을 적용하였다. 이 이론은 개인의 대안별 선호(우선순위)로부터 집단의 선호관계(우선순위)를 도출할 수 있다. 또한 도출된 투자우선순위결과에 대한 집단의 동의수준(만족도)을 추정할 수 있다. 이러한 정보는 최종 정책결정자의 중요한 판단자료로서 사용되어 질 수 있다. 그 동안 교통사업의 투자우선순위결정과정의 객관성의 부족으로 사업간 우선순위결과에 대한 신뢰가 그리 높지 않았다. 본 논문에서 제시한 방법은 기존의 투자우선순위의 평가방법을 보완하여 대외적인 신뢰성을 제고할 수 있을 것으로 판단된다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.05a
• /
• pp.68-71
• /
• 2002

의사들은 환자들의 건강 상태와 관련하여 다양한 유형의 정보들을 수집하고 분석하여 개별적인 환자들의 진단을 내리게 된다. 의사들이 한 명의 환자와 관련된 다양한 정보로부터 질환을 결정 내리기까지에는 여러 단계에서 다양한 의사결정이 필요하며 매우 복잡한 과정을 거치게 된다. 그러므로 의사들에게 또는 환자들에게 보조적인 도움을 주고자 많은 의료진단 시스템들이 개발되었다. 현재까지 개발된 대부분의 의료 진단시스템들은 특정한 의사의 경험이나 한 유형의 질환에 고정되어 있다. 그래서 환자들이 급성복통과 같은 여러 가지 유형의 질환에 관련되어 있는 증상을 호소할 때 의사들이 적절한 의사결정을 내리기가 쉽지 않다. 본 논문에서는 급성복통과 관련된 여러 가지 유형의 질환을 진단할 수 있는 시스템을 퍼지 논리를 이용하여 설계하고 구현해 본다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.1
• /
• pp.139-144
• /
• 2008

Generally, real system that is the object of decision-making is very variable and sometimes it lies situations with uncertainty. To solve these problem, it has used statistical methods as significance level, certainty factor, sensitivity analysis and so on. In this paper, we propose a method for fuzzy decision-making based on MFAC(Multiple Fuzzy Alpha Cut) to improve the definiteness of classification results with similarity evaluation. In the proposed method, MFAC is used for extracting multiple a ${\alpha}$-level with proximity degree at proximity relation between relative Hamming distance and max-min method and for minimizing the number of data which are associated with the partition intervals extracted by MFAC. To determine final alternative of decision-making, we compute the weighted value between extracted data by MFAC From the experimental results, we can see the fact that the proposed method is simpler and more definite than classification performance of the conventional methods and determines an alternative efficiently for decision-maker by testing significance of sample data through statistical method.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.17-18
• /
• 2008

다변량 퍼지 의사결정트리(이하 MFDT)는 학습 모델의 구조가 간소하고 분류율이 높다는 장점 때문에 일반 퍼지 의사결정트리를 대신해 손동작 인식 시스템의 분류기로 사용되었다. 다양한 사용자의 손동작 특성을 분류하기 위해 여러 개의 인식 모델을 만들고 새로운 사용자에게 가장 적합한 모델을 선택해 사용하는 모델 선택 기법도 손동작 인식에 적용되었다. 모델 선택 과정을 통해 선택된 모델은 기존 모델 중에서 새로운 사용자의 특성에 가장 가깝지만 해당 사용자에 최적화된 모델이라고는 할 수 없다. 이 논문에서는 MFDT 모델을 새로 입력된 데이터를 이용해 적응시키는 방법을 설명하고 실험 결과를 통해 적응 성능을 검증한다.

• Journal of the Korea Concrete Institute
• /
• v.14 no.6
• /
• pp.1022-1031
• /
• 2002

This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.10
• /
• pp.2584-2590
• /
• 1998

In the existing fuzzy aggregation method, the operators such as t-norm, t conorm, mean operator, Yafer's operator and $\gamma$ operator are used to aggregate the values of membership functions. However, these methods have problems in that they do not reflect the decision situation properlyin the decision process. In order to solve these problems we suggest a situation assessment model(SAM) to reflect the decision situation in the decision proess. In the fuzzy decision environment, we propose a new aggregation method to reflect the decision situation using the result of SAM. We call it the aggregation based on situation assessment (ASA) method. It makes the stepwise aggregation with derection according to the decision situation. Moreover, we compare ASA method with the existing aggregation methods.

• Journal of Internet Computing and Services
• /
• v.12 no.4
• /
• pp.107-117
• /
• 2011

The appearance of Web has brought an substantial revolution to all fields of society such knowledge management and business transaction as well as traditional information retrieval. In this paper, we propose an EFASIT(Extended Fuzzy AHP and SImilarity Technology) model considering the emotion analysis. And we combine the Extended Fuzzy AHP Method(EFAM) with SImilarity Technology(SIT) based on the domain corpus information in order to efficiently retrieve the document on the Web. The proposed the EFASIT model can generate the more definite rule according to integration of fuzzy knowledge of various decision-maker, and can give a help to decision-making, and confirms through the experiment.

• Journal of KIISE:Software and Applications
• /
• v.34 no.6
• /
• pp.519-529
• /
• 2007

In this paper, we investigate the use of fuzzy rules for efficient intrusion detection. We use evolutionary algorithm to optimize the set of fuzzy rules for intrusion detection by constructing fuzzy decision trees. For efficient execution of evolutionary algorithm we use supervised clustering to generate an initial set of membership functions for fuzzy rules. In our method both performance and complexity of fuzzy rules (or fuzzy decision trees) are taken into account in fitness evaluation. We also use evaluation with data partition, membership degree caching and zero-pruning to reduce time for construction and evaluation of fuzzy decision trees. For performance evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that our method outperformed the existing methods. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of Digital Convergence
• /
• v.14 no.9
• /
• pp.229-239
• /
• 2016

The aim of this paper is to extend the TOPSIS(Technique for Order Performance by Similarity to Ideal Solution) to the fuzzy environment for solving the new professor selection problem in a university. In order to achieve the goal, the rating of each candidate and the weight of each criterion are described by linguistic terms which can be expressed in trapezoidal fuzzy numbers. In this paper, a vertex method is proposed to calculate the distance between two trapezoidal fuzzy numbers. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all candidates. This research derived; 1) 4 evaluation criteria(research results, education and research competency, personality, major suitability) for new professor selection, 2) the 5 step procedure of the proposed fuzzy TOPSIS method for the group decision, 3) priorities of 4 candidates in the new professor selection case. The results of this paper will be useful to practical expert who is interested in analyzing fuzzy data and its multi-criteria decision-making tool for personal selection problem in personal management. Finally, the theoretical and practical implications of the findings were discussed and the directions for future research were suggested.

• Journal of Korean Society of Environmental Engineers
• /
• v.22 no.4
• /
• pp.699-712
• /
• 2000

Juam lake is a major water resource for the industrial and agricultural activities as well as the resident life of Kwangju and Chonnam regions. However, the water quality of the lake is getting worse due to a large quantity of pollutant inflowing to the lake. Thus, the strategy for achieving the water quality goal of the lake should be developed as soon as possible. When there are various alternatives that can be used as the strategy, several criteria based on the achievement degree of water quality goal, the applicability of technique and social environment, and the reasonableness of the cost required are made to evaluate and rank the alternatives. However, it is difficult to make a decision when there are multiple criteria and conflicting objectives and specifically the estimated values of criteria contain elements of uncertainty. The uncertainty stems from the lack of available information, the randomness of future situation, and the incomplete knowledge of expert. As the degree of uncertainty is higher, the decision becomes more difficult. In this study, a fuzzy decision-making method is presented to assist decision makers in evaluating various alternatives under uncertainty. The method allows decision makers to characterize the associated uncertainty by applying fuzzy theory and incorporate the uncertainty directly into the decision making process for selecting the "best" alternative so decisions can be made that are more appropriate and realistic than those made without taking uncertainty in account.