• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.407-410
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• 1996

This paper presents how to build a human sensibility database by multivariate method. And, we discribe a fuzzy based inference system which converts human sensibility data to design factors using the human sensibility database. We are able to obtain the values of multiple correlation coeffcient, partial correlation coefficient, and categories by the quantification theory which is multivariate analysis. So, the human sensibility database is constructed from those values. The inference system will be more useful, if the human sensibility database and graphic design factor database were integrated.

• Molecules and Cells
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• 제25권2호
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

• 산업기술연구
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• 제15권
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• pp.187-193
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• 1995

In this paper, a short-term load forecasting expert system considering the load variation due to the change in temperature is presented. The change in temperature is an important load variation factor that varies the normal load pattern. The conventional load forecasting methods by artificial neural networks have used the technique where the temperature variables were included in the input neurons of artificial neural networks. However, simply adding the input units of temperature data may make the forecasting accuracy worse, since the accuracy of the load forecasting in this method depends on the accuracy of weather forecasting. In this paper, the fuzzy expert system that modifies the forecasted load using fuzzy rules representing the relations of load and temperature is presented and compared with a conventional load forecasting technique. In the test case of 1991, the proposed model provided a more accurate forecast than the conventional technique.

• 한국정밀공학회지
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• 제17권7호
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• pp.74-79
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• 2000

Integrating a 3D solid modeler with a general purpose FEM code, an automatic nonlinear analysis system of the 3D crack problems has been developed. A geometry model, i.e. a solid containing one or several 3D cracks is defined. Several distributions of local node density are chosen, and then automatically superposed on one another over the geometry model by using the fuzzy knowledge processing. Nodes are generated by the bucketing method, and ten-noded quadratic tetrahedral solid elements are generated by the Delaunay triangulation techniques. The complete finite element(FE) model generated, and a stress analysis is performed. In this system, burden to analysts fur introducing 3D cracks to the FE model as well as fur estimating their fracture mechanics parameters can be dramatically reduced. This paper describes the methodologies to realize such functions, and demonstrates the validity of the present system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.859-861
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• 1996

This paper presents the novel fault diagnostic expert system based on dissolved gas analysis(DGA) techniques in power transformer. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set concept. The uncertainty of rules are handled by fuzzy measures. Trend analysis through the monthly increment of key gas and DGA analysis are combined by the Dempster-Shafer theory, and the state of transformer and confidence factor are yielded by using this combined analysis. To verify the effectiveness of the proposed diagnosis technique, the expert system has been tested by using KEPCO's transformer gas records.

• 한국항해학회지
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• 제24권1호
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• pp.35-45
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• 2000

Recently, in the shipping companies have been employing prudently in order to prevent from sea accidents occurred by human factors. Also the students of merchant marine universities are choosing prudently the shipping companies when taking a job. But many qualitative and quantitative factors are considered in decision making for the employment and the choice of a company. FSM(Fuzzy Structural Modeling) has been widely used in modeling the system composed of such qualitative and quantitative factor. In this paper, a case study is discussed for the analysis of the consciousness of the employment of shining companies and students' choice of such company in maritime university using FSM. Also this paper proposed the planes for educating and recruitment guiding the student in maritime university.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1176-1182
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• 2009

Due to recession in real estate market, interest of risk analysis is increasing. Feasibility study in the first stage takes a great role in a project. There are not objectified tools which are able to cope with uncertainty of project, and feasibility study based on selected method of determinism does not include liquidity of weight risk. Also, shortage of consideration for subjective and atypical external factors causes inappropriate results. Therefore, this study proposes feasibility study model focused on risk factor influences in construction cost and sales cost. Considering effective level of cost based on objective risk factors and probable weight of risk by this model, real workers are able to bring correct and scientific decisions better than former method based on selective analysis of real estate development.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.185-199
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• 2024

In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms-K-means, fuzzy C-means, TSC, EIGENGAP, DBSCAN, and density peak-were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.

• 한국ITS학회 논문지
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• 제7권3호
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• pp.29-37
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• 2008

본 논문은 RFID 시스템을 통해 수집되는 데이터를 '국가도로교통량조사'에 활용하는 것에 목적을 두고 있다. 연구 수행에 있어 RFID 전자태그 보급의 한계성을 극복하기 위해서, 먼저 RFID 시스템을 통하여 표본데이터를 수집하고 이를 전수화(표본데이터를 조사지점을 통과하는 모든 차량의 수로 만드는 과정) 하였다. 최적의 전수화 방법론을 선정하기 위하여 세가지 방법론(시간계수 모델, 퍼지 모델, 신경망 모델)을 적용하였으며 분석결과 시간계수를 이용한 모델이 최적의 전수화 방법론으로 선정되었다. RFID 시스템을 '국가도로교통량조사'에 활용할 수 있는 방안을 모색하기 위해 '제주도'를 모델로 하여 분석한 결과 인프라 구축의 한계로 인하여 상시조사를 대체할 수는 없으나, 수시조사는 활용에 대한 새로운 가능성을 확인하였다. 따라서 교통량 조사(상시조사)에 있어 RFID 시스템을 활용한다면 기존 검지기에 비해 비용저감 효과가 있을 것으로 기대된다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권5호
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• pp.341-346
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• 2013

본 연구에서는 수정된 구조의 FMM 신경망으로부터 패턴 인식을 위한 규칙 추출 방법을 제안한다. 제안된 방법은 학습데이터에서 특징값에 대한 빈도 요소를 반영하는 하이퍼박스 정의를 기반으로 하는데, 이로부터 특징과 패턴클래스 간의 상호 연관도 요소를 정의 하였다. 이는 기존의 모델에서 사용되는 하이퍼박스 중첩테스트 및 축소(contraction) 기법을 사용하지 않아도 하이퍼박스의 중첩에 의한 분류의 모호성을 해결할 수 있게 한다. 본 연구에서는 패턴 클래스의 각 차원별로 퍼지 분할을 기반으로 하는 수정된 하이퍼박스 멤버쉽 함수와 이를 사용하는 학습방법을 제시한다. 제안된 기법으로부터 특정패턴의 분류를 위한 자극성(excitatory) 특징 및 억제성(inhibitory) 특징을 구분하고 이들 정보는 규칙 생성과정에 적용된다. 수화 인식에 관한 실험에 제안된 방법론을 적용함으로써 제안된 이론의 타당성을 실험적으로 고찰하였다.