• Proceedings of the Korea Contents Association Conference
• /
• 2006.11a
• /
• pp.612-615
• /
• 2006

The management of image information settles as an important field with the advent of multimedia age and we are in need of the effective retrieval method to manage systematically image information. It is used to color, texture, and shape features for content-based image retrieval. And existing methods using multiple features have problems that the retrieval process is embarrassed because each weight is set up manually. So we have solved these problems by assignment of weight applying fuzzy integral. This paper proposed the optimized values of fuzzy measure by experiments.

• The KIPS Transactions:PartB
• /
• v.11B no.4
• /
• pp.485-490
• /
• 2004

In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

• The Journal of the Korea Contents Association
• /
• v.7 no.1
• /
• pp.40-47
• /
• 2007

In the protein macro array image, it is important to find out the feature of the each protein chip. A decision error by the personal sense of sight occurred from long time observation while making an experiment in many protein chip image. So the feature extraction is needed by a simulator. In the case of feature analysis for macro array scan image the efficiency is maximized. In the fluorescence scan image, the response for each cell have been depend on R, G, B distribution of color image. But it is difficult to be classified as one color feature in the case of mixed color image. In this paper, the response color of a protein chip is classified according to the fuzzy integral value with respect to fuzzy measure as the user desired color. The result of the experiment for the macro array fluorescence image with the Scan Array 5000 shows that the proposed method using the fuzzy integral is important fact to be make decision for the ambiguous color.

• Journal of Navigation and Port Research
• /
• v.28 no.3
• /
• pp.177-184
• /
• 2004

The purpose of this study is to identify and evaluate the competitiveness of container ports in ASEAN(Association of Southeast Asian Nations) and Korea, which plays a leading role in basing the hub of international logistics strategies at the onset of the 21st century. Its ultimate purpose is to consider the relevant policy-making by comparing the competitiveness of ASEAN and Korea's container ports. This paper adopted the HFP method, which is an empirical analysis that evaluated the port competitiveness by quantifying it a, a qualitative attribute in the aforementioned area, where both ASEAN and Korea vie with each other for increasing container throughput. The results of this study showed that Singapore ranked the first in the subject of study in view of the competitiveness, followed by Busan(2) and Manila(2) as a leading group of the relevant ports in international logistics strategies. This analytic evaluation contributes to the empirical approach applied to policy-making by the HFP method, which is the newest research technique in social science through the comparative study of port competitiveness between ASEAN and Korea.

• Fire Science and Engineering
• /
• v.10 no.3
• /
• pp.19-28
• /
• 1996

This paper present the way the fire detector determines whether a fire has broken out or not using the fuzzy measure. This method is based on Dempster's combination rule using the belief measure. The detector indicate a 'Fire'(F) or 'Nonfire'(N) when it determines whether a fire has broken out or not. To determine this, the fuzzy rule is applied in the setting value for the heat and smoke detector which is used. As a result, It is proved that the final decision can be determined more exactly whether a fire has broken out or not in proportion to the frequency of the fuzzy measure and the value of Bel (F).

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2009.01a
• /
• pp.35-40
• /
• 2009

FCM 알고리즘은 입력 벡터와 각 클러스터의 유클리드 거리를 이용하여 구해진 소속도만를 비교하여 데이터를 분류하기 때문에 클러스터링 된 공간에서의 데이터들의 분포에 따라 바람직하지 못한 클러스터링 결과를 보일 수 있다. 이러한 문제점을 개선하기 위해 대칭적 성질을 이용하는 대칭성 측도에 퍼지 이론을 적용하여 군집간의 거리에 따른 변화와 군집 중심의 위치, 그리고 군집 형태에 따라 영향을 덜 받는 개선된 FCM이 제안되었다. 본 논문에서는 효과적으로 패턴을 분류하기 위해 개선된 FCM 알고리즘을 적용한 개선된 하이브리드 네트워크를 제안한다. 제안된 하이브리드 네트워크는 개선된 FCM 알고리즘을 입력층과 중간층의 학습구조 적용하고 중간층과 출력층의 학습구조는 일반화된 델타학습법을 적용한다. 제안된 방법의 인식성능을 평가하기 위해 2차원 좌표평면 상의 데이터를 기존의 Max_Min 신경망을 이용한 FCM 기반 RBF 네트워크와 FCM 기반 RBF 네트워크, HCM 기반 네트워크와 제안된 방법 간의 학습 및 인식 성능을 비교 및 분석하였다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.9
• /
• pp.1905-1912
• /
• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.