• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.255-258
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• 2000

We prepose fuzzy statistical test of fuzzy hypotheses membership function with fuzzy number data. Finding the maximum grade of the meeting point for fuzzy hypotheses membership function and membership function of confidence interval. By the maximum grade, we obtain the results to acceptance or reject for the test of fuzzy hypotheses.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.189-196
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• 1999

Until now, Many approach to use the layer or attribute items in GIS the classification results of remotely sensed images is going on, but It was rarely ever tried to use the results of fuzzy classification in GIS. The fuzzy classification can be accurate than any other classification methods of remotely sensed images and can separately extract the result for each classification items. In this study, We applied to GIS database implementation with fuzzy classification result. In the process of this study, We convert the fuzzy classification image into the grid of GIS database, and Membership Grade Value files transformed to table database into the GIS. And then Membership Grade Values related to each grid-cell of database be able to verify with pointer layer. Finally, we can use the fuzzy classification images in GIS database.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.10a
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• pp.144-147
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• 1994

본 연구에서는 진도우파수 성분과 진동진폭에 대한 이상진동의 멤버쉽함수(membership function)를 고려하여, 멤버쉽정도(membership grade)를 구하고, 퍼지연산에 의하여 회전동기와 비동기진동을 구별하는 1차 진단을 한후, 각각에 대한 진동진폭의 멤버쉽함수와 인과매트릭스(decision table)를 이용하여 보다 세분된 2차 진단을 수행하는 2단계의 진단수법을 제안한다. 그리고 실험장치에서 여러가지의 결함을 인위적으로 만들고, 이 계측자료와 관련자료를 이용하여 본 진단법의 유용성을 검토하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.109-116
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• 2005

In this paper, we investigate the visual and quantitative analysis at the same time with an electronic tongue(e-tongue) system using an array of ISE(ion-selective electrode). We apply the FCM(fuzzy c-means) algorithm combined with PCA(principal component analysis), which can be reduced multi-dimensional data to third-dimensional data, to classify data patterns detected by E-Tongue system. The proposed technique can be designed to solve the cluster centers and membership grade of patterns combined with the output results obtained by PCA method. According to the proposed technique, the membership grade of unknown pattern, which does not shown previously can be determined and analyzed visually. Conclusionally, the relationship between the standard patterns and unknown pattern can be easily analyzed. Throughout the experimental trials, the proposed technique has been confirmed using developed E-Tongue system.

• Proceedings of the IEEK Conference
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• summer
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.97-101
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• 2004

This paper is about the use of existing evaluation methods, which evaluate learning determined by the score of an exam, which is either a multiple-choice type or single choice type question. These scores don't show the objective evaluations that cause some negative opinions about the scores. In this paper, we propose that the evaluation of the methods of self-directed learning use the triangle-type function of the fuzzy theory so that the learner can objectively evaluate their own learning ability. The proposed method classifies the result of learning into three fuzzy grades to calculate membership, and evaluate the result of an exam according to the final fuzzy grade degree as applied to the fuzzy grade sheets.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.133-137
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• 2005

In this paper, we investigate visual and quantitative analysis of different tastes in the liquids using multi-array chemical sensor (MACS) based on the ion-selective electrodes (ISEs), which is so called the electronic tongue (E-Tongue) system. We apply the Fuzzy C-means (FCM) algorithm combined with Principal Component Analysis (PCA), which can be used to reduce multi-dimensional data to two- or three-dimensional data, to classify visually data patterns detected by E-Tongue system. The proposed technique can be determined the cluster centers and membership grade of patterns through the unsupervised way. The membership grade of an unknown pattern, which does not shown previously, can be visually and analytically determined. Throughout the experimental trails, the E-tongue system combined with the proposed algorithms is demonstrated robust performance for visual and quantitative analysis for different tastes in the liquids.

• Journal of dental hygiene science
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• v.6 no.3
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• pp.163-167
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• 2006

The purpose of this study is to position the Korean Dental Hygienists Association(KDHA) for reserve dental hygienists as undergraduates, and thereby suggest KDHA's future potential businesses and its promising directions from comprehensive perspectives. To meet this goal, total 430 undergraduates in dental hygienics were asked to join questionnaire survey dating from November 28 to December 9, 2005. Then, the resulting data collected were analyzed using SPSS WIN 12.0. The results of data analysis can be outlined as follows: 1. Almost all of respondents(95.1%) recognized KDHA mainly via departmental faculty(37.7%), Internet(26.7%) and more. 2. It was found that KDHA's future potential businesses should be devoted primarily to promoting the right and benefit of dental hygienists, and secondly to business for their capability development. 3. In terms of joining the membership of KDHA, 73.0% of respondents showed desires to join KDHA certainly if they get relevant qualifications and 81.2% of respondents answered that it is necessary to pay membership fee to KDHA, if they join it. 4. A test about any possible associations with KDHA's positioning according to general characteristics showed that there were more or less significant differences in KDHA membership experience depending upon age(P = .022), and so was in the intention to join KDHA depending upon grade(P = .000), and in the membership fee payment depending upon both age(P = .000) and grade(P = .000) on statistical level.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1371-1373
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• 1996

A clustering algorithms with dynamic adjustment of learning rate for GBFCM(Gradient Based FCM) is proposed in this paper. This algorithm combines two idea of dynamic K-means algorithms and GBFCM : learning rate variation with entropy concept and continuous membership grade. To evaluate dynamic GBFCM, we made comparisons with Kohonen's Self-Organizing Map over several tutorial examples and image compression. The results show that DGBFCM(Dynamic GBFCM) gives superior performance over Kohonen's algorithm in terms of signal-to-noise.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.202-214
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• 2002

In this paper, we propose a modified FCM (MFCM) algorithm to solve the problems of the FCM algorithm and the fuzzy clustering algorithm using an average intracluster distance (FCAID). The MFCM algorithm grants the regular grade of membership in the small size of cluster. And it clears up the convergence problem of objective function because its objective function is designed according to the grade of membership of it, verified, and used for clustering data. So, it can solve the problem of the FCM algorithm in different size of cluster and the FCAID algorithm in the convergence problem of objective function. To verify the MFCM algorithm, we compared with the result of the FCM and the FCAID algorithm in data clustering. From the experimental results, the MFCM algorithm has a good performance compared with others by classification entropy.