• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.202-209
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• 2004

Quality control is essential to the final product in BGA-type PCB fabrication. So, many automatic vision systems have been developed to achieve speedy, low cost and high quality inspection. A multiple threshold selection algorithm is a very important technique for machine vision based inspection. In this paper, an inspected image is modeled by using fuzzy sets and then the parameters of specified membership functions are estimated to be in maximum fuzzy entropy with the probability of the fuzzy sets, using the exhausted search method. Fuzzy c-partitions with the estimated parameters are automatically generated, and then multiple thresholds are selected as the crossover points of the fuzzy sets that form the estimated fuzzy partitions. Several experiments related to flip chip BGA images show that the proposed algorithm outperforms previous ones using both entropy and variance, and also can be successfully applied to AVI systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1390-1397
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• 1996

In this paper, in case of segmenting an image by a fuzzy entropy, an image segmentation algorithm is derived under an extended fuzzy entropy including the probabilistic including the probabilistic information in order to cover the toal uncertainty of information contained in fuzzy sets. By describing the image with fuzzysets, the total uncertainty of a fuzzy set consists of the uncertain information arising from its fuzziness and the uncertain information arising from the randomness in its ordinary set. To optimally segment all the boundary regions in the image, the total entropy function is computed by locally applving the fuzzy and Shannon entropies within the width of the fuzzy regions and the image is segmented withthe global maximum andlocal maximawhich correspond to the boundary regions. Comtional one by detecting theboundary regions more than 5 times.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.547-552
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• 2007

In this note, we extends some of the results of Liu [Fuzzy Sets and systems 157 (2006) 869-878]. This extension consists of a simple proof involving weighted functions and their preference index. We also give an elementary simple proof of the maximum entropy weighting function problem with a given preference index value without using any advanced theory like variational principles or without using Lagrangian multiplier methods.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.131-138
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• 1998

In usual statistical data analysis, we describe statistical data by exact values. However, in modem complex and large-scale systems, it is difficult to treat the systems using only exact data. In this paper, we define these data as fuzzy data(ie. Linguistic variable applied to make the member-ship function.) and Propose a new method to get an analysis of fuzzy survey data based on the maximum entropy Principle. Also, we propose a new method of discrimination by measuring distance between a distribution of the stable state and estimated distribution of the present state using the Kullback - Leibler information. Furthermore, we investigate the validity of our method by computer simulations under realistic situations.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.527-535
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• 2000

For a reliable patient set-up verification, better portal films are needed to track relevant features. Simulator films are compared with portal films as a reference image in radiotherapy planning. This shows some possibilities of the use of image information of simulator images for enhancement and restorations of portal images which are very poor in quality compared with the simulator images. This paper present an approach that combine an associative memory, a kind of artificial neural networks with fuzzy image enhancement technique using genetic algorithm which determines the fuzzy region of membership function by the use of maximum entropy principles. A higher portal image quality than conventional technique is achieved.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.23-23
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• 2017

기상레이더는 강우량을 바로 추정하지 못하는 특성으로 인해 정량적 강우산출 과정 중에 다양한 원인으로 인해 불확실성 발생 요소가 존재하나 이를 정량화하고 저감하는데 많은 어려움이 있다. 원인을 살펴보면, 첫째, 기상레이더의 관측에서부터 정량적 강우량 추정까지 일련의 과정에 대한 포괄적으로 불확실성 정량화와 분석이 이루어지지 못하며, 둘째, 전체 불확실성이 어느 정도 되는지 제시하지 못하므로 각 단계별 불확실성이 전체 불확실성 대비 어느 정도 비율이 되는지 제시하지 못한다. 마지막으로 기존 연구들은 불확실성을 줄이고자 여러 방법을 사용하고 있으나 어느 정도 효용성이 있는지 불확실성 측면에서 제시하지 못하고 있다. 따라서 본 연구에서는 Maximum Entropy(ME)와 Uncertainty Delta Method(UMD)를 이용한 접근방법을 제안하여 기상레이더를 활용하여 정량적 강우량을 추정하는 일련의 과정에서 단계별로 불확실성이 어떻게 전파되는지 추정하였다. 본 연구에서는 한반도 전역을 대상으로 2012년 여름철(6~8월)에 발생한 18개 강우사례를 이용하여 품질관리(Open Radar Product Generator 품질관리 알고리즘, fuzzy 알고리즘), 강우추정(Window Probability Matching Method, Marshall-Palmer 관계식), 후처리보정(Local Gauge Correction 기법, Gauge to Radar ratio 기법)단계만을 수행하였으며, 이 결과를 바탕으로 기상레이더 정량적 강우추정 단계별 불확실성을 정량화하였다. 정량화결과, 최종적으로 관측단계의 불확실성보다 최종 불확실성이 줄어들었으나, 강우추정 단계에서 불확실성이 증가하는 것으로 나타났다. 이는 어떤 강우추정식을 적용하느냐에 따라 레이더 강우추정결과가 매우 달라질 수 있음을 의미한다. 따라서 본 연구에서 제시한 불확실성 정량화 방법을 통하여 첫째, 전체 및 단계별 불확실성을 정량화할 수 있고, 둘째, 최종 불확실성 대비 각 단계별 불확실성을 비율을 제시할 수 있으며, 마지막으로 수행단계별로 불확실성 전파과정을 파악할 수 있다. 이는 향후 정량적 레이더 강우추정 과정에 있어서 불확실성을 발생시키는 주요 원인파악과 이에 대한 집중적인 투자를 가능하게 한다. 이러한 과정을 통하여 보다 정확한 정량적 레이더 강우추정이 가능할 것으로 판단된다.