• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.581-587
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• 2003

독립 요소 분석 (Independent Component Analysis: ICA)는 텍스처를 의미 있는 특징으로 변환하는 강인한 영상 필터를 생성하기 위한 확률적 방법이다. ICA는 고차통계적 특성을 사용하여 ICA 필터와 독립 요소를 동시에 학습한다. 제안한 분류 방법은 fast ICA 알고리즘을 사용하여 KOMPSAT 영상으로부터 ICA 필터를 생성한 다음, 필터에 의해 투영된 텍스처들의 특징들을 독립 평면상에서 무감독 방법으로 분류한다. KOMPSAT 영상은 텍스처 성분이 뚜렷하지 않는 영역이 존재하기 때문에 본 논문에서는 투영된 특징 값들과 윈도우 내의 정규화된 평균 화소값으로 특징 벡터를 재구성하였다. 분류 방법으로는 K-means 클러스터링을 적용하였다. 6.6m 해상도를 가진 KOMPSAT 흑백 영상에 대해 제안한 방법은 우수한 분류 성능을 보인다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.472-478
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• 2012

The inspection machine in PCB (printed circuit board) assembly line checks assembly errors by inspecting the images inside of the component inspection region. The component inspection region consists of region of component package and region of soldering. It is necessary to extract the regions automatically for auto-teaching system of the inspection machine. We propose an image segmentation method to extract the component inspection regions automatically from images of PCB. The acquired image is transformed to HSI color model, and then segmented by several regions by clustering method. We develop a modified K-means algorithm to increase the accuracy of extraction. The heuristics generating the initial clusters and merging the final clusters are newly proposed. The vertical and horizontal projection is also developed to distinguish the region of component package and region of soldering. The experimental results are presented to verify the usefulness of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.749-752
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• 2004

CT 영상에서 폐암 추출을 위한 컴퓨터지원진단시스템(CAD)에서 전처리 시스템은 매우 중요한 역할을 담당한다. 본 논문에서는 CT 영상에서 폐암 추출을 위한 전처리 기법을 소개한다. CT 영상에서 폐 영역 추출 과정에서 가장 먼저 수행되는 이진화를 위해 k-means 클러스터링 알고리즘을 이용하고, 비관심 영역 제거 방법으로 연결요소를 분석하고, 이진화 과정에서 발생한 폐 외곽 분실을 재구성하기 위해 Rolling Ball 알고리즘을 수행한다. 또한 분할된 폐 영역에서 폐암 후보자를 선출하기 위해 분할과정에서 수행하였던 이진화 방법을 폐 영역에 다시 한번 적용하고 잡음제거를 위해 모폴러지 기법을 사용한 전처리 기법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.945-947
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• 2013

입력 모듈의 결함경향성을 결정하는 결함 예측 모델 연구들은 대부분 훈련 데이터 집합을 사용하는 감독형 모델에 관련된 것들이었다. 하지만 과거 데이터 집합이 없거나 현재 프로젝트 성격이 다른 경우는 비감독형 모델이 필요하며, 이들에 관한 연구들은 모델 구축의 어려움 때문에 극소수 존재한다. 본 논문에서는 대표적인 클러스터링 알고리즘들을 사용한 비감독형 모델들을 제작하여, 기존 모델들이 많이 사용한 K-means 모델과 나머지 모델들의 성능을 비교하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.853-856
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• 2008

최근 모바일 기기 사용자의 수가 증가함에 따라 모바일 기기 사용자의 위치정보를 관리하기 위한 기법들이 활발히 연구되고 있다. 기존의 모바일 시스템에서 위치정보를 관리하기 위한 방법으로 two-tier 방식과 two-tier 방식을 개선한 구조적 기법이 제시되었다. 구조적 기법에서는 어떻게 위치 데이터베이스를 군집화시키는 것이 매우 중요하다. 왜냐하면 데이터베이스를 군집하는 방법에 따라 업데이트 비용의 차이가 크기 때문이다. 구조적 기법을 위한 이전 연구는 set-cover 알고리즘을 기반한 bottom-up 방식의 시스템 이다. 본 논문에서는 구조적 기법의 데이터베이스 군집화를 위해 K-means clustering 알고리즘을 기반한 top-down 방식의 시스템을 사용하였고, 실험을 통해 본 논문에서 제시된 방식의 시스템이 기존 방식의 시스템보다 데이터베이스 업데이트측면에서 13.67%의 성능이 향상되었음을 보였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.380-385
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• 2019

This paper is concerned with departure passengers' dwell time analysis using real system data. Previous researches emphasize the importance of dwell time analysis from perspective of airport terminal planning and non-aeronautical revenue. However, short-term airport operation using passengers' dwell time is considered impossible due to absence of passengers' behavior data. Recently, in accordance with the wave of smart airport, world leading airports are systematically collecting passenger data. So there is high possibility of analyzing passengers' dwell time with the data stacked in the airport database. We conducted dwell time analysis using data from Incheon Int'l airport. In order to handle passenger data, we adapted clustering algorithm which is one of data mining techniques. As a clustering result, passengers are divided into 3 clusters. One is the cluster for passengers whose dwell time is relatively short and who tend to spend longer time in the airside. Another is the cluster for passengers who have near 3 hours dwell time. The other is the cluster for passengers whose total dwell time is extremely long.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.