• 한국컴퓨터산업학회논문지
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• 제4권4호
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• pp.479-490
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• 2003

컴포넌트 기반 소프트웨어개발 (CBD: Component Based Development)은 재사용 부품을 기반하여 소프트웨어 개발, 수정, 유지보수를 용이하게 지원한다. 따라서 컴포넌트는 강한 응집력과 양한 결합력으로 개발되어야 한다. 본 논문에서는use case와 클래스를 간에 유사성을 통한 클러스터링 분석에 기반 하여 컴포넌트 식별에 대해 연구한다. 컴포넌트 참조 모델과 프레임워크를 제시하여 사례를 통해 검증한다. 컴포넌트 식별 방법은 추출, 명세 및 아키？쳐를 지원한다. 이들 방법론은 기존의 객체지향 방법론을 참조하며 분석에서 구현까지의 추적성을 지원하며 재사용 컴포넌트의 모듈성 지원을 위해 강한 응집력과 약한 결합력을 반영한다.

• International Journal of Contents
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• 제10권2호
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• pp.47-58
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• 2014

Physiological signals provide important clues in the diagnosis and prediction of disease. Analyzing these signals is important in health and medicine. In particular, data preprocessing for physiological signal analysis is a vital issue because missing values, noise, and outliers may degrade the analysis performance. In this paper, we propose PhysioCover, a system that can recover missing values of physiological signals that were monitored in real time. PhysioCover integrates a gradual method and EM-based Principle Component Analysis (PCA). This approach can (1) more readily recover long- and short-term missing data than existing methods, such as traditional EM-based PCA, linear interpolation, 5-average and Missing Value Singular Value Decomposition (MSVD), (2) more effectively detect hidden variables than PCA and Independent component analysis (ICA), and (3) offer fast computation time through real-time processing. Experimental results with the physiological data of an intensive care unit show that the proposed method assigns more accurate missing values than previous methods.

• 정보처리학회논문지B
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• 제8B권4호
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• pp.389-394
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• 2001

본 논문에서는 독립 성분 해석(Independent Component Analysis, ICA) 기법과 인근 평균 및 정규화를 이용한 영상 분류 방법을 제안하였다. ICA에 잡음을 주어 영상을 분류하였을 때, 잡음에 대한 강인성을 증가시키기 위하여, 제안된 인근 평균 및 정규화를 전처리로 적용하였다. 제안된 방법은 전처리 없이 ICA에 주성분 해석(Principal Component Analysis, PCA)을 이용한 것에 비해 잡음에 대한 강인성을 증가시키는 것을 모의 실험을 통하여 확인하였다.

• 한국환경과학회지
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• 제11권1호
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• pp.63-68
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• 2002

In this study, we examined statistical analysis between sewage plant operations parameters and effluent quality We got six components from principle component analysis of the operation parameters and secondary effluent quality. 91.8% of the total variance was explained by the six components. The components were identified in the following order : 1) organic matter removal by aeration basin microbe, 2) settleability on secondary clarifier load, 3) removal of nutrients, 4) microbial number increasement and species diversity, 5) microbial activity in aeration basin, 6) oxidation in aeration basin.

• Genomics & Informatics
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• 제18권1호
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• pp.11.1-11.3
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• 2020

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

• 한국토양비료학회지
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• 제34권3호
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• pp.173-177
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• 2001

This study was conducted to investigate a potential utilitization of multivariate statistical analysis(Factor analysis, Discrimination analysis) on interpretation of rice plant lodging reason. Rice plants were sampled in paddy around Taegu city at from 25 to 29 of September in 2000. Mineral nutrient content(phosphate, potassium) of rice plant were significantly higher at 99% level, Silicate content were lower at 95% level in lodged samples than in normal. Plant characteristics associate with lodging(Culm length, second and third internode length, bight of center gravity) were significantly longer in lodged rice plant than in non lodged. Result of Factor analysis were that first principle component were culm length, second(N2) and third internode length(N3), second principle component were Ca content, first internode length(N1) and N3/culm length, third principle component were center gravity length(G) and G/culm length, fourth were nitrogen, phosphate, and potassium content, fifth were N2/culm length, N2+N3/culm length, Sixth was silicate content of rice plant. Linear discriminant equation distinguished lodged rice plants with non lodged rice plants very well. Prediction value was 100%, most explainable variable were phosphate content, culm length and third length.

• 한국환경과학회지
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• 제29권11호
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• pp.1065-1078
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• 2020

The statistical characteristics of aerosol-cloud interactions over East Asia were investigated using Moderate Resolution Imaging Spectroradiometer satellite data. The long-term relationship between various aerosol and cloud parameters was estimated using correlation analysis, principle component analysis, and Aerosol Indirect Effect (AIE) estimation. In correlation analysis, Aerosol Optical Depth (AOD) was positively Correlated with Cloud Condensation Nuclei (CCN) and Cloud Fraction (CF), but negatively correlated with Cloud Top Temperature (CTT) and Cloud Top Pressure (CTP). Fine Mode Fraction (FMF) and CCN were positively correlated over the ocean because of sea spray. In principle component analysis, AOD and FMF were influenced by water vapor. In particular, AOD was positively influenced by CF, and negatively by CTT and CTP over the ocean. In AIE estimation, the AIE value in each cloud layer and type was mostly negative (Twomey effect) but sometimes positive (anti-Twomey effect). This is related to regional, environmental, seasonal, and meteorological effects. Rigorous and extensive studies on aerosol-cloud interactions over East Asia should be conducted via micro- and macro-scale investigations, to determine chemical characteristics using various meteorological instruments.

• 대한공간정보학회지
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• 제25권1호
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• pp.9-17
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• 2017

초분광 영상(hyperspectral imagery)은 주성분분석이나 최소잡음비율 등을 이용하여 자료의 차원과 잡음을 감소시켜 토지피복분류에 사용되는 것이 일반적이다. 최근에는 분광정보와 공간적 특성을 가진 다양한 입력 자료를 이용한 감독분류에 관한 연구가 활발히 진행되고 있다. 본 연구에서는 초분광 영상을 이용한 토지피복분류를 위해 principle component(PC) 밴드와 normalized difference vegetation index(NDVI) 자료를 감독분류의 입력자료로 활용하였다. NDVI 자료는 초분광 영상에서 추출된 PC 밴드가 포함하고 있지 않는 추가적인 정보를 활용하여 식생지역에 대한 토지피복분류 정확도를 높이고자 사용하였으며, morphological filter를 통해 각 밴드의 extended attribute profiles(EAP)를 제작하여 분류를 위한 입력 자료로 사용하였다. 감독분류기법은 random forest 알고리즘을 이용하였으며, EAP를 기반으로 다양한 입력 자료의 적용에 따른 분류정확도를 비교하고자 하였다. 연구지역으로는 두 대상지를 선정하였으며, 영상 내에서 취득한 참조자료를 이용하여 정량적인 평가를 수행하였다. 본 연구에서 제안한 기법의 분류정확도는 85.72%와 91.14%로 다른 입력 자료들을 이용한 경우와 비교하여 가장 높은 분류정확도를 나타냈다. 향후, 초분광 영상을 이용한 토지피복분류의 정확도를 높이기 위한 분류 알고리즘 개발과 대상지역 특성에 맞는 추가 입력자료 개발에 관한 연구가 필요할 것으로 사료된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.185-189
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• 2004

For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.481-486
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• 2007

Wireless Sensor Network(WSNs) consists of small sensor nodes with sensing, computation, and wireless communication capabilities. The large number of sensor nodes in a WSN means that there will often be some nodes which give erroneous sensor data owing to several reasons such as power shortage and transmission error. Generally, these sensor data are gathered by a sink node to monitor and diagnose the current environment. Therefore, this can make it difficult to get an effective monitoring and diagnosis. In this paper, to overcome the aforementioned problems, intelligent sensor data validation method based on PCA(Principle Component Analysis) is utilized. Furthermore, a practical implementation using embedded system is given to show the feasibility of the proposed scheme.