• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.479-490
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• 2003

CBD is a software development approach based on reusable component and supports easy modification and evolution of software. For the success of this approach, a component must be developed with high cohesion and low coupling. In this paper, we propose the two types of clustering analysis technique based on affinity between use-cases and classes and propose component identification method applying to this technique. We also propose component reference model and CBD methodology framework and perform a ease study to demonstrate how the affinity-based clustering technique is used in component identification method. Component identification method contains three tasks such as component extraction, component specification and component architecting. This method uses object-oriented concept for identifying component, which improves traceability from analysis to implementation and can automatically extract component. This method reflects the low coupling-high cohesion principle for good modularization about reusable component.

• International Journal of Contents
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• v.10 no.2
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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.

• The KIPS Transactions:PartB
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• v.8B no.4
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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.

• Journal of Environmental Science International
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• v.11 no.1
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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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• v.18 no.1
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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.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.3
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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.

• Journal of Environmental Science International
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• v.29 no.11
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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.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.9-17
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• 2017

Hyperspectral imagery is used in the land cover classification with the principle component analysis and minimum noise fraction to reduce the data dimensionality and noise. Recently, studies on the supervised classification using various features having spectral information and spatial characteristic have been carried out. In this study, principle component bands and normalized difference vegetation index(NDVI) was utilized in the supervised classification for the land cover classification. To utilize additional information not included in the principle component bands by the hyperspectral imagery, we tried to increase the classification accuracy by using the NDVI. In addition, the extended attribute profiles(EAP) generated using the morphological filter was used as the input data. The random forest algorithm, which is one of the representative supervised classification, was used. The classification accuracy according to the application of various features based on EAP was compared. Two areas was selected in the experiments, and the quantitative evaluation was performed by using reference data. The classification accuracy of the proposed algorithm showed the highest classification accuracy of 85.72% and 91.14% compared with existing algorithms. Further research will need to develop a supervised classification algorithm and additional input datasets to improve the accuracy of land cover classification using hyperspectral imagery.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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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.