• 한국재난정보학회 논문집
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• 제12권1호
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• pp.10-18
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• 2016

본 연구는 건설 시공 계획 및 관리 업무에 NOS를 적용하기 위하여 다음과 같은 제안을 과정을 통해서 새로운 NOS 모델을 제시하였다. 먼저, 건설공사의 특성을 반영한 시설 단위 - 구조 단위 - 시공 단위 - 자원 단위의 흐름으로 공사정보분류체계의 개념을 제시하였다. 이 체계를 근거로 네트워크의 구성하고, 성과측정관리기준선의 추이를 분석하는 기준계획시간(MT : Master Target)식과 집행실적을 분석해 주는 수정계획시간(WT : Work Target) 식을 제안하여 NOS 모델을 제시하기 위한 성과측정 관리 기준선의 수립방법을 제안하였다. 최종적으로 성과측정 집행 분석 방법의 이론적 검증을 통해서 확정계약과 실비정산보수가산방식에 적합한 NOS 적용 방안을 제시하였다.

• 한국해양공학회지
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• 제26권4호
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• 한국군사과학기술학회지
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• 제14권4호
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• pp.719-725
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• 2011

To apply TMAH-based Py-MS to a field biological detection system for real-time classification of cell-type, reproducible patterns of the TMAH-based Py-MS spectra was known as a critical factor for classification but was seriously disturbed by quantity of cells injected into pyro-tube. This factor is an exterior variable that could not be complemented by improving the performance of the TMAH-based Py-MS instrument. One of idea to solve the knotty problem has been flashed from "Top-down proteomics for identification of intact microoganisms". That is, biomarker peaks are selected from complicate Py-MS spectra for intact microoganisms by tracing out their origins, based on Py-MS spectra for the featured components of different cell-types, in Top-down approach. This idea has been tested in classification of different Gram-type microoganisms. Through the analyses of spectra for the featured components - peptidoglycan and lipoteichoic acid for Gram-positive cells and lipopolysaccharide and lipid A for Gram-negative cells - with comparing to the spectra the corresponding Gram-type cells in the Top-down approach, biomarker peaks were selected to carry out PCA(Principal Component Analysis) in order to see classification of different Gram-types, resulting in significant improvement of their classification. Furthermore, weighting biomarker peaks on intact cell's spectra, based on the data for the featured components of the Gram-types, contributed to elevate classification performance.

• 대한원격탐사학회지
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• 제31권4호
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• pp.303-319
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• 2015

The objectives of this paper are to measure surface imperviousness using three different classification methods: per-pixel, sub-pixel, and object-oriented classification. They are tested on high-spatial resolution QuickBird data at 2.4 meters (four spectral bands and three principal component bands) as well as a medium-spatial resolution Landsat TM image at 30 meters. To measure impervious surfaces, we selected 30 sample sites with different land uses and residential densities across image representing the city of Phoenix, Arizona, USA. For per-pixel an unsupervised classification is first conducted to provide prior knowledge on the possible candidate spectral classes, and then a supervised classification is performed using the maximum-likelihood rule. For sub-pixel classification, a Linear Spectral Mixture Analysis (LSMA) is used to disentangle land cover information from mixed pixels. For object-oriented classification several different sets of scale parameters and expert decision rules are implemented, including a nearest neighbor classifier. The results from these three methods show that the object-oriented approach (accuracy of 91%) provides more accurate results than those achieved by per-pixel algorithm (accuracy of 67% and 83% using Landsat TM and QuickBird, respectively). It is also clear that sub-pixel algorithm gives more accurate results (accuracy of 87%) in case of intensive and dense urban areas using medium-resolution imagery.

• 반도체디스플레이기술학회지
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• 제15권3호
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• pp.6-11
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• 2016

Silicon wafer is one of main materials in solar cell. Micro-cracks in silicon wafer are one of reasons to decrease efficiency of energy transformation. They couldn't be observed by human eye. Also, their shape is not only various but also complicated. Accordingly, their shape classification is absolutely needed for manufacturing process quality and its feedback. The performance of typical classification techniques which is principal component analysis(PCA), neural network, fusion model to integrate PCA with neural network, and support vector machine(SVM), are evaluated using pattern features of micro-cracks. As a result, it has been confirmed that the SVM gives good results in micro-crack classification.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• 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.

• 한국농공학회논문집
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• 제54권1호
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• pp.73-81
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• 2012

For planning and developing rural regions, it is very important to understand and utilize regional characteristics including social, demographic, and economic aspects. The purpose of this study is to find effective analysis techniques and provide a procedure design for mining regional characteristics in South Korea through reviewing and analyzing 41 related studies. The engaged research methods can be classified into five categories (PCA+CA, PCA, CA, GIS, and PCA+GIS) with the combination of three methodologies: principal component analysis (PCA), cluster analysis (CA), and geographical information system (GIS). The combination of PCA and CA occupied about 40 % of research methods used in related studies. The analysis tool of Korean Rural Information Supporting System (KRISS) is designed based on the outcomes of this study and applied to classify the regional capacity of agriculture using agricultural census data (2000) for evaluating its applicability.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.69-72
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• 2003

The paper considers partial least squares (PLS) as a new dimension reduction technique for the feature vector to overcome the small sample size problem in face recognition. Principal component analysis (PCA), a conventional dimension reduction method, selects the components with maximum variability, irrespective of the class information. So PCA does not necessarily extract features that are important for the discrimination of classes. PLS, on the other hand, constructs the components so that the correlation between the class variable and themselves is maximized. Therefore PLS components are more predictive than PCA components in classification. The experimental results on Manchester and ORL databases show that PLS is to be preferred over PCA when classification is the goal and dimension reduction is needed.

• The Journal of the Acoustical Society of Korea
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• 제25권3E호
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• pp.89-94
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• 2006

For audio indexing and targeted search of specific audio or corresponding visual contents, the MPEG-7 standard has adopted a sound classification framework, in which dimension-reduced Audio Spectrum Projection (ASP) features are used to train continuous hidden Markov models (HMMs) for classification of various sounds. The MPEG-7 employs Principal Component Analysis (PCA) or Independent Component Analysis (ICA) for the dimensional reduction. Other well-established techniques include Non-negative Matrix Factorization (NMF), Linear Discriminant Analysis (LDA) and Discrete Cosine Transformation (DCT). In this paper we compare the performance of different dimensional reduction methods with Gaussian mixture models (GMMs) and HMMs in the classifying video sound clips.