• Korean Journal Plant Pathology
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• v.13 no.1
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• pp.46-56
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• 1997

Based on the principal component analysis (PCA) of Richards' parameter estimates, ginseng field soils were grouped as the principal component 1 (PC1) and the principal component 2 (PC2). The microflora and physico-chemical characteristics of each soil group were compared to elucidate soil environmental factors affecting the disease development of root rot of ginseng seedling. Among 3 soil groups by PC1, there were differences in the populations of total fungi (TF) and Cylindrocarpon plus Fusarium (C+F), and the population ratio of Cylindrocarpon plus Fusarium to total fungi or total bacteria (C+F/TF, C+F/TB) in rhizoplane of ginseng seedlings, the population of total actinomycetes (TA) and the population ratio of total Fusarium to total actinomycetes (Fus/TA) in soil, and soil chemical properties (EC, NO3-N, K, Mn, ect.). Among 4 soil groups by PC2, there were differences in TF, C+F, TB, C+F/TF and C+F/TB in the rhizoplane, Trichoderma plus Gliocladium (T+G) in soil, and P2O5 content in soil. Especially, EC, NO3-N, K, K/Mg and Mn were positively correlated to PC1, and TA was negatively to PC1; however, TF, C+F, TB, C+F/TF and C+F/TB in the rhizoplane were significantly correlated to PC2 positively. On the other hand, microbes in the rhizoplane were not significantly correlated to the stand-missing rate (SMR), although TA and Fe/Mn were negatively correlated, and pH and Ca were positively correlated to SMR.

• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.422-431
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• 2000

This paper proposes a new codebook generation method, called a PCA-Based VQ, that incorporates the PCA (Principal Component Analysis) technique into VQ (Vector Quantization) codebook design. The PCA technique reduces the data dimensions by transforming input image vectors into the feature vectors. The cluster of feature vectors in the transformed domain is bisectioned into two subclusters by an optimally chosen partitioning hyperplane. We expedite the searching of the optimal partitioning hyperplane that is the most time consuming process by considering that (1) the optimal partitioning hyperplane is perpendicular to the first principal axis of the feature vectors, (2) it is located on the equilibrium point of the left and right cluster's distortions, and (3) the left and right cluster's distortions can be adjusted incrementally. This principal axis bisectioning is successively performed on the cluster whose difference of distortion between before and after bisection is the maximum among the existing clusters until the total distortion of clusters becomes as small as the desired level. Simulation results show that the proposed PCA-based VQ method is promising because its reconstruction performance is as good as that of the SOFM (Self-Organizing Feature Maps) method and its codebook generation is as fast as that of the K-means method.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.25-31
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• 2006

In this paper, This study proposes a method that use the whole face as features by using a color information and KLT that overcome the weak points of existing face extraction and face recognition. The significant information among the features of face is extracted by PCA which uses KLT. In this paper, you will find that the recognition efficiency is over 90% for the faces that have various size and angle by proposing the face recognition method using color information and the KLT.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.465-468
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• 2000

얼굴 영상은 똑같은 표정의 같은 사람이라도 조명에 따라 매우 다른 얼굴 영상으로 나타난다. 따라서 본 논문에서는 조명 변화에 강인한 얼굴 인식 방법을 제안한다. 제안된 방법은 오프라인 훈련(off-line training)과 온라인 인식(on-line recognition)의 두 부분으로 이루어져 있다. 오프라인 훈련은 PCA(principal component analysis)를 기반으로 한다. 온라인 인식에서는 조명 변화에 대한 보상, 얼굴 특징의 추출, 그리고 인식을 위한 분류 과정의 3 단계로 구성되어 있다. 오프라인 훈련에서는 전체 훈련 얼굴 영상 데이터에 PCA를 적용하여 조명 변화가 최대한 제외된 특징 벡터 공간을 생성한다. 실제 인식 단계에서는 첫 번째로 입력 영상으로 들어온 얼굴 영상에서 조명의 영향을 보상하기 위해 준동형 필터링(homomorphic filtering) 후 밝기 정규화(normalization)를 취한다. 두 번째 단계에서는 입력 데이터의 차원을 줄이고 얼굴 특징 벡터를 구하기 위해 PCA를 수행한다. 마지막 과정으로서 입력 영상의 특징 벡터들과 오프라인에서 미리 구하여진 특징 벡터들의 유사도를 측정하여 얼굴을 인식하게 된다. 실험 결과 제안된 방법은 기존의 Eigenface 방법에 비해 우수한 성능을 나타내었다.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.177-182
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• 2010

This paper proposes a new document clustering method using PCA and fuzzy association. The proposed method can represent an inherent structure of document clusters better since it select the cluster label and terms of representing cluster by semantic features based on PCA. Also it can improve the quality of document clustering because the clustered documents by using fuzzy association values distinguish well dissimilar documents in clusters. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1857_1858
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• 2009

본 논문에서는 PCA와 입자 군집 최적화 알고리즘을 이용한 얼굴이미지에서 특징선택 방법에 대하여 제안한다. 2차원 얼굴이미지의 히스토그램 분표값에서 정규화합 연산을 이용한 히스토그램 평활화 기법을 거쳐 대비효과를 주어 화질을 개선시켜 준다. PCA는 2차원 얼굴이미지를 이용하여 공분산 행렬을 구한 후 그것의 고유값에 따른 고유벡터를 구하여 얼굴인식에 사용될 특징 벡터들을 추출한다. 또한 추출된 특징벡터 중에서 얼굴인식 성능에 중요한 요소가 되는 특징 벡터들을 입자 군집 최적화 알고리즘을 이용하여 최적화한다. 다항식 기반 RBF 신경회로망을 사용하여 얼굴인식 성능을 평가한다. 본 논문에서 제안된 방법을 통해 최적화된 특징벡터와 얼굴인식률과의 관계를 알 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.405-405
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• 2000

We designed the artificial olfactory sensing system (Electronic Nose) using MOS type sensor array fur recognizing and analyzing odour. The response of individual sensors of sensor array, each processing a slightly different response towards the sample volatiles, can provide enough information to discriminate between sample odours. In this paper, we applied clustering algorithm for dimension reduction, such as linear projection mapping (PCA method), nonlinear mapping (Sammon mapping method) and the combination of PCA and Sammon mapping having a better discriminating ability. The odours used are VOC (Volatile chemical compound) and Toxic gases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1475-1479
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• 2004

High dimensional data requires efficient feature extraction techliques. Though PCA(Principal Component Analysis) is a famous feature extraction method it requires huge memory space and computational cost is high. In this paper we use incremental PCA for feature extraction on high dimensional data. Through experiment we show that proposed method is superior to APEX model.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.339-342
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• 2011

Recently, researches are studied in developing efficient techniques for accessing, querying, and storing XML documents which are frequently used in the Internet. In this paper, we propose a new method to cluster XML documents efficiently. We use a K-means algorithm with a Principal Component Analysis(PCA) to cluster XML documents after they are represented by vectors in the feature vector space by transferring them as names and levels of the elements of the corresponding trees. The experiment shows that our proposed method has a good result.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.479-483
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• 2011

The automation of production process is actively expanding for the purpose of the cost reduction and quality assurance. Among these, automatic tracking of the product along the whole process of the production is also important topic. Typically this is done by adopting OCR technology. Conventional OCR technology operates well on the rather good quality of the image like as printed characters on the paper. In industrial application, IDs are marked on the metal surface, and this cause the height difference between background material and character. Illumination systems that guarantee an image with good quality may be a solution, but it is rather difficult to design such an illumination system. This paper proposes an algorithm for the recognition of vehicle's ID characters using edge projection and PCA (Principal Component Analysis). Proposed algorithm robustly operates under illumination change using the same parameters. Experimental results show the feasibility of the proposed algorithm.