• 제목/요약/키워드: Principal component Analysis

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Determination of Differences in the Nonvolatile Metabolites of Pine-Mushrooms (Tricholoma matsutake Sing.) According to Different Parts and Heating Times Using $^1H$ NMR and Principal Component Analysis

  • Cho, In-Hee;Kim, Young-Suk;Lee, Ki-Won;Choi, Hyung-Kyoon
    • Journal of Microbiology and Biotechnology
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    • 제17권10호
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    • pp.1682-1687
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    • 2007
  • The differences in the nonvolatile metabolites of pine-mushrooms (Tricholoma matsutake Sing.) according to different parts and heating times were analyzed by applying principal component analysis (PCA) to $^1H$ nuclear magnetic resonance (NMR) spectroscopy data. The $^1H$ NMR spectra and PCA enabled the differences of nonvolatile metabolites among mushroom samples to be clearly observed. The two parts of mushrooms could be easily discriminated based on PC 1, and could be separated according to different heattreated times based on PC 3. The major peaks in the $^1H$ NMR spectra that contributed to differences among mushroom samples were assigned to trehalose, succinic acid, choline, leucine/isoleucine, and alanine. The content of trehalose was higher in the pileus than in the stipe of all mushroom samples, whereas succinic acid, choline, and leucine/isoleucine were the main components in the stipe. Heating resulted in significant losses of alanine and leucine/isoleucine, whereas succinic acid, choline, and trehalose were the most abundant components in mushrooms heat-treated for 3 min and 5 min, respectively.

Application of principal component analysis and wavelet transform to fatigue crack detection in waveguides

  • Cammarata, Marcello;Rizzo, Piervincenzo;Dutta, Debaditya;Sohn, Hoon
    • Smart Structures and Systems
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    • 제6권4호
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    • pp.349-362
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    • 2010
  • Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defect-sensitive features to perform a multivariate diagnosis of damage. This diagnosis is based on the PCA. The framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a PXI platform that controls the generation and measurement of the ultrasonic signals by means of piezoelectric transducers made of Lead Zirconate Titanate. Although the approach is demonstrated in a beam test, it is argued that the proposed method is general and applicable to any structure that can sustain the propagation of UGWs.

Early warning of hazard for pipelines by acoustic recognition using principal component analysis and one-class support vector machines

  • Wan, Chunfeng;Mita, Akira
    • Smart Structures and Systems
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    • 제6권4호
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    • pp.405-421
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    • 2010
  • This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

주성분 분석기법을 적용한 사면 계측데이터 평가 (Slope Displacement Data Estimation using Principal Component Analysis)

  • 정수정;김용수;안상로
    • 한국지반공학회:학술대회논문집
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    • 한국지반공학회 2010년도 춘계 학술발표회
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    • pp.1358-1365
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    • 2010
  • Estimating condition of slope is difficult because of nonlinear time dependency and seasonal effects, which affect the displacements. Displacements and displacement patterns of landslides are highly variable in time and space, and a unique approach cannot be defined to model landslide movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. In the non-parametric approaches, no physical assumptions of target systems are required. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, non-parametric approaches are advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured. Non-parametric approaches are consequently more flexible in modeling than parametric approaches. This method is expected to be a useful tool for the slope management of and alarm systems.

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PCA에 기반한 압축영역에서의 MPEG Video 검색기법 (PCA-Based MPEG Video Retrieval in Compressed Domain)

  • 이경화;강대성
    • 대한전자공학회논문지SP
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    • 제40권1호
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    • pp.28-33
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    • 2003
  • 본 논문에서는 동영상 압축 부호화에 대한 표준안인 MPEG 기반의 압축 비디오 스트림으로부터 DCT DC 계수를 추출하구 이들로 구성된 DE 영상으로부터 장면 전환 검출을 수행한 후 대표 프레임을 추출한다. 또한 추출된 대표 프레임을 PCA(Principal Component Analysis) 방법을 이용하여 데이터베이스의 색인 정보로 저장한 후 입력된 질의 영상과 가장 유사한 대표 영상들을 검색하는 방법에 대해 제안한다. 즉, 추출된 대표 프레임에 대해 주성분해석 기법을 적용하여 통계적인 특성을 가진 데이터를 특징으로 추출함으로써 코드워드의 수에 따른 코드북을 생성하고 이를 데이터베이스의 색인 정보로 저장한다. 실험 결과 제안된 방법이 검색에 있어 우수한 성능을 나타내고 또한 통계적인 데이터의 특성을 이용하기 때문에 처리 시간과 상당한 양의 메모리 공간을 줄일 수 있음을 확인하였다.

한국산(韓國産) 산공재(散孔材)의 해부학적(解剖學的) 특성(特性)에 관한 비교연구(比較硏究)(I) -단순상관(單純相關)과 주성분(主成分) 분석(分析)에 의한 특성(特性)- (Comparative Anatomy of Diffuse-Porous Woods Grown in Korea (I) -Characteristics by Simple Correlation and Principal Component Analysis-)

  • 정연집;이필우
    • Journal of the Korean Wood Science and Technology
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    • 제23권4호
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    • pp.46-53
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    • 1995
  • The anatomy of Korean diffuse-porous woods, 36 families, 75 genera, 145 species, 215 specimens was described and analyzed. Sixteen wood anatomical characters, habit and phenology factors were determined by simple correlation and principal component analysis. Strong positive correlations were found between vessel element length and fiber length, ray width and ray height, simple pits of fiber wall and paratracheal parenchyma distribution. The results of principal component analysis (PCA) disclose the primitive characteristics and the direction of xylem evolution of Korean diffuse-porous woods. The xylem evolution scenario for Korean dicotyledonous woods is considered to be developed in the direction of decreasing trends of vessel frequency, vessel element length, and length/diameter(L/D) ratio of vessel element but increasing trends of vessel diameter, fiber length/vessel element length(F/V) ratio, libriform wood fibers, simple perforation, and homogeneous ray composition. Increase of vessel diameter and decrease of vessel frequency seem to be related to the improvement of conductive efficiency, and increase of the vessel element length and occurrence of scalariform perforation in vessel element may be related to enhanced of conductive safety. Also the libriform wood fibers and ray features appear to have relationship with mechanical support and nutrient metabolism, respectively.

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계통간 분류거리에 의한 한국 재래종 옥수수의 게통분류 (Classification of Korean Local Corn Lines by the Taxonomic Distance Based on Principal Component Analysis.)

  • 이인섭
    • 생명과학회지
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    • 제14권1호
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    • pp.57-60
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    • 2004
  • 육종재료를 얻기 위하여 부산ㆍ경남지역에서 수집된 우리나라 재래종 옥수수 49계통에 대하여 주성분분두법을 이용하여 계통간 거리를 구하였고, 이를 이용하여 계통분류를 실시하였던 바 다음과 같은 결과를 얻었다. 계통간 거리에 의해 49계통은 4개의 계통군으로 분류되었고, 계통군 I 에는 11계통, 계통군 II에는 20계통, 계통군 III에 는 14계통, 그리고 계통군 IV에는 4계통이 속하였다. 계통군 I은 조생, 단간, 소수, 소분얼형의 계통들이었고, 계통군 II는 조생, 장간, 대수, 다수, 소분얼형의 계통이었고, 계통군 III은 만생, 단간, 소수, 다분얼형 의 계통이었고, 계통군 IV는 중생, 장간, 대수, 다수, 다분얼형의 계통들이었다.

Modified Principal Component Analysis for In-situ Endpoint Detection of Dielectric Layers Etching Using Plasma Impedance Monitoring and Self Plasma Optical Emission Spectroscopy

  • Jang, Hae-Gyu;Choi, Sang-Hyuk;Chae, Hee-Yeop
    • 한국진공학회:학술대회논문집
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    • 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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    • pp.182-182
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    • 2012
  • Plasma etching is used in various semiconductor processing steps. In plasma etcher, optical- emission spectroscopy (OES) is widely used for in-situ endpoint detection. However, the sensitivity of OES is decreased if polymer is deposited on viewport or the proportion of exposed area on the wafer is too small. Because of these problems, the object is to investigate the suitability of using plasma impedance monitoring (PIM) and self plasma optical emission spectrocopy (SPOES) with statistical approach for in-situ endpoint detection. The endpoint was determined by impedance signal variation from I-V monitor (VI probe) and optical emission signal from SPOES. However, the signal variation at the endpoint is too weak to determine endpoint when $SiO_2$ and SiNx layers are etched by fluorocarbon on inductive coupled plasma (ICP) etcher, if the proportion of $SiO_2$ and SiNx area on Si wafer are small. Therefore, modified principal component analysis (mPCA) is applied to them for increasing sensitivity. For verifying this method, detected endpoint from impedance monitoring is compared with optical emission spectroscopy.

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주성분 분석을 이용한 문서 주제어 추출 (Document Thematic words Extraction using Principal Component Analysis)

  • 이창범;김민수;이기호;이귀상;박혁로
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제29권10호
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    • pp.747-754
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    • 2002
  • 본 논문에서는 문서의 내용을 대표할 수 있는 주제어를 추출하는데 있어 다변량 통계 분석 기법 중의 하나인 주성분 분석을 이용하는 모델을 제안한다. 제안한 모델은 고유값과 고유벡터를 이용하여 문서 자체내의 단어의 흐름을 파악한 후 주제어를 추출하는 방법이다. 제안한 모델을 문서 요약에 적용하여 그 성능을 평가하였다. 신문기사를 대상으로 실험한 결과 제안한 모델이 단어의 출현 빈도를 고려하는 방법, 시소러스를 이용하는 방법 모두에 비해 더 좋은 성능을 보였다. 제안한 모델은 정보검색, 정보추출, 문서요약 등에 이용될 수 있으리라 기대된다.

PCA 기반 파라메타를 이용한 숫자음 인식 (The Recognition of Korean Syllables using Parameter Based on Principal Component Analysis)

  • 박경훈;표창수;김창근;허강인
    • 융합신호처리학회 학술대회논문집
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    • 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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    • pp.181-184
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    • 2000
  • 본 논문에서는 음성 특징추출의 한 방법으로서 기존의 방법들과는 달리 음성의 통계적인 특성들을 고려하여, 입력 공간내에서 변동량이 가장 많은 방향으로 주축을 발견한 다음 그 정보를 이용하여 데이터의 중복성을 제거하는 주성분 해석(PCA:Principal Component Analysis)기법을 사용하여 음성의 특징을 추출하는 방법을 제안한다. 본 논문의 숫자음 인식실험 결과와 비교하기 위하여 기존의 음성특징 파라메타인 Mel-Cepstrum과 비교하였을 때, 0.5%의 인식률 차이가 있었으나, 음성특징 추출시 기존의 파라메타에 비하여 비교적 짧은 시간에 구해지는 점과 데이터의 통계적 특성을 이용한 최적의 기저벡터를 이용한다면 단어나 문장 인식시에 보다 나은 인식률을 얻으리라 사료된다.

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