• 분석과학
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• 제17권3호
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• pp.217-224
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• 2004

소형의 이온선택성 전극을 흐름계 어레이로 구성하여 전자혀 시스템을 제작하였다. 제작된 전자혀 시스템에 음용수 및 알코올성 음료를 저 농도의 완충용액 (0.005 M Tris-$H_2SO_4$ pH 7.2)에 묽힌 후 9개의 전극이 장착된 흐름계에 흘려 보내어 전위차를 측정하였다. 얻어진 자료는 주성분 분석 (principal component analysis; PCA)을 통하여 식음료의 맛을 평가할 수 있도록 2차원 및 3차원 좌표계에 표시하였다. 본 연구에서 제작한 전자혀 시스템은 식품의 생산공정관리의 목적으로 또는 시중에서 구할 수 있는 식음료 맛의 변화 추이를 구분해 내는 목적으로 유용하게 사용될 수 있다.

• 전기화학회지
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• 제7권4호
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• pp.206-210
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• 2004

금속산화물 $(TiO_2,\;RuO_2,\;PbO_2,\;Ni(OH)_2)$과 Prussian blue (PB)를 각각 탄소반죽에 혼합한 후 스크린 프린팅 기법으로 6종의 탄소반죽 전극들을 제작하였다. 제작된 탄소반죽 전극들로 전자혀 시스템을 제작하여 다양한 음료수와 식품에 대한 감응을 0.1M carbonate buffer, pH 9.6완충 용액에 묽힌 후 대시간전류법의 방법으로 측정하였다 얻어진 자료를 주성분 분석법 (principal component analysis; PCA)으로 처리한 후 식음료의 맛을 평가할 수 있도록 2차원 좌표계에 표시하였으며, 그 결과 본 실험에서 제작한 시스템 및 분석법은 다양한 식음료의 종류를 뚜렷이 구분해 낼 수 있음을 확인하였다.

• 전력전자학회논문지
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• 제25권5호
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• pp.376-384
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• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.

• 한국가스학회지
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• 제23권4호
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• pp.19-27
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• 2019

세계 환경규제가 강화되면서 액화천연가스의 사용량이 지속해서 증가하고 있다. 안정적이고 효율적인 액화천연가스 생산을 위해서는 운전 조건을 세분화하여 감시하는 시스템 구축이 필수적이다. 본 연구에서는 천연가스 액화플랜트 성분 분리공정을 해석하여 구축한 동적 모델 데이터를 대상으로 다중 모드 감시시스템 개발 방법을 제안하였다. 먼저 전체 정상 데이터를 주성분분석과 k-평균 군집화 방법론을 사용하여 다중 정상 운전 모델로 구분하였다. 그 다음, 새로운 데이터 값을 k-최근접 알고리즘으로 구축된 다중 정상 모드와 매칭하였다. 마지막으로, 다중 모드 주성분분석 감시 기법을 통해 공정 데이터의 이상 여부를 판별하였다. 제시된 방법론은 45가지 이상경우에 적용하였고, 기본 주성분분석 방법론과 단변수 감시 방법론과의 비교를 통해 속도와 정확도 지표에서 평균 약 5~10%이상 우수함을 입증하였다.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2005년도 학술발표논문집
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• pp.461-466
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• 2005

The purpose of this study is to classify rural villages through the amenity investigation by a village unit. PCA(Principal component analysis) is used for the classification of rural villages. The principal components of rural villages are deduced scale, population, infrastructure, traffic, education welfare and sightseeing by PCA.

• 한국통신학회논문지
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• 제29권1C호
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• pp.65-71
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• 2004

본 논문은 주성분분석(PCA, Principle Component Analysis) 혹은 독립성분분석(ICA, Independent Principle Component Analysis)를 이용하여 HMM(Hidden Markov Model) 파라메타의 차수를 감소시킴으로써 MLLR(Maximum Likelihood Linear Regression) 화자 적응 알고리즘을 개선하였다. 데이터의 특징을 잘 나타내는 PCA와 ICA를 통해 모델 mixture component의 상관관계를 줄이고 상대적으로 데이터의 분포가 적은 축을 삭제함으로써 추정해야 하는 적응 파라메타의 수를 줄였다. 기존의 MLLR 알고리즘은 SI(Speaker Independent)모델 보다 좋은 인식성능을 나타내기 위해 30초 이상의 적응 데이터가 요구되었고, 반면 제안한 알고리즘은 적응 파라메타의 수를 감소시킴으로써 10초 이상의 적응데이터가 요구되었다. 또한, 36차의 HMM 파라메타는 기존의 MLLR 알고리즘과 비슷한 인식성능을 나다내는 10차의 주성분이나 독릭성분을 사용함으로써 MLLR 알고리즘에서 적응파라메타를 추정할 때 요구되는 연산량을 1/167로 감소시켰다.

• 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.

• 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.

• 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.

• 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.