• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.715-720
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• 2005

This paper presents a hybrid recognition method of first moment of face image and principal component analysis(PCA). First moment is applied to reduce the dimension by shifting to the centroid of image, which is to exclude the needless backgrounds in the face recognitions. PCA is implemented by single layer neural network which has a teaming rule of Foldiak algorithm. It has been used as an alternative method for numerical PCA. PCA is to derive an orthonormal basis which directly leads to dimensionality reduction and possibly to feature extraction of face image. The proposed method has been applied to the problems for recognizing the 48 face images(12 Persons $\ast$ 4 scenes) of 64$\ast$64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• Analytical Science and Technology
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• v.17 no.3
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• pp.217-224
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• 2004

A taste sensor system composed of mini electrode array was built in a flow cell. Potentiometric signals from 9 electrodes were collected for drinking waters and alcoholic beverages which were diluted in a low concentration buffer solution (0.005 M Tris-$H_2SO_4$ pH 7.2) for the measurement. The measured results were treated with the principal component analysis (PCA), and grouped on a two or three dimensional PCA coordinate to discriminate the tastes of each beverage. It is demonstrated that the taste sensor system of this work may be used for the quality control of beverages in production or the examination of their taste variation in the market.

• Journal of the Korean Electrochemical Society
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• v.7 no.4
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• pp.206-210
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• 2004

All array of carbon paste-based electrodes was prepared by screen printing the carbon paste modified with metal oxides $(TiO_2,\;RuO_2,\;PbO_2,\;Ni(OH)_2)$ and Prussian blue (PB). An electronic tongue system was assembled with the carbon-paste electrode array, and applied to discriminate the tastes of various commercial beverages and foods by measuring the chronoamperometric responses to the samples diluted in 0.1M carbonate buffer (pH 9.6). The results were analyzed with principal component analysis(PCA) method and plotted on two dimensional PCA coordinate; it was apparent that the amperometric electronic tongue system could discriminate the types of various foods and beverages.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.5
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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.

• Journal of the Korean Institute of Gas
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• v.23 no.4
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• pp.19-27
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• 2019

The consumption of liquefied natural gas (LNG) has increased annually due to the strengthening of international environmental regulations. In order to produce stable and efficient LNG, it is essential to divide the global (overall) operating condition and construct a quick and accurate monitoring system for each operation condition. In this study, multi-mode monitoring system is proposed to the LNG plant fractionation process. First, global normal operation data is divided to local (subdivide) normal operation data using global principal component analysis (PCA) and k-means clustering method. And then, the data to be analyzed were matched with the local normal mode. Finally, it is determined the state of process abnormality through the local PCA. The proposed method is applied to 45 fault case and it proved to be more than 5~10% efficient compared to the global PCA and univariate monitoring.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.65-71
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• 2004

We improved the MLLR speaker adaptation algorithm with reduction of the order of HMM parameters using PCA(Principle Component Analysis) or ICA(Independent Component Analysis). To find a smaller set of variables with less redundancy, we adapt PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible, minimize the correlations between data elements, and remove the axis with less covariance or higher-order statistical independencies. Ordinary MLLR algorithm needs more than 30 seconds adaptation data to represent higher word recognition rate of SD(Speaker Dependent) models than of SI(Speaker Independent) models, whereas proposed algorithm needs just more than 10 seconds adaptation data. 10 components for ICA and PCA represent similar performance with 36 components for ordinary MLLR framework. So, compared with ordinary MLLR algorithm, the amount of total computation requested in speaker adaptation is reduced by about 1/167 in proposed MLLR algorithm.

• Journal of Microbiology and Biotechnology
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• v.17 no.10
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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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• v.6 no.4
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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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• v.6 no.4
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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.