• Speech Sciences
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• v.10 no.4
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• pp.149-157
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• 2003

In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker's PCA transformation matrix to reduce the correlation among the elements. Then, the fuzzy mixture model for speaker is obtained from these transformed feature vectors with reduced dimensions. The orthogonal Gaussian Mixture Model (GMM) can be derived as a special case of PCA fuzzy mixture model. In our experiments, with having the number of mixtures equal, the proposed method requires less training time and less storage as well as shows better speaker identification rate compared to the conventional GMM. Also, the proposed one shows equal or better identification performance than the orthogonal GMM does.

• KSBB Journal
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• v.21 no.1 s.96
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• pp.59-67
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• 2006

2D spectrofluorometer produces many spectral data during fermentation processes. The fluorescence spectra are analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). Analysis of the spectral data by PCA results in scores and loadings that are visualized in score-loading plots and used to monitor a few fermentation processes by S. cerevisae and recombinant E. coli. Two chemometric models were established to analyze the correlation between fluorescence spectra and process variables using PCR and PLS, and PLS was found to show slightly better calibration and prediction performance than PCR.

• Plant Biotechnology Reports
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• v.3 no.1
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• pp.87-93
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• 2009

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivars metabolically, leaves and fruits of five commercial strawberry cultivars were subjected to Fourier transform infrared (FT-IR) spectroscopy. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA) and Fisher's linear discriminant function analysis. The dendrogram based on hierarchical clustering analysis of these spectral data separated the five commercial cultivars into two major groups with originality. The first group consisted of Korean cultivars including 'Maehyang', 'Seolhyang', and 'Gumhyang', whereas in the second group, 'Ryukbo' clustered with 'Janghee', both Japanese cultivars. The results from analysis of fruits were the same as of leaves. We therefore conclude that the hierarchical dendrogram based on PCA of FT-IR data from leaves represents the most probable chemotaxonomical relationship between cultivars, enabling discrimination of cultivars in a rapid and simple manner.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.45-50
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• 2004

In this paper, we propose a new PCA-based LDA Mixture Algorithm(PLMA) for real-time face recognition system. This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction part we applied subtraction image, color filtering, eyes and mouth region detection, and normalization method, and in the face recognition part we used the method mixing PCA and LDA in extracted face candidate region images. The existing recognition system using only PCA showed low recognition rates, and it is hard in the recognition system using only LDA to apply LDA to the input images as it is when the number of image pixels ire small as compared with the training set. To overcome these shortcomings, we reduced dimension as we apply PCA to the normalized images, and apply LDA to the compressed images, therefore it is possible for us to do real-time recognition, and we are also capable of improving recognition rates. We have experimented using self-organized DAUface database to evaluate the performance of the proposed system. The experimental results show that the proposed method outperform PCA, LDA and ICA method within the framework of recognition accuracy.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.6-17
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• 2013

Recently, the color of vehicle license plate has been changed from green to white. Thus the vehicle plate recognition system used for parking management systems, speed and signal violation detection systems should be robust to the both colors. This paper presents a vehicle license plate recognition system, which works on both of green and white plate at the same time. In the proposed system, the image of license plate is taken from a captured vehicle image by using morphological information. In the next, each character region in the license plate image is extracted based on the vertical and horizontal projection of plate image and the relative position of individual characters. Finally, for the recognition process of extracted characters, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis) are sequentially utilized. In the experiment, vehicle license plates of both green background and white background captured under irregular illumination conditions have been tested, and the relatively high extraction and recognition rates are observed.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.1
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• pp.8-18
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• 2024

The present study concerns reduced order modeling of a marine diesel engine, which can be used for outlier detection in status monitoring and carbon intensity index calculation. Principal Component Analysis (PCA) is introduced for the reduced order modeling, focusing on the feasibility of detecting and treating nonlinear variables. By cross-correlation, it is found that there are seven non-linear data channels among 23 data channels, i.e., fuel mode, exhaust gas temperature after the turbocharger, and cylinder coolant temperatures. The dataset is handled so that the mean is located at the nominal continuous rating. Polynomial presentation of the dataset is also applied to reflect the linearity between the engine speed and other channels. The first principal mode shows strong effects of linearity of the most data channels to show the linearity of the system. The non-linear variables are effectively explained by other modes. second mode concerns the temperature of the cylinder cooling water, which shows small correlation with other variables. The third and fourth modes correlates the fuel mode and turbocharger exhaust gas temperature, which have inferior linearity to other channels. PCA is proven to be applicable to data given in binary type of fuel mode selection, as well as numerical type data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.10
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• pp.1139-1145
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• 2014

Creation of a human skeleton model and characterization of the variation in the bone shape are fundamentally important in many applications of biomechanics. In this paper, we present a parametric shape modeling method for femurs that is based on extracting the main parameter of variations of the femur shape from a 3D model database by using statistical shape analysis. For this shape analysis, principal component analysis (PCA) is used. Application of the PCA to 3D data requires bringing all the models in correspondence to each other. For this reason, anatomical landmarks are used for guiding the deformation of the template model to fit the 3D model data. After subsequent application of PCA to a set of femur models, we calculate the correlation between the dominant components of shape variability for a target population and the anatomical parameters of the femur shape. Finally, we provide tools for visualizing and creating the femur shape using the main parameter of femur shape variation.

• Bulletin of the Korean Chemical Society
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• v.34 no.3
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• pp.815-819
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• 2013

Micropatterns of streptavidin and human epidermal carcinoma A431 cells were successfully imaged, as received and without any labeling, using cluster $Au_3{^+}$ ion beam-based time-of-flight secondary ion mass spectrometry (TOF-SIMS) together with a principal component analysis (PCA). Three different analysis ion beams ($Ga^+$, $Au^+$ and $Au_3{^+}$) were compared to obtain label-free TOF-SIMS chemical images of micropatterns of streptavidin, which were subsequently used for generating cell patterns. The image of the total positive ions obtained by the $Au_3{^+}$ primary ion beam corresponded to the actual image of micropatterns of streptavidin, whereas the total positive-ion images by $Ga^+$ or $Au^+$ primary ion beams did not. A PCA of the TOF-SIMS spectra was initially performed to identify characteristic secondary ions of streptavidin. Chemical images of each characteristic ion were reconstructed from the raw data and used in the second PCA run, which resulted in a contrasted - and corrected - image of the micropatterns of streptavidin by the $Ga^+$ and $Au^+$ ion beams. The findings herein suggest that using cluster-ion analysis beams and multivariate data analysis for TOF-SIMS chemical imaging would be an effectual method for producing label-free chemical images of micropatterns of biomolecules, including proteins and cells.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.7-17
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• 2017

Loan consultants assist clients with loan application processing and loan decisions. Their duties may include contacting people to ask if they want a loan, meeting with loan applicants and explaining different loan options. We studied the efficiency of service quality of loan consultants contracted to a bank in Korea. They do not work as a team, but do work independently. Since he/she is not an employee of the bank, the consultant is paid solely in proportion to how much he/she sell loans. In this study, a consultant is considered as a decision making unit (DMU) in the DEA (Data Envelopment Analysis) model. We use a principal component analysis-data envelopment analysis (PCA-DEA) model integrated with Shannon's Entropy to evaluate quality efficiency of the consultants. We adopt a three-stage process to calculate the efficiency of service quality of the consultants. In the first stage, we use PCA to obtain 6 synthetic indicators, including 4 input indicators and 2 output indicators, from survey results in which questionnaire items are constructed on the basis of SERVQUAL model. In the second stage, 3 DEA models allowing negative values are used to calculate the relative efficiency of each DMU. In the third stage, the weight of each result is calculated on the basis of Shannon's Entropy theory, and then we generate a comprehensive efficiency score using it. An example illustrates the proposed process of evaluating the relative quality efficiency of the loan consultants and how to use the efficiency to improve the service quality of the consultants.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.82-86
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• 2006

The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.