• 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.

• The Korean Journal of Pain
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• v.13 no.2
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• pp.218-223
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• 2000

Background: This study was conducted to evaluate the efficacy of a parenteral nonsteroidal anti-inflammatory agent for management of post-surgical pain and its effect on hospital stay and long-term surgical outcome. Methods: Total of 40 patients undergoing lumbar discectomy were randomly assigned to two groups, receiving either 1) 30 mg intravenous ketorolac upon surgical closure, every 6 hours for 36 hours, and morphine IV PCA (intravenous patient controlled analgesia), or 2) only morphine PCA. A blinded investigator recorded; the visual analog pain scores, total postoperative narcotic consumption, complications by morphine PCA, length of hospitalization (from surgery to discharge), and long-term outcome at 6 weeks. Results: The patients who received IV ketorolac and morphine PCA reported significantly lower visual analog pain scores than patients receiving only morphine PCA. Cumulative morphine doses were significantly lower in the ketorolac group (P<0.001). There was no significant difference between groups in the frequency of side effects related to morphine PCA. Mean length of hospitalization was longer for patients receiving only morphine PCA, but there was no statistical significance. Six weeks after surgery, four (20.0%) patients who received only morphine PCA suffered persistent back pain. In contrary, all those patients who received ketorolac were free of back pain at follow-up (P<0.05). Conclusions: These results suggest that intermittent IV bolus ketorolac, when used with opioid IV PCA is more effective than opioid IV PCA alone for postoperative pain following lumbar disc surgery. However, this strategy did not contribute to early discharge from hospital after lumbar disc surgery. The effect to long-term surgical outcome was not conclusive.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.441-451
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• 2017

In this paper, we review principal component analysis (PCA) procedures in the frequency domain and apply them to analyze sea surface temperature data. The classical PCA defined in the time domain is a popular dimension reduction technique. Extending the conventional PCA to the frequency domain makes it possible to define PCA in the frequency domain, which is useful for dimension reduction as well as a feature extraction of multiple time series. We focus on two PCA methods in the frequency domain, Hilbert PCA (HPCA) and frequency domain PCA (FDPCA). We review these two PCAs in order for potential readers to easily understand insights as well as perform a numerical study for comparison with conventional PCA. Furthermore, we apply PCA methods in the frequency domain to sea surface temperature data on the tropical Pacific Ocean. Results from numerical experiments demonstrate that PCA in the frequency domain is effective for the analysis of time series data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.2
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• pp.152-159
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• 2005

In this paper, we propose a diagnosis algorithm using fusion wかd based on PCA and LDA to detect fault states of the induction motor that is applied to various industrial fields. After yielding a feature vector from the current value measured by an experiment using PCA and LDA, training data is made to produce each matching value. In a diagnostic step, two matching values yielded by PCA and LDA are fused by probability model and finally verified. Since the proposed diagnosis algorithm takes only merits of PCA and LDA it shows excellent results under noisy environments. The simulation results to verify the usability of the proposed algorithm showed better performance than the case just using conventional PCA or LDA.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.492-497
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• 2005

Feature extraction is important to classify data with large dimension such as image data. The representative feature extraction methods lot feature extraction ate PCA, ICA, LDA and MLP, etc. These algorithms can be classified in two groups: unsupervised algorithms such as PCA, LDA, and supervised algorithms such as LDA, MLP. Among these two groups, supervised algorithms are more suitable to extract the features for classification because of the class information of input data. In this paper we suggest a new feature extraction algorithm PCA-FX which uses class information with PCA to extract ieatures for classification. We test our algorithm using Yale face database and compare the performance of proposed algorithm with those of other algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2180-2188
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• 2009

This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

• Elastomers and Composites
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• v.47 no.1
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• pp.36-42
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• 2012

More than 3 wt% of polycyclic aromatics (PCAs) in process oil is known to cause skin cancer. The criterion of distinguishing between low PCA oil and high PCA oil is based on 3 wt% of PCA. High PCA oil is called as a carcinogen like distillate aromatic extract (DAE). Low PCA oil is considered as safety oils like treated distillate aromatic extract (TDAE), mild extract solvate (MES), and paraffinic oil. Four types of process oils such as DAE, TDAE, MES, and paraffinic oil purified by solvent extraction and separation skills from SBR vulcanizates were measured by FT-IR techniques. The effects of rubber chemicals such as N-1,3-dimethylbutyl-N'-phenyl-p-phenylnenediamine (HPPD), polymerized 2,2,4-trimethyl-1,2-dihydroquinoline (TMDQ), paraffin wax as antidegradants, and processing aid like Structol 40MS on paraffinic oil from SBR vulcanizates were also studied. The type of low or high PCA was identified by the relative abundance of absorbance at the aromatic substitution patterns of 864, 810, and $754cm^{-1}$ and at the paraffinic or naphthenic pattern of $721cm^{-1}$.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.81-88
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• 2014

The researcher of this study applied PCA on aerial hyper-spectral sensor and selectively combined bands which contain high amount of information, creating five types of PCA images. By applying Spectral Angle Mapping-supervised classification technique on each type of image, classification process was carried out and accuracy was evaluated. The test result showed that the amount of information contained in the first band of PCA-transformation image was 76.74% and the second accumulated band contained 98.40%, suggesting that most of information were contained in the first and the second PCA components. Quantitative classification accuracy evaluation of each type of image showed that total accuracy, producer's accuracy and user's accuracy had similar patterns. What drew the researcher's attention was the fact that the first and the second bands of the PCA-transformation image had the highest accuracy according to the classification accuracy although it was believed that more than four bands of PCA-transformation image should be contained in order to secure accuracy when doing the qualitative classification accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.651-656
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• 2010

This paper proposes a new dynamic PCA algorithm to recognize types of electric poles, which is necessary for a mobile robot moving along the neutral line for inspecting high-voltage facilities. Since the mobile robot needs to pass over the electric poles and grasp the neutral wire again for the next region inspection, the detection of the electric pole type is a critical factor for the successful passing-over the electric pole. The CCD camera installed on the mobile robot captures the image of the electric pole while it is approaching to the electric pole. Applying the dynamic PCA algorithm to the CCD image, the electric pole type has been classified to provide the stable grasping operation for the mobile robot. The new dynamic PCA algorithm replaces the reference image in real time to improve the robustness of the PCA algorithm, adjusts the brightness to get the clear images, and applies the Laplacian edge detection algorithm to increase the recognition rate of electric pole type. Through the real experiments, the effectiveness of this proposed dynamic PCA algorithm method using Laplacian edge detecting method has been demonstrated, which improves the recognition rate about 20% comparing to the conventional PCA algorithm.

• Proceedings of the SCSK Conference
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• 2003.09b
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• pp.145-148
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• 2003

Previously, we reported that L-PCA enhanced blood circulation by modulating constitutive NO production. It was that L-PCA increased L-Arg uptake into endothelial cell, followed by the enhancement of NO production. Then we recommended the use of L-PCA for cosmetics, not only as humectants but also as enhancer of blood circulation. Since L-Arg is transported into endothelial cells by CAT (cationic amino acid transporter), it is expected that L-PCA also increase the uptake of basic amino acid, L-Lys. In this study, the uptakes of some amino acids into cells were evaluated by using 3H-labelled amino acid. Then we found the tendency that the uptake of L-Lys into endothelial cells was also enhanced by L-PCA. And the evident effect was observed in the epidermal fibroblasts, which had also CAT. Furthermore, it was found that the transportation of the other type of amino acids were not enhanced by L-PCA. That is to say, a famous moisturizer, L-PCA, has some effects on basic amino acid transport into cells.