• International Journal of Contents
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• v.10 no.2
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• pp.47-58
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• 2014

Physiological signals provide important clues in the diagnosis and prediction of disease. Analyzing these signals is important in health and medicine. In particular, data preprocessing for physiological signal analysis is a vital issue because missing values, noise, and outliers may degrade the analysis performance. In this paper, we propose PhysioCover, a system that can recover missing values of physiological signals that were monitored in real time. PhysioCover integrates a gradual method and EM-based Principle Component Analysis (PCA). This approach can (1) more readily recover long- and short-term missing data than existing methods, such as traditional EM-based PCA, linear interpolation, 5-average and Missing Value Singular Value Decomposition (MSVD), (2) more effectively detect hidden variables than PCA and Independent component analysis (ICA), and (3) offer fast computation time through real-time processing. Experimental results with the physiological data of an intensive care unit show that the proposed method assigns more accurate missing values than previous methods.

• Korean Journal of Applied Biomechanics
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• v.25 no.2
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• pp.157-165
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• 2015

Object : The goals of this research were to make Performance Enhanced Model(PE) taken the largest performance index (PI) through artificial variation of principle components calculated by principle component analysis for trial data, and to verify the effect through comparing kinematic factors between trial data (Raw) and PE. Method : Ten subjects (5 men, 5 women) were recruited and 80% of their maximal record was considered. The PI is a regression equation. In order to develop PE, we extracted Principle components from trial position data (by Principle Components Analysis (PCA)). Before PCA, we made 17 position data to 3 row matrix according to components. We calculated 3 eigen value (principle components) through PCA. And except Y (medial-lateral direction) component (because motion of Y component is small), principle components of X (anterior-posterior direction) and Z (vertical direction) components were changed as following. Changed principle components = principle components + principle components ${\times}$ k. After changing the each principle component, we reconstructed position data using the changed principle components and calculated performance index (PI). A Paired t-test was used to compare Raw data and Performance Enhanced Model data. The level of statistical significance was set at $p{\leq}0.05$. Result : The PI was significantly increased about 12.9kg at PE ($101.92{\pm}6.25$) when compared to the Raw data ($91.29{\pm}7.10$). It means that performance can be increased by optimizing 3D positions. The difference of kinematic factors as follows : the movement distance of the bar from start to lock out was significantly larger (about 1cm) for PE, the width of anterior-posterior bar position in full phase was significantly wider (about 1.3cm) for PE and the horizontal displacement toward the weightlifter after beginning of descent from maximal height was significantly greater (about 0.4cm) for PE. Additionally, the minimum knee angle in the 2-pull phase was significantly smaller (approximately 2.7cm) for the PE compared to that of the Raw. PE was decided at proximal position from the Raw (origin point (0,0)) of PC variation). Conclusion : PI was decided at proximal position from the Raw (origin point (0,0)) of PC variation). This means that Performance Enhanced Model was decided by similar motion to the Raw without a great change. Therefore, weightlifters could be accept Performance Enhanced Model easily, comfortably and without large stress. The Performance Enhance Model can provide training direction for athletes to improve their weightlifting records.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.4
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• pp.285-291
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• 2015

In this paper, we propose a classification algorithm for postures of sitting person by using IMU(inertial measurement unit). This algorithm uses PCA(principle component analysis) for decreasing the number of feature vectors to three and SVM(support vector machine) with RBF(radial basis function) kernel for classifying posture types. In order to collect the data, we designed neckband-shaped earphones with IMU, and applied it to three subjects who are healthy adults. Subjects were experimented three sitting postures, which are neutral posture, smartphoning, and writing. As the result, our PCA-SVM algorithm showed 95% confidence while the dimension of the feature vectors was reduced to 25%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.305-309
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• 2006

In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory(ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.209-211
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• 2017

We performed Principle Component Analysis (PCA) over 264 galaxies in the IRAS Revised Bright Galaxy Sample (Sanders et al., 2003) using 12, 25, 60 and $100{\mu}m$ flux data observed by IRAS and 9, 18, 65, 90 and $140{\mu}m$ flux data observed by AKARI. We found that (i)the first principle component was largely contributed by infrared to visible flux ratio, (ii)the second principal component was largely contributed by the flux ratio between IRAS and AKARI, (iii)the third principle component was largely contributed by infrared colors.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.67-79
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• 2019

The reliable Typical Meteorological Year (TMY) data, sometimes called Test Reference Year (TRY) data, are necessary in the feasibility study of renewable energy installation as well as zero energy building. In Korea, there are available TMY data; TMY from Korea Institute of Energy Research (KIER), TRY from the Korean Solar Energy Society (KSES) and TRY from Passive House Institute Korea (PHIKO). This study aims at examining their characteristics by using Principle Component Analysis (PCA) at six ground observing stations. First step is to investigate the annual averages of meteorological elements from TMY data and their standard deviations. Then, PCA is done to find which principle components are derived from different TMY data. Temperature and solar irradiance are determined as the main principle component of TMY data produced by KIER and KSES at all stations whereas TRY data from PHIKO does not show similar result from those by KIER and KSES.

• The KIPS Transactions:PartB
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• v.8B no.4
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• pp.389-394
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• 2001

본 논문에서는 독립 성분 해석(Independent Component Analysis, ICA) 기법과 인근 평균 및 정규화를 이용한 영상 분류 방법을 제안하였다. ICA에 잡음을 주어 영상을 분류하였을 때, 잡음에 대한 강인성을 증가시키기 위하여, 제안된 인근 평균 및 정규화를 전처리로 적용하였다. 제안된 방법은 전처리 없이 ICA에 주성분 해석(Principal Component Analysis, PCA)을 이용한 것에 비해 잡음에 대한 강인성을 증가시키는 것을 모의 실험을 통하여 확인하였다.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1134-1141
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• 2005

With ever more popularity of video web-publishing, popular content is being compressed, reformatted and modified, resulting in excessive content duplication. Such overlapped data can cause problem of search speed and rate of searching. However, duplicated data on other site can provide alternatives while specific site cause problem. This paper proposes the efficient method, for retrieving. similar video data in large database. In this research we have used the method to compare summarized video image instead of the raw video data, and detected similar videos through clustering in that dimension feature vector through PCA(principle component analysis). We show that our proposed method is efficient and accurate through our experiment.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2272-2275
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• 2002

PCA(Principle Component Analysis) has emerged as a useful tool for process monitoring and fault diagnosis. The general approach requires the user to identify the root cause by interpreting the residual or principle components. This could be tedious and often impossible for a large process. In this paper, PCA scheme is combined with the FCM-based fault diagnostic algorithm to enhance the diagnosistic results. The implementation of the PCA-FCM based fault diagnostic system is done and its application is illustrated on the two-tank system.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.283-284
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• 2006

In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, we extract the local entropy and then segment the document image to separate the background and the foreground using the Otsu's method. Finally, we classify the segmented regions into each component using PCA(principle component analysis) algorithm based on the texture features that are extracted from the co-occurrence matrix for the entropy image. The entropy-based segmentation is robust to not only noise and the change of light, but also skew and rotation. Texture features are not restricted from any form of the document image and have a superior discrimination for each component. In addition, PCA algorithm used for the classifier can classify the components more robustly than neural network.