• ETRI Journal
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• v.33 no.3
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• pp.443-453
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• 2011

This paper evaluates the efficiency of mobile content firms through a hybrid approach combining data envelopment analysis (DEA) to analyze the relative efficiency and performance of firms and principal component analysis (PCA) to analyze data structures. We performed a DEA using the total amount of assets, operating costs, employees, and years in business as inputs, and revenue as output. We calculated fifteen combinations of DEA efficiency in the mobile content firms. We performed a PCA on the results of the fifteen DEA models, dividing the mobile content firms into those having either 'asset-oriented' or 'manpower and experience-oriented' efficiency. Discriminant analysis was used to validate the relationship between the efficiency models and mobile content types. This paper contributes toward the construction of a framework that combines the DEA and PCA approaches in mobile content firms for use in comprehensive measurements. Such a framework has the potential to present major factors of efficiency for sustainable management in mobile content firms and to aid in planning mobile content industry policies.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.113-118
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• 1996

Principal component analysis(PCA) is an essential technique for data compression and feature extraction, and has been widely used in statistical data analysis, communication theory, pattern recognition, and image processing. Oja(1992) found that a linear neuron with constrained Hebbian learning rule can extract the principal component by using stochastic gradient ascent method. In practice real data often contain some outliers. These outliers will significantly deteriorate the performances of the PCA algorithms. In order to make PCA robust, Xu & Yuille(1995) applied statistical physics to the problem of robust principal component analysis(RPCA). Devlin et.al(1981) obtained principal components by using techniques such as M-estimation. The propose of this paper is to investigate from the statistical point of view how Xu & Yuille's(1995) RPCA works under the same simulation condition as in Devlin et.al(1981).

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.329-336
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• 2006

In this paper, we propose a method of significant term extraction within a document. The technique used is Principal Component Analysis(PCA) which is one of the multivariate analysis methods. PCA can sufficiently use term-term relationships within a document by term-term correlations. We use a correlation matrix instead of a covariance matrix between terms for performing PCA. We also try to find out thresholds of both the number of components to be selected and correlation coefficients between selected components and terms. The experimental results on 283 Korean newspaper articles show that the condition of the first six components with correlation coefficients of |0.4| is the best for extracting sentence based on the significant selected terms.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.181-184
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• 2000

The new method of feature extraction is proposed, considering the statistic feature of human voice, unlike the conventional methods of voice extraction. PCA(principal Component Analysis) is applied to this new method. PCA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. Then the new method is applied to real voice recognition to assess performance. When results of the number recognition in this paper and the conventional Mel-Cepstrum of voice feature parameter are compared, there is 0.5% difference of recognition rate. Better recognition rate is expected than word or sentence recognition in that less convergence time than the conventional method in extracting voice feature. Also, better recognition tate is expected when the optimum vector is used by statistic feature of data.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.779-782
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• 2002

본 논문에서는 주성분분석(principal component analysis; 이하 PCA) 및 첨도(Kurtosis)에서의 그레이 블록 거리 알고리즘(grey block algorithms; 이하 GBD)을 이용, 영상간의 거리를 측정하여 어느 정도 영상간의 상대적 식별을 용이하게 하여 영상 분류가 되는지 모의실험을 통하여 확인하고자 한다. 모의실험 결과로부터, PCA에서는 k가 9에서 상대적 식별이 불가능함을 보였고, 첨도에서는 k가 4까지만 블록을 택할 할 수 있음을 모의실험을 통하여 확인할 수 있었다.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.257-258
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• 1998

To reveal the interconnected brain regions involved in human working memory, their functional connectivity was analyzed using principal component analysis (PCA). rCBF PET scans were peformed on 5 normal volunteers during the verbal and visual working memory tasks and PCA was applied. PCA produced the first principal components related with the increase of the difficulty and the second one which demonstrate the dissociation of verbal and visual memory system.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.64-74
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• 2014

The primary and ambient signal decomposition of a stereo sound is a key step to the stereo upmix. The principal component analysis (PCA) is one of the most widely used methods of primary-ambient signal decomposition. However, previous PCA-based decomposition algorithms assume that stereo sound sources are only amplitude-panned without any consideration of phase difference. So it occurs some performance degradation in case of live recorded stereo sound. In this paper, we propose a new PCA-based stereo decomposition algorithm that can consider the phase difference between the channel signals. The proposed algorithm overcomes limitation of conventional signal model using PCA with phase alignment. The phase alignment is realized by using inter-channel phase difference (IPD) which is widely used in parametric stereo coding. Moreover, Enhanced Modified PCA(EMPCA) is combined to solve the problem of conventional PCA caused by Primary to Ambient energy Ratio(PAR) and panning angle dependency. The simulation results are presented to show the improvements of the proposed algorithm.

• Smart Structures and Systems
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• v.22 no.1
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• pp.57-70
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• 2018

For the long-term structural health monitoring of civil structures, the effect of ambient temperature variation has been regarded as one of the critical issues. In this study, a principal component analysis (PCA)-based algorithm is proposed to filter out temperature effects on electromechanical impedance (EMI) monitoring of prestressed tendon anchorages. Firstly, the EMI monitoring via a piezoelectric interface device is described for prestress-loss detection in the tendon anchorage system. Secondly, the PCA-based temperature filtering algorithm tailored to the EMI monitoring of the prestressed tendon anchorage is outlined. The proposed algorithm utilizes the damage-sensitive features obtained from sub-ranges of the EMI data to establish the PCA-based filter model. Finally, the feasibility of the PCA-based algorithm is experimentally evaluated by distinguishing temperature changes from prestress-loss events in a prestressed concrete girder. The accuracy of the prestress-loss detection results is discussed with respect to the EMI features before and after the temperature filtering.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.772-776
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• 2006

A research about facial image recognition is studied in the most of images in a full race. A representative part, effecting a facial image recognition, is eyes and a mouth. So, facial image recognition researchers have studied under the central eyes, eyebrows, and mouths on the facial images. But most people in front of a camera in everyday life are difficult to recognize a fast change of pupils. And people wear glasses. So, in this paper, we try using Principal Component Analysis(PCA) for facial image recognition in blindfold case.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.35-41
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• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.