• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.144-146
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• 2020

Discrete Wavelet Transform (DWT) is an effective technique that is commonly used for detecting noise in collected data of an individual sensor. In addition, the detection accuracy can be significant improved by exploiting the correlation in the data of neighboring sensors of Wireless Sensor Networks (WSNs). Principal component analysis is the powerful technique to analyze the correlation in the multivariate data. In this paper, we propose a DWT-PCA combination scheme for noise detection (DWT-PCA-ND). Experimental results on a real dataset show a remarkably higher performance of DWT-PCA-ND comparing to conventional PCA scheme in detection of noise that is a popular anomaly in collected data of WSN.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.355-360
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• 2015

In this paper, we propose the design of Radial Basis Function Neural Network based on PCA in order to recognize handwritten digits. The proposed pattern classifier consists of the preprocessing step of PCA and the pattern classification step of pRBFNNs. In the preprocessing step, Feature data is obtained through preprocessing step of PCA for minimizing the information loss of given data and then this data is used as input data to pRBFNNs. The hidden layer of the proposed classifier is built up by Fuzzy C-Means(FCM) clustering algorithm and the connection weights are defined as linear polynomial function. In the output layer, polynomial parameters are obtained by using Least Square Estimation (LSE). MNIST database known as one of the benchmark handwritten dataset is applied for the performance evaluation of the proposed classifier. The experimental results of the proposed system are compared with other existing classifiers.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.67-76
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• 2008

Reliable face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. Gabor feature vectors are known to be more robust to variations of pose and illumination than any other feature vectors so that they are popularly adopted for face recognition. However, they are not completely independent of illuminations. In this paper, we propose an illumination-robust face recognition method based on the Gabor feature vector illumination PCA model. We first construct the Gabor feature vector illumination PCA model where Gator feature vector space is rendered to be decomposed into two orthogonal illumination subspace and face identity subspace. Since the Gabor feature vectors obtained by projection into the face identity subspace are separated from illumination, the face recognition utilizing them becomes more robust to illumination. Through experiments, it is shown that the proposed face recognition based on Gabor feature vector illumination PCA model performs more reliably under various illumination and Pose environments.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.04a
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• pp.121.2-122
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• 2003

다슬기분말(PSB)과 그 회분(ASB)으로 제조한 칼슘락테이트(PCaL 및 ACaL)를 0.5%씩 첨가한 반죽의 발효와 빵의 품질 및 저장성에 미치는 영향을 조사하였다. 반죽의 pH는 4.85~4.98로 ACaL.PCaL.대조군의 순으로 나타났다. 반죽의 부피와 빵의 loaf volume index는 대조군이 높았고 ACaL 첨가군이 낮았으며, pH를 5.50으로 조정하여 제조한 반죽의 부피와 빵의 loaf volume은 대조군과 큰 차이를 보이지 않았다. PCaL 및 ACaL을 첨가한 빵의 Ca함량은 29.4~29.7 mg/100 g-f.w로 대조군의 13.0 mg/100 g-f.w에 비하여 높았으며, 첨가군의 미량 무기질로 Mg, Fe, Zn이 0.03~0.98 mg/100 g-f.w 범위로 검출되었다. 빵의 L$^{*}$ 값은 대조군과 실험군의 유의적인 차이가 없었으며, a＊값, b＊값은 PCaL 첨가군이 가장 높아 황갈색을 띄었다. 빵의 hardness, gumminess는 대조군$^{\circ}C$ 실온에 두면서 저장한 결과 대조군은 3일째부터, ACaL 첨가군은 5일째부터, PCaL 첨가군은 6일째부터 곰팡이가 번식하였다.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6121-6125
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• 2014

Background: Prostate-specific antigen (PSA) is a potential biomarker for early detection of prostate cancer (PCa) but its level is known to be affected by many background factors and roles of ubiquitous toxicants have not been determined. Endocrine disrupting chemicals (EDCs) are ubiquitous reproductive toxicants used in consumer products, which promote tumor formation in some reproductive model systems by binding to AhR, but human data on its expression in prostate cancer as well as its association with PSA levels are not clear. This study aimed to evaluate the expression levels of AhR and its association with serological levels of PSA and to detect possible effects of background factors and EDC exposure history on PSA levels in PCa cases. Materials and Methods: A cross-sectional study was conducted on the tissue levels of AhR and serum levels of PSA in 53 PCa cases from 2008-2011 and associations between each and background and lifestyle related factors were determined. Results: Although the AhR was overexpressed in PCa and correlated with the age of patients, it did not correlate with PSA levels.Of nutritional factors, increased intake of polysaturated fats and fish in the routine regimen of PCa cases increased the PSA levels significantly. Conclusions: AhR overexpression in PCa pontws to roles of EDCs in PCa but without any direct association with PSA levels. However, PSA levels are affected by exposure to possible toxicants in foods whichneed to be assessed as possible risk factors of PCa in future studies.

• 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.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.205-208
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• 2013

Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.

• Journal of the Korean Institute of Gas
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• v.28 no.2
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• pp.38-46
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• 2024

The city gas pipeline network is characterized by being widely distributed and hierarchically connected in a complex manner over a wide area. In order to monitor the status of the widely distributed network pressures with high precision, Multi-block PCA(MBPCA) is recommended. However, while MBPCA has excellent performance in identifying faulty sensors as the number of sensors increases, the fault detection performance deteriorates, and also there is a problem that the model needs to be updated entirely even if minor changes occur. In this study, we developed fault detectability index and fault identificability index to determine the effectiveness of MBPCA application block by block. Based on these indices, we distinguished MBPCA and PCA blocks and developed a fault detection and diagnostic system for the city gas pipeline network of Haean Energy Co., Ltd., and were able to solve the problems that arise when there are many sensors.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.35-49
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• 2004

This paper proposes a new glasses removal method from color frontal facial image to generate gray glassless facial image. The proposed method is based on recursive PCA reconstruction. For the generation of glassless images, the occluded region by glasses should be found, and a good reconstructed image to compensate with should be obtained. The recursive PCA reconstruction Provides us with both of them simultaneously, and finally produces glassless facial images. This paper shows the effectiveness of the proposed method by some experimental results. We believe that this method can be applied to removing other type of occlusion than the glasses with some modification and enhancing the performance of a face recognition system.