• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.342-345
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• 2013

본 논문은 모바일 환경에서의 다중생체인식을 통한 개인인증 시스템을 제안한다. 다중생체인식을 위하여 얼굴인식과 화자인식을 선택하였으며, 시스템의 인식 시나리오는 다음을 따른다. 얼굴인식을 위하여 Modified census transform (MCT) 기반의 얼굴검출과 k-means 클러스터 분석 (cluster analysis) 알고리즘 기반의 눈 검출을 통해 얼굴영역 전처리를 수행하고, principal component analysis (PCA) 기반의 얼굴인증 시스템을 구현한다. 화자인식을 위하여 음성의 끝점 추출과 Mel frequency cepstral coefficient(MFCC) 특징을 추출하고, dynamic time warping (DTW) 기반의 화자 인증 시스템을 구현한다. 그리고 각각의 생체인식을 본 논문에서 제안된 방법을 기반으로 융합하여 인식률을 향상시킨다.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.13-22
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• 2007

Fault detection of cycle-based signals is typically performed using statistical approaches. Univariate SPC using few representative statistics and multivariate analysis methods such as PCA and PLS are the most popular methods for analyzing cycle-based signals. However, such approaches are limited when dealing with information-rich cycle-based signals. In this paper, process fault defection method based on wavelet analysis is proposed. Using Haar wavelet, coefficients that well reflect the process condition are selected. Next, Hotelling's $T^2$ chart using selected coefficients is constructed for assessment of process condition. To enhance the overall efficiency of fault detection, the following two steps are suggested, i.e. denoising method based on wavelet transform and coefficient selection methods using variance difference. For performance evaluation, various types of abnormal process conditions are simulated and the proposed algorithm is compared with other methodologies.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.456-463
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• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• Journal of the Korea Computer Graphics Society
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• v.24 no.2
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• pp.11-20
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• 2018

We propose a new method particle-based method that explicitly preserves thin liquid sheets for animating liquids on CPU-GPU heterogeneous computing framework. Our primary contribution is a particle-based framework that splits at thin points and collapses at dense points to prevent the breakup of liquid on GPU. In contrast to existing surface tracking methods, the our method does not suffer from numerical diffusion or tangles, and robustly handles topology changes on CPU-GPU framework. The thin features are detected by examining stretches of distributions of neighboring particles by performing PCA(Principle component analysis), which is used to reconstruct thin surfaces with anisotropic kernels. The efficiency of the candidate position extraction process to calculate the position of the fluid particle was rapidly improved based on the CPU-GPU heterogeneous computing techniques. Proposed algorithm is intuitively implemented, easy to parallelize and capable of producing quickly detailed thin liquid animations.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.90-97
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• 1999

Recently, there have been many researches to automate processing and analysing image data in medical field, due to the advance of image processing techniques, the fast communication network and high performance hardware. In this paper, we design and implement the system based on the multi-layer neural network model to be able to analyze, differentiate and count blood cells in the peripheral blood image. To do these, we segment red and white-blood cell in blood image acquired from microscope with CCD(Charge-coupled device) camera and then apply the various feature extraction algorithms to classify. In addition to, we reduce multi-variate feature number using PCA(Principle Component Analysis) to construct more efficient classifier. So, in this paper, we are sure that the proposed system can be applied to a pathological guided system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.3
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• pp.75-84
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• 2005

Since an unexpected fault of induction motor drive systems can cause serious troubles in many industrial applications, which the technique is required to diagnose faults of a voltage-fed PWM inverter for induction motor drives. The considered fault types are rectifier diodes, switching devices and input terminals with open-circuit faults, and the signal for diagnosis is derived from motor currents. The magnitude of dq-current trajectory is used for the feature extraction of a fault and PCA LDA are applied to diagnose. Also, we show results with respect to the execution time because of the possibility to use that a diagnosis software is embedded in the controllers of medium and small size induction motors drive for real-time diagnosis. After we performed various simulations for the fault diagnosis of the inverter, the usefulness of proposed algerian was verified.

• Journal of Sensor Science and Technology
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• v.8 no.6
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• pp.461-468
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• 1999

In this paper, we have implemented a gas recognition system for classification and identification of explosive gases such as methane, propane, and butane using a sensor array and an artificial neural network. Such explosive gases which can be usually detected in the oil factory and LPG pipeline are very dangerous for a human being. We analyzed the characteristics of a multi-dimensional sensor signals obtained from the nine sensors using the principal component analysis(PCA) technique. Also, we implemented a gas pattern recognizer using a multi-layer neural network with error back propagation learning algorithm, which can classify and identify the sorts of gases and concentrations for each gas. The simulation and experimental results show that the proposed gas recognition system is effective to identify the explosive gases. And also, we used DSP board(TMS320C31) to implement the proposed gas recognition system using the neural network for real time processing.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.6
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• pp.579-589
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• 2004

The radar signals are generally very sensitive to relative orientations between radar and target. Thus, the performance of a target recognition system significantly deteriorates as the region of aspect angles becomes broader. To address this difficulty, in this paper, we propose a method based on the multi-aspect information in order to improve the classification capability ever for a wide angular region. First, range profiles are used to extract feature vectors based on the central moments and principal component analysis(PCA). Then, a classifier with the use of multi-aspect information is applied to them, yielding an additional improvement of target recognition capability. There are two different strategies among the classifiers that can fuse the information from multi-aspect radar signals: independent methodology and dependent methodology. In this study, the performances of the two strategies are compared within the frame work of target recognition. The radar cross section(RCS) data of six aircraft models measured at compact range of Pohang University of Science and Technology are used to demonstrate and compare the performances of the two strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.1987-1996
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• 2009

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 Korea Multimedia Society
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• v.16 no.3
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• pp.302-308
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• 2013

In this research, we have explored personal authentication system through multimodal biometrics for mobile computing environment. We have selected face and speaker recognition for the implementation of multimodal biometrics system. For face recognition part, we detect the face with Modified Census Transform (MCT). Detected face is pre-processed through eye detection module based on k-means algorithm. Then we recognize the face with Principal Component Analysis (PCA) algorithm. For speaker recognition part, we extract features using the end-point of voice and the Mel Frequency Cepstral Coefficient (MFCC). Then we verify the speaker through Dynamic Time Warping (DTW) algorithm. Our proposed multimodal biometrics system shows improved verification rate through combining two different biometrics described above. We implement our proposed system based on Android environment using Galaxy S hoppin. Proposed system presents reduced false acceptance ratio (FAR) of 1.8% which shows improvement from single biometrics system using the face and the voice (presents 4.6% and 6.7% respectively).