• 한국정보통신학회논문지
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• 제12권9호
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• pp.1547-1553
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• 2008

본 논문은 간소화된 주성분 벡터를 이용한 벡터 그래픽 캐릭터의 다양한 얼굴 표정 생성 방법을 제안한다. 먼저 Russell의 내적 정서 상태에 기반하여 재정의된 벡터 그래픽 캐릭터들의 9가지 표정에 대해 주성분 분석을 수행한다. 이를 통해 캐릭터의 얼굴 특성과 표정에 주된 영향을 미치는 주성분 벡터를 찾아내고, 간소화된 주성분 벡터로부터 얼굴 표정을 생성한다. 또한 캐릭터의 특성과 표정의 가중치 값을 보간함으로써 자연스러운 중간 캐릭터 및 표정을 생성한다. 이는 얼굴 애니메이션에서 종래의 키프레임 저장 공간을 상당히 줄일 수 있으며, 적은 계산량으로 중간 표정을 생성할 수 있다. 이에 실시간 제어를 요구하는 웹/모바일 서비스, 게임 등에서 캐릭터 생성 시스템의 성능을 상당히 개선할 수 있다.

• International Journal of Advanced Culture Technology
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• 제6권3호
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 춘계학술발표대회논문집
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• pp.314-317
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• 2003

기존의 침입탐지 시스템에서는 오용탐지모델이 널리 사용되고 있다. 이 모델은 낮은 오판율(False Alarm rates)을 가지고 있으나, 새로운 공격에 대해 전문가시스템(Expert Systems)에 의한 규칙추가를 필요로 한다. 그리고 그 규칙과 완전히 일치되는 시그너처만 공격으로 탐지하므로 변형된 공격을 탐지하지 못한다는 문제점을 가지고 있다 본 논문에서는 이러한 문제점을 보완하기 위해 주성분분석(Principle Component Analysis; 이하 PCA)과 서포트 벡터 머신(Support Vector Machines; 이하 SVM)을 이용한 침입탐지 시스템을 제안한다. 네트워크 상의 패킷은 PCA를 이용하여 결정된 주성분 공간에서 해석되고, 정상적인 흐름과 비정상적인 흐름에 대한 패킷이미지패턴으로 정규화 된다. 이러한 두 가지 클래스에 대한 SVM 분류기를 구현한다. 개발하는 침입탐지 시스템은 알려진 다양한 침입유형뿐만 아니라, 새로운 변종에 대해서도 분류기의 유연한 반응을 통하여 효과적으로 탐지할 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.327-344
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• 2013

Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.

• 한국해양공학회지
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• 제26권4호
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• 대한기계학회논문집
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• 제17권12호
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• pp.2949-2961
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• 1993

Dynamic cutting process can be represented by a closed-loop0 system consisted of machine tool structure and pure cutting process. On this paper, cutting system is modeled as a six degrees of freedom system using MARV(Modified Autoregressive Vector) model in face milling, and the modeled dynamic cutting process is used to predict dynamic cutting force component. Based on the double modulation principle, a dynamic cutting force model is developed. From the simulated relative displacements between tool and workpiece the dynamic force domponents can be calculated, and the dynamic force can be obtained by superposition of the static force and dynamic force components. The simulated dynamic cutting forces have a good agreement with the measured cutting force.

• 대한전자공학회논문지SP
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• 제42권1호
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• pp.25-31
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• 2005

본 논문에서는 컬러 영상에서 Support Vector Domain Description (SVDD)를 이용한 얼굴 검출 방법을 제안한다. 기존의 훈련을 통한 얼굴 검출 방법은 얼굴 영상과 얼굴이 아닌 영상을 모두 사용해야 한다. 그러나, SVDD를 이용한 얼굴 검출은 단지 훈련을 위해 얼굴 영상만이 사용된다. SVDD의 훈련을 통해 나오는 값인 반지름과 중심 좌표를 통해 얼굴을 검출한다. 또한, 엔트로피를 이용한 임계값 추출 방법(Entropic Threshold)을 통해 얼굴 특징을 추출하고, 슬라이딩 윈도우(sliding window)기법을 통해 성능을 개선한다. 주성분 분석(Principle Component Analysis) 과 SVDD를 이용한 얼굴 검출 방법의 비교 실험을 통해 본 논문이 제안한 방법의 효율성을 확인한다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제16권4호
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• pp.405-414
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• 2016

IC-implementations provide high performance for solving the high computational cost of pattern matching but have relative low flexibility for satisfying different applications. In this paper, we report an associative memory architecture for k nearest neighbor (KNN) search, which is one of the most basic algorithms in pattern matching. The designed architecture features reconfigurable vector-component parallelism enabled by programmable switching circuits between vector components, and a dedicated majority vote circuit. In addition, the main time-consuming part of KNN is solved by a clock mapping concept based weighted frequency dividers that drastically reduce the in principle exponential increase of the worst-case search-clock number with the bit width of vector components to only a linear increase. A test chip in 180 nm CMOS technology, which has 32 rows, 8 parallel 8-bit vector-components in each row, consumes altogether in peak 61.4 mW and only 11.9 mW for nearest squared Euclidean distance search (at 45.58 MHz and 1.8 V).

• 한국멀티미디어학회논문지
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• 제9권11호
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• pp.1435-1444
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• 2006

본 논문은 얼굴 영상에서 추출된 특징 값들을 주성분 분석(Principle Component Analysis; 이하 PCA)을 이용하여 재해석하고, 서포트 벡터 머신(Support Vector Machine; 이하 SVM)을 이용한 이진 분류를 통하여 효과적이면서 실시간으로 얼굴을 검출할 수 있는 방법론을 제안한다. 얼굴과 얼굴이 아닌 영상들로 학습데이터를 구성하여, 이 영상들로부터 Haar-like 특징값들을 추출한다. 추출된 다량의 특징 값들 중에 얼굴과 얼굴이 아닌 영역에 대하여 판별 능력이 우수한 특징값들은 PCA를 이용하여 재해석되고 유용한 특징들을 선별한다. 선별된 특징들을 SVM의 입력 차원으로 사용하여 최종 분류기를 학습 및 구성한다. 제안하는 분류기는 학습데이터 집단의 구성에 크게 영향을 받지 않고, 소량의 학습데이터만으로도 90.1%의 만족할만한 얼굴 검출률을 보여주며, $320{\times}240$ 크기의 영상에 대하여 실시간 얼굴 검출에 사용 가능한 초당 8프레임의 처리속도를 보여주었다.