• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.571-579
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• 2005

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.37-46
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• 2006

It is known to be hard to apply cryptographic one-way functions to the recognition system using bio-information directly. As one of the solutions about that problem there is a permutation transformation scheme. However, they did not show my algorithmic behavior or any performance analysis of the transformation by experiment. In this paper, by showing the recognition ratio of the transformed scheme by experiment, we prove that that scheme is sound. Also, we adopt their transformation to LDA(Linear Discriminant Analysis) to show the experimental results. In the negative side, we introduce a new type of attack against the permutation transformation schemes. finally, we briefly mention a generalization of the permutation transformation for countermeasure of the attack at the end of this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.300-307
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• 2008

This paper is concerned with face recognition for human-robot interaction (HRI) in robot environments. For this purpose, we use Tensor Subspace Analysis (TSA) to recognize the user's face through robot camera when robot performs various services in home environments. Thus, the spatial correlation between the pixels in an image can be naturally characterized by TSA. Here we utilizes face database collected in u-robot test bed environments in ETRI. The presented method can be used as a core technique in conjunction with HRI that can naturally interact between human and robots in home robot applications. The experimental results on face database revealed that the presented method showed a good performance in comparison with the well-known methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in distant-varying environments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.295-298
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• 2007

본 논문에서는 사람의 얼굴표정을 통해 4개의 기본감정(기쁨, 슬픔, 화남, 놀람)에 대한 특징을 추출하고 인식하여 그 결과를 이용하여 감정표현 시스템을 구현한다. 먼저 주성분 분석(Principal Component Analysis)법을 이용하여 고차원의 영상 특징 데이터를 저차원 특징 데이터로 변환한 후 이를 선형 판별 분석(Linear Discriminant Analysis)법에 적용시켜 좀 더 효율적인 특징벡터를 추출한 다음 감정을 인식하고, 인식된 결과를 얼굴 표현 시스템에 적용시켜 감정을 표현한다.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.487-492
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• 2008

Facial expression recognition is an intensive research area for designing Human Computer Interfaces. In this work, we present a new facial expression recognition system utilizing Enhanced Independent Component Analysis (EICA) for feature extraction and discrete Hidden Markov Model (HMM) for recognition. Our proposed approach for the first time deals with sequential images of emotion-specific facial data analyzed with EICA and recognized with HMM. Performance of our proposed system has been compared to the conventional approaches where Principal and Independent Component Analysis are utilized for feature extraction. Our preliminary results show that our proposed algorithm produces improved recognition rates in comparison to previous works.

• Speech Sciences
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• v.14 no.4
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• pp.131-144
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• 2007

In an effort to improve the performance of mel frequency cepstral coefficients (MFCC), we investigate the effects of varying the parameters for the filter banks and their associated windows on speech recognition rates. Specifically, the mel and bark scales are combined with various types of filter bank windows. Comparison and evaluation of the suggested methods are performed by two independent ways of speech recognition and the Fisher discriminant objective function. It is shown that the Hanning window based on the bark scale yields 28.1% relative performance improvements over the triangular window with the mel scale in speech recognition error rate. Further work on incorporating PCA and/or LDA would be desirable as a postprocessor to MFCC extraction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.965-968
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• 2007

In this paper, we propose an efficient method to classify human facial impression using face image. The features that represent the shape of eye, jaw and face are used. The proposed method employs PCA, LDA and SVM in series. Human face has been classified for 8 facial impressions. The experiments have been performed for many face images, and show encouraging result.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.431-434
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• 2005

본 논문은 유비쿼터스 컴퓨팅 환경 기반에서의 서명인식 시스템 구현을 기술한다. 구현된 시스템은 PDA와 전자 서명입력기를 이용하여 서명데이터를 획득하고 이 데이터를 유비쿼터스 환경인 무선랜을 이용하여 인증 서버로 전송하여 서버로부터 인증된 결과를 받도록 하였다. 본 시스템의 구성은 터치 스크린을 통한 서명입력이 가능한 PDA와 전자서명입력기를 장착한 무선단말기를 사용하는 클라이언트 부분과 서명을 검증하는 서버 부분으로 나누어 구현 하였다. 본 논문에서 인식알고리즘으로는 서명영상을 구간분할한 후 PCA와 LDA를 사용하여 특징값을 추출한다. 학습과정에서 미리 구한 고유값을 이용하여 서명입력기로부터 획득한 서명데이터를 같은 공간에 투영시켜 서로간의 유사도를 비교하도록 하여 서명인식 속도 및 성능을 개선하였다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.797-798
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• 2006

In this paper, a codec design method is pro-posed for the face images based on JPEG and its application to face recognition is presented. Quantization table design is dis-cussed using R-D optimization for Yale face data. For the usage in the embedded systems, fast codec design is also considered. The proposed codec has better performance than JPEG codec for face images. Through the recognition experiment using PCA and LDA, it has been shown that the proposed codec has better performance than JPEG codec.