• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1055-1063
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• 2014

Recently, various methods have been proposed for improving the performance of the side channel analysis using the power consumption. Of those method, waveform compression method applies to reduce the noise component in pre-processing step. In this paper, we propose the new LDA(Linear Discriminant Analysis)-based signal compression method finding unique feature vector. Through experimentations, we are comparing the proposed method with the PCA(Principal Component Analysis)-based method which has known for the best performance among existing signal compression methods.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.817-824
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• 2010

Linear Discriminant Analysis (LDA) has been successfully applied for dimension reduction in face recognition. However, LDA requires the transformation of a face image to a one-dimensional vector and this process can cause the correlation information among neighboring pixels to be disregarded. On the other hand, 2D-LDA uses 2D images directly without a transformation process and it has been shown to be superior to the traditional LDA. Nevertheless, there are some problems in 2D-LDA. First, it is difficult to determine the optimal number of feature vectors in a reduced dimensional space. Second, the size of rectangular windows used in 2D-LDA makes strong impacts on classification accuracies but there is no reliable way to determine an optimal window size. In this paper, we propose a new algorithm to overcome those problems in 2D-LDA. We adopt an ensemble approach which combines several classifiers obtained by utilizing various window sizes. And a practical method to determine the number of feature vectors is also presented. Experimental results demonstrate that the proposed method can overcome the difficulties with choosing an optimal window size and the number of feature vectors.

• The Journal of Advanced Prosthodontics
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• v.9 no.6
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• pp.409-415
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• 2017

PURPOSE. Accurate information is essential in dentistry. The image information of missing teeth is used in optically based medical equipment in prosthodontic treatment. To evaluate oral scanners, the standardized model was examined from cases of image recognition errors of linear discriminant analysis (LDA), and a model that combines the variables with reference to ISO 12836:2015 was designed. MATERIALS AND METHODS. The basic model was fabricated by applying 4 factors to the tooth profile (chamfer, groove, curve, and square) and the bottom surface. Photo-type and video-type scanners were used to analyze 3D images after image capture. The scans were performed several times according to the prescribed sequence to distinguish the model from the one that did not form, and the results confirmed it to be the best. RESULTS. In the case of the initial basic model, a 3D shape could not be obtained by scanning even if several shots were taken. Subsequently, the recognition rate of the image was improved with every variable factor, and the difference depends on the tooth profile and the pattern of the floor surface. CONCLUSION. Based on the recognition error of the LDA, the recognition rate decreases when the model has a similar pattern. Therefore, to obtain the accurate 3D data, the difference of each class needs to be provided when developing a standardized model.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.380-387
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• 2010

For the fault diagnosis of three-phase induction motors, we propose a diagnosis algorithm based on mutual information and linear discriminant analysis (LDA). The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by mutual information As the next step, feature extraction is performed by LDA, and then diagnosis is evaluated by k-NN classifier. The results to verify the usability of the proposed algorithm showed better performance than various conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.167-175
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• 2016

The volatile component analysis of 82 commercialized liquors(44 samples of single malt whisky, 20 samples of blended whisky and 18 samples of brandy) was carried out by gas chromatography after liquid-liquid extraction with dichloromethane. Pattern recognition techniques such as principle component analysis(PCA), cluster analysis(CA), linear discriminant analysis(LDA) and partial least square discriminant analysis(PLSDA) were applied for the discrimination of different liquor categories. Classification rules were validated by considering sensitivity and specificity of each class. Both techniques, LDA and PLSDA, gave 100% sensitivity and specificity for all of the categories. These results suggested that the common characteristics and identities as typification of whiskies and brandys was founded by using multivariate data analysis method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.299-301
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• 2005

본 연구는 생체정보를 이용하여 개인을 인증하고 확인하기 위한 방법으로 기존 단일 생체인식 기법의 단점을 보완하기 위해 홍채와 얼굴을 이용한 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구하였다. 중국 홍채 데이터베이스 CASIA(Chinese Academy of Science)에 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 사용하여 특징벡터를 획득하였으며, FERET(FERET(Face Recognition Technology) 얼굴영상데이터를 사용하여 FERET 연구에서 매우 우수한 성능을 보인 EBGM알고리듬으로 특징벡터를 획득하였다. 이로부터 얻어진 두 score 값에 대하여 다양한 균등화 과정을 시도해 보았으며, 등록자와 침입자를 구분하기 위한 Fusion Algorithm으로 Bayesian Classifier, Support vector machine, Fisher's linear discriminant를 사용하였다. 또한, 널리 사용되는 방법 중 Weighted Summation을 이용하여 다중생체인식의 성능을 비교해 보았다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.515-518
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• 2017

I used Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) to utilize biometric technology for security. I used 14 korean consonants(ㄱ to ㅎ). And It has both information of gestures for each consonants and identity of user. So this experiment is set for this two aspects. I used database including 20 people's images. Each person did 140 action for every consonant with 10 trials. PCA and LDA must be applied on self-collected database using MATLAB programming. Equal Error Rate (EER) is used for evaluate performance of this analysis.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.61-64
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• 2001

본 논문에서는 class간의 변별력을 증가시키기 위한 유용한 방법인 LDA(Linear Discriminant Analysis)를 사용하여 한국어 숫자음간의 변별력을 높여 연결숫자 인식성능을 높이고자 하였다. 한국어 숫자음은 모두 단음절이며 또한 혼동가능성이 높은 숫자쌍이 존재하여 이것이 전체 인식률을 저하시킨다. LDA를 사용한 경우 숫자열 오인식률이 $8\%$ 감소하였다. 그리고, 음성특징 벡터의 차수를 감소시키고 LDA 사용전 보다 약간의 인식률 증가를 보였다. 그러나, 선형적인 방법으로 분리가 불가능한 class들의 분포가 존재할 때는 LDA를 사용하여도 변별력 향상은 기대하기 어렵다. 이와 같은 분포의 class사이의 변별력을 증가시키기 위해 between-scatter covariance matrix를 구할 때 class 사이에 혼동가능성 정도를 나타내는 weighting factor를 적용하였으며, 그 결과 숫자열 오인식률이 LDA 사용전보다 $9.7\%$ 감소하였다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1960-1961
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• 2011

얼굴 인식에서 가장 많이 사용되고 있는 PCA(Principal Component Analysis)는 고차원의 얼굴 데이터를 낮은 차원으로 표현할 수 있다는 장점이 있다. LDA(Linear Discriminant Analysis)는 서로 다른 데이터를 잘 분리할 수 있으며, 얼굴 인식에서 우수한 성능을 보인다. 본 연구에서는 서로의 장점을 결합하여 PCA와 LDA를 혼합, 적용하였다. 고차원의 얼굴데이터를 PCA로 차원 축소한 후 LDA를 이용해 더욱 효과적인 분류가 되어 얼굴 인식률을 향상시킨다. 인식 모듈로는 pRBFNN(Polynomial Based Radial Basis Function Neural Networks) 모델을 구축하여 고차원 패턴인식 문제에 대한 해결책을 제시하고자 한다. 그리고 제안된 패턴분류기는 얼굴 데이터를 사용하여 성능을 확인한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.718-720
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• 2004

얼굴 인증에서 가장 보편적으로 사용되고 있는 주성분 분석(PCA . Principal Component Analysis)은 정면 얼굴과 같은 특징 패턴에 대해서 비교적 높은 성능을 보인다. 인식률을 떨어뜨리지 않으면서 데이터량을 줄일 수 있는 효과가 있어 클래스를 잘 축약하여 표현하기에 유용하다. 하지만 조명이나 표정의 변화에 대해서는 성능을 보장할 수 없다 이를 보완하기 위해 성분이 다른 클래스간의 분리가 수월하도록 선형판별분석(LDA Linear Discriminant Analysis)을 사용한다 LDA는 데이터의 양이 적을 때는 성능이 떨어지는 단점이 있다 그래서 PCA와 LDA를 융합한 기술을 사용하면 더 나은 성능을 얻을 수 있는데 Min, Max, Mean, Append, Majority voting방법 등이 이에 해당된다. 하지만 기존 연구에서는 제한적 데이터베이스에 대한 실험에 그쳐 실험 결과의 객관성이 부족했다. 본 논문에서는 정형화된 환경에서 여러 가지 데이터베이스를 사용해 실험함으로써 Min, Max, Mean 융합 알고리즘의 성능을 비교 분석한다. 융합 알고리즘이 언제나 좋은 성능을 내는 것은 아니지만 얼굴영상에서 조명이나 표정 등이 변화함에 상관없이 일정 수준의 인증율을 보장하고 있다.