• Journal of the Korean Magnetics Society
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• v.19 no.3
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• pp.81-84
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• 2009

We examine the effect of U term (U = 3 eV) describing the Coulomb interactions between electrons on the results of electronic band structure calculations carried out for bcc Fe bulk, monolayer, and chain. We investigated the properties of the three Fe structures by using the all-electron total-energy full-potential linearized augmented plane wave method. The U term was included in the exchange - correlation functionals constructed on the basis of local density approximation (LDA) and general gradient approximation (GGA). We found that in the case of bcc Fe bulk structure inclusion of the U term leads to the overestimated values of magnetic moment on Fe atom. The values of magnetic moment calculated for Fe in monolayer and chain are in accordance with calculations in which the U term was not included. In general, for each system the calculated values of magnetic moment on Fe sites were larger when the U term was incorporated in the energy functional. In Fe bulk, the value of magnetic moment $2.54{\mu}_B$ for LDA+U larger than $2.25{\mu}_B$ for LDA.

• Journal of the Optical Society of Korea
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• v.12 no.2
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• pp.88-92
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• 2008

This paper discusses a photon-counting linear discriminant analysis (LDA) with computational integral imaging (II). The computational II method reconstructs three-dimensional (3D) objects on the reconstruction planes located at arbitrary depth-levels. A maximum likelihood estimation (MLE) can be used to estimate the Poisson parameters of photon counts in the reconstruction space. The photon-counting LDA combined with the computational II method is developed in order to classify partially occluded objects with photon-limited images. Unknown targets are classified with the estimated Poisson parameters while reconstructed irradiance images are trained. It is shown that a low number of photons are sufficient to classify occluded objects with the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.897-904
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• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.131-136
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• 2020

Wake Up Word (WUW) is a short utterance used to convert speech recognizer to recognition mode. The WUW defined by the user who actually use the speech recognizer is called user-defined WUW. In this paper, to recognize user-defined WUW, we construct traditional Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), Linear Discriminant Analysis (LDA)-GMM-HMM and LDA-Deep Neural Network (DNN)-HMM based system and compare their performances. Also, to improve recognition accuracy of the WUW system, a threshold method is applied to each model, which significantly reduces the error rate of the WUW recognition and the rejection failure rate of non-WUW simultaneously. For LDA-DNN-HMM system, when the WUW error rate is 9.84 %, the rejection failure rate of non-WUW is 0.0058 %, which is about 4.82 times lower than the LDA-GMM-HMM system. These results demonstrate that LDA-DNN-HMM model developed in this paper proves to be highly effective for constructing user-defined WUW recognition system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.10-23
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• 2016

With requiring circumspections using big data, this study attempts to develop and apply the search method for audit issues relating to the ITS policy or program. For the foregoing, the auditing process of the board of audit and inspection was converged with the theoretical frame of boundary analysis proposed by William Dunn as an analysis tool for audit issues. Moreover, we apply the text mining technique in order to computerize the analysis tool, which is similar to the boundary analysis in the concept of approaching meta-problems. For the text mining analysis, specific model we applied the antisymmetry-symmetry compound lexeme-based LDA model based on the Latent Dirichlet Allocation(LDA) methodologies proposed by David Blei. The several prime issues were founded through a case analysis as follows: lack of collection of traffic information by the urban traffic information system, which is operated by the National Police Agency, the overlapping problems between the Ministry of Land, Infrastructure and Transport and the Advanced Traffic Management System and fabrication of the mileage on digital tachograph.

• MALSORI
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• no.44
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• pp.105-113
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• 2002

In Korean, each digit is monosyllable and some pairs are known to have high confusability, causing performance degradation of connected digit recognition systems. To improve the performance, in this paper, we employ various discriminant analyses (DA) including Linear DA (LDA), Weighted Pairwise Scatter LDA WPS-LDA), Heteroscedastic Discriminant Analysis (HDA), and Maximum Likelihood Linear Transformation (MLLT). We also examine several combinations of various DA for additional performance improvement. Experimental results show that applying any DA mentioned above improves the string accuracy, but the amount of improvement of each DA method varies according to the model complexity or number of mixtures per state. Especially, more than 20% of string error reduction is achieved by applying MLLT after WPS-LDA, compared with the baseline system, when class level of DA is defined as a tied state and 1 mixture per state is used.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.555-558
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• 2003

본 논문은 PCA(Principal Component Analysis)와 LDA(Linear Discriminant Analysis)를 적용하여 입력된 사용자 얼굴 사진과 가장 유사한 아바타를 자동으로 생성하기 위한 방법을 제안한다. 입력된 사진으로부터 알려진 영상처리 기법들을 이용하여 얼굴 영역을 추출하고, 추출된 얼굴로부터 얼굴 구성요소(눈썹,눈,코,입)를 추출한다. 추출된 얼굴 구성요소와 미리 분류하여 구축한 실제 얼굴 사진에서의 얼굴 구성요소 라이브러리를 PCA와 LDA를 적용하여 유사도를 계산한다. 최종적으로 계산된 유사도 값이 가장 큰 영상의 대표 아바타가 결과영상으로 나오게 된다. 실험결과 기존의 아바타 추출방법에서 드러난 입력영상과 2진화된 아바타 영상과의 속성 차이로 인한 문제점을 보안하고 좀 더 정확하고 자동화된 방법으로 아바타를 추출 할 수 있다는 것을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.87-95
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• 2006

In this paper, we propose a novel feature extraction method for face recognition, based on Discrete Cosine Transform (DCT), Energy Probability (EP), and Linear Discriminant Analysis (LDA). We define an energy probability as magnitude of effective information and it is used to create a frequency mask in OCT domain. The feature extraction method consists of three steps; i) the spatial domain of face images is transformed into the frequency domain called OCT domain; ii) energy property is applied on DCT domain that acquire from face image for the purpose of dimension reduction of data and optimization of valid information; iii) in order to obtain the most significant and invariant feature of face images, LDA is applied to the data extracted using frequency mask. In experiments, the recognition rate is 96.8% in ETRI database and 100% in ORL database. The proposed method has been shown improvements on the dimension reduction of feature space and the face recognition over the previously proposed methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Smart Media Journal
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• v.7 no.4
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• pp.90-98
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• 2018

A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification Electrocardiogram signals, included in the cardiac electrical activity, are often analyzed and used for various purposes such as heart rate measurement, heartbeat rhythm test, heart abnormality diagnosis, emotion recognition and biometrics. The objective of this paper is to perform individual identification operation based on Multilinear Linear Discriminant Analysis (MLDA) with the tensor feature. The MLDA can solve dimensional aspects of classification problems in high-dimensional tensor, and correlated subspaces can be used to distinguish between different classes. In order to evaluate the performance, we used MPhysionet's MIT-BIH database. The experimental results on this database showed that the individual identification by MLDA outperformed that by PCA and LDA.