• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.76-82
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• 2013

We evaluate the performance of the time-reversal (TR) prefilter and the weighted TR prefilter in an indoor wireless communication system with channel estimation errors. The TR prefilter uses a time-reversed channel as a prefilter to maximize received peak power. The equivalent channel of the TR prefilter is an 공분산 of the channel and the received peak power is maximized. When there are channel estimation errors, the equivalent channel is not an 공분산 of the channel and the received peak power cannot be maximized. The weighted TR prefilter minimizes the inter-symbol interference and maintains the received peak power. Thus, even when there are some channel estimation errors, the weighted TR prefilter can guarantee the received peak power.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.315-316
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• 2011

일반적으로 영상에서 얼굴 영상을 검출하고 인식하는 알고리즘은 패턴 인식 연구에 있어서 인간과 컴퓨터의 상호작용의 연구라는 면에서 아주 중요한 문제로 연구되어 왔다. 본 논문에서는 고유얼굴을 이용하여 유클리디언 거리법과 퍼지기법의 인식률을 비교해보고자 한다. PCA(Principal Component Analysis) 방식은 우수한 인식 결과를 보장하는 얼굴인식 기법중의 하나이며, 얼굴 영상을 이용하여 공분산 행렬을 계산하고, 공분산 행렬을 통해 생성된 저차원의 벡터, 즉 고유얼굴(Eigenface)을 이용하여 가중치를 계산하고, 이 가중치를 기준으로 인식을 수행하는 기법이다. 이를 기반으로 하여, 본 논문에서는 전처리 과정, 고유얼굴 과정, 유클리디언 거리법 및 퍼지 소속도 함수 설계 과정, 신경망 학습과정, 인식과정으로 구성된 5단계의 얼굴 인식 알고리즘을 제안한다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.903-906
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• 2001

본 논문에서는 조명의 변화에 의해 컬러 영상의 컬러 성분이 달라지더라도 영상 내 컬러간의 편차값을 나타내는 공분산 행렬(covariance matrix)의 고유벡터(eigenvector)와 영상 내 화소들의 컬러 성분과의 상관관계는 거의 변화하지 않는 특징을 이용한 조명 변화에 강인한 영상 검색 방법을 제안한다. 제안된 방법은 영상에서 컬러 성분들의 공분산 행렬과 공분산 행렬의 고유치(eigenvalue), 고유벡터를 계산한 후, 가장 큰 고유치에 관계된 고유벡터로 화소를 투영시키고, 투영된 벡터의 크기 성분으로 영상을 재구성한다. 재구성된 영상으로부터 7개의 불변 모멘트(moment)를 계산하고, 공분산의 가장 큰 고유치를 가중치로 부과하여 특징벡터를 추출한다. 7개의 불변 모멘트로부터 구한 특징벡터는 영상 내 물체의 이동, 영상의 회전, 크기 변화뿐만 아니라, 조명의 변화에 의해 컬러가 변화할 경우에도 유사한 영상을 잘 검색한다. 제안된 방법의 성능 확인을 위하여 5가지 조명에서 얻은 영상 데이터베이스를 이용하여 실험하였으며, 실험 결과 히스토그램 인터섹션에 비해 적은 특징량으로 검색이 가능하면서 조명 변화에도 대응할 수 있는 검색 결과를 얻을 수 있었다.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.395-407
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• 2007

Outliers distort many measures for data analysis. We can propose dandelion seed plot as a graphical tool for evaluating the effect of outliers in one-and two-variate data. We can draw mean-variance dandelion seed plots using linked curves which are made by changing weights from 1 to 0 for each datum. Similarly we can also draw covariance-correlation-coefficient dandelion seed plots. This graphical method can be a useful tool for elementary statistics education in college.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.3
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• pp.459-464
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• 2001

We propose a transmit beamforming algerian for array antenna in FDD (Frequency Division Duplex) environments. The proposed method estimates the directions and spectra of the users, and constructs the spatial covariance matrix of the interferences at the downlink frequency. The weights are computed by that covariance matrix and desired user's direction vector Simulations are performed under Rayleigh fading environments. The proposed method don't need the data feedback, has the enhanced performance in BER (Bit Error Rate).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.3
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• pp.224-228
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• 2015

In this paper, we study the method for desired signals estimation that array antennas are received signals. We apply sub spatial method of direction of arrival algorithm and adaptive array antennas in order to remove interference and noise signal of received antenna signals. Array response vector of adaptive array antenna is probability, it is correctly estimation of direction of arrival of targets to update weight signal. Desired signals are estimated updating covariance matrix after moving interference and noise signals among received signals. We estimate signals using eigen decomposition and eigen value, high resolution direction of arrival estimation algorithm is devided signal sub spatial and noise sub spatial. Though simulation, we analyze to compare proposed method with general method.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.151-158
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• 2015

This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.27-36
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• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.229-250
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• 2022

Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.400-404
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• 2015

In this paper, we study for direction of arrival MUSIC spatial spectrum algorithm in order to desired signal estimation in spatial. Proposal MUSIC spatial spectrum algorithm in paper use model error and Bayesian method to estimation on correct target position. Receiver array response vector using adaptive array antenna use Bayesian method, and target position estimate to update weight value with model error method. Target's signal estimation of desired direction of arrival in this paper apply weight value of signal covariance matrix for array response vector after removing incident signal interference and noise, respectively. Though simulation, we analyze to compare proposed method with general method.