• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.8
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• pp.615-623
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• 2018

The probability density function(PDF) for the amplitude of the reflectivity of low-grazing-angle sea clutter has generally been modeled by a compound-Gaussian distribution, rather than by the Rayleigh distribution, owing to the intensity variation of each clutter patch over time. The texture component forming the reflectivity has been simulated by combining Gamma distribution and memory-less nonlinear transformation(MNLT). On the other hand, there is no typical method available that can be used to simulate the speckle component. We first review Watt's method, wherein the speckle is simulated starting from the Doppler spectrum of the received echoes that is modeled as having a Gaussian shape. Then, we introduce a newly proposed method. The proposed method simulates the speckle by manipulating a clutter covariance matrix through the Cholesky decomposition after minimizing the effect of adjacent clutter patches using an equalizer. The feasibility of the proposed method is validated through simulation, wherein the results from two methods are compared in terms of the Doppler spectrum and the correlation function.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Journal of the Chungcheong Mathematical Society
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• v.10 no.1
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• pp.147-159
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• 1997

It is important to solve the large sparse linear system appeared in many application field such as $AA^Ty={\beta}$ efficiently. In solving this linear system, the sparse solver using the splitting method for the relatively dense column is experimentally better than the direct solver using the Cholesky method.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.99-106
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• 1999

An accelerated optimization technique combined with a stepwise deflation procedure is presented for the efficient evaluation of a few of the smallest eigenvalues and their corresponding eigenvectors of the generalized eigenproblems. The optimization is performed on the Rayleigh quotient of the deflated matrices by the aid of a preconditioned conjugate gradient scheme with the incomplete Cholesky factorization.

• Journal of the Chungcheong Mathematical Society
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• v.11 no.1
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• pp.129-142
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• 1998

In this paper, we propose a blocked variant of the Conjugate Gradient method which performs as well as and has coarser parallelism than the classical Conjugate Gradient method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.158-165
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• 1999

비행시험을 통해 획득한 데이터의 해석과정에서 대상 항공기의 크기가 소형인 경우에는 엔진진동이나 외부의 교란에 의한 잡음이나 바이어스 등의 강도가 높기 때문에 데이터의 처리과정에서 많은 문제점을 산출하게 된다. 이와 같은 문제점을 해결하기 위해 상태추정 알고리즘이 사용되며, 본 논문에서는 항공기의 비선형 세로운동 방정식의 경우에 확장형 칼만 필터를 적용하여 항공기 세로운동의 상태변수들을 추정하였으며, 또한 확률근사과정, 이노베이션에 대한 궤환 적응 등 적응형 칼만 필터를 사용하여 수렴속도와 정확도 둥을 향상시킨 알고리즘을 제안하고 그 결과를 나타내었다.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.544-551
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• 2015

In this paper, the design and implementation results of the optical flow estimator (OFE) for moving object detection (MOD) in advanced driver assistance system (ADAS). In the proposed design, Brox's algorithm with global optimization is considered, which shows the high performance in the vehicle environment. In addition, Cholesky factorization is applied to solve Euler-Lagrange equation in Brox's algorithm. Also, shift register bank is incorporated to reduce memory access rate. The proposed optical flow estimator was designed with Verilog-HDL, and FPGA board was used for the real-time verification. Implementation results show that the proposed optical flow estimator includes the logic slices of 40.4K, 155 DSP48s, and block memory of 11,290Kbits.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.1
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• pp.35-44
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• 2004

We Propose the methods to design the finite impulse response (FIR) and the infinite impulse response (IIR) lattice filters using Schur algorithm through the spectral factorization of the covariance matrix by circulant matrix factorization (CMF). Circulant matrix factorization is also very powerful tool used for spectral factorization of the covariance polynomial in matrix domain to obtain the minimum phase polynomial without the polynomial root finding problem. Schur algorithm is the method for a fast Cholesky factorization of Toeplitz matrix, which easily determines the lattice filter parameters. Examples for the case of the FIR filter and for the case of the In filter are included, and performance of our method check by comparing of our method and another methods (polynomial root finding and cepstral deconvolution).

• Transactions of Materials Processing
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• v.7 no.5
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• pp.474-480
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• 1998

In the present study a domain decomposition scheme using the substructuring method is developed for the computational efficiency of the finite element analysis of metal forming processes. in order to avoid calculation of an inverse matrix during the substructuring procedure, the modified Cholesky decomposition method is implemented. As obtaining the data independence by the substructuring method the program is easily paralleized using the Parallel Virtual machine(PVM) library on a work-station cluster connected on networks. A numerical example for a simple upsetting is calculated and the speed-up ratio with respect to various number of subdomains and number of processors. The efficiency of the parallel computation is discussed by comparing the results.

• Korean Management Science Review
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• v.11 no.3
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• pp.1-11
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• 1994

Recent advances in linear programming solution methodology have focused on interior point methods. This powerful new class of methods achieves significant reductions in computer time for large linear programs and solves problems significantly larger than previously possible. These methods can be examined from points of Fiacco and McCormick's barrier method, Lagrangian duality, Newton's method, and others. This study presents a primal-dual log barrier algorithm of interior point methods for linear programming. The primal-dual log barrier method is currently the most efficient and successful variant of interior point methods. This paper also addresses a Cholesky factorization method of symmetric positive definite matrices arising in interior point methods. A special structure of the matrices, called supernode, is exploited to use computational techniques such as direct addressing and loop-unrolling. Two dense matrix handling techniques are also presented to handle dense columns of the original matrix A. The two techniques may minimize storage requirement for factor matrix L and a smaller number of arithmetic operations in the matrix L computation.