• 융합신호처리학회논문지
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• 제11권1호
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• pp.41-46
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• 2010

본 논문은 도래방향 추정법의 하나인 유니터리 MUSIC(MUltiple SIgnal Classification) 알고리즘의 하드웨어 구현에 대한 것이다. 이 알고리즘은 복소 상관행렬을 유니터리 변환(Unitary transform)을 통해 실수 상관행렬로 변환하여 하드웨어 구현을 쉽게 할 수 있다. 실수 상관행렬의 고유치와 고유벡터는 Jacobi법에 ADD와 SHIFT만으로 구현이 가능한 CORDIC(COordinate Rotation DIgital Computer) 알고리즘을 접목한 Jacobi-CORDIC 알고리즘으로 구하였다. 또한 256점 DFT(Discrete Fourier Transform)를 적용하여 각도 스펙트럼을 구하고, 스펙트럼의 검색으로 도래각을 추정하였다. 본 논문에서는 알고리즘의 하드웨어 구현을 위해 System Generator를 이용하여 설계하였다. 최종 설계된 DoA 추정 시스템은 Matlab 시뮬레이션 결과와 비교하여 일치된 결과를 얻었고, Hardware Co-Sim을 통해 System Generator 설계 결과를 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.1998-2016
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• 2012

Reliable spectrum sensing algorithm is a fundamental component in cognitive radio. In this paper, a non-cooperative spectrum sensing algorithm which needs only one cognitive radio node named CORDIC (Coordinate Rotation Digital Computer) Jacobi based method is proposed. The algorithm computes the eigenvalues of the sampled covariance of received signal mainly by shift and additional operations, which is suitable for hardware implementation. Based the latest random matrix theory (RMT) about the distribution of the limiting maximum and minimum eigenvalue ratio, the relationship between the probability of false alarm and the decision threshold is derived. Simulations and discussions show the method is effective. Real captured digital television (DTV) signals and Universal Software Radio Peripheral (USRP) are also employed to evaluate the performance of the algorithm, which prove the proposed algorithm can be applied in practical spectrum sensing applications.

• 한국통신학회논문지
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• 제14권4호
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• pp.388-397
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• 1989

본 논문에서는 불규칙하게 분포된 non-zero 원소를 가진 대형 space 행렬로서 표시되는 선형시스템의 해를 능률적으로 얻기 위한 반복 병렬 알고리즘에 대하여 기술하고, 이 알고리즘을 수행하는데 적절한 컴퓨터로서 dataflow컴퓨터 구조를 제안하였다. 이 알고리즘에서는 Jacobi 반복법을 사용하였으며 행렬의 내적을 구하는데 소요되는 시간을 단축함으로서 병렬 수행시간을 단축시켰다.

• 한국컴퓨터정보학회논문지
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• 제19권9호
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• pp.21-31
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• 2014

특이치 분해는 다양한 분야의 데이터 집단에서 고유한 특성을 찾는 특징 추출 분야에 많이 활용되고 있다. 하지만 특이치 분해의 복잡 행렬 연산은 많은 연산 시간을 요구한다. 본 논문에서는 특이치 분해의 대표적인 알고리즘인 one-sided block Jacobi를 고속 처리하기 위해 2차원 멀티코어 시스템을 이용하여 효율적으로 병렬 구현하고 성능을 향상시킨다. 또한, one-sided block Jacobi 알고리즘의 다양한 행렬 ($128{\times}128$, $64{\times}64$, $32{\times}32$, $16{\times}16$)을 서로 다른 2차원 PE 구조에 구현하고 성능 및 에너지를 분석함으로써 각 행렬에 대한 최적의 멀티코어 구조를 탐색한다. 더불어 동일한 행렬의 one-sided block Jacobi 알고리즘에 대해 선택된 멀티코어 구조와 상용 고성능 그래픽스 프로세싱 유닛 (GPU)과의 성능 비교를 통해 제안한 2차원 멀티코어 방법의 잠재 가능성을 확인한다.

• Journal of applied mathematics & informatics
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• 제40권1_2호
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• pp.267-281
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• 2022

Herein, an algorithm for efficient evaluation of oscillatory Fourier-integrals with Jacobi-Cauchy type singularities is suggested. This method is based on the use of the traditional Clenshaw-Curtis (CC) algorithms in which the given function is approximated by the truncated Chebyshev series, term by term, and the oscillatory factor is approximated by using Bessel function of the first kind. Subsequently, the modified moments are computed efficiently using the numerical steepest descent method or special functions. Furthermore, Algorithm and programming code in MATHEMATICA^® 9.0 are provided for the implementation of the method for automatic computation on a computer. Finally, selected numerical examples are given in support of our theoretical analysis.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.412-415
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• 1995

Previously obtained results of L$_{2}$-gain and H$_{\infty}$ control via state feedback of nonlinear systems are extended to a class of nonlinear system with uncertainties. The required information about the uncertainties is that the uncertainties are bounded in Euclidian norm by known functions of the system state. The conditions are characterized in terms of the corresponding Hamilton-Jacobi equations or inequalities (HJEI). An algorithm for finding an approximate local solution of Hamilton-Jacobi equation is given. This results and algorithm are illustrated on a numerical example..

• 대한수학회지
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• 제49권2호
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• pp.293-314
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• 2012

We prove that the Siegel modular form of D'Hoker and Phong that gives the chiral superstring measure in degree two is a lift. This gives a fast algorithm for computing its Fourier coefficients. We prove a general lifting from Jacobi cusp forms of half integral index t/2 over the theta group ${\Gamma}_1$(1, 2) to Siegel modular cusp forms over certain subgroups ${\Gamma}^{para}$(t; 1, 2) of paramodular groups. The theta group lift given here is a modification of the Gritsenko lift.

• 대한전자공학회논문지
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• 제26권4호
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• pp.7-15
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• 1989

Gauss-Jacobi relaxation 方法으로부터 非線形 마이크로波 回路解析 알고리듬을 薄出하고, Harmonic Balance 技法을 應用하여 갈륨비소 쇼트키 障壁 다이오드를 이용한 混合器의 非線形 및 線形 回路解析 方法을 提示하였다. 本 硏究에서의 解析方法으로부터 마이크로波 混合器의 變換損失을 正確히 豫測하고, 컴퓨터 시뮬레이션으로부터 MMIC設計에 有用함을 보였다. 寬察로 Ku-밴드用 混合器를 MIC로 設計, 製作하여 시뮬레이션 結果의 妥當性을 立證하였다.

• 대한기계학회논문집A
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• 제24권5호
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• pp.1215-1230
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• 2000

Parallel Approaches using Cray T3E which is NIPP (Massively Parallel Processors) machine are presented for the efficient computation of the finite element analysis of 3-D shape rolling processes. D omain decomposition method coupled with parallel linear equation solver is used. Domain decomposition is applied for obtaining element tangent stifffiess matrices and residual vectors. Direct and iterative parallel algorithms are used for solving the linear equations. Direct algorithm is_parallel version of direct banded matrix solver. For iterative algorithms, the well-known preconditioned conjugate gradient solver with Jacobi preconditioner is also employed. Moreover a new effective iterative scheme with block inverse matrix preconditioner, which is named by present authors, is presented and its results are compared with the one using Jacobi preconditioner. PVM and MPI are used for message passing and synchronization between processors. The performance and efficiency of each algorithm is discussed and comparisons are made among different algorithms.

• 대한수학회지
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• 제59권2호
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• pp.311-335
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• 2022

This paper treats Merton's classical portfolio optimization problem for a market participant who invests in safe assets and risky assets to maximize the expected utility. When the state process is a d-dimensional Markov diffusion, this problem is transformed into a problem of solving a Hamilton-Jacobi-Bellman (HJB) equation. The main purpose of this paper is to solve this HJB equation by a deep learning algorithm: the deep Galerkin method, first suggested by J. Sirignano and K. Spiliopoulos. We then apply the algorithm to get the solution to the HJB equation and compare with the result from the finite difference method.