• Journal of the Korean Statistical Society
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• 제31권3호
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• pp.315-328
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• 2002

As an extension of the well-known Pickands (1975) estimate. for the extreme value index, Yun (2002) introduced a generalized Pickands estimator. This paper searches for a minimax estimator in the sense of minimizing the maximum asymptotic relative efficiency of the Pickands estimator with respect to the generalized one. To reduce the asymptotic variance of the resulting estimator, convex combinations of the minimax estimator are also considered and their asymptotic normality is established. Finally, the optimal combination is determined and proves to be superior to the generalized Pickands estimator.

• 한국정보전자통신기술학회논문지
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• 제11권6호
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• pp.800-806
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• 2018

소프트웨어 효율성은 운용 환경에서 사용시간에 따라 고장없이 사용할 수 있는 확률이며, 소프트웨어 시스템 안정성에 영향을 미치는 가장 근본적인 요인이다. 정보기술 분야에서 활용되고 있는 컴퓨터 시스템의 오작동은 관련 산업분야에 중요한 손실을 야기할 수도 있다. 따라서, 본 연구에서는 소프트웨어 고장시간 자료를 가지고 유한고장 NHPP 모형에 기반하여 다양한 위험함수에 의존한 소프트웨어 신뢰성 모형의 속성을 분석 하였다. 제안한 모형의 위험함수 패턴은 Goel-Okumoto모형은 상수가 되고, Minimax 모형과 Rayleigh모형은 증가패턴을 따르지만, 위험함수의 증가폭은 Minimax 모형이 Rayleigh모형과 Goel-Okumoto모형 보다 작은 것으로 나타났다. 또한, 평균값 함수m(t)의 참값 오차와 평균제곱오차(MSE)는 Minimax 모형이 모두 Rayleigh 모형과 Goel-Okumoto모형 보다 작아서 상대적으로 효율적이였다. 본 연구의 결과는 소프트웨어 개발자에게 위험함수에 관한 기본정보로 활용될 수 있을 것으로 기대한다.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.619-626
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• 2003

Given a prior guess for the unknown value of P(Y

• 전기학회논문지
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• 제67권12호
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• pp.1657-1664
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• 2018

This paper proposes a decision making scheme for choosing the best move at each state of game in order to implement an artificial intelligence othello game player. The proposed decision making scheme predicts the various possible states of the game when the game has progressed from the current state, evaluates the degree of possibility of winning or losing the game at the states, and searches the best move based on the evaluation. In this paper, we generate learning data by decomposing the records of professional players' real game into states, matching and accumulating winning points to the states, and using the Artificial Neural Network that learned them, we evaluated the value of each predicted state and applied the Minimax search to determine the best move. We implemented an artificial intelligence player of the Othello game by applying the proposed scheme and evaluated the performance of the game player through games with three different artificial intelligence players.

• 한국게임학회 논문지
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• 제18권5호
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• pp.59-66
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• 2018

이 논문에서는 전통게임중 하나인 고누게임에 대한 구현과 실험이 수행된다. 고누게임을 구현하기 위한 기법으로 미니맥스알고리즘이 적용되었다. 미니맥스 알고리즘에 게임을 구현하기 위해 평가함수를 제안하였다. 고누게임의 구현 이후 성능향상을 위해 알파베타 가지치기에 대한 알고리즘의 효율성을 분석한다. 게임의 승패에 영향을 미치는 최적의 분석을 위해 가중치 분석이 수행되었다. 가중치 분석을 위하여, 사람과 컴퓨터의 대국, 컴퓨터와 컴퓨터의 대국으로 실험하였다. 그 결과 최적의 공격과 방어를 할 수 있는 가중치를 제시하였다.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제9권1호
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• pp.81-90
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• 2002

The least informative (favorable) distributions, minimizing Fisher information for a multivariate location parameter, are derived in the parametric class of the exponential-power spherically symmetric distributions under the following characterizing restrictions; (i) a bounded variance, (ii) a bounded value of a density at the center of symmetry, and (iii) the intersection of these restrictions. In the first two cases, (i) and (ii) respectively, the least informative distributions are the Gaussian and Laplace, respectively. In the latter case (iii) the optimal solution has three branches, with relatively small variances it is the Gaussian, them with intermediate variances. The corresponding robust minimax M-estimators of location are given by the $L_2$-norm, the $L_1$-norm and the $L_{p}$ -norm methods. The properties of the proposed estimators and their adaptive versions ar studied in asymptotics and on finite samples by Monte Carlo.

• Korean Journal of Mathematics
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• 제6권1호
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• pp.27-46
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• 1998

We are concerned with an optimal control problem governed by a Poisson equation in which body force acts like a control parameter. The cost functional to be optimized is taken to represent the error from the desired observation and the cost due to the control. We recast the problem into the mixed formulation to take advantage of the minimax principle for the duality method. The existence of a saddle point for the Lagrangian shall be shown and the optimality system will be derived therein. Finally, to attain an optimal control, we combine the optimality system with an operational technique. By achieving the gradient of the cost functional, a convergent algorithm based on the projected gradient method is established.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

• 한국게임학회 논문지
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• 제18권4호
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• pp.99-106
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• 2018

보드게임에서는 많은 경우의 수의 말들과 많은 상태공간들을 가지고 있다. 그러므로 게임은 학습을 오래 하여야 한다. 본 논문에서는 Q러닝 알고리즘을 이용했다. 그러나 강화학습은 학습초기에 학습속도가 느려지는 단점이 있다. 그러므로 학습을 하는 동안에 같은 최선의 값이 있을 때, 게임트리를 고려한 문제영역의 지식을 활용한 휴리스틱을 사용하여 학습의 속도향상을 시도하였다. 기존 구현된 말과 개선하여 구현된 말을 비교하기 위하여 보드게임을 제작했다. 그래서 일방적으로 공격하는 말과 승부를 겨루게 하였다. 개선된 말은 게임트리를 고려하여 상대방 말을 공격하였다. 실험결과 개선하여 구현된 말이 학습속도적인 면에서 향상됨 것을 알 수 있었다.

• Structural Engineering and Mechanics
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• 제68권5호
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• pp.549-561
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• 2018

Sobol method is applied as a powerful variance decomposition technique in the field of global sensitivity analysis (GSA). The paper is devoted to increase convergence speed of the extracted Sobol indices using a new proposed sampling technique called genetic based Latine hypercube sampling (GBLHS). This technique is indeed an improved version of restricted Latine hypercube sampling (LHS) and the optimization algorithm is inspired from genetic algorithm in a new approach. The new approach is based on the optimization of minimax value of LHS arrays using manipulation of array indices as chromosomes in genetic algorithm. The improved Sobol method is implemented to perform factor prioritization and fixing of an uncertain comprehensive high speed rotor-bearing system. The finite element method is employed for rotor-bearing modeling by considering Eshleman-Eubanks assumption and interaction of axial force on the rotor whirling behavior. The performance of the GBLHS technique are compared with the Monte Carlo Simulation (MCS), LHS and Optimized LHS (Minimax. criteria). Comparison of the GBLHS with other techniques demonstrates its capability for increasing convergence speed of the sensitivity indices and improving computational time of the GSA.