• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.1
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• pp.90-100
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• 1999

유전자 알고리즘은 전통적인 등반 알고리즘을 이용하여 구하기 어려웠던 최적화 문제를 해결하기 위한 강인한(Robust) 탐색 기법이다. 특히 목적함수가 (1)여러 개의 국부 최대치를 가지는 경우, (2)수학적으로 표현이 불가능하거나 어려운 경우, (3)목적함수에 교란 항(disturbance term)이 섞여 있을 경우도 우수한 탐색 능력을 갖는 것으로 알려져 있다. 본 논문에서는 유전자 알고리즘을 이용하여 나타나는 다양한 해집합을 형성하는 개체군을 군집성 분석(cluster analysis)을 이용하여 군집화하고, 각 군집에 부여된 군집 적합도에 따라서 최적해를 구함으로써 단순 유전자 알고리즘에 의한 최적화보다 훨씬 향상된 탐색 알고리즘을 제안하였다. 반응표면의 형태가 정형화한 테스트 함수의 형태로 나타난다고 가정한 경우에 대하여 몬테 칼로 시뮬레이션을 통하여 본 알고리즘을 적용하여 평가하고 분석하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.1006-1009
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• 2003

In a motion estimation method for use in encoding a moving picture, a full-pixel motion vector is estimated by stochastically sampling a pixel to be processed in a predetermined-sized block of a previous frame or a next frame as a reference frame for each of a plurality of equal-sized blocks in a current frame. Then, a half-pixel motion vector is estimated based on the full-pixel motion vector. Accordingly, both the calculation amount and the calculation time required for the motion estimation are effectively reduced. Further, it can be prevented that the hardware becomes complicated. .

• The Journal of the Acoustical Society of Korea
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• v.15 no.4E
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• pp.21-27
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• 1996

In this paper, we propose a discriminative training algorithm for the stochastic segment model (SSM) in continuous speech recognition. As the SSM is usually trained by maximum likelihood estimation (MLE), a discriminative training algorithm is required to improve the recognition performance. Since the SSM does not assume the conditional independence of observation sequence as is done in hidden Markov models (HMMs), the search space for decoding an unknown input utterance is increased considerably. To reduce the computational complexity and starch space amount in an iterative training algorithm for discriminative SSMs, a hybrid architecture of SSMs and HMMs is programming using HMMs. Given the segment boundaries, the parameters of the SSM are discriminatively trained by the minimum error classification criterion based on a generalized probabilistic descent (GPD) method. With the discriminative training of the SSM, the word error rate is reduced by 17% compared with the MLE-trained SSM in speaker-independent continuous speech recognition.

• The KIPS Transactions:PartC
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• v.8C no.1
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• pp.105-112
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• 2001

In this paper, we present the advanced QCELP codebook search improving the qualification of speech, which can make QCELP vocoder used in noise robust system. While conventional QCELP usually searches stochastic codebook once, we can find that two times search is the most suitable for improving the quality of speech after we did 2-5 times search. Consequently, the advanced QCELP vocoder represents excitation signal in detail using two times precise quantization and so improve the qualification of speech. In our experiment, we use the speeches collected from circumstance (such as lecture room, house, street, laboratory etc.) without regarding noise as input dat and measure the speech Qualification using SNR, segSNR. As the result of the experiment, we find that the advanced QCELP makes SNR and segSNR improved by 38.35% and 65.51% respectively compared with conventional QCELP.

• Journal of computational fluids engineering
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• v.17 no.4
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• pp.32-40
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• 2012

Efficient Global Optimization (EGO) method is a global optimization technique which can select the next sample point automatically by infill sampling criteria (ISC) and search for the global minimum with less samples than what the conventional global optimization method needs. ISC function consists of the predictor and mean square error (MSE) provided from the kriging model which is a stochastic metamodel. Also the constrained EGO method can minimize the objective function dealing with the constraints under EGO concept. In this study the constrained EGO method applied to the RAE2822 airfoil shape design formulated with the constraint. But the noisy CFD data caused the kriging model to fail to depict the true function. The distorted kriging model would make the EGO deviate from the correct search. This distortion of kriging model can be handled with the interpolation(p=free) kriging model. With the interpolation(p=free) kriging model, however, the search of EGO solution was stalled in the narrow feasible region without the chance to update the objective and constraint functions. Then the accuracy of EGO solution was not good enough. So the three-step search method was proposed to obtain the accurate global minimum as well as prevent from the distortion of kriging model for the noisy constrained CFD problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.114-122
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• 2010

The Probabilistic Traveling Salesman Problem (PTSP) is an important topic in the study of traveling salesman problem and stochastic routing problem. The goal of PTSP is to find a priori tour visiting all customers with a minimum expected length, which simply skips customers not requiring a visit in the tour. There are many existing researches for the homogeneous version of the problem, where all customers have an identical visiting probability. Otherwise, the researches for the heterogeneous version of the problem are insufficient and most of them have focused on search base algorithms. In this paper, we propose a simple construction algorithm to solve the heterogeneous PTSP. The Minimum Expected Length Insertion (MELI) algorithm is a construction algorithm and consists of processes to decide a sequence of visiting customers by inserting the one, with the minimum expected length between two customers already in the sequence. Compared with optimal solutions, the MELI algorithm generates better solutions when the average probability is low and the customers have different visiting probabilities. We also suggest a local search method which improves the initial solution generated by the MELI algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.6
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• pp.478-487
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• 2002

This paper presents a hybrid method to compute the solutions of an optimization Problem. The present hybrid algorithm is the synthesis of an artificial life algorithm and the random tabu search method. The artificial life algorithm has the most important feature called emergence. The emergence is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The conventional artificial life algorithm for optimization is a stochastic searching algorithm using the feature of artificial life. Emergent colonies appear at the optimum locations in an artificial ecology. And the locations are the optimum solutions. We combined the feature of random-tabu search method with the conventional algorithm. The feature of random-tabu search method is to divide any given region into sub-regions. The enhanced artificial life algorithm (EALA) not only converge faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. The enhanced artificial life algorithm is applied to the optimum design of high-speed, short journal bearings and its usefulness is verified through an optimization problem.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.24-29
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• 1997

Recently, Genetic Algorithm(GA), which is a stochastic direct search strategy that mimics the process of genetic evolution, is widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA is very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GA. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher porbability of convergence to global optimum compared to traditional techniques which take one-point search method. The methods consist of three genetics opera- tions named selection, crossover and mutation. In this study, a method of finding the omtimum size and topology of drilling machine is proposed by using the GA, For rapid converge to optimum, elitist survival model,roulette wheel selection with limited candidates, and multi-point shuffle cross-over method are adapted. And pseudo object function, which is the combined form of object function and penalty function, is used to include constraints into fitness function. GA shows good results of weight reducing effect and convergency in optimal design of drilling machine.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.178-188
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• 2019

When using a distributed system, it is very important to know the intention of a target ship in order to prevent collisions. The action taken by a certain ship for collision avoidance and the action of the target ship it intends to avoid influence each other. However, it is difficult to establish a collision avoidance plan in consideration of multiple-ship situations for this reason. To solve this problem, a Distributed Stochastic Search Algorithm (DSSA) has been proposed. A DSSA searches for a course that can most reduce cost through repeated information exchange with target ships, and then indicates whether the current course should be maintained or a new course should be chosen according to probability and constraints. However, it has not been proven how the parameters used in DSSA affect collision avoidance actions. Therefore, in this paper, I have investigated the effect of the parameters and weight factors of DSSA. Experiments were conducted by combining parameters (time window, safe domain, detection range) and weight factors for encounters of two ships in head-on, crossing, and overtaking situations. A total of 24,000 experiments were conducted: 8,000 iterations for each situation. As a result, no collision occurred in any experiment conducted using DSSA. Costs have been shown to increase if a ship gives a large weight to its destination, i.e., takes selfish behavior. The more lasting the expected position of the target ship, the smaller the sailing distance and the number of message exchanges. The larger the detection range, the safer the interaction.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.813-826
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• 2015

This research deals with an estimation method for kinetic reaction model. The kinetic reaction model is a model to explain spread or changing process based on interaction between species on the Biochemical area. This model can be applied to a model for disease spreading as well as a model for system Biology. In the search, we assumed that the spread of species is stochastic and we construct the reaction model based on stochastic movement. We utilized Gillespie algorithm in order to construct likelihood function. We introduced a Bayesian estimation method using Markov chain Monte Carlo methods that produces more stable results. We applied the Bayesian estimation method to the Lotka-Volterra model and gene transcription model and had more stable estimation results.