• Journal of the Korean Geotechnical Society
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• v.35 no.4
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• pp.27-35
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• 2019

This study confirmed the applicability to the field of geotechnical engineering for relatively complicated space and many target design variables in back analysis. The Sharan's equation and the Blum's method were used for the tunnel field and the retaining wall as a model for the multi-variate problem of geotechnical engineering. Optimization methods are generally divided into a deterministic method and a stochastic method. In this study, Simulated Annealing Method (SA) was selected as a deterministic method and Differential Evolution Algorithm (DEA) and Particle Swarm Optimization Method (PSO) were selected as stochastic methods. The three selected optimization methods were compared by applying a multi-variate model. The problem of deterministic method has been confirmed in the multi-variate back analysis of geotechnical engineering, and the superiority of DEA can be confirmed. DEA showed an average error rate of 3.12% for Sharan's solution and 2.23% for Blum's problem. The iteration number of DEA was confirmed to be smaller than the other two optimization methods. SA was confirmed to be 117.39~167.13 times higher than DEA and PSO was confirmed to be 2.43~6.91 times higher than DEA. Applying a DEA to the multi-variate back analysis of geotechnical problems can be expected to improve computational speed and accuracy.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.153-162
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• 2011

The minimum cut problem is to minimize c(S,T), that is, to determine source S and sink T such that the capacity of the S-T cut is minimal. The flow-based algorithm is mostly used to find the bottleneck arcs by calculating flow network, and does not presents the minimum cut. This paper suggests an algorithm that simply includes the maximum capacity vertex to adjacent set S or T and finds the minimum cut without obtaining flow network in advance. On applying the suggested algorithm to 13 limited graphs, it can be finds the minimum cut value $_{\min}c$(S, T) with simply and correctly.

• Journal of the military operations research society of Korea
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• v.8 no.1
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• pp.53-70
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• 1982

This thesis develops a more realistic and applicable new set covering model that is adjusted and supplied by the existing set covering models, and induces an algorithm for solving the new set covering model, and applies the new model and the algorithm to an actual set covering problems. The new set covering model introduces a probabilistic covering aistance ($0{\eqslantless}p{\eqslantless}1$)or time($0{\eqslantless}p{\eqslantless}1$) instead of a deterministic covering distance(0 or 1) or time (0 or 1) of the existing set covering model. The existing set covering model has not considered the merit of the overcover of customers. But this new set covering model leads a concept of this overcover to a concept of the parallel system reliability. The algorithm has been programmed on the UNIVAC 9030 for solving large-scale covering problems. An application of the new set covering model is presented in order to determine the locations of the air surveillance radars as a set covering problem for a case-study.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.740-749
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• 1998

This paper proposes compression of image data using neural networks based on conjugate gradient method and dynamic tunneling system. The conjugate gradient method is applied for high speed optimization .The dynamic tunneling algorithms, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Converging to the local minima by using the conjugate gradient method, the new initial point for escaping the local minima is estimated by dynamic tunneling system. The proposed method has been applied the image data compression of 12 ${\times}$12 pixels. The simulation results shows the proposed networks has better learning performance , in comparison with that using the conventional BP as learning algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.115-125
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• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.44-51
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• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.2
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• pp.54-60
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• 2018

This paper contains the multi-mission scheduling optimization of UAV within a given operating time. Mission scheduling optimization problem is one of combinatorial optimization, and it has been shown to be NP-hard(non-deterministic polynomial-time hardness). In this problem, as the size of the problem increases, the computation time increases dramatically. So, we applied the genetic algorithm to this problem. For the application, we set the mission scenario, objective function, and constraints, and then, performed simulation with MATLAB. After 1000 case simulation, we evaluate the optimality and computing time in comparison with global optimum from MILP(Mixed Integer Linear Programming).

• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.259-268
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• 1999

In order to reduce the cost of product and save the processing time, optimal nesting of two-dimensional part is an important application in number of industries like shipbuilding and garment making. There have been many studies on finding the optimal solution of two-dimensional nesting. The problem of two-dimensional nesting has a non-deterministic characteristic and there have been various attempts to solve the problem by reducing the size of problem rather than solving the problem as a whole. Heuristic method and linearlization are often used to find an optimal solution of the problem. In this paper, theoretical and practical nesting algorithm for rectangular, circular and irregular shape of two-dimensional parts is proposed. Both No-Fit-Polygon and Minkowski-Sum are used for solving the overlapping problem of two parts and the dynamic programming technique is used for reducing the number search spae in order to find an optimal solution. Also, nesting designer's expertise is complied into the proposed algorithm to supplement the heuristic method.

• Geomechanics and Engineering
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• v.15 no.4
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• pp.937-945
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• 2018

A probabilistic study of a reinforced earth wall in a frictional soil using the surface response methodology (RSM) is presented. A deterministic model based on numerical simulations is used (Abdelouhab et al. 2011, 2012b) and the serviceability limit state (SLS) is considered in the analysis. The model computes the maximum horizontal displacement of the wall. The response surface methodology is utilized for the assessment of the Hasofer-Lind reliability index and is optimized by the use of a genetic algorithm. The soil friction angle and the unit weight are considered as random variables while studying the SLS. The assumption of non-normal distribution for the random variables has an important effect on the reliability index for the practical range of values of the wall horizontal displacement.