• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.237-250
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• 2009

We consider a nonparametric test procedure for the multi-sample problem with grouped data. We construct the test statistics based on the scores obtained from the likelihood ratio principle and derive the limiting distribution under the null hypothesis. Also we illustrate our procedure with an example and obtain the asymptotic properties under the Pitman translation alternatives. Also we discuss some concluding remarks. Finally we derive the covariance between components in the Appendix.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.825-835
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• 2017

Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.137-146
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• 2015

Singh (2013) considered the dual problem to the calibration of design weights to obtain a new generalized linear regression estimator (GREG) for the finite population total. In this work, we have made an attempt to suggest a way to use the dual calibration of the design weights in case of multi-auxiliary variables; in other words, we have made an attempt to give an answer to the concern in Remark 2 of Singh (2013) work. The same idea is also used to generalize the GREG estimator proposed by Deville and S$\ddot{a}$rndal (1992). It is not an easy task to find the optimum values of the parameters appear in our approach; therefore, few suggestions are mentioned to select values for such parameters based on a random sample. Based on real data set and under simple random sampling without replacement design, our approach is compared with other approaches mentioned in this paper and for different sample sizes. Simulation results show that all estimators have negligible relative bias, and the multivariate case of Singh (2013) estimator is more efficient than other estimators.

• 산업경영시스템학회지
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• 제40권1호
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• 한국시뮬레이션학회논문지
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• 제9권3호
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• pp.91-102
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• 2000

The distribution routing problem is one of the important problems in distribution and supply center management. This research is concerned with an integrated distribution routing problem for multi-supply centers based on improved genetic algorithm and GUI-type programming. In this research, we used a three-step approach; in step 1 a sector clustering model is developed to transfer the multi-supply center problem to single supply center problems which are more easy to be solved, in step 2 we developed a vehicle routing model with time and vehicle capacity constraints and in step 3, we developed a GA-TSP model which can improve the vehicle routing schedules by simulation. For the computational purpose, we developed a GUI-type computer program according to the proposed methods and the sample outputs show that the proposed method is very effective on a set of standard test problems, and it could be potentially useful in solving the distribution routing problems in multi-supply center problem.

• 한국산업정보학회논문지
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• 제28권1호
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• pp.27-34
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• 2023

최상의 에이전트-작업 할당을 결정하는 문제는 조합 최적화(combinatorial optimization)의 대표적인 문제이자 NP-난해(NP-hard)임이 알려져 있다. 본 연구에서는 에이전트와 작업의 할당 시 결정되는 작업 수행 확률(completion probability)이 불확실한 상황에서의 문제를 다룬다. 에이전트나 작업 내부의 요인 혹은 시스템 외적인 요소로 인한 작업 수행 확률은 고정적이기보다 불확실성을 갖는 것이 일반적이다. 불확실성을 고려하지 않은 할당 결정은 변동성이 있는 현실 상황에서 효과적이지 않은 결정이 될 수 있다. 작업 수행 확률의 불확실성을 수학적으로 반영하기 위해 본 연 구에서는 추계적 계획법(stochastic programming)을 활용한 수리 모형을 제시한다. 본 연구에서는 효율적으로 문제를 풀기 위해 표본 평균 근사법(sample average approximation)을 활용한 알고리즘을 제안한다. 본 문제 해결 방법론을 이용해 효과적인 할당 결정과 상한값과 하한값을 구할 수 있고, 결과의 성능을 확인하기 위해 최적 격차(optimality gap)와 분산을 실험을 통해 제시한다. 이를 통해 알고리즘으로 구한 할당 결정의 우수성 및 강건성을 보인다.

• 전자공학회논문지CI
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• 제45권5호
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• pp.15-24
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• 2008

얼굴인식 등과 같은 고차원 식별문제에서는 샘플패턴의 수가 패턴의 차원보다 작아지게 된다. 이러한 상황에서 차원을 축소하기위해 선형판별분석법을 적용할 경우, 희소성(Small Sample Size: SSS)문제가 발생한다. 최근, SSS 문제를 해결하기 위하여 비유사도에 기반 한 식별법(Dissimilarity-Based Classification: DBC)을 이용하는 방법이 검토되었다. DBC에서는 특징 벡터 대신에 학습 샘플들로부터 추출한 프로토타입들과의 비유사도를 측정하여 입력 패턴을 식별하는 방법이다. 본 논문에서는 비유사도 표현단계와 DBC 학습단계에서 퓨전기법을 중복 적용하는 다단계 퓨전기법(Multi-level Fusion Strategies: MFS)으로 DBCs를 최적화시키는 방법을 제안한다. 제안 방법을 벤취마크 얼굴영상 데이터베이스를 대상으로 실험한 결과, 식별률을 향상시킬 수 있음을 확인하였다.

• 정보처리학회논문지B
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• 제8B권6호
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• pp.601-608
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• 2001

The automated assignment of multiple tasks to executing agents is a key problem in the area of multi-agent systems. In many domains, significant savings can be achieved by reallocating tasks among agents with different costs for handling tasks. The automation of task reallocation among self-interested agents requires that the individual agents use a common negotiation protocol that prescribes how they have to interact in order to come to an agreement on "who does what". In this paper, we introduce the multi-agent Traveling Salesman Problem(TSP) as an example of task reallocation problem, and suggest the Vickery auction as an interagent negotiation protocol for solving this problem. In general, auction-based protocols show several advantageous features: they are easily implementable, they enforce an efficient assignment process, and they guarantce an agreement even in scenarios in which the agents possess only very little domain-specific Knowledge. Furthermore Vickrey auctions have the additional advantage that each interested agent bids only once and that the dominant strategy is to bid one′s true valuation. In order to apply this market-based protocol into task reallocation among self-interested agents, we define the profit of each agent, the goal of negotiation, tasks to be traded out through auctions, the bidding strategy, and the sequence of auctions. Through several experiments with sample multi-agent TSPs, we show that the task allocation can improve monotonically at each step and then finally an optimal task allocation can be found with this protocol.

• Structural Engineering and Mechanics
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• 제26권5호
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• pp.591-615
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• 2007

An improvement is introduced to solve the plane problems of linear elasticity by reciprocal theorem for orthotropic materials. This method gives an integral equation with complex kernels which will be solved numerically. An artificial boundary is defined to eliminate the singularities and also an algorithm is introduced to calculate multi-valued complex functions which belonged to the kernels of the integral equation. The chosen sample problem is a plate, having a circular or elliptical hole, stretched by the forces parallel to one of the principal directions of the material. Results are compatible with the solutions given by Lekhnitskii for an infinite plane. Five different orthotropic materials are considered. Stress distributions have been calculated inside and on the boundary. There is no boundary layer effect. For comparison, some sample problems are also solved by finite element method and to check the accuracy of the presented method, two sample problems are also solved for infinite plate.

• Industrial Engineering and Management Systems
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• 제13권1호
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• pp.29-42
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• 2014

Extensive research has been conducted on supplier evaluation and selection as a strategic and crucial component of supply chain management in recent years. However, few articles in the previous literature have been dedicated to the use of fuzzy inference systems as an aid in decision-making. Therefore, this essay attempts to demonstrate the application of this method in evaluating suppliers, based on a comprehensive framework of qualitative and quantitative factors besides the effect of gradual coverage distance. The purpose of this study is to investigate the applicability of the numerous measures and metrics in a multi-objective optimization problem of the supply chain network design with the aim of managing the allocation of orders by coordinating the production lines to satisfy customers' demand. This work presents a dynamic non-linear programming model that examines the important aspects of the strategic planning of the manufacturing in supply chain. The effectiveness of the configured network is illustrated using a sample, following which an exact method is used to solve this multi-objective problem and confirm the validity of the model, and finally the results will be discussed and analyzed.