• Journal of Advanced Marine Engineering and Technology
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• v.33 no.3
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• pp.403-410
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• 2009

The aim of this research is to develop the Bees Algorithm named 'the dynamic allocated Bees Algorithm' for multi-objective problem, especially in order to be suit for Pareto optimality. In addition two new neighbourhood search methods have been developed to produce enhanced solutions for a multi-objective problem named 'random selection neighbourhood search' and 'weighted sum neighbourhood search' and they were compared with the basic neighbourhood search in the dynamic allocated Bees Algorithm. They were successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem and simulation results presented for the standard IEEE 30-bus system and they were compared to those obtained using other approaches. The comparison shows the superiority of the proposed dynamic allocated Bees Algorithms and confirms its suitability for solving the multi-objective EED problem.

• Economic and Environmental Geology
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• v.28 no.4
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• pp.425-431
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• 1995

Genetic algorithms are so named because they are analogous to biological processes. The model parameters are coded in binary form. The algorithm then starts with a randomly chosen population of models called chromosomes. The second step is to evaluate the fitness values of these models, measured by a correlation between data and synthetic for a particular model. Then, the three genetic processes of selection, crossover, and mutation are performed upon the model in sequence. Genetic algorithms share the favorable characteristics of random Monte Carlo over local optimization methods in that they do not require linearizing assumptions nor the calculation of partial derivatives, are independent of the misfit criterion, and avoid numerical instabilities associated with matrix inversion. An additional advantage over converntional methods such as iterative least squares is that the sampling is global, rather than local, thereby reducing the tendency to become entrapped in local minima and avoiding the dependency on an assumed starting model.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.49-57
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• 2005

Construction labor productivity, which is the greatest source of variation in overall construction productivity, is the critical factor for determining the project performance in terms of time and cost, especially during scheduled overtime when extra time and cost are invested. The objective of this research is to select an appropriate type of probability distribution function representing the variability of daily labor productivity during overtime. Based on the results of statistical data analysis of labor performance during different weekly work hours, lognormal distribution is selected in order to take advantage of easiness of generating correlated random numbers. The selected lognormal distribution can be used for development of a simulation model in construction scheduling, cost analysis, and other applications areas where representation of the correlations between variables are essential.

• Journal of Family Resource Management and Policy Review
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• v.19 no.3
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• pp.1-16
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• 2015

The purpose of this study was to synthesize the results of studies on the effects of multi-cultural education program for Korean children. Using the author's own selection criteria, 17 studies were finally selected and 31 effect sizes were calculated from these studies and used for meta analysis. The overall effect size for all studies on the random effect model was .802, and it was positive and high. Given the heterogeneity among the effect size, subgroup analysis was conducted. According to the analysis, effect sizes significantly differed depending on program goal, concerned multi-cultural higher than others. Result also showed that the high scored effect sizes were the general family, pre-school age children group, and the program were 11-20 children group in size, and 11~20 times in frequency of education.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.559-570
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• 2008

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can be added to the updating principle, so that particles have strong exploration ability than those of conventional PSO. The conditions and main behaviors of PSO-CREV are described. Two properties in terms of "divergence before convergence" and "controllable exploration behavior" are presented, which promote the performance of PSO-CREV. An experimental method based on a complex test function is proposed by which the proper parameters of PSO-CREV used in practice are figured out, which guarantees the high exploration ability, as well as the convergence rate is concerned. The benchmarks and applications on FCRNN training verify the improvements brought by PSO-CREV.

• ETRI Journal
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• v.29 no.1
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• pp.116-119
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• 2007

In this letter, we design a collision resolution protocol for optical burst switching ring networks to avoid burst collision. We define the offset time condition for no burst transmission collision and manage the free time list of nodes for no burst reception collision. In order to improve the throughput, we use a fiber delay line, void-filling, and void-compression. This protocol does not require any additional procedures for bandwidth reservation such as centralized assignment of bandwidth, lightpath setup of WDM ring networks, or token capturing for the burst transmission. The simulation results show that the proposed protocol can achieve high throughput while saving 70% of wavelengths when compared to round robin with random selection, round robin with persistent, and round robin with non-persistent with only destination delay.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1319-1325
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• 2010

Quantile regression provides a more complete statistical analysis of the stochastic relationships among random variables. Sometimes quantile functions estimated at different orders can cross each other. We propose a new non-crossing quantile regression method applying support vector median regression to restricted regression quantile, restricted support vector quantile regression. The proposed method provides a satisfying solution to estimating non-crossing quantile functions when multiple quantiles for high dimensional data are needed. We also present the model selection method that employs cross validation techniques for choosing the parameters which aect the performance of the proposed method. One real example and a simulated example are provided to show the usefulness of the proposed method.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.1-6
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• 1997

The scheduling problem of satisfying both 0/1 constraint and the timing constraint while minimizing the total error is NP-complete when the optional parts have arbitrary processing times. In this paper, we present a heuristic scheduling algorithm for 0/1 constraint imprecise systems which consist of communicating tasks running on multiple processors. The algorithm is based on the program graph which is similar to the one presented in[4]. To check the schedulability, we apply Lawler and Moore's theorem. To analyze the performance of the proposed algorithm, intensive simulation is done. The results of the simulation shows that the longest processing first selection strategy outperforms random or minimal laxity policies.

• ETRI Journal
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• v.36 no.4
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• pp.609-616
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• 2014

Even though many computational methods (recursive formulae) for blocking probabilities in finite-capacity M/D/1 queues have already been produced, these are forms of transforms or are limited to single-node queues. Using a distinctly different approach from the usual queueing theory, this study introduces explicit (transform-free) formulae for a blocking probability, a stationary probability, and mean sojourn time under either production or communication blocking policy. Additionally, the smallest buffer capacity subject to a given blocking probability can be determined numerically from these formulae. With proper selection of the overall offered load ${\rho}$, the approach described herein can be applicable to more general queues from a computational point of view if the explicit expressions of random vector $D_n$ are available.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1127-1135
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• 2014

Genome-wide association studies (GWAS) are designed to discover genetic variants such as single nucleotide polymorphisms (SNPs) that are associated with human complex traits. Although there is an increasing interest in the application of GWAS methodologies to population-based cohorts, many published GWAS have adopted a case-control design, which raise an issue related to a sampling bias of both case and control samples. Because of unequal selection probabilities between cases and controls, the samples are not representative of the population that they are purported to represent. Therefore, non-random sampling in case-control study can potentially lead to inconsistent and biased estimates of SNP-trait associations. In this paper, we proposed inverse-probability of sampling weights based on disease prevalence to eliminate a case-control sampling bias in estimation and testing for association between SNPs and quantitative traits. We apply the proposed method to a data from the Korea Association Resource project and show that the standard estimators applied to the weighted data yield unbiased estimates.