• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.4
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• pp.351-362
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• 2020

Adaptive behaviors are one of ubiquitous features in evolutionary dynamics of populations, and certain adaptive behaviors can be witnessed by individuals' movements which are generally affected by local environments. In this paper, by revisiting the previous work, we investigate the sensitivity of species coexistence in the system of cyclic competition where species movement can be affected by local environments. By measuring the extinction probability through Monte-Carlo simulations, we find the relativistic effect of weights of local fitness and exchange rate for adaptive movement on species biodiversity which promotes species coexistence as the relativistic effect is intensified. In addition, by means of basins of initial conditions, we also found that adaptive movement can also affect species biodiversity with respect to the choice of initial conditions. The strong adaptive movement can eventually lead the coexistence as a globally stable state in the spatially extended system regardless of mobility.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.12
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• pp.1671-1677
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• 2006

A total of two hundred twenty eight half-sib chickens were scored for allele size at 20 microsatellite loci to estimate individual heterozygosity and mean $d^2$. The averages of microsatellite heterozygosity, allele per locus and mean $d^2$ were 0.39, 3.6 and 49, respectively. The body weight was measured biweekly from birth to twelve weeks of age. Gompertz function was assumed to simulate body weight and to estimate the growth model parameters. Due to sex effect on body weight, the regression of body weight on heterozygosity as well as on mean $d^2$ in males and females was analyzed separately in the present study. Positive correlations were found between microsatellite heterozygosity and body weight in males and females (p<0.05). Positive correlation also observed between individual heterozygosity and simulated maximum daily gain estimated from Gompertz function in female chickens (p<0.05). There were no significant correlations between mean $d^2$ and body weight. The results suggest that local effect hypothesis could explain the correlations between heterozygosity and fitness-related traits in the domesticated chicken population, rather than the general effect hypothesis does.

• Journal of Korean Society of Forest Science
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• v.107 no.2
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• pp.184-192
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• 2018

In this study was implemented to develop tree stem weight prediction equation of Larix kaempferi in central region by selecting a standard site, taking into account of diameter and position of the local trees. Fifty five sample trees were selected in total. By utilizing actual data of the sample trees, 11 models were compared and analyzed in order to estimate four different kinds of weights which include fresh weight, ovendry outside bark weight, ovendry inside bark weight and merchantable weight. As to estimate its weight, the study has classified its model according to three parameters: DBH, DBH and height, and volume. The optimal model was chosen by comparing the performance of model using the fit index and standard error of estimate and residual distribution. As a result, the formula utilizing DBH (Variable 1) is $W=a+bD+cD^2$ (3) and its fit index was 90~92%. The formula for DBH and height (Variable 2) is $W=aD^bH^C$ (8) and its fit index was 97~98%. In summation, Variable 2 model showed higher fitness than Variable 1 model. Moreover, fit index of formula for total volume and merchantable volume (W=aV) showed high rate of 98~99%, as well as resulting 7.7-17.5 with SEE and 8.0-10.0 with CV(%) which lead to predominately high fitness in conclusion. This study is expected to provide information on weights for single trees and furthermore, to be used as a basic study for weight of stand unit and biomass estimation equations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.2
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• pp.406-414
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• 2002

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.215-236
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• 2017

Obtaining accurate location information is important in practical applications of wireless sensor networks (WSNs). The distance vector hop (DV-Hop) is a frequently-used range-free localization algorithm in WSNs, but it has low localization accuracy. Moreover, despite various improvements to DV-Hop-based localization algorithms, maintaining a balance between high localization accuracy and good stability and convergence is still a challenge. To overcome these shortcomings, we proposed an improved DV-Hop localization algorithm based on the bat algorithm (IBDV-Hop) for WSNs. The IBDV-Hop algorithm incorporates optimization methods that enhance the accuracy of the average hop distance and fitness function. We also introduce a nonlinear dynamic inertial weight strategy to extend the global search scope and increase the local search accuracy. Moreover, we develop an updated solutions strategy that avoids premature convergence by the IBDV-Hop algorithm. Both theoretical analysis and simulation results show that the IBDV-Hop algorithm achieves higher localization accuracy than the original DV-Hop algorithm and other improved algorithms. The IBDV-Hop algorithm also exhibits good stability, search capability and convergence, and it requires little additional time complexity and energy consumption.