• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1462-1469
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• 1997

This study presents a genetic algorithm-based method for optimizing control parameters in fluid power systems. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics. A genetic algorithm seeks control parameters maximizing a measure that evaluates system performance. Five control gains of the PID-PD cascade controller fr an electrohydraulic speed control system with a variable displacement hydraulic motor are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that optimization of the five gains by manual tuning should be a task of great difficulty and that a genetic algorithm is an efficient scheme giving economy of time and in labor in optimizing control parameters of fluid power systems.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.4
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• pp.316-321
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• 2003

Analysis of breeding gains in grain yield has been intensively conducted in wheat, barley, oat, maize, and soybean. Such information is limited in rice. The objective of this study was to compare the breeding gains and cultural gains contributed to yield gains of Korean rice varieties since early 1900s. Two sets of yield data were used for analysis; the historical yield data of 1908 for old japonica cultivars, and present yield data in the years from 1996 to 1998 for the six cultivars, consisting of previous two old cultivars and four contemporary cultivars. The old cultivars were two native cultivars, Jodongi and Damageum, while contemporary cultivars were two premium quality japonica cultivars, Hwaseongbyeo and Dongjinbyeo, and two Tongil-type cultivars, high yielding cultivars developed from indica/japonica hybridization, Milyang23 and Dasanbyeo. The yield differences of old cultivars between the experiments in 1908 and the experiments from 1996 to 1998 were estimated as cultural gains (1.84 tons $\textrm{ha}^{-1}$) due to the improvement of cultivation technology. Yield differences between the old cultivars and contemporary cultivars were considered total yield gains during the periods. These were 2.51 tons $\textrm{ha}^{-1}$ for japonica cultivars and 3.81 tons $\textrm{ha}^{-1}$ for Tongil-type cultivars. From these data, the genetic gain of 0.67 tons $\textrm{ha}^{-1}$ and 1.97 tons $\textrm{ha}^{-1}$ were estimated for japonica cultivars and Tongil-type cultivars respectively. The ratio between cultural gain and genetic gain appeared to be 2.7:1 for japonica cultivars and 1:1 for Tongil-type cultivars. This analysis clearly showed the higher genetic contribution in Tongil-type cultivars than in japonica cultivars, suggesting a guideline to be used when planning new yield improvement programs. Additional implication has emerged when a better yield response to modem cultivation technology was found in one of the old cultivars, suggesting the combined improvement between breeding and cultural improvement is necessary for attaining the maximum yield capacity of a crop.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.698-706
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• 2016

In order to move mobile robots to desired locations in a minimum time, optimal control problems have to be solved; however, their analytic solutions are almost impossible to obtain due to robot nonlinear equations. This paper presents a method to get optimal control gains of mobile robots using genetic algorithms. Since the optimal control gains of mobile robots depend on the initial conditions, the initial condition range is discretized to form some grid points, and genetic algorithms are applied to provide the optimal control gains for the corresponding grid points. The optimal control gains for general initial conditions may be obtained by use of neural networks. So the optimal control gains and the corresponding grid points are used to train neural networks. The trained neural networks can supply pseudo-optimal control gains. Finally simulation studies have been conducted to verify the effectiveness of the method presented in this paper.

• Korean Journal of Plant Resources
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• v.19 no.6
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• pp.677-679
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• 2006

The objectives of this study were to examine the genetic variation of 20-year-old tree height and to estimate heritabilities and genetic gains of Korean white pine. Analysis of variance showed that families and family x block interaction had the significant (p=0.01) effects on tree height. However, family variation appears to be much greater than the variation due to family x block interaction. Individual tree heritability was higher ($h_I^2=0.73$) than family heritability, ($h_F^2=0.83$) therefore, combined selection showed the largest genetic gain (17.76%) in a given equal intensity of selection.

• Animal Bioscience
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• v.34 no.8
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• pp.1303-1308
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• 2021

Objective: The objective of this study was to define a new composite trait for Holstein dairy cows and evaluate the possibility of joint improvement in milk and fertility traits. Methods: A data set consisting 35,882 fertility related records (days open [DO], calving interval [CI], and number of services per conception [NSC], and total milk yield in each lactation [TMY]) was collected from 1998 to 2016 in Polish Holstein-Friesian breed herds. In this study TMY, DO, CI, and lactation length of each cow was used to obtain composite milk and fertility traits (CMF). Results: Moderate heritability (0.15) was estimated for composite trait that was higher than heritability of female fertility related traits: DO 0.047, CI 0.042, and NSC 0.014, and slightly lower than heritability of TMY 0.19. Favourable genetic correlations (-0.87) were estimated between CMF with TMY. Spearman rank correlation coefficients between breeding value of CMF with DO, CI, and TMY were high (>0.94) but with NSC were moderate (0.64). Selection on CMF caused favourable correlated genetic gains for DO, CI, and TMY. Different selection indices with different emphasis on fertility and milk production were constructed. The amount of correlated genetic gains obtained for DO and total milk production according to selection in CMF were higher than of genetic gains obtained for DO and TMY in selection indices with different emphasis on milk and fertility. Conclusion: The animal selection only based on a composite trait - CMF proposed in current study would simultaneously lead to favourable genetic gains for both milk and fertility related traits. In this situation CMF introduced in current study can be used to overcome to limitations of selection index and CMF could be useful for countries that have problems in recording traits, especially functional traits.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1460-1463
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• 1997

The aim of this paper is to suggest a design method of the model following optimal boiler-turbine H.inf. control system using genetic algorithm. This boiler-turbine H.inf. control system is designed by applying genetic algortihm with reference model to the optimal determination of weighting functions and design parameter .gamma. that are given by Glover-Doyle algornithm whch can design H.inf. contrlaaer in the sate. space. The first method to do this is ghat the gains of weightinf functions and .gamma. are optimized simultaneously by genetic algroithm. And the second method is that not only the gains and .gamma. but also the dynamics of weighting functions are optimized at the same time by genetic algonithm. The effectiveness of this boiler-turbine H.inf. control system is verified and compared with LQG/LTR control system by computer simulation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.126-132
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• 2004

This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.48-55
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• 2004

This study presents a genetic algorithm-based method fur optimizing control parameters in the pressure control of electro-hydraulic pump with variable displacement. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics and search the optimal control parameters maximizing a measure that evaluates the performance of a system. Four control gains of the PI-PD cascade controller for an electro-hydraulic pressure control system are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that genetic algorithm is an efficient scheme in optimizing control parameters of the pressure control of electro-hydraulic pump with variable displacement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.1811-1817
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• 2014

In this paper, a PID controller with interpolated gains by use of neural networks is proposed for the formation control problem that following robots track a leading robot with constant distances and angles when there are changes in the mass of the following robot. The whole control system is composed of a kinematic controller and a dynamic controller considering the robot dynamics. The dynamic controller is the PID controller with varying gains, and the proper gains are obtained for some representative masses of the follower robot by the genetic algorithm. Neural networks is trained using the genetic algorithm with the gain data obtained in the previous step. The trained neural network determines optimal PID gains for a random mass of following robot. Simulation studies show that for arbitrary masses of the tracking robot, the PID controller with interpolated gains by the trained neural network has better tracking performance than that of the PID controller with fixed gains.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.183-191
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• 2019

Objective: The purpose of this study was to compare intended and actual yearly genetic gains for milk production and conformation traits and to investigate the simple selection criterion practiced among milk production and conformation traits during the last two decades in Japan. Learning how to utilize the information on intended and actual genetic gains during the last two decades into the genomic era is vital. Methods: Genetic superiority for each trait for four paths of selection (sires to breed bulls [SB], sires to breed cows [SC], dams to breed bulls [DB], and dams to breed cows [DC]) was estimated. Actual practiced simple selection criteria were investigated among milk production and conformation traits and relative emphasis on milk production and conformation traits was compared. Results: Selection differentials in milk production traits were greater than those of conformation traits in all four paths of selection. Realized yearly genetic gain was less than that intended for milk production traits. Actual annual genetic gain for conformation traits was equivalent to or greater than intended. Retrospective selection weights of milk production and conformation traits were 0.73:0.27 and 0.56:0.44 for intended and realized genetic gains, respectively. Conclusion: Selection was aimed more toward increasing genetic gain in milk production than toward conformation traits over the past two decades in Japan. In contrast, actual annual genetic gain for conformation traits was equivalent to or greater than intended. Balanced selection between milk production and conformation traits tended to be favored during actual selection. Each of four paths of selection (SB, SC, DB, and DC) has played an individual and important role. With shortening generation interval in the genomic era, a young sire arises before the completion of sire's daughters' milk production records. How to integrate these four paths of selection in the genomic era is vital.