• Journal of Power System Engineering
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• v.10 no.4
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• pp.147-152
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• 2006

The current type amplifier is the device that is used for an actuator as the motor's torque controller. However, it is too difficult to select the parameter value that has the desired output because the current type amplifier's transfer function is too complex. This study concern about the design of the current type amplifier with the desired output. From the modeled transfer function of the current type amplifier, the optimal parameter values of the transfer function can be selected in order to have the desired output using the Real Coded Genetic Algorithm(RCGA). The real circuit is made with the selected parameter value. The step response of the real circuit is in good agreement with the desired step response.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.663-671
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• 2017

Some genetic association tests include an unidentifiable nuisance parameter under the null hypothesis of no association. When the mode of inheritance (MOI) is not specified in a case-control design, the Cochran-Armitage (CA) trend test contains an unidentifiable nuisance parameter. The transmission disequilibrium test (TDT) in a family-based association study that includes the unaffected also contains an unidentifiable nuisance parameter. The hypothesis tests that include an unidentifiable nuisance parameter are typically performed by taking a supremum of the CA tests or TDT over reasonable values of the parameter. The p-values of the supremum test statistics cannot be obtained by a normal or chi-square distribution. A common method is to use a Davies's upper bound of the p-value instead of an exact asymptotic p-value. In this paper, we provide a unified sine-cosine process expression of the CA trend test that does not specify the MOI and the TDT that includes the unaffected. We also present a closed form expression of the exact asymptotic formulas to calculate the p-values of the supremum tests when the score function can be written as a linear form in an unidentifiable parameter. We illustrate how to use the derived formulas using a pharmacogenetics case-control dataset and an attention deficit hyperactivity disorder family-based example.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.4
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• pp.468-474
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• 2006

Real time ultrasound data was generated on 10,596 live Hanwoo cows to study genetic variation on ultrasonic beef quality traits and to assess the best model to estimate genetic parameters on these traits. Pedigree stacking and data validation was done using the SAS statistical software and the genetic parameter estimates were obtained by EM-REML algorithm. Out of the five different multi-trait mixed animal models constructed, the optimal model included fixed effects of herd, year-season-appraisal, body condition score, linear and quadratic covariates for chest girth, the linear covariate effect of age and the random animal and residual effect of the five models studied. The heritability of longissimus muscle area (LMA), $12^{th}$ rib measurement of back fat thickness (BF) and marbling score (MS) was 0.11, 0.17 and 0.15, respectively. Genetic correlation of LMA vs. BF, LMA vs. MS and BF vs. MS was -0.15, 0.06 and 0.61, respectively. The results showed presence of genetic variation in these ultrasonic beef quality traits in Hanwoo cows and suggest that the selection of Hanwoo cows may be possible by performing ultrasonic scans on live animals, which will ultimately be helpful in reducing the generation interval and the cost of selection procedure.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.741-747
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• 2016

The aim of study was to estimate genetic parameter of carcass traits in commercial Hanwoo steer using national animal model for selection of superior bull. Analyzed data (n=5,843) on carcass traits was collected from 107,020 Hanwoo steer. The animal model was used to estimate heritability and genetic correlations. The estimated heritability of carcass traits were 0.19, 0.17, 0.20 and 0.23 for carcass weight, eye muscle area, backfat thickness and marbling score, respectively. The estimated heritability for carcass traits in commercial Hanwoo are low than estimated heritability of national progeny test population for selection of superior bull because breeding environment, genetic performance of cow and feeding day was different. Therefore, we suggests that animal model can include practical genetic variable based on national animal model to improve genetic performance in commercial Hanwoo.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1161-1164
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• 1996

In this paper, a parameter optimization technique for a mobile robot navigation is discussed. Authors already have proposed a navigation algorithm for mobile robots with sonar sensors using fuzzy decision making theory. Fuzzy decision making selects the optimal via-point utilizing membership values of each via-point candidate for fuzzy navigation goals. However, to make a robot successfully navigate through an unknown and cluttered environment, one needs to adjust parameters of membership function, thus changing shape of MF, for each fuzzy goal. Furthermore, the change in robot configuration, like change in sensor arrangement or sensing range, invokes another adjusting of MFs. To accomplish an intelligent way to adjust these parameters, we adopted a genetic algorithm, which does not require any formulation of the problem, thus more appropriate for robot navigation. Genetic algorithm generates the fittest parameter set through crossover and mutation operation of its string representation. The fitness of a parameter set is assigned after a simulation run according to its time of travel, accumulated heading angle change and collision. A series of simulations for several different environments is carried out to verify the proposed method. The results show the optimal parameters can be acquired with this method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.177-182
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• 2010

The forward convener is one of power supplies used widely. This paper presents parameter tuning methods to obtain optimal circuit element values for the forward converter to minimize the output voltage variation under various load changing environments. The conventional method using the concept of the phase margin is extended to have optimal phase margin that gives slightly improved performance in the output voltage response. For this, the phase margin becomes the tuning parameter and is optimized with the genetic algorithm. Next, the circuit element values are directly chosen as the tuning parameters and also optimized using the genetic algorithm to have very improved performance in the output voltage control of the forward converter.

• Journal of Ocean Engineering and Technology
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• v.18 no.5
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• pp.29-35
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• 2004

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is proposed for identifying a global solution, using continuous zooming factors for optimization problems. In order to improve the local fine-tuning of the GA, we introduced a new method whereby the search space is zoomed around the design variable with the best fitness per 100 generation, resulting in an improvement of the convergence. Furthermore, the reliability of the optimized solution is determined based on the theory of probability, and the parameter used for the successive zooming method is optimized. With parameter optimization, we can eliminate the time allocated for deciding parameters used in SZGA. To demonstrate the superiority of the proposed theory, we tested for the minimization of a multiple function, as well as simple functions. After testing, we applied the parameter optimization to a truss problem and wicket gate servomotor optimization. Then, the proposed algorithm identifies a more exact optimum value than the standard genetic algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.105-111
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• 1999

This Paper is about how to deside the best parameter of PID controller, using Genetic Algorithms and Neural Networks. Control by Genetic Algorithms, which is off-line pass, has weakness for disturbance. So we want to improve like followings by adding Neural Network to controller and putting it on line. First we find PID parameter by Genetic Algorithms in forward pass of Neural Network and set the best output condition according to the increasing number of generation. Second, we explain the adaptability for disturbance with simulation by correcting parameter by backpropagation learning rule by using the learning ability of Neural Network.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1163-1169
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• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.