• KSBB Journal
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• v.14 no.4
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• pp.495-502
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• 1999

The optimization of fed-batch yeast fermentation process has been performed using genetic algorithm(GA). Three strategies were designed and applied to obtain the optimal feed rate profiles. Genes in the chromosome (input variables for optimization) included feed rates on fixed time intervals (strategy I), or swiching times $t_s1\;and\;t_s2$, and feed rates on singular arc (strategy II), or feed rates and the length of time interval (strategy III). Strategy III showed the best results for all initial conditions due to efficient utilization of genetic information. Simulation results using GA showed similar or better performance compared with previous results by variational caculus and singular control approach.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

• Clinical and Experimental Pediatrics
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• v.55 no.1
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• pp.6-10
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• 2012

Human puberty is a complex, coordinated biological process with multiple levels of regulations. The timing of puberty varies greatly in children and is influenced by both environmental and genetic factors. The key genes of pubertal onset, $KISS1$, $GPR54$, $GNRH1$ and $GNRHR$, may be major causal factors underlying gonadotropin-releasing hormone-dependent precocious puberty (GDPP). Two gain-of-function mutations in $KISS1$ and $GPR54$ have been identified recently as genetic causes of GDPP. $GNRH1$ and $GNRHR$ are also gene candidates for GDPP; however no mutations have been identified in these genes. Presently potential genetic causes like $LIN28B$ continues to appear; many areas of research await exploration in this context. In this review, I focus primarily on the genetic causes of GDPP.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.514-519
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• 2003

The aircraft engine is usually supported by rolling element bearings and has a small damping rate, which is vol y sensitive to external force. The high-performance requirement of the rotors leads to complex assembly designs and are more flexible. Squeeze film dampers (SFDs) are introduced to provide damping while crossing the critical speeds and stability to the rotor s :stem. Hence, the focus of the present investigation is on the decision of an optimal size of the flexible rotor system supported by the squeeze film dampers to minimize the maximum transmitted load and unbalance response over a range operating speeds. The enhanced genetic algorithm (EGA), which was developed by authors, is used in the optimization process. This algorithm is based on the synthesis of a modified genetic algorithm and simplex method. The results show significant benefits in using EGA when compared with nonlinear programming (NLP).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.468-475
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• 2003

In this Paper, we design an optimal binary phase computer generated hologram for Pattern generation using combined genetic algorithm and simulated annealing algorithm together. To design an optimal binary phase computer generated hologram, in searching process of the proposed method, the simple genetic algorithm is used to get an initial random transmittance function of simulated annealing algorithm. Computer simulation shows that the proposed algorithm has better performance than the genetic algorithm or simulated annealing algorithm of terms of diffraction efficiency

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.58-65
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• 2001

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

• Computers and Concrete
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• v.11 no.3
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• pp.183-199
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• 2013

To select a most desired mix proportion that meets required performances according to the quality of recycled aggregate, a large number of experimental works must be carried out. This paper proposed a new design method for the mix proportion of recycled aggregate concrete to reduce the number of trial mixes. Genetic algorithm is adapted for the method, which has been an optimization technique to solve the multi-criteria problem through the simulated biological evolutionary process. Fitness functions for the required properties of concrete such as slump, density, strength, elastic modulus, carbonation resistance, price and carbon dioxide emission were developed based on statistical analysis on conventional data or adapted from various early studies. Then these fitness functions were applied in the genetic algorithm. As a result, several optimum mix proportions for recycled aggregate concrete that meets required performances were obtained.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.158-165
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• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.

• Journal of KSNVE
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• v.10 no.1
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• pp.33-40
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• 2000

With the increased requirement for automobile noise, a design fo mufflers with higher performances becomes more important in recent days. For a design of some mufflers, it must satisfy both minimizing back pressure and maximizing sound attenuation in broad range of frequecny. Even for a simple Helmholtz resonator, an important element in a muffler, a resonator design with accurate resonant frequency is difficult if one want to consider standing waves within the cavity. In this paper, the genetic algorithm, one of the optimization technique with high capability of global fittest solution and robust convergence, is applied to the design process of mufflers. Results show that the genetic algorithm can be successfully and efficiently used to find the fittest model for both mufflers and Helmoltz resonators.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.1
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• pp.1-6
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• 2021

Next-generation sequencing (NGS) technologies have changed the process of genetic diagnosis from a gene-by-gene approach to syndrome-based diagnostic gene panel sequencing (DPS), diagnostic exome sequencing (DES), and diagnostic genome sequencing (DGS). A priori information on the causative genes that might underlie a genetic condition is a prerequisite for genetic diagnosis before conducting clinical NGS tests. Theoretically, DPS, DES, and DGS do not require any information on specific candidate genes. Therefore, clinical NGS tests sometimes detect disease-related pathogenic variants in genes underlying different conditions from the initial diagnosis. These clinical NGS tests are expensive, but they can be a cost-effective approach for the rapid diagnosis of rare disorders with genetic heterogeneity, such as the glycogen storage disease, familial intrahepatic cholestasis, lysosomal storage disease, and primary immunodeficiency. In addition, DES or DGS may find novel genes that that were previously not linked to human diseases.