• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2146-2148
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• 2002

In this paper, PID control method using genetic algorithm to control the nonlinear multivariable system is presented. Genetic algorithms are global search techniques for nonlinear optimization. For experiment, the x-y rod balancing system with driver circuit board is fabricated. Experiments such as angle and position control for system are performed. The validity and control performance of the GA-based PID controller are confirmed by experimental results.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.1
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• pp.1-17
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• 1997

Genetic algorithms have proved to be a versatile and effectvie approach for solving optimization problems. Nevertheless, there are many situations that the genetic algorithm does not perform particularly well, and so various methods of hybridization have been proposed. Thus, this paper develop a hybrid method and a precision search method around optimum in the gentic algorithm and the conventional optimization techniques in finding global or near optimum.

• Bulletin of the Korean Chemical Society
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• v.34 no.6
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• pp.1791-1794
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• 2013

Phytochemical investigation on dragon's blood of Dracaena cambodiana led to the discovery of two new flavonoid derivatives, cambodianin G (1) and cambodianin H (2). Their structures were elucidated on the basis of detailed spectroscopic analysis, including 1D and 2D NMR techniques and chemical methods. The two compounds were observed to exhibit antibacterial activities against Staphylococcus aureus, and compound 1 showed cytotoxicities against K562 and SGC-7901 cell lines.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.3 s.3
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• pp.73-82
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• 2001

This study examines the applicability of the Nash model and the Diskin model, which are linear and nonlinear runoff models, respectively, by applying optimization techniques to the parameter calibration of the two models. Nonlinear programming which is one of traditional optimization techniques and Genetic Algorithm which has been actively applied recently are used in this study. The Nash and Diskin models which use the calibrated parameter with a flood events are applied to a different flood event in Soyang Dam basin. The results obtained from the parameter calibration show slight discrepancy depending upon the flood events. It has been found in the comparion between the observed hydrograph and the hydrographs obtained from the parameter calibration that the Diskin model can better simulate the observed hydrograph than the Nash model can, especially, for the peak flow. This can be analyzed that the Diskin model which is a nonlinear runoff model is better off in simulating the nonlinear characteristic of the rainfall-runoff process.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.2
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• pp.144-156
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• 2016

Soil is a dynamic biological system, in which it is difficult to determine the composition of microbial communities. Knowledge of microbial diversity and function in soils are limited because of the taxonomic and methodological limitations associated with studying the organisms. In this review, approaches to measure microbial diversity in soil were discussed. Research on soil microbes can be categorized as structural diversity, functional diversity and genetic diversity studies, and these include cultivation based and cultivation independent methods. Cultivation independent technique to evaluate soil structural diversity include different techniques such as Phospholipid Fatty Acids (PLFA) and Fatty Acid Methyl Ester (FAME) analysis. Carbon source utilization pattern of soil microorganisms by Community Level Physiological Profiling (CLPP), catabolic responses by Substrate Induced Respiration technique (SIR) and soil microbial enzyme activities are discussed. Genetic diversity of soil microorganisms using molecular techniques such as 16S rDNA analysis Denaturing Gradient Gel Electrophoresis (DGGE) / Temperature Gradient Gel Electrophoresis (TGGE), Terminal Restriction Fragment Length Polymorphism (T-RFLP), Single Strand Conformation Polymorphism (SSCP), Restriction Fragment Length Polymorphism (RFLP) / Amplified Ribosomal DNA Restriction Analysis (ARDRA) and Ribosomal Intergenic Spacer Analysis (RISA) are also discussed. The chapter ends with a final conclusion on the advantages and disadvantages of different techniques and advances in molecular techniques to study the soil microbial diversity.

• Journal of Plant Biotechnology
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• v.37 no.4
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• pp.343-356
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• 2010

Plant micropropagation techniques include bud cultures using apical or axillary buds, organogenesis through callus culture or adventitious bud induction, and somatic embryogenesis. In Korea Forest Research Institute (KFRI), the first tissue culture trial in woody plant was initiated from the bud culture of hybrid poplars (Populus alba x P. glandulosa) in 1978. Since then several mass propagation techniques have developed from conifer and hardwood species, resulting in allowing practical application to Poplars, Birches and some oak species. In addition, useful micropropagation and genetic resources conservation techniques were established in some rare and endangered tree species including Abeliophyllum distichum. Among various in vitro propagation techniques, somatic embryogenesis is known to be the most efficient plant regeneration system. Since the first somatic embryo induction was reported in Tilia amurensis by KFRI in 1986, various protocols for direct or indirect somatic embryogenesis systems have developed in conifer and hardwood species including Larix leptolepis, Pinus rigida x P. taeda F1, Kalopanax septemlobus and Liliodendron tulipifera, etc. However, most of these technologies have been developed using juvenile tissues, i.e. immature zygotic embryos or mature embryos. Therefore it has been difficult to directly application to tree breeding program due to their unproven genetic background. Recently remarkable progresses and new approaches have been achieved in mature tree somatic embryogenesis. In this article we reviewed several micropropagation techniques, which have been mainly developed by KFRI and recent international progresses.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.5
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• pp.636-642
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• 2018

Objective: The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods: A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results: All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from $0.78{\pm}0.01$ to $0.82{\pm}0.03$, between the first and second parities, from $0.73{\pm}0.03$ to $0.8{\pm}0.04$ between the first and third parities, and from $0.82{\pm}0.02$ to $0.84{\pm}0.04$ between the second and third parities. Conclusion: These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.191-205
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• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.11
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• pp.989-996
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• 2016

This paper intends to develop the rotor track and balance (RTB) algorithm using the nonlinear RTB models and a real-coded hybrid genetic algorithm. The RTB response data computed using the trim solutions with variation of the adjustment parameters have been used to build nonlinear RTB models based on the quadratic interpolation functions. Nonlinear programming problems to minimize the track deviations and the airframe vibration responses have been formulated to find optimum settings of balance weights, trim-tab deflections, and pitch-link lengths of each blade. The results are efficiently resolved using the real-coded genetic algorithm hybridized with the particle swarm optimization techniques for convergence acceleration. The nonlinear RTB models and the optimized RTB parameters have been compared with those computed using the linear models to validate the proposed techniques. The results showed that the nonlinear models lead to more accurate models and reduced RTB responses than the linear counterpart.