• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.65-75
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• 2012

In setting an experimental design for the prediction outside the experimental region (extrapolation design), it is natural for the experimenter to be very careful about the validity of the model for the design because the experimenter is not certain whether the model can be extended beyond the design region or not. In this paper, a hybrid constrained type approach was adopted in dealing model uncertainty as well as the prediction error using the three basic principles available in literature, maxi-min, constrained, and compound design. Furthermore, the effect of the distance of the extrapolation design point from the design region is investigated. A search algorithm was used because the classical exchange algorithm was found to be complex due to the characteristic of the problem.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.818-821
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• 2004

With a rapid development in the area of micro and ultra precision technology, the micro surface machining of small size parts are explosively increased. Especially, to improve efficiency of various beams in lens and reflector, non-rotational symmetric form and several mm level heights changeable surface can be machined at a time. These geometric complex 3D surface cannot be machined by general short stroke FTS. The long stroke FTS if firmly needed to move directly several mm and have nm level positioning accuracy for the complex surface form. The long stroke FTS used linear motors to drive moving unit long and fine, aero static bearings to decrease friction and moving errors in guide way, optical linear scale with nm level resolution to measure position of FTS. Furthermore, to increase the performance of acceleration of FTS, the light material, such as AL is used for the structure and the high stiffness box type structure is selected. In this paper, the genetic algorithm approach is described to determine a set of design parameters for auto tuning. The authors have attempted to model the design problem with the objective of minimizing the error, such as variable pattern change. This method can give the better alternative than existing other method.

• Proceedings of the Botanical Society of Korea Conference
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• 1995.06a
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• pp.40-56
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• 1995

During the past two decades, China has seen her great progress in plant biotechnology. Since the Chinese market of herb medicine is huge, while the plant resources are shrinking, particular emphasis has been placed in plant tissue and cell cultures of medicinal plants, this includes fast propagation, protoplast isolation and regeneration, cell suspension cultures and large scale fermentation. To optimize culture conditions for producing secondary compounds in vitro, various media, additives and elicitors have been tested. Successful examples of large scale culture for the secondary metabolite biosynthesis are quite limited : Lithospermum ery throrhizon and Arnebia euchroma for shikonin derivatives, Panax ginseng, P. notoginseng, P. quinquefolium for saponins, and a few other medicinal plants. Recent development of genetic transformation systems of plant cells offered a new approach to in vitro production of secondary compounds. Hairy root induction and cultures, by using Ri-plasmid, have been reported from a number of medicinal plant species, such as Artemisia annua that produces little artemisinin in normal cultured cells, and from Glycyrrhiza uralensis. In the coming five years, Chinese scientists will continue their work on large scale cell cultures of a few of selected plant species, including Taxus spp. and A. annua, for the production of secondary metabolites with medicinal interests, one or two groups of scientists will be engaged in molecular cloning of the key enzymes in plant secondary metabolism.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.4
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• pp.303-315
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• 2005

In this paper we address a design problem for hub-and-spoke networks and then consider a capacitated hub locations problem with direct shipment (CHLPwD). We determine the location of hubs, the allocation of nodes to hubs, and direct shipment paths in the network, with the objective of minimizing the total cost in the network. In this paper, CHLPwD is formulated as 0-1 integer programming. We develop an evolutionary algorithm here to solve the large sized CHLPwD. To do this, we present the representation and the genetic operators suitable for the problem and propose a heuristic method for the allocation of nodes to hubs. To enhance the search capability, problem-specific information is used in our evolutionary algorithm. The proposed algorithm is compared with the heuristic method in terms of solution quality and computation time. The experimental results show that our algorithm can provide better solutions than the heuristic.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.131-142
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• 2000

Facility layout is the early stage of system design that requires a mid-term or long-term plan. Since improper facility layout might incur substantial logistics cost including material handling and re-installment costs, due consideration must be given to decisions on facility layout. Facility layout is concerned with low to arrange equipment necessary for production in a given space. Its objective is to minimize the sum of all the products of each equipment's amount of flow multiplied by distance. Facility layout also is related to the issue of NP-complete, i.e., calculated amounts exponentially increase with the increase of the number of equipment. This study discusses Hybrid GA developed, as an algorithm for facility layout, to solve the above-mentioned problems. The algorithm, which is designed to efficiently place equipment, automatically produces a horizontal passageway by the block, if a designer provides the width and length of the space to be handled. In addition, this study demonstrates the validity of the Algorithm by comparing with existing algorithms that have been developed. We present a Hybrid GA approach to the facility layout problem that improves on existing work in terms of solution quality and method. Experimental results show that the proposed algorithm is able to produce better solution quality and more practical layouts than the ones obtained by applying existing algorithms.

• Korean Journal of Biological Psychiatry
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• v.10 no.1
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• pp.3-19
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• 2003

The use of atypical antipsychotics is limited by occurrence of adverse reactions such as weight gain, despite of their benefits. This article provides a comprehensive review and discussion of the most significant findings regarding obesity-related pathways and integrates these with the known mechanism of atypical antipsychotic action. The focus of this article is primarily on the genetics of obesity related pathways that may be disrupted by atypical antipsychotics. This review also discussed weight gain, hyperglycemia or occurrence of diabetes while being treated with atypical antipsychotics from the point of view of pharmacogenetics. Pharmacogenetic research seeks to uncover genetic factors that will help clinicians identify the best treatment strategies for their patients. It will aid clinically in the prediction of response and side effects, such as antipsychotic-induced weight gain, and minimize the current "trial and error" approach to prescribing in the near future. This article also presents the genetics of both central and peripheral pathways putatively involved in antipsychotic-induced weight gain while providing a comprehensive review of the obesity literature. This article also review obesity related candidate molecules which may be disrupted during atypical antipsychotic drug treatment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.422-427
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• 2002

This paper focuses on the formulation and validation of an automatic strategy for the selection of the locations and directions of strain gauges to capture at best the modal response of a blade in a series of modes. These locations and directions are selected to render the strain measurements as robust as possible with respect to random mispositioning of the gauges and gauge failures. The approach relies on the evaluation of the signal-to-noise ratios of the gauge measurements from finite element strain data and includes the effects of gauge size. A genetic algorithm is used to find the strain gauge locations-directions that lead to the largest possible value of the smallest modal strain signal-to-noise ratio, in the absence of gauge failure, or of its expected value when gauge failure is possible. A fan blade is used to exemplify the applicability of the proposed methodology and to demonstrate the effects of the essential parameters of the problem, i.e. the mispositioning level, the probability of gauge failure, and the number of gauges.

• Toxicological Research
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• v.25 no.2
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• pp.85-92
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• 2009

4,4'-Methylenedianiline (MDA) is an aromatic amine that is widely used in the industrial synthetic process. Genotoxic MDA forms DNA adducts in the liver and is known to induce liver damage in human and rats. To elucidate the molecular mechanisms associated with MDA-induced hepatotoxicity, we have identified genes differentially expressed by microarray approach. BALB/c male mice were treated once daily with MDA (20 mg/kg) up to 7 days via intraperitoneal injection (i.p.) and hepatic damages were revealed by histopathological observation and elevation of serum marker enzymes such as AST, ALT, ALP, cholesterol, DBIL, and TBIL. Microarray analysis showed that 952 genes were differentially expressed in the liver of MDA-treated mice and their biological functions and canonical pathways were further analyzed using Ingenuity Pathways Analysis (IPA). Toxicological functional analysis showed that genes related to hepatotoxicity such hyperplasia/hyperproliferation (Timp1), necrosis/cell death (Cd14, Mt1f, Timp1, and Pmaip1), hemorrhaging (Mt1f), cholestasis (Akr1c3, Hpx, and Slc10a2), and inflammation (Cd14 and Hpx) were differentially expressed in MDA-treated group. This gene expression profiling should be useful for elucidating the genetic events associated with aromatic amine-induced hepatotoxicity and for discovering the potential biomarkers for hepatotoxicity.

• The Plant Pathology Journal
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• v.33 no.6
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• pp.529-542
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• 2017

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.