• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.117-126
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• 2003

GA(genetic algorithm) has a powerful searching ability and is comparatively easy to use and to apply as well. By that reason, GA is in the spotlight these days as an optimization skill for mechanical systems.$^1$However, GA has a low efficiency caused by a huge amount of repetitive computation and an inefficiency that GA meanders near the optimum. It also can be shown a phenomenon such as genetic drifting which converges to a wrong solution.$^{8}$ These defects are the reasons why GA is not widdy applied to real world problems. However, the low efficiency problem and the meandering problem of GA can be overcomed by introducing parallel computation$^{7}$ and gray code$^4$, respectively. Standard GA(SGA)$^{9}$ works fine on small to medium scale problems. However, SGA done not work well for large-scale problems. Large-scale problems with more than 500-bit of sere's have never been tested and published in papers. In the result of using the SGA, the powerful searching ability of SGA doesn't have no effect on optimizing the problem that has 96 design valuables and 1536 bits of gene's length. So it converges to a solution which is not considered as a global optimum. Therefore, this study proposes ExpGA(experience GA) which is a new genetic algorithm made by applying a new probability parameter called by the experience value. Furthermore, this study finds the solution throughout the whole field searching, with applying ExpGA which is a optimization technique for the structure having genetic drifting by the standard GA and not making a optimization close to the best fitted value. In addition to them, this study also makes a research about the possibility of GA as a optimization technique of large-scale design variable problems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.1 s.71
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• pp.27-37
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• 2006

A Metropolis genetic algorithm (MGA) is developed and applied for the structural design optimization. In MGA, favorable features of Metropolis criterion of simulated annealing (SA) are incorporated in the reproduction operations of simple genetic algorithm (SGA). This way, the MGA maintains the wide varieties of individuals and preserves the potential genetic information of early generations. Consequently, the proposed MGA alleviates the disadvantages of premature convergence to a local optimum in SGA and time consuming computation for the precise global optimum in SA. Performances and applicability of MGA are compared with those of conventional algorithms such as Holland's SGA, Krishnakumar's micro GA, and Kirkpatrick's SA. Typical numerical examples are used to evaluate the computational performances, the favorable features and applicability of MGA. The effects of population sizes and maximum generations are also evaluated for the performance reliability and robustness of MGA. From the theoretical evaluation and numerical experience, it is concluded that the proposed MGA Is a reliable and efficient tool for structural design optimization.

• Clinical and Experimental Pediatrics
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• v.49 no.12
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• pp.1308-1314
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• 2006

Purpose : This study aimed to evaluate the usefulness of auditory evoked potential (AEP) in clarifying neuronal development in premature small for gestational age (SGA), and appropriate for gestational age (AGA) infants. Methods : A total of 183 premature infants who were born from August 2002 to July 2005, were examined with AEP. They were divided into three groups; AGA, symmetric-SGA and asymmetric-SGA group. Results : Statistically significant differences in the head circumference were observed in three groups. Among the risk factors, prevalence of hypoglycemia and hypoalbuminemia between AGA and asymmetric SGA infants were significantly different. V absolute peak latency (APL) in the right side of AGA infants was delayed were than that of asymmetric SGA infants. III-V interpeak latency (IPL) of asymmetric SGA infants was delayed more than that of symmetric SGA infants. Moreover, I-V IPL on both sides of symmetric SGA infants was shortened more than that of AGA infants. However, all the results of AEP were within the reference range, according to gestational age. Birth weight of, only asymmetric SGA, was related to the III APL on both sides and the III-V IPL on right side. Conclusion : This study shows that the values of APL and IPL of premature SGA infants are different than that of premature AGA infants. These data could be an indicator in evaluating the neurologic functions of small for gestational age infants.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.1
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• pp.64-73
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• 2004

This paper addresses a problem of machining determination of input quantity in a multi-stage flexible flow system with non-identical parallel machines considers a subcontracting, machining restraint, and machine yield. We develop a nonlinear programing with the objective of minimizing the sum of in-house processing cost and subcontracting cost. To solve this model, we introduce a single-processor parallel genetic algorithm(SPGA) to improve a weak point for the declined robustness of simple algorithm(SGA). The efficiency of the SPGA is examined in comparison with the SGA for the same problem. In of examination the SPGA is to provide the excellent solution than the solution of the SGA.

• Clinical and Experimental Pediatrics
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• v.52 no.2
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• pp.137-141
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• 2009

Depending on the definition used, between 3% and 10% of live neonates are small for gestational age (SGA). The definition of SGA requires the following: (1) accurate knowledge of gestational age; (2) accurate measurements at birth of weight, length, and head circumference; (3) a cutoff, which has been variably set at the 10th percentile, 3rd percentile, or at less than 2 standard deviation from the mean, and (4) race and ethnicity-specific growth curve. Consensus statements are needed on the management of growth hormone therapy in SGA children, as well as treatment and long-term health outcomes such as impaired cognitive function, increased risk of adult cardiovascular disease, and type 2 diabetes.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1013-1016
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• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we propose an extended schema theorem associated with a schema co-evolutionary algorithm(SCEA), which explains why the co-evolutionary algorithm works better than SGA. The experimental results show that the SCEA works well in optimization problems including deceptive functions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.77-80
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• 1998

The theoretical foundations of simple genetic algorithm(SGA) are the Schema Theorem and the Building Block Hypothesis. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and cooperate each other. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. Also the experimental results show a co-evolutionary algorithm works well in optimization problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.115-122
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• 2003

A Metropolis genetic algorithm(MGA) is developed and applied for the structural design optimization. In MGA favorable features of Metropolis algorithm in simulated annealing(SA) are incorporated in simple genetic algorithm(SGA), so that the MGA alleviates the disadvantage of finding imprecise solution in SGA and time-consuming computation in SA. Performances of MGA are compared with those of conventional algorithms such as Holland's SGA, Krishnakumar's micro genetic algorithm(μGA), and Kirkpatrick's SA. Typical numerical examples are used to evaluate the favorable features and applicability of MGA From the theoretical evaluation and numerical experience, it is concluded that the proposed MGA is a reliable and efficient tool for structural design optimization.

• KSBB Journal
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• v.23 no.6
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• pp.499-503
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• 2008

The bioethanol for use as a liquid fuel by fermentation of renewable biomass as an alternative to petroleum is important from the viewpoint of global environmental protection. Recently, many scientists have attempted to increase the productivity of bioethanol process by developing specific microorganism as well as optimizing the process conditions. In the present study, which is based on our previous investigation on the pretreatment process, theproductivity of bioethanol obtained from simultaneous saccharification and fermentation (SSF) process was compared between various domestic materials including barley, brown rice, corn and sweet potato. Additionally, Solid glucoamylase (SGA; developed in Changhae Co.), from modified strain with UV, was used. The result was compared to commercial glucoamylase (GA). It was observed that the fermentation rate was increased together with the yield which can be derived from the final ethanol concentration. Especially, in the case of brown rice, compared to the experimental results using GA, the final ethanol concentration was 1.25 times higher and 18.4 g/L of the yield was increased. Also, the time required for reaching 95% of the maximum ethanol concentration is significantly reduced, which is approximately 36 hours, compared to 88 hours using GA. It means that SGA has excellent saccharogenic power.

• Korean Journal of Community Nutrition
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• v.26 no.4
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• pp.280-295
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• 2021

Objectives: This study aimed to examine the characteristics of patients according to their nutritional status as assessed by five nutritional screening tools: Patient-Generated Subjective Global Assessment (PG-SGA), NUTRISCORE, Nutritional Risk Index (NRI), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT) and to compare the agreement, sensitivity, and specificity of these tools. Methods: A total of 952 gastric cancer patients who underwent gastrectomy and chemotherapy from January 2009 to December 2012 at the Samsung Medical Center were included. We categorized patients into malnourished and normal according to the five nutritional screening tools 1 month after surgery and compared their characteristics. We also calculated the Spearman partial correlation, Cohen's Kappa coefficient, the area under the curve (AUC), sensitivity, and specificity of each pair of screening tools. Results: We observed 86.24% malnutrition based on the PG-SGA and 85.82% based on the NUTRISCORE among gastric cancer patients in our study. When we applied NRI or CONUT, however, the malnutrition levels were less than 30%. Patients with malnutrition as assessed by the PG-SGA, NUTRISCORE, or NRI had lower intakes of energy and protein compared to normal patients. When NRI, PNI, or CONUT were used to identify malnutrition, lower levels of albumin, hemoglobin, total lymphocyte count, total cholesterol, and longer postoperative hospital stays were observed among patients with malnutrition compared to those without malnutrition. We found relatively high agreement between PG-SGA and NUTRISCORE; sensitivity was 90.86% and AUC was 0.78. When we compared NRI and PNI, sensitivity was 99.64% and AUC was 0.97. AUC ranged from 0.50 to 0.67 for comparisons between CONUT and each of the other nutritional screening tools. Conclusions: Our study suggests that PG-SGA and NRI have a relatively high agreement with the NUTRISCORE and PNI, respectively. Further cohort studies are needed to examine whether the nutritional status assessed by PG-SGA, NUTRISCORE, NRI, PNI, and CONUT predicts the gastric cancer prognosis.