• Journal of agriculture & life science
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• v.46 no.6
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• pp.43-49
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• 2012

This study was conducted to develop a stem volume table for the Robinia pseudoacacia using stem taper equations. Specifically, Kozak's model was used in the estimation of each model parameter. The fitness of the estimated model was statistically verified and results of the residual analysis were found significant. Therefore, this model is considered applicable in the preparation of stem volume table for R. pseudoacacia. Furthermore, volume with bark and without bark table were developed based on the bark thickness estimation equation. The bark thickness estimation equation was also statistically significant, The stem volume table developed for R. pseudoacacia, which was first in Korea, is vital in managing these forests.

• Journal of Technologic Dentistry
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• v.41 no.2
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• pp.53-61
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• 2019

Purpose: The purpose of this study is to evaluate the discrepancy of scan process in dental intra oral scanner by comparing model scanner and anticipate possibility to introduce intra oral scan technique. Methods: 3D superimposition test was conducted to compare the scan discrepancy. The scanners used in this study are the e-oral scanner, the D750 model scanner, and the high precision CMM(3D Coordinate Measuring Machine). The standard of accuracy verification is ISO 5725-1; trueness and precision. Master model was manufactured by dental stone and scanned 5 times by intra oral, model scanner. Reference data was scanned 5 times by high accuracy CMM to evaluate the trueness. Results: Trueness of D750 scanner were $7.4{\mu}m$ $5.1{\mu}m$ $6.8{\mu}m$ at an abutment, an occluasal, a specific area. and trueness of e-scanner were $20.2{\mu}m$ $27.4{\mu}m$ $37.8{\mu}m$ at an abutment, an occluasal, a specific area. Precision of D750 scanner was $7.04{\mu}m$, e-scanner was $15.95{\mu}m$. Conclusion: When conducting in vitro test, The mean difference of trueness between e-scanner and D750 were $12.8{\mu}m$ at an abutment area, $22.3{\mu}m$ at an occlusal area, $31.0{\mu}m$ at a specific area and $8.91{\mu}m$ in precision. The scan discrepancies are within the range of clinical acceptance.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.13-21
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• 2023

This paper proposes a method of automatically generating a model through a machine learning technique by setting a hypothetical model in the form of a gray box or black box with unknown parameters, when the big data of the actual system is given. We implements the proposed framework and conducts experiments to find an appropriate model among various hypothesis models and compares the cost and fitness of them. As a result we find that the proposed framework works well with continuous systems that could be modeled with ordinary differential equation. This technique is expected to be used well for the purpose of automatically updating the consistency of the digital twin model or predicting the output for new inputs using recently generated big data.

• Journal of Microbiology and Biotechnology
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• v.33 no.9
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• pp.1238-1249
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• 2023

In this study, we sought to investigate the production and optimization of biosurfactants by soil fungi isolated from petroleum oil-contaminated soil in Saudi Arabia. Forty-four fungal isolates were isolated from ten petroleum oil-contaminated soil samples. All isolates were identified using the internal transcribed spacer (ITS) region, and biosurfactant screening showed that thirty-nine of the isolates were positive. Aspergillus niger SA1 was the highest biosurfactant producer, demonstrating surface tension, drop collapsing, oil displacement, and an emulsification index (E₂₄) of 35.8 mN/m, 0.55 cm, 6.7 cm, and 70%, respectively. This isolate was therefore selected for biosurfactant optimization using the Fit Group model. The biosurfactant yield was increased 1.22 times higher than in the nonoptimized medium (8.02 g/l) under conditions of pH 6, temperature 35℃, waste frying oil (5.5 g), agitation rate of 200 rpm, and an incubation period of 7 days. Model significance and fitness analysis had an RMSE score of 0.852 and a p-value of 0.0016. The biosurfactant activities were surface tension (35.8 mN/m), drop collapsing (0.7 cm), oil displacement (4.5 cm), and E₂₄ (65.0%). The time course of biosurfactant production was a growth-associated phase. The main outputs of the mathematical model for biomass yield were Yx/s (1.18), and µ_max (0.0306) for biosurfactant yield was Y_p/s (1.87) and Y_p/x (2.51); for waste frying oil consumption the So was 55 g/l, and Ke was 2.56. To verify the model's accuracy, percentage errors between biomass and biosurfactant yields were determined by experimental work and calculated using model equations. The average error of biomass yield was 2.68%, and the average error percentage of biosurfactant yield was 3.39%.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.155-160
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• 2003

Reliability assessment of power systems has been an important topic for the past several decades. It is becoming even more important nowadays as the power market moves toward a new competitive environment. This paper deals with two topics on reliability assessment. The first is how to select probability distributions and determine their parameters to model the probabilistic events in a power system. The second is how to consider restorative actions in the assessment, which directly influence reliability indices. This paper proposes simple but convincing alternative solutions on the two topics. In the case study, this paper shows the influences of the probability distributions that are used in power system modeling.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.52-65
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• 1997

For the degradation of severe noise and ill-conditioned blur the optimization function has the solution spaces which have many local optima around global solution. General restoration methods such as inverse filtering or gradient methods are mainly dependent on the properties of degradation model and tend to be isolated into a local optima because their convergences are determined in the convex space. Hence we introduce genetic algorithm as a searching method which will search solutions beyond the convex spaces including local solutins. In this paper we introudce improved evaluation square error) and fitness value for gray scaled images. Finally we also proposed the local fine tunign of window size and visit number for delicate searching mechanism in the vicinity of th global solution. Through the experiental results we verified the effectiveness of the proposed genetic operators and evaluation function on noise reduction over the conventional ones, as well as the improved performance of local fine tuning.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.67-80
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• 2000

Symbiotic evolutionary algorithms, also called cooperative coevolutionary algorithms, are stochastic search algorithms that imitate the biological coevolution process through symbiotic interactions. In the algorithms, the fitness evaluation of an individual required first selecting symbiotic partners of the individual. Several partner selection strategies are provided. The goal of this study is to analyze how much partnering strategies can influence the performance of the algorithms. With two types of test-bed problems: the NKC model and the binary string covering problem, extensive experiments are carried out to compare the performance of partnering strategies, using the analysis of variance. The experimental results indicate that there does not exist statistically significant difference in their performance.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.71-74
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• 1998

HMM (hidden Markov model)을 이용한 음성인식은 현재 가장 널리 쓰여지고 있는 방법으로, 이 중 CDHMM (continuous observation density HMM)은 상태에서 관측심볼확률을 연속확률밀도를 사용하여 표현한다. 본 논문에서는 가우스 혼합밀도함수를 사용하는 CDHMM의 상태천이확률과, 관측심볼확률을 표현하기 위한 인자인 평균벡터, 공분산 행렬, 가지하중값을 유전자 알고리듬을 사용하여 최적화하는 방법을 제안하였다. 유전자 알고리듬은 매개변수 최적화문제에 대하여 자연의 진화원리를 모방한 알고리듬으로, 염색체 형태로 표현된 개체군 (population) 중에서 환경에 대한 적합도 (fitness)가 높은 개체가 높은 확률로 살아남아 재생 (reproduction)하게 되며, 교배 (crossover)와 돌연변이 (mutation) 연산 후에 다음 세대 개체군을 형성하게 되고, 이러한 과정을 반복하면서 최적의 개체를 구하게 된다. 본 논문에서는 상태천이확률, 평균벡터, 공분산행렬, 가지하중값을 부동소수점수 (floating point number)의 유전자형으로 표현하여 유전자 알고리듬을 수행하였다. 유전자 알고리듬은 복잡한 탐색공간에서 최적의 해를 찾는데 효과적으로 적용되었다.

• Journal of Ocean Engineering and Technology
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• v.23 no.6
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• pp.131-135
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• 2009

We present an optimum design method for a rectangular reinforced concrete beam using Genetic Algorithms. The optimum design procedure in this paper employs 2 design cases: i) all of the design variables (b, d, As) of the rectangular reinforced concrete section are used pseudo-continuously, ii) one is pseudo-continuous for the concrete cross section (b, d) and the other is discrete, using an index for the steel area (As). The optimum design in this paper uses Chakrabarty's model. In this paper, the Genetic Algorithms use the method of Elitism and penalty parameters to improve the fitness in the reproduction process, which leads to very practical designs. The optimum design of the steel area in the examples uses ASTM standard reinforcing bars (#3~#11, #14, #18).

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.93_94
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• 2009

Load modeling has a significant impact on power system analysis and control. Estimating model parameters can be considered as important as stability analysis itself for accurate analysis and control. This paper presents a method for estimating parameters for load models, which include static and dynamic parts, based on particle swarm optimization. The method effectively searches a suitable set of parameters minimizing the fitness function. This paper applies the method to simulation data obtained from 8-bus test system including induction motors.