• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.617-631
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• 2019

In the four-step demand model, a gravity mode is used most commonly at the trip distribution stage. The purpose of this study was to develop a new friction factor that can express the accessibility property as a single friction factor to compensate for the variable limits of the gravity model parameters (travel time, travel cost). To derive a new friction factor, a new friction factor was derived using the space syntax that can quantify the characteristics of the urban space structure, deriving the link-unit integration degree and then using the travel time and travel distance relationship. Calibration of the derived friction factor resulted in a similar level to that of the existing friction factor. As a result of verifying the various indicators, the explanatory power was found to be excellent in the short - and long - distance range. Therefore, it is possible to derive and apply the new friction factor using the integration index, which can complement the accessibility beyond the limit of the existing shortest distance, and it is believed to be more advantageous in future utilization.

• 대한두경부종양학회:학술대회논문집
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• 2009.05a
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• pp.73-73
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• 2009

• Journal of The Korean Radiological Technologist Association
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• v.28 no.1
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• pp.323-323
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• 2002

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.272-275
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• 2004

플라즈마 식각공정은 소자제조를 위한 미세 패턴닝 제작에 이용되고 있다. 공정 메커니즘의 정성적 해석, 최적화, 그리고 제어를 위해서는 컴퓨터 예측모델의 개발이 요구된다. 역전파 신경망 (backpropagation neural network-BPNN) 모델을 개발하는 데에는 다수의 학습인자가 관여하고 있으며, 가장 그 최적화가 어려운 학습인자는 초기웨이트이다. 모델개발시, 초기웨이트는 random 값으로 설정이 되며, 이로 인해 초기웨이트의 최적화가 어렵다. 본 연구에서는 유전자 알고리즘 (genetic algorithm-GA)을 이용하여 BPNN의 초기웨이트를 최적화하였으며, 이를 식각공정 모델링에 적용하여 평가하였다. 실리카 식각공정 데이터는 $2^3$ 인자 실험계획법을 이용하여 수집하였으며, GA에 관여하는 두 확률인자의 영향을 42 인자 실험계획법을 이용하여 최적화 하였다. 종래의 모델에 비해, 최적화된 모델은 실리카 식각률, Al 식각률, Al 선택비, 그리고 프로파일 응답에 대해서 각 기 24%, 13%,, 16%, 그리고 17%의 향상률을 보였다. 이는 제안된 최적화 기법이 플라즈마 모델의 예측성능을 증진하는데 효과적으로 응용될 수 있음을 의미한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.135-136
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• 2009

• The Science & Technology
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• no.5 s.456
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• pp.88-93
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• 2007

• 월간산업보건
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• s.351
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• pp.22-27
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• 2017

• Fire Science and Engineering
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• v.28 no.3
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• pp.80-86
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• 2014

Based on 2009 Korea Health Panel, this study investigated socio-economic and clinical characteristics associated with emergency medical transport use, and analyzed a simple predictive model of emergency medical transport use. Analysis results were summarized as follows: First, emergency medical transports such as 119 ambulance were more used than private cars, taxis, or walk-in. Second, between a user group and a non-user group of emergency medical transports, there were statistically significant differences in age, the level of education, family composition, house type, household income, the relationship with the head of household, insurance types, the presence of handicap, the presence of chronic disease, reasons to emergency medical service use, and treatment after emergency medical service completed. Third, age, household income, the presence of handicap, reasons to emergency medical service use, and treatment after emergency medical service completed were statistically significant predictors associated with emergency medical transports use. To improve emergency medical service system, the characteristics and predictors associated with emergency medical transports are more concerned.

• Korean journal of food and cookery science
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• v.3 no.2
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• pp.68-74
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• 1987

Objective and subjective methods were performed together for TPA analysis of acorn mook, and their correlations were analyzed. As the result of sensory evaluation, hardness and fracturability were most important factors for prediction of preference. Meanwhile, compression test with Instron Universal Testing Machine revealed that P1(maximum peak in first bite) was very effective factor representing the cheracteristics of first bite, and that P2 the latter peak in first bite) was valuable for prediction of characteristics of second bite.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.149-156
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• 2020

The genesis of ore bodies is a very diverse and complex process, and the target depth of mineral exploration increases. These create a need for predictive mineral exploration, which may be facilitated by the advancement of machine learning and geological database. In this study, we confirm that the faults and igneous rocks distributions and magnetic data can be used as input data for potential mapping using deep neural networks. When the input data are constructed with faults, igneous rocks, and magnetic data, we can build a potential mapping model of the metal deposit that has a predictive accuracy greater than 0.9. If detailed geological and geophysical data are obtained, this approach can be applied to the potential mapping on a mine scale. In addition, we confirm that the magnetic data, which provide the distribution of the underground igneous rock, can supplement the limited information from the surface igneous rock distribution. Therefore, rather than simply integrating various data sets, it will be more important to integrate information considering the geological correlation to genesis of minerals.