• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2749-2751
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• 2002

일반화된 회귀 신경망 (GRNN)을 이용하여 플라즈마을 분류하는 새로운 알고리즘을 보고한다. 데이터분포를 통계적인 평균치와 표준편차를 이용하여 특징지었으며, 바이어스 인자을 이용하여 9 종류의 데이터을 발생하였다. 각 데이터에 대하여 GRNN의 학습인자를 최적화하였으며, 모델성능은 예측과 분류 정확도로 나누어 바이어스와 학습인자의 함수로 분석하였다. 바이어스는 모델성능에 상당한 영향을 주었으며, 학습인자와의 상호작용을 통하여 완전 분류를 이루었다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.315-324
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• 2022

The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.183-191
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• 2018

In this study a traffic volume dependent displacement estimation model for Gwangan Bridge was developed using bridge monitoring big data. Traffic volume data for four different vehicle types and the vertical displacement data in the central position of the Gwangan Bridge were used to develop and validate the estimation model. Two statistical estimation models were developed using multiple regression analysis (MRA) and principal component analysis (PCA). Estimation performance of those two models were compared with actual values. The results show that both the MRA and the PCA based models are successfully estimating the vertical displacement of Gwangan Bridge. Based on the results, it is concluded that the developed model can effectively be used to predict the traffic volume dependent displacement behavior of Gwangan Bridge.

• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.39-48
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• 2011

To predict the shelf-life of ammunition stockpiled in intermediate have practical meaning as a core value of combat support. This research is to Predict the shelf-life of ammunition by applying time series analysis based on report from ASRP of the 155mm, KD541 performed for 6 years. This study applied time series analysis using 'Mini-tab program' to measure the amount of stabilizer as time passes by is different from the other one that uses regression analysis. The average shelf-life of KD541 drawn by time series analysis was 43 years and the lowest shelf-life assessed on the 95% confidence level was 35 years.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.227-234
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• 2000

지금까지 수행된 대부분의 유가예측은 주고 계량 데이터를 기반으로 하는 여러 가지 계량 모델을 구성하여 수행되었으며, 그 결과 산유국 동향과 같은 국제 유가시장의 불확실성을 제대로 반영하지 못했다. 따라서, 본 논문에서는 이러한 문제점을 해결하기 위하여 계량경제학적인 접근방법과 전문가시스템을 통합한 유가예측 시스템을 설계 및 구현하였다. 즉, 계량 데이터를 기초로 유가예측 모델을 구성하고, 산유국동향과 같은 비계량적인 요인이 유가에 미치는 영향에 대한 실무자의 경험적인 지식은 지식베이스로 구축함으로써, 유가예측과 관련된 다양한 요인들을 폭넓게 고려할 수 있는 통합된 시스템을 개발하였다. 유가예측 모델로는 대표 유종의 유가 및 수급 전망을 위한 동적 선형연립 모델과 유종간 유가의 균형차액을 예측하기 위한 Fully Modified 공적분 회귀분석 모델을 구성하였으며, 유가예측 모델에서 반영하기 어려운 산유국 동향이나 OPEC정책, 선물시장 동향 등은 실무자의 경험적인 지식을 바탕으로 시스템 예측변수로 설정하여 유가예측에 반영할 수 있도록 지식베이스를 구축하였다. 또한, 본 시스템에서는 유가예측 이외에 석유 수급을 전망하고, 유가 및 수급과 관련된 다양한 정보를 제공하고 관리하는 기능을 제공하고 있다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.119-124
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• 2023

자원이 풍부한 언어를 사용하여 훈련된 모델을 만들고 해당 모델을 사용해 자원이 부족한 언어에 대해 전이 학습하는 방법인 교차언어 전송(Cross-Lingual Transfer)은 다국어 모델을 사용하여 특정한 언어에 맞는 모델을 만들 때 사용되는 일반적이고 효율적인 방법이다. 교차언어 전송의 성능은 서비스하는 언어와 전송 모델을 만들기 위한 훈련 데이터 언어에 따라 성능이 매우 다르므로 어떤 언어를 사용하여 학습할지 결정하는 단계는 효율적인 언어 서비스를 위해 매우 중요하다. 본 연구에서는 교차언어 전송을 위한 원천언어를 찾을 수 있는 특성이 무엇인지 회귀분석을 통해 탐구한다. 또한 교차언어전송에 용이한 원천 학습 언어를 찾는 기존의 방법론들 간의 비교를 통해 더 나은 방법을 도출해내고 한국어의 경우에 일반적으로 더 나은 원천 학습 언어를 찾을 수 있는 방법론을 도출한다.

• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.173-173
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• 2001

목적： 1.5T 생체용 자기공명영상기기를 이용한 수소자기공명분광법으로 용액 내 물질의 정량분석에 대한 가능성을 알아보고자 하였다. 대상 및 방법： 0.01％에서 50％까지의 여러 농도를 갖는 포도당+증류수 혼합액의 모델용액을 만들어 생체용 자기공명영상기기와 시험관 nuclear magnetic resonance (NMR) 분광기에서 각각 수소 자기공명분광법을 시행하여 스펙트럼을 얻었다. 또한 12명의 당뇨환자에서 방광내의 소변에 대해 생체용 자기공명영상기기에서 스펙트럼을 얻고 소변을 추출하여 시험관 NMR 분광기에서 수소자기공명분광법을 시행하였다 각각의 방법으로 얻은 스펙트럼 상에서 포도당 농도에 따른 포도당/물 피크의 면적 비의 변화를 구하였고, 통계처리는 상관분석과 단순선형회귀분석을 시행하였고 회귀식을 산출하였다. 또한 생체용 자기공명영상기기를 이용하여 얻은 결과가 객관적인지 알아보기 위해 시험관 NMR 분광기에서 얻은 결과와의 상관관계를 분석하였다.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.649-661
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• 2020

Cut-slope management system (CSMS) has been investigated all slopes on the road of the whole country to evaluate risk rating of each slope. Based on this evaluation, the decision-making for maintenance can be conducted, and this procedure will be helpful to establish a consistent and efficient policy of safe road. CSMS has updated the database of all slopes annually, and this database is constructed based on a basic and detailed investigation. In the database, there are two type of data: first one is an objective data such as slopes' location, height, width, length, and information about underground and bedrock, etc; second one is subjective data, which is decided by experts based on those objective data, e.g., degree of emergency and risk, maintenance solution, etc. The purpose of this study is identifying an data application plan to utilize those CSMS data. For this purpose, logistic regression, which is a basic machine-learning method to construct a prediction model, is performed to predict a judging-type variable (i.e., subjective data) based on objective data. The constructed logistic model shows the accurate prediction, and this model can be used to judge a priority of slopes for detailed investigation. Also, it is anticipated that the prediction model can filter unusual data by comparing with a prediction value.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.167-175
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• 2018

By conducting multiple regression analysis, we analyzed the major independent factors affecting user convenience and emotional factors, and identified the important functional elements in the design of the VR device, so that the functional elements to be developed can be grasped in advance. As a result of the study, satisfaction of handling of VR bicycle and satisfaction of speed control by paddling were considered as the most important technical factors as independent factors which have the greatest influence on user convenience and emotional factor among technical satisfaction. Also, it is possible to increase the probabilities of successful design by setting a model that predicts user convenience and the emotional part from the technical factors.