• 대한토목학회논문집
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• 제32권3D호
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• pp.223-229
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• 2012

도로 포장면과 타이어 사이의 마찰 저항을 미끄럼 저항이라고 한다. 미끄럼 저항은 도로 안전에 매우 중요한 요소이며, 다양한 요인이 복합적으로 작용한다. 미끄럼 저항 측정법의 한계를 극복하기 위해 컴퓨터 모델링을 이용한 해석 수행 결과, 속도가 증가하거나 수막두께가 두꺼울수록 미끄럼 저항은 감소하였다. 해석 결과를 이용해 수막두께와 속도에 따라 수막 위를 주행하는 타이어에 발생하는 양력을 계산할 수 있으며, IFI(International Friction Index) 미끄럼 저항 예측모델과 실측 미끄럼 저항과의 차이를 줄이기 위해 양력을 반영한 수정 IFI 미끄럼 저항 예측모델을 개발하였다. 예측모델과 실측 데이터의 상관관계 분석 결과, 기존 IFI 예측모델의 $R^2$는 0.49로, 수정 IFI 예측모델의 $R^2$는 0.64로 나타나 수정 IFI 예측모델이 기존모델에 비해 예측 효과가 우수하였다. 포장면의 상태에 따른 수막두께를 수정 예측모델에 반영할 경우 더욱 정확한 예측모델을 얻을 수 있을 것이다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 추계학술대회논문집
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• pp.709-714
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• 2008

The various computer programs are used in computer simulation of the traffic noise prediction. But the difference or problem of calculation method used for road traffic noise prediction is not exactly investigated. In this paper, Road traffic noise is predicted on the specific regions by using four prediction methods such as XPS31-133 model(France), RLS-90 model(Germany), ASJ RTN model(Japan) and FHWA model(U.S.A.), which are operated by a program named SoundPLAN, a program to predict road traffic noise. Those prediction values are compared with a measurement value. The results show that four prediction values for taraffic noise are a little different, because of various input factors according to the prediction methods.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2009년도 제7회 압연 심포지엄
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• pp.97-102
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• 2009

In hot strip rolling, sound prediction of the temperature of the strip is vital for achieving the desired finishing mill draft temperature (FDT). In this paper, a precision on-line model for the prediction of temperature distributions along the thickness of the strip in the finishing mill is presented. The model consists of an analytic model for the prediction of temperature distributions in the inter-stand zone, and a semi-analytic model for the prediction of temperature distributions in the bite zone in which thermal boundary conditions as well as heat generation due to deformation are predicted by finite element-based, approximate models. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model.

• 한국항공운항학회지
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• 제27권4호
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• pp.27-36
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• 2019

An accurate trajectory prediction is a key to the safe and efficient operations of aircraft. One way to improve trajectory prediction accuracy is to develop a model for aircraft ground speed prediction. This paper proposes a generative model for posterior aircraft ground speed prediction. The proposed method fits the Gaussian Mixture Model(GMM) to historical data of aircraft speed, and then the model is used to generates probabilistic speed profile of the aircraft. The performances of the proposed method are demonstrated with real traffic data in Incheon Flight Information Region(FIR).

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• 전기학회논문지
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• 제65권5호
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• pp.851-856
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• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1343-1346
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• 2005

In the cold rolling mill, coil breakage that generated in rolling process makes the various types of troubles such as the degradation of productivity and the damage of equipment. Recent researches were done by the mechanical analysis such as the analysis of roll chattering or strip inclining and the prevention of breakage that detects the crack of coil. But they could cover some kind of breakages. The prediction of Coil breakage was very complicated and occurred rarely. We propose to build effective prediction modes for coil breakage in rolling process, based on data mining model. We proposed three prediction models for coil breakage: (1) decision tree based model, (2) regression based model and (3) neural network based model. To reduce model parameters, we selected important variables related to the occurrence of coil breakage from the attributes of coil setup by using the methods such as decision tree, variable selection and the choice of domain experts. We developed these prediction models and chose the best model among them using SEMMA process that proposed in SAS E-miner environment. We estimated model accuracy by scoring the prediction model with the posterior probability. We also have developed a software tool to analyze the data and generate the proposed prediction models either automatically and in a user-driven manner. It also has an effective visualization feature that is based on PCA (Principle Component Analysis).

• 한국건축시공학회지
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• 제2권4호
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• pp.177-182
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• 2002

The purpose of this study is to develope the strength prediction model by Maturity Method. A maturity function is a mathematical expression to account for the combined effects of time and temperature on the strength development of a cementious mixture. The method of equivalent ages is to use Arrhenius equation which indicates the influence of curing temperature on the initial hydration ratio of cement. For the experimental factors of this study, we selected the concrete mixing of W/C ratio 45, 50, 55 and 60% and curing temperature 5, 10, 20 and $30^{\circ}C$. And we compare and evaluate with logistic model that is existing strength prediction model, because we have to verify adaption possibility of new strength prediction model which is proposed by maturity method. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor.

• 대한토목학회논문집
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• 제35권2호
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• pp.465-470
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• 2015

에폭시 아스팔트 혼합물은 에폭시 수지와 경화제의 화학반응이 진행되어 양생시간을 거쳐 성능 발현이 이루어진다. 에폭시 아스팔트의 양생수준은 후속공정의 진행과 교통개방 및 공정계획의 수립에 절대적인 영향을 미치므로 정확한 예측모델의 개발이 중요하다. 본 연구에서는 기존 예측식에 사용되는 인자들의 화학적 의미 분석을 통하여 에폭시 수지의 농도와 경화특성을 반영하여 기존식보다 확대된 적용 범위를 갖는 양생수준 예측식을 제시하였다. 실외양생 실험과 비교 결과 상관계수가 0.971 이상으로 나타나 조성이 다른 에폭시 아스팔트 혼합물의 온도와 시간에 따른 양생수준을 예측할 수 있는 것으로 나타났다.

• 대한토목학회논문집
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• 제32권6D호
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• pp.599-605
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• 2012

교통개방시점의 예측은 공사 계획을 위해 중요하며 이를 위해 에폭시 아스팔트 혼합물의 양생에 따른 강도를 예측하는 것이 필요하다. 본 연구에서는 에폭시 아스팔트 혼합물의 양생온도와 시간에 따른 마샬안정도를 측정하고 이를 이용해 강도 발현식을 구하였으며, 변화하는 온도와 강도에 따른 반응속도를 반영할 수 있도록 화학적 반응속도론을 이용하여 에폭시 아스팔트 강도 예측모델을 개발하였다. 예측모델을 사용하여 에폭시 아스팔트 포장이 적용된 국내 교량에 대해 교통개방시기를 예측하였다. 2009년~2011년의 기상조건에 따라 가정된 포장체 온도를 사용한 예측결과는 실제 교통개방일과 17일의 차이가 발생했으나 이는 2012년의 실제 기상상태와의 차이 때문이다. 실제 측정된 포장 온도를 예측모델에 대입할 경우 2일의 교통개방가능일 차이가 있었으며, 상관관계 분석 결과 R2가 0.95로 실제 강도값과 매우 유사한 결과를 얻을 수 있었다. 기상상태와 포장체의 온도에 대한 충분한 데이터를 확보한다면 에폭시 아스팔트 강도 예측모델을 사용하여 상당히 신뢰도 있는 교통개방 가능 시기의 예측이 가능한 것으로 나타났다.