• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 춘계 종합학술대회 논문집
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• pp.545-548
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• 2006

GPS 측량은 Multi-path 오차가 발생하기 때문에 도심 지역이나 숲이 있는 항공사진에서는 지상기준점을 잡기위해 GPS측량이 어렵다. 본 논문은 소규모 지역에서 3차원 정사사진 구현을 위한 GPS와 EDM의 적용에 관한 연구이다. 삼각점에서 연구대상지역에 대한 기준점을 내린 다음 EDM으로 정밀삼각측량으로 지상기준점을 선정하였다. 그리고 항공사진을 정사투영사진으로 변환하기위해 획득된 지상기준점을 적용하였다. 그리고 연구대상지역에 대하여 항공정사사진을 이용한 3차원 모델링을 구현하여 향후 추진될 3차원 GIS 구축을 위한 항공사진의 활용방안에 대해서 연구 하였다. Multi-path 오차로 기인하는 GPS수신이 어려운 지점을 EDM을 이용하여 영상에서 균등히 분포된 지상기준점의 획득이 가능하였고 3차원 공간정보 자료구축에서 가장 기본이 되는 소규모 지역에서 항공사진의 활용이 가능할 것으로 판단되었다.

• 한국항해항만학회지
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• 제28권3호
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• pp.221-225
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• 2004

본 논문은 신경 회로망과 유전 알고리즘을 이용한 비선형 시스템 모델링을 다룬다. 비선형 함수의 근사성 때문에 시스템을 식별하고 제어하기 위해서 신경 회로망을 응용한 연구가 실제로 많이 이루어지고 있다. 빠른 응답시간과 최소의 오차를 위해서는 최적구조 신경 회로망을 설계하는 것이 중요하다. 유선 알고리즘은 최근에 단순성과 견고성 때문에 점점 많이 이용되는 추세이다. 따라서 본 논문에서는 유선알고리즘을 이용하여 신경회로망을 최적화한다. 오차와 응답시간을 최소화하는 신경 회로망 구조를 위해서 유전알고리즘의 유전자로 이진 코딩하여 최적 신경회로망을 탐색하고자 한다. 시뮬레이션을 통해서, 최적 신경회로망 구조가 비선형 시스템 식별에 효과적인 것을 입증하고자 한다.

• Steel and Composite Structures
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• 제27권4호
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• pp.427-438
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• 2018

In this paper, the single and multi-objective optimization of thin-walled conical tubes with different types of indentations under axial impact has been investigated using surrogate models called metamodels. The geometry of tapered thin-walled tubes has been studied in order to achieve maximum specific energy absorption (SEA) and minimum peak crushing force (PCF). The height, radius, thickness, tapered angle of the tube, and the radius of indentation have been considered as design variables. Based on the design of experiments (DOE) method, the generated sample points are computed using the explicit finite element code. Different surrogate models including Kriging, Feed Forward Neural Network (FNN), Radial Basis Neural Network (RNN), and Response Surface Modelling (RSM) comprised to evaluate the appropriation of such models. The comparison study between surrogate models and the exploration of indentation shapes have been provided. The obtained results show that the RNN method has the minimum mean squared error (MSE) in training points compared to the other methods. Meanwhile, optimization based on surrogate models with lower values of MSE does not provide optimum results. The RNN method demonstrates a lower crashworthiness performance (with a lower value of 125.7% for SEA and a higher value of 56.8% for PCF) in comparison to RSM with an error order of $10^{-3}$. The SEA values can be increased by 17.6% and PCF values can be decreased by 24.63% by different types of indentation. In a specific geometry, higher SEA and lower PCF require triangular and circular shapes of indentation, respectively.

• Advances in environmental research
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• 제5권3호
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• 한국항만경제학회지
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• 제27권2호
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• pp.27-38
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• 2011

본고는 우리나라 항만별 승용차 수출행태의 차이를 밝히는데 목적을 둔다. 수출은 미국의 경기와 미국 달러의 일본 엔화표시 환율의 함수로 한다. 경제이론에 의하면 미국 경기의 상승은 우리나라 항만의 승용차 수출의 증가로 나타나며, 엔화 환율의 상승은 엔화 가치하락에 따른 일본 승용차의 가격경쟁력 상승으로 우리의 승용차 수출이 감소한다. 먼저 항만별 수출모형의 안정성을 GPH 기법을 이용하여 모형의 안정성을 확보한 후 오차수정계수를 도출하여 항만간 계수의 차이가 크며, 군산항에서 가장 작고 울산항에서 가장 크다는 것을 밝힌다. OLS를 이용한 모형의 추정을 통해 3개 항만의 수출행태가 경제이론과 일치한다는 것을 보인다. 그리고 예측오차의 분산분해를 통해 항구별 승용차 수출이 경기와 환율에 대해 내생변수라는 것과 역사적 분해를 통해 경기쇼크가 3개 항만 수출변동의 주요 변수라는 것을 밝힌다.

• Membrane and Water Treatment
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• 제9권2호
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• pp.115-121
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• 2018

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

• 한국기후변화학회지
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• 제6권4호
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• 터널과지하공간
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• 제32권6호
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• pp.586-597
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• 2022

본 연구에서는 국내 주요 연약지반으로 알려진 낙동강 조간대 지역의 압밀침하 취약성 평가에 활용할 상부 점성토층의 위치별 두께 정보를 추정할 수 있는 모델을 개발하였다. 두께정보 추정을 위하여 기계학습 알고리즘인 RF (Random Forest), SVR (Support Vector Regression), GPR (Gaussian Process Regression)과 지구통계기법인 정규크리깅(Ordinary Kriging)을 이용한 4가지 공간추정 모델을 개발하고 상호 비교하였다. 모델 개발을 위하여 수집한 연구지역의 시추공 자료 4,712개 중 상부점성토층이 존재하는 2,948개의 시추공 자료를 사용하였으며, 개발된 모델들의 성능을 정량적으로 평가하기 위하여 피어슨(Pearson) 상관계수와 오차제곱평균(mean squared error)을 사용하였다. 또한, 정성적 평가를 위하여 연구지역 전역에 상부점성토층의 두께를 추정하여 점성토층의 지역별 분포 특성을 상호 비교하였다.

• 토지주택연구
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• 제8권1호
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• pp.23-29
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• 2017

The BIM design is expected to expand to the domestic and overseas construction industries, depending on the effect of construction productivity and quality improvement. However, with the obligation of Public Procurement Service to design the BIM design, it includes a design error and the problem of utilization of 3D design by choosing a simple 2D to 3D remodelling method that can not be modelled in 3D modeling or use of the construction and maintenance phases. The results reviewed by BIM design results were largely underutilized and were not even performed with the verification of the error. In order to resolve this, one must develop the check system that secures the quality of BIM design and ensure that the reliability of BIM results are available. In this study, it is designed to develop a program that can automatically verify the design of the BIM design results such as violation of the rules of the BIM design, design flaws, and improve the usability of the BIM design. In particular, this programs were developed not only to identify programmes that were not commercially available, but also to validate drawings in low-light computer environments. The developed program(LH-BIM) store the information of attribute extracted from the Revit file(ArchiCAD, IFC file included) in the integrated DB. This provides the ability to freely lookup the features and properties of drawings delivered exclusively by the LH-BIM Program without using the Revit tools. By doing so, it was possible to resolve the difficulties of using traditional commercial programs and to ensure that they operate only with traditional PC performance. Further, the results of the various BIM software can be readily validated, which can be solved the conversion process error of IFC in the case of SMC. Additionally, the developed program has the ability to automatically check the error and design criteria of the drawings, as well as the ability to calculate the area estimation. These functions allow businesses to apply simple and easy tasks to operate tasks of BIM modelling. The developed system(LH-BIM) carried out a verification test by reviewing the review of the BIM Design model of the Korea Land & Housing Corporation. It is hoped that the verification system will not only be able to achieve the Quality of BIM design, but also contribute to the expansion of BIM and future construction BIM.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.205-208
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• 2008

수치해석에서는 온도, 압력, 비체적, 엔탈피, 엔트로피 등의 수치값이 필요하다. 그런데 증기표의 대부분의 열역학적 성질들은 측정된 값이기 때문에 기본적으로 측정 오차를 가지고 있다. 본 연구에서는 압력 기준의 물의 포화 상태에 대해, 난수를 발생시켜 적절한 크기로 조절한 다음 원래의 성질들에 더하여 인위적으로 노이즈가 포함된 데이터를 만들었다. 이 데이터를 신경회로망과 스플라인 보간법으로 함수 근사를 하였다. 해석 결과 신경회로망이 2차 스플라인 보간법보다 훨씬 더 적은 백분율 오차를 보였으며 이로부터 신경회로망이 측정 오차의 영향을 적게 받는 함수 근사에 적절한 방법임을 확인하였다.