• Journal of the Korean GEO-environmental Society
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• v.14 no.9
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• pp.31-37
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• 2013

In this study installation diameter, interval, area replacement ratio and ground hardness of applicable ground in C.G.S method should be mastered through surrounding ground by conducting modeling. Optimum artificial neural network was selected through the study of the parameter of artificial neural network and prediction model was developed by the relationship with numerical analysis and artificial neural network. As this result, C.G.S pile settlement and ground settlement were found to be equal in terms of diameter, interval, area replacement ratio and ground hardness, presented in a single curve, which means that the behavior pattern of applied ground in C.G.S method was presented as some form, and based on such a result, learning the artificial neural network for 3D behavior was found to be possible. As the study results of artificial neural network internal factor, when using the number of neural in hidden layer 10, momentum constant 0.2 and learning rate 0.2, relationship between input and output was expressed properly. As a result of evaluating the ground behavior of C.G.S method which was applied to using such optimum structure of artificial neural network model, is that determination coefficient in case of C.G.S pile settlement was 0.8737, in case of ground settlement was 0.7339 and in case of ground heaving was 0.7212, sufficient reliability was known.

• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.83-97
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• 2000

In the present study, artificial neural network based on the multi-layer perceptron is used and an optimum model is chosen through the process of efficiency evaluation in order to develop a system predicting maximum displacements of the earth retaining walls at various excavation stages. By analyzing the measured field data collected at various urban excavation sites in Korea, factors influencing on the behaviors of the excavation wall are examined. Among the measured data collected, reliable data are further selected on the basis of the performance ratio and are used as a data base. Data-based measurements are also utilized for both teaming and verifying the artificial neural network model. The learning is carried out by using the back-propagation algorithm based on the steepest descent method. Finally, to verify a validity of the formulated artificial neural network system, both the magnitude and the occurring position of the maximum horizontal displacement are predicted and compared with measured data at real excavation sites not included in the teaming process.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.1-9
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• 2021

In this study, we evaluated the accuracy of deep learning-based tree species classification model trained by using high-resolution images. We selected five species classed, i.e., pine, birch, larch, korean pine, mongolian oak for classification. We created 5,000 datasets using high-resolution orthophoto and forest type map. CNN deep learning model is used to tree species classification. We divided training data, verification data, and test data by a 5:3:2 ratio of the datasets and used it for the learning and evaluation of the model. The overall accuracy of the model was 89%. The accuracy of each species were pine 95%, birch 89%, larch 80%, korean pine 86% and mongolian oak 98%.

• Journal of the Korean Geotechnical Society
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• v.32 no.2
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• pp.5-17
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• 2016

Collapse of a tunnel can occur anytime, anywhere due to the special characteristics of tunnel structures and unexpected geological conditions during construction. Tunnel collapse will lead to economic losses and casualties. So various studies are continually being conducted to prevent economic losses, casualties and accidents. In this study, we analyzed data from 56 domestic construction tunnel collapse sites, and input factors to be applied to the artificial neural network were selected by the sensitivity analysis. And for the artificial neural network model design studies were carried out with the selected input factors and optimized ANN model to predict the type of tunnel collapse was determined. By using it, in 12 sites where tunnel collapse occurred applicability evaluation was conducted. Thus, the tunnel collapse type predictability was verified. These results will be able to be used as basic data for preventing and reinforcing collapse in the tunnel construction site.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.307-309
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• 2001

분류문제에서 유용한 학습패턴은 클래스들간의 분류경계에 근접한 정상패턴들을 말한다. 본 연구에서는 다양한 구조와 학습 파라미터를 가진 신경망 앙상블을 구성하고 그 출력값의 편기와 분산에 기초한 패턴절수를 정의한다. 전체 학습패턴 중 일정한 임계값 이상의 패턴점수를 가진 패턴들만이 학습패턴으로 선정된다. 제안한 방법은 두 개의 인공문제와 두 개의 실제문제 (UCI Repository)에 적응, 검증되었다. 그 결과 선택된 패턴만으로 학습한 경우, 메모리 공간 절약 및 계산시간 단축의 효과뿐만 아니라 복잡도가 큰 모델이라도 과적합을 하지 않았고 실험적으로 안정된 결과를 산출했으며, 적은 수의 학습패턴만으로도 일반화 성능을 향상시키거나 적어도 저하시키지 않았다는 것을 보였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.419-422
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• 2004

본 논문에서는 신경망 학습을 위한 데이터 획득시 생길 수 있는 오차를 줄이기 위해 획득 데이터에 대한 전처리 과정을 퍼지로써 구현하는 알고리즘을 제안하였다 신경망은 주어진 정보를 이용하여 학습을 가능하게 함으로써 시스템의 특징을 추출하는데 매우 우수한 능력을 발휘하고 있다 그러나 이는 학습에 사용하는 데이터에 오차가 포함되지 않는다는 점을 전제로 하고 있다. 그런데 데이터 획득과정이 인간의 주관적 판단에 의해 수작업으로 이루어지는 경우 학습 데이터는 오차가 존재할 수 있다. 학습 데이터의 오차를 줄이기 위해 조기에 획득된 데이터를 분석하고 추가적인 후보 데이터를 선정하여 데이터 획득 과정에서 큰 영향을 미치는 물체의 거리와 크기를 모두 고려할 수 있도록 퍼지 모델로써 구현하고자 한다.

• Journal of the Korean GEO-environmental Society
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• v.13 no.8
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• pp.25-33
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• 2012

Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to consider various geographies and geotechnical conditions. When the tunnel is under construction, examination of accurate safety and prediction of behavior are overcome the limits of predicting behavior by Artificial Neural Network in this study. First, construct the suitable structure after the data of field was made sure by the multi-layer back propagation, then apply with algorithm. Employ the result of measured data from database, and consider the influence factor of tunnel, like supporting pattern, RMR, Q, the types of rock, excavation length, excavation shape, excavation over, to carry out the reliable analysis through field applicability of Artificial Neural Network. After studying, using the ANN model to predict the shearing displacement, convergence displacement, underground displacement, Rock bolt output follow the excavation over of tunnel construction field, then determine the field applicability with ANN through field measured value and comparison analysis when tunnel is being constructed.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.43-43
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• 2022

유해 조류 대발생은 전국 각지의 인공호소나 하천에서 다발적으로 발생하며, 경관을 해치고 수질을 오염시키는 등 수자원에 부정적인 영향을 미친다. 본 연구에서는 인공호소에서 발생하는 유해 조류 대발생을 예측하기 위해 심층학습 기법을 이용하여 예측 모델을 개발하고자 하였다. 대상 지점은 대청호의 추동 지점으로 선정하였다. 대청호는 금강유역 중류에 위치한 댐으로, 약 150만명에 달하는 급수 인구수를 유지 중이기에 유해 남조 대발생 관리가 매우 중요한 장소이다. 학습용 데이터 구축은 대청호의 2011년 1월부터 2019년 12월까지 측정된 수질, 기상, 수문 자료를 입력 자료를 이용하였다. 수질 예측 모델의 구조는 다중 레이어 퍼셉트론(Multiple Layer Perceptron; MLP)으로, 입력과 한 개 이상의 은닉층, 그리고 출력층으로 구성된 인공신경망이다. 본 연구에서는 인공신경망의 은닉층 개수(1~3개)와 각각의 레이어에 적용되는 은닉 노드 개수(11~30개), 활성함수 5종(Linear, sigmoid, hyperbolic tangent, Rectified Linear Unit, Exponential Linear Unit)을 각각 하이퍼파라미터로 정하고, 모델의 성능을 최대로 발휘할 수 있는 조건을 찾고자 하였다. 하이퍼파라미터 최적화 도구는 Tensorflow에서 배포하는 Keras Tuner를 사용하였다. 모델은 총 3000 학습 epoch 가 진행되는 동안 최적의 가중치를 계산하도록 설계하였고, 이 결과를 매 반복마다 저장장치에 기록하였다. 모델 성능의 타당성은 예측과 실측 데이터 간의 상관관계를 R2, NSE, RMSE를 통해 산출하여 검증하였다. 모델 최적화 결과, 적합한 하이퍼파라미터는 최적화 횟수 총 300회에서 256 번째 반복 결과인 은닉층 개수 3개, 은닉 노드 수 각각 25개, 22개, 14개가 가장 적합하였고, 이에 따른 활성함수는 ELU, ReLU, Hyperbolic tangent, Linear 순서대로 사용되었다. 최적화된 하이퍼파라미터를 이용하여 모델 학습 및 검증을 수행한 결과, R2는 학습 0.68, 검증 0.61이었고 NSE는 학습 0.85, 검증 0.81, RMSE는 학습 0.82, 검증 0.92로 나타났다.

• Journal of the Korean Institute of Gas
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• v.17 no.2
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• pp.59-69
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• 2013

This study presents development of the ANN simulator for well placement of infill drilling in gas fields. The input data of the ANN simulator includes the production time, well location, all inter well distances, boundary inter well distance, infill well position, productivity potential, functional links, reservoir pressure. The output data includes the bottomhole pressure in addition to the production rate. Thus, it is possible to calculate the productivity and bottomhole pressure during production period simultaneously, and it is expected that this model could replace conventional simulators. Training for the 20 well placement scenarios was conducted. As a result, it was found that accuracy of ANN simulator was high as the coefficient of correlation for production rate was 0.99 and the bottomhole pressure 0.98 respectively. From the resultes, the validity of the ANN simulator has been verified. The term, which could produce Maximum Daily Quantity (MDQ) at the gas field and the productivity according to the well location was analyzed. As a result, the MDQ could be maintained for a short time in scenario C-1, which has the three infill wells nearby aquifer boundary, and a long time in scenario A-1. In conclusion, it was found that scenario A maintained the MDQ up to 21% more than those of scenarios B and C which include parameters that might affect the productivity. Thus, the production rate can be maximized by selecting the location of production wells in comprehensive consideration of parameters that may affect the productivity. Also, because the developed ANN simulator could calculate both production rate and bottomhole pressure, respectively, it could be used as the forward simulator in a various inverse model.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.24-32
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• 2006

Using a deterministic design optimization, the effect of uncertainty can result in violation of constraints and deterioration of performances. For this reason, design optimization is required to guarantee reliability for constraints and ensure robustness for an objective function under uncertainty. Therefore, this study drew Monte Carlo Simulation(MCS) for the evaluation of reliability and robustness, and selected an artificial neural network as an approximate model that is suitable for MCS. Applying to the aero-structural optimization problem of aircraft wing, we can explore robuster optima satisfying the sigma level of reliability than the baseline.