• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.449-459
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• 2016

This paper proposes a new method to improve the three problems of existing automatic composition method using artificial neural networks. The first problem is that the existing beat post-processing to fit into music theories could not handle all the cases of occurring. The second one is that the pitch space generated by artificial neural networks is distorted because the rest is trained with the pitch on the same neural network with large values. The last problem is caused by the difference between the initial melody and beats given by user and those generated by an artificial neural network in the process of new composition. In order to treat these problems, we propose an enhanced post-processing of beats, initial melody processing, and adoption of artificial neural network for rest. It was found from experiments that the proposed methods totally resolved the three problems.

• Journal of the Korean Geotechnical Society
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• v.19 no.4
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• pp.287-298
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• 2003

The feasibility of using neural networks to model the complex relationship between piezocone measurements and the undrained shear strength of clays has been investigated. A three layered back propagation neural network model was developed based on actual undrained shear strengths, which were obtained from the isotrpoically and anisotrpoically consolidated triaxial compression test(CIUC and CAUC), and piezocone measurements compiled from various locations around the world. It was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was compared with conventional empirical method, direct correlation method, and theoretical method. It was found that the neural network model is not only capable of inferring a complex relationship between piezocone measurements and the undrained shear strength of clays but also gives a more precise and reliable undrained shear strength than theoretical and empirical approaches. Furthermore, neural network model has a possibility to be a generalized relationship between piezocone measurements and undrained shear strength over the various places and countries, while the present empirical correlations present the site specific relationship.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.641-647
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• 2017

In this paper, we propose a method to automatically generate a configured song with melodies composed of front/middle/last parts by using hierarchical artificial neural networks in automatic composition. In the first layer, an artificial neural network is used to learn an existing song or a random melody and outputs a song after performing rhythm post-processing. In the second layer, the melody created by the artificial neural network in the first layer is learned by three artificial neural networks of front/middle/last parts in the second layer in order to make a configured song. In the artificial neural network of the second layer, we applied a method to generate repeatability using measure identity in order to make song with repeatability and after that the song is completed after rhythm, chord, tonality post-processing. It was confirmed from experiments that our proposed method produced configured songs well.

• 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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• 2017.05a
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• pp.120-120
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• 2017

인천지역의 상수도공급은 팔당댐을 취수원으로 하여 도수, 송수관을 거쳐 인천지역 내 정수장을 통하여 각 급수지역까지 일원화된 관로시스템으로 공급되고 있다. 관망에서의 적절한 수압관리, 노후관로 교체사업 등은 급수관망 내 관로 사고위험을 줄일 수 있고, 누수량을 저감하여 무수율의 감소로 이어질 수 있다. 상수관망 내 누수에 영향을 주는 물리적, 운영적 요소를 파악하고, 이를 이용하여 누수해결을 위한 방법론을 제시하는 것은 매우 중요하다. 본 연구에서는 인천시 배수관망 데이터를 활용하여 통계분석 및 인공신경망을 통하여 무수율에 영향을 미치는 인자를 선별하고, 무수율과의 연관성을 분석하고자 하였다. 이를 위해 대상지역에 대한 시설현황 및 운영자료를 취득하고, 무수율 분석에 활용하였다. 인천시의 소블럭을 대상으로 관로노후도, 배수관연장, 평균관경, 급수전당 공급량, 누수발생 횟수, 용도지역, 관망구성 형태 등을 고려하여 무수율과의 관계분석을 위한 통계분석을 수행하였다. 특히 급수에 필요한 최소에너지와 관망에서 공급되는 에너지를 비교하기 위하여 관망해석 프로그램인 EPANET을 이용하여 관망내 절점에서의 수압과 수요량이 적용된 최소공급에너지를 활용하였고, 이를 통하여 블록 내 과잉공급에너지와 무수율의 영향성을 비교하였다. 최종적으로 산출된 주요인자에 대한 주성분분석, 분산분석, 다중회귀분석 등의 통계분석과 인공신경망에 의해 학습된 알고리즘을 통하여 산정된 무수율을 실측 무수율과 비교, 분석하였다. 인공신경망에 의해 산정된 무수율과 실측 무수율의 정확도를 평가하기 위하여 MAE, MSE, PBIAS 등의 정확도 평가와 산점도 분석을 수행하고, 상관계수를 도출하여 가장 정확한 방법을 결정하였다. 분석 결과 통계분석에 의한 다중회귀식으로 산출된 무수율 보다 인공신경망에 의한 무수율이 실측값에 더욱 근접한 것으로 나타났으며 이용된 뉴런의 수의 따라 산출결과가 상이하기 때문에 최적 뉴런의 수를 산정해야 할 필요가 있음을 확인하였다. 특히 사용된 상수관망 주요인자 중 주성분분석을 통하여 선정된 각 성분을 인공신경망에 적용시 더욱 정확한 무수율 예측이 가능한 것으로 나타났다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.359-366
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• 2020

In this study, a simulation-based damage estimation method for helidecks is proposed using an artificial neural network. The structural members that share a connecting node in the helideck are regarded as a damage group, and a total of 37,400 damage scenarios are numerically generated by applying randomly assigned damage to up to three damage groups. Modal analysis is then performed for all the damage scenarios, which are selectively used as either training or validation or verification sets based on the purpose of use. An artificial neural network with three hidden layers is constructed using a PyTorch program to recognize the patterns of the modal responses of the helideck model under both damaged and undamaged states, and the network is successively trained to minimize the loss function. Finally, the estimated damage rate from the proposed artificial neural network is compared to the actual assigned damage rate using 400 verification scenarios to show that the neural network is able to estimate the location and amount of structural damage precisely.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.29-32
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• 2019

인공신경망은 인간의 신경세포인 뉴런을 모델로서 사용했다. 인간은 외부에서 오는 정보를 뇌에서 받아들이고 판단한다. 받아들인 정보를 통해 어떻게 산출할 것인지에 대한 일들을 기능하게 된다. 그러한 일련의 과정을 필기체 숫자 데이터를 통하여 사람이 유도하는 예측 값을 인식해내고, 학습된 예측 값을 실제 값과 비교해 분석하였다. 그리고 더 나아가 인공신경망에 대해 어떻게 응용할 것인지 논의하였다.

• Computational Structural Engineering
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• v.9 no.2
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• pp.93-101
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• 1996

An artificial neural network is a computational model that mimics the biological system of the brain and it consists of a number of interconnected processing units where it can reasonably infer by them. Because the neural network is particularly useful for evaluating systems with a multitude of nonlinear variables, it can be used in experimental results predictions, in structural planning and in optimum design of structures. This paper describes the basic theory related to the neural networks and discusses the applicability of neural networks to predict the ultimate shear capacity of the precast concrete vertical joints by comparing the neural networks with a conventional method such as regression.

• Proceedings of the KSEEG Conference
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• 2001.04a
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• pp.181-184
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• 2001