• 대한토목학회논문집
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• 제26권1B호
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• pp.39-50
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• 2006

홍수를 예측하기 위해서 국내 5대강 유역의 홍수통제소는 저류함수모형을 사용하고 있으며 현재까지 홍수예측에 대한 많은 연구가 이루어지고 있다. 이에 본 논문에서는 현재 홍수통제소에서 사용되고 있는 저류함수모형과 과거의 강우-수위 관계를 이용한 회귀분석(regression analysis), 그리고 인공신경망(artificial neural network)을 이용하여 홍수를 예측하고 이를 비교, 분석하고자 하였다. 저류함수모형의 경우는 홍수통제소의 대표매개변수와 보정된 최적(평균)매개변수를 적용하였다. 그리고 회귀분석과 인공신경망은 1995~2001년까지의 홍수사상 중 4개의 홍수사상을 선택하여 회귀계수를 구하고 역전파(backpropagation) 알고리즘을 사용하여 학습을 시켰다. 그 결과 저류함수모형의 경우 최적 매개변수를 이용하였을 때 기존의 홍수통제소에서 사용하고 있는 대표매개변수보다 예측이 개선되었으며, 회귀분석의 방법인 다중회귀분석, Robust 회귀분석, Stepwise 회귀분석을 이용한 홍수예측은 비교적 정확한 결과를 얻을 수 있었다. 역전파 알고리즘을 사용한 인공신경망의 경우도 회귀분석을 이용한 홍수예측보다는 다소 못하였지만 정확한 결과를 얻을 수 있었다.

• 전기학회논문지P
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• 제55권3호
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• pp.117-122
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• 2006

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also this paper is proposed. speed control of induction motor using FLC-FNN and estimation of speed using ANN controller. The back Propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권10호
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 한국정밀공학회지
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• 제29권12호
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• pp.1313-1320
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• 2012

Condition monitoring of the reciprocating compressor is important task. As a traditional method, health monitoring system of refrigerator depends on decision of a skilled person based on his experience. However, the skilled person cannot monitor all the compressors completely. If a sampled compressor is faulty, thousands of compressors manufactured at that place are regarded as faulty compressors. Therefore it is necessary to monitor all compressors in the production line. In the paper real time health monitoring system is developed based on high order time frequency method and artificial neural network. The system is installed in the mass production line. The result of the application has been very successful, and currently the system is working very well on the production line.

• Earthquakes and Structures
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• 제10권4호
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• pp.757-768
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• 2016

In the past decades, effect of near field earthquake on the historical monuments has attracted the attention of researchers. So, many analyses in this regard have been presented. Tunnels as vital arteries play an important role in management after the earthquake crisis. However, digging tunnels and seismic effects of earthquake on the historical monuments have always been a challenge between engineers and historical supporters. So, in a case study, effect of near field earthquake on the historical monument was investigated. For this research, Finite Element Analysis (FEM) in soil environment and soil-structure interaction was used. In Plaxis 2D software, different accelerograms of near field earthquake were applied to the geometric definition. Analysis validations were performed based on the previous numerical studies. Creating a nonlinear relationship with space parameter, time, angular and numerical model outputs was of practical and critical importance. Hence, artificial Neural Network (ANN) was used and two linear layers and Tansig function were considered. Accuracy of the results was approved by the appropriate statistical test. Results of the study showed that buildings near and far from the tunnel had a special seismic behavior. Scattering of seismic waves on the underground tunnels on the adjacent buildings was influenced by their distance from the tunnel. Finally, a static test expressed optimal convergence of neural network and Plaxis.

• Animal cells and systems
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• 제16권2호
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• pp.121-126
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• 2012

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.

• 융합정보논문지
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• 제11권7호
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• pp.1-6
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• 2021

본 연구의 목적은 이미지 랜드마크 선정 기법을 기반으로, 인공신경망 안면 영상분석 시스템의 성능을 향상하기 위한 내용이다. 랜드마크 선정을 위하여 안면 이미지 연령을 분류를 위한 CNN 기반의 다층 ResNet 모델의 구성이 필요하며, ResNet 모델에서 입력 노드의 변화에 따른 출력 노드의 변화를 감지하는 히트 맵을 추출한다. 추출된 다수의 히트 맵을 결합하여 연령 구분 예측과 관계된 안면 랜드마크를 구성한다. 이를 통하여, 안면 랜드마크를 통하여 픽셀의 위치별 중요도를 분석할 수 있으며, 가중치가 낮은 픽셀의 제거함으로서 상당량의 입력 데이터 감소가 가능해졌다. 이러한 기법은 인공신경망 시스템의 연산 성능 향상에 기여하게 된다.

• Steel and Composite Structures
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• 제49권2호
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• pp.231-244
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• 2023

Many of the recent investigations in the field of geotechnical engineering focused on the bearing capacity theories of multilayered soil. A number of factors affect the bearing capacity of the soil, such as soil properties, applied overburden stress, soil layer thickness beneath the footing, and type of design analysis. An extensive number of finite element model (FEM) simulation was performed on a prototype slope with various abovementioned terms. Furthermore, several non-linear artificial intelligence (AI) models are developed, and the best possible neural network system is presented. The data set is from 3443 measured full-scale finite element modeling (FEM) results of a circular shallow footing analysis placed on layered cohesionless soil. The result is used for both training (75% selected randomly) and testing (25% selected randomly) the models. The results from the predicted models are evaluated and compared using different statistical indices (R² and RMSE) and the most accurate model BBO (R²=0.9481, RMSE=4.71878 for training and R²=0.94355, RMSE=5.1338 for testing) and TLBO (R²=0.948, RMSE=4.70822 for training and R²=0.94341, RMSE=5.13991 for testing) are presented as a simple, applicable formula.

• 대한조선학회논문집
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• 제52권1호
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• pp.88-95
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• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.

• 대한공간정보학회지
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• 제19권3호
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• pp.33-40
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• 2011

본 연구에서는 제주지역의 산사태가능성을 분석하기 위하여 사람의 발길이 많은 사라봉, 별도봉 지역과 송악산 지역의 지형 및 토질공학적 사면 붕괴 유발 인자들을 이용하여 로지스틱회귀분석기법과 인공신경망기법을 GIS기법과 결합하여 예측지도를 작성하고 비교분석하였다. 산사태 예측지도를 작성하기 위해서 산사태 발생에 영향을 주는 사면경사, 고도, 건조밀도, 투수계수, 간극율을 선택하였으며 선정된 지역을 대상으로 실시한 야외조사와 토양물성시험 결과를 정리한 후 이를 토대로 GIS기법을 적용하여 각 레이어별 주제도를 작성하였다. 생성된 주제도를 각각 로지스틱회귀분석기법과 인공신경망기법으로 작성하여 비교분석한 결과 사면경사와 간극율의 경중률이 가장 높게 나타났고, 예측지도는 로지스틱회귀분석기법이 더욱 정확한 결과를 나타내었으며, 도로변과 산책로를 중심으로 산사태 발생가능성이 높게 분포하고 있음을 알 수 있었다.