• Journal of the Korean Geotechnical Society
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• v.16 no.5
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• pp.169-178
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• 2000

본 논문에서는 단일 및 군말뚝의 수평변위와 최대 휨모멘트를 예측하기 위하여 인공신경망을 도입하였다. 인공신경망에 의한 결과는 낙동강 모래지반에서 단일 및 군말뚝에 대하여 수행한 일련의 모형실험결과와 비교하였다. 인공신경망 중의 하나인 오류 역전파 신경망(EBIPNN)의 적용성 검증을 위하여 600개의 모형실험결과들을 이용하였다. 그리고 신경망의 구조는 한개의 입력층과 두개의 은닉층 그리고 한개의 출력층으로 구성되었다. 전체 데이터의 25%, 50% 그리고 75% 결과는 각각 신경망의 학습에 이용되었으며 학슴에 이용하지 않은 데이터들은 예측에 이용되었다. 인공신경망 학습결과와 실험결과의 비교에 의하면, 신경망의 최적학습을 위하여 최적학습을 위하여 적합한 은닉층의 뉴런수는 각각 30개로 그리고 학습률은 0.9로 결정되었다. 전체 데이터의 50%이상으로 학습을 수행한 신경망의 모델은 정확한 예측을 하는 것으로 나타났다. 따라서, 인공신경망 모델리 수평하중을 받는 말뚝의 수평변위와 최대 휨모멘트의 예측에 적용될 수 있는 가능성을 보여주었다.

• Journal of the Korean Geotechnical Society
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• v.17 no.1
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• pp.47-56
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• 2001

본 논문에서는 단일 및 군말뚝의 수평변위를 예측하기 위하여 신경망 학습속도의 향상과 지역 최소점 수렴을 방지하는 Readjusting 기법을 적용한 인공신경망을 도입하였다. 이 인공신경망을 M-EBPNN 이라고 한다. M-EBPNN에 의한 결과는 낙동강 모래지반에서 단일 및 군말뚝에 대하여 수행한 일련의 모형실험결과와 비교하였으며, 그리고 신경망의 학습속도와 지역 최소점의 수렴성을 평가하기 위하여 오류 역전파 신경망(EBPNN)의 결과와도 비교 분석하였다. M-EBPNN의 적용성 검증을 위하여 200개의 모형실험결과들을 이용하였으며, 신경망의 구조는 EBPNN의 구조와 동일한 한 개의 입력층과 두 개의 은닉층 그리고 한 개의 출력층으로 구성되었다. 전체 데이터의 25%, 50% 그리고 75% 결과는 각각 신경망의 학습에 이용되었으며 학습에 이용하지 않은 데이터들은 예측에 이용되었다. 그리고, 신경망의 최적학습을 위하여 적합한 은닉층의 뉴런 수와 학습률은 EBPNN에서 결정한 값들을 본 신경망에 이용하였다. 해석결과들에 의하면, 동일한 학습패턴에서의 M-EBPNN이 학습 반복횟수는 EBPNN 보다 최고 88% 감소하였으며 지역 최소점에 수렴하는 현상은 거의 나타나지 않았다. 따라서, 인공신경망 모델이 수평하중을 받는 말뚝의 수평변위 예측에 적용될 수 있는 가능성을 보여 주었다.

• Journal of KIISE
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• v.44 no.12
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• pp.1282-1289
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• 2017

Since the study of deep convolutional neural network became prevalent, one of the important discoveries is that a feature map from a convolutional network can be extracted before going into the fully connected layer and can be used as a saliency map for object detection. Furthermore, the model can use features from each different layer for accurate object detection: the features from different layers can have different properties. As the model goes deeper, it has many latent skip connections and feature maps to elaborate object detection. Although there are many intermediate layers that we can use for semantic segmentation through skip connection, still the characteristics of each skip connection and the best skip connection for this task are uncertain. Therefore, in this study, we exhaustively research skip connections of state-of-the-art deep convolutional networks and investigate the characteristics of the features from each intermediate layer. In addition, this study would suggest how to use a recent deep neural network model for semantic segmentation and it would therefore become a cornerstone for later studies with the state-of-the-art network models.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.487-494
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• 2023

In this study, performance comparison was performed on R and TensorFlow, which are free deep learning tools. In the experiment, six types of deep neural networks were built using each tool, and the neural networks were trained using the 10-year Korean temperature dataset. The number of nodes in the input layer of the constructed neural network was set to 10, the number of output layers was set to 5, and the hidden layer was set to 5, 10, and 20 to conduct experiments. The dataset includes 3600 temperature data collected from Gangnam-gu, Seoul from March 1, 2013 to March 29, 2023. For performance comparison, the future temperature was predicted for 5 days using the trained neural network, and the root mean square error (RMSE) value was measured using the predicted value and the actual value. Experiment results shows that when there was one hidden layer, the learning error of R was 0.04731176, and TensorFlow was measured at 0.06677193, and when there were two hidden layers, R was measured at 0.04782134 and TensorFlow was measured at 0.05799060. Overall, R was measured to have better performance. We tried to solve the difficulties in tool selection by providing quantitative performance information on the two tools to users who are new to machine learning.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.228-235
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• 2010

This paper deals with an autonomous flight controller design problem for a tilt-rotor aircraft in rotary-wing mode. The inner-loop algorithm is designed using the output-based approximate feedback linearization. The model error originated from the feedback linearization is cancelled within allowable tolerance by using single-hidden-layer neural network. According to Lyapunov direct stability theory, the adaptive update law is derived to run the neural network on-line, which is based on the linear observer dynamics. Moreover, the outer-loop algorithm is designed to track the trajectory generated from way-point guidance. Especially, heading and flight-path angle line-of-sight guidance are applied to the outer-loop to improve accuracy of the landing tracking performance. The 6-DOF nonlinear simulation shows that the overall performance of the flight control algorithm is satisfactory even though the collective input response shows instantaneous actuator saturation for a short time due to the lack of the neural network and the saturation protection logic in that loop.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.456-459
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• 2001

In this paper, a wavelet neural network for adaptive control was proposed. The structure of this network is similar to that of the multilayer perceptron(MLP), except that here the sigmoid functions are replated by mother wavelet function in the hidden units. The simulation result showed the effectiveness of using the wavelet neural network structure in the adaptive control of one-link manipulator.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.370-374
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• 1999

The proposed algorithm used the Hybrid teaming rule in the input and hidden layer, and Back-Propagation teaming rule in the hidden and output layer. From the results of simulation of tracking control with one link manipulator as a plant, we verify the usefulness of the proposed control method to compare with common direct adaptive neural network control method; proposed hybrid teaming rule showed faster loaming time faster settling time than the direct adaptive neural network using Back-propagation algorithm. Usefulness of the proposed control method is that it is faster the learning time and settling time than common direct adaptive neural network control method.