• 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 Korea Water Resources Association Conference
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• 2017.05a
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• pp.170-170
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• 2017

본 연구의 목표는 극한 지역의 대비 시스템을 구축하기 위하여 인공 신경망(Artificial Neural Networks)을 이용하여 보다 관측하기 쉬운 기상 인자들로부터 적설량을 실시간 측정 가능성을 제시하는 것이다. 본 연구에서 사용한 데이터베이스는 기상청의 기상자료개방포털에서 사람이 직접 측정한 종관기상관측의 자료다. 이 중에서 일최대 기온, 일최저 기온, 일평균 기온, 강수량을 사용하여 오차를 줄여나가는 최적화방법으로 인공 신경망 시스템을 설계하였다. 설계된 시스템으로 500회 시뮬레이션한 연구 결과는 상관계수가 적설량 측정에 대한 인공 신경망의 크기(노드의 개수)와 관계없이 평균적으로 0.8627인 것을 보여준다. 추가적으로 보조 입력 값인 고도를 사용한 결과, 성능은 좋아졌지만 상관계수의 차이는 평균 0.0044로 미세했다. 또한 Cross-Validation을 통해 기존의 보간법인 Kriging기법과 비교하여 미 관측 지역에서 인공 신경망(ANNs) 사용이 Kriging기법 보다 우수하다는 것을 2차원 Regression's map을 통해 나타냈다. 마지막으로 오차가 크게 발생했을 경우 보안할 수 있는 확률적인 방안을 제시하였다.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.127-132
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• 2016

Several methods have been proposed to apply artificial neural networks to text compression in the past. However, the networks and targets are both limited to the small size due to hardware capability in the past. Modern GPUs have much better calculation capability than CPUs in an order of magnitude now, even though CPUs have become faster. It becomes possible now to train greater and complex neural networks in a shorter time. This paper proposed a method to transform the distribution of original data with a probabilistic neural predictor. Experiments were performed on a feedforward neural network and a recurrent neural network with gated-recurrent units. The recurrent neural network model outperformed feedforward network in compression rate and prediction accuracy.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2977-2979
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• 1999

본 논문에서는 MEBP(Modified Error Back-Propagation) 학습 규칙을 간단한 비선형 회로를 이용하여 구현하였다. 인공 신경 회로망(ANNs : Artificial Neural Networks)은 많은 수의 뉴런을 필요하기 때문에 표준 CMOS 기술을 이용하는 간단한 비선형 시냅스(synapse) 회로는 인공 신경 회로망 구현에 적합하다. 학습회로는 비선형 시냅스 회로. 시그모이드(sigmoid) 회로. 그리고 선형 곱셈기로 구성되어 있다. 학습 회로의 출력은 각 입력 패턴에 따라 유일한 값으로 결정되어진다. 제안한 학술회로를 $2{\times}2{\times}1$과 $2{\times}3{\times}1$ 다층 feedforward 신경 회로망 모델에 적용하였다. MEBP 하드웨어 구현은 HSPICE 회로 시뮬레이터를 이용하여 검증하였다. 제안한 학술 회로는 on-chip 학습회로를 포함한 대규모 신경회로망 구현에 매우 적합하리라 예상된다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.71-78
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• 2016

This paper proposes the new electric power demand forecast model which is based on an artificial neural network and considers time and weather factors. Time factors are selected by measuring the autocorrelation coefficients of load demand in summer and winter seasons. Weather factors are selected by using Pearson correlation coefficient The important weather factors are temperature and dew point because the correlation coefficients between these factors and load demand are much higher than those of the other factors such as humidities, air pressures and wind speeds. The experimental results show that the proposed model using time and seasonal weather factors improves the load demand forecasts to a great extent.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.17-24
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• 2017

Recently Deep-learning, Technologies using Large or Deep Artificial Neural Networks, have Shown Remarkable Performance, and the Increasing Size of the Network Contributes to its Performance Improvement. However, the Increase in the Size of the Neural Network Leads to an Increase in the Calculation Amount, which Causes Problems Such as Circuit Complexity, Price, Heat Generation, and Real-time Restriction. In This Paper, We Propose and Test a Method to Reduce the Number of Network Connections by Effectively Pruning the Redundancy in the Connection and Showing the Difference between the Performance and the Desired Range of the Original Neural Network. In Particular, we Proposed a Simple Method to Improve the Performance by Re-learning and to Guarantee the Desired Performance by Allocating the Error Rate per Layer in Order to Consider the Difference of each Layer. Experiments have been Performed on a Typical Neural Network Structure such as FCN (full connection network) and CNN (convolution neural network) Structure and Confirmed that the Performance Similar to that of the Original Neural Network can be Obtained by Only about 1/10 Connection.

• Clean Technology
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• v.7 no.2
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• pp.119-126
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• 2001

The prediction of ozone formation was studied using the neural network and the stochastic method. Parameter estimation method and artificial neural network(ANN) method were employed in the stochastic scheme. In the parameter estimation method, extended least squares(ELS) method and recursive maximum likelihood(RML) were used to achieve the real time parameter estimation. Autoregressive moving average model with external input(ARMAX) was used as the ozone formation model for the parameter estimation method. ANN with 3 layers was also tested to predict the ozone formation. To demonstrate the performance of the ozone formation prediction schemes used in this work, the prediction results of ozone formation were compared with the real data. From the comparison it was found that the prediction schemes based on the parameter estimation method and ANN method show an acceptable accuracy with limited prediction horizon.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.2
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• pp.126-132
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• 2010

An approach to calculating expected damage of breakwater assisted by artificial neural network was developed. Wave height in front of a breakwater was predicted by a trained artificial neural network with inputs of wave height in deep ocean and tidal level. Prediction results by the neural network can be comparable to that by professional numerical model for wave transformation. Using the wave prediction neural network, it was very easy and fast to obtain a number of significant waves at breakwater and finally analysis time for expected damage can be shortened. In addition, the effect of considering tidal level in the calculation of expected damage was revealed by comparing the expected damages with and without tidal variation. Therefore, it was pointed out that tidal variation should be considered to improve prediction accuracy.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.2
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• pp.166-174
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• 1999

On-line efficiency optimization control of an induction motor drive using neural network is important from the v viewpoints of energy saving and controlling a nonlinear system whose charact81istics are not fully known. This paper p presents a neural networklongleftarrowbased on-line efficiency optimization control for an induction motor drive, which adopts an optimal slip an밍J.lar frequency control. In the proposed scheme, a neuro-controller provides minimal loss operating point i in the whole range of the measured input power. Both simulation and experimental results show that a considerable e energy saving is achieved compared with the conventional constant vlf ratio operation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.1-1
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• 2000

본 논문에서는 3축이 연성되어 비선형 운동 방정식으로 표현되는 3축 안정화 인공위성 시스뎀에 입릭외란과 시스템의 불확실성이 존재할 경우에도 자제 정밀도를 유지하는 제어기를 설계한다. 비선헝 운동 방정식으로 표현되는 운동 방정식을 선형화하고 PID제어기를 구성하였다 선형화에 의한 시스템의 불확실성과 입력 외란을 신경회로망으로 추정하여 외란의 엉향을 제거하도록 구성된 PR제어기의 제어입력을 수정한다 수정된 제어입력은 외란을 상쇠시켜 시스템 출력에서 외란의 효과를 제거하게 된다. 신경회로망은 제어입력과 시스템 출력, 기준 운동 방정식간의 관계를 이용하여 외간과 시스템의 불확실성을 추정하며, 역전파 알고리즘을 사용한 학습 알고리즘으로 신경 회로망을 교육한다. 제안된 신경회로망을 이용한 외란 제거 제어기는 시뮬레이션을 통하여 자세 정밀도의 향상을 검증한다