• JSTS:Journal of Semiconductor Technology and Science
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• 제6권3호
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• pp.182-188
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• 2006

An artificial neural network model for the microwave characteristics of an InGaAs/InP hemt for 70 nm gate length has been developed. The small-signal microwave parameters have been evaluated to determine the transconductance and drain-conductance. We have further investigated the frequency characteristics of the device. The neural network training have been done using the three layer architecture using Levenberg-Marqaurdt Backpropagation algorithm. The results have been compared with the experimental data, which shows a close agreement and the validity of our proposed model.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2004년도 품질경영모델을 통한 가치 창출
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• pp.655-673
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• 2004

This paper is concerned with analysing the bankruptcy prediction power of three models: Multivariate Discriminant Analysis(MDA ), Logit Analysis, Neural Network. The after-crisis bankrupted companies were limited to the research data and the listed companies belonging to manufacturing industry was limited to the research data so as to improve prediction accuracy and validity of the model. In order to assure meaningful bankruptcy prediction, training data and testing data were not extracted within the corresponding period. The result is that prediction accuracy of neural network model is more excellent than that of logit analysis and MDA model when considering that execution of testing data was followed by execution of training data.

• 에너지공학
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• 제6권1호
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• pp.68-76
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• 1997

본 논문에서는 단기 부하예측을 위하여 인공신경망 모형을 제안하였다. 본 논문에서 제안된 인공신경망의 학습알고리즘은 기존의 역전파 알고리즘 보다 효과적으로 학습수렴이 빠르며 모수결정과 초기가중치 값들에 대한 의존도가 낮은 동적 적응 학습알고리즘을 개발하여 단기 부하예측에 그 적용 가능성을 시험하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권7호
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• pp.531-535
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• 2004

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. The GA-GRNN was applied to classify 4 different Promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. Compared to conventional GRNN, GA-GRNN significantly improved the total classification sensitivity as well as the total prediction accuracy. As a result, the proposed GA-GRNN demonstrated improved classification sensitivity and prediction accuracy over the convention GRNN.

• 대한인간공학회지
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• 제21권1호
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• pp.1-14
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• 2002

The main purpose of this paper is to present selection criteria for ROK Airforce pilot training candidates in order to save costs involved in sequential pilot training. We use classification models such Decision Tree, Logistic Regression and Neural Network based on aptitude test results of 288 ROK Air Force applicants in 1994-1996. Different models are compared in terms of classification accuracy, ROC and Lift-value. Neural network is evaluated as the best model for each sequential flight test result while Logistic regression model outperforms the rest of them for discriminating the last flight test result. Therefore we suggest a pilot selection criterion based on this logistic regression. Overall. we find that the factors such as Attention Sharing, Speed Tracking, Machine Comprehension and Instrument Reading Ability having significant effects on the flight results. We expect that the use of our criteria can increase the effectiveness of flight resources.

• Structural Engineering and Mechanics
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• 제31권2호
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• pp.153-162
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• 2009

The reduction of the dynamic response of an offshore structure subjected to wind-generated random ocean waves is of extreme significance in the aspects of serviceability, fatigue life and safety of the structure. In this study, a new neuro-control scheme is applied to the vibration control of a fixed offshore platform under random wave loads to examine the applicability of the proposed method. It is called the Lattice Probabilistic Neural Network (LPNN), as it utilizes lattice pattern of state vectors as the training data of PNN. When control results of the LPNN are compared with those of the NN and PNN, LPNN showed better performance in effectively suppressing the structural responses in a shorter computational time.

• 제어로봇시스템학회논문지
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• 제15권5호
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• pp.555-561
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• 2009

The established mathematical modeling methods have limitation to know the hydraulic characteristics at the wastewater treatment plant which are complex and nonlinear systems. So, an artificial neural network (ANN) model based on hydraulic characteristics is applied for modeling wastewater quality of a full-scale wastewater treatment plant using DNR (Daewoo nutrient removal) process. ANN was trained using data which are influents (TSS, BOD, COD, TN, TP) and effluents (COD, TN, TP) components in a year, and predicted the effluent results based on the training. To raise the efficiency of prediction, inputs of ANN are added the influent and effluent information that are in yesterday and the day before yesterday. The results of training data tend to have high accuracy between real value and predicted value, but test data tend to have lower accuracy. However, the more hydraulic characteristics are considered, the results become more accuracy.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.234-236
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• 2020

Content-adaptive training and transmission of the model parameters of neural networks can boost up the SR performance with higher restoration fidelity. In this case, efficient transmission of neural network parameters are essentially needed. Thus, we propose a novel method of compressing the network model parameters based on the training of network model parameters in the sense that the residues of filter parameters and content loss are jointly minimized. So, the residues of filter parameters are only transmitted to receiver sides for different temporal portions of video under consideration. This is advantage for image restoration applications with receivers (user terminals) of low complexity. In this case, the user terminals are assumed to have a limited computation and storage resource.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1256-1258
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• 1990

A large number of trainings are requested for the artificial neural network using the backpropagation algorithm. It is shown that one dimensional search technique is effective to reduce the number of trainings through some numerical simulations.

• Structural Engineering and Mechanics
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• 제13권2호
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• pp.187-209
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• 2002

The infrastructure system in the United States has been aging faster than the resource available to restore them. Therefore decision for allocating the resources is based in part on the condition of the structural system. This paper proposes to use neural network to predict the overall rating of the structural system because of the successful applications of neural network to other fields which require a "symptom-diagnostic" type relationship. The goal of this paper is to illustrate the potential of using neural network in civil engineering applications and, particularly, in bridge evaluations. Data collected by the Tennessee Department of Transportation were used as "test bed" for the study. Multi-layer feed forward networks were developed using the Levenberg-Marquardt training algorithm. All the neural networks consisted of at least one hidden layer of neurons. Hyperbolic tangent transfer functions were used in the first hidden layer and log-sigmoid transfer functions were used in the subsequent hidden and output layers. The best performing neural network consisted of three hidden layers. This network contained three neurons in the first hidden layer, two neurons in the second hidden layer and one neuron in the third hidden layer. The neural network performed well based on a target error of 10%. The results of this study indicate that the potential for using neural networks for the evaluation of infrastructure systems is very good.