• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.552-560
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• 2010

This paper presents an application of layered Artificial Neural Network controller to study load frequency control problem in power system. The objective of control scheme guarantees that steady state error of frequencies and inadvertent interchange of tie-lines are maintained in a given tolerance limitation. The proposed controller has been designed for a two-area interconnected power system. Only one artificial neural network controller (ANN), which controls the inputs of each area in the power system together, is considered. In this study, back propagation-through time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and ANN controller, separately. For the first time comparative study has been carried out between SMES and CES unit, all of the areas are included with SMES and CES unit separately. By comparing the results for both cases, the performance of ANN controller with CES unit is found to be better than conventional controllers with SMES, CES and ANN with SMES.

• Structural Engineering and Mechanics
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• 제75권5호
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• pp.633-642
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• 2020

Artificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gâteaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E_t/E_b), a shear correction factor (k_s), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.

• 한국농공학회논문집
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• 제59권3호
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• 한국산학기술학회논문지
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• 제5권1호
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• pp.1-6
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• 2004

휘발성유기 화합물(Vo1ati1e Organic Compounds : VOCs)은 탄화수소 화합물을 총칭한다. 이는 오존 및 광화학 스모그의 원인물질일 뿐 아니라 인체에는 암을 유발시키는 유해 물질이다. 또한 대기 중 악취 물질로서 환경 및 건강에 영향을 초래하는 유해성 물질이다. 본 논문은 대기 중에 포함된 암을 유발시키는 유해성 물질인 BTX(Benzene, Toluene, Xylene)의 존재 유무와 농도 측정에 대해서 연구하였다. 다종의 가스센서를 어레이하여 BTX 가스를 측정하고 인공신경망(Artificial Neural Network : ANN)의 역전파(Back propagation : BP) 알고리즘으로 시뮬레이션과 실험을 통해 농도를 추론하였다. ANN모듈은 기준 데이터를 시뮬레이션을 통해 학습시키고, 가스를 주입하여 실험 할 때 학습된 델타 모델에 근거하여 추론을 할 수 있는 추론 알고리즘 모듈이다. 이 모듈은 기준데이터를 MATLAB 코드로 시뮬레이션을 하여 생성된 parameter를 가지고 수행했으며, 시뮬레이션 결과를 실험을 통해 비교 테스트하여 검증하였다.

• Computers and Concrete
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• 제15권2호
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• pp.167-181
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• 2015

"Mortar or concrete pneumatically projected at high velocity onto a surface" is called Shotcrete. Models that predict shotcrete design parameters (e.g. compressive strength, slump etc) from any mixing proportions of admixtures could save considerable experimentation time consumed during trial and error based procedures. Artificial Neural Network (ANN) has been widely used for similar purposes; however, such models have been rarely applied on shotcrete design. In this study 19 samples of shotcrete test panels with varying quantities of water, steel fibers and silica fume were used to determine their slump, cost and compressive strength at different ages. A number of 3-layer Back propagation Neural Network (BPNN) models of different network architectures were used to train the network using 15 samples, while 4 samples were randomly chosen to validate the model. The predicted compressive strength from linear regression lacked accuracy with $R^2$ value of 0.36. Whereas, outputs from 3-5-3 ANN architecture gave higher correlations of $R^2$ = 0.99, 0.95 and 0.98 for compressive strength, cost and slump parameters of the training data and corresponding $R^2$ values of 0.99, 0.99 and 0.90 for the validation dataset. Sensitivity analysis of output variables using ANN can unfold the nonlinear cause and effect relationship for otherwise obscure ANN model.

• 조명전기설비학회논문지
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• 제24권5호
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• pp.16-28
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• 2010

본 논문은 AIPI 및 ANN에 의한 SynRM 드라이브의 최대토크 제어를 제시한다. 본 논문은 인버터의 정격 전압과 전류의 한계 조건을 고려하여 전 속도영역에서 최대토크제어를 제시한다. 속도에 따라 각 제어모드에서 최대토크를 발생하기 위한 최적의 전류값을 계산하고. 계산된 최적전류를 이용하여 최대토크 제어를 수행한다. 제시된 최대토크 제어 알고리즘은 AIPI와 ANN 제어기와 함께 SynRM 드라이브에 적용하여 동작특성을 분석하고 그 타당성을 제시한다.

• Bulletin of the Korean Chemical Society
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• 제26권1호
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• pp.139-145
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• 2005

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q$^+$), dipole moment ($\mu$), the Hildebrand solubility parameter (${\delta}_H^2$) and total charges in molecule (q$_t$) are inputs and output of ANN is log k$_2$ . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k$_2$ of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

• Advances in Energy Research
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• 제6권2호
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• Advances in Computational Design
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• 제1권3호
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• Computers and Concrete
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• 제13권4호
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• pp.547-567
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• 2014

Precast Seismic Structural Systems (PRESSS) provided an iterative procedure for obtaining optimum design of unbonded post-tensioned coupled precast concrete wall systems. Although PRESSS procedure is effective, however, it is lengthy and laborious. The purpose of this research is to employ Artificial Neural Network (ANN) to predict the optimum design parameters for such wall systems while avoiding the demanding iterative process. The developed ANN model is very accurate in predicting the nondimensional optimum design parameters related to post-tensioning reinforcement area, yield force of shear connectors and ratio of moment resisted by shear connectors to the design moment. The Mean Absolute Percent Error (MAPE) for the test data for these design parameters is around %1 and the correlation coefficient is almost equal to 1.0. The developed ANN model is then used to study the effect of different design parameters on wall behavior. It is observed that the design moment and the concrete strength have the most influence on the wall behavior as compared to other parameters. Several design examples were presented to demonstrate the accuracy and effectiveness of the ANN model.