• 소성∙가공
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• 제29권4호
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Earthquakes and Structures
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• 제8권5호
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• pp.1171-1190
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• 2015

Enhancement of strength and ductility is the main reason for the extensive use of FRP jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP-confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of CFRP (carbon fiber reinforced polymer) confined concrete cylinders by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of geometrical and mechanical parameters. The accuracy of the proposed ANN model is quite satisfactory when compared to experimental results. Moreover, the results of the proposed ANN model are compared with five important theoretical models proposed by researchers so far and considered to be in good agreement.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2005년도 총회 및 춘계학술발표회
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• pp.348-351
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• 2005

In this study, solute breakthrough curves through the unsaturated zone were predicted using artificial neural network (ANN) by numerical tests and laboratory experiments. In the numerical tests, applicability of ANN model to prediction of breakthrough curves was evaluated using synthetic data generated by HYDRUS-2D. An appropriate strategy of ANN application and input data form were recommended. The ANN model was validated by laboratory experiments comparing with HYDRUS-2D simulations. The results show that the ANN model can be an effective method for forecasting solute breakthrough curves through the unsaturated zone when hydraulic data are available.

• Steel and Composite Structures
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• 제42권6호
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• pp.733-745
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• 2022

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

• Environmental Engineering Research
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• 제25권3호
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• pp.418-425
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• 2020

In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R² values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

• 한국건설관리학회논문집
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• 제19권4호
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• pp.82-92
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• 2018

건설사업 초기단계가 환경부하량의 절감 가능성에 큰 영향을 미친다는 것을 감안한다면, 효과적인 의사결정을 지원할 수 있는 시스템을 초기에 구축하고 활용하는 것이 중요하다. 이에 본 연구에서는 기본적인 설계 요인들을 고려하여 초기 설계단계에서 활용될 수 있는 환경부하량 산정 모델을 구축하였다. 이 모델은 인공신경망 기법을 활용하고 설계단계 업무가 진행됨에 따라 얻을 수 있는 가용정보 수준을 고려하여 기획단계 적용 모델(ANN-1)과 기본 설계단계 적용 모델(ANN-2)로 구분하여 환경부하량을 산정하도록 구축되었다. 모델의 실험결과 ANN-1, ANN-2모델의 절대평균오차율과 표준편차는 각각 11.19% / 5.30% 및 9.59% / 3.09%로 높은 신뢰성을 갖는 것으로 나타났다. 본 모델은 프로젝트 초기단계별 기초적인 가용정보만을 활용하여 신속하고 정확하게 환경부하량을 추정하고 합리적인 의사결정을 지원할 수 있을 것으로 기대된다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.347-356
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• 2000

This paper compares four models of artificial neural networks (ANN) supported by genetic algorithms the prediction of stock price index. Previous research proposed many hybrid models of ANN and genetic algorithms(GA) in order to train the network, to select the feature subsets, and to optimize the network topologies. Most these studies, however, only used GA to improve a part of architectural factors of ANN. In this paper, GA simultaneously optimized multiple factors of ANN. Experimental results show that GA approach to simultaneous optimization for ANN (SOGANN3) outperforms the other approaches.

• 한국환경복원기술학회지
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• 제9권5호
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• pp.22-31
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• 2006

AnnAGNPS model would be applied to simulate the pollutant removal capacity with the buffer strip in the Deachung reservoir watershed. In 2002, 2,270 tons of TN and 221 tons of TP were discharged from the nonpoint source pollutants in this watershed. During the rainy season, from June to September, 66.4% of TN and 71.9% of TP resulted from nonpoint source loads. AnnAGNPS model was also used to simulate the nutrients removal capacity from the buffer strip under the condition that the present landuse would be changed to forest. As the result of simulation, the removal rates of nutrients from the buffer strip of Daecheong reservoir watershed are 406 tons of TN, 39 tons of TP, which means reduction rates are TN 17.9%, TP 17.8%, respectively.

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.

• Membrane and Water Treatment
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• 제9권6호
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.