• Structural Engineering and Mechanics
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• v.75 no.5
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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.

• Journal of Sport and Applied Science
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• v.4 no.2
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• pp.1-9
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• 2020

Purpose: This study sought to investigate the critical features of Word of mouth (WOM) advertising and their impact on sport consumer behavior. Research design, data, and methodology: Target population of the study consisted of all sports consumer of the Federation of Special Patients and Organ Transplantation, Tehran (Iran), who had indirectly watched the World Organ Transplant Competition documentary at least once on others' advice. For this purpose, 360 sports consumers of the federation were purposefully selected and they were asked to complete the standard WOM advertising questionnaire of Asda and Ko. Pearson correlation coefficient test and modeling of structural equations were performed using Spss24 and Smart PLS software at an error level of 0.05 used to analyze the data. Results: The findings show that there is a significant relationship between experience and expertise, trust and validity, content richness, and the power of message transmission through WOM advertising and its predictability. Finally, interpersonal relationships and work involvement also had a moderating role in this regard. Conclusions: The general conclusion is that the components of WOM advertising as well as involvement and homophily with the mediating role directly as one of the presuppositions for persuasion. The sports consumer was promoting WOM.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.84-88
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• 1999

The accurate estimation of segmental duration is crucial for natural-sounding text-to-speech synthesis. For predicting Korean segmental durations, conventional methods utilized phonemic context, part-of-speech context and locational information in prosodic phrase. In this paper, the tonal information of phonemes is employed for more accurate prediction. After defining two non-boundary tones and six boundary tones, we annotated the tonal label on each syllable of 400 sentences. To predict segmental duration using tonal information, we constructed neural networks with a real-valued output node predicting phonemic duration and trained them by backpropagation algorithm. Experimental results showed that the proposed features are effective for predicting Korean segmental durations, and we got 0.863 correlation coefficient of the observed durations and predicted ones.

• Bulletin of the Korean Chemical Society
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• v.28 no.4
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• pp.561-566
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• 2007

Adenosine kinase (AK) is a ubiquitous intracellular enzyme, which catalyzes the phosphorylation of adenosine (ADO) to adenosine monophosphate (AMP). AK inhibitors have therapeutic potential as analgesic and antiinflammatory agents. A chemical feature based pharmacophore model has been generated from known AK inhibitors (26 training set compounds) by HypoGen module implemented in CATALYST software. The top ranked hypothesis (Hypo1) contained four features of two hydrogen-bond acceptors (HBA) and two hydrophobic aromatics (Z). Hypo1 was validated by 124 test set molecules with a correlation coefficient of 0.905 between experimental and estimated activity. It was also validated by CatScramble method. Thus, the Hypo1 was exploited for searching new lead compounds over 238,819 chemical compounds in NCI database and then the selected compounds were screened based on restriction estimated activity and Lipinski's rules to evaluate their drug-like properties. Finally we could obtain 72 new lead candidates and the two best compound structures from them were posted.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• Advances in concrete construction
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• v.10 no.3
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• pp.195-209
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• 2020

This study investigates the behavior of ultra-high-performance fiber-reinforced concrete (UHPFRC) with hybrid macro-micro steel and macro steel-polypropylene (PP) fibers. Compression, direct and indirect tension tests were carried out on cubic and cylindrical, dogbone and prismatic specimens, respectively. Three types of macro steel fibers, i.e., round crimped (RC), crimped (C), and hooked (H) were combined with micro steel (MS) and PP fibers in overall ratios of 2% by volume. Additionally, numerical analyses were performed to validate the test results. Parameters studied included, fracture energy, tensile strength, compressive strength, flexural strength, and residual strength. Tests showed that replacing PP fibers with MS significantly improves all parameters particularly flexural strength (17.38 MPa compared to 37.71 MPa). Additionally, the adopted numerical approach successfully captured the flexural load-deflection response of experimental beams. Lastly, the proposed regression model for the flexural load-deflection curve compared very well with experimental results, as evidenced by its coefficient of correlation (R²) of over 0.90.

• Journal of Powder Materials
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• v.26 no.5
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• pp.369-374
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• 2019

The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson's r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.

• Nuclear Engineering and Technology
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• v.47 no.7
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• pp.867-874
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• 2015

Separation of cesium chloride (CsCl) and strontium chloride ($SrCl_2$) from the lithium chloride-potassium chloride (LiCl-KCl) salt was studied using a melt crystallization process similar to the reverse vertical Bridgeman growth technique. A ternary $SrCl_2-LiCl-KCl$ salt was explored at similar growth rates (1.8-5 mm/h) and compared with CsCl ternary results to identify similarities. Quaternary experiments were also conducted and compared with the ternary cases to identify trends and possible limitations to the separations process. In the ternary case, as much as 68% of the total salt could be recycled per batch process. In the quaternary experiments, separation of Cs and Sr was nearly identical at the slower rates; however, as the growth rate increased, $SrCl_2$ separated more easily than CsCl. The quaternary results show less separation and rate dependence than in both ternary cases. As an estimated result, only 51% of the total salt could be recycled per batch. Furthermore, two models have been explored to further understand the growth process and separation. A comparison of the experimental and modeling results reveals that the nonmixed model fits reasonably well with the ternary and quaternary data sets. A dimensional analysis was performed and a correlation was identified to semipredict the segregation coefficient.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.369-378
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• 2018

Purpose: The aim of this study was to construct a saponin content-predicting model using shortwave infrared imaging spectroscopy. Methods: The experiment used a shortwave imaging spectrometer and ENVI spectral acquisition software sampling a spectrum of 910 nm-2500 nm. The corresponding preprocessing and mathematical modeling analysis was performed by Unscrambler 9.7 software to establish a ginsenoside nondestructive spectral testing prediction model. Results: The optimal preprocessing method was determined to be a standard normal variable transformation combined with the second-order differential method. The coefficient of determination, $R^2$, of the mathematical model established by the partial least squares method was found to be 0.9999, while the root mean squared error of prediction, RMSEP, was found to be 0.0043, and root mean squared error of calibration, RMSEC, was 0.0041. The residuals of the majority of the samples used for the prediction were between ${\pm}1$. Conclusion: The experiment showed that the predicted model featured a high correlation with real values and a good prediction result, such that this technique can be appropriately applied for the nondestructive testing of ginseng quality.

• Geosystem Engineering
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• v.21 no.5
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• pp.273-281
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• 2018

In this study, the activated red mud was used as a new and appropriate adsorbent for the removal of ferrocyanide and ferricyanide from aqueous solution. Predicting the removal percentage and adsorption capacity of ferro-ferricyanide by activated red mud during the adsorption process is necessary which has been done by modeling and simulation. The artificial neural network (ANN) was used to develop new models for the predictions. A back propagation algorithm model was trained to develop a predictive model. The effective variables including pH, absorbent amount, absorbent type, ionic strength, stirring rate, time, adsorbate type, and adsorbate dosage were considered as inputs of the models. The correlation coefficient value ($R^2$) and root mean square error (RMSE) values of the testing data for the removal percentage and adsorption capacity using ANN models were 0.8560, 12.5667, 0.9329, and 10.8117, respectively. The results showed that the proposed ANN models can be used to predict the removal percentage and adsorption capacity of activated red mud for the removal of ferrocyanide and ferricyanide with reasonable error.