• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.

• YAKHAK HOEJI
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• v.20 no.1
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• pp.63-69
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• 1976

Prednisolone tablet product design problem was structured as constrained optimization problem and subsequently solved by multiple regression analysis and Lagrangian method of optimixation. Prednisolone was the drug chosen and anhydrous lactose and corn starch were the adjuvants. The effect of anhydrous lactose and corn starch concentrations on tablet hardness, volume, disintegration time and in vitro release rate was studied. The concentrations of anhydrous lactose and corn starch used in this experiment were 30-60 percent and 5-30 percent, respectively. A full second-order (quadratic) model with all possible two-factor interactions was employed. To obtain the values of anhydrous lactose and corn starch which miniumize the in vitro : release time (t$_{60%}$) subject to the constraint on tablet hardness, disintegration time and volume, we solved the Lagrange function. Multiple correlation coefficients for the regression models were correlated at less than 0.05 level and it was found that the optimum concentrations of anhydrous lactose and corn starch were 45 percent and 21 percent, respectively.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.318-323
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• 2016

In the current study, factors affecting cheese brands products in grocery stores were evaluated with an emphasis on diversity. The sample data were collected from Noushad and Pegah Milk Industry in 2015 and data were extracted, reviewed, and analyzed from 435 grocery stores in Mashhad using seemingly unrelated regression model and particle swarm optimization algorithm. Results showed that optimum amount of Kalleh product diversity is higher than other competitors in the market, and Kalleh UF diversity is 100 to 250 grams, and Kalleh UF diversity with weight of 300 to 500 grams is more than other modes of diversity, and Kalleh brand must remove tin cheese from the market. Sabah Brand also should eliminate its glass and creamy diversity from market, UF diversity is mostly welcomed in market.

• Journal of the Optical Society of Korea
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• v.9 no.3
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• pp.107-110
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• 2005

An optical method of measuring hematocrit noninvasively is presented. An LED Light with multiple wavelengths was irradiated on fingernail and transmitted light from the finger was measured to predict hematocrit. A finger probe contained an LED array and detector. Our previous experience showed that prediction accuracy was sensitive to reliability of the finger probe hardware and we optimized the finger probe parameters such as the internal color, detector area and the emission area of a light source based on Design of Experiment. Using the optimized finger probe, we developed a hematocrit monitoring system and tested with 549 persons. For the calibration model with 368 persons, a regression coefficient of 0.74 and a standard deviation of 3.67 and the mean percent error of $8\%$ were obtained. Hematocrits for 181 persons were predicted. We achieved a mean percent error of $8.2\%$ where the regression coefficient was 0.68 and the standard deviation was 3.69.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• Journal of the Korean GEO-environmental Society
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• v.25 no.11
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• pp.5-12
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• 2024

In this study, the liquefaction potential index was assessed using actual borehole data and seismic waves, and a predictive model was developed based on machine learning methods. A total of 10 features were selected including factors reflecting the characteristics of the seismic waves. To identify candidate methods, a preliminary test was conducted using commonly used machine learning methods for regression, followed by Bayesian optimization to optimize the hyperparameters for these candidate methods. Among artificial neural networks, Gaussian process regression, and random forest, it was found that the random forest effectively predicted the liquefaction potential index, as indicated by a low root mean square error, a high coefficient of determination, and considerations regarding overfitting. However, it was noted that the model tends to underestimate the liquefaction potential index when the index was 5 or higher.

• Journal of Environmental Science International
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• v.18 no.11
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• pp.1225-1234
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• 2009

The aim of this research was to apply experimental design methodology in the optimization condition of electrochemical oxidation of Rhodamine B(RhB). The reactions of electrochemical oxidation were mathematically described as a function of parameters amounts of current, NaCl dosage, pH and time being modeled by the use of the central composite design, which was used for fitting quadratic response surface model. The application of response surface methodology using central composite design(CCD) technique yielded the following regression equation, which is an empirical relationship between the removal efficiency of RhB and test variable in actual variables: RhB removal (%) = 3.977 + 23.279$\cdot$Current + 49.124$\cdot$NaCI - 5.539$\cdot$pH - 8.863$\cdot$time - 22.710$\cdot$Current$\cdot$NaCl + 5.409$\cdot$Current$\cdot$time + 2.390$\cdot$NaCl$\cdot$time + 1.061$\cdot$pH$\cdot$time - $0.570{\cdot}time^2$. The model predicted also agree with the experimentally observed result($R^2$ = 91.9%).

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.21 no.11
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• pp.987-994
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• 2008

The optimal structure of the Tonpilz transducer was designed. First, the FE model of the transducer was constructed, that included all the details of the transducer which used practical environment. The validity of the FE model was verified through the impedance analysis of the transducer. Second, the resonance frequency, the sound pressure, the bandwidth, and the impulsive shock pressure of the transducer in relation to its structural variables were analyzed. Third, the design method of $2^n$ experiments was employed to reduce the number of analysis cases, and through statistical multiple regression analysis of the results, the functional forms of the transducer performances that could consider the cross-coupled effects of the structural variables were derived. Based on the all results, the optimal geometry of the Tonpilz transducer that had the highest sound pressure level at the desired working environment was determined through the optimization with the SQP-PD method of a target function composed of the transducer performance. Furthermore, for the convenience of a user, the automatic process program making the optimal structure of the acoustic transducer automatically at a given target and a desired working environment was made. The developed method can reflect all the cross-coupled effects of multiple structural variables, and can be extended to the design of general acoustic transducers.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Environmental Engineering Research
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• v.23 no.4
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• pp.374-382
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• 2018

Petroleum hydrocarbons pollutants, such as diesel fuel, have caused ecosystem damage in terrestrial and aquatic habitats. They have been recognized as one of the most hazardous wastes. This study was designed to optimize the effect of Tween 80 concentration, nitrogen (N)/phosphorus (P) ratio and salinity level on diesel biodegradation by Vibrio alginolyticus (V. alginolyticus). Response surface methodology with Box-Behnken design was selected with three factors of Tween 80 concentration (0, 5, 10 mg/L), N/P ratio (5, 10, 15) and salinity level (15‰, 17.5‰, 20‰) as independent variables. The percentage of diesel degradation was a dependent variable for 14 d of the remediation period. The results showed that the percentages of diesel degradation generally increased with an increase in the amount of Tween 80 concentration, N/P ratio and salinity level, respectively. The optimization condition for diesel degradation by V. alginolyticus occurred at 9.33 mg/L of Tween 80, 9.04 of N/P ratio and 19.47‰ of salinity level, respectively, with percentages of diesel degradation at 98.20%. The statistical analyses of the experimental results and model predictions ($R^2=0.9936$) showed the reliability of the regression model and indicated that the addition of biostimulant can enhance the percentage of diesel biodegradation.