• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.593-606
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• 2016

Computer simulation is widely used in a variety of computational science and engineering fields, including computational fluid dynamics, nano physics, computational chemistry, structural dynamics, and computer-aided optimal design, to simulate the behavior of a system. As the demand for the accuracy and complexity of the simulation grows, however, the cost of executing the simulation is rapidly increasing. It, therefore, is very important to lower the total execution time of the simulation especially when that simulation makes a huge number of repetitions with varying values of input parameters. In this paper we develop a simulation service system that provides the ability to predict the result of the requested simulation without actual execution for that simulation: by recording and then returning previously obtained or predicted results of that simulation. To achieve the goal of avoiding repetitive simulation, the system provides two main functionalities: (1) storing simulation-result records into database and (2) predicting from the database the result of a requested simulation using statistical machine learning techniques. In our experiments we evaluate the prediction performance of the system using real airfoil simulation result data. Our system on average showed a very low error rate at a minimum of 0.9% for a certain output variable. Using the system any user can receive the predicted outcome of her simulation promptly without actually running it, which would otherwise impose a heavy burden on computing and storage resources.

• Journal of Korean Physical Therapy Science
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• v.8 no.2
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• pp.977-987
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• 2001

The purpose of this study was to examine the effects of sensory integration therapy (SIT) on sensory' motor development and adaptive behavior of cerebral palsy children. The design of this study was quasi experiments with a non-equivalent pre- and post-test control design. Subjects of the study were arbitrarily chosen based on predetermined selection criteria among the cerebral palsy children who were treated as out-patients at two rehabilitation hospitals one in Seoul, and the other in Kyunggi-do. The study was conducted between early April and late July in 2000. Fifteen children were in the experimental group and eleven in the control group. The allocation was done based on ease of experimental treatment. A five-step SIT program was devised from a combination of SIT programs suggested by Ayres(1985) and Finks(1989), and an author-designed SIT program for cerebral palsy children. The experimental group was subjected to 20 to 30 minutes of SIT per session. two sessions a week for ten -week period. The effects of SIT were measured with respect to 9 sub-areas that can be administered to cerebral palsy children out of a total of 17 sub-areas in the Southern California Sensory Integration Test (SCSIT) developed by Ayres (1980). In addition. the scale developed by Russell (1993) for Gross Motor Function Measure (GMFM). and Perception Motor Development Test developed by 中司利一 et al.(1987) were also applied. Adaptive behavior was analyzed using guidelines in two unpublished documents - School-Age Checklist for Occupational Therapy by the Wakefield Occupational Therapy Associates, and the OTA-Watertown Clinical Assessment by the Watertown Occupational Therapy Associates-, and an author-developed Adaptive Behavior Checklist. Collected data were statistically analyzed by SPSS PC for chi square test, Mann-Whitney test, Wilcoxon signed rank test, and paired t-test. The results were as follows: 1. In sensory development, the experimental group exhibited a score increase compared to the control group, but the difference was not statistically significant, Although the experimental group showed improvements in all. 9 sub-areas compared to the control group, only right-left discrimination exhibited statistically significant change. 2. In gross motor development, the experimental group showed improvements in score compared to the control group, but it was not statistically significant. In fine motor development, the experimental group exhibited statistically significant improvements compared to the control group. In sub-area analysis, figure synthesis showed positive change. 3. In adaptive behavior development, post-experimental adaptive behavior scores were higher compared to pre-experimental scores with statistical significance. Furthermore, sub-areas emotional behavior, perception behavior, gross-fine motor function, oral-respiration function, motor behavior, motor planning, and adaptive response exhibited higher scores after SIT. In conclusion SIT was found to be partially effective in sensory and fine motor development, effective in all adaptive behavior areas, and not effective in gross motor development. Thus, this study has shown that SIT is an effective intervention for sensory development, fine motor development, and adaptive behavior for cerebral palsy children. But, for the effectiveness of SIT on gross motor development, further studies employing longer-time experiments are recommended.

• Journal of the Korean GEO-environmental Society
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• v.12 no.9
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• pp.55-61
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• 2011

This study investigate the use of ultraviolet(UV) light with hydrogen peroxide($H_2O_2$) for Methyl Tert Butyl Ether(MTBE) degradation in photolysis reactor. The process in general demands the generation of OH radicals in solution at the presence of UV light. These radicals can then attack the MTBE molecule and it is finally destroyed or converted into a simple harmless compound. The MTBE removal by photolysis were mathematically described as the independent variables such as irradiation intensity, initial concentration of MTBE and $H_2O_2$/MTBE ratio, and these were modeled by the use of response surface methodology(RSM). These experiments were carried out as a Box-Behnken Design(BBD) consisting of 15 experiments. Regression analysis term of Analysis of Variance(ANOVA) shows significantly p-value(p<0.05) and high coefficients for determination values($R^2$=94.60%) that allow satisfactory prediction of second-order regression model. And Canonical analysis yields the stationery point for response, with the estimate ridge of maximum responses and optimal conditions for Y(MTBE removal efficiency, %) are $x_1$=25.75 W of irradiation intensity, $x_2$=7.69 mg/L of MTBE concentration and $x_3$=11.04 of $H_2O_2$/MTBE molecular ratio, respectively. This study clearly shows that RSM is available tool for optimizing the operating conditions to maximize MTBE removal.

• Journal of KIISE
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• v.42 no.5
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• pp.609-620
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• 2015

Viewers of videos are likely to absorb more information from the part of the screen that attracts visual attention. This fact has led to the visual attention models that are being used in producing and evaluating videos. In this paper, we investigate the factors that are significant to visual attention and the mathematical form of the visual attention model. We then estimated the visual attention probability using the statistical design of experiments. The analysis of variance (ANOVA) verifies that the motion velocity, distance from the screen, and amount of defocus blur affect human visual attention significantly. Using the response surface modeling (RSM), we created a visual attention score model that concerns the three factors, from which we calculate the visual attention probabilities (VAPs) of image pixels. The VAPs are directly applied to existing gradient based 3D effect perception measurement. By giving weights according to our VAPs, our algorithm achieves more accurate measurement than the existing method. The performance of the proposed measurement is assessed by comparing them with subjective evaluation as well as with existing methods. The comparison verifies that the proposed measurement outperforms the existing ones.

• Journal of Science Education
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• v.41 no.1
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• pp.80-97
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• 2017

The purpose of this study was to develop understanding-oriented materials based on backward course design model and analyze their effects on 'biology and environment' unit of elementary school science. Backward Design starts from a specification of learning outcomes and decisions on methodology and syllabus are developed from the learning outcomes. This method has a strength maintaining consistency between educational contents-evaluation-learning activities and also promoting student's authentic understanding. The 78 students 6th graders participated in this experiments. Data was collected using project activities, the science academic emotion scale and academic achievement. The collected data was analyzed by t-test and ANCOVA analysis using the SPSS 23 statistical program. The following major conclusions were drawn on the basis of data analysis. First, the experimental group showed a relatively accurate understanding of the contents of science but they could not produce creative output in two project activities. Second, the interaction effect of the instruction based on backward curriculum design and science academic emotion was not significant statistically. Third, the experimental group showed a significant improvement in the academic achievement of 'biology and environment' unit.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.267-280
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• 2007

Various single-valued design optimality criteria such as D-, G-, and V-optimality are used often in constructing optimal experimental designs for mixture experiments in a constrained region R where lower and upper bound constraints are imposed on the ingredients proportions. Even though they are optimal in the strict sense of particular optimality criterion used, it is known that their performance is unsatisfactory with respect to the prediction capability over a constrained region. (Vining et at., 1993; Khuri et at., 1999) We assume the quadratic polynomial model as the mixture response surface model and are interested in finding efficient designs in the constrained design space for a mixture. In this paper, we make an expanded list of candidate design points by adding interior points to the extreme vertices, edge midpoints, constrained face centroids and the overall centroid. Then, we want to propose a robust design with respect to D-optimality, G-optimality, V-optimality and distance-based U-optimality. Comparing scaled prediction variance quantile plots (SPVQP) of robust designs with that of recommended designs in Khuri et al. (1999) and Vining et al. (1993) in the well-known examples of a four-component fertilizer experiment as well as McLean and Anderson's Railroad Flare Experiment, robust designs turned out to be superior to those recommended designs.

• Journal of Life Science
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• v.30 no.6
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• pp.522-531
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• 2020

The aim of this study was to establish the optimal medium composition for enhancing L(+)-lactic acid (LLA) production using response surface methodology (RSM). Lactobacillus paracasei SRCM201474 was selected as the LLA producer by productivity analysis from nine candidates isolated from kimchi and identified by 16S rRNA gene sequencing. Plackett-Burman design was used to assess the effect of eleven media components on LLA production, including carbon (glucose, sucrose, molasses), nitrogen (yeast extract, peptone, tryptone, beef extract), and mineral (NaCl, K₂HPO₄, MgSO₄, MnSO₄) materials. Glucose, sucrose, molasses, and peptone were subsequently chosen as promising media for further optimization studies, and a hybrid design experiment was used to establish their optimal concentrations as glucose 15.48 g/l, sucrose 16.73 g/l, molasses 39.09 g/l, and peptone 34.91 g/l. The coefficient of determination of the equation derived from RSM regression for LLA production was mathematically reliable at 0.9969. At optimum parameters, 33.38 g/l of maximum LLA increased by 193% when compared with MRS broth as unoptimized medium (17.66 g/l). Our statistical model was confirmed by subsequent validation experiments. Increasing the performance of LLA-producing microorganisms and establishing an effective LLA fermentation process can be of particular benefit for bioplastic technologies and industrial applications.

• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.205-217
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• 2022

The thermodynamic properties of shaft lining concrete (SLC) are important evidence for the design and construction, and the spatial variability of concrete materials can directly affect the stochastic thermal analysis of the concrete structures. In this work, an array of field experiments of the concrete materials are carried out, and the statistical characteristics of thermophysical parameters of SLC are obtained. The coefficient of variation (COV) and scale of fluctuation (SOF) of uncertain thermophysical parameters are estimated. A three-dimensional (3-D) stochastic thermal model of concrete materials with heat conduction and hydration heat is proposed, and the uncertain thermodynamic properties of SLC are computed by the self-compiled program. Model validation with the experimental and numerical temperatures is also presented. According to the relationship between autocorrelation functions distance (ACD) and SOF for the five theoretical autocorrelation functions (ACFs), the effects of the ACF, COV and ACD of concrete materials on the uncertain thermodynamic properties of SLC are analyzed. The results show that the spatial variability of concrete materials is subsistent. The average temperatures and standard deviation (SD) of inner SLC are the lowest while the outer SLC is the highest. The effects of five 3-D ACFs of concrete materials on uncertain thermodynamic properties of SLC are insignificant. The larger the COV of concrete materials is, the larger the SD of SLC will be. On the contrary, the longer the ACD of concrete materials is, the smaller the SD of SLC will be. The SD of temperature of SLC increases first and then decreases. This study can provide a reliable reference for the thermodynamic properties of SLC considering spatial variability of concrete materials.

• Structural Engineering and Mechanics
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• v.86 no.1
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• pp.119-137
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• 2023

The purpose of this research is to assess the performance of CBN and ceramic tools during the dry turning of gray cast iron EN GJL-350. During the turning operation, the variable machining parameters are cutting speed, feed rate, depth of cut and type of the cutting material. This contribution consists of two sections, the first one deals with the performance evaluation of four materials in terms of evolution of flank wear, surface roughness (2D and 3D) and cutting forces. The focus of the second section is on statistical analysis, followed by modeling and optimization. The experiments are conducted according to the Taguchi design L₃₂ and based on ANOVA approach to quantify the impact of input factors on the output parameters, namely, the surface roughness (Ra), the cutting force (Fz), the cutting power (Pc), specific cutting energy (Ecs). The RSM method was used to create prediction models of several technical factors (Ra, Fz, Pc, Ecs and MRR). Subsequently, the desirability function approach was used to achieve a multi-objective optimization that encompasses the output parameters simultaneously. The aim is to obtain optimal cutting regimes, following several cases of optimization often encountered in industry. The results found show that the CBN tool is the most efficient cutting material compared to the three ceramics. The optimal combination for the first case where the importance is the same for the different outputs is Vc=660 m/min, f=0.116 mm/rev, ap=0.232 mm and the material CBN. The optimization results have been verified by carrying out confirmation tests.

• Computers and Concrete
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• v.34 no.1
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• pp.93-122
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• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.