• Advances in Energy Research
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• v.4 no.2
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• pp.107-120
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• 2016

The aim of this research is to apply the eQuest model to investigate the energy conservation in a multifamily building located in Dayton, Ohio by using a Trombe wall and an ammonia ground source heat pump (R-717 GSHP). Integration of the Trombe wall into the building is the first retrofitting measure in this study. Trombe wall as a passive solar system, has a simple structure which may reduce the heating demand of buildings significantly. Utilization of ground source heat pump is an effective approach where conventional air source heat pump doesn't have an efficient performance, especially in cold climates. Furthermore, the type of refrigerant in the heat pumps has a substantial effect on energy efficiency. Natural refrigerant, ammonia (R-717), which has a high performance and no negative impacts on the environment, could be the best choice for using in heat pumps. After implementing the eQUEST model in the said multifamily building, the total annual energy consumption with a conventional R-717 air-source-heat-pump (ASHP) system was estimated as the baseline model. The baseline model results were compared to those of the following scenarios: using R-717 GSHP, R410a GSHP and integration of the Trombe wall into the building. The Results specified that, compared to the baseline model, applying the R-717 GSHP and Trombe wall, led to 20% and 9% of energy conservation in the building, respectively. In addition, it was noticed that by using R-410a instead of R-717 in the GSHP, the energy demand increased by 14%.

• Food Science of Animal Resources
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• v.30 no.3
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• pp.487-494
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• 2010

Cooked pork can be easily contaminated with Staphylococcus aureus during carriage and serving after cooking. This study was performed to develop growth prediction models of S. aureus to assure the safety of cooked pork. The Baranyi and Gompertz primary predictive models were compared. These growth models for S. aureus in cooked pork were developed at storage temperatures of 5, 15, and $25^{\circ}C$. The specific growth rate (SGR) and lag time (LT) values were calculated. The Baranyi model, which displayed a $R^2$ of 0.98 and root mean square error (RMSE) of 0.27, was more compatible than the Gompertz model, which displayed 0.84 in both $R^2$ and RMSE. The Baranyi model was used to develop a response surface secondary model to indicate changes of LT and SGR values according to storage temperature. The compatibility of the developed model was confirmed by calculating $R^2$, $B_f$, $A_f$, and RMSE values as statistic parameters. At 5, 15 and $25^{\circ}C$, $R^2$ was 0.88, 0.99 and 0.99; RMSE was 0.11, 0.24 and 0.10; $B_f$ was 1.12, 1.02 and 1.03; and $A_f$ was 1.17, 1.03 and 1.03, respectively. The developed predictive growth model is suitable to predict the growth of S. aureus in cooked pork, and so has potential in the microbial risk assessment as an input value or model.

• Journal of Information Technology Applications and Management
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• v.30 no.6
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• pp.91-111
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• 2023

This study aims to identify the influencing factors of intention to use cloud services using the SEM-ANN two-step approach. In previous studies of SEM-ANN, SEM presented R² and ANN presented MSE(mean squared error), so analysis performance could not be compared. In this study, R² and MSE were calculated and presented by SEM and ANN, respectively. Then, analysis performance was compared and feature importances were compared by sensitivity analysis. As a result, the ANN default model improved R² by 2.87 compared to the PLS model, showing a small Cohen's effect size. The ANN optimization model improved R² by 7.86 compared to the PLS model, showing a medium Cohen effect size. In normalized feature importances, the order of importances was the same for PLS and ANN. The contribution of this study, which links structural equation modeling to artificial intelligence, is that it verified the effect of improving the explanatory power of the research model while maintaining the order of importance of independent variables.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.673-678
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• 2005

The objective of this paper is to develop the microorganism concentration simulation model for the health related effect analysis while farmers and water managers reuse the wastewater for agricultural irrigation. This model consists of the CE-QUAL-R1 model and the CREAMS-PADDY model. The CE-QUAL-R1 model is the 1-D numerical model to analyze the water quality of the reservoir and the CREAMS-PADDY model is modified from CREAMS model for considering the hydrologic cycles in paddy field. This model was applied to examine the application by the observed data from 2003 in Byoungjum study area. From this research, the average root mean square error (RMSE) for the simulated concentration during the calibration period was 0.51 MPN/100ml and correlation coefficient $(R^2)$ was 0.71. And the RMSE for the simulated concentration during the verification period was 0.46 MPN/100ml and $R^2$ was 0.73. This simulation results show that the coliform inflow concentrations by the wastewater irrigation wield great influence upon the temporal coliform concentrations in paddy field.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.118-129
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• 2021

The sediment transportation caused by soil erosion due to rainfall-discharge in the large watershed scale plays critical role in human society. The relationship between rainfall-discharge-sediment transportation is depending on the start time of rainfall and end of rainfall but, the studies related with rainfall characteristics are insufficient. In this study, The Soil and Water Assession Tool (SWAT) model was used to study the relationship between rainfall-discharge-sediment transportation at the Sook river watershed which is monitored by the Ministry of Environment. To do this, first of all, the sensitivity analysis about model attributes was performed using monitored data. The accuracy analysis of SWAT model was conducted using the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and the coefficient of determination (R²). After that, it was studied what results could be obtained according to changes in rainfall timing and end points. In the result of discharge simulation, the modified rainfall values (sum of total rainfall starting time and end time) showed more high accuracy values (R²:0.90, NSE: 0.8) than original rainfall values (R²:0.76, NSE: 0.72). In the result of sediment transportation simulation, during calibration had more resonable results(R²:0.87, NSE: 0.86) than compared with original rainfall values (R²:0.44, NSE: 0.41). However, validation results of sediment transportation simulation showed low accuracy values compared with calibration results. This results maybe cause monitoring periods of sediment flow compared with discharge monitoring periods. Nevertheless, since rainfall characteristic plays critical rule in model results, continuous research on rainfall characteristic is needed.

• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.184-193
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• 2021

This paper investigates the performance of technology innovation activities performed by firms in 16 major regions in Korea using 2002-2010 survey data by STEPI. The theoretical and empirical analysis is carried out via the 2 models which are the simple R&D - total revenue model and Cobb-Douglas model based on the simple model adding labor variable. The main results shows that for simple model, the R&D elasticity for total revenue is 0.42 for all areas and Ul-San shows the highest elasticity level, 0.66 and Bu-San the lowest level, 0.2. In case of Cobb Douglas model the R&D elasticities are not statistically significant for many regions. To overcome the low statistical significance, we grouped the 15 regions for 3 wider regions using ANOVA based on the R&D intensity for the homogeneity of R&D activities. By grouping, each region has more observations to analyze and the results from the empirical analysis shows higher statistical significance level and data explanation capability. In this case, Group 3 which shows larger firm size and slightly higher export share shows the highest level of R&D elasticity, 0.088 and Group 1 which has the smallest firm size and the lowest revenue growth rate shows the lowest level, 0.31. For the labor elasticity, Group 1 shows the higest level, 1.16 and Group2 the lowest level, 1.096. These results show that the regions which have many middle and small firms reveal low R&D-revenue elasticity and high labor-revenue elasticity.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.924-935
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• 2023

Purpose: The central aim of this study is to leverage machine learning techniques for the classification of Intrusion Detection System (IDS) data, with a specific focus on identifying the variables responsible for enhancing overall performance. Method: First, we classified 'R2L(Remote to Local)' and 'U2R (User to Root)' attacks in the NSL-KDD dataset, which are difficult to detect due to class imbalance, using seven machine learning models, including Logistic Regression (LR) and K-Nearest Neighbor (KNN). Next, we use the SHapley Additive exPlanation (SHAP) for two classification models that showed high performance, Random Forest (RF) and Light Gradient-Boosting Machine (LGBM), to check the importance of variables that affect classification for each model. Result: In the case of RF, the 'service' variable and in the case of LGBM, the 'dst_host_srv_count' variable were confirmed to be the most important variables. These pivotal variables serve as key factors capable of enhancing performance in the context of classification for each respective model. Conclusion: In conclusion, this paper successfully identifies the optimal models, RF and LGBM, for classifying 'R2L' and 'U2R' attacks, while elucidating the crucial variables associated with each selected model.

• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.46-54
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• 1993

Analytic and simulation models for prioritized token ring are presented in this paper. Its protocol is based on prioritized token ring with reservation (R-PTR). Since the protocol of the R-PTR is simple and the performance of the R-PTR is not inferior to that of the IEEE-PTR under almost all traffic load environments, we use the R-PTR as our token ring model. By using the properties of Markovian process, the expressions for average throughput and average packet transmission delay are derived. The results obtained from the analytic model are compared with that of the discrete event simulation model.

• Journal of Microbiology and Biotechnology
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• v.20 no.4
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• pp.737-748
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• 2010

Glutaminase production in Zygosaccharomyces rouxii by solid-state fermentation (SSF) is detailed. Substrates screening showed best results with oatmeal (OM) and wheatbran (WB). Furthermore, a 1:1 combination of OM:WB gave 0.614 units/gds with artificial sea water as a moistening agent. Evaluation of additional carbon, nitrogen, amino acids, and minerals supplementation was done. A central composite design was employed to investigate the effects of four variables (viz., moisture content, glucose, corn steep liquor, and glutamine) on production. A 4-fold increase in enzyme production was obtained. Studies were undertaken to analyze the time-course model, the microbial growth, and nutrient utilization during SSF. A logistic equation ($R^2$=0.8973), describing the growth model of Z. rouxii, was obtained with maximum values of ${\mu}_m$ and $X_m$ at $0.326h^{-1}$ and 7.35% of dry matter weight loss, respectively. A goodfit model to describe utilization of total carbohydrate ($R^2$=0.9906) and nitrogen concentration ($R^2$=0.9869) with time was obtained. The model was used successfully to predict enzyme production ($R^2$=0.7950).