• Journal of Korean Society of Environmental Engineers
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• v.36 no.10
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• pp.691-697
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• 2014

Though the upper stream basin area of Gwanpyung-Cheon in Daejeon, Korea is protected as Green Belt Zone, the stream is under constant environmental pressure due to current agricultural practices and infrastructure development in its basin area. To develop appropriate integrated water resources management plan for the stream, it is necessary to consider not only water quality problems but also water quantity aspect. In this study, Storm Water Management Model (SWMM) was calibrated and validated with sets of field measurements to predict for future water flow and water quality conditions for any rainfall event. While flow modeling results showed good agreement by showing correlation coefficient is greater than 0.9, water quality modeling results showed relatively less accurate levels of agreements with correlation coefficient between 0.67 and 0.87. Hypothetical basin development scenarios were developed to compare effect on stream water quality and quantity when Low Impact Development (LID) technologies are applied in the basin. The results of this study can be used effectively in decision making processes of urban development Gwanpyung-Cheon area by comparing basin management alternatives such as LID methods.

• Journal of Environmental Science International
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• v.15 no.1
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• pp.1-7
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• 2006

The purpose of this study is to illustrate the characteristics of soil organic matter (SOM) and partition coefficient $(K_{DOC})$. Humic substances (HS) from eight soils of varying properties were extracted by two different methods. The dissolved organic carbon (DOC) concentration was stabilized in 22hrs. The ratio of UV absorbance at 465nm and 665nm (E4/E6 ratio) for HS were similar pattern for 8 soils. The extraction with increasing pH increased dissolution of SON. The ratio of organic carbon (OC) associated with HA and FA (the HA:FA ratio) was varied widely in accordance with the soils and was highly correlated to OC $content(\%)$ of the soils. in modeling metal speciation in soils and soil solutions, assumptions that all DOC in soil solution is associated with FA and that HA:FA ratio in SOM is constant have been made. The results of this study indicate that the validity of these assumptions is questionable. By sequential pH extraction, the $K_{DOC}$ showed in a linear correlation with pH.

• The Journal of Industrial Distribution & Business
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• v.10 no.4
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• pp.33-41
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• 2019

Purpose - This study investigates the effect of strategic orientation on market performance with emphasis on the mediative role of innovation capability, economic value and relational value in food producer companies. Research design, data, and methodology - In this descriptive study, a population of 244 managers and employees of Food industry companies in Tehran were investigated. The respondents filled a questionnaire on strategic orientation, innovation capability, economic value, relational value and market performance, during January to August 2018. Reliability and validity were evaluated by Cronbach's alpha coefficient and confirmatory factor analysis. To analyze the data, Spearman's correlation coefficient and structural equation modeling were used by SmartPLS software. Results - Effects of competitor's orientation and technology orientation on all three intermediary variables were positive and significant. The effect of customer orientation on innovation and economic value was positive and significant, but the effect of customer orientation on the value of the relationship was insignificant. Furthermore, entrepreneurial orientation has a positive and significant effect on innovation capability. The effects of three mediator variables on market performance are positive and significant. Conclusions - As the relationship between the mediator variables and market performance were positive and significant, companies should have a comprehensive plan of focus on strengthening these variables.

• Smart Structures and Systems
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• v.19 no.4
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• pp.361-369
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• 2017

Electromechanical impedance method as an efficient tool in Structural Health Monitoring (SHM) utilizes the electromechanical impedance of piezoelectric materials which is directly related to the mechanical impedance of the host structure and will be affected by damages. In this paper, electromechanical impedance of piezoelectric patches attached to simply support rectangular plate is determined theoretically and experimentally in order to detect damage. A pairs of piezoelectric wafer active sensor (PWAS) patches are used on top and bottom of an aluminum plate to generate pure bending. The analytical model and experiments are carried out both for undamaged and damaged plates. To validate theoretical models, the electromechanical impedances of PWAS for undamaged and damaged plate using theoretical models are compared with those obtained experimentally. Both theoretical and experimental results demonstrate that by crack generation and intensifying this crack, natural frequency of structure decreases. Finally, in order to evaluate damage severity, damage metrics such as Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), and Correlation Coefficient Deviation (CCD) are used based on experimental results. The results show that generation of crack and crack depth increasing can be detectable by CCD.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Computers and Concrete
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• v.13 no.2
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• pp.255-270
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• 2014

Impact performance of high-performance concrete (HPC) and SFRC at 28-day and 56-day under the action of repeated dynamic loading was studied. Silica fume replacement at 10% and 15% by mass and crimped steel fiber ($V_f$ = 0.5%- 1.5%) with aspect ratios of 80 and 53 were used in the concrete mixes. Results indicated that addition of fibers in HPC can effectively restrain the initiation and propagation of cracks under stress, and enhance the impact strengths and toughness of HPC. Variation of fiber aspect ratio has minor effect on improvement in impact strength. Based on the experimental data, failure resistance prediction models were developed with correlation coefficient (R) = 0.96 and the estimated absolute variation is 1.82% and on validation, the integral absolute error (IAE) determined is 10.49%. On analyzing the data collected, linear relationship for the prediction of failure resistance with R= 0.99 was obtained. IAE value of 10.26% for the model indicates better the reliability of model. Multiple linear regression model was developed to predict the ultimate failure resistance with multiple R= 0.96 and absolute variation obtained is 4.9%.

• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• The Korean Journal of Health Service Management
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• v.9 no.1
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• pp.67-79
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• 2015

The aim of this descriptive cross-sectional study was determine the effect of critical thinking and clinical decision-making on ethical dilemmas. A survey of dental hygienists residing in Busan and South Gyeongsang, Korea was conducted using convenience sampling between September and December, 2013. A total of 153 responses were used in the final analysis. Data analysis and structural equation modeling were performed with IBM SPSS Statistics(version 21.0) and AMOS(version 18.0) programs. A negative(-) correlation coefficient(-0.37) was observed between critical thinking and ethical dilemmas on statistical analysis, i.e., higher critical thinking led to less ethical dilemmas(p=0.024, CR=-2.264). The values from the structural equation model were ${\chi}^2=98.124$ df=66, GFI=0.919, AGFI=0.871, and RMSEA=0.057. This study proposed a theoretical model in which critical thinking, ethical values, and decision-making skills should be firmly established to effectively respond to specific situations, such as ethical dilemmas, and that greater tendencies for critical thinking led to less ethical dilemmas, thereby demonstrating a negative(-) correlation between the two parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.69-69
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• 2021

From the unprecedented 2011 spring flood, the residens reside by Lake Champlain and Richelieu River encountered enormous damages. The International Joint Committee (IJC) released the Lake Champlain-Richelieu River (LCRR) Plan of Study (PoS). One of the major tasks for the PoS is to investigate the possible scenarios that might happen in the LCRR basin based on the stochastic simulation of the Net Basin Supplies that calculates the amount of flow into the lake and the river. Therefore, the current study proposed a novel apporach that simulate the annual NBS teleconnecting the climate index. The proposed model employed the bivariate empirical decomposition to contamporaneously model the long-term evolution of nonstationary oscillation embeded in the annual NBS and the climate signal (here, Artic Oscillation: AO). In order to represent the variational behavior of NBS correlation structure along with the temporal revolution of the climate index, a new nonstationary parameterization concept is proposed. The results indicate that the proposed model is superior performance in preserving long and short temporal correlation. It can even preserve the hurst coefficient better than any other tested models.