• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.875-886
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• 1994

This study is to develop an advanced storm runoff analysis program which takes geomorphological characteristics of watershed into consideration in determining model parameters. Basic concept of storm runoff modelling is based upon the kinematic wave theory. And numerical solution is obtained by the characteristic curve method. The storm runoff analysis program developed by this study is composed of multiple equivalent roughness sub-basins, each of which has two equivalent catchments on both side of a stream. Because it is based upon the stream-order of the Strahler system, the equivalent catchment-stream network reflects the stochastic geomorphological characteristics in the model parameter. Applicability and reliability of the storm runoff analysis program is evidenced by model calibration and verification process utilizing geographical and hydrological data of the Bocheong-river area which is a representative watershed of IHP projects in Korea. This study will hopefully contribute to hydrological calculation essentially required to understand water quality effect caused by regional development.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.113-121
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• 2015

This study is to identify that the efforts of business ethics build customer loyalty, and it makes customers lead to purchase behavior in the viewpoint of grasping convergence effect. The survey has proved that ethical management influences consumers' purchase behaviors, and consumers' image perception and trust on corporate play parameter role in that process. This results imply that corporate's ethical management gives increase in revenue and reputation to corporate. So managers must recognize business goes on when the social legitimacy is approved by the public, and make efforts to arrange systems and programs to foster ethical management. In the future study, it is required that an analysis to differentiate target range and level of implementation of ethical management and research associated with ethical issue of high public interest.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.14-24
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• 2020

This paper aims to establish an efficient national R&D Application Programming Interface (API) management system for national R&D data-driven management and explore the possibility of using operational data according to the recent global data openness and sharing policy. In accordance with the trend of opening and sharing of national R&D data, we plan to improve management efficiency by analyzing operational data of the national R&D API service. For this purpose, we standardized the parameters for the national R&D APIs that were distributed separately by integrating the individual APIs to build a national R&D API management system. The results of this study revealed that the service call traffic of the national R&D API has shown 554.5% growth in the year as compared to the year 2015 when the measurement started. In addition, this paper also evaluations the possibility of using operational data through data preparation, analysis, and prediction based on service operations management data in the actual operation of national R&D integrated API management system.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Atmosphere
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• v.29 no.4
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• pp.417-427
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• 2019

The purpose of this study is to build urban canopy model (Met Office Reading Urban Surface Exchange Scheme, MORUSES) based to Unified Model (UM) by using urban building information data in Seoul, and then to compare the improving urban canopy model simulation result with that of Seoul Automatic Weather Station (AWS) observation site data. UM-MORUSES is based on building information database in London, we performed a sensitivity experiment of UM-MOURSES model using urban building information database in Seoul. Geographic Information System (GIS) analysis of 1.5 km resolution Seoul building data is applied instead of London building information data. Frontal-area index and planar-area index of Seoul are used to calculate building height. The height of the highest building in Seoul is 40m, showing high in Yeoido-gu, Gangnam-gu and Jamsil-gu areas. The street aspect ratio is high in Gangnam-gu, and the repetition rate of buildings is lower in Eunpyeong-gu and Gangbuk-gu. UM-MORUSES model is improved to consider the building geometry parameter in Seoul. It is noticed that the Root Mean Square Error (RMSE) of wind speed is decreases from 0.8 to 0.6 m s^-1 by 25 number AWS in Seoul. The surface air temperature forecast tends to underestimate in pre-improvement model, while it is improved at night time by UM-MORUSES model. This study shows that the post-improvement UM-MORUSES model can provide detailed Seoul building information data and accurate surface air temperature and wind speed in urban region.

• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.719-727
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• 2004

Water quality models can be applied to manage the regional water quality problems and to estimate the target and allowable pollution load in watershed effectively. The optimization of state variables in the given water quality model Is necessary to build up more effective model. The least-squares method is applied to fit field observations in QUAL2E developed by U.S. EPA, which is most widely used one in the world to simulate the stream water quality, and the optimization model with constraints is constructed to estimate the parameters. The objective function of the optimization model is solved by Solver in Microsoft Excel and Monte Carlo simulation is conducted to know the influence of parameter in conventional pollutants. It is found that this technique is easily implemented and rapidly convergent computational procedure to calibrate the parameters after appling this approach in Anyang stream located in Kyonggi province mainly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2708-2714
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• 2009

Due to poor linguistic communication skills of sucklings and infants, crying mostly is only means of communication to express their body conditions and desires. We, therefore, developed an infant auscultation system which detects which part of the body has a pathological problem, by analysing infant's crying sound patterns. Specifically, in this paper, we accomplished an auscultation system for congenital heart diseases detection by performing pitch, intensity and spectrum analysis of the crying sounds between the normal infants group and the congenital heart diseases group. With this system, we can diagnose congenital heart diseases of infants with poor communication capacity, and, in the near future, can build a home care diagnosis system based on crying sound analysis technologies through additional experiments on medical data.

• Journal of the Korean Geotechnical Society
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• v.28 no.5
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• pp.95-107
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• 2012

Even though the importance of predicting build-up of pore pressure under cyclic loading is recognized, effective stress analysis is rarely performed due to difficulties in selecting the parameters for the pore pressure model. In this paper, a new stress based numerical model for predicting pore pressure under cyclic loading is developed. The main strength of the model is that it is easy-to-use, requiring only the CSR-N curve in selecting the parameters. Another advantage of the model is that it can be used for any loading pattern and therefore can be implemented in an effective stress time-domain dynamic analysis code. The accuracy of the model is validated through its comparisons with measurements in literature and laboratory test data collected in Korea. Further comparisons with another stress based pore pressure model highlighted the superiority of the proposed model.

• Journal of Animal Environmental Science
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• v.8 no.3
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• pp.171-176
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• 2002

Physical parameters of milk cow were measured to design and build RMS(Robotic Milking System) with a tape-measurer and body parameter measurer. The parameters are very important variables to design an RMS. For the working zone space of an RMS manipulator and the movement blunting of milk cow, an interval frame was installed on the stall bottom, and then cow's behavioral reactions were tested. The results from this study is summarized as follow. 1. On the general physical condition measurement, the maximum, minimum and average body length of cow which is related to the space that the manipulator could work into the RMS were 175cm, 144cm, and 163cm respectively. It appeared that the average distance between bottom and chest was 60cm. 2. The average length between fore teats, fore and hind teats and hind teats were 178mm, 150mm and 95mm respectively. It appeared that the average length between bottom and teat attachments was 544mm, and the average length between fore teats and tail-end was 331mm. 3. Although a cow kept a some extent length between hind legs for milking, it looked a stable pose. However, the cow kept a some extent distance between front legs for milking, it looked a unstable pose. Based on results of this test, an interval frame of stall bottom should be installed around the position which was located at its hind legs.

• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.