• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.8
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• pp.1317-1323
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• 2003

In this paper, In-air and in-water vibration characteristics of Rotary Specimen Rack(RSR) are estimated through 3D finite element analysis by using ANSYS software. Added mass is calculated by using Blevins' equation. To confirm the accuracy of the results presented in this study, obtained results are compared to those of using a theoretical equation. It is confirmed that in-water natural frequencies of the RSR are lower than in-air ones due to tile added mass effect of the fluid. Also, good agreement is founded between natural frequency ratios obtained by a theoretical equation and those of using ANSYS.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.759-767
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• 2017

Two main parameters of NRCS-CN method are curve numbers and intial loss ratio. They are generally selected according to the guideline of US National Engineering Handbook, however, they might cause errors on estimated runoff in Korea because there are differences between soil types and hydrological characteristics of Korean watersheds and those of United States. In this study, applying asymptotic CN regression method, we suggested eight modified NRCS-CN models to decide optimum runoff estimation model for Korean watersheds. RSR (RMSE-observations standard deviation ratio) and NSE (Nash-Sutcliffe efficiency) were used to evaluate model performance, consequently M6 for gauged basins (Avg. RSR was 0.76, Avg. NSE was 0.39) and M7 for ungauged basins (Avg. RSR was 0.82, Avg. NSE was 0.31) were selected. Furthermore it was observed that initial loss ratios ranging from 0.01 to 0.10 were more adequate than the fixed ${\lambda}=0.20$ in most of basins.

• Journal of Korea Water Resources Association
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• v.52 no.6
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• pp.381-395
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• 2019

The purpose of this study is to predict agricultural reservoir storage rate (RSR) in a month. This algorithm was developed by multiple linear regression model (MLRM) which included the past 3 months RSRs data and the future climate change scenarios. In order to improve use of predicted RSR, this study need the severe criteria in terms of drought. So, the predicted RSR was indexed as the 3 months reservoir drought index (RDI3) and then it was disaggregated into drought duration, severity, and intensity. For the future RSR estimation by climate change scenarios, the 6 RCP 8.5 scenarios of HadGEM2-ES, CESM1-BGC, MPI-ESM-MR, INM-CM4, FGOALS-s2, and HadGEM3-RA were used in three future evaluation periods (S1: 2011~2040, S2: 2041~2070, S3: 2071~2099). The future S3 period of HadGEM2-ES scenario which has the biggest increase in precipitation and temperature showed the largest decrease to 60.2% among the 6 scenarios compared to the historical RSR (1976~2005) 77.3%. In contrast, INM-CM4 scenario which has smallest changes in precipitation and temperature in S3 period showed the smallest decrease to 72.8%. For the CESM1-BGC and MPI-ESM-MR, FGOALS-s2, and HadGEM3-RA, the S3 period RSR showed 72.6%, 72.6%, 67.4%, and 64.5% decrease respectively. The future severe drought condition of RDI3 below -0.25 showed the increase trend for the number and severity up to -2.0 during S3 period.

• Proceedings of the Zoological Society Korea Conference
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• 1998.10b
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• pp.333.2-333
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• 1998

No Abstract, See Full Text

• Journal of Korean Society of Water and Wastewater
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• v.36 no.4
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• Transactions of Materials Processing
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• v.27 no.1
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• pp.12-17
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• 2018

This paper outlines the research findings on the microstructure and mechanical properties of AZ31 Mg alloy fabricated by differential speed rolling (DSR) with respect to processing variables such as temperature, roll speed ratio (RSR), and deformation route. The resultant microstructure of the sample, deformed by 2-pass DSRs at 473 K, comprised finer grains with more uniform distribution than those at 573 and 623 K. This was due to active recrystallization, which was expected to appear during DSR at temperatures higher than 573 K. When the sample was deformed via DSR with RSR of 1:4 for the upper and lower rolls at 453 K, the values of yield and ultimate tensile strength were observed to be higher than their counterpart with RSR of 1:1. The application of sample rotation around the longitudinal axis would give rise to an excellent combination of tension strength (~330 MPa) and ductility (~20 %) at ambient temperatures. This is discussed based on its uniform fine grained structure and the softening of basal texture.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.952-956
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• 2007

In industrial site, as the technology of FA developes, many robots can take the place of human labor. Generally, robots require high precision reducers which have cycloid or involute tooth profile. These reducers provide high reduction ratios with low backlash and high accuracy but need a high stiffness of tooth. In this study, to improve the high precision, the cycloid reducers which have straight line teeth profile are used. Also the FEM analysis, which consist of straight line teeth profile, was performed to simulate the stress of teeth profile.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Journal of the Korean Geosynthetics Society
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• v.14 no.3
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• pp.31-42
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• 2015

The Reinforced subgrade for railroad (RSR) was constructed for one way railway line with the dimension of 5 m high, 6 m wide and 20 m long to evaluate its performance under train design load. The RSR has characteristics of short length (0.3-0.4 H) of reinforcement and rigid wall, 30 and 40 cm vertical spacing of reinforcement installation. To enhance economics and constructability, three kinds of connections (welding, hinge & bolt, bold wire) were also designed to realize the integration between rigid wall and reinforced subgrade. Two times of static loading tests were done on the full size railroad subgrade. The maximum applied pressure was 0.98 MPa (the maximum test load 5.88 MN), which corresponds to 19.6 times of the design load for railroad subgrade, 50 kPa. The performance on the RSR was evaluated with the safety on the failure, subgrade bearing capacity and settlement, horizontal displacement of wall, and reinforcement strain. Based on the full scale test, we confirmed that the RSR with the conditions of 0.35 H (35% of height) short reinforcement length, hinge & bolt type connection for integration between rigid wall and reinforced subgrade, and 40cm vertical spacing of reinforcement installment shows good performance under train design load.

• The Korean Journal of Pesticide Science
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• v.5 no.1
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• pp.61-67
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• 2001

Five hundred sixteen isolates of Botrytis elliptica were isolated from infected leaves of Lilium longiflorum from Kangwon alpine areas in Korea during tile seasons from 1998 to 2000 and resistance of these isolates against some fungicides were examined. The isolation frequency of phenotypes resistant to benomyl, procymidone, and diethofencarb were 90.1, 32.4, and 40.9%, respectively. The isolates were divided into six phenotypic groups; RSS, RRS, SSR, SRR, RSR and RRR, representing sensitive (S) or resistant (R) to benzimidazole, dicarboximide, and N-phenylcarbamate fungicides in order. The percentage of six phenotypes were 40.7, 8.5, 7.2, 2.7, 19.8, and 21.1%, respectively. The RSS phenotype was the most frequently isolated, and tile SRR consisted of the extremely minor populations. In comparison studies on tile overwintering ability of each phenotype in relation to the others, the most frequently isolated RSS and SSR had the higher fitness ability than the less frequently isolated RSR, SRR, and RRR. Recently, population increase of tile RSR and RRR phenotypes may have resulted from the increased applications of the mixture of carbendazim and diethofencarb to control benzimidazole-resistant B. elliptica since 1998. The results of this study indicate that careful application of the fungicides is necessary to achieve effective control of leaf blight on lily in Korea.