• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1237-1246
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• 2021

The purpose of the research is to evaluate how education influences the income of household heads, who are young adult in rural Vietnam. In order to examine the impact of education on the households where their heads are young adults, in this paper, the authors employ two research methods. First, ordinary least squares (OLS) regression is used to study the impact of education on different groups of income; second, quantile regression is applied to find out how education influences the income of households. The dataset includes a survey of 800 young households aged between18 and 35 who are the head of agricultural farms in rural areas. The findings indicate that education has a positive impact on income of young households. Furthermore, the results prove that the longer schooling years, the higher income youth can attain. The results showed that, at the survey time (Sep 2019), the average monthly income of rural young adults who are joining the production process shows a big gap between low and high incomes. Moreover, the study has revealed that other factors positively affect the incomes, namely, joining job-related associations, land resource, hired labour, hi-tech application as well as extension of producing unit.

• Korean Journal of Cognitive Science
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• v.31 no.1
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• pp.1-23
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• 2020

This paper introduces SNUDM, an analysis program for Ratcliff's diffusion model, which has been one of the most important models in cognitive psychology over the past 35 years and which has come to occupy an important place in cognitive neuroscience in recent years. The analysis tool is designed with the basic principles of easy comprehension and simplicity in use. A diffusion process was programmed as the limit of a simple random walk in a manner resembling Ratcliff & Tuerlinckx(2002). The response time distribution of the model was constructed by simulating the time taken by a random walk until it reaches a threshold with small steps. The optimal parameter values in the model are found to be the smallest value of the chi-square values obtained by comparing the resulting distribution and the experimental data using Simplex method. For simplicity and ease of use, the input file used here is created as a file containing the quantile of the reaction time, the trials and other information. The number of participants and the number of conditions required for such work programs are given in a way that answers the question. Using this analysis tool, the experimental data of Ratcliff, Gomez, & McKoon(2004) were analyzed. We found the very similar pattern of parameter values to Ratcliff et al.(2004) found. When comparing DMAT, fast-dm and SNUDM with the generated data, we found that when the number of trials is small, SNUDM estimates the boundary parameter to a value similar to fast-dm and less than the DMAT. In addition, when the number of trials was large, it was confirmed that all three tools estimate parameters similarly.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.1-12
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• 2015

The study aimed to project inflows and demmands for the agricultural reservoir watersheds in South Korea considering a variety of regional characteristics and the uncertainty of future climate information. The study bias-corrected and spatially downscaled retrospective daily Global Climate Model (GCM) outputs under Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios using non-parametric quantile mapping method to force Soil and Water Assessment Tool (SWAT) model. Using the historical simulation, the skills of un-calibrated SWAT model (without calibration process) was evaluated for 5 reservoir watersheds (selected as well-monitored representatives). The study then, evaluated the performance of 9 GCMs in reproducing historical upstream inflow and irrigation demand at the five representative reservoirs. Finally future inflows and demands for 58 watersheds were projected using 9 GCMs projections under the two RCP scenarios. We demonstrated that (1) un-calibrated SWAT model is likely applicable to agricultural watershed, (2) the uncertainty of future climate information from different GCMs is significant, (3) multi-model ensemble (MME) shows comparatively resonable skills in reproducing water balances over the study area. The results of projection under the RCP 4.5 and RCP 8.5 scenario generally showed the increase of inflow by 9.4% and 10.8% and demand by 1.4% and 1.7%, respectively. More importantly, the results for different seasons and reservoirs varied considerably in the impacts of climate change.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.47-58
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• 2018

In this study, we evaluated the uncertainty in the process of selecting GCM and downscaling method for assessing the impact of climate change, and influence of user-centered climate change information on reproducibility of Chungju Dam inflow was analyzed. First, we selected the top 16 GCMs through the evaluation of spatio-temporal reproducibility of 29 raw GCMs using 30-year average of 10-day precipitation without any bias-correction. The climate extreme indices including annual total precipitation and annual maximum 1-day precipitation were selected as the relevant indices to the dam inflow. The Simple Quantile Mapping (SQM) downscaling method was selected through the evaluation of reproducibility of selected indices and spatial correlation among weather stations. SWAT simulation results for the past 30 years period by considering limitations in weather input showed the satisfactory results with monthly model efficiency of 0.92. The error in average dam inflow according to selection of GCMs and downscaling method showed the bests result when 16 GCMs selected raw GCM analysi were used. It was found that selection of downscaling method rather than selection of GCM is more is important in overall uncertainties. The average inflow for the future period increased in all RCP scenarios as time goes on from near-future to far-future periods. Also, it was predicted that the inflow volume will be higher in the RCP 8.5 scenario than in the RCP 4.5 scenario in all future periods. Maximum daily inflow, which is important for flood control, showed a high changing rate more than twice as much as the average inflow amount. It is also important to understand the seasonal fluctuation of the inflow for the dam management purpose. Both average inflow and maximum inflow showed a tendency to increase mainly in July and August during near-future period while average and maximum inflows increased through the whole period of months in both mid-future and far-future periods.

• Journal of Korean Society of Disaster and Security
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• v.8 no.1
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• pp.21-27
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• 2015

Recent researches show that climate change has impact on the rainfall process at different temporal and spatial scales. The present paper is focused on climate change impact on sub-daily rainfall quantile of Han River basin in South Korea. Climate change simulation outputs from ECHO-G GCM under the A2 scenario were used to estimate daily extreme rainfall. Sub-daily extreme rainfall was estimated using the scale invariance concept. In order to assess sub-daily extreme rainfall from climate change simulation outputs, precipitation time series were generated based on NSRPM (Neyman-Scott Rectangular Pulse Model) and modified using the ratio of rainfall over projection periods to historical one. Sub-daily extreme rainfall was then estimated from those series. It was found that sub-daily extreme rainfall in the future displayed increasing or decreasing trends for estimation methods and different periods.