• 산업경영시스템학회지
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• 제44권3호
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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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• 제8권2호
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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.

• 인지과학
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• 제31권1호
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• pp.1-23
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• 2020

이 논문에서는 지난 40여 년 동안 인지심리학에서 가장 중요한 모형 가운데 하나이며 근래에는 인지신경과학에서도 중요한 자리를 차지하고 있는 Ratcliff의 확산(diffusion)모형을 분석하는 도구 SNUDM을 소개한다. SNUDM은 확산과정을 Ratcliff & Tuerlinckx(2002)에 소개된 방식으로 단순 무작위걷기(random walk)를 묘사했다. 구체적으로, 모형이 생성하는 반응시간 분포는 주어진 파라미터 값들에서 작은 걸음으로 무작위걷기를 하여 일정 수준에 다다를 때까지 걸린 시간들로 이루어졌고, 모형의 파라미터 추정치는 단순도형(Simplex) 방식을 이용하여 실험 자료와 생성된 분포를 비교하기 위해 계산된 카이제곱값을 최소화하는 파라미터의 값을 사용한다. 사용의 간편함을 위해, 입력 파일은 반응시간의 분위수(quantile), 시행수와 기타 정보를 담은 파일로 간단하게 했고, 프로그램 작동에 필요한 피험자 수와 조건 수 등은 질문에 답을 하는 방식으로 입력하도록 했으며, 조건에 따라 비교할 파라미터와 그렇지 않고 고정할 파라미터도 미리 지정하도록 했다. 분석도구 SNUDM이 파라미터 값을 제대로 찾아내는지를 알아보기 위해 Ratcliff, Gomez, & McKoon(2004)의 실험1 자료를 써서 검토한 결과, 그들이 보고한 실험 조건들 사이에서 보인 상대적인 표집율의 크기에서 동일한 패턴을 얻었다. 또한 SNUDM으로 생성된 자료를 DMAT과 fast-dm의 자료와 비교해 보았을 때 SNUDM은 시행수가 적을 경우에는 경계 파라미터를 fast-dm과는 비슷한 값을 추정하였고 DMAT보다는 작은 값으로 추정했으나 시행수가 많은 경우에는 세 도구 모두 비슷하게 파라미터를 추정하는 것을 확인하였다.

• 한국농공학회논문집
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• 제57권5호
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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.

• 한국기후변화학회지
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• 제9권1호
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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.

• 한국방재안전학회논문집
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• 제8권1호
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• pp.21-27
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• 2015

전세계적으로 기상이변이 빈번하게 발생하면서 기후변화가 수문환경에 미치는 영향에 대한 연구가 활발히 진행되고 있다. 기후변화 연구에는 대체로 이산화탄소 배출 시나리오에 근거한 GCM 모의 결과가 사용되며, GCM 자료를 바탕으로 미래의 수문량 변화를 예측하는 방법으로 진행된다. 기후변화가 강우에 미치는 영향과 관련해서는 기후변화가 총강우량에 미치는 영향에 대한 연구가 주를 이뤄왔으나 극한강우량에 미치는 영향에 대한 연구는 미흡한 실정이다. 또한 상세화 된 강우 자료가 월단위 또는 일 단위이기 때문에 극한홍수량 산정에 필요한 시단위 극한강우량 추정에는 한계가 있다. 본 연구에서는 기후변화가 극한강우량에 미치는 영향을 분석하기 위해 A2 시나리오에 근거한 ECHO-G GCM 모델의 모의 결과를 상세화 시켜 얻은 한강 유역내의 9개 강우 관측 지점의 일강우 자료를 바탕으로 강우의 scale invariance 특성에 근거한 시단위 확률강우량을 추정하였고, NSRPM(Neymann-Scott Rectangular Pulse Model)을 적용하여 시단위 확률강우량을 추정하였다. 이러한 방법으로 추정된 9개 지점의 확률강우량과 한강유역종합치수계획(국토해양부, 2008)에서 산정한 확률강우량을 비교하여 미래의 확률강우량 변화를 분석하였다. 분석된 한강 유역 내 강우 관측 지점의 확률강우량 변화 추이는 지점에 따라, 미래기간에 따라 상이하게 나타났으나 대체로 scaling에 의한 결과가 관측값에 근거한 확률강우량보다 대체로 큰 값을 보였고, NSRPM에 의한 결과는 미래 기간에 따라 관측값보다 크거나 작은 값을 보였다.