• 센서학회지
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• 제19권5호
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• pp.375-384
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• 2010

In this paper, we proposed a new shaft distortion correction system having an adaptive PID controller using displacement sensors, which is adaptively reflecting variations of shaft strength owing to irregular heat treatment during an annealing process and sensitivity to the seasonal temperature changes. Generally, the shafts are annealed by heat treatment in order to enlarge the strength of the shaft, which causes an distortion of a shaft such as irregular bending of the shaft. In order to correct such a distortion of the shaft, a mechanical pressure is properly impacted to the distorted shaft. However, the strength of every shaft is different from each other owing to irregular annealing and seasonal temperature changes. Especially, the strength of a thin shaft such as a car transmission shaft is much more sensitive than that of a thick shaft. Therefore, it is very important for considering the strength of each shaft during correction of the car transmission shaft distortion in order to generate proper mechanical pressure. The conventional PID controller for the shaft distortion correction system does not consider each different strength of each shaft, which causes low productivity. Therefore, we proposed a new PID controller considering variations of shaft strength caused by seasonal temperature changes as well as irregular heat treatment and different cooling time. Three displacement sensors are used to measure a degree of distortion of the shaft at three different location. The proposed PID controller generates adaptively different coefficients according to different strength of each shaft using appropriately obtained pressure times from long-term experiments. Consequently, the proposed shaft distortion correction system increases the productivity about 30 % more than the conventional correction system in the real factory.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.695-703
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• 2005

This paper extends the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. The finite sample distribution of the associated rank test for dally data is also presented.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 춘계 학술발표회 논문집
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• pp.13-15
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• 2005

In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.

• Journal of the Korean Statistical Society
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• 제36권4호
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• pp.501-522
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• 2007

We propose an estimation procedure that can be used for detecting structural changes in the seasonal cointegrated vector autoregressive model. The asymptotic properties of the estimates and the test statistics for the parameter change are provided. A simulation example is presented to illustrate this method and its concept.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

• 대기
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• 제17권4호
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• pp.421-433
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• 2007

We have investigated the seasonal characteristics of surface turbulent fluxes observed at Ieodo Ocean Research Station from 2005 to 2006. Both 10Hz and 30 minutes flux data are quality controled, and tilt correction is performed in 10Hz data before quality control. The turbulent fluxes of open sea shows clear seasonal variations, though diurnal variations are barely shown. The seasonal ratio of stable and unstable conditions are closely related to the temperature difference between sea surface and air. In stable and semi-stable condition, latent and sensible heat fluxes have very small values without any relationship with wind speed. Though friction velocity shows slightly increasing trend with wind speed, it has many outliers. In unstable condition, turbulent fluxes increased with wind speed. Especially, latent heat flux increased rapidly during DJF. The latent heat flux at high wind speeds is more scatter.

• 한국수자원학회논문집
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• 제50권8호
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• pp.551-562
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• 2017

2014년부터 기상청에서 현업으로 활용하고 있는 전지구 계절예측시스템 GloSea5의 최대 6개월 예측 강수량을 수자원 및 여러 응용분야에 활용하기 위해서는 예측모델이 가지는 관측자료와의 정량적인 편의를 보정할 필요가 있다. 본 연구에서는 GloSea5의 예측 강수량에서 나타나는 편의를 보정하기 위해 확률분포형을 활용한 편의보정기법, 매개변수 및 비매개변수적 편의보정기법 등 총 11개의 기법을 활용하여 계절예측모델의 적용성을 평가하고 최적의 편의보정기법을 선정하고자 하였다. 과거재현기간에 대한 편의보정 결과, 비매개변수적 편의보정기법이 다른 기법에 비해 가장 관측자료와 유사하게 보정하는 것으로 분석되었으나 예측기간에 대해서는 상대적으로 많은 이상치를 발생시켰다. 이와는 대조적으로 매개변수적 편의보정기법은 과거재현기간 및 예측기간 모두 안정된 결과를 보여주고 있음을 확인할 수 있었다. 본 연구의 결과는 수자원운영 및 관리, 수력, 농업 등 계절예측모델을 활용한 여러 응용분야에 적용이 가능할 것으로 기대된다.

• 응용통계연구
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• 제21권5호
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• pp.783-789
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• 2008

We investigate the effects of the misspecification of cointegrating(CI) ranks at other frequencies on the inference of seasonal models at the frequency of interest; our study includes tests for CI ranks and estimation of CI vectors. Earlier studies focused mostly on a single frequency corresponding to one seasonal root at a time, ignoring possible cointegration at the remaining frequencies. We investigate the effects of the mis-specification, especially in finite samples, by adopting Gaussian reduced rank(GRR) estimation by Ahn and Reinsel (1994) that considers cointegration at all frequencies of seasonal unit roots simultaneously. It is observed that the identification of the seasonal CI rank at the frequency of interest is sensitive to the mis-prespecification of the CI ranks at other frequencies, mainly when the CI ranks at the remaining frequencies are underspecified.

• Communications for Statistical Applications and Methods
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• 제15권3호
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• pp.411-420
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• 2008

This paper considers a feasible two-step estimator for seasonal cointegration as the extension of $Br{\ddot{u}}ggeman$ and $L{\ddot{u}}tkepohl$ (2005). It is shown that the reducedrank maximum likelihood(ML) estimator for seasonal cointegration can still produce occasional outliers as that for non-seasonal cointegration even though the sizes of them are not extreme as those in non-seasonal cointegration. The ML estimator(MLE) is compared with the two-step estimator in a small Monte Carlo simulation study and we find that the two-step estimator can be an attractive alternative to the MLE, especially, in a small sample.

• 대기
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• 제30권2호
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• pp.155-167
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• 2020

In order to produce more detailed and accurate information of river discharge and freshwater discharge, global high-resolution hydrodynamic model (CaMa-Flood) is applied to an operational land surface model of global seasonal forecast system. In addition, bias correction to grid runoff for the hydrodynamic model is attempted. CaMa-Flood is a river routing model that distributes runoff forcing from a land surface model to oceans or inland seas along continentalscale rivers, which can represent flood stage and river discharge explicitly. The runoff data generated by the land surface model are bias-corrected by using composite runoff data from UNH-GRDC. The impact of bias-correction on the runoff, which is spatially resolved on 0.5° grid, has been evaluated for 1991~2010. It is shown that bias-correction increases runoff by 30% on average over all continents, which is closer to UNH-GRDC. Two experiments with coupled CaMa-Flood are carried out to produce river discharge: one using this bias correction and the other not using. It is found that the experiment adapting bias correction exhibits significant increase of both river discharge over major rivers around the world and continental freshwater discharge into oceans (40% globally), which is closer to GRDC. These preliminary results indicate that the application of CaMa-Flood as well as bias-corrected runoff to the operational global seasonal forecast system is feasible to attain information of surface water cycle from a coupled suite of atmospheric, land surface, and hydrodynamic model.