• Title/Summary/Keyword: A Posteriori Prediction

Search Result 13, Processing Time 0.021 seconds

A Numerical Analysis of Porewater Pressure Predictions on Hillside Slopes (수치해석을 이용한 산사면에서의 간극수압 예측에 관한 연구)

  • 이인모;서정복
    • Geotechnical Engineering
    • /
    • v.10 no.1
    • /
    • pp.47-62
    • /
    • 1994
  • It has been well known that the rainfall-triggered rise of groundwater levels is one of the most important factors resulting the instability of the hillside slopes. Thus, the prediction of porewater pressure is an essential step in the evaluation of landslide hazard. This study involves the development and verification of numerical groundwater flow model for the prediction of groundwater flow fluctuations accounting for both of unsatu나toed flow and saturated flow on steep hillside slopes. The first part of this study is to develop a nomerical groundwater flow model. The numerical technique chosen for this study is the finitro element method in combination with the finite difference method. The finite element method is used to transform the space derivatives and the finite difference method is used to discretize the time domain. The second part of this study is to estimate the unknown model parameters used in the proposed numerical model. There were three parameters to be estimated from input -output record $K_e$, $\psi_e$, b. The Maximum -A-Posteriori(MAP) optimization method is utilized for this purpose, . The developed model is applied to a site in Korea where two debris avalanches of large scale and many landslides of small scale were occurred. The results of example analysis show that the numerical groundwater flow model has a capacity of predicting the fluctuation of groundwater levels due to rainfall reasonably well.

  • PDF

A Study on the Tool Fracture Detection Algorithm Using System Identification (시스템인식을 이용한 공구파손검출 알고리듬에 관한 연구)

  • Sa, Seung-Yun;Yu, Eun-Lee;Ryu, Bong-Hwan
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.21 no.6
    • /
    • pp.988-994
    • /
    • 1997
  • The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, digital image of time series sequence was acquired by taking advantage of optical technique. Mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR(auto regressive) model was selected for system model and fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, it was found that there was a system stability.

Prospective validation of a novel dosing scheme for intravenous busulfan in adult patients undergoing hematopoietic stem cell transplantation

  • Cho, Sang-Heon;Lee, Jung-Hee;Lim, Hyeong-Seok;Lee, Kyoo-Hyung;Kim, Dae-Young;Choe, Sangmin;Bae, Kyun-Seop;Lee, Je-Hwan
    • The Korean Journal of Physiology and Pharmacology
    • /
    • v.20 no.3
    • /
    • pp.245-251
    • /
    • 2016
  • The objective of this study was to externally validate a new dosing scheme for busulfan. Thirty-seven adult patients who received busulfan as conditioning therapy for hematopoietic stem cell transplantation (HCT) participated in this prospective study. Patients were randomized to receive intravenous busulfan, either as the conventional dosage (3.2 mg/kg daily) or according to the new dosing scheme based on their actual body weight (ABW) ($23{\times}ABW^{0.5}mg\;daily$) targeting an area under the concentration-time curve (AUC) of $5924{\mu}M{\cdot}min$. Pharmacokinetic profiles were collected using a limited sampling strategy by randomly selecting 2 time points at 3.5, 5, 6, 7 or 22 hours after starting busulfan administration. Using an established population pharmacokinetic model with NONMEM software, busulfan concentrations at the available blood sampling times were predicted from dosage history and demographic data. The predicted and measured concentrations were compared by a visual predictive check (VPC). Maximum a posteriori Bayesian estimators were estimated to calculate the predicted AUC ($AUC_{PRED}$). The accuracy and precision of the $AUC_{PRED}$ values were assessed by calculating the mean prediction error (MPE) and root mean squared prediction error (RMSE), and compared with the target AUC of $5924{\mu}M{\cdot}min$. VPC showed that most data fell within the 95% prediction interval. MPE and RMSE of $AUC_{PRED}$ were -5.8% and 20.6%, respectively, in the conventional dosing group and -2.1% and 14.0%, respectively, in the new dosing scheme group. These findings demonstrated the validity of a new dosing scheme for daily intravenous busulfan used as conditioning therapy for HCT.