Incremental Data Driven Modelling for Plug and Play Process Control

Abstract—This paper studies the data driven update of a model for a system where the number of inputs or outputs increased. Often existing control systems are equipped with an additional sensor or actuator to improve performance. If a good model for the present system is available it is advantageous to only estimate the additional part while keeping the present model, compared to estimating the whole model from scratch. The capabilities with convex methods are investigated. It is shown that model updating for static sensor/actuators can be done consistently for the deterministic part. The stochastic part is far more complicated and here convex methods gives a approximate solution. The total solution is demonstrated by simulation to improve state prediction and control performance. I

Stochastic Differential Equations with State Dependent Diffusion - 2 Order Statistics and State Estimation

Estimation of states in stochastic differential equations with state dependent diffusion is known to be difficult. Previous research recommend the higher order extended Kalman filter or the Lamperti transform method for this case. This paper shows that a new developed method, based on the unscented Kalman filter, is superior for two simulated stochastic differential equation systems.</p

Identifying admitted patients at risk of dying:a prospective observational validation of four biochemical scoring systems

OBJECTIVES: Risk assessment is an important part of emergency patient care. Risk assessment tools based on biochemical data have the advantage that calculation can be automated and results can be easily provided. However, to be used clinically, existing tools have to be validated by independent researchers. This study involved an independent external validation of four risk stratification systems predicting death that rely primarily on biochemical variables. DESIGN: Prospective observational study. SETTING: The medical admission unit at a regional teaching hospital in Denmark. PARTICIPANTS: Of 5894 adult (age 15 or above) acutely admitted medical patients, 205 (3.5%) died during admission and 46 died (0.8%) within one calendar day. INTERVENTIONS: None. MAIN OUTCOME MEASURES: The main outcome measure was the ability to identify patients at an increased risk of dying (discriminatory power) as area under the receiver-operating characteristic curve (AUROC) and the accuracy of the predicted probability (calibration) using the Hosmer-Lemeshow goodness-of-fit test. The endpoint was all-cause mortality, defined in accordance with the original manuscripts. RESULTS: Using the original coefficients, all four systems were excellent at identifying patients at increased risk (discriminatory power, AUROC ≥0.80). The accuracy was poor (we could assess calibration for two systems, which failed). After recalculation of the coefficients, two systems had improved discriminatory power and two remained unchanged. Calibration failed for one system in the validation cohort. CONCLUSIONS: Four biochemical risk stratification systems can risk-stratify the acutely admitted medical patients for mortality with excellent discriminatory power. We could improve the models for use in our setting by recalculating the risk coefficient for the chosen variables