A Model Validation Approach for Various Design Configurations with Insufficient Experimental Data for Model Accuracy Check

2012 ◽  
Author(s):  
Zhimin Xi ◽  
Yan Fu ◽  
Ren-Jye Yang
Keyword(s):  
Experimental Data ◽  
Model Validation ◽  
Model Accuracy ◽  
Accuracy Check

Model Validation Metric and Model Bias Characterization for Dynamic System Responses Under Uncertainty

2012 ◽  
Author(s):  
Zhimin Xi ◽  
Yan Fu ◽  
Ren-Jye Yang
Keyword(s):  
Experimental Data ◽  
Dynamic System ◽  
Random Process ◽  
Model Validation ◽  
Model Prediction ◽  
Uncertain System ◽  
Model Bias ◽  
Model Accuracy ◽  
Accuracy Check

Quantification of the accuracy of analytical models (math or computer simulation models) and characterization of the model bias are two essential processes in model validation. Available model validation metrics, whether qualitative or quantitative, do not consider the influence of the number of experimental data for model accuracy check. In addition, quantitative measure from the validation metric does not directly reflect the level of model accuracy, i.e. from 0% to 100%, especially when there is a lack of experimental data. If the original model prediction does not satisfy accuracy criteria compared to the experimental data, instead of revising the model conceptually, characterization of the model bias may be a more practical approach to improve the model accuracy because there is probably no ideal model which can predict the actual physical system with no error. So far, there is a lack of effective approaches that can accurately characterize the model bias for multiple dynamic system responses. To overcome these limitations, the first objective of this study is to develop a model validation metric for model accuracy check considering different number of experimental data. Specifically, a validation metric using the Bhattacharya distance (B-distance) is proposed with three notable benefits. First of all, the metric directly compares the distributions of two set of uncertain system responses from model prediction and experiment rather than the distribution parameters (e.g. mean and variance). Second, the B-distance quantitatively measures the degree of accuracy from 0% to 100% between the distributions of the uncertain system responses. Third, reference accuracy metric with respect to different number of experimental data can be effectively obtained so that hypothesis test can be performed to identify whether the two distributions are identical or not in a probability manner. The second objective of this study is to propose an effective approach to accurately characterize the model bias for dynamic system responses. Specially, the model bias is represented by a generic random process, where realizations of the model bias at each time step could follow arbitrary distributions. Instead of using the traditional Bayesian or Maximum Likelihood Estimation (MLE) approach, we propose a novel and efficient approach to identify the model bias using a generic random process modeling technique. A vehicle safety system with 11 dynamic system responses is used to demonstrate the effectiveness of the proposed approach.

scholarly journals Generalisability through local validation: overcoming barriers due to data disparity in healthcare

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
William Greig Mitchell ◽  
Edward Christopher Dee ◽  
Leo Anthony Celi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Global Health ◽  
Model Validation ◽  
Health Inequity ◽  
Diverse Populations ◽  
Single Centre ◽  
Model Accuracy ◽  
South Korean ◽  
Deep Learning Model

AbstractCho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity.Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation.The importance of external ML model validation on diverse populations should be highlighted where possible – especially for models built with single-centre data.

Dynamic Model of Dual Clutch Lever-Based Electromechanical Actuator

2011 ◽  
Author(s):  
Vladimir Ivanovic´ ◽  
Josˇko Deur ◽  
Milan Milutinovic´ ◽  
H. Eric Tseng
Keyword(s):  
Experimental Data ◽  
Dynamic Model ◽  
Model Validation ◽  
Bond Graph ◽  
Test Rig ◽  
Electromechanical Actuator ◽  
Actuator Dynamics ◽  
Operating Modes ◽  
Graph Modeling ◽  
Bond Graph Modeling

The paper presents a dynamic model of a dual clutch lever-based electromechanical actuator. Bond graph modeling technique is used to describe the clutch actuator dynamics. The model is parameterized and thoroughly validated based on the experimental data collected by using a test rig. The model validation results are used for the purpose of analysis of the actuator behavior under typical operating modes.

A dispersion based model for anaerobic digestion of solid cattle wastes in a stratified thermophilic accumulation system

2005 ◽  
Vol 52 (1-2) ◽  
pp. 195-202 ◽  
Author(s):  
H.M. El-Mashad ◽  
W.K.P. van Loon ◽  
G. Zeeman ◽  
G.P.A. Bot ◽  
G. Lettinga
Keyword(s):  
Experimental Data ◽  
Anaerobic Digestion ◽  
Aspect Ratio ◽  
Dynamic Model ◽  
Model Validation ◽  
Methane Production ◽  
System Performance ◽  
Aspect Ratios ◽  
Accumulation System ◽  
Good Agreement

A dynamic model has been developed to describe the anaerobic digestion of solid cattle waste in an accumulation system (AC). To calibrate the model an experiment was carried out at a lab-scale AC at 50 °C. The predicted methane production shows a very good agreement (i.e. R2=0.998) with the experimental data. However less agreement is evident for the intermediates. After model validation the model was applied to study the effect of different aspect ratios on the system performance. An optimum aspect ratio of 2–3 could be determined.

Thermal model validation of an electric-driven smart window through experimental data and evaluation of the impact on a case study

2020 ◽  
Vol 181 ◽  
pp. 107134 ◽  
Author(s):  
Giovanni Ciampi ◽  
Michelangelo Scorpio ◽  
Yorgos Spanodimitriou ◽  
Antonio Rosato ◽  
Sergio Sibilio
Keyword(s):  
Experimental Data ◽  
Model Validation ◽  
Thermal Model ◽  
Smart Window ◽  
The Impact
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