Measuring variable importance for computational models or measured data is an important
task in many applications. It has drawn our attention that the variable importance analysis …

The present work is the first effort to apply machine learning approaches and variable
importance analysis (VIA) to the determination of environmental attributes that regulate the …

Estimating the functional relation between the probabilistic response of a computational
model and the distribution parameters of the model inputs is especially useful for 1) …

This paper proposes a complete sensitivity analysis of the use of Autoregressive models
(AR) and Mahalanobis Squared Distance in the field of Structural Health Monitoring (SHM) …

Abstract Robust Parametric Sensitivity Analysis (PSA) is a prerequisite for efficient shape
optimisation via parametric modelling. A major challenge PSA has to handle is related to the …

Reliability sensitivity analysis is crucial for efficient design optimisation based on parametric
modelling. A common fact in applications is that reliability shows different degrees of …

A new set of variance-based sensitivity indices, called W-indices, is proposed. Similar to the
Sobol's indices, both main and total effect indices are defined. The W-main effect indices …

The existing predictive failure probability (PFP)-based global sensitivity analysis methods
may get stuck in practical application due to the large amount of calculation or insufficient …

To efficiently execute the variance-based global sensitivity analysis, the law of total variance
in the successive intervals without overlap** is proved at first, on which an efficient space …

目前, 上肢运动的疲劳状态监测, 一般单纯依赖表面肌电信号 (sEMG) 对疲劳进行识别和分类,
导致结果不稳定, 存在一定局限. 为此, 本文将 sEMG 信号识别与动作捕捉技术引入到疲劳状态 …