Search results for " Robustness"
showing 10 items of 25 documents
Temperature-dependent mutational robustness can explain faster molecular evolution at warm temperatures, affecting speciation rate and global pattern…
2015
Distribution of species across the Earth shows strong latitudinal and altitudinal gradients with the number of species decreasing with declining temperatures. While these patterns have been recognized for well over a century, the mechanisms generating and maintaining them have remained elusive. Here, we propose a mechanistic explanation for temperature-dependent rates of molecular evolution that can influence speciation rates and global biodiversity gradients. Our hypothesis is based on the effects of temperature and temperature-adaptation on stability of proteins and other catalytic biomolecules. First, due to the nature of physical forces between biomolecules and water, stability of biomo…
Using a hazard-independent approach to understand road-network robustness to multiple disruption scenarios
2021
Abstract A range of predictable and unpredictable events can cause road perturbations, disrupting traffic flows and more generally the functioning of society. To manage this threat, stakeholders need to understand the potential impact of a multitude of predictable and unpredictable events. The present paper adopts a hazard-independent approach to assess the robustness (ability to maintain functionality despite disturbances) of the Sioux Falls network to all possible disruptions. This approach allows understanding the impact of a wide range of disruptive events, including random, localised, and targeted link failures. The paper also investigates the predictability of the link combinations wh…
Robustified smoothing for enhancement of thermal image sequences affected by clouds
2015
Obtaining radiometric surface temperature information with both high acquisition rate and high spatial resolution is still not possible through a single sensor. However, in several earth observation applications, the fusion of data acquired by different sensors is a viable solution for so called image sharpening. A related issue is the presence of clouds, which may impair the performance of the data fusion algorithms. In this paper we propose a robustified setup for the sharpening of thermal images in a non real-time scenario, capable to deal with missing thermal data due to cloudy pixels, and robust with respect to cloud mask misclassifications. The effectiveness of the presented technique…
Nonlinear Robust Control of a Quadratic Boost Converter in a Wide Operation Range, Based on Extended Linearization Method
2022
This paper proposes a control system for a quadratic boost DC/DC converter in a wide range of operations, based on an inner loop with a sliding mode controller, for reaching a desired equilibrium state, and an outer loop with integral-type controller, for assuring robustness against load and input voltage variations and converter parameter uncertainties. The sliding mode controller is designed with the extended linearization method and assures local asymptotic stability, whereas the integral controller is designed using classical frequency methods, and assures input–output stability. It is shown that the proposed controller also deals with the sudden changes in the nominal operating conditi…
Applications and Limitations of Robust Bayesian Bounds and Type II MLE
1994
Three applications of robust Bayesian analysis and three examples of its limitations are given. The applications that are reviewed are the development of an automatic Ockham’s Razor, outlier detection, and analysis of weighted distributions. Limitations of robust Bayesian bounds are highlighted through examples that include analysis of a paranormal experiment and a hierarchical model. This last example shows a disturbing difference between actual hierarchical Bayesian analysis and robust Bayesian bounds, a difference which also arises if, instead, a Type II MLE or empirical Bayes analysis is performed.
Data-driven design of robust fault detection system for wind turbines
2014
Abstract In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct …
Velocity sensorless control of uncertain load using RKF tuned with an evolutionary algorithm and mu-analysis
2010
Abstract In case of a velocity control scheme for a load directly driven by an actuator, large variations of its parameters are problematic due to possible instability and large variations of the final performances. This performances are then decreasing if a sensorless control is implemented due to cost, reliability or application constraints. This paper proposes solutions to quickly and accurately tune an observer with a lower computer time consumption and lower conception time. A previous calculated state feedback is used as base for a Kalman filter with special noise matrices. An evolutionary algorithm optimizes the observers degrees of freedom all over the variations. The mu-analysis th…
Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement
2022
Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, th…
Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems
2017
Cultivation of horticultural species under organic management has increased in importance in recent years. However, the sustainability of this new production method needs to be supported by scientific research, especially in the field of virology. We studied the prevalence of three important virus diseases in agroecosystems with regard to its management system: organic versus non-organic, with and without greenhouse. Prevalence was assessed by means of a Bayesian correlated binary model which connects the risk of infection of each virus within the same plot and was defined in terms of a logit generalized linear mixed model (GLMM). Model robustness was checked through a sensitivity analysis …
Macroelement Model for the Progressive-Collapse Analysis of Infilled Frames
2021
A new multistrut macromodel for the analysis of the progressive-collapse response of infilled reinforced concrete (RC) frames is presented in this paper. The model consists of three struts: two outer infinitely rigid and resistant struts and one inner fiber-section strut. The inclination of the struts as well as the stress-strain response are modulated by two parameters that are obtained by means of analytical correlations provided in the paper. The latter link the geometric and mechanical properties of an infilled frame to the geometric configuration and mechanical response of the equivalent strut model. This confers the model the capability to adapt to consider different collapse configur…