... of robustness to uncertainty as developed in info-gap decision theory. We explain that maximizing the robustness is complementary to—but distinct from—minimizing a measure of effort of the learning procedure. 1 Corresponding author. e-mail:  yakov@technion.ac.il e-mail:  scott.cogan@univ-fcomte.fr...

...Kalman Ziha The probabilistic safety analysis evaluates system reliability and failure probability by using statistics and probability theory. However, it cannot estimate the system safety uncertainties due to variabilities of probabilities in different system states. The article first summarizes...

... sufficient because a target variable value often varies due to diverse sources of uncertainty. For instance, in the fused deposition modeling process, the inconsistent results of replications are associated with natural randomness, environmental condition, and noisy process parameters. The point value...

... boundary conditions, were considered for the uncertainty analysis. It is shown that, in each case, the interval values of the response parameters given by the universal gray number theory are consistent with the ranges of the input parameters, compared to those given by the interval analysis. It is also...

.... A dataset of the pin-on-disk test by ASTM-G99 standard is used for additional model validation. The rail system data are employed for validation of the model in the practical systems. Uncertainty is analyzed by Monte Carlo simulation to determine the variations of the predicted material loss. Finally...

.... In this work, evidence theory has been coupled with BN to address inconsistency, conflict and incompleteness in the expert opinions. It combines the acquired knowledge from various subjective sources, thereby rendering accuracy in probability estimation. Another source of uncertainty in BN is model uncertainty...

... of the vehicle. The proposed approach consists of two main elements, namely uncertainty modeling of traffic conditions and model-based reliability analysis. In the uncertainty modeling step, a recently developed Gaussian mixture copula (GMC) method is employed to accurately represent the uncertainty in the road...

...Harshini Devathi; Sunetra Sarkar A novel uncertainty quantification routine in the genre of adaptive sparse grid stochastic collocation (SC) has been proposed in this study to investigate the propagation of parametric uncertainties in a stall flutter aeroelastic system. In a hypercube stochastic...

... to the particular stress–strain curves used for the materials in the model. We desire to estimate or bound response variation when only a few stress–strain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty...

...Fazel Khayatian; Maryam MeshkinKiya; Piero Baraldi; Francesco Di Maio; Enrico Zio Optimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables...

...Arun Veeramany; James T. Woodward; Donald J. Hammerstrom Valuation of transactive energy (TE) systems should be supported by a structured and systematic approach to uncertainty identification, assessment, and treatment in the interest of risk-informed decision making. The proposed approach...

... of learning BNs without discretizing the continuous variables or making assumptions about their conditional probability densities (CPDs). The applications of the learned BN to uncertainty quantification and model calibration are also investigated. The results of a mathematical example and an engineering...

... to quantitative analysis, particularly on resource allocation optimization under uncertainty, such as with differentiation between disruption and regular supply variability [ 57 ], facility location design [ 58 , 59...

... manuscript received October 18, 2017; published online December 20, 2017. Assoc. Editor: Sankaran Mahadevan. 28 01 2016 18 10 2017 Reliability Structural reliability Uncertainty Reliability is the ability that a component or system performs its intended function in routine...

... Uncertainty Computer numerical control (CNC) machine tools widely function as key equipment to process large-scale products in manufacturing. With the increasing importance of CNC machine tools in the high-end manufacturing field, it is more and more necessary to improve the accuracy of machine tools...

...Stefan Erschen; Fabian Duddeck; Matthias Gerdts; Markus Zimmermann In the early development phase of complex technical systems, uncertainties caused by unknown design restrictions must be considered. In order to avoid premature design decisions, sets of good designs, i.e., designs which satisfy all...

... of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model...

.... C. , 2016 , “ Handling Uncertainty in the Remnant Fatigue Life Assessment of Offshore Process Pipework ,” ASME Paper No. IMECE2016-65504. 10.1115/IMECE2016-65504 [5] Khan , F...

..., uncertainty is estimated by using the Monte Carlo simulation. It is observed that by increasing the crack length, the standard deviation (the measure of uncertainty) increases. Also, it is noted that the reduction in stress ratio has the similar effect. Then, stochastic crack growth model, proposed by Yang...

...Javad Sovizi; Rahul Rai; Venkat Krovi Loosely interconnected cooperative systems such as cable robots are particularly susceptible to uncertainty. Such uncertainty is exacerbated by addition of the base mobility to realize reconfigurability within the system. However, it also sets the ground...