A successful image-guided surgical intervention requires accurate measurement of coordinate systems. Uncertainty is introduced every time a pose is measured by the optical tracking system. When we transform a measured pose into a different coordinate system, the covariance (which encodes the uncertainty of the pose) must be propagated to this new coordinate system. In this paper, we describe a ...

Within the domain of Systems Engineering the need to perform property aggregation to understand, analyze and manage complex systems is unequivocal. This can be seen in numerous domains such as capability analysis, Mission Essential Competencies (MEC) and Critical Design Features (CDF). Furthermore, the need to consider uncertainty propagation as well as the sensitivity of related properties wit...

A new technique o f uncertainty management in expert systems is proposed. It is suggested that the possibility theory-based treatment o f uncertainty (developed by D. Dubois and H. Prade) in inference engines be simplified by using a combination o f possibility and necessity measures, namely N + FI 1, rather than using the possibility measure 17 and the necessity measure N separately. This comb...

Uncertainty propagation and filtering can be interpreted as gradient flows with respect to suitable metrics in the infinite dimensional manifold of probability density functions. Such a viewpoint has been put forth in recent literature, and a systematic way to formulate and solve the same for linear Gaussian systems has appeared in our previous work where the gradient flows were realized via pr...

In this work, propagation of uncertainty during calibration process of TRANUS, an integrated land use and transport model (ILUTM), has been investigated. It has also been examined, through a sensitivity analysis, which input parameters affect the variation of the outputs the most. Moreover, a probabilistic verification methodology of calibration process, which equates the observed and calculate...

Many robotics applications involve motion planning with uncertainty. In this paper, we focus on path planning for planar systems by optimizing the probability of successfully arriving at a goal. We approach this problem with a modified version of the Path-of-Probability (POP) algorithm. We extend the POP algorithm to allow for a moving target and to optimize the number of steps to reach the goa...

A spectral formalism has been developed for the “non-intrusive” analysis of parametric uncertainty in reacting-flow systems. In comparison to conventional Monte Carlo analysis, this method quantifies the extent, dependence, and propagation of uncertainty through the model system and allows the correlation of uncertainties in specific parameters to the resulting uncertainty in detailed flame str...

In long-term forecasting it is important to estimate the confidence of predictions, as they are often affected by errors that are accumulated over the prediction horizon. To address this problem, an effective novel iterative method is developed for Gaussian structured learning models in this study for propagating uncertainty in temporal graphs by modeling noisy inputs. The proposed method is ap...

One of the biggest challenges in speaker recognition is incomplete observations in test phase caused by availability of only short duration utterances. The problem with short utterances is that speaker recognition needs to be handled by having information from only limited amount of acoustic classes. By considering limited observations from a test speaker, the resulting i-vector as a representa...

In order to assess the reliability of computational algorithms it is necessary to consider the uncertainty associated with the results of the computation. This is true for visualization as it is for other computational sciences, and realization of this important concept has led researchers to develop methods for assessing uncertainty in visualization. In this work we propose a framework for ver...