This lecture is about some of the basics of uncertainty: the use of variances and covariances to express statistical uncertainty (“error bars”, error ellipses); we are concentrating on the uncertainties of geographic co-ordinates. This makes it necessary to talk about geodetic datums – alternative ways of fixing the starting point(s) used for fixing geodetic co-ordinates in a network solution. ...

While it is often fairly straightforward to estimate the reliability of speech features in the time-frequency domain, this may not be true in other domains more amenable to speech recognition, such as e.g. for RASTA-PLP features or those obtained with the ETSI advanced front end. In such cases, one useful approach is to estimate the uncertainties in the domain where noise-reduction preprocessin...

the problem of crack identification in continuous beam like structures is considered. the cracks’ locations and their depths are identified by employing experimental modal test results performed on the structure. the cracks are modeled using generic elements to include the coupling effects between shear forces and bending moments at the crack section. in the identification procedure eigen- sens...

This report investigates a technique to calculate the distributions of discretization errors for a model of advection-diffusion-reaction with stochastic noise in problem data. The focus is on operator-split discretization methods. The error is decomposed into components due to the splitting and due to the discretization within each component. We present a method to estimate the distributions of...

Time-of-Flight (TOF) cameras are active real time depth sensors. One issue of TOF sensors is measurement noise. In this paper, we present a method for providing the uncertainty associated to 3D TOF measurements based on noise modelling. Measurement uncertainty is the combination of pixel detection error and sensor noise. First, a detailed noise characterization is presented. Then, a continuous ...

The application of radar quantitative precipitation estimation (QPE) to hydrology and water quality models can be preferred to interpolated rainfall point measurements because of the wide coverage that radars can provide, together with a good spatio-temporal resolutions. Nonetheless, it is often limited by the proneness of radar QPE to a multitude of errors. Although radar errors have been wide...

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...

Soil hydrological models are inherently imperfect because they abstract and simplify “real” hydrological patterns and processes. Indeed, an important aim of modeling is to establish the simplest description possible for adequately addressing a particular problem. Also, models are frequently based on input data that are known to be inadequate for some practical purpose. Thus, uncertainties in mo...

In a typical reinforcement learning (RL) setting details of the environment are not given explicitly but have to be estimated from observations. Most RL approaches only optimize the expected value. However, if the number of observations is limited considering expected values only can lead to false conclusions. Instead, it is crucial to also account for the estimator’s uncertainties. In this pap...

Multirobot symbolic planning (MSP) aims at computing plans, each in the form of a sequence of actions, for a team of robots to achieve their individual goals while minimizing overall cost. Solving MSP problems requires modeling limited domain resources (e.g., corridors that allow at most one robot at a time) and the possibility of action synergy (e.g., multiple robots going through a door after...