Search results for " Uncertainty"
showing 10 items of 777 documents
Near-Real-Time Estimation of Water Vapor Column From MSG-SEVIRI Thermal Infrared Bands: Implications for Land Surface Temperature Retrieval
2015
The Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) instrument provides observations of half the globe every 15 min, at low spatial resolution. These data are an invaluable tool to observe daily to yearly cycle of land surface temperature (LST), as well as for various early warning systems. However, advanced algorithms for LST estimation requires a previous estimation of the water vapor (WV) column above the observed pixel, for which no instantaneous retrieval methods are yet available, and therefore hinders their implementation in a near-real-time processing chain for MSG-SEVIRI data. This work analyzes three different formulations for such WV retrieva…
A C1-generic dichotomy for diffeomorphisms: Weak forms of hyperbolicity or infinitely many sinks or sources
2003
We show that, for every compact n-dimensional manifold, n > 1, there is a residual subset of Diff (M) of diffeomorphisms for which the homoclinic class of any periodic saddle of f verifies one of the following two possibilities: Either it is contained in the closure of an infinite set of sinks or sources (Newhouse phenomenon), or it presents some weak form of hyperbolicity called dominated splitting (this is a generalization of a bidimensional result of Mafine [Ma3]). In particular, we show that any Cl-robustly transitive diffeomorphism admits a dominated splitting.
A proof of Carleson's $\varepsilon^2$-conjecture
2019
In this paper we provide a proof of the Carleson $\varepsilon^2$-conjecture. This result yields a characterization (up to exceptional sets of zero length) of the tangent points of a Jordan curve in terms of the finiteness of the associated Carleson $\varepsilon^2$-square function.
Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation
2022
The movement of patients and respiratory motion during MRI acquisition produce image artefacts that reduce the image quality and its diagnostic value. Quality assessment of the images is essential to minimize segmentation errors and avoid wrong clinical decisions in the downstream tasks. In this paper, we propose automatic multi-task learning (MTL) based classification model to detect cardiac MR images with different levels of motion artefact. We also develop an automatic segmentation model that leverages k-space based motion artefact augmentation (MAA) and a novel compound loss that utilizes Dice loss with a polynomial version of cross-entropy loss (PolyLoss) to robustly segment cardiac st…
Uncertainty in urban stormwater quality modelling: The influence of likelihood measure formulation in the GLUE methodology
2009
In the last years, the attention on integrated analysis of sewer networks, wastewater treatment plants and receiving waters has been growing. However, the common lack of data in the urban water-quality field and the incomplete knowledge regarding the interpretation of the main phenomena taking part in integrated urban water systems draw attention to the necessity of evaluating the reliability of model results. Uncertainty analysis can provide useful hints and information regarding the best model approach to be used by assessing its degrees of significance and reliability. Few studies deal with uncertainty assessment in the integrated urban-drainage field. In order to fill this gap, there ha…
Consistent measurements of alpha(s) from precise oriented event shape distributions
2000
An updated analysis using about 1.5 million events recorded at $\sqrt{s} = M_Z$ with the DELPHI detector in 1994 is presented. Eighteen infrared and collinear safe event shape observables are measured as a function of the polar angle of the thrust axis. The data are compared to theoretical calculations in ${\cal O} (\alpha_s^2)$ including the event orientation. A combined fit of $\alpha_s$ and of the renormalization scale $x_{\mu}$ in $\cal O(\alpha_s^2$) yields an excellent description of the high statistics data. The weighted average from 18 observables including quark mass effects and correlations is $\alpha_s(M_Z^2) = 0.1174 \pm 0.0026$. The final result, derived from the jet cone energ…
R&D alliances timing under uncertainty: from theory toward experiments
2015
There is a growing awareness that managers have cognitive biases when making investments decisions under uncertainty. There is evidence of deviation from the predictions derived using normative models, such as real options models. The proposed research sheds light on the importance of integrating normative models with experimental methods in order to predict and explain such cognitive limitations. To this aim, starting from a real options model dealing with alliance timing decisions, we propose a simple design of an experiment, that can been used to test some of the fundamental insights of real options theory in the context of R&D alliances.
A Bayesian approach to assess data from radionuclide activity analyses in environmental samples
2007
A Bayesian statistical approach is introduced to assess experimental data from the analyses of radionuclide activity concentration in environmental samples (low activities). A theoretical model has been developed that allows the use of known prior information about the value of the measurand (activity), together with the experimental value determined through the measurement. The model has been applied to data of the Inter-laboratory Proficiency Test organised periodically among Spanish environmental radioactivity laboratories that are producing the radiochemical results for the Spanish radioactive monitoring network. A global improvement of laboratories performance is produced when this pri…
Handling epistemic uncertainty in the Fault Tree Analysis using interval valued expert information
2014
Risk Analysis ( RA) is a meaningful process in every industrial context, particularly referring to major hazard plants. In such sites, input reliability data are generally poor so leading to a partial or incomplete knowledge of the failure process. As a consequence, RA is always affected by the so called epistemic uncertainty which presence makes inappropriate the classical probabilistic approach. Therefore, the present work deals with such a kind of uncertainty in the Fault Tree Analysis (FTA) and proposes a new aggregation rule to combine the interval-valued information supplied by a team of experts about each Basic Event ( BE). The aggregation leads to an interval which bounds are modell…
A bottom-up procedure to calculate the Top Event probability in presence of epistemic uncertainty
2012
Industrial plants may be subjected to very dangerous events. Different methodologies are employed to evaluate the probability of their occurrence, as Process Safety Analysis (PSA) or Risk Analysis (RA). However, since for rare events reliability data are poor, the epistemic uncertainty needs to be considered. In this context, the classical probabilistic approach cannot be successfully used and then different approaches must be taken into account. Actually, this paper proposes the use of the Evidence Theory or Dempster-Shafer Theory (DST) to deal with data characterizing rare events in high risk industrial sites. In particular, a classical Fault Tree Analysis (FTA) is considered when the onl…