10,452 research outputs found
Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications
Nowadays ontologies present a growing interest in Data Fusion applications.
As a matter of fact, the ontologies are seen as a semantic tool for describing
and reasoning about sensor data, objects, relations and general domain
theories. In addition, uncertainty is perhaps one of the most important
characteristics of the data and information handled by Data Fusion. However,
the fundamental nature of ontologies implies that ontologies describe only
asserted and veracious facts of the world. Different probabilistic, fuzzy and
evidential approaches already exist to fill this gap; this paper recaps the
most popular tools. However none of the tools meets exactly our purposes.
Therefore, we constructed a Dempster-Shafer ontology that can be imported into
any specific domain ontology and that enables us to instantiate it in an
uncertain manner. We also developed a Java application that enables reasoning
about these uncertain ontological instances.Comment: Workshop on Theory of Belief Functions, Brest: France (2010
A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes
Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence
Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems
Abstract— Nowadays careful measurement applications are
handed over to Wired and Wireless Sensor Network. Taking
the scenario of train location as an example, this would lead to
an increase in uncertainty about position related to sensors
with long acquisition times like Balises, RFID and
Transponders along the track. We take into account the data
without any synchronization protocols, for increase the
accuracy and reduce the uncertainty after the data fusion
algorithms. The case studies, we have analysed, derived from
the needs of the project partners: train localization, head of an
auger in the drilling sector localization and the location of
containers of radioactive material waste in a reprocessing
nuclear plant. They have the necessity to plan the maintenance
operations of their infrastructure basing through architecture
that taking input from the sensors, which are localization and
diagnosis, maps and cost, to optimize the cost effectiveness and
reduce the time of operation
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