Fuzzy quantifiers (like many, few, ) are an important research topic not only due to their abundance in natural language (NL), but also because an adequate account of these quantifiers would provide a class of powerful yet human-understandable operators for information aggregation and data fusion. We introduce the DFS theory of fuzzy quantification, present a model of the theory, and describe a...

This study develops a travel time estimation process by integrating a missing data treatment and data-fusion-based approaches. In missing data treatment, this study develops a grey time-series model and a grey-theory-based pseudo-nearest-neighbor method to recover, respectively, temporal and spatial missing values in traffic detector data sets. Both spatial and temporal patterns of traffic data...

⎯This paper presents a new approach for solving the paradoxical Blackman's Association Problem. It utilizes the recently defined new class fusion rule based on fuzzy Tconorm/T-norm operators together with DezertSmarandache theory based, relative variations of generalized pignistic probabilities measure of correct associations, defined from a partial ordering function of hyper-power set. The abi...

In the last few decades, a new way of interpreting multi-sensor array processing techniques and artificial intelligence systems has been introduced. This theory focuses on interactions and fusion between heterogeneous/homogeneous information at different layers in order to guarantee data completeness and robust resolution of hard-to-solve problems. Our research investigates this theory, well kn...

A Two-Layer Conflict Solving data fusion approach is proposed in this work, with an aim to provide another approach to data fusion community. Since the evidence of DempsterShafer Theory, algorithms for combining pieces of evidence have drawn a considerable attention from data fusion researchers, along with many alternatives invented. However, none of these approaches receive an agreement for be...

Two main humanitarian mine action types may benefit from multi-sensor data fusion techniques: 1) close range antipersonnel (AP) mine detection and 2) mined area reduction. Data fusion for these two applications is presented here. Close range detection consists of detection of (sub-)surface anomalies that may be related to the presence of mines (e.g., detection of metals using a metal detector, ...

Data sources are often dispersed geographically in real life applications. Finding a knowledge model may require to join all the data sources and to run a machine learning algorithm on the joint set. We present an alternative based on a Multi Agent System (MAS): an agent mines one data source in order to extract a local theory (knowledge model) and then merges it with the previous MAS theory us...

This article presents a distributed vision system for tracking of mobile objects over wide areas. A temporal data fusion is used in order to improve the decision making at the data association stage. The temporal fusion is performed with a possibilistic M.H.T. (Multiple Hypothesis Tracking). We have decided to use the Possibility Theory which handle efficiently uncertainties. The originality of...