One of the problems usually discussed in autonomous mobile robot navigation is how to build a map of the environment where the robot is navigating. In the case of indoor navigation, a suitable method to do this mapping is the fusion of the data that are gathered from the several sensors, which are mounted on the robot. Here, a mobile robot equipped just with range finding sensors has been suppo...

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for pos...

This paper presents a novel fingerprint classifier fusion algorithm using Dempster-Shafer theory concomitant with update rule. The proposed algorithm accurately matches fingerprint evidences and also efficiently adapts to dynamically evolving database size without compromising accuracy or speed. We experimentally validate our approach using three fingerprint recognition algorithms based on minu...

Image understanding applications are often tainted with a high degree of complexity, uncertainty, and imprecision. The large amount of data makes it necessary to select the most useful information. The active fusion system proposed in this paper is able to eeectively select information sources, to control the acquisition process, to select processing strategies, to integrate results, and to dra...

This paper introduces an evidential reasoning-based approach for recognizing and extracting manufacturing features from solid model description of objects. A major di4culty faced by previously proposed methods for feature extraction has been the interaction between features due to non-uniqueness and ambiguousness in feature representation. To overcome this di4culty, we introduce a Dempster–Shaf...

The research described in this paper addresses issues of designing a computationally effective decision support system which can assist a decision maker in making an optimal choice between several discrete alternatives. A new hybrid approach to multi-attribute decision making under uncertainty incorporating Neural Networks and the Dempster-Shafer theory of evidence is introduced. A neural netwo...

This paper deals with the problem of statistical unsupervised fusion of dependent sensors with its potential applications to multisensor image segmentation. On the one hand, Bayesian fusions can be of great efficiency, particularly when using hidden Markov models. On the other hand, we give some examples showing that there are situations in which the Dempster-Shafer fusion can be usefully integ...

Dempster-Shafer Evidence Theory(DST) enables multi-sensor data fusion, which makes it possible to infer the context. In this paper, we propose about how to use multi-sensor data fusion to infer context in the dynamic circumstances. Dynamic circumstance means a changing of the situation or the surrounding itself, and particularly signifies that there is a changeable factor in a specific environm...

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster-Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a propositio...

The financial statement audit is the process of collecting, evaluating, and aggregating relevant items of evidence pertaining to various management assertions related to the financial statement accounts to determine whether the company’s financial statements present fairly its financial position. The Dempster-Shafer theory [1] of belief functions has been argued to be an appropriate framework f...