Application of D-S Theory in Information Fusion of Water Quality Measurement Sensors

2012 ◽  
Vol 241-244 ◽  
pp. 956-959 ◽  
Author(s):  
Hai Feng Li ◽  
Hui Zuo ◽  
Xu Feng Hua
Keyword(s):  
Information Fusion ◽  
Measurement System ◽  
Evidence Theory ◽  
Quality Measurement ◽  
Fusion Rule ◽  
Combination Rule ◽  
Sensor Measurement ◽  
System Information ◽  
Water Quality Measurement ◽  
Main Factor

This paper issues the D-S evidence theory and modifies the fusion rule according to the relation of time and the NH3 content in water. A few concepts of D-S evidence theory and D-S evidence combination rule are presented. The modified D-S algorithm is simulated in time and space fusion of multi-sensor measurement system information. It could distinguish the main factor that changing the NH3 content in water. The measurement system cost could be decreased.

scholarly journals An Extended Base Belief Function in Dempster–Shafer Evidence Theory and Its Application in Conflict Data Fusion

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2137
Author(s):  
Dingyi Gan ◽  
Bin Yang ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Data Management ◽  
Information Fusion ◽  
Evidence Theory ◽  
Belief Function ◽  
Mass Function ◽  
Combination Rule ◽  
Basic Probability ◽  
Belief Entropy ◽  
Management Method

The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time. In order to solve this problem, the base belief function is proposed to modify the basic probability assignment (BPA) in the exhaustive frame of discernment (FOD). However, in the non-exhaustive FOD, the mass function value of the empty set is nonzero, which makes the base belief function no longer applicable. In this paper, considering the influence of the size of the FOD and the mass function value of the empty set, a new belief function named the extended base belief function (EBBF) is proposed. This method can modify the BPA in the non-exhaustive FOD and obtain intuitive fusion results by taking into account the characteristics of the non-exhaustive FOD. In addition, the EBBF can degenerate into the base belief function in the exhaustive FOD. At the same time, by calculating the belief entropy of the modified BPA, we find that the value of belief entropy is higher than before. Belief entropy is used to measure the uncertainty of information, which can show the conflict more intuitively. The increase of the value of entropy belief is the consequence of conflict. This paper also designs an improved conflict data management method based on the EBBF to verify the rationality and effectiveness of the proposed method.

scholarly journals A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

2020 ◽  
Author(s):  
Lifan Sun ◽  
Yuting Chang ◽  
Jiexin Pu ◽  
Haofang Yu ◽  
Zhe Yang
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
Uncertain Information ◽  
Combination Rule ◽  
Simulation Experiments ◽  
Sensor Information ◽  
Bodies Of Evidence ◽  
Probability Distance ◽  
Deng Entropy

The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.

Fault Diagnosis of Engine Based on Improved Dempster-Shafer Information Fusion Method

2009 ◽  
Vol 16-19 ◽  
pp. 1310-1317
Author(s):  
Wei Zhou ◽  
Ying Ji Liu ◽  
Qing Fu Cao ◽  
Tian Xia Zhang
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
New Combination ◽  
Fusion Technology ◽  
Combination Rule ◽  
Experiment Research ◽  
New Approach ◽  
Weight Value

In order to enhance the accuracy of engine fault diagnosis, information fusion technology was applied and a novel combination method is proposed based on D-S evidence theory. The evidence groups were classified by evidence conflict coefficient, the importance of each highly conflict evidence was calculated, and the credibility of each evidence was determined with a distance function of evidence bodies. Then the weight value of each evidence was revised with its importance and credibility respectively. Finally, the Dempster combination rule was used to realize the information fusion. The effectiveness of the new approach proposed was verified by theoretical analysis and experiment research results. Comparing with D-S evidence theory and the improved synthesis formula, the new combination method is more efficient in improving the accuracy and the certainty degree of engine fault diagnosis.

Milling Tool Wear Identification Using Information Fusion

2014 ◽  
Vol 496-500 ◽  
pp. 1681-1684
Author(s):  
Chuang Wen Xu
Keyword(s):  
Tool Wear ◽  
Information Fusion ◽  
Evidence Theory ◽  
Reliability Function ◽  
Belief Functions ◽  
Combination Rule ◽  
Literature Study ◽  
Wear Characteristics ◽  
Milling Tool ◽  
Information Characteristics

Milling tool wear identification literature study, some information characteristics was generated by a number of possible milling tool wear. In theory, the characteristics of each of the information under the milling tool wear has a certain probability of occurring. DS evidence theory, belief functions used to express the size of the probability. the milling tool wear objects was identified through multi-sensor testing, then, measured the information features of each sensor belong to belief functions was obtained, then use the DS combination rule for information fusion, information fusion can be milling tool wear characteristics belong to the reliability function, the final determination based on certain criteria for determining status of milling tool wear type and size of tool wear.

Conflicting evidence combination based on Belief Mover’s Distance

2021 ◽  
pp. 1-17
Author(s):  
Shenshen Bai ◽  
Longjie Li ◽  
Xiaoyun Chen
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Weighted Average ◽  
Combination Rule ◽  
The Earth ◽  
Conflicting Evidence ◽  
Evidence Combination ◽  
Final Weight ◽  
The Difference ◽  
New Evidence

The Dempster-Shafer evidence theory has been extensively used in various applications of information fusion owing to its capability in dealing with uncertain modeling and reasoning. However, when meeting highly conflicting evidence, the classical Dempster’s combination rule may give counter-intuitive results. To address this issue, we propose a new method in this work to fuse conflicting evidence. Firstly, a new evidence distance metric, named Belief Mover’s Distance, which is inspired by the Earth Mover’s Distance, is defined to measure the difference between two pieces of evidence. Subsequently, the credibility weight and distance weight of each piece of evidence are computed according to the Belief Mover’s Distance. Then, the final weight of each piece of evidence is generated by unifying these two weights. Finally, the classical Dempster’s rule is employed to fuse the weighted average evidence. Several examples and applications are presented to analyze the performance of the proposed method. Experimental results manifest that the proposed method is remarkably effective in comparison with other methods.

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