scholarly journals A novel weighted evidence combination rule based on improved entropy function with a diagnosis application

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882399 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Medical Diagnosis ◽  
Real Life ◽  
Entropy Function ◽  
New Method ◽  
Combination Rule ◽  
Initial Weight ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

Managing conflict in Dempster–Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989.

scholarly journals Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Fault Diagnosis ◽  
Conflict Management ◽  
Information Fusion ◽  
Uncertainty Measure ◽  
Combination Rule ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Weight Value ◽  
Shafer Theory

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.

scholarly journals A New Method to Handle Conflict when Combining Evidences Using Entropy Function and Evidence Angle with an Effective Application in Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Fault Diagnosis ◽  
Entropy Function ◽  
Initial Weight ◽  
Research Directions ◽  
New Approach ◽  
Dempster Shafer Theory ◽  
Final Weight ◽  
Novel Method ◽  
Effectiveness And Efficiency ◽  
Shafer Theory

Addressing the problem of fusing highly conflicting evidences in Dempster–Shafer theory is one of the most necessary, important, and difficult research directions all the time, and so far we have published two papers related to it. In this paper, another novel method to handle conflict when combining evidences is proposed, where evidence distance, evidence angle, and improved entropy function, three key tools, are used for constructing the final weight of each body of evidence. This newly proposed approach mainly consists of three steps: firstly, both evidence distance and evidence angle determine the initial weight together; secondly, making use of the improved entropy modifies the initial weight to get the final weight; lastly, the classical D-S combination rule will be applied to obtain final fusion results. Still a classical numeric example and a real fault diagnosis application both demonstrate its effectiveness and efficiency, and compared with other current popular methods including two of our previous works, this new approach can converge fast and reduce most uncertainty of decision-making when fusing highly conflicting evidences.

Modified Interval Dempster-Shafer Theory and its Application for Target Identification

2012 ◽  
Vol 190-191 ◽  
pp. 1153-1156
Author(s):  
Hai Jun Liu ◽  
Hong Shan Nie ◽  
Hong Qi Yu ◽  
Hong Hu Hua ◽  
Zheng Liu
Keyword(s):  
Measurement Uncertainty ◽  
Target Identification ◽  
Identification Algorithm ◽  
Combination Rules ◽  
Feature Measurement ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

To deal with the problem of target identification caused by the achieved reliability of multi sensors and the feature measurement uncertainty of the target, this paper proposes a new identification algorithm based on Modified Interval Dempster-Shafer Theory (MIDST), which models sensor’s reliability as scalar value and identification outputs of each sensor as interval values, and then combines the actual interval outputs through interval evidence combination rules. At last, one simulation is presented to demonstrate the identification capability of the MIDST algorithm.

A new method based on Dempster–Shafer theory and fuzzy c-means for brain MRI segmentation

2015 ◽  
Vol 26 (10) ◽  
pp. 105402 ◽  
Author(s):  
Jie Liu ◽  
Xi Lu ◽  
Yunpeng Li ◽  
Xiaowu Chen ◽  
Yong Deng
Keyword(s):  
Brain Mri ◽  
New Method ◽  
Mri Segmentation ◽  
Fuzzy C Means ◽  
Dempster Shafer Theory ◽  
Shafer Theory

scholarly journals Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

2021 ◽  
Author(s):  
Ias Sri Wahyuni ◽  
Rachid Sabre
Keyword(s):  
Image Fusion ◽  
Mass Function ◽  
New Method ◽  
Dempster Shafer Theory ◽  
Local Distance ◽  
Focus Image ◽  
Fusion Images ◽  
Shafer Theory ◽  
Quadratic Distance ◽  
Local Variability

In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show that our method gives the significant and better result by comparing it to other methods.

scholarly journals A Signature Analysis Method for IC Failure Analysis

1996 ◽  
Author(s):  
C.L. Henderson ◽  
J.M. Soden
Keyword(s):  
Failure Analysis ◽  
Failure Mechanism ◽  
Experiential Knowledge ◽  
New Method ◽  
Signature Analysis ◽  
Analysis Method ◽  
Dempster Shafer Theory ◽  
Shafer Theory

Abstract A new method of signature analysis is presented and explained. This method of signature analysis can be based on either experiential knowledge of failure analysis, observed data, or a combination of both. The method can also be used on low numbers of failures or even single failures. It uses the Dempster-Shafer theory to calculate failure mechanism confidence. The model is developed in the paper and an example is given for its use.

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