A new method to rank fuzzy numbers using Dempster–Shafer theory with fuzzy targets

2016 ◽  
Vol 346-347 ◽  
pp. 302-317 ◽  
Author(s):  
Kok Chin Chai ◽  
Kai Meng Tay ◽  
Chee Peng Lim
Keyword(s):  
Fuzzy Numbers ◽  
New Method ◽  
Dempster Shafer Theory ◽  
Shafer Theory

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.

Research and Application of the Improved DST New Method Based on Fuzzy Consistent Matrix and the Weighted Average

2014 ◽  
Vol 1030-1032 ◽  
pp. 1764-1768 ◽  
Author(s):  
Wei Xiao Xu ◽  
Ji Wen Tan ◽  
Hong Zhan
Keyword(s):  
Fault Diagnosis ◽  
Evidence Synthesis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
New Method ◽  
Cnc Machine ◽  
Bearing Fault Diagnosis ◽  
Dempster Shafer Theory ◽  
High Conflict ◽  
Shafer Theory

Aiming at the existing defects of evidence dempster-shafer theory (DST) in dealing with high conflict evidence, we proposed a new method to improve DST. By introducing concept of fuzzy consistent matrix, calculate the weights of factors, and put different sources of evidence into distinguish, and finally cast more than one vote to prevent the phenomenon, the average convergence of evidence. What’s more, the improved DST new method is applied to the rolling bearing fault diagnosis of CNC machine workbench .The test results show that the improved new synthetic formula increases the accuracy of fault diagnosis Ball, the conflict of evidence synthesis results better, to achieve better results.

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.

Fuzzy-DSS Human Health Risk Assessment Under Uncertain Environment

2018 ◽  
pp. 316-347
Author(s):  
Palash Dutta
Keyword(s):  
Risk Assessment ◽  
Health Risk ◽  
Human Health ◽  
Health Risk Assessment ◽  
Fuzzy Numbers ◽  
Human Health Risk ◽  
Human Health Risk Assessment ◽  
Model Parameters ◽  
Dempster Shafer Theory ◽  
Shafer Theory

It is always utmost essential to accumulate knowledge on the nature of each and every accessible data, information, and model parameters in risk assessment. It is noticed that more often model parameters, data, information are fouled with uncertainty due to lack of precision, deficiency in data, diminutive sample sizes. In such environments, fuzzy set theory or Dempster-Shafer theory (DST) can be explored to represent this type of uncertainty. Most frequently, both types of uncertainty representation theories coexist in human health risk assessment and need to merge within the same framework. For this purpose, this chapter presents two algorithms to combine Dempster-Shafer structure (DSS) with generalized/normal fuzzy focal elements, generalized/normal fuzzy numbers within the same framework. Computer codes are generated using Matlab M-files. Finally, human health risk assessment is carried out under this setting and it is observed that the results are obtained in the form of fuzzy numbers (normal/generalized) at different fractiles.

Highly Iterative Planning of Manufacturing Technologies: Evaluation of Manufacturing Technology Capabilities Considering Information Uncertainties

2019 ◽  
Author(s):  
Jan Rey ◽  
Timm Grünebaum ◽  
Daniel Trauth ◽  
Thomas Bergs
Keyword(s):  
Product Development ◽  
Development Process ◽  
New Method ◽  
Product Development Process ◽  
Product Characteristics ◽  
Dempster Shafer Theory ◽  
Iterative Development ◽  
Manufacturing Technologies ◽  
Shafer Theory ◽  
Theory Of Evidence

Abstract Highly iterative product development is a promising approach, which enables a continuous inclusion of customers in the product development process. A stronger involvement of customers results in more frequent changes of the required product characteristics while the product is being developed. For the planning of manufacturing technologies, which takes place in parallel to product development, this means that very uncertain product and technology information have to be processed. In order to consider these uncertainties when designing technology chains, technology planners have to be able to model and quantify them. Moreover, due to the frequent product changes during the highly iterative development process, an evaluation of how capable manufacturing technologies are for handling future changes of product characteristics is essential for technology planners. This paper presents a new methodology, which enables the evaluation of manufacturing technologies regarding their capability to react to future product changes within the development process. Firstly, a new method based on fuzzy sets and the Dempster-Shafer theory of evidence is presented. It allows an aggregation of uncertain product and technology information from different sources. Afterwards, the influences of manufacturing technologies within a technology chain on the product characteristics are modeled considering the different uncertainties. Finally, a new method to evaluate the capability of manufacturing technologies to cope with future product changes is introduced. This allows technology planners to predict the capability of manufacturing technologies to manufacture the future, fully developed product and hence to identify alternatives to reduce the information uncertainties, for example by executing prototype experiments.

scholarly journals Bridge Condition Assessment UsingDNumbers

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xinyang Deng ◽  
Yong Hu ◽  
Yong Deng
Keyword(s):  
Condition Assessment ◽  
Complex Problem ◽  
New Method ◽  
Illustrative Case ◽  
Uncertain Information ◽  
Uncertain Environment ◽  
Dempster Shafer Theory ◽  
Bridge Condition Assessment ◽  
Shafer Theory ◽  
Assessment Results

Bridge condition assessment is a complex problem influenced by many factors. The uncertain environment increases more its complexity. Due to the uncertainty in the process of assessment, one of the key problems is the representation of assessment results. Though there exists many methods that can deal with uncertain information, however, they have more or less deficiencies. In this paper, a new representation of uncertain information, calledDnumbers, is presented. It extends the Dempster-Shafer theory. By usingDnumbers, a new method is developed for the bridge condition assessment. Compared to these existing methods, the proposed method is simpler and more effective. An illustrative case is given to show the effectiveness of the new method.

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