Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications-Reference-Cited by-同舟云学术

Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications

Published:2020-12 Issue: Volume:162 Page:113887
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Ghosh Nimisha,Paul Rourab,Maity Satyabrata,Maity Krishanu,Saha Sayantan

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference47 articles.

1. An informational approach for sensor and actuator fault diagnosis for autonomous mobile robots;Abci;Journal of Intelligent and Robotic Systems,2019

2. Outlier detection for wireless sensor networks using density-based clustering approach;Abid;IET Wireless Sensor Systems,2017

3. Outlier analysis;Aggarwal,2016

4. A novel fuzzy approach for combining uncertain conflict evidences in the dempster-shafer theory;An;IEEE Access,2019

5. A comprehensive review of the multi-sensor data fusion architectures;Azimirad;Journal of Theoretical & Applied Information Technology,2015

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