Proceedings ArticleDOI
Multi-sensor Information Fusion Based on Rough Set Theory
Xiujiang Lv,Yan Zhao,Guang-shun Yao,Qiao-chu Lv,Ning Wang +4 more
- Vol. 1, pp 28-30
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TLDR
Aiming at the problem that the data in the information fusion often overloads, the method that rough set application in neural network was proposed, in which useful attributes were extracted from given training data and redundant attributes were deleted utilizing numerical analysis ability of rough set theory, so sample size can be reduced.Abstract:
Aiming at the problem that the data in the information fusion often overloads, the method that rough set application in neural network was proposed, in which useful attributes were extracted from given training data and redundant attributes were deleted utilizing numerical analysis ability of rough set theory, so sample size can be reduced. While reducing training time and increasing efficiency, the useful information in the source data set wasn't lost.read more
Citations
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Journal ArticleDOI
Information Fusion in a Multi-Source Incomplete Information System Based on Information Entropy
Mengmeng Li,Xiaoyan Zhang +1 more
TL;DR: This paper presents a method for fusing incomplete multi-source systems based on information entropy, and by comparison with another method, the fusion method is validated and experimental results indicate that multi- source information fusion approaches significantly outperform other approaches to fusion.
Proceedings ArticleDOI
Multi-sensor information fusion method and its applications on fault detection of diesel engine
TL;DR: The experiment results indicate that the problem of multi-sensor information fusion in diesel engine fault detection is solved by using Dempster-Shafer evidential theory, and the uncertainty of single sensor information is avoided.
Proceedings ArticleDOI
Traffic status estimate based on data fusion
TL;DR: In the paper, Rough Sets Theory is utilized to fuse different data to more accurately estimate the traffic status, including current GPS data from uplink and downlink lanes and historical data.
Book ChapterDOI
Feature Level Information Fusion Based Deep Learning
TL;DR: This work makes facial comparison by improved Siamese network, which can be efficiently optimized with different perspectives and thus guarantee good robustness and achieves the best performance on LWF by 97.98%.
Proceedings ArticleDOI
A Reduction Algorithm Based on Rough Set and Information Granulation Theory for Multi-radar Data
TL;DR: Simulation results show that the algorithm can reduce redundant data effectively and the coordinate estimated after reducing data is closer to the true location of the civil aircraft.
References
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Journal Article
Research on Rough Set Application in Neural Network
TL;DR: This paper discusses and summarizes the key technology, current status and the trends of rough set application in neural network, and brings out an application framework for it.