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Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Donghui Li1, Cong Shen2,*, Xiaopeng Dai1, Xinghui Zhu1, Jian Luo1, Xueting Li1, Haiwen Chen3, Zhiyao Liang4

1 College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China.
2 College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
3 College of Computer Science and Engineering, National University of Defense Technology, Changsha, 410073, China.
4 Faculty of Information Technology, Macau University of Science and Technology, Macau.
* Corresponding Author: Cong Shen. Email:

Computers, Materials & Continua 2019, 61(3), 1217-1231.


Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor. The data of water quality in the environment comes from different sensors, thus the data must be fused. In our research, self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value, temperature, oxygen dissolved and NH3 concentration of water quality environment. Based on the fusion, the Grubbs method is used to detect the abnormal data so as to provide data support for estimation, prediction and early warning of the water quality.


Cite This Article

APA Style
Li, D., Shen, C., Dai, X., Zhu, X., Luo, J. et al. (2019). Research on data fusion of adaptive weighted multi-source sensor . Computers, Materials & Continua, 61(3), 1217-1231.
Vancouver Style
Li D, Shen C, Dai X, Zhu X, Luo J, Li X, et al. Research on data fusion of adaptive weighted multi-source sensor . Comput Mater Contin. 2019;61(3):1217-1231
IEEE Style
D. Li et al., "Research on Data Fusion of Adaptive Weighted Multi-Source Sensor ," Comput. Mater. Contin., vol. 61, no. 3, pp. 1217-1231. 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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