Donghui Li¹, Cong Shen^2,*, Xiaopeng Dai¹, Xinghui Zhu¹, Jian Luo¹, Xueting Li¹, Haiwen Chen³, Zhiyao Liang⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1217-1231, 2019, DOI:10.32604/cmc.2019.06354

Abstract 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. More >

He Xufei¹, Deng Zhongmin², Song Zhitao¹

Structural Durability & Health Monitoring, Vol.8, No.3, pp. 193-208, 2012, DOI:10.32604/sdhm.2012.008.193

Abstract This paper describes an information optimizing scheme which is developed by integrating rough set and hierarchical data fusion. The novel structural damage indices are extracted using the information from different sources and then imported into probabilistic neural network (PNN) for classification and health assessment. In order to enhance the accuracy of diagnosis, results from separate PNN classification are fused to achieve comprehensive decision. Rough set is employed to decrease the spatial dimension of data. The predictive accuracy of optimizing scheme is demonstrated on a helicopter, taken as an example, with varied sensors, for multiple damage identification. More >