Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder  

作　　者：Xiaoxiong Feng Jianhua Liu Xiaoxiong Feng;Jianhua Liu(College of Railway Transportation, Hunan University of Technology, Zhuzhou, China)

机构地区：[1]College of Railway Transportation, Hunan University of Technology, Zhuzhou, China

出　　处：《Journal of Sensor Technology》2023年第4期69-85,共17页传感技术（英文）

摘　　要：To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

关 键 词：Multi-Mode Data Fusion Coupling Convolutional Auto-Encoder Adaptive Optimization Deep Learning 

分 类 号：G63[文化科学—教育学]