Masci, J., Masci, J., Meier, U., et al. (2011) Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction. In: Honkela, T., Duch, W., Girolami, M. and Kaski, S., Eds., ICANN 2011: Artificial Neural Networks and Machine Learning—ICANN 2011, Springer, Berlin, 52-59.
https://doi.org/10.1007/978-3-642-21735-7_7

has been cited by the following article:

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

  AUTHORS: Xiaoxiong Feng, Jianhua Liu

  KEYWORDS: Multi-Mode Data Fusion, Coupling Convolutional Auto-Encoder, Adaptive Optimization, Deep Learning

  JOURNAL NAME: Journal of Sensor Technology, Vol.13 No.4, December 26, 2023

  ABSTRACT: 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.