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

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.

Extraction of Flavonoids from Leaves of Coix lacryma-jobi L.

[Objectives] To extract the flavonoids from leaves of Coix lacryma-jobi L. [Methods] Ethanol extraction method was adopted,spectrophotometry was used,and single factor experiment and orthogonal experiment were carried out to study the effects of ethanol percentage,extraction temperature,solid-to-liquid ratio and extraction time on the extraction of total flavonoids from leaves of C. lacryma-jobi L.[Results] The order of 4 factors influencing the extraction of flavonoids from leaves of C. lacryma-jobi L. was: solid-to-liquid ratio > extraction time > ethanol percentage > extraction temperature. When the extraction temperature was 70℃,the extraction time was 1. 5 h and the solid-liquid ratio was 1: 10,the ethanol percentage was 60%,the extraction effect was the best,extraction of flavonoids was 0. 107 5 mg/m L.[Conclusions] This study is expected to provide a theoretical basis for further development and utilization of C. lacryma-jobi L.