Autoencoder basics Autoencoders are based on neural networks, and the network consists of two parts: an encoder and a decoder. Encoder compresses the N-dimensional input (e.g. a frame of sensor data) ...

This paper introduces GeneA-SLAM2, an RGB-D SLAM system for dynamic environments. It eliminates dynamic object interference via depth statistical information and enhances keypoint distribution ...

In recent years, data-driven soft sensors, especially deep learning soft sensors show great potential for application in the process industry. As a typical deep network, stacked autoencoder (SAE) has ...