Fault detection in flotation processes based on deep learning and support vector machine

This publication has 10 references indexed in Scilit:

• Froth image analysis by use of transfer learning and convolutional neural networks
  Minerals Engineering, 2018
• The Method of Reagent Control Based on Time Series Distribution of Bubble Size in a Gold‐Antimony Flotation Process
  Asian Journal of Control, 2017
• Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
  2015
• Nonparametric density estimation of froth colour texture distribution for monitoring sulphur flotation process
  Minerals Engineering, 2013
• Color co-occurrence matrix based froth image texture extraction for mineral flotation
  Minerals Engineering, 2013
• Recognition of the operational statuses of reagent addition using dynamic bubble size distribution in copper flotation process
  Minerals Engineering, 2013
• Flotation process fault detection using output PDF of bubble size distribution
  Minerals Engineering, 2012
• Online monitoring and control of froth flotation systems with machine vision: A review
  International Journal of Mineral Processing, 2010
• A SURVEY ON PATTERN RECOGNITION APPLICATIONS OF SUPPORT VECTOR MACHINES
  International Journal of Pattern Recognition and Artificial Intelligence, 2003
• Gradient-based learning applied to document recognition
  Proceedings of the IEEE, 1998