A layered working condition perception integrating handcrafted with deep features for froth flotation-Reference-Cited by-同舟云学术

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A layered working condition perception integrating handcrafted with deep features for froth flotation

Published:2021-08 Issue: Volume:170 Page:107059
ISSN:0892-6875
Container-title:Minerals Engineering
language:en
Short-container-title:Minerals Engineering

Author:

Gao Xiaoliang,Tang Zhaohui,Xie Yongfang,Zhang Hu,Gui Weihua

Funder

China National Funds for Distinguished Young Scientists

National Natural Science Foundation of China-Guangdong Joint Fund

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Mechanical Engineering,Geotechnical Engineering and Engineering Geology,General Chemistry,Control and Systems Engineering

Reference31 articles.

1. Online monitoring and control of froth flotation systems with machine vision: A review;Aldrich;Int. J. Mineral Process,2010

2. Control of a flotation column using fuzzy logic inference;Carvalho;Fuzzy Sets Syst.,2002

3. Froth collapse in column flotation: a prevention method using froth density estimation and fuzzy expert systems;Chuk;Miner. Eng.,2005

4. Froth image analysis by use of transfer learning and convolutional neural networks;Fu;Miner. Eng.,2018

5. Machine-vision-based online measuring and controlling technologies for mineral flotation——A Review;GUI;Acta Autom. Sin.,2013

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1. Upstream process condition monitoring for froth flotation based on feature performance evaluation and parameter-mapped GRNN;Expert Systems with Applications;2025-01

2. Dual-Rule-Based Weighted Fuzzy Interpolative Reasoning Module and Temporal Encoder–Decoder Bayesian Network for Reagent Addition Control;IEEE Transactions on Fuzzy Systems;2024-07

3. Multi-condition flotation process condition recognition method based on deep learning and multivariate data pre-processing;2024 36th Chinese Control and Decision Conference (CCDC);2024-05-25

4. Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning;Engineering Applications of Artificial Intelligence;2024-03

5. Measurement of Froth-Layer Depth With Binocular Camera Based on Intensity Correction and Its Application to Pulp-Grade Monitoring;IEEE Transactions on Instrumentation and Measurement;2024

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