Sintering conditions recognition of rotary kiln based on kernel modification considering class imbalance-Reference-Cited by-同舟云学术

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Sintering conditions recognition of rotary kiln based on kernel modification considering class imbalance

Published:2020-11 Issue: Volume:106 Page:271-282
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Wang Dingxiang,Zhang Xiaogang,Chen Hua,Zhou Yicong,Cheng Fanyong

Funder

National Key Technologies Research and Development Program of China

Hunan Natural Science Foundation

National Natural Science Foundation of China

Scientific Research Launch Project of Anhui Polytechnic University

Macau Science and Technology Development Fund

University of Macau

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference40 articles.

1. Recognition of the temperature condition of a rotary kiln using dynamic features of a series of blurry flame images;Chen;IEEE Trans Ind Inf,2015

2. A new camera-based method for measuring the flame stability of non-oscillating and oscillating combustions;Matthes;Exp Therm Fluid Sci,2019

3. Temperature measurement of coal fired flame in the cement kiln by raw image processing;Xiangyu;Measurement,2018

4. Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques;Bai;Fuel,2017

5. Identification and abnormal condition detection of a cement rotary kiln;Makaremi;IFAC Proc Vol,2008

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1. Spatio-Temporal Graph Attention Network for Sintering Temperature Long-Range Forecasting in Rotary Kilns;IEEE Transactions on Industrial Informatics;2023-02

2. Working Condition Recognition Based on Transfer Learning and Attention Mechanism for a Rotary Kiln;Entropy;2022-08-25

3. Multi-objective optimization-based adaptive class-specific cost extreme learning machine for imbalanced classification;Neurocomputing;2022-07

4. An efficient unsupervised image quality metric with application for condition recognition in kiln;Engineering Applications of Artificial Intelligence;2022-01

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