On-line burning state recognition for sintering process using SSIM index of flame images

Yanjun Lin; Chai Li; Jingxin Zhang; Xiaojie Zhou

doi:10.1109/WCICA.2014.7053089

On-line burning state recognition for sintering process using SSIM index of flame images

Yanjun Lin, Chai Li, Jingxin Zhang, Xiaojie Zhou

Electrical and Computer Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

6 Citations (Scopus)

Abstract

Recognition of burning state based on flame images has been an important issue in sintering process of rotary kiln. Existing methods usually adopt techniques of image segmentation, pattern recognition and machine learning, which have high demands on the quality of samples and computational power. It is challenging to the on-line recognition of the burning state, which is essential in realtime control systems. This paper proposes a new approach to the burning state
recognition by comparing the structural similarity (SSIM) index of flame images. The burning state is identified according to the maximum SSIM index between the real time flame image and images in two standard libraries which consist reference images with normal-burning state and under-burning state respectively. This method has low computational complexity and is suitable for
online control in the rotary kiln system. Simulation results show that the proposed method achieves high recognition accuracy with low computation.

Original language	English
Title of host publication	2014 11th World Congress on Intelligent Control and Automation (WCICA)
Editors	Hong Wang
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	2352 - 2357
Number of pages	6
ISBN (Print)	9781479958269
DOIs	https://doi.org/10.1109/WCICA.2014.7053089
Publication status	Published - 2015
Event	World Congress on Intelligent Control and Automation 2014 - Shenyang, China Duration: 29 Jun 2014 → 4 Jul 2014 Conference number: 11th

Conference

Conference	World Congress on Intelligent Control and Automation 2014
Abbreviated title	WCICA 2014
Country/Territory	China
City	Shenyang
Period	29/06/14 → 4/07/14

Keywords

On-line burning state recognition
structural similarity (SSIM)
flame images
low-pass filters

Access to Document

10.1109/WCICA.2014.7053089

Cite this

@inproceedings{e62946f3379a45b5aadf6654c1719783,

title = "On-line burning state recognition for sintering process using SSIM index of flame images",

abstract = "Recognition of burning state based on flame images has been an important issue in sintering process of rotary kiln. Existing methods usually adopt techniques of image segmentation, pattern recognition and machine learning, which have high demands on the quality of samples and computational power. It is challenging to the on-line recognition of the burning state, which is essential in realtime control systems. This paper proposes a new approach to the burning staterecognition by comparing the structural similarity (SSIM) index of flame images. The burning state is identified according to the maximum SSIM index between the real time flame image and images in two standard libraries which consist reference images with normal-burning state and under-burning state respectively. This method has low computational complexity and is suitable foronline control in the rotary kiln system. Simulation results show that the proposed method achieves high recognition accuracy with low computation.",

keywords = "On-line burning state recognition, structural similarity (SSIM), flame images, low-pass filters",

author = "Yanjun Lin and Chai Li and Jingxin Zhang and Xiaojie Zhou",

year = "2015",

doi = "10.1109/WCICA.2014.7053089",

language = "English",

isbn = "9781479958269",

pages = "2352 -- 2357",

editor = "Hong Wang",

booktitle = "2014 11th World Congress on Intelligent Control and Automation (WCICA)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "World Congress on Intelligent Control and Automation 2014, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",

}

Lin, Y, Li, C, Zhang, J & Zhou, X 2015, On-line burning state recognition for sintering process using SSIM index of flame images. in H Wang (ed.), 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 2352 - 2357, World Congress on Intelligent Control and Automation 2014, Shenyang, China, 29/06/14. https://doi.org/10.1109/WCICA.2014.7053089

On-line burning state recognition for sintering process using SSIM index of flame images. / Lin, Yanjun; Li, Chai; Zhang, Jingxin et al.

2014 11th World Congress on Intelligent Control and Automation (WCICA). ed. / Hong Wang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 2352 - 2357.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - On-line burning state recognition for sintering process using SSIM index of flame images

AU - Lin, Yanjun

AU - Li, Chai

AU - Zhang, Jingxin

AU - Zhou, Xiaojie

N1 - Conference code: 11th

PY - 2015

Y1 - 2015

N2 - Recognition of burning state based on flame images has been an important issue in sintering process of rotary kiln. Existing methods usually adopt techniques of image segmentation, pattern recognition and machine learning, which have high demands on the quality of samples and computational power. It is challenging to the on-line recognition of the burning state, which is essential in realtime control systems. This paper proposes a new approach to the burning staterecognition by comparing the structural similarity (SSIM) index of flame images. The burning state is identified according to the maximum SSIM index between the real time flame image and images in two standard libraries which consist reference images with normal-burning state and under-burning state respectively. This method has low computational complexity and is suitable foronline control in the rotary kiln system. Simulation results show that the proposed method achieves high recognition accuracy with low computation.

AB - Recognition of burning state based on flame images has been an important issue in sintering process of rotary kiln. Existing methods usually adopt techniques of image segmentation, pattern recognition and machine learning, which have high demands on the quality of samples and computational power. It is challenging to the on-line recognition of the burning state, which is essential in realtime control systems. This paper proposes a new approach to the burning staterecognition by comparing the structural similarity (SSIM) index of flame images. The burning state is identified according to the maximum SSIM index between the real time flame image and images in two standard libraries which consist reference images with normal-burning state and under-burning state respectively. This method has low computational complexity and is suitable foronline control in the rotary kiln system. Simulation results show that the proposed method achieves high recognition accuracy with low computation.

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KW - structural similarity (SSIM)

KW - flame images

KW - low-pass filters

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