Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/186299
Title: Riemannian Alternative Matrix Completion for Image-Based Flame Recognition
Authors: Zhichao Wang;Min Liu;Mingyu Dong;Lian Wu
Year: 2017
Publisher: IEEE
Abstract: The flame image has important significance in combustion state recognition and judgment, which can be used effectively for control of energy consumption and exhaust emissions. Due to the harsh industrial environments, flame images are usually corrupted by transmission errors or coding issues, which makes the combustion state analysis very challenging. This paper proposes a novel flame combustion state analysis framework, which provides new insight into two crucial issues: corrupted flame image recovery and combustion state recognition. First, we propose Riemannian alternative optimization (RAO) with fast convergence and the global optimization ability to recover the corrupted flame image. More specifically, RAO constructs a low-rank factorization model and exploits the geometric nature of the flame image to perform the optimization on Riemannian manifolds. Second, we use Fisher discriminant analysis to exploit discriminative features of the recovered flame image and provide well-separated classes of the combustion state for recognition. The experiments show that the proposed framework recovers the corrupted flame image efficiently and achieves satisfying performance of combustion state recognition.
URI: http://localhost/handle/Hannan/186299
volume: 27
issue: 11
More Information: 2490,
2503
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7506004.pdf5.58 MBAdobe PDF
Title: Riemannian Alternative Matrix Completion for Image-Based Flame Recognition
Authors: Zhichao Wang;Min Liu;Mingyu Dong;Lian Wu
Year: 2017
Publisher: IEEE
Abstract: The flame image has important significance in combustion state recognition and judgment, which can be used effectively for control of energy consumption and exhaust emissions. Due to the harsh industrial environments, flame images are usually corrupted by transmission errors or coding issues, which makes the combustion state analysis very challenging. This paper proposes a novel flame combustion state analysis framework, which provides new insight into two crucial issues: corrupted flame image recovery and combustion state recognition. First, we propose Riemannian alternative optimization (RAO) with fast convergence and the global optimization ability to recover the corrupted flame image. More specifically, RAO constructs a low-rank factorization model and exploits the geometric nature of the flame image to perform the optimization on Riemannian manifolds. Second, we use Fisher discriminant analysis to exploit discriminative features of the recovered flame image and provide well-separated classes of the combustion state for recognition. The experiments show that the proposed framework recovers the corrupted flame image efficiently and achieves satisfying performance of combustion state recognition.
URI: http://localhost/handle/Hannan/186299
volume: 27
issue: 11
More Information: 2490,
2503
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7506004.pdf5.58 MBAdobe PDF
Title: Riemannian Alternative Matrix Completion for Image-Based Flame Recognition
Authors: Zhichao Wang;Min Liu;Mingyu Dong;Lian Wu
Year: 2017
Publisher: IEEE
Abstract: The flame image has important significance in combustion state recognition and judgment, which can be used effectively for control of energy consumption and exhaust emissions. Due to the harsh industrial environments, flame images are usually corrupted by transmission errors or coding issues, which makes the combustion state analysis very challenging. This paper proposes a novel flame combustion state analysis framework, which provides new insight into two crucial issues: corrupted flame image recovery and combustion state recognition. First, we propose Riemannian alternative optimization (RAO) with fast convergence and the global optimization ability to recover the corrupted flame image. More specifically, RAO constructs a low-rank factorization model and exploits the geometric nature of the flame image to perform the optimization on Riemannian manifolds. Second, we use Fisher discriminant analysis to exploit discriminative features of the recovered flame image and provide well-separated classes of the combustion state for recognition. The experiments show that the proposed framework recovers the corrupted flame image efficiently and achieves satisfying performance of combustion state recognition.
URI: http://localhost/handle/Hannan/186299
volume: 27
issue: 11
More Information: 2490,
2503
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7506004.pdf5.58 MBAdobe PDF