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Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods

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Abstract

Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.

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Authors and Affiliations

  1. School of Metallurgical Science and Engineering, Central South University, Changsha, 410083, China

    Zhang Hong-liang  (张红亮), Zou Zhong  (邹 忠) & Li Jie  (李 劼)

  2. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Chen Xiang-tao  (陈湘涛)

Authors
  1. Zhang Hong-liang  (张红亮)
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  2. Zou Zhong  (邹 忠)
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  3. Li Jie  (李 劼)
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  4. Chen Xiang-tao  (陈湘涛)
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Corresponding author

Correspondence to Zhang Hong-liang  (张红亮).

Additional information

Foundation item: Project(60634020) supported by the National Natural Science Foundation of China

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Zhang, Hl., Zou, Z., Li, J. et al. Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods. J. Cent. South Univ. Technol. 15, 39–43 (2008). https://doi.org/10.1007/s11771-008-0009-8

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  • DOI: https://doi.org/10.1007/s11771-008-0009-8

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