A soft-sensor for estimating copper quality by image analysis technology

IEEE International Conference on Control and Automation, ICCA

Volumn , Issue , 2013, Pages 991-996

  Yuan, Xiaofeng ^a   Zhang, Hongwei ^a   Song, Zhihuan ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR CHANNELS; COPPER CONTENT; ESTIMATION METHODS; INPUT VARIABLES; LEAST SQUARES REGRESSION; PRINCIPAL COMPONENTS; REGRESSION MODEL; TESTING SAMPLES;

COLOR; COPPER; COPPER ALLOYS; LEAST SQUARES APPROXIMATIONS; LIGHT SOURCES; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS;

ESTIMATION;

EID: 84882376362     PISSN: 19483449     EISSN: 19483457     Source Type: Conference Proceeding    
DOI: 10.1109/ICCA.2013.6565042     Document Type: Conference Paper

References (15)

1. M. M. Haque, et al., "Recycling copper from waste copper wire using an applied voltage", International Journal of Precision Engineering and Manufacturing, vol. 13, pp. 1251-1254, Jul. 2012.
2. J. M. Prats-Montalban, et al., "Multivariate image analysis: A review with applications", Chemometrics and Intelligent Laboratory Systems, vol. 107, pp. 1-23, May 2011.
3. X. Wang, et al., "Fabric Texture Analysis Using Computer Vision Techniques", IEEE Transactions on Instrumentation and Measurement, vol. 60, pp. 44-56, Jan 2011.
4. S. Kucheryavski, "Extracting useful information from images", Chemometrics and Intelligent Laboratory Systems, vol. 108, pp. 2-12, Aug. 2011.
5. W. I. W. Ishak et al., "Image based modeling for oil palm fruit maturity prediction", Journal of Food Agriculture & Environment, vol. 8, pp. 469-476, Apr. 2010.
6. A. I. Martynenko, "Computer-vision system for control of drying processes", Drying Technology, vol. 24, pp. 879-888, 2006.
7. Y. M. M. Jusoh, et al., "Bread crust thickness measurement using digital imaging and L a b color system", Journal of Food Engineering, vol. 94, pp. 366-371, Oct. 2009.
8. C. W. Kim, et al., "A study on the composition determination of Cu alloys by image processing technology", in Semi-Solid Processing of Alloys and Composites. vol. 116-117, C. G. Kang, et al., Eds., ed, 2006, pp. 795-798.
9. C. Aldrich, et al., "Online monitoring and control of froth flotation systems with machine vision: A review", International Journal of Mineral Processing, vol. 96, pp. 1-13, Sep. 2010.
10. H. Muller, et al., "A review of content-based image retrieval systems in medical applications - clinical benefits and future directions", International Journal of Medical Informatics, vol. 73, pp. 1-23, Feb. 2004.
11. Y. P. Zhang, et al., "Quantitative research on surface color of binary brass", The Chinese Journal of Nonferrous Metals, vol. 11, pp. 152-155, 2001.
12. B. Li and X. Zhang, "Color Characteristic of Cu-Zn-Al Alloys", Transactions of NF soc, vol. 4, pp. 89-92, 1994.
13. Y. H. Yang, et al., A new fault detection and diagnosis method based on principal component analysis in multivariate continuous processes. New York: IEEE, 2002.
14. S. Wold, et al., "Principal Component Analysis", Chemometrics and Intelligent Laboratory Systems, vol. 2, pp. 37-52, Aug. 1987.
15. Y. Zhu, Multivariable system identification for process control, 1st ed. Amsterdam; New York: Pergamon, 2001.