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Reseach Article

Distillation Column Malfunctions Identification using Higher Order Statistics

by M. E. Hammad, H. Kasban, Sayed M. Elaraby, Moawad I. Dessouky, O. Zahran, Fathi E. Abd El-samie
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 1
Year of Publication: 2014
Authors: M. E. Hammad, H. Kasban, Sayed M. Elaraby, Moawad I. Dessouky, O. Zahran, Fathi E. Abd El-samie
10.5120/18873-0129

M. E. Hammad, H. Kasban, Sayed M. Elaraby, Moawad I. Dessouky, O. Zahran, Fathi E. Abd El-samie . Distillation Column Malfunctions Identification using Higher Order Statistics. International Journal of Computer Applications. 108, 1 ( December 2014), 7-13. DOI=10.5120/18873-0129

@article{ 10.5120/18873-0129,
author = { M. E. Hammad, H. Kasban, Sayed M. Elaraby, Moawad I. Dessouky, O. Zahran, Fathi E. Abd El-samie },
title = { Distillation Column Malfunctions Identification using Higher Order Statistics },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 1 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 7-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number1/18873-0129/ },
doi = { 10.5120/18873-0129 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:50.437053+05:30
%A M. E. Hammad
%A H. Kasban
%A Sayed M. Elaraby
%A Moawad I. Dessouky
%A O. Zahran
%A Fathi E. Abd El-samie
%T Distillation Column Malfunctions Identification using Higher Order Statistics
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 1
%P 7-13
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital images have been spread all over the world. With rapidly development and ease of use of digital image editing tools like Photoshop TM and paint TM, it is important to authenticate or detect the forged regions in any suspicious digital image. In this paper, new and robust method for authenticating and identifying the forged regions in the scanned images is proposed. The method is based on using dust/scratch and source imperfection pattern of scanned images to identify the forged regions. Each suspicious image is divided into non-overlapping blocks. The correlation features between each block and acquisition scanner's template have been extracted to identify the correct scanned blocks and the tampered ones. Four groups of the tampered images are tested. The experimental results have approved the validity, efficiency, and robustness of the proposed method to identify the tampered images and define their forged regions. The proposed results are then compared to the results of the two previously published methods. The proposed method is simple and easy to apply to all types of the tampered images, regardless the acquisition source noise or the image contents.

References
  1. V. A. S. Pablo, F. E. Costa, P. R. Rela, W. A. P. Calvo, and M. M. Hamada, "Gamma Scanning Evaluation for Random Packed Columns", IEEE Nuclear Science Symposium Conference, pp. 519-523, 2005.
  2. K. Salahshoor and S. GHaribshaiyan, "Online Multivariable Identification of a Nonlinear Distillation Column using an Adaptive Takagi-Sugeno Fuzzy Model", IEEE Conference, pp. 527-532, 2008.
  3. M. Muddu and C. S. Patwardhan, "Adaptive Predictive Control of a High Purity Distillation Column Using Irregularly Sampled Multi-rate Data", International Symposium, pp. 192-197, 2011.
  4. D. Ugryumova, G. Vandersteen, B. Huyck, F. Logist, J. V. Impe, and B. De Moor, "Identification and Modeling of Distillation Columns From Transient Response Data", Instrumentation and Measurement Technology Conference (I2MTC), IEEE conference, pp. 2098-2103, 2012.
  5. M. Sanches, M. Hanaguchi, F. Beckmann and W. Calvo, "Radiological Safety in the Gamma Scan Procedures", Proc. of 2007 International Nuclear Atlantic Conference, Santos, Brazil, 2007.
  6. P. Vasquez, C. Mesquita, G. LeRoux and M. Hamada, "Methodological Analysis of Gamma Tomography System for Large Random Packed Columns", 7th International Topical Meeting on Industrial Radiation and Radioisotope Measurements Application, Prague, Czech Republic, 2008.
  7. C. A. P. Wilson, H. M. Margarida, F. E. Sprenger, P. A. S. Vasquez, P. R. Rela, J. F. T. Martins, J. C. S. Pereira, N. M. Omi and C. H. Mesquita, "Gamma-ray Computed Tomography Scanners for Applications in Multiphase System Columns", Nukleonika, Vol. 54, No. 2, pp. 129?133, 2009.
  8. Dr Jaafar, "Gamma-Ray Scanning for Troubleshooting, Optimization and Predictive Maintenance of Distillation Columns", Hydrocarbon Asia, pp. 62-65, 2005.
  9. A. E Hills, "A window into Radioactive Sealed Source Diagnostics", IAEA/AFRA Regional Training Course on Capacity Building for Enhanced Gamma Scanning of Industrial Process, Tunis, Tunisia, October 2012.
  10. C. L. Nikias and J. M. Mendel, "Signal Processing with Higher Order Spectra", IEEE Signal Processing Magazine, pp. 10-37, July 1993.
  11. J. G. Proakis, C. M. Rader, F. Ling, and C. L. Nikias, "Advanced Digital Signal Processing", Macmillan Publishing Company, a division of Macmillan Inc. 1992.
  12. R. V Pawar, P. P. Kajave and S . N. Mali, " Speaker Identification using Neural Networks", Proceedings of World Academy of Science, Engineering and Technology, Vol. 7, pp. 1307- 6884, 2005.
  13. G. Dreyfus, "Neural Networks Methodology and Applications," Springer Verlag Berlin Heidelberg, 2005.
  14. A. I. Galushkin, "Neural Networks Theory," Springer-Verlag Berlin Heidelberg, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Bispectrum Cumulant moment and Trispectrum.