International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.14, No.1, Feb. 2022
Digital Method of Automated Non-destructive Diagnostics for High-power Magnetron Resonator Blocks
Full Text (PDF, 680KB), PP.40-49
The paper reveals the problem of the lack of standard non-destructive diagnostic methods for high-power microwave devices aimed at regeneration. The issue is understudied and requires further research. The conducted analysis of state of the art on the subject area exhibited that image processing was used to specify the examined object's target characteristics in a wide range of research. Having summarized the considered image comparison methods on the subject area of this work, the authors formulated several requirements for the selected image analysis method based on the automated non-destructive diagnosis of resonator units for high-power magnetrons. The primary requirement is using non-iterative algorithms; the second condition is a chosen method of image analysis, and the third option is the number of pixels for a processed image. It must significantly exceed the number of descriptors required for making a decision. Guided by the analysis results and based on the results of previous studies conducted by the authors, the algorithm for identifying a defect in the resonator unit of a microwave device based on the image of the frequency-azimuthal distribution for the probing field phase difference expressed by the Zernike moments is proposed. MATLAB R14a was used as a modeling environment. The descriptor vector was restricted to the Zernike moments, including the 7th order. The work is interdisciplinary and written at the intersection of technical diagnostics, microwave engineering, and digital image processing.
Cite This Paper
Serge Olszewski, Yaroslav Tanasiichuk, Viktor Mashkov, Volodymyr Lytvynenko, Irina Lurie, " Digital Method of Automated Non-destructive Diagnostics for High-power Magnetron Resonator Blocks", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.1, pp. 40-49, 2022.DOI: 10.5815/ijigsp.2022.01.04
S. Patgiri, M.Devi, A.K. Barbara, “Adaptation of Image Processing technique in identifying earthquake-induced anomalous feature from TEC: A New Approach,” International Journal of Electronics and Applied Research (IJEAR), Vol.5, Iss.2, 2018.
S.S. Ittannavar, R.H. Havaldar, B.P. Khot, “Comparative Assessment of Image Processing Techniques for the Early Detection of Breast Cancer: A Review,” Biosci. Biotech. Res. Comm., Vol.13, No.13, pp.181-194, 2020.
N. Jayanthi, S. Indu, “Comparison of Image Matching Techniques,” International Journal of Latest Trends in Engineering and Technology, Vol.7, Iss.3, pp. 396-401, 2020.
R. Katukam, P. Sindhoora, “Image Comparison Methods & Tools: A Review,” the 1st National Conference on. Emerging Trends in Information Technology, pp. 35-42, 2015.
V. Mashkov, “New approach to system-level self-diagnosis,” Proc. of 11th IEEE International Conference on Computer and Information Technology (CIT 2011), pp. 579-584, 2011.
C. Guada, D. Gomez, J.T. Rodriguez, J. Yoсez, J. Montero, “Classifying image analysis techniques from their output,” International Journal of Computational Intelligence Systems, Vol.9, pp. 43–68, 2016.
Y.G. Byun, Y.K. Han, T.B. Chae, “A multispectral image segmentation approach for object-based image classification of high-resolution satellite imagery,” KSCE Journal of Civil Engineering, Vol.17, No.2, pp. 486–497, 2013.
P. Malkani, A. Sagar, K.R. Asha, A. Dubey, P. Singh, “An overview on crop - weed discrimination based on digital image processing using textural features,” International Journal of Chemical Studies, Vol.7, No.6, pp. 2514-2520, 2019.
M. Krishna, G. Satyanarayana, V. Devi Satya Sri, “Digital Image Processing Techniques in Character Recognition - A Survey,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), Vol.2, Iss.6, pp. 2456-3307, 2017.
S.V. Olszewski, Ya.V. Tanasiichuk, “Investigation of the influence of the defect in the magnetron resonator system on the parameters of its microwave field,” Scientific notes of Tavrida National V.I. Vernadsky University Series: Engineering Sciences, Vol.31(70), No.5, pp. 43-48, 2020.
S.V. Olszewski, Ya.V. Tanasiichuk, “The structural scheme synthesis of the system of test diagnostics of magnetron resonator blocks aimed to regeneration,” Scientific notes of Tavrida National V.I. Vernadsky University Series: Engineering Sciences, Vol.32(71), No.1, pp. 28-32, 2021.
P. Sumathi, S.Murugan, " GNVDF: A GPU-accelerated Novel Algorithm for Finding Frequent Patterns Using Vertical Data Format Approach and Jagged Array ", International Journal of Modern Education and Computer Science (IJMECS), Vol.13, No.4, pp. 28-41, 2021. DOI: 10.5815/ijmecs.2021.04.03.
Hanan A. Al-Jubouri, " Integration Colour and Texture Features for Content-based Image Retrieval", International Journal of Modern Education and Computer Science (IJMECS), Vol.12, No.2, pp. 10-18, 2020. DOI: 10.5815/ijmecs.2020.02.02.
Ye. Sulema, E. Kerre, O. Shkurat, " Vector Image Retrieval Methods Based on Fuzzy Patterns", International Journal of Modern Education and Computer Science (IJMECS), Vol.12, No.3, pp. 8-16, 2020. DOI: 10.5815/ijmecs.2020.03.02.
U. Ali, S. Aftab, A. Iqbal, Z. Nawaz, M. Salman Bashir, M. Anwaar Saeed, " Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques", International Journal of Modern Education and Computer Science (IJMECS), Vol.12, No.5, pp. 29-40, 2020. DOI: 10.5815/ijmecs.2020.05.03.
Kh.M. Hosny, M.A. Hafez, “An Algorithm for Fast Computation of 3D Zernike Moments for Volumetric Images,” Mathematical Problems in Engineering Volume, Article ID 353406, 17 p., 2012, doi:10.1155/2012/353406.
A. Padilla-Vivanco, A. Martinez-Ramirez, F.-S. Granados-Agustin, "Digital image reconstruction using Zernike moments," Proc. SPIE 5237 Optics in Atmospheric Propagation and Adaptive Systems VI, 2004, DOI: 10.1117/12.514248.
A. Gуrniak, E. Skubalska-Rafajlowicz, “Object Classification Using Sequences of Zernike Moments,” 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), pp.99-109, 2017, 10.1007/978-3-319-59105-6_9. HAL-01656212.
K.V. Kale, P.D. Deshmukh, S.V. Chavan, M.M. Kazi, Y.S. Rode, “Zernike Moment Feature Extraction for Handwritten Devanagari (Marathi) Compound Character Recognition,” International Journal of Advanced Research in Artificial Intelligence (IJARAI), Vol.3, No.1, 2014.