Prof. Lik-Kwan Shark Profile

Prof. Lik-Kwan Shark

Professor at Univ of Central Lancashire

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Publications (4)

Proceedings Article | 21 June 2019 Paper

Robust principal component analysis of ultrasonic sectorial scans for defect detection in weld inspection

L.-K. Shark, B. Cassels, S. J. Mein, A. Nixon, T. Barber, R. Turner

Proceedings Volume 11059, 110590E (2019) https://doi.org/10.1117/12.2527622

KEYWORDS: Principal component analysis, Inspection, Ultrasonics, Defect detection, Nondestructive evaluation, Data acquisition, Structural engineering, Phased arrays, Image processing, Statistical analysis

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A single weld defect in a safety-critical engineering structure has the potential to incur high monetary costs, damage to the environment or loss of human life. This makes comprehensive non-destructive internal and external inspection of these welds essential. For non-destructive internal inspection the ultrasonic phased array supports a number of methods for producing a cross sectional image at a fixed location. Full coverage of the weld requires a sequence of images to be taken along the full length, each image at a unique incremental step. If the weld has a geometrically regular structure, such as that corresponding to a long linear section or the circumference of a pipe, automation becomes possible and data is now provided for post processing and auditing. Particularly in a production process this may provide many thousands of images a day, all of which must be manually examined by a qualified inspector. Presented in this paper is an approach for rapid identification of anomalies in sequences of ultrasonic sector images taken at equally spaced index points. The proposed method is based on robust principal component analysis (PCA). An assumption is that most sectors are anomaly free and have a statistically similar geometrical structure. Unsupervised multivariate statistical analysis is now performed to yield an initial low dimensional principal subspace representing the variation of the common weld background. Using the Mahalanobis distance outliers, observations with extreme variations and likely to correspond to sector scans containing anomalies, are removed from the reference set. This ensures a robust PCA-based reference model for weld background, against which a sectorial scan is identified as defect free or not. Using a comprehensive set of sector scan data acquired from test blocks, containing different types and sizes of weld defects at different locations and orientations, the paper concludes that PCA has potential for anomaly detection in this context. Although trimming improves the accuracy of the system eigenvectors, it is shown that greater accuracy of the low rank subspace is possible through principal component pursuit (PCP). This is evident by an almost 100% anomaly detection rate with a false alarm rate of well below 10%.

Proceedings Article | 23 October 2010 Paper

Site-specific land clutter modelling based on radar remote sensing images and digital terrain data

Kenneth Lever, Darren Radford, Dave Marshall, Lik-Kwan Shark, Andriy Kurekin

Proceedings Volume 7830, 783013 (2010) https://doi.org/10.1117/12.864692

KEYWORDS: Radar, Remote sensing, Backscatter, Modeling, Data modeling, Sensors, Target detection, Calibration, Image fusion, Earth observing sensors

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Proceedings Article | 23 October 2010 Paper

Classification of filtered multichannel images

Benoit Vozel, Andriy Kurekin, Nikolay Ponomarenko, Lik-Kwan Shark, Vladimir Lukin, Kacem Chehdi, Dmitriy Fevralev

Proceedings Volume 7830, 78300M (2010) https://doi.org/10.1117/12.864215

KEYWORDS: Image filtering, Image classification, Remote sensing, 3D image processing, Linear filtering, Visualization, Filtering (signal processing), Denoising, Landsat, Earth observing sensors

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Proceedings Article | 19 August 1998 Paper

Classification of multispectral remote sensing image using an improved backpropagation neural network

Lik-kwan Shark, Wenbo Mei, Huiqian Du

Proceedings Volume 3561, (1998) https://doi.org/10.1117/12.319726

KEYWORDS: Neural networks, Image classification, Remote sensing, Multispectral imaging, Landsat, Network architectures, Electronics engineering, Evolutionary algorithms, Statistical modeling, Earth observing sensors

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