Comparative Calibration of Corrosion Measurements Using K-Nearest Neighbour Based Techniques

Every measuring equipment or inspection tool is known to have its own accuracy, which may affect the reliability of its measurements. This includes oil and gas pipeline corrosion defects measurements. The inspection tolerance occurred in the measurements should be treated carefully for each equipment to prevent misinterpretation of the data which could lead to incorrect assessment. This paper presents a comparison between two K-Nearest Neighbour (KNN) interpolation techniques used to calibrate corrosion measurements collected by Magnetic Flux Leakage Intelligent Pig (MFL-IP) with the readings of Ultrasonic Testing (UT) scan device. The comparison has relied on the position of the interpolators, the weight sequence, and the error in the final enhanced metrics compared to the original measurements. Both techniques have the potential to calibrate and enhance IP measurements, with relative advantage for one technique in reducing over fitting problem. This enhancement will be used to improve the integrity assessment report that depends on the disturbed corrosion metrics of oil and gas pipelines, to decide whether the pipeline is fit for service or needs certain maintenance

Comparative Calibration of Corrosion Measurements Using K-Nearest Neighbour Based Techniques

Every measuring equipment or inspection tool is known to have its own accuracy, which may affect the reliability of its measurements. This includes oil and gas pipeline corrosion defects measurements. The inspection tolerance occurred in the measurements should be treated carefully for each equipment to prevent misinterpretation of the data which could lead to incorrect assessment. This paper presents a comparison between two K-Nearest Neighbour (KNN) interpolation techniques used to calibrate corrosion measurements collected by Magnetic Flux Leakage Intelligent Pig (MFL-IP) with the readings of Ultrasonic Testing (UT) scan device. The comparison has relied on the position of the interpolators, the weight sequence, and the error in the final enhanced metrics compared to the original measurements. Both techniques have the potential to calibrate and enhance IP measurements, with relative advantage for one technique in reducing over fitting problem. This enhancement will be used to improve the integrity assessment report that depends on the disturbed corrosion metrics of oil and gas pipelines, to decide whether the pipeline is fit for service or needs certain maintenance

Image Interpolation Using a Rational Bi-Cubic Ball

This study deals with the application of new rational bi-cubic Ball function with six parameters in image interpolation, especially for the grayscale image. These six free parameters can be modified to get better and quality image resolution, and refine the shape of the interpolating surface. This bivariate rational Ball function has been extended from univariate cases by using a tensor product approach. The proposed scheme is tested for image upscaling with factors of two and four through an efficient algorithm. The effectiveness of the proposed scheme is measured by using an image quality assessment (IQA), such as peak-signal-to-noise-ratio (PSNR), root mean square error (RMSE) or feature similarity (FSIM) index. Numerical and graphical results with comparisons against some existing scheme are presented by using MATLAB. The proposed scheme resulted in higher PSNR and FSIM, and smaller RMSE. Thus, the new rational bi-cubic Ball with six parameters is better than the existing scheme via an efficient algorithm