STATISTICAL CALIBRATION OF PIPELINE IN-LINE INSPECTION DATA
This paper describes a statistical methodology for the calibration of in-line inspection (ILI) tools which is based on the comparison of the ILI readings with field-measurements results. The systematic and random errors that affect the ILI and the field tools are estimated and from this information, an unbiased estimation of the true depth of the defects detected by the ILI tool is produced. The influence of the number of field verifications on the reliability of the calibration process is addressed. The methodology is tested through Monte Carlo simulations and illustrated using a real-life ILI dataset produced by an UT tool. Introduction: Today in-line inspection (ILI) tools used to detect, locate and size pipeline anomalies such as dents, cracks and corrosion metal loss are based on the magnetic flux leakage (MFL) and ultrasonic (UT) principles [1]. The information provided by ILI tools consists of geometrical data describing each detected anomaly, e.g. its length, depth, width and orientation. This information is affected by built-in measurement errors, both systematic and random, that have to be considered when the ILI data is used to conduct integrity studies and fitness-forpurpose investigations. Pipeline integrity analysts and ILI vendors are now aware of the fact that in assessing the severity of a defect, the key issue is how accurately its geometry has been measured. In this sense, the two important parameters that ILI vendors provide are the probability of detection (POD) and the sizing accuracy of the tool. The sizing accuracy associated with an ILI tool is quoted as an accuracy level and a percent confidence. The depth sizing accuracy associated with corrosion metal loss of today high resolution (HR) MFL tools is typically claimed to be ±10% of the pipe wall thickness (wt) with a confidence level of 80%. In the case of extra high resolution (XHR) MFL tools, the sizing accuracy is ±5% wt at 80% confidence. On the other hand, today HR and XHR UT tools are claimed to have a sizing accuracy of ±0.6 mm and ±0.3 mm at a confidence level of 80%, respectively [1]. Pipeline operators assess the accuracy of a pipeline inspection through the statistical comparison of the metal loss sizes predicted by the ILI tool with the sizes obtained through field inspections. (a) 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
Two steps hybrid calibration algorithm of support vector regression and K-nearest neighbors
Abstract Errors in the measurements of pipeline nondestructive tools may lead to faulty decisions causing economic and environmental loss, or system failures. Calibration models are an effective tool that is used to enhance the quality of corrosion measurements affected by inline inspection tools sizing accuracy. Parametric calibration models are limited to datasets with Gaussian behavior. On the other hand, non-parametric calibration models can overcome the normality limitation, however, they provide only a local or general estimation. This paper presents a new hybrid calibration model that is based on two steps K nearest neighbor interpolation and support vector regression. The suggested hybrid model uses both general and local estimation behaviors for the calibration process, hence resulting in a better prediction. The hybrid algorithm was evaluated using a dataset of pipeline corrosion measurements collected by a Magnetic Flux Leakage (MFL) sensor (with an error margin of ±20% of the true values), and an Ultrasonic (UT) device (with an error margin of ±4%). The suggested approach resulted in reducing the errors in MFL corrosion measurements to be only ±6.82% instead of the original ±20%.
Teaching learning disability to undergraduate medical students
The education of doctors shapes their professional identity: who they are and how they practise. Where does the issue of learning disability fit in? What is it and of what factors should doctors in training be aware?

The training of medical students has been defined by the General Medical Council (