The condition monitoring of damaged steel structures.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN012487 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Deep Learning Based Novelty Detection

Given a set of image instances from known classes, the goal of novelty detection is to determine whether an observed image during inference belongs to one of the known classes. In this thesis, deep learning-based approaches to solve novelty detection are studied under four different settings. In the first two settings, availability of out-of- distributional data (OOD) is assumed. With this assumption, novelty detection can be studied for cases where there are multiple known classes and a single known class separately. The thesis further explores this problem in a more constrained setting where only the data from known classes are considered for training. Finally, we study a practical application of novelty detection in mobile Active Authentication (AA) where latency and efficiency are as important as the detection accuracy

Machine learning for advanced characterisation of silicon solar cells

Improving the efficiency, reliability, and durability of photovoltaic cells and modules is key to accelerating the transition towards a carbon-free society. With tens of millions of solar cells manufactured every day, this thesis aims to leverage the available characterisation data to identify defects in solar cells using powerful machine learning techniques. Firstly, it explores temperature and injection dependent lifetime data to characterise bulk defects in silicon solar cells. Machine learning algorithms were trained to model the recombination statistics’ inverse function and predict the defect parameters. The proposed image representation of lifetime data and access to powerful deep learning techniques surpasses traditional defect parameter extraction techniques and enables the extraction of temperature dependent defect parameters. Secondly, it makes use of end-of-line current-voltage measurements and luminescence images to demonstrate how luminescence imaging can satisfy the needs of end-of-line binning. By introducing a deep learning framework, the cell efficiency is correlated to the luminescence image and shows that a luminescence-based binning does not impact the mismatch losses of the fabricated modules while having a greater capability of detecting defects in solar cells. The framework is shown in multiple transfer learning and fine-tuning applications such as half-cut and shingled cells. The method is then extended for automated efficiency-loss analysis, where a new deep learning framework identifies the defective regions in the luminescence image and their impact on the overall cell efficiency. Finally, it presents a machine learning algorithm to model the relationship between input process parameters and output efficiency to identify the recipe for achieving the highest solar cell efficiency with the help of a genetic algorithm optimiser. The development of machine learning-powered characterisation truly unlocks new insight and brings the photovoltaic industry to the next level, making the most of the available data to accelerate the rate of improvement of solar cell and module efficiency while identifying the potential defects impacting their reliability and durability

A Bayes Linear Approach to Making Inferences from X-rays

X-ray images are often used to make inferences about physical phenomena and the entities about which inferences are made are complex. The Bayes linear approach is a generalisation of subjective Bayesian analysis suited to uncertainty quantification for complex systems. Therefore, Bayes linear is an appropriate tool for making inferences from X-ray images. In this thesis, I will propose methodology for making inferences about quantities, which may be organised as multivariate random fields. A number of problems will be addressed: anomaly detection, emulation, inverse problem solving and transferable databases. Anomaly detection is deciding whether a new observation belongs to the same population as a reference population, emulation is the task of building a statistical model of a complex computer model, inverse problem solving is the task of making inferences about system values, given an observation of system behaviour and transferable databases is the task of using a data-set created using a simulator to make inferences about physical phenomena. The methods we use to address these problems will be exemplified using applications from the X-ray industry. Anomaly detection will be used to identify plastic contaminants in chocolate bars, emulation will be used to efficiently predict the scatter present in an X-ray image, inverse problem solving will be used to infer an entity's composition from an X-ray image and transferable databases will be used to improve image quality and return diagnostic measures from clinical X-ray images. The Bayes linear approach to making inferences from an X-ray image enables improvements over the state-of-the-art approaches to high impact problems

Sensitivity study and first prototype tests for the CHIPS neutrino detector R&D program

CHIPS (CHerenkov detectors In mine PitS) is an R&D project aiming to develop novel cost-effective detectors for long baseline neutrino oscillation experiments. Water Cherenkov detector modules will be submerged in an existing lake in the path of an accelerator neutrino beam, eliminating the need for expensive excavation. In a staged approach, the first detectors will be deployed in a flooded mine pit in northern Minnesota, 7 mrad off-axis from the existing NuMI beam. A small proof-of-principle model (CHIPS-M) has already been tested and the first stage of a fully functional 10 kt module (CHIPS-10) is planned for 2018. The main physics aim is to measure the CP-violating neutrino mixing phase (δCP). A sensitivity study was performed with the GLoBES package, using results from a dedicated detector simulation and a preliminary reconstruction algorithm. The predicted physics reach of CHIPS-10 and potential bigger modules is presented and compared with currently running experiments and future projects. One of the instruments submerged on board CHIPS-M in autumn 2015 was a prototype detection unit, constructed at Nikhef. The unit contains hardware borrowed from the KM3NeT experiment, including 16 3 inch photomultiplier tubes and readout electronics. In addition to testing the mechanical design and data acquisition, the detector was used to record a large sample of cosmic ray muon events. A preliminary analysis of the collected data was performed, in order to measure the cosmic background interaction rates and validate the Monte Carlo simulation used to optimise future designs. The first in situ measurement of the cosmic muon rate at the bottom of the Wentworth Pit is presented, and extrapolated values for CHIPS-10 show that the dead time due to muons is below 0.3 %

JUNO Conceptual Design Report

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.Comment: 328 pages, 211 figure

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

A Multidisciplinary Analysis of Frequency Domain Metal Detectors for Humanitarian Demining

This thesis details an analysis of metal detectors (low frequency electromagnetic induction devices) with emphasis on Frequency Domain (FD) systems and the operational conditions of interest to humanitarian demining. After an initial look at humanitarian demining and a review of their basic principles we turn our attention to electromagnetic induction modelling and to analytical solutions to some basic FD direct (forward) problems. The second half of the thesis focuses then on the analysis of an extensive amount of experimental data. The possibility of target classification is first discussed on a qualitative basis, then quantitatively. Finally, we discuss shape and size determination via near field imaging