Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.

Pipeline corrosion and leakage monitoring based on the distributed optical fiber sensing technology

Accelerated finite element elastodynamic simulations using the GPU

High-resolution guided wave tomography

Breast ultrasound tomography has the potential to improve the cost, safety, and reliability of breast cancer screening and diagnosis over the gold-standard of mammography. Vital to achieving this potential is the development of imaging algorithms to unravel the complex anatomy of the breast and its mechanical properties. The solution most commonly relied upon is time-of-flight tomography, but this exhibits low resolution due to the presence of diffraction effects. Iterative full-wave inversion methods present one solution to achieve higher resolution, but these are slow and are not guaranteed to converge to the correct solution. Presented here is HARBUT, the hybrid algorithm for robust breast ultrasound tomography, which utilizes the complementary strengths of time-of-flight and diffraction tomography resulting in a direct, fast, robust and accurate high resolution method of reconstructing the sound speed through the breast. The algorithm is shown to produce accurate reconstructions with realistic data from a complex three-dimensional simulation, with masses as small as 4 mm being clearly visible.

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High-resolution imaging without iteration: a fast and robust method for breast ultrasound tomography.

Guided wave tomography offers a method to accurately quantify wall thickness losses in pipes and vessels caused by corrosion, using ultrasonic waves transmitted over distances of approximately 1 to 2 m, and measured by an array of transducers. These measurements are then used to reconstruct a map of wall thickness throughout the inspected region. To achieve accurate estimations of remnant wall thickness, it is vital that a suitable Lamb mode is chosen. This paper presents a detailed evaluation of the two most suitable modes, S0 and A0, to compare their performance using both numerical and experimental data. The sensitivity of A0 to thickness variations was shown to be superior to S0; however, the attenuation from A0 when a liquid loading was present was much higher than S0. A0 was less sensitive to the presence of coatings on the surface than was S0. Finally, it was shown that both modes could achieve a similar level of resolution in the plane of the plate surface.

Mode selection for corrosion detection in pipes and vessels via guided wave tomography

Finite element modelling is a promising tool for further progressing the development of ultrasonic non-destructive evaluation of polycrystalline materials. Yet its widespread adoption has been held back due to a high computational cost, which has restricted current works to relatively small models and to two dimensions. However, the emergence of sufficiently powerful computing, such as highly efficient solutions on graphics processors, is enabling a step improvement in possibilities. This article aims to realise those capabilities to simulate ultrasonic scattering of longitudinal waves in an equiaxed polycrystalline material in both two (2D) and three dimensions (3D). The modelling relies on an established Voronoi approach to randomly generate a representative grain morphology. It is shown that both 2D and 3D numerical data show good agreement across a range of scattering regimes in comparison to well-established theoretical predictions for attenuation and phase velocity. In addition, 2D parametric studies illustrate the mesh sampling requirements for two different types of mesh to ensure modelling accuracy and present useful guidelines for future works. Modelling limitations are also shown. It is found that 2D models reduce the scattering mechanism in the Rayleigh regime.

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Peter Huthwaite

Papers

Accelerated finite element elastodynamic simulations using the GPU

High-resolution guided wave tomography

High-resolution imaging without iteration: a fast and robust method for breast ultrasound tomography.

Mode selection for corrosion detection in pipes and vessels via guided wave tomography

Finite element modelling of elastic wave scattering within a polycrystalline material in two and three dimensions