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 chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Introduction to the Finite Element Method

Guided Lamb waves for identification of damage in composite structures: A review

Research into ultrasonic NDE techniques for the inspection of multilayered structures relies strongly on the use of modeling tools which calculate dispersion curves and reflection and transmission spectra. These predictions are essential to enable the best inspection strategies to be identified and their sensitivities to be evaluated. General purpose multilayer modeling tools may be developed from a number of matrix formulations which have evolved in the latter half of this century and there is now a formidable number of publications on the subject. This paper presents a review of the main developments of the matrix techniques, and their use in response and modal models, with emphasis on ultrasonics applications. >

Matrix techniques for modeling ultrasonic waves in multilayered media

Defect detection in pipes using guided waves

The application of guided waves in NDT can be hampered by the lack of readily available dispersion curves for complex structures. To overcome this hindrance, we have developed a general purpose program that can create dispersion curves for a very wide range of systems and then effectively communicate the information contained within those curves. The program uses the global matrix method to handle multi-layered Cartesian and cylindrical systems. The solution routines cover both leaky and non-leaky cases and remain robust for systems which are known to be difficult, such as large frequency-thicknesses and thin layers embedded in much thicker layers. Elastic and visco-elastic isotropic materials are fully supported; anisotropic materials are also covered, but are currently limited to the elastic, non-leaky, Cartesian case.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=3475&context=qnde

Disperse: a general purpose program for creating dispersion curves

The reflection of the L(0, 2), axially symmetric guidea elastic wave from notches in pipes is examined, using laboratory experiments and finite element simulation The result show that the reflection coefficient of this mode is very close to a linear function of the circumferential extent of the notch, and is a stronger function of the through thickne depth of the notch. The motivation for the work was the development of a technique for inspecting chemical plant pipework, but the study addresses the nature of the reflection function and has general applicability.

The Reflection of Guided Waves From Circumferential Notches in Pipes

A quantitative study of the reflection of the T(0,1) mode from defects in pipes in the frequency range 10-300 kHz has been carried out, finite element predictions being validated by experiments on selected cases. Both cracklike defects with zero axial extent and notches with varying axial extents have been considered. The results show that the reflection coefficient from axisymmetric cracks increases monotonically with depth at all frequencies and increases with frequency at any given depth. In the frequency range of interest there is no mode conversion at axisymmetric defects. With nonaxisymmetric cracks, the reflection coefficient is a roughly linear function of the circumferential extent of the defect at relatively high frequencies, the reflection coefficient at low circumferential extents falling below the linear prediction at lower frequencies. With nonaxisymmetric defects, mode conversion to the F(1,2) mode is generally seen, and at lower frequencies the F(1,3) mode is also produced. The depth and circumferential extent are the parameters controlling the reflection from cracks; when notches having finite axial extent, rather than cracks, are considered, interference between the reflections from the start and the end of the notch causes a periodic variation of the reflection coefficient as a function of the axial extent of the notch. The results have been explained in terms of the wave-number-defect size product, ka. Low frequency scattering behavior is seen when ka 1.

Michael J. S. Lowe

Papers

Matrix techniques for modeling ultrasonic waves in multilayered media

Defect detection in pipes using guided waves

Disperse: a general purpose program for creating dispersion curves

The Reflection of Guided Waves From Circumferential Notches in Pipes

The reflection of the fundamental torsional mode from cracks and notches in pipes