The Grid 2: Blueprint for a New Computing Infrastructure

Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.

A Survey on Gas Sensing Technology

Applications of ultrasound in analysis, processing and quality control of food: A review

An ultrasonic array is a single transducer that contains a number of individually connected elements. Recent years have seen a dramatic increase in the use of ultrasonic arrays for non-destructive evaluation. Arrays offer great potential to increase inspection quality and reduce inspection time. Their main advantages are their increased flexibility over traditional single element transducer methods, meaning that one array can be used to perform a number of different inspections, and their ability to produce immediate images of the test structure. These advantages have led to the rapid uptake of arrays by the engineering industry. These industrial applications are underpinned by a wide range of published research which describes new piezoelectric materials, array geometries, modelling methods and inspection modalities. The aim of this paper is to bring together the most relevant published work on arrays for non-destructive evaluation applications, comment on the state-of the art and discuss future directions. There is also a significant body of published literature referring to use of arrays in the medical and sonar fields and the most relevant papers from these related areas are also reviewed. However, although there is much common ground, the use of arrays in non-destructive evaluation offers some distinctly different challenges to these other disciplines.

Ultrasonic arrays for non-destructive evaluation: A review

This review is devoted to some inverse problems arising in the context of linear elasticity, namely the identification of distributions of elastic moduli, model parameters or buried objects such as cracks. These inverse problems are considered mainly for three-dimensional elastic media under equilibrium or dynamical conditions, and also for thin elastic plates. The main goal is to overview some recent results, in an effort to bridge the gap between studies of a mathematical nature and problems defined from engineering practice. Accordingly, emphasis is given to formulations and solution techniques which are well suited to general-purpose numerical methods for solving elasticity problems on complex configurations, in particular the finite element method and the boundary element method. An underlying thread of the discussion is the fact that useful tools for the formulation, analysis and solution of inverse problems arising in linear elasticity, namely the reciprocity gap and the error in constitutive equation, stem from variational and virtual work principles, i.e., fundamental principles governing the mechanics of deformable solid continua. In addition, the virtual work principle is shown to be instrumental for establishing computationally efficient formulae for parameter or geometrical sensitivity, based on the adjoint solution method. Sensitivity formulae are presented for various situations, especially in connection with contact mechanics, cavity and crack shape perturbations, thus enriching the already extensive known repertoire of such results. Finally, the concept of topological derivative and its implementation for the identification of cavities or inclusions are expounded.

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Inverse problems in elasticity

Ultrasonic Nondestructive Evaluation Systems: Models and Measurements provides the latest information and techniques available for ultrasonic nondestructive evaluation (NDE) inspections. Using a systems level approach, this book employs aspects of Fourier analysis, linear system theory, and wave propagation and scattering theory to develop a comprehensive model of an entire ultrasonic measurement system. The book also describes in detail the measurements needed to obtain all the system model parameters. This integrated approach leads to a new model-based engineering technology for designing, using and optimizing ultrasonic nondestructive evaluation inspections. Practicing engineers, teachers, and students alike will learn about the latest developments in NDE technology, including a recently developed pulse-echo method for measuring the sensitivity of an ultrasonic transducer, and the use of Gaussian beam theory in simulating the wave fields generated by ultrasonic transducers. In addition, this unique book incorporates MATLAB examples and exercises which allow readers to conduct simulated inspections and implement the latest modeling technology. Written by recognized experts in NDE research, Ultrasonic Nondestructive Evaluation Systems: Models and Measurements is designed to combine well-developed techniques with the latest advances in technology.

Ultrasonic Nondestructive Evaluation Systems: Models and Measurements

Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions

Development of an ultra sonic phased array system for nondestructive tests of nuclear power plant components

The industrial application of deep neural networks to automate the ultrasonic weldment flaw classification system has some limitations. The major problem that affects the classification performance of deep neural networks is the noise in the ultrasonic signals. So, in this article, a deep neural network, also known as autoencoder is investigated to remove noise from ultrasonic signals before feeding them to deep learning classifiers. A database was generated from specimens that were closely resembled with pipe weldment geometry having counterbore and weldment defects. Those signals were, later on, corrupted with noise to mimic industrial applicability. An autoencoder was then employed to remove noise from counterbore, planer and volumetric weldment defect signals. The classification performance of the convolutional neural network (CNN) was evaluated in three different ways. At first, without employing the autoencoder, secondly, on the denoised outputs of the autoencoder and on third CNN was trained with the noiseless signals but was tested on the denoised outputs of the autoencoder. The results demonstrate that the autoencoder can successfully remove noise from the ultrasonic weldment defect signals, which consequently improve the defect classification accuracy of the artificially intelligent deep learning classifiers.

Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder

Ultrasonic signal classification of defects in weldment, in automatic fashion, is an active area of research and many pattern recognition approaches have been developed to classify ultrasonic signals correctly. However, most of the developed algorithms depend on some statistical or signal processing techniques to extract the suitable features for them. In this work, data driven approaches are used to train the neural network for defect classification without extracting any feature from ultrasonic signals. Firstly, the performance of single hidden layer neural network was evaluated as almost all the prior works have applied it for classification then its performance was compared with deep neural network with drop out regularization. The results demonstrate that given deep neural network architecture is more robust and the network can classify defects with high accuracy without extracting any feature from ultrasonic signals.

Sung-Jin Song

Papers

Ultrasonic Nondestructive Evaluation Systems: Models and Measurements

Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions

Development of an ultra sonic phased array system for nondestructive tests of nuclear power plant components

Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder

Investigation of deep neural network with drop out for ultrasonic flaw classification in weldments