There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.

A review of structural health monitoring literature 1996-2001

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

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

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

Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.

Activation Functions: Comparison of trends in Practice and Research for Deep Learning

This paper describes a theoretical and experimental study for determination of the through-air system impulse response and insertion loss with different air-coupled ultrasonic transducers. Wide-band piezopolymer transducers (PVDF) are employed in both transmission and reception modes and their behavior assessed by means of mathematical modeling and experiment. Specifically, a linear systems approach, modified to include the influence of attenuation in the propagation medium, was used to design suitable PVDF transducers for wide-band operation in air. Suitable devices were then manufactured for determination of the transmission and reception response characteristics of piezocomposite and electrostatic transducers when operating in the air environment. A range of transducers was evaluated, including 1-3 connectivity composites of different ceramic volume fraction and mechanical matching conditions, in addition to electrostatic devices of varying design. To complement the investigation, relative performances for narrow-band operation are also presented under transmission and transmit-receive conditions. Despite the obvious measurement difficulties, good agreement between theory and experiment was observed and the methodology is shown to provide a convenient and robust procedure for comparison of through-air transducers operating in the frequency range 50 KHz to 2 MHz. Although highly resonant, the most effective composite transducers under consideration demonstrate an improvement in two-way insertion loss of 22.4 dB and 11.5 dB over a corresponding electrostatic pair, under narrow-band and wide-band operation, respectively.

Characterization of air-coupled transducers

A combination of theoretical modeling and experimental analysis is used to predict and assess the performance of 1–3 composite transducers for operation in an air‐coupled environment. Specifically, finite element analysis, supported by linear systems modeling, is employed to evaluate transmission and reception characteristics over the complete volume fraction range, for operating frequencies in the region of 500 kHz, and the theory confirmed using novel experimental techniques. The theoretical approach is then extended to assess the influence of matching and backing on transducer sensitivity and bandwidth. Throughout, the influence of air propagation is included in the simulation approach and highlighted where this factor is significant. The work is expected to provide useful design guidelines for the practical ultrasonic engineer.

An evaluation of 1-3 connectivity composite transducers for air-coupled ultrasonic applications

Applications involving high-power ultrasound are expanding rapidly as ultrasonic intensification opportunities are identified in new fields. This is facilitated through new technological developments and an evolution of current systems to tackle challenging problems. It is therefore important to continually update both the scientific and commercial communities on current system performance and limitations. To achieve this objective, this paper addresses two key aspects of high-power ultrasonic systems. In the first part, the review of high-power applications focuses on industrial applications and documents the developing technology from its early cleaning applications through to the advanced sonochemistry, cutting, and water treatment applications used today. The second part provides a comprehensive overview of measurement techniques used in conjunction with high-power ultrasonic systems. This is an important and evolving field which enables design and process engineers to optimize the behavior and/or operation of key metrics of system performance, such as field distribution or cavitation intensity.

/pdf/review-of-high-power-ultrasound-industrial-applications-and-4j1yyshja1.pdf

Anthony Gachagan

Papers

Activation Functions: Comparison of trends in Practice and Research for Deep Learning

Characterization of air-coupled transducers

An evaluation of 1-3 connectivity composite transducers for air-coupled ultrasonic applications

Review of high-power ultrasound-industrial applications and measurement methods

Alignment of nanostructured tripeptide gels by directional ultrasonication