Full wave numerical modelling of terahertz systems for nondestructive evaluation of dielectric structures

Łopato, Przemysław; Chady, Tomasz; Sikora, R.; Gratkowski, Stanislaw; Ziolkowski, Marcin

doi:10.1108/03321641311305719

Cited by 33 publications

(15 citation statements)

References 8 publications

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“…There are many methods and algorithms used in optimization problems [52,53,54,55,56,57]. In this article, the authors chose deterministic algorithms based on the Gauss-Newton method as a reference to the machine learning methods.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Rymarczyk

Kłosowski

Kozłowski

et al. 2019

Sensors

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The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other.

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Section: Methodsmentioning

confidence: 99%

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Rymarczyk

Kłosowski

Kozłowski

et al. 2019

Sensors

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“…A well-posed problem is likely to be solved on a computer using a stable algorithm [6,7,8]. If the problem is not well-posed, it should be reformulated in a way that allows the use of a numeric algorithm [9]. In such cases, additional assumptions are usually applied, e.g., a smoothness of the solution.…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression for Machine Learning in Process Tomography

Rymarczyk

Kozłowski

Kłosowski

et al. 2019

Sensors

117

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The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasound transmission tomography (UST). The test object was a tank filled with water in which reconstructed objects were placed. For both EIT and UST, a novel approach was used in which each pixel of the output image was reconstructed by a separately trained prediction system. Therefore, it was necessary to use many predictive systems whose number corresponds to the number of pixels of the output image. Thanks to this approach the under-completed problem was changed to an over-completed one. To reduce the number of predictors in logistic regression by removing irrelevant and mutually correlated entries, the elastic net method was used. The developed algorithm that reconstructs images pixel-by-pixel is insensitive to the shape, number and position of the reconstructed objects. In order to assess the quality of mappings obtained thanks to the new algorithm, appropriate metrics were used: compatibility ratio (CR) and relative error (RE). The obtained results enabled the assessment of the usefulness of logistic regression in the reconstruction of EIT and UST images.

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“…There is a clear tendency in the industry to implement more optimal related functions with an emphasis on active inspection and monitoring [24][25][26][18][19][20][21][22]. There are many optimization methods [1,[8][9][10][11][12][13][14][15], but to solve the inverse problem in the ultrasound tomography can use [2][3][4][5][6][7]16,17,23].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound tomography measuring system for acquisition and analysis data

Rymarczyk¹

2019

ELECTROTECHNICAL REVIEW

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Ultrasound tomography provides the ability to analyse processes occurring inside the facility without interfering with the production, analysis and detection of obstacles, defects and various anomalies. The presented measuring system has a specially designed measuring structure (including electrodes, thanks to which it is an innovative solution in the field, particularly effective in the analysis). Knowledge of the characteristics of each tomographic technique solution, the application allows you to choose the appropriate method of image reconstruction. A reverse problem is the process of identifying optimization or synthesis, in which the goal is to determine the parameters describing the data field.Streszczenie. Tomografia ultradźwiękowa daje możliwość analizowania procesów zachodzących wewnątrz obiektu bez zakłócania produkcji, analizy i wykrywania przeszkód, wad i różnych anomalii. Prezentowany układ pomiarowy ma specjalnie zaprojektowaną strukturę pomiarową (w tym elektrody, dzięki czemu jest innowacyjnym rozwiązaniem w terenie, szczególnie skutecznym w analizie). Znajomość charakterystyki każdego rozwiązania techniki tomograficznej, aplikacja pozwala wybrać odpowiednią metodę rekonstrukcji obrazu. Odwrotnym problemem jest proces identyfikacji optymalizacji lub syntezy, w którym celem jest określenie parametrów opisujących pole danych (System pomiarowy tomografii ultradźwiękowej do pozyskiwania i analizy danych.

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Full wave numerical modelling of terahertz systems for nondestructive evaluation of dielectric structures

Cited by 33 publications

References 8 publications

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Logistic Regression for Machine Learning in Process Tomography

Ultrasound tomography measuring system for acquisition and analysis data

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