Ultrasonic tomography for reinforced concrete inspection using algebraic reconstruction technique with Iterative Kaczmarz method

Kevin, Soetomo; Rahma, Talitha F.; Juliastuti, Endang; Kurniadi, Deddy

doi:10.1109/ica.2016.7811468

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…The main purpose of tomographic studies is to non-invasively measure the interior properties of objects at their edges without interfering with their physicochemical characteristics [24]. Ultrasound waves are short waves and have properties of radiation and propagation.…”

Section: Ultrasound Transmission Tomographymentioning

confidence: 99%

Optimisation of Technological Processes by Solving Inverse Problem through Block-Wise-Transform-Reduction Method Using Open Architecture Sensor Platform

et al. 2021

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This paper presents an open architecture for a sensor platform for the processing, collection, and image reconstruction from measurement data. This paper focuses on ultrasound tomography in block-wise-transform-reduction image reconstruction. The advantage of the presented solution, which is part of the project “Next-generation industrial tomography platform for process diagnostics and control”, is the ability to analyze spatial data and process it quickly. The developed solution includes industrial tomography, big data, smart sensors, computational intelligence algorithms, and cloud computing. Along with the measurement platform, we validate the methods that incorporate image compression into the reconstruction process, speeding up computation and simplifying the regularisation of solving the inverse tomography problem. The algorithm is based on discrete transformation. This method uses compression on each block of the image separately. According to the experiments, this solution is much more efficient than deterministic methods. A feature of this method is that it can be directly incorporated into the compression process of the reconstructed image. Thus, the proposed solution allows tomographic sensor-based process control, multidimensional industrial process control, and big data analysis.

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Section: Ultrasound Transmission Tomographymentioning

confidence: 99%

Optimisation of Technological Processes by Solving Inverse Problem through Block-Wise-Transform-Reduction Method Using Open Architecture Sensor Platform

et al. 2021

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“…Among the existing problems of process tomography, the following topics can be mentioned: corrosion control of wall loss in places of pipe support [39], imaging of hidden defects inside metal elements [40], reinforced concrete inspection [23,41], detection of cracks or voids in nonmetallic materials [42], monitoring flaws of gas pipelines [36], bubble detection for two-phase liquid and gas [15,20,43], monitoring of flood embankments [11,34,44] and others.…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression for Machine Learning in Process Tomography

Rymarczyk

Kozłowski

Kłosowski

et al. 2019

Sensors

118

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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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