Assessment of the Spatial Distribution of Moisture Content in Granular Material Using Electrical Impedance Tomography

Porzuczek, Jan

doi:10.3390/s19122807

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…1. Scheme of the measuring stand [6] The forward problem is solved by determining the potential distribution within the region given the boundary conditions and complete information about the region, i.e. by solving Laplace's equation: On the surface of the damp brick wall, there are 16 electrodes arranged vertically in a straight line.…”

Section: Methodsmentioning

confidence: 99%

Application of Convolutional Neural Networks in Wall Moisture Identification by Eit Method

Kłosowski

Rymarczyk

2022

IAPGOS

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The article presents the results of research in the area of using deep neural networks to identify moisture inside the walls of buildings using electrical impedance tomography. Two deep neural networks were used to transform the input measurements into images of damp places - convolutional neural networks (CNN) and recurrent long short-term memory networks LSTM. After training both models, a comparative assessment of the results obtained thanks to them was made. The conclusions show that both models are highly utilitarian in the analyzed problem. However, slightly better results were obtained with the LSTM method.

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Section: Methodsmentioning

confidence: 99%

Application of Convolutional Neural Networks in Wall Moisture Identification by Eit Method

Kłosowski

Rymarczyk

2022

IAPGOS

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“…A pixel is a single finite element placed on a mesh. The finite element method and Eidors toolbox for Matlab were used to construct the simulation algorithm [42][43][44]. Although the problem in the proposed research is binary, it is solved using regression models.…”

Section: Training Data Preparationmentioning

confidence: 99%

The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography

et al. 2021

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This paper refers to an original concept of tomographic measurement of brick wall humidity using an algorithm based on long short-term memory (LSTM) neural networks. The measurement vector was treated as a data sequence with a single time step in the presented study. This approach enabled the use of an algorithm utilising a recurrent deep neural network of the LSTM type as a system for converting the measurement vector into output images. A prototype electrical impedance tomograph was used in the research. The LSTM network, which is often employed for time series classification, was used to tackle the inverse problem. The task of the LSTM network was to convert 448 voltage measurements into spatial images of a selected section of a historical building’s brick wall. The 3D tomographic image mesh consisted of 11,297 finite elements. A novelty is using the measurement vector as a single time step sequence consisting of 448 features (channels). Through the appropriate selection of network parameters and the training algorithm, it was possible to obtain an LSTM network that reconstructs images of damp brick walls with high accuracy. Additionally, the reconstruction times are very short.

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“…Thanks to tomography, it is possible to obtain images of the interior of the object, which is why it is not a technique of measuring absolute physical quantities, but a method of visualizing relative differences in the structure of the analyzed object. In industry, various types of tomography can be used, which, due to the physical phenomenon used, can be divided into: electrical tomography, computed tomography, ultrasound, radiotomographic imaging, and even optical tomography [1][2][3][4][5][6][7][8]. There are basically two types of electrical tomography -capacitive and impedance.…”

Section: Introductionmentioning

confidence: 99%

The use of dual machine learning in industrial electrical tomography

Rymarczyk

Kłosowski

Cieplak

et al. 2022

J. Phys.: Conf. Ser.

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Machine learning techniques are playing a key role in tomography. Process tomography, also known as industrial tomography, uses a variety of physical phenomena. Contrary to the commonly used computed tomography in medicine, electrical, ultrasound, radio and even optical tomography are used in industry. In electrical tomography we distinguish between impedance and capacitance tomography. This manuscript presents an algorithmic method to allow accurate measurements of reactors and industrial vessels using electrical impedance tomography. Reactors may contain liquids which undergo phase changes resulting in crystallization or gassing. The tomograph can detect gas crystals or bubbles. The innovative contribution of the authors is the development of an original algorithm that allows the conversion of input measurements to 2D images. First, the algorithm trains multiple single-output neural networks, each of which generates a single image pixel. Secondly, two models were used (support vector machines and artificial neural networks), which were assigned to individual pixels of the image. The image was reconstructed using two methods, not one, so the new method was called dual machine learning (DML). In order to assess the effectiveness of the new approach, both homogeneous methods (SVM and ANN) were compared with the new DML method. The results confirmed the higher effectiveness of the new approach.

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Assessment of the Spatial Distribution of Moisture Content in Granular Material Using Electrical Impedance Tomography

Cited by 11 publications

References 30 publications

Application of Convolutional Neural Networks in Wall Moisture Identification by Eit Method

Application of Convolutional Neural Networks in Wall Moisture Identification by Eit Method

The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography

The use of dual machine learning in industrial electrical tomography

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