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.
The article presents a non-destructive method using multisensor electrodes to study cross-sections of interior objects such as landfills and flood embankments. Special sensors have been developed for deep measurements using electrical resistive tomography (ERT). It is an innovative approach to testing water and waste reservoirs, both due to the reconstruction model and the measurement method. The combination of tomographic techniques and original reconstruction algorithms allowed non-invasive and more accurate, spatial assessment of seepages and other damages of flood embankments. Streszczenie. W artykule przedstawiono metodę nieniszczącą z wykorzystaniem elektrod wielosensorowych do badania przekrojów wnętrza obiektów takich jak wysypiska i wały przeciwpowodziowe. Opracowano specjalne czujniki przeznaczone do pomiarów głębinowych za pomocą elektrycznej tomografii rezystancyjnej (ERT). Jest to innowacyjne podejście do testowania zbiorników wodnych i odpadowych, zarówno z uwagi na model rekonstrukcji, jak i metodę pomiaru. Połączenie technik tomograficznych i oryginalnych algorytmów rekonstrukcyjnych pozwoliło na nieinwazyjną i bardziej dokładną, przestrzenną ocenę infiltracji i innych uszkodzeń wałów przeciwpowodziowych. (Monitorowanie obszaru metodą ERT za pomocą elektrod wielosensorowych).
This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first decade of the 19th century. Using the prototype of an electric tomograph of our own design, a number of voltage measurements were made on selected parts of the building. Many algorithmic methods have been preliminarily analyzed. Ultimately, the three models based on machine learning were selected: linear regression with SVM (support vector machine) learner, linear regression with least squares learner, and a multilayer perceptron neural network. The classical Gauss–Newton model was also used in the comparison. Both the experiments based on real measurements and simulation data showed a higher efficiency of machine learning methods than the Gauss–Newton method. The tomographic methods surpassed the point methods in measuring the dampness in the walls because they show a spatial image of the interior and not separate points of the examined cross-section. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. Machine learning has a greater potential to create correct tomographic reconstructions than traditional mathematical methods. In this research, linear regression models performed slightly worse than neural networks.
The article deals with the problem of detecting moisture in the walls of historical buildings. As part of the presented research, the following four methods based on mathematical modeling and machine learning were compared: total variation, least-angle regression, elastic net, and artificial neural networks. Based on the simulation data, the systems for the reconstruction of “pixel by pixel” tomographic images were trained. In order to test the reconstructive algorithms obtained during the research, images were generated based on real measurements and simulation cases. The method comparison was performed on the basis of three indicators: mean square error, relative image error, and image correlation coefficient. The above indicators were applied to four selected variants that corresponded to various parts of the walls. The variants differed in the dimensions of the tested wall sections, the number of electrodes used, and the resolution of the 3D image meshes. In all analyzed variants, the best results were obtained using the elastic net algorithm. In addition, all machine learning methods generated better tomographic reconstructions than the classic Total Variation method.
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