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**8**spośród

**8**dla zapytania:

*authorDesc:"Edward KOZŁOWSKI"*###
**Implementation of the LARS method to solve the inverse problem in electrical tomography**
DOI:10.15199/48.2018.12.31

The tomographic method makes it possible to obtain moisture distribution inside the wall in a digital form. This is extremely useful when you need to obtain a high quality image in a non-invasive way. Visualization of the moisture inside the wall enables the implementation of effective protection of walls against moisture and in the case of old buildings - effective and fast drainage of walls. This is of particular importance for thick walls. The most important advantages of the proposed measurement system include non-invasive and non-destructive measurement of the tested object thanks to specially designed surface electrodes and the ability to display the moisture distribution inside the wall both on the plane (2D) and spatially (3D). Due to the fact that wall conductivity depends mainly on the degree of humidity, it is possible to determine the distribution of moisture inside the wall using the indirect method - based on the conductivity map. In the case of brick walls, this is the only cheap and non-invasive method, unlike the weight method, in which the wall must be drilled, and the heat generated evaporates a certain amount of moisture. This is, therefore, an invasive method of quality, not a quantitative method, thus subject to an additional error. We are interested only in differential (relative) images, on which we can distinguish specific colors from a dry background. Thanks to this, moisture content can be assessed in the tested cross-sections of walls or bricks. There are many different methods to optimize the solution mentioned above. problem [8-14,22,23]. This article presents the method of using the smallest angle algorithm [4] to solve the inverse problem in electrical tomography for damp wall [1-3,5-7,15-21]. Statistical method Reduction of adverse effects of multi-polarization between predictors can be achieved by applying the lowest angle regression algorithm for this solution. The algorithm in question [...]

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**The concept of the technological process control using a distributed industrial tomography system**
DOI:10.15199/48.2018.12.36

Industrial tomography enables non-invasive, dynamic observation of physical and chemical phenomena without the need of mechanical interference into the interior of the investigated object [1-14,16,19-22]. Thanks to the features mentioned above, this type of tomography is ideal for automatic optimization of design and production processes. Process tomography systems can operate autonomously in the field of monitoring, measurement and control of the correct functioning of industrial processes. A network of sensors connected to the system provides a constant data flow enabling tracking of technological processes even in closed technical facilities, such as fermenters. Process tomography is also used to acquire data on the flow of fluids and loose components in pipelines that act as transport media [24-29, 31-37]. The data obtained from the sensors are delivered to the data warehouse, where they are further processed. As a consequence, data warehouses enable building a knowledge base on operating systems and processes. Data analysis results can be displayed in a suitable form on the monitor screen. In semi-automatic systems they can be used by the operator as elements of supporting decision-making processes, and in automatic systems, decisions are made by IT systems, and the information about the history of these decisions is a log file. The production process control tasks carried out in this way allow increasing the efficiency and quality of products, as well as increasing the company's competitiveness level. Methods of analysis and control of processes include issues related to the processing of data obtained from various sensors located in remote nodes. Monitoring is based on acquired and processed data due to appropriately elaborated algorithms for parameter automation [15,17,18,23,30,38]. This paper concerns the issues of processes control in a cyber-physical system based on the concept of a production process managemen[...]

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**Area monitoring using the ERT method with multisensor electrodes**
DOI:10.15199/48.2019.01.39

Tomography is a technique that allows obtaining a cross-sectional image of the examined object on the basis of data from the measurement of a given physical value (radiation, capacity, resistance, etc.) at selected points usually lying on the edge of the tested area. The resulting measurement vector is used to reconstruct the crosssection image using appropriate algorithms. The obtained image represents the distribution of a certain feature of the examined object depending on the type of tomography used. It can be material density, concentration, electrical permittivity, conductivity, etc. Electrical tomography covers many tomographic imaging methods based on the processing of various electrical parameters [1,3,4,7,9,10,12-14]. Despite the fact that many methods have already been developed for assessing damage to flood embankments, there is no single universal tool for their diagnosis and monitoring. In this paper, a new method for testing flood embankments and landfills by means of electrical resistive tomography (ERT) was presented. For the needs of the research, a special measuring system was developed with special multisensor electrodes for depth measurements using ERT. The algorithms used for image reconstruction were based on gradient and topological methods. After minor modifications, it is possible to apply the discussed technique to solving reverse problems in electrical tomography [6, 18-23]. The combination of tomographic techniques with reconstruction algorithms allowed non-invasive and more accurate spatial assessment of seepages and damages to flood protections. Model Electric tomography including ERT enables non-invasive measurements of various types of technical objects. The i[...]

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**Industrial processes control with the use of a neural tomographic algorithm**
DOI:10.15199/48.2019.02.22

Process tomography is picking up in significance alongside innovative advancement [1], [2]. At present, a significant trend can be watched for the robotization of modern procedures, which is firmly identified with process control. The need to automate the control of innovative procedures is one of the fundamental purposes behind the dynamic improvement of IT information handling strategies [3], [4]. Simulation and experimental tests are an important condition for optimizing the control of processes carried out by liquid and suspension mixing systems that under certain circumstances can crystallize [5]. An example of such a substance is biodiesel. Common measurement tools used to quantify physicochemical processes, such as sensors and markers, are often characterized by evaluation capabilities limited to specific points. Due to the high degree of difficulty in modeling the mixing and heating processes of crystallizing substances [6], which are characterized by a distinct non- Newtonian flow, traditional Computational Fluid Dynamics models do not provide a suitable basis for dimensioning mixing and heating systems, and therefore become useless. Classical models do not take into account granulometric parameters. The method of determining the rheological properties of liquids is difficult. In addition, traditional models used to simulate the mixing and heating of multiphase systems are still inaccurate [7], [8]. This fact may lead to misinterpretations, especially with regard to modeling and simulation of mixing and heating processes of non-Newtonian liquids, viscous and loaded with foreign particles. For this reason, reliable forecasts regarding the course of such processes are virtually impossible. The above-mentioned problems are an important reason to intensify efforts to develop an effective method of monitoring and supervising liquid crystallization processes [9]. Electrical impedance tomography (EIT) is a modality with[...]

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**Application of Gaussian Kernel with Regard to Correlations for Image Reconstruction in Electrical Tomography**
DOI:10.15199/48.2019.05.14

This article proposes a new solution based on the analysed methods that enable the proper reproduction of the image. This work gives promising results as a new horizon to solve practical problems. The Support Vector Machine for Regression with Gaussian kernel was implemented. A regression method gives more accurate and stable reconstruction results in solving the inverse problem in electrical tomography. There are many ways to solve the optimization problem [1-10]. The statistical methods [11] were used to reconstruct the image in electrical impedance tomography. The main objective of the tomography is to perform image reconstruction. During the measurements, we can see that the measured values from some electrodes are strongly correlated (due to the way of measurement). In this case, we have a multicollinearity problem. Electrical impedance tomography (EIT) is an ill-posed inverse problem. In the EIT, the electrical voltages are injected into the object using a set of electrodes attached to the object's surface, and the potentials are measured. The object's conductivity is reconstructed on the basis of known voltages and measured potentials. Reconstruction of electrical impedance tomography requires accurate modelling. EIT is a method of imaging in which the conductivity distribution of the tested object is estimated on the basis of measurements of electrical voltages and potentials of electrodes at the boundary. To obtain quantitative information on the change in conductivity, it would be better to use a non-linear model in the differential imaging solution [12-14]. In the case when the objects are different (in the sense of size), the grid is made, then the model parameters are estimated, and only in the final phase the reconstruction is a labour-intensive process. The approach used below is an attempt to create a model that would analyse similar objects of different sizes - we learn on a smaller object, but we recogn[...]

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**Detection of seepages in flood embankments using the ElasticNET method**
DOI:10.15199/48.2019.01.40

Electric tomography is based on the transformation of data taken from the surface of the tested object into the image of its cross-section. There are many methods to optimize the obtained image by solving the appropriate objective function [1-5,13,15,16,20-25,32]. The algorithm based on the ElasticNET presented in this article is a new proposal in tomography. Fig. 1. Model of measuremnt system. The way of working of electrical impedance tomography (EIT) consists in introducing electrical voltage to the tested object by means of a set of electrodes located on the surface of the object. Next, the measured values of electrical potentials between individual electrode pairs are collected. Conductance of individual sections of the crosssection of the tested object is reconstructed on the basis of known values of voltages and measured values of potentials. Reconstruction of the image obtained by electrical tomography requires sophisticated modeling. This method of imaging consists in the fact that the conductivity distribution of the tested object is estimated on the basis of measurements of electrical voltages and electrode potentials on the surface of their contact with the tested object. In order to obtain quantitative data on changes in the conductivity inside an object, it is more effective to apply a non-linear model in differential imaging [1,6-12,14,17- 199,26-31]. In Fig. 1 shows the model of the measurement system. ElasticNET Let’s consider the problem of recognizing linear dependencies (1) Y X where Y Rn , X Rnk1 are the observation matrices of a output variable and predictive variables respectively, Rk1means a matrix of structural parameters, while Rn vector of independent random variables. The wellknown method of least squares consists in estimating unknown parameters &[...]

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**Machine learning in image reconstruction by multi-sensor electrodes**
DOI:10.15199/48.2019.12.42

In electrical impedance tomography (EIT), voltage or current is injected into an object using a set of electrodes attached to its surface [1-6]. Voltage values are measured on the remaining electrodes. The conductivity of the object is reconstructed based on the measurements taken and the appropriate algorithm to solve the inverse problem [7-19]. Measurement system The measuring system was based on an electric tomography device, which assumes the use of two measuring methods and allows measurements to be made to 32 channels. Figure 1 shows laboratory measurement models with one and two multi-sensor electrodes. The device measures the tested object based on measurements of the potential distribution on the surface. The system collects measured data from the electrodes. The solution assumes the use of two measuring methods and allows measurements to be made to 32 channels. The device consists of several separate modules: power generator, measuring block, multiplexer and controller. You can connect more than one multiplexer to the device. Data acquisition systems require equipment for voltage measurement, filtering, demodulation and conversion to digital units and signal processing in order to send data to the computing unit. Depending on the method of use, the system can be adapted to measurements in large areas or small sections. In both cases, all infrastructure is centrally managed to collect data and record measurements. The system allows you to collect measurements, manage data and monitor devices. An electric tomograph constructed for this type of measurement is shown in Fig. 2. Methods There are many numerical methods used in optimization problems [20-35]. This article uses machine learning methods [36-39] that use information to learn directly from data without first determining a mathematical model. The machine learning algorithms used utilize the availability of large amounts of data and computing resources. The [...]

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**Application of multi-source data for process analysis in electrical tomography**
DOI:10.15199/48.2019.12.43

Industrial tomography solves the inverse problem that the optimization, identification or synthesis process. Such problems are difficult to analyze [1-5]. They do not have clear solutions. Knowledge of the process can make image reconstruction more resistant to incomplete information [6- 9]. There are many numerical methods [10-26].Tomography enables the analysis of processes taking place in the facility without interfering with them [27-30]. It enables better understanding and monitoring of industrial processes and facilitates real-time process control. Figure 1 shows the structure of the system using an electric tomograph with image reconstruction. Fig. 1. The idea of system structure with a hybrid tomography scanner with flow measurement, image processing in the cloud computing. Methods Electrical tomography is an imaging technique that uses different electrical properties of materials. In this method, the current source is connected to the tested object, and then the voltage distribution on its edge is measured (Fig, 2). The collected information is processed and reconstructed by a specific algorithm. The basic theory can be obtained from Maxwell's equations. A complex 'admittivity' can define as follows: (1) i where ε is the permittivity, σ is the electrical conductivity, ω is the angular frequency. Potential distribution in a heterogeneous, isotropic area: (2) [...]

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