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A hybrid tomography for assessing the moisture level of walls and building condition

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Warianty tytułu
PL
Tomografia hybrydowa do oceny poziomu wilgotności ścian i stanu budynku
Języki publikacji
EN
Abstrakty
EN
The article presents an innovative solution for assessing the moisture level of walls and building condition. The use of modern tomographic techniques allows for a non-destructive and very precise spatial assessment of the humidity level. Prepared constructions contain special electrodes for measuring humidity in a brick wall. The proposed application solves the inverse problem in electrical tomography. A level set method was used to reconstruct the images.
PL
W artykule przedstawiono innowacyjne rozwiązanie do oceny poziomu wilgotności ścian i stanu budynku. Zastosowanie nowoczesnych technik tomograficznych pozwala na nieniszczącą i bardzo precyzyjną ocenę przestrzenną poziomu wilgotności. Przygotowane konstrukcje zawierają specjalne elektrody do pomiaru wilgotności w ścianie z cegły. Proponowane zastosowanie rozwiązuje problem odwrotny w tomografii elektrycznej. Do rekonstrukcji obrazów użyto metody zbiorów poziomicowych.
Rocznik
Strony
100--103
Opis fizyczny
Bibliogr. 34 poz., rys.
Twórcy
  • University of Economics and Innovation, Projektowa 4, Lublin, Poland
  • Research & Development Centre Netrix S.A.
  • Research & Development Centre Netrix S.A.
  • Research & Development Centre Netrix S.A.
  • Research & Development Centre Netrix S.A.
  • Research & Development Centre Netrix S.A.
autor
  • Research & Development Centre Netrix S.A.
Bibliografia
  • [1] Rymarczyk T., Oleszek M., Szumowski J., Tchórzewski P., Adamkiewicz P. and Sikora J., A hybrid tomography system for the analysis of wall dampness, PTZE — 2018 Applications of Electromagnetic in Modern Techniques and Medicine, 0912 September 2018, Racławice, Poland
  • [2] Allaire G., Gournay F. De,. Jouve F., Toader A. M., Structural optimization using topological and shape sensitivity via a level set method”, Control and Cybernetics, 34 (2005), 59–80.
  • [3] Banasiak R., Wajman R., Jaworski T., Fiderek P., Fidos H., Nowakowski J., Study on two-phase flow regime visualization and identification using 3D electrical capacitance tomography and fuzzy-logic classification, International Journal of Multiphase Flow, 58 (2014), 1-14.
  • [4] Bartušek K.; Fiala P., Mikulka J., Numerical Modeling of Magnetic Field Deformation as Related to Susceptibility Measured with an MR System”, Radioengineering, 17 (2008), No. 4, 113-118.
  • [5] Borcea L, Electrical impedance tomography, Inverse Problems, 18 (2002), 99–136.
  • [6] Borsoi R. A., Aya J. C. C., Costa G. H., and Bermudez J. C. M., Super-resolution reconstruction of electrical impedance tomography images,” Comput. Electr. Eng., 69 (2018), 1–13.
  • [7] Chen C., Woźniak PW., Romanowski A. et al., Using Crowdsourcing for Scientific Analysis of Industrial Tomographic Images”, ACM Transactions on Intelligent Systems and Technology, 7 (2016), No. 4, 52:1--52:25.
  • [8] Donno G. De, Giambattista L. Di, Orlando L., High-resolution investigation of masonry samples through GPR and electrical resistivity tomography. Construction and Building Materials, 154 (2017), 1234-1249.
  • [9] Duda K., Adamkiewicz A., Rymarczyk T., Nondestructive Method to Examine Brick Wall Dampness”, International Interdisciplinary Phd Workshop 2016, (2016), 68-71.
  • [10] Filipowicz S.F., Rymarczyk T., The Shape Reconstruction of Unknown Objects for Inverse Problems”, Przegląd Elektrotechniczny, 88 (2012), No. 3A, 55-57.
  • [11] Garbaa H., Jackowska-Strumiłło L., Grudzień K., Romanowski A., Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure”, Archives of Electrical Engineering 65 (2016), No. 4, 657-669
  • [12] Gola, A. Świć A., Computer-Aided Machine Tool Selection for Focused Flexibility Manufacturing Systems Using Economical Criteria,” Actual Problems of Economics, 124 (2011), No. 10, 383-389.
  • [13] Grudzien K., Romanowski A., Chaniecki Z., Niedostatkiewicz M.,. Sankowski D., Description of the silo flow and bulk solid pulsation detection using ECT”, Flow Measurement and Instrumentation, 21 (2010), No. 3, 198-206.
  • [14] Holder D., Introduction to biomedical electrical impedance tomography Electrical Impedance Tomography Methods, History and Applications”, Bristol, Institute of Physics, 2005.
  • [15] Hoła J., Matkowski Z., Schabowicz K., Sikora J., Nita K., Wójtowicz S., Identification of moisture content in brick walls by means of impedance tomography. COMPEL-The international journal for computation and mathematics in electrical and electronic engineering, 31 (2012), No. 6, 17741792.
  • [16] Kosicka E., Kozłowski E., and Mazurkiewicz D., Intelligent Systems of Forecasting the Failure of Machinery Park and Supporting Fulfilment of Orders of Spare Parts, 2018, 54–63.
  • [17] Kryszyn J., Wanta D., Smolik W., Gain Adjustment for Signalto-Noise Ratio Improvement in Electrical Capacitance Tomography System EVT4”,IEEE Sensors Journal, 17 (2017), Np. 24, 8107-8116.
  • [18] Kryszyn J., Smolik W., Toolbox for 3d modelling and image reconstruction in electrical capacitance tomography”, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (IAPGOŚ), 7 (2017), No. 1, 137-145; DOI: 10.5604/01.3001.0010.4603
  • [19] Kryszyn J., Smolik W., Toolbox for 3d modelling and image reconstruction in electrical capacitance tomography, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (IAPGOŚ) , 7, no. 1, (2017), 137-145; DOI: 10.5604/01.3001.0010.4603
  • [20] Lopato P., Herbko M. A Circular Microstrip Antenna Sensor for Direction Sensitive Strain Evaluation, Sensors, 1, (2018), 310; https://doi.org/10.3390/s18010310
  • [21] Majchrowicz M., Kapusta P., Jackowska-Strumiłło L., Sankowski D., Optimization of Distributed Multi-node, MultiGPU, Heterogeneous System for 3D Image Reconstruction in Electrical Capacitance Tomography”, Image processing & communications, 21 (2016), No. 3, 81-90.
  • [22] Osher S., Sethian J.A., Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations”, J. Comput. Phys. 79 (1988), 12-49.
  • [23] Osher S. and Fedkiw R., Level Set Methods: An Overview and Some Recent Results”, Journal of Computational Physics, 169 (2001), 463–502.
  • [24] Korzeniewska E., Gałązka-Czarnecka I., Czarnecki A., Piekarska A., Krawczyk A., Influence of PEF on antocyjans in wine, Przeglad Elektrotechniczny, 94 (2018), No. 1, 57-60.
  • [25] Korzeniewska E., Szczesny A., Parasitic parameters of thin film structures created on flexible substrates in PVD process , Microelectronic Engineering, 193 (2018), 62-64.
  • [26] Polakowski K., Filipowicz S.F., Sikora J., Rymarczyk T., Tomography technology application, Przeglad Elektrotechniczny, 84 (2008), No. 12, 227-229.
  • [27] Psuj G. Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements, Sensors, 1, (2018), 292; https://doi.org/10.3390/s18010292
  • [28] Romanowski A., Big Data-Driven Contextual Processing Methods for Electrical Capacitance Tomography, IEEE Transactions on Industrial Informatics, (2018), 1551-3203, DOI: 10.1109/TII.2018.2855200
  • [29] Rymarczyk T., Tchórzewski P., Adamkiewicz P., Duda K., Szumowski J., Sikora J., Practical Implementation of Electrical Tomography in a Distributed System to Examine the Condition of Objects”, IEEE Sensors Journal, 17 (2017), No. 24, 81668186.
  • [30] Rymarczyk T., Sikora J., Applying industrial tomography to control and optimization flow systems, Open Physics, 16, (2018); 332–345, DOI: https://doi.org/10.1515/phys-20180046
  • [31] Rymarczyk T., Kłosowski G., Application of neural reconstruction of tomographic images in the problem of reliability of flood protection facilities, Eksploatacja i Niezawodnosc – Maintenance and Reliability 20 (2018), No. 3, 425–434, http://dx.doi.org/10.17531/ein.2018.3.11
  • [32] Rymarczyk T., Kłosowski G., Kozłowski E., Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze Moisture of Buildings, Sensors, 7 (2018), 2285.
  • [33] Wang M., Industrial Tomography: Systems and Applications”, Elsevier, 2015.
  • [34] Ziolkowski M., Gratkowski S., and Zywica A. R., Analytical and numerical models of the magnetoacoustic tomography with magnetic induction,” COMPEL - Int. J. Comput. Math. Electr. Electron. Eng., 37 (2018), No. 2, 538–548.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aae8de0c-6847-4ca6-9d7a-9ef68f45eb71
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