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Cyber-Physical System for Collecting Data on Moisture Inside the Walls of Buildings

Published: 15 November 2021 Publication History

Abstract

This paper presents the results of research on the identification of moisture inside the walls of buildings with the use of non-invasive electrical impedance tomography (EIT). The novelty and contribution of this research is the development of an original algorithmic method to solve the ill posedness, inverse problem. Since the new algorithm optimizes the method for each pixel of the tomographic image, taking into account a specific measurement vector, regardless of what and how many homogeneous methods are included in the algorithm, the obtained results are more accurate than those obtained with the use of homogeneous methods. As part of the research, prototypes of the EIT tomograph and electrodes for examining walls were designed and manufactured.

References

[1]
Freimanis Ritvars, Rasa Vaiskunaite, Tereza Bezrucko, and Andra Blumberga, 2019. In-situ moisture assessment in external walls of historic building using nondestructive methods. Environ. Clim. Technol., 23, 1 (Mar, 2019), 122--134.
[2]
Tomasz Rymarczyk and Grzegorz Kłosowski, 2018. Application of neural reconstruction of tomographic images in the problem of reliability of flood protection facilities. Eksploat. i Niezawodn. -- Maint. Reliab., 20, 3 (Jun 2018), 425--434.
[3]
Edward Kozłowski, Tomasz Rymarczyk, Tomasz Cieplak, Grzegorz Kłosowski, and Paweł Tchorzewski, 2019. Application of logistic regression to image reconstruction in EIT. In 2019 International Interdisciplinary PhD Workshop (IIPhDW), 2019, 80--83.
[4]
Tomasz Rymarczyk, Grzegorz Kłosowski, and Edward Kozłowski, 2018. A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings. Sensors, 18, 7 (Jul 2018), 2285.

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  1. Cyber-Physical System for Collecting Data on Moisture Inside the Walls of Buildings

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      cover image ACM Conferences
      SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems
      November 2021
      686 pages
      ISBN:9781450390972
      DOI:10.1145/3485730
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 15 November 2021

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      Author Tags

      1. Dampness analysis
      2. Electrical tomography
      3. Machine learning
      4. Moisture inspection
      5. Nondestructive tests

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      SenSys '21 Paper Acceptance Rate 25 of 139 submissions, 18%;
      Overall Acceptance Rate 174 of 867 submissions, 20%

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