IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0241665.html
   My bibliography  Save this article

Development of the automated software and device for determination of wicking in textiles using open-source tools

Author

Listed:
  • Predrag M Milanovic
  • Snezana B Stankovic
  • Milada Novakovic
  • Dragana Grujic
  • Mirjana Kostic
  • Jovana Z Milanovic

Abstract

The development of automated software and the device for determination of wicking of textile materials, using open-source ImageJ libraries for image processing, and newly designed additional algorithm for the determination of threshold, is presented in this paper. The description of the device, design of the open-source software “Kapilarko”, as well as an explanation of the steps: image processing, threshold determination and reading of wicking height, are provided. We have also investigated the possibility of using the artificial neural networks for automatic recognition of the wicking height. The results showed that the recognition of the wet area of the sample, based on the application of artificial neural networks was in a very good agreement with the experimental data. The device's utility for the measurement of wicking ability of textile materials was proved by testing various knitted fabrics. The constructed device has the advantages of providing automated measurement and minimization of the subjective errors of the operators; extremely fast or long-term measurements; digital recording of results; consistency of experimental conditions; possibility of using water instead of colors and, last but not least, low cost of the device. Considering the importance and frequent measurements of wicking ability of textile materials, the advantages of the presented device, as well as the fact that commercial software without publishing the source-code, are used for most of the available devices, we believe that our idea to design the automated software and device by applying the "open-source" approach, will be of benefit to scientists and engineers in using or improving wicking experiments.

Suggested Citation

  • Predrag M Milanovic & Snezana B Stankovic & Milada Novakovic & Dragana Grujic & Mirjana Kostic & Jovana Z Milanovic, 2020. "Development of the automated software and device for determination of wicking in textiles using open-source tools," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
  • Handle: RePEc:plo:pone00:0241665
    DOI: 10.1371/journal.pone.0241665
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241665
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0241665&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0241665?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Al-Zahrani & Amin Abo-Monasar, 2015. "Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3651-3662, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kanhua Yu & Lili Liu & Zhe Chen, 2021. "An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources," Mathematics, MDPI, vol. 9(12), pages 1-26, June.
    2. Dália Loureiro & Aisha Mamade & Marta Cabral & Conceição Amado & Dídia Covas, 2016. "A Comprehensive Approach for Spatial and Temporal Water Demand Profiling to Improve Management in Network Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3443-3457, August.
    3. Md Mahmudul Haque & Amaury Souza & Ataur Rahman, 2017. "Water Demand Modelling Using Independent Component Regression Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 299-312, January.
    4. El Hassene Ait Mokhtar & Radouane Laggoune & Alaa Chateauneuf, 2016. "Utility-Based Maintenance Optimization for Complex Water-Distribution Systems Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4153-4170, September.
    5. Dong-Her Shih & Ching-Hsien Liao & Ting-Wei Wu & Huan-Shuo Chang & Ming-Hung Shih, 2022. "WSI: A New Early Warning Water Survival Index for the Domestic Water Demand," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    6. Magnus Moglia & Christian Andi Nygaard, 2024. "The Responsiveness of Urban Water Demand to Working from Home Intensity," Sustainability, MDPI, vol. 16(5), pages 1-21, February.
    7. Quoc Bao Pham & S. I. Abba & Abdullahi Garba Usman & Nguyen Thi Thuy Linh & Vivek Gupta & Anurag Malik & Romulus Costache & Ngoc Duong Vo & Doan Quang Tri, 2019. "Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5067-5087, December.
    8. Azar Niknam & Hasan Khademi Zare & Hassan Hosseininasab & Ali Mostafaeipour & Manuel Herrera, 2022. "A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?," Sustainability, MDPI, vol. 14(9), pages 1-25, April.
    9. Xiao-Jun Wang & Jian-Yun Zhang & Shamsuddin Shahid & Wei Xie & Chao-Yang Du & Xiao-Chuan Shang & Xu Zhang, 2018. "Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 911-924, April.
    10. Jorge Alejandro Silva, 2022. "Implementation and Integration of Sustainability in the Water Industry: A Systematic Literature Review," Sustainability, MDPI, vol. 14(23), pages 1-28, November.
    11. Adam P. Piotrowski & Maciej J. Napiorkowski & Monika Kalinowska & Jaroslaw J. Napiorkowski & Marzena Osuch, 2016. "Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1217-1237, February.
    12. Saeed Ghavidelfar & Asaad Y. Shamseldin & Bruce W. Melville, 2017. "A Multi-Scale Analysis of Single-Unit Housing Water Demand Through Integration of Water Consumption, Land Use and Demographic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2173-2186, May.
    13. Yanhu He & Jie Yang & Xiaohong Chen & Kairong Lin & Yanhui Zheng & Zhaoli Wang, 2018. "A Two-stage Approach to Basin-scale Water Demand Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 401-416, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0241665. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.