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Tytuł artykułu

Tracking the transport of pollutants by means of imaging methods

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A method for identification and tracking of pollutants plumes in water is presented and applied to laboratory data. This method uses the intensity threshold and associated image processing algorithms to identify the pollutant’s plume within a footage. Quantitative geometrical parameters are then extracted on each frame as proxies of the turbulent diffusion (i.e. area and perimeter) and advection (i.e. centroid location). From the determined plume location in each frame, it is then possible to devise a tracking algorithm which can determine the trajectory and eventual fate of the plume. The developed method is applied to two different types of plumes: one generated by a liquid pollutant (rhodamine) and another by a granular matrix type material (coal) to compare its capability of tracking different plumes. Although developed with laboratory images, the presented method is general and can be applied to field images as well. The advantages and limitations of the proposed methodology are also discussed.
Czasopismo
Rocznik
Strony
2437--2450
Opis fizyczny
Bibliogr. 34 poz.
Twórcy
  • Graduate School of Engineering, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto-shi, Kyoto 606-8501, Japan
autor
  • CERIS-Civil Engineering Research and Innovation for Sustainability, Universidade de Lisboa, Avenida Rovisco Pais 1, 1049-001 Lisboa, Portugal
  • Centro de Investigación y Gestión de Desastres Naturales (CIGIDEN), Av. Vicuña Mackenna, 4860 Macul, Chile
  • Centro de Investigación y Gestión de Desastres Naturales (CIGIDEN), Av. Vicuña Mackenna, 4860 Macul, Chile
  • Centro de Observación Marino para estudios de Riesgos del Ambiente Costero (COSTAR-UV), Universidad de Valparaíso, Avenida Brasil 1786, 2340000 Valparaíso, Chile
  • Escuela de Ingeniería Civil Oceánica, Universidad de Valparaíso, Avenida Brasil 1786, 2340000 Valparaíso, Chile
  • Escuela de Ingeniería Civil Oceánica, Universidad de Valparaíso, Avenida Brasil 1786, 2340000 Valparaíso, Chile
Bibliografia
  • 1. Aleixo R, Soares-Frazão S, Zech Y (2011) Velocity-field measurements in a dam-break flow using a PTV Voronoï imaging technique. Exp Fluids 50(6):1633–1649
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  • 5. Capart H, Young D, Zech Y (2002) Voronoï imaging methods for the measurement of granular flows. Exp Fluids 32:121–135
  • 6. Czernuszenko W (1987) Dispersion of pollutants in rivers. Hydrol Sci J 32(1):59–67. https://doi.org/10.1080/02626668709491162
  • 7. Fischer H, List J, Koh C, Imberger J, Brooks N (1979) Mixing in inland and coastal waters. Academic Press, New York
  • 8. Fox JF, Patrick A, Wood S (2006) The use of lspiv to measure large streamwise vortices. In: Graham R (ed) World environmental and water resource congress 2006: examining the con uence of environmental and water concerns
  • 9. Gill DA, Ritchie LA, Picou JS (2016) Sociocultural and psychosocial impacts of the exxon valdez oil spill: twentyfour years of research in cordova, alaska. Extr Ind Soc 3(4):1105–1116. https://doi.org/10.1016/j.exis.2016.09.004
  • 10. Gonzalez RC, Woods RE (1993) Digital image processing. Addison-Wesley, New York
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  • 13. LabOceano (2021) Laboceano. Retrieved from https://ingenieriaoceanica.uv.cl/laboceano
  • 14. Lemoine F, Wolff M, Lebouche M (1996) Simultaneous concentration and velocity combined laser-induced uorescence and application to turbulent transport. Exp Fluids 20:319–327. https://doi.org/10.1007/BF00191013
  • 15. Lewis QW, Rhoads BL (2018) LSPIV measurements of two-dimensional flow structure in streams using small unmanned aerial systems: 1. accuracy assessment based on comparison with stationary camera platforms and in-stream velocity measurements. Water Resour Res 54:8000–8018. https://doi.org/10.1029/2018WR022550
  • 16. Lindeberg T (2013a) Image matching using generalized scale-space interest points. In: Scale space and variational methods in computer vision, springer lecture notes in computer science, vol 7893. Springer
  • 17. Lindeberg T (2013b) Scale selection properties of generalized scale-space interest point detectors. J Math Imaging Vis 46(2):177–210
  • 18. Matuszewski J, and Rajkowski A (2019) The use of machine learning algorithms for image recognition. In: Proceedings volume 11442, radioelectronic systems conference 2019
  • 19. Meynart R (1982) Digital image processing for speckle flow velocimetry. Res Sci Instrum 53:110–111
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  • 21. Pak M, and Kim S (2017) A review of deep learning in image recognition. In: 4th international conference on computer applications and information processing technology (caipt), pp 1–3. https://doi.org/10.1109/CAIPT.2017.8320684
  • 22. Raffel M, Willert C, Werely S, Kompenhans J (2007) Particle image velocimetry—a practical guide. Springer, Berlin
  • 23. Rodriguez A, Sánchez-Arcilla A, Redondo JM, Bahia E, Sierra JP (1995) Pollutant dispersion in the nearshore region: modelling and measurements. Water Sci Technol 32:169–178. https://doi.org/10.2166/wst.1995.0682
  • 24. Snavely K (2008) Scene reconstruction and visualization from internet photo collections. Phd., thesis, University of Washington
  • 25. Spetsakis M, Aloimonos Y (1991) A multi-frame approach to visual motion perception. Int J Comput Vis 6:245–255
  • 26. Spinewine B, Capart H, Larcher M, Zech Y (2003) Three-dimensional Voronoï imaging methods for the measurement of near-wall particulate flows. Exp Fluids 34(2):227–241
  • 27. Sridevi N, Meenakshi M (2020) Efficient motion compensation and detection algorithm using modified Kalman filtering. In: Communication and electronics systems (icces) 2020, pp 264–268
  • 28. Swati and Dixit G (2014) Improved algorithm for blob detection in document images uence. 6949314. In: 5th international conference–confluence the next generation information technology summit, pp 703–708. https://doi.org/10.1109/con
  • 29. Tauro F, Pagano C, Phamduy P, Grimaldi S, Porfiri M (2015) Large-scale particle image velocimetry from an unmanned aerial vehicle. IEEE/ASME Trans Mechatron 20(6):3269–3275. https://doi.org/10.1109/TMECH.2015.2408112
  • 30. Tropea C, Yarin A, Foss J (eds) (2007) Handbook of experimental fluid mechanics. Springer, New York
  • 31. Westob J, Brasington J, Glasser N, Hambrey M, Reynolds J (2012) structure-from-motion photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300–314. https://doi.org/10.1016/j.geomorph.2012.08.021
  • 32. Winckler P, Molteni F, Reyes M, Gubler A, Sandoval J, Aleixo R (2022) Is rhodamine a good tracer to predict coal transport in water? Obras y Proyectos 30:16–29
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Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e91de580-da1f-4e0c-bdcd-04f15652d500
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