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Ensuring aerodrome development processes and using sensory networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Using new technology to track service movement and logistics equipment, passengers or wild animals in the airport area can significantly reduce runway incursion occurrence. Sensor implementation networks will allow for foreign entity identification in a timely manner and take measures to prevent unauthorized access to the track. Modern technologies, which include sensor networks, multifunctional camera systems and radio frequency identification access chips facilitate the creation of complex safety nets at active points and on access roads. Due to their mobility and possible changes in range and direction, sensory networks are an effective method for achieving the desired level of security. Combining elements of modern technology creates space for automated airport security. Security risk portfolios are now defined for 10 different operating domains and give advice to the decision-making process, which the European Plan for Aviation Safety (EPAS) has supported. The aim of the article is to analyse safety in commercial air transport for the period 2006-2015 in comparison with 2016 and propose a method that would reduce the number of incidents through sensor networks and using texture analysis.
Rocznik
Tom
Strony
99--117
Opis fizyczny
Bibliogr. 36 poz.
Twórcy
autor
  • Department of Air Force, Faculty of Military Technology, University of Defence in Brno, Kounicova 65 Street, 662 10 Brno, Czech Republic
autor
  • Department of Air Force, Faculty of Military Technology, University of Defence in Brno, Kounicova 65 Street, 662 10 Brno, Czech Republic
autor
  • Department of Air Force, Faculty of Military Technology, University of Defence in Brno, Kounicova 65 Street, 662 10 Brno, Czech Republic
autor
  • Department of Air Force, Faculty of Military Technology, University of Defence in Brno, Kounicova 65 Street, 662 10 Brno, Czech Republic
Bibliografia
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  • 3. Bentyn Z. 2017. “Logistic performance development of the countries on the northern corridor of the new silk road”. European Transport \ Trasporti Europei. Issue 63. Paper no 4: 1-15.
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  • 5. Brodatz P. 1966. Textures - A Photographic Album for Artists and Designers. Mineola, NY: Dover.
  • 6. Campbell N.D.F., K. Subr, J. Kautz. N.d. “Fully-connected CRFs with Non-parametric Pairwise Potentials”. Available at: http://homepages.inf.ed.ac.uk/ksubr/Files/Papers/PrwPotPaper.pdf.
  • 7. Chen Y., E. Dougherty. 1994. “Grey-scale Morphological Granulometric Texture Classification”. Optical Engineering 33(8): 2713-2722.
  • 8. Courty N., T. Corpetti. “Crowd Motion Capture”. Computer Animation and Virtual Worlds 18(4-5): 361-370.
  • 9. D’amato A.A., S. Kgoed, G. Swanepoel, J. Walters, A. Drotskie, P.J. Kilbourn. “Convergence of logistics planning and execution in outsourcing”. Journal of Transport and Supply Chain Management 9(1)(a159): 1-9. DOI: https://doi.org/10.4102/jtscm.v9i1.159.
  • 10. Dukiewicz T. 2016. “Systém ISTAR jako determinanta vojenského informačního prostředí”. International Military Technical Conference Tactics 2016. Brno. [In Czech: “ISTAR as a Determinant of the Military Information Environment”.] ISBN 978-80-7231-398-3.
  • 11. EASA. Reduced Interface Taxonomy.
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  • 16. Haralick R. 1979. “Statistical and Structural Approaches to Texture”. Proceedings of the IEEE 67(5): 786-804.
  • 17. Heinrich H.W. 1931. Industrial Accident Prevention: A Scientific Approach. New York, NY: McGraw-Hill. Quoted in E. Hollnagel. 2009. Safer Complex Industrial Environments: A Human Factors Approach. Boca Raton, FL: CRC Press. ISBN 1-4200-9248-0.
  • 18. Hotař V. 2009. “Definice fraktálů”. [In Czech: “Definition of fractals”.] In Metodika popisu průmyslových dat pomocí fraktální geometrie. [In Czech: Methodology for the Description of Industrial Data Using Fractal Geometry.] Liberec: Technická univerzita. Available at: http://www.ksr.tul.cz/fraktaly/definice.html.
  • 19. ICAO. 2000. ADREP 2000 Taxonomy.
  • 20. ICAO. 2007. Doc 9870 - AN/463, Manual on the Prevention of Runway Incursions. International Civil Aviation Organization.
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  • 27. Musse S.R., C.R. Jung, J.C.S. Jacques, Jr., A. Braun. 2007. “Using Computer Vision to Simulate the Motion of Virtual Agents”. Computer Animation and Virtual Worlds 18(2): 83-93.
  • 28. Musse S.R., M. Paravisi, R. Rodrigues, J.C.S. Jacques, Jnr., C.R. Jung. 2007. “Using Synthetic Ground Truth Data to Evaluate Computer Vision Techniques”. Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance: 25-32.
  • 29. Paravisi M., A. Werhli, J.C.S. Jacques, Jr., R. Rodrigues, A. Bicho, C. Jung, S.R. Musse. 2008. “Continuum Crowds with Local Control”. Proceedings of Computer Graphics International (CGI’08): 108-115. Istanbul, Turkey. June 2008.
  • 30. Pham T.A. 2010. Optimization of Texture Feature Extraction Algorithm. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.827.8904&rep=rep1&type=pdf
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  • 34. Solera F., S. Calderara, R. Cucchiara. 2016. “Socially Constrained Structural Learning for Groups Detection in Crowd”. IEEE Transaction on Pattern Analysis and Machine Intelligence 38 (5): 995-1008.
  • 35. Stroeve S., P. Som, B. van Doorn, B. Bakker. 2015 “A Risk-based Framework for Assessment of Runway Incursion Events”. 11th USA/Europe Air Traffic Management Research and Development Seminar (ATM2015). Available at: http://www.atmseminar.org/seminarContent/seminar11/papers/366_Stroeve_0121150328-Final-Paper-4-23-15.pdf.
  • 36. Taylor G., A. Chosak, P. Brewer. 2007. “OVVV: Using Virtual Worlds to Design and Evaluate Surveillance Systems”. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 1-8.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f805046d-0f6f-4a0a-97ae-b4b0838959b4
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