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Tytuł artykułu

Hardware-Software Complex for Predicting the Development of an Ecologically Hazardous Emergency Situation on the Railway

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A hardware-software system has been implemented to monitor the environmental state (EnvState) at the site of railway (RY) accidents and disasters. The proposed hardware-software system consists of several main components. The first software component, based on the queueing theory (QT), simulates the workload of emergency response units at the RY accident site. It also interacts with a central data processing server and information collection devices. A transmitter for these devices was built on the ATmega328 microcontroller. The hardware part of the environmental monitoring system at the RY accident site is also based on the ATmega328 microcontroller. In the hardwaresoftware system for monitoring the EnvState at the RY accident site, the data processing server receives information via the MQTT protocol from all devices about the state of each sensor and the device's location at the RY accident or disaster site, accompanied by EnvState contamination. All data is periodically recorded in a database on the server in the appropriate format with timestamps. The obtained information can then be used by specialists from the emergency response headquarters.
Rocznik
Strony
707--712
Opis fizyczny
Bibliogr. 31 poz., fot., rys., wykr.
Twórcy
  • National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine
  • Kazakh University Ways of Communications, Almaty, Kazakhstan
  • State University of Trade and Economics, Kyiv, Ukraine
  • State University of Trade and Economics, Kyiv, Ukraine
  • State University of Trade and Economics, Kyiv, Ukraine
  • Department of Software Engineering, SatbayevUniversity, Almaty, Kazakhstan
Bibliografia
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  • [16] Dvorak, Zdenek, Bohus Leitner, Michal Ballay, Lenka Mocova, and Pavel Fuchs. 2021. "Environmental Impact Modeling for Transportation of Hazardous Liquids" Sustainability 13, no. 20: 11367. https://doi.org/10.3390/su132011367
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  • [18] Elkins, J. A., & Carter, A. (1993). Testing and analysis techniques for safety assessment of rail vehicles: the state-of-the-art. Vehicle System Dynamics, 22(3-4), 185-208.
  • [19] Katsman M. D. Matematichna model viznachennya ymovirnostey mozhlivih ekologichnih naslidkiv zaliznichnih avarIy, Zb. nauk. Prats HUPS. Harkiv, 2013, 1(34), 182-185.
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  • [21] Lakhno, Valerii & Tsiutsiura, Svitlana & Ryndych, Yevhen & Blozva, A. & Desiatko, Alona & Usov, Y. & Kaznadiy, S.. (2019). Optimization of information and communication transport systems protection tasks. International Journal of Civil Engineering and Technology. 10. 1-9.
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  • [23] Akhmetov, Bakhytzhan; Lakhno, Valeriy; Oralbekova, Ayaulym; Kaskatayev, Zhanat; Mussayeva, Gulmira (2019) Automated Self-trained System of Functional Control and State Detection of Railway Transport Nodes. International Journal of Electronics and Telecommunications; 2019; vol. 65; No 3; 491-496 https://doi.org/10.24425/ijet.2019.129804; eISSN 2300-1933 (since 2013) ; ISSN 2081-8491 (until 2012)
  • [24] Bing Song, Xiaoping Ma, Yong Qin, Hao Hu, Zhipeng Zhang. (2023) Railroad accident causal analysis with unstructured narratives using bidirectional encoder representations for transformers. Journal of Transportation Safety & Security 15:7, pages 717-736.
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  • [26] Zhao, Kaigong & Zhang, Xiaolei & Wang, Hui & Gai, Yongling & Wang, Haiyan. (2022). Allocation of Resources for Emergency Response to Coal-To-Oil Hazardous Chemical Accidents under Railway Transportation Mode. Sustainability. 14. 16777. 10.3390/su142416777.
  • [27] Ebrahimi, Hadis & Sattari, Fereshteh & Lefsrud, Lianne & Macciotta, Renato. (2023). A machine learning and data analytics approach for predicting evacuation and identifying contributing factors during hazardous materials incidents on railways. Safety Science. 164. 106180. https://doi.org/10.1016/j.ssci.2023.106180
  • [28] Panasiuk, І & Tretiakova, L. & Mitiuk, L.. (2023). Method of identifying hazards and predicting the emergency situations in case of soil contamination by heavy metal compounds. Compounds Power Engineering: ING: economics, technique, ecology. https://doi.org/10.20535/1813-420.3.2022.272097
  • [29] Abuova, Akbala & Lakhno, Valerii & Oshanova, Nurzhamal & Yagaliyeva, Bagdat & Anosov, Andrew. (2019). Conceptual Model of the Automated Decision-Making Process in Analysis of Emergency Situations on Railway Transport. https://doi.org/10.1007/978-3-030-37632-1_14
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-27cbe136-f472-4cf5-9f42-cd49e7a563a6
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