A cloud-based urban monitoring system by using a quadcopter and intelligent learning techniques

• Autorzy: Khanmohammadi Sohrab , Samadi Mohammad

Identyfikatory

DOI

10.14313/JAMRIS/2-2022/11

• Języki publikacji: EN

Abstrakty

EN

The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. The main objectives of this system are to monitor climate status, temperature, humidity, and smoke, as well as to detect fire occurrences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the predetermined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status and effects of the sensors’ data changes on climate status. An illustrative example is used to draw the simulated area using RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices.

Słowa kluczowe

EN

urban monitoring; cloud computing; quadcopter; deep learning; fuzzy system; image processing; pattern recognition; bayesian network; intelligent techniques; learning systems

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 2
• Strony: 11--19
• Opis fizyczny: Bibliogr. 40 poz., rys.

Twórcy

autor

Khanmohammadi Sohrab

• Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

autor

Samadi Mohammad

• Polytechnic Institute of Porto, Portugal

Bibliografia

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Uwagi

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).