Department of Computer Engineering

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    A Fire-Detection and Control System in Automobiles: Implementing a Design That Uses Fuzzy Logic to Anticipate and Respond
    (IEEE Industry Applications Magazine, 2019-01) Sowah, R.; Ampadu, K.O.; Ofoli, A.R.; Koumadi, K.; Mills, G.A.; Nortey, J.
    Despite the immense benefits of fire detection in road transport, more than 2,000 vehicles are damaged by unexpected fires on a daily basis. On a global scale, incendiary-based losses for the automobile and insurance industries have run in to the billions of dollars in the past decade. One contributing factor is the lack of a sophisticated fire safety system in automobiles. This has been addressed by designing and implementing fuzzy logic control systems with feedback over an Arduino microcontroller system. The automatic system, consisting of flame, temperature, and smoke sensors as well as a re-engineered mobile carbon dioxide (CO 2 ) air-conditioning unit, was tested on a medium-sized physical car. Results suggest that the automobile fire-detection and control system, devoid of false alarms, detects and extinguishes fire in under 20s. An innovative, very promising modular solution for hardware implementation in fire detection and control for automobiles has been developed by using new algorithms and fuzzy logic.
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    Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)
    (Hindawi Journal of Engineering, 2020-02-01) Sowah, R.A.; Apeadu, K.; Gatsi, F.; Ampadu, K.O.; Mensah, B.S.
    )is paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. )ese offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. )e system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. )e incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. )e efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. )e final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.