Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)
المؤلفون المشاركون
Sowah, Robert A.
Apeadu, Kwaku O.
Gatsi, Francis
Ampadu, Kwame O.
Mensah, Baffour S.
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-01
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
This 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.
These 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.
The 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.
The 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.
The 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.
The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sowah, Robert A.& Apeadu, Kwaku O.& Gatsi, Francis& Ampadu, Kwame O.& Mensah, Baffour S.. 2020. Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs). Journal of Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1183677
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sowah, Robert A.…[et al.]. Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs). Journal of Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1183677
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sowah, Robert A.& Apeadu, Kwaku O.& Gatsi, Francis& Ampadu, Kwame O.& Mensah, Baffour S.. Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs). Journal of Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1183677
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1183677
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر