The Experts below are selected from a list of 2037 Experts worldwide ranked by ideXlab platform
Michael - One of the best experts on this subject based on the ideXlab platform.
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The Search & Rescue Crew want you! | National Maritime Museum Cornwall | Falmouth, Cornwall
2012Co-Authors: MichaelAbstract:Be inspired by the National Maritime Museum Cornwall's new Search & Rescue exhibition and take part in a range of hands-on creative craft activities
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The Search & Rescue Crew want you at the Maritime Museum. | National Maritime Museum Cornwall | Falmouth, Cornwall
2012Co-Authors: MichaelAbstract:Be inspired by our Search & Rescue exhibition and take part in hands-on activities. Build a Rescue rocket, create a lifeboat moneybox and more.
Robert A Sowah - One of the best experts on this subject based on the ideXlab platform.
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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)
Journal of Engineering, 2020Co-Authors: Robert A Sowah, Kwaku O. Apeadu, Francis Gatsi, Kwame O. Ampadu, Baffour S. MensahAbstract: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%.
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hardware design and web based communication modules of a real time multisensor fire detection and notification system using fuzzy logic
IEEE Transactions on Industry Applications, 2017Co-Authors: Robert A Sowah, Abdul R Ofoli, Selase Krakani, Seth Y FiawooAbstract:This paper presents the design and development of a fuzzy logic based multisensor fire detection system and a web-based notification system. Until recently, most consumer grade fire detection systems relied solely on smoke detectors. The protection provided by these has been established to be limited by the type of fire present and the detection technology at use. The problem is further compounded by the lack of adequate alert and notification mechanisms. A typical system relies on the physical presence of a human being to act on the alert. In developing countries, poor planning and addressing negatively affects the fire and Rescue Crew's response time. To address this problem, a fuzzy logic system was implemented using an Arduino development board with inputs from an MQ2 smoke sensor, a TMP102 temperature sensor, and a DFRobot flame sensor. The output of the detection system is sent over short message service (SMS) using a SIM900 global system for mobile communication (GSM) module to the web-based system and the house owner or caretaker in real-time. With access granted to the web-based system, the fire and Rescue Crew also get notified in real-time with location information. A comparison between the efficiency of the notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements in the form of timely detection, alerting, and response.
Seth Y Fiawoo - One of the best experts on this subject based on the ideXlab platform.
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hardware design and web based communication modules of a real time multisensor fire detection and notification system using fuzzy logic
IEEE Transactions on Industry Applications, 2017Co-Authors: Robert A Sowah, Abdul R Ofoli, Selase Krakani, Seth Y FiawooAbstract:This paper presents the design and development of a fuzzy logic based multisensor fire detection system and a web-based notification system. Until recently, most consumer grade fire detection systems relied solely on smoke detectors. The protection provided by these has been established to be limited by the type of fire present and the detection technology at use. The problem is further compounded by the lack of adequate alert and notification mechanisms. A typical system relies on the physical presence of a human being to act on the alert. In developing countries, poor planning and addressing negatively affects the fire and Rescue Crew's response time. To address this problem, a fuzzy logic system was implemented using an Arduino development board with inputs from an MQ2 smoke sensor, a TMP102 temperature sensor, and a DFRobot flame sensor. The output of the detection system is sent over short message service (SMS) using a SIM900 global system for mobile communication (GSM) module to the web-based system and the house owner or caretaker in real-time. With access granted to the web-based system, the fire and Rescue Crew also get notified in real-time with location information. A comparison between the efficiency of the notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements in the form of timely detection, alerting, and response.
Baffour S. Mensah - One of the best experts on this subject based on the ideXlab platform.
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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)
Journal of Engineering, 2020Co-Authors: Robert A Sowah, Kwaku O. Apeadu, Francis Gatsi, Kwame O. Ampadu, Baffour S. MensahAbstract: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%.
Abdul R Ofoli - One of the best experts on this subject based on the ideXlab platform.
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hardware design and web based communication modules of a real time multisensor fire detection and notification system using fuzzy logic
IEEE Transactions on Industry Applications, 2017Co-Authors: Robert A Sowah, Abdul R Ofoli, Selase Krakani, Seth Y FiawooAbstract:This paper presents the design and development of a fuzzy logic based multisensor fire detection system and a web-based notification system. Until recently, most consumer grade fire detection systems relied solely on smoke detectors. The protection provided by these has been established to be limited by the type of fire present and the detection technology at use. The problem is further compounded by the lack of adequate alert and notification mechanisms. A typical system relies on the physical presence of a human being to act on the alert. In developing countries, poor planning and addressing negatively affects the fire and Rescue Crew's response time. To address this problem, a fuzzy logic system was implemented using an Arduino development board with inputs from an MQ2 smoke sensor, a TMP102 temperature sensor, and a DFRobot flame sensor. The output of the detection system is sent over short message service (SMS) using a SIM900 global system for mobile communication (GSM) module to the web-based system and the house owner or caretaker in real-time. With access granted to the web-based system, the fire and Rescue Crew also get notified in real-time with location information. A comparison between the efficiency of the notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements in the form of timely detection, alerting, and response.