Intelligent classifier for incipient phase fire in building
Early fire detection is one of the most promising sub-fields in indoor air quality research. Ability to give early fire indication can help the building occupants to take responsive actions in order to prevent the fire. Delay in having such indication not only leading to property and money losses,...
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| Format: | Thesis |
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| Language: | English |
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| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77988/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77988/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77988/4/Allan%20Melvin.pdf |
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| Summary: | Early fire detection is one of the most promising sub-fields in indoor air quality research. Ability to give early fire indication can help the building occupants to take responsive actions in order to prevent the fire. Delay in having such indication not
only leading to property and money losses, but also life losses. This research is a preliminary research intended to detect the early fire and the material (common fire sources and building construction materials) involved in the fire using intelligent
classifier. Indoor Air Quality (IAQ) database is formed as the testing database, while Portable Electronic Nose 3 (PEN3) database is formed to verify the IAQ database. The databases consist of gas sensor inputs from the test materials, heated up at different
temperatures in the testbed. Seven temperatures, range from 50°C up to 250°C have been tested. For incipient phase fire, data for temperature range 75°C up to 125°C shows a very significant result. The data is pre-processed and normalised into five
types of normalised features. Out of the five normalised features, only three were statistically selected for proposed multi- stage feature selection and feature fusion process. As an output to the proposed process, a new robust feature, IAQ-Hybrid
feature is formed. IAQ-Hybrid feature is consisting of dimensionally reduced principal components fused by the feature fusion technique. ANOVA F- Test and Principal Component Analysis are used for selecting the useful and non- redundant
data for the proposed feature formulation. The proposed feature and the other normalised features (three types of normalised features which were statistically selected earlier) are tested with various common unsupervised, semi- supervised and
supervised classifiers. |
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