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Smart fire detector could slash false alarms

作者:薛浇    发布时间:2019-02-28 03:16:02    

By Kurt Kleiner A fire detector that can tell the difference between burning toast and a burning building could save money, annoyance, and possibly even lives, by cutting down on false alarms. German company Siemens will start selling the detector in the UK to commercial users by January 2006, and the technology could eventually make its way into homes, says the firm’s fire safety manager, Andrew Morgan. The detector uses four sensors and a neural network to determine if the smoke and heat it’s detecting are from a fire or are just part of the normal room environment. In the UK more than half of the 872,000 fire call-outs in 2004 were bogus, and 285,000 of those false alarms were due to fire detectors. Responding to false alarms costs money, and in the home annoying false alarms encourage people to disable their alarms. Most home alarms are designed to go off when smoke in the air exceeds a certain concentration. But as most people have discovered, the detectors do not distinguish between smoke from frying bacon, steam from a hot shower, or fumes from a smouldering mattress. Some commercial systems are more sophisticated, feeding data from a number of different sensors to a central computer and letting the computer decide whether the readings indicate a fire. The Siemens detector is different, Morgan says, because it builds artificial intelligence into each individual detector, using custom-designed integrated circuits. The detector contains two thermal sensors and two optical sensors. The thermal sensors monitor temperature and its rate of change. The optical sensors monitor the size and colour of the smoke, with one sensor optimised for thick black smoke. Thick black smoke is, paradoxically, relatively hard for an optical sensor to detect, since the sensor works by reflecting light off of the smoke. These four sensors input their data into a neural network – a computer circuit that approximates the basic structure of biological brains. Neural nets are good at considering a number of inputs and recognizing patterns in them. When they are installed, the detectors are programmed with a set of typical parameters for the kind of room they are in, so that they know what patterns of temperature, smoke colour, and particle size to consider normal. For instance, a detector in a workshop would know to ignore dust and machinery exhaust, and one in a bathroom would ignore steam, but both would react to unusual smoke or temperature readings that could signal a fire. Although Siemens has not manufactured a home-consumer model yet,

 

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