Intra-pulse Cavity Enhanced Measurements of Carbon Monoxide in a Rapid Compression Machine

Nasir, Ehson F.; Farooq, Aamir

doi:10.1364/cleo_at.2018.atu4i.5

Cited by 1 publication

(1 citation statement)

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“…A sensor that can swiftly response to the effects of CO has successfully been developed using Eu-doped Ceria film [19]. The use of laser as a sensor for CO has also been developed to reduce measurement noise and this system is capable of measuring CO concentration to as small as 2.4 ppm [20]. Concentration of CO can also be measured using spectroscopy method.…”

Section: Related Workmentioning

confidence: 99%

Real-time decision support system for carbon monoxide threat warning using online expert system

Suryono¹,

Surarso²,

Saputra³

et al. 2019

J Appl Eng Science

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This paper describes a decision support system to mitigate the danger that carbon monoxide pose via internet-based measurement. This system is required because in high concentration above threshold, carbon monoxide can trigger many diseases and even cause death. However, a system that is capable of detection and making online decision in real-time against that threat is not yet available. Therefore, decisions on carbon monoxide threat are often taken too late as they are made manually with expert analysis. This research proposes the design of a sensor node composed of a gas sensor, a microcontroller, a WIFI router, and an Internet modem to acquire data and communicate them via the internet. The pollution index value and rule-based algorithm, which are used to determine carbon monoxide gas pollution categories in the web server program, are in accordance to data stated in the Indonesia Air Pollutant Index. An expert system programming based on expert knowledge is then used to make decision on pollution. Results show that the sensor node built is capable of sending data online to the cloud station, with Root Mean Square Error of 3.46 µg/m 2 and relative error of 0.78% for a measurement range of 0-440 µg/m 2. This system is also capable of sending data fast with a transfer rate of 764 milliseconds. Further testing also revealed that using expert system in cloud computing results in speedy warning of carbon monoxide threat, at 15.6 seconds.

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Section: Related Workmentioning

confidence: 99%