5 resultados para sensor uncertainty

em eResearch Archive - Queensland Department of Agriculture


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Metal oxide semiconductor (MOS) sensors are a class of chemical sensor that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares. Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity.

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Metal oxide semiconductor (MOS) sensors are a class of chemical sensors that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares (PLS). Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity. Special Issue: Selected Paper from the 12th International Symposium on Olfaction and Electronic Noses - ISOEN 2007, International Symposium on Olfaction and Electronic Noses.

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Identification of major contributors to odour annoyance in areas with multiple emission sources is necessary to address and resolve odour disputes. In an effort to develop an appropriate tool for this task, odour samples were collected on-site at a piggery and an abattoir (the major odour sources in the area) and at surrounding off-site areas, then analysed using a commercial non-specific chemical sensor array to develop an odour fingerprint database. The developed odour fingerprint database was analysed using two pattern recognition algorithms including a partial least squares-discriminant analysis (PLS-DA) and a Kohonen self-organising map (KSOM). The KSOM model could identify odour samples sourced from the piggery shed 15, piggery pond 8, piggery pond 9, abattoir, motel and others with mean percentage values of 77.5, 65.0, 90.2, 75.7, 44.8 and 64.6%, respectively.

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Report on evidence of shrinkage of live coral trout during professional fishing operations on the Great Barrier Reef in 2000. Excel data includes the following fields: Column A. Fish (fish number from 1 -24) Column B. Bin (1-8, container the fish was held in during the experiment) Column C. Measure (1-7, number of the measurement of each fish) Column D. Observer (1 or 2, making the measurement) Column E. Time 2 Column F. Time (time of the day the measurement was made) Column G. FL (Fork Length) Column H. TL (Total Length) Column I. Difference (difference in length between measures) Column J. Order Column K. Temperature (surface water temp under the boat)

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In collaboration with the New South Wales Department of Primary Industries we compared the effectiveness of the spanner crab monitoring systems used by New South Wales and Queensland and developed a fishery-independent survey protocol acceptable to both states. The objectives of this project were to: 1. Determine the age at which spanner crabs (Ranina ranina) recruit to the fishery 2. Develop a common methodology for monitoring and assessing the Australian spanner crab stock 3. Investigate sources of variability in apparent population density.