A technique for intrinsic and extrinsic calibration of laser line scanners, also called laser triangulation sensors (LTSs), for integration in a coordinate measuring machine (CMM) is presented in this paper. Setting out from the modeling of a commercial LTS for use in a CMM and the algorithms implemented for image capture and processing, with the use of a gauge object, a one-step calibration procedure has been developed to obtain both intrinsic parameters—laser plane, CCD sensor and camera geometry—and extrinsic parameters which relate the LTS's reference system to the CMM's reference system, integrating both mathematical models. This method performs both calibrations in a single step, thus avoiding the digitalization of a reference sphere in order to obtain the extrinsic parameters, or optimization procedures subsequent to LTS calibration. The results obtained in accuracy and repeatability tests performed on gauge geometric primitives attest to the viability of this technique for the integration of LTSs in CMMs.
Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures.
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