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COP 1: Data-acquisition and virtual instruments
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- Task 1: Good practice guidance for data-acquisition procedures (including virtual instruments)
- Task 2: Application of existing uncertainty best practice to measurement set-ups
- Task 3: Actual (informal) evaluations of data-acquisition procedures and software
| 1 | Title: SAODR: sequence analysis for outlier data rejection | Author: Franco Pavese, Daniela Ichim | | Institute: Istituto di Metrologia G Colonnetti - CNR, Torino, Italy | Owner: Fernando Sparasci | Attachments: 1 | Abstract:
In automatic data acquisition, a sample is generally made up of several
instrumental readings. A series of readings is generally reduced to a single
value by simple methods, such as averaging. However, outlying values can
affect the series. The paper introduces an algorithm, named ‘sequence analysis
outlier data rejection’ (SAODR), which takes into account one of
the most common problems affecting the measurand during the acquisition,
i.e. a nonlinear drift with embedded sequences of outliers due to pulse-noise
peaks. The algorithm uses a time-ordering procedure and the ‘distances’
between successive readings. The frequent case of constant sampling rate is
discussed. The reported tests show the results obtained with Fortran 77 and
MATLAB implementations of the algorithm. A rejection efficiency higher
than 99% was obtained.
| | Created: 9/6/2004 6:52:15 PM | Last Modified: 9/6/2004 7:02:31 PM |  Details |
| 2 | Title: SAODR software | Author: Franco Pavese, Daniela Ichim | | Institute: IMGC, Torino, Italy | Owner: Franco Pavese | Attachments: 1 | Abstract:
In automatic data acquisition, valid sample is generally made up of several instrumental readings. These series of readings are generally reduced to single value by simple methods, such as averaging. However, outlying values can affect the series. The paper introduces an algorithm, named ”sequence-analysis outlier data rejection ”(SAODR), which takes into account one of the most common problems affecting the measurand during the acquisition, i.e.non linear drift with embedded sequences of outliers due to noise peaks. The algorithm uses a time-ordering procedure and the ”distances” between successive readings. The frequent case of equally-time-spaced data is discussed. Results on tests performed for this case are reported using simulated data with Fortran 77 and MatLab(c) algorithm implementations. A rejection efficiency higher than 99% was obtained. | | Created: 3/2/2004 9:45:20 AM | Last Modified: 3/23/2004 10:45:21 AM | Details |
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