Analysis of the random measurement error of areal 3D coordinate measurements exclusively based on measurement repetitions

2021 ◽  
Vol 88 (2) ◽  
pp. 71-77
Author(s):  
Andreas Michael Müller ◽  
Tino Hausotte
Keyword(s):  
Measurement Error ◽  
Measurement Uncertainty ◽  
Measurement System ◽  
Measurement Errors ◽  
Random Measurement Error ◽  
Additional Information ◽  
Random Measurement ◽  
Random Components ◽  
Measurement Object ◽  
X Ray Computed

Abstract The measurement uncertainty characteristics of a measurement system are an important parameter when evaluating the suitability of a certain measurement system for a specific measurement task. The measurement uncertainty can be calculated from observed measurement errors, which consist of both systematic and random components. While the unfavourable influence of systematic components can be compensated by calibration, random components are inherently not correctable. There are various measurement principles which are affected by different measurement error characteristics depending on specific properties of the measurement task, e. g. the optical surface properties of the measurement object when using fringe projection or the material properties when using industrial X-ray computed tomography. Thus, it can be helpful in certain scenarios if the spatial distribution of the acquisition quality as well as uncertainty characteristics on the captured surface of a certain measurement task can be found out. This article demonstrates a methodology to determine the random measurement error solely from a series of measurement repetitions without the need of additional information, e. g. a reference measurement or the nominal geometry of the examined part.

Time-Dependent Corrosion Growth Modeling Using Multiple ILI Data

2012 ◽  
Author(s):  
S. Zhang ◽  
W. Zhou ◽  
M. Al-Amin ◽  
S. Kariyawasam ◽  
H. Wang
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Oil And Gas ◽  
Time Dependent ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Proposed Model ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects ◽  
Pipeline Corrosion

This paper describes a non-homogeneous gamma process-based model to characterize the growth of the depth of corrosion defect on oil and gas pipelines. All the parameters in the growth model are assumed to be uncertain; the probabilistic characteristics of these parameters are evaluated using the hierarchical Bayesian methodology by incorporating the defect information reported by the multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random measurement error associated with the ILI tools as well as the correlation between the measurement errors associated with different ILI tools are taken into account in the analysis. The application of the model is illustrated using an example involving real ILI data on a pipeline that is currently in service. The results suggest that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

scholarly journals Modeling Errors in Physical Activity Recall Data

2012 ◽  
Vol 9 (s1) ◽  
pp. S56-S67 ◽  
Author(s):  
Sarah M. Nusser ◽  
Nicholas K. Beyler ◽  
Gregory J. Welk ◽  
Alicia L. Carriquiry ◽  
Wayne A. Fuller ◽  
...  
Keyword(s):  
Physical Activity ◽  
Measurement Error ◽  
Measurement Errors ◽  
Activity Measurement ◽  
Model Parameters ◽  
Usual Activity ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Recall Data ◽  
Physical Activity Recall

Background:Physical activity recall instruments provide an inexpensive method of collecting physical activity patterns on a sample of individuals, but they are subject to systematic and random measurement error. Statistical models can be used to estimate measurement error in activity recalls and provide more accurate estimates of usual activity parameters for a population.Methods:We develop a measurement error model for a short-term activity recall that describes the relationship between the recall and an individual’s usual activity over a long period of time. The model includes terms for systematic and random measurement errors. To estimate model parameters, the design should include replicate observations of a concurrent activity recall and an objective monitor measurement on a subsample of respondents.Results:We illustrate the approach with preliminary data from the Iowa Physical Activity Measurement Study. In this dataset, recalls tend to overestimate actual activity, and measurement errors greatly increase the variance of recalls relative to the person-to-person variation in usual activity. Statistical adjustments are used to remove bias and extraneous variation in estimating the usual activity distribution.Conclusions:Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.

scholarly journals Automated evaluation of the surface point quality in dimensional X-ray computed tomography

2020 ◽  
Vol 87 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Andreas Michael Müller ◽  
Lorenz Butzhammer ◽  
Florian Wohlgemuth ◽  
Tino Hausotte
Keyword(s):  
Computed Tomography ◽  
Surface Point ◽  
Random Measurement Error ◽  
X Ray ◽  
Random Measurement ◽  
Automated Evaluation ◽  
Dimensional Measurements ◽  
X Ray Computed ◽  
Model Measurement

AbstractX-ray computed tomography (CT) enables dimensional measurements of numerous measurands with a single scan, including the measurement of inner structures. However, measurement artefacts complicate the applicability of the technology in some cases. This paper presents a methodology to assess the surface point quality of computed tomography measurements without the requirement of a CAD model. Measurement artefacts lowering the surface point quality can therefore automatically be detected. The correlation of quality values with the random measurement error is demonstrated. The presented method can in principle be used to weight single fit points to reduce the measurement uncertainty of CT measurements.

scholarly journals Validation of a new signal processing scheme for the MST radar at Aberystwyth

2008 ◽  
Vol 26 (11) ◽  
pp. 3253-3268 ◽  
Author(s):  
D. A. Hooper ◽  
J. Nash ◽  
T. Oakley ◽  
M. Turp
Keyword(s):  
Signal Processing ◽  
Measurement Error ◽  
Gravity Wave ◽  
Wave Activity ◽  
Horizontal Wind ◽  
Time Averaging ◽  
Random Measurement Error ◽  
Processing Scheme ◽  
Random Measurement ◽  
Short Period

Abstract. This paper describes a new signal processing scheme for the 46.5 MHz Doppler Beam Swinging wind-profiling radar at Aberystwyth, in the UK. Although the techniques used are similar to those already described in literature – i.e. the identification of multiple signal components within each spectrum and the use of radial- and time-continuity algorithms for quality-control purposes – it is shown that they must be adapted for the specific meteorological environment above Aberystwyth. In particular they need to take into account the three primary causes of unwanted signals: ground clutter, interference, and Rayleigh scatter from hydrometeors under stratiform precipitation conditions. Attention is also paid to the fact that short-period gravity-wave activity can lead to an invalidation of the fundamental assumption of the wind field remaining stationary over the temporal and spatial scales encompassed by a cycle of observation. Methods of identifying and accounting for such conditions are described. The random measurement error associated with horizontal wind components is estimated to be 3.0–4.0 m s−1 for single cycle data. This reduces to 2.0–3.0 m s−1 for data averaged over 30 min. The random measurement error associated with vertical wind components is estimated to be 0.2–0.3 m s−1. This cannot be reduced by time-averaging as significant natural variability is expected over intervals of just a few minutes under conditions of short-period gravity-wave activity.

Random measurement error in visual acuity measurement in clinical settings

2005 ◽  
Vol 83 (3) ◽  
pp. 328-332 ◽  
Author(s):  
Jaakko Leinonen ◽  
Eero Laakkonen ◽  
Leila Laatikainen
Keyword(s):  
Visual Acuity ◽  
Measurement Error ◽  
Clinical Settings ◽  
Random Measurement Error ◽  
Random Measurement

Effects of Random Measurement Error on Lung Cancer Screening Decisions

CHEST Journal ◽  
2020 ◽  
Author(s):  
Tanner J. Caverly ◽  
Xuefei Zhang ◽  
Rodney A. Hayward ◽  
Ji Zhu ◽  
Akbar K. Waljee
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Measurement Error ◽  
Lung Cancer Screening ◽  
Random Measurement Error ◽  
Random Measurement

Influence of Random Measurement Error on Estimated Rates of Headache Chronification and Remission

2013 ◽  
Vol 53 (6) ◽  
pp. 920-929 ◽  
Author(s):  
Timothy T. Houle ◽  
Dana P. Turner ◽  
Todd A. Smitherman ◽  
Donald B. Penzien ◽  
Richard B. Lipton
Keyword(s):  
Measurement Error ◽  
Random Measurement Error ◽  
Random Measurement

scholarly journals Minke whale abundance estimation from the NASS 1987 and 2001 aerial cue–counting surveys taking appropriate account of distance estimation errors

2009 ◽  
Vol 7 ◽  
pp. 95 ◽  
Author(s):  
David L Borchers ◽  
Daniel G Pike ◽  
Thorvaldur Gunnlaugsson ◽  
Gísli A Víkingsson
Keyword(s):  
Measurement Error ◽  
North Atlantic ◽  
Measurement Errors ◽  
Distance Estimation ◽  
Minke Whale ◽  
Balaenoptera Acutorostrata ◽  
Estimation Errors ◽  
Random Measurement Error ◽  
Minke Whales ◽  
Coastal Shelf

We estimate the abundance of minke whales (Balaenoptera acutorostrata) from the Icelandic coastal shelf aerial surveys carried out as part of the 1987 and 2001 North Atlantic Sightings Surveys (NASS). In the case of the 1987 survey, the probability of detecting animals at distance zero (g(0)) is very close to 1 but there is substantial random measurement error in estimating distances. To estimate abundance from these data, we use methods which assume g(0)=1 but which includea distance measurement error model. In the case of the 2001 survey, measurement errors were sufficiently small to be negligible, and we use double platform methods which estimate g(0) and assume no measurement error to estimate abundance. From the 1987 survey, we estimate abundance to be 24,532 animals, with 95% CI (13,399; 44,916). From the 2001 NASS survey data, minke whale abundance is estimated to be 43,633 animals, with 95% CI (30,148; 63,149).

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