Comparison of Polar Motion Data from the 1980 Project Merit Short Campaign

AbstractPolar motion coordinates obtained during the Merit short campaign by various techniques, as well as their variations, have been compared directly and through a smoothing procedure. In the direct comparison, values at selected dates were differenced in all combinations and the rms values computed. Through the smoothing procedure the amplitudes of the annual and Chandler motions as well as the centers of the polhodes were estimated and compared with predicted values based on the previous six years of polar motion data. The results are somewhat inconclusive, mainly due to the shortness of the campaign and the (practically) real time data reduction techniques. To resolve the issue of possible systematic differences, the proposed MERIT main campaign seems an absolute necessity.

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A Guide to Uncertainty Analysis of Hot-Wire Data

Journal of Fluids Engineering ◽

10.1115/1.3243096 ◽

1984 ◽

Vol 106 (2) ◽

pp. 181-186 ◽

Cited By ~ 87

Author(s):

S. Yavuzkurt

Keyword(s):

Uncertainty Analysis ◽

Real Time ◽

Data Reduction ◽

Plate Boundary ◽

Time Averaging ◽

Hot Wire ◽

Time Data ◽

Uncertainty Distribution ◽

Reduction Techniques ◽

Real Time Data

Uncertainty analysis of hot-wire data in turbulent flows are made for time-averaging and real-time data reduction techniques. Equations of uncertainty are derived in general form for every step of data taking and evaluation. Numerical examples are supplied for two-dimensional flat plate boundary layer data. Results show that the real-time data reduction techniques generally have a lower and much more uniform uncertainty distribution than the time-averaging techniques (whose uncertainties are much higher for the near-wall data).

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Machine Learning for Real-time Data Reduction in Cloud of Things

2020 2nd International Conference on Computer and Information Sciences (ICCIS) ◽

10.1109/iccis49240.2020.9257645 ◽

2020 ◽

Author(s):

Atheer Alahmed ◽

Amal Alrasheedi ◽

Maha Alharbi ◽

Norah Alrebdi ◽

Marwan Aleasa ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Data Reduction ◽

Time Data ◽

Real Time Data

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Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems

Big Data Management and the Internet of Things for Improved Health Systems - Advances in Healthcare Information Systems and Administration ◽

10.4018/978-1-5225-5222-2.ch004 ◽

2018 ◽

pp. 52-64

Author(s):

Masoud Hemmatpour ◽

Renato Ferrero ◽

Filippo Gandino ◽

Bartolomeo Montrucchio ◽

Maurizio Rebaudengo

Keyword(s):

Real Time ◽

High Speed ◽

Time Data ◽

Reduction Techniques ◽

Fall Prediction ◽

Real Time Data ◽

Data Volume ◽

Prediction Systems ◽

Unintentional Falls ◽

Health Service Costs

Unintentional falls are a frequent cause of hospitalization that mostly increases health service costs due to injuries. Fall prediction systems strive to reduce injuries and provide fast help to the users. Typically, such systems collect data continuously at a high speed through a device directly attached to the user. Whereas such systems are implemented in devices with limited resources, data volume is significantly important. In this chapter, a real-time data analyzer and reducer is proposed in order to manage the data volume of fall prediction systems.

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