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Number of results: 6
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Abstract

The article presents results of the influence of the GMDH (Group Method of Data Handling) neural network input data preparation method on the results of predicting corrections for the Polish timescale UTC(PL). Prediction of corrections was carried out using two methods, time series analysis and regression. As appropriate to these methods, the input data was prepared based on two time series, ts1 and ts2. The implemented research concerned the designation of the prediction errors on certain days of the forecast and the influence of the quantity of data on the prediction error. The obtained results indicate that in the case of the GMDH neural network the best quality of forecasting for UTC(PL) can be obtained using the time-series analysis method. The prediction errors obtained did not exceed the value of ± 8 ns, which confirms the possibility of maintaining the Polish timescale at a high level of compliance with the UTC.

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Authors and Affiliations

Wiesław Miczulski
Łukasz Sobolewski
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Abstract

This paper summarizes the activity of the chosen Polish geodetic research teams in 2019–2022 in the fields of the Earth rotation and geodynamics. This publication has been prepared for the needs of the presentation of Polish scientists’ activities on the 28th International Union of Geodesy and Geodynamics General Assembly, Berlin, Germany. The part concerning Earth rotation is mostly focused on the estimation of the geophysical excitation of polar motion using data from Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions, and on the improvement of the determination of Earth rotation parameters based on the Satellite Laser Ranging (SLR), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), and Global Navigation Satellite System (GNSS) satellite techniques. The part concerning geodynamics is focused on geodetic time series analysis for geodynamical purposes and monitoring of the vertical ground movements induced by mass transport within the Earth’s system, monitoring of the crustal movements using GNSS and newly applied Interferometric Synthetic Aperture Radar (InSAR), discussing the changes of the landslides and its monitoring using geodetic methods as well as investigations of seismic events and sea-level changes with geodetic methods. Finally, the recent research activities carried out by Polish scientists in the international projects is presented.
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Authors and Affiliations

Janusz Bogusz
1
ORCID: ORCID
Aleksander Brzeziński
2 3
ORCID: ORCID
Walyeldeen Godah
4
ORCID: ORCID
Jolanta Nastula
3
ORCID: ORCID

  1. Military University of Technology, Warsaw, Poland
  2. Warsaw University of Technology, Warsaw, Poland
  3. Space Research Centre, Polish Academy of Sciences, Warsaw, Poland
  4. Institute of Geodesy and Cartography, Centre of Geodesy and Geodynamics, Warsaw, Poland
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Abstract

With the developing technology and increasing construction, the importance of structural observations, which are of great significance in disaster management, has increased. Geodetic methods have been preferred in recent years due to their high accuracy and ease of use in Structural Health Monitoring (SHM) Surveys. In this study, harmonic oscillation tests have been carried out on a shake table to determine the usability of the Single Base and the Network Real-Time Kinematic (RTK) Global Navigation Satellite Systems (GNSS) method in SHM studies. It is aimed to determine the harmonic movements of different amplitudes and frequencies created by the shake table with 20 Hz multi-GNSS equipment. The amplitude and frequency values of the movements created using Fast Fourier Transform (FFT) and Time Series Analysis have been calculated. The precision of the analysis results has been determined by comparing the LVDT (Linear Variable Differential Transformer) data, which is the position sensor of the shake table, with the GNSS data. The advantages of the two RTK methods over each other have been determined using the calculated amplitude and frequency differences. As a result of all experiments, it has been determined that network and single base RTK GNSS methods effectively monitor structural behaviours and natural frequencies.
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Authors and Affiliations

Güldane Oku Topal
1
ORCID: ORCID
Burak Akpinar
1
ORCID: ORCID

  1. Yildiz Technical Universty, Istanbul, Turkey
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Abstract

Statistical Process Control (SPC) based on the well known Shewhart control charts, is widely used in contemporary manufacturing

industry, including many foundries. However, the classic SPC methods require that the measured quantities, e.g. process or product

parameters, are not auto-correlated, i.e. their current values do not depend on the preceding ones. For the processes which do not obey this

assumption the Special Cause Control (SCC) charts were proposed, utilizing the residual data obtained from the time-series analysis. In the

present paper the results of application of SCC charts to a green sand processing system are presented. The tests, made on real industrial

data collected in a big iron foundry, were aimed at the comparison of occurrences of out-of-control signals detected in the original data

with those appeared in the residual data. It was found that application of the SCC charts reduces numbers of the signals in almost all cases

It is concluded that it can be helpful in avoiding false signals, i.e. resulting from predictable factors.

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Authors and Affiliations

M. Perzyk
A. Rodziewicz
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Abstract

The aim of the paper was an attempt at applying the time-series analysis to the control of the melting process of grey cast iron in production conditions. The production data were collected in one of Polish foundries in the form of spectrometer printouts. The quality of the alloy was controlled by its chemical composition in about 0.5 hour time intervals. The procedure of preparation of the industrial data is presented, including OCR-based method of transformation to the electronic numerical format as well as generation of records related to particular weekdays. The computations for time-series analysis were made using the author’s own software having a wide range of capabilities, including detection of important periodicity in data as well as regression modeling of the residual data, i.e. the values obtained after subtraction of general trend, trend of variability amplitude and the periodical component. The most interesting results of the analysis include: significant 2-measurements periodicity of percentages of all components, significance 7-day periodicity of silicon content measured at the end of a day and the relatively good prediction accuracy obtained without modeling of residual data for various types of expected values. Some practical conclusions have been formulated, related to possible improvements in the melting process control procedures as well as more general tips concerning applications of time-series analysis in foundry production.

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Authors and Affiliations

M. Perzyk
A. Rodziewicz
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Abstract

The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in

production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data

concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The

computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the

real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of

important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was

labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results

of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the

predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data.

The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease

fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.

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Authors and Affiliations

A. Rodziewicz
M. Perzyk

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