scholarly journals AMBIGUITIES IN DETERMINATION OF SELF-AFFINITY IN THE AE-INDEX TIME SERIES

Fractals ◽  
2001 ◽  
Vol 09 (04) ◽  
pp. 471-479 ◽  
Author(s):  
N. W. WATKINS ◽  
M. P. FREEMAN ◽  
C. S. RHODES ◽  
G. ROWLANDS
Keyword(s):  
Time Series ◽  
Dynamic Range ◽  
Solar Wind Plasma ◽  
Linear Scaling ◽  
Ae Index ◽  
Other Information ◽  
Confinement System ◽  
Dilation Factor ◽  
Functional Forms

The interaction between the Earth's magnetic field and the solar wind plasma results in a natural plasma confinement system which stores energy. Dissipation of this energy through Joule heating in the ionosphere can be studied via the Auroral Electrojet (AE) index. The apparent broken power law form of the frequency spectrum of this index has motivated investigation of whether it can be described as fractal coloured noise. One frequently-applied test for self-affinity is to demonstrate linear scaling of the logarithm of the structure function of a time series with the logarithm of the dilation factor λ. We point out that, while this is conclusive when applied to signals that are self-affine over many decades in λ, such as Brownian motion, the slope deviates from exact linearity and the conclusions become ambiguous when the test is used over shorter ranges of λ. We demonstrate that non self-affine time series made up of random pulses can show near-linear scaling over a finite dynamic range such that they could be misinterpreted as being self-affine. In particular, we show that pulses with functional forms such as those identified by Weimer within the AL index, from which AE is partly derived, will exhibit nearly linear scaling over ranges similar to those previously shown for AE and AL. The value of the slope, related to the Hurst exponent for a self-affine fractal, seems to be a more robust discriminator for fractality, if other information is available.

New determinations of tides on the north-western Ross Ice Shelf

2020 ◽  
pp. 1-14
Author(s):  
Richard D. Ray ◽  
Kristine M. Larson ◽  
Bruce J. Haines
Keyword(s):  
Time Series ◽  
Tide Gauge ◽  
High Rate ◽  
Ice Shelf ◽  
Ross Ice Shelf ◽  
The North ◽  
Global Positioning ◽  
North Western

Abstract New determinations of ocean tides are extracted from high-rate Global Positioning System (GPS) solutions at nine stations sitting on the Ross Ice Shelf. Five are multi-year time series. Three older time series are only 2–3 weeks long. These are not ideal, but they are still useful because they provide the only in situ tide observations in that sector of the ice shelf. The long tide-gauge observations from Scott Base and Cape Roberts are also reanalysed. They allow determination of some previously neglected tidal phenomena in this region, such as third-degree tides, and they provide context for analysis of the shorter datasets. The semidiurnal tides are small at all sites, yet M2 undergoes a clear seasonal cycle, which was first noted by Sir George Darwin while studying measurements from the Discovery expedition. Darwin saw a much larger modulation than we observe, and we consider possible explanations - instrumental or climatic - for this difference.

scholarly journals Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements

Landslides ◽  
2021 ◽  
Author(s):  
Chuang Song ◽  
Chen Yu ◽  
Zhenhong Li ◽  
Veronica Pazzi ◽  
Matteo Del Soldato ◽  
...  
Keyword(s):  
Time Series ◽  
Seismic Noise ◽  
Slip Surface ◽  
Inversion Method ◽  
Subsurface Structures ◽  
Slip Surfaces ◽  
Landslide Volume ◽  
Noise Measurements ◽  
Landslide Body

AbstractInterferometric Synthetic Aperture Radar (InSAR) enables detailed investigation of surface landslide movements, but it cannot provide information about subsurface structures. In this work, InSAR measurements were integrated with seismic noise in situ measurements to analyse both the surface and subsurface characteristics of a complex slow-moving landslide exhibiting multiple failure surfaces. The landslide body involves a town of around 6000 inhabitants, Villa de la Independencia (Bolivia), where extensive damages to buildings have been observed. To investigate the spatial-temporal characteristics of the landslide motion, Sentinel-1 displacement time series from October 2014 to December 2019 were produced. A new geometric inversion method is proposed to determine the best-fit sliding direction and inclination of the landslide. Our results indicate that the landslide is featured by a compound movement where three different blocks slide. This is further evidenced by seismic noise measurements which identified that the different dynamic characteristics of the three sub-blocks were possibly due to the different properties of shallow and deep slip surfaces. Determination of the slip surface depths allows for estimating the overall landslide volume (9.18 · 107 m3). Furthermore, Sentinel-1 time series show that the landslide movements manifest substantial accelerations in early 2018 and 2019, coinciding with increased precipitations in the late rainy season which are identified as the most likely triggers of the observed accelerations. This study showcases  the potential of integrating InSAR and seismic noise techniques to understand the landslide mechanism from ground to subsurface.

scholarly journals How to correctly apply Gaussian statistics in a non-stationary climate?

2021 ◽  
Author(s):  
Reinhold Steinacker
Keyword(s):  
Time Series ◽  
Actual Data ◽  
Temperature Record ◽  
Positive Trend ◽  
Positive Temperature ◽  
Climatic Trend ◽  
Filter Methods ◽  
Periodic Time Series ◽  
Past Data

AbstractTime series with a significant trend, as is now being the case for the temperature in the course of climate change, need a careful approach for statistical evaluations. Climatological means and moments are usually taken from past data which means that the statistics does not fit to actual data anymore. Therefore, we need to determine the long-term trend before comparing actual data with the actual climate. This is not an easy task, because the determination of the signal—a climatic trend—is influenced by the random scatter of observed data. Different filter methods are tested upon their quality to obtain realistic smoothed trends of observed time series. A new method is proposed, which is based on a variational principle. It outperforms other conventional methods of smoothing, especially if periodic time series are processed. This new methodology is used to test, how extreme the temperature of 2018 in Vienna actually was. It is shown that the new annual temperature record of 2018 is not too extreme, if we consider the positive trend of the last decades. Also, the daily mean temperatures of 2018 are not found to be really extreme according to the present climate. The real extreme of the temperature record of Vienna—and many other places around the world—is the strongly increased positive temperature trend over the last years.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

scholarly journals Nonlinear analysis of magnetospheric data Part I. Geometric characteristics of the AE index time series and comparison with nonlinear surrogate data

1999 ◽  
Vol 6 (1) ◽  
pp. 51-65 ◽  
Author(s):  
G. P. Pavlos ◽  
M. A. Athanasiu ◽  
D. Kugiumtzis ◽  
N. Hatzigeorgiu ◽  
A. G. Rigas ◽  
...  
Keyword(s):  
Time Series ◽  
State Space ◽  
Null Hypothesis ◽  
Surrogate Data ◽  
Statistical Testing ◽  
Geometrical Parameters ◽  
Geometric Characteristics ◽  
Geometrical Characteristics ◽  
Ae Index ◽  
Index Time Series

Abstract. A long AE index time series is used as a crucial magnetospheric quantity in order to study the underlying dynainics. For this purpose we utilize methods of nonlinear and chaotic analysis of time series. Two basic components of this analysis are the reconstruction of the experimental tiine series state space trajectory of the underlying process and the statistical testing of an null hypothesis. The null hypothesis against which the experimental time series are tested is that the observed AE index signal is generated by a linear stochastic signal possibly perturbed by a static nonlinear distortion. As dis ' ' ating statistics we use geometrical characteristics of the reconstructed state space (Part I, which is the work of this paper) and dynamical characteristics (Part II, which is the work a separate paper), and "nonlinear" surrogate data, generated by two different techniques which can mimic the original (AE index) signal. lie null hypothesis is tested for geometrical characteristics which are the dimension of the reconstructed trajectory and some new geometrical parameters introduced in this work for the efficient discrimination between the nonlinear stochastic surrogate data and the AE index. Finally, the estimated geometric characteristics of the magnetospheric AE index present new evidence about the nonlinear and low dimensional character of the underlying magnetospheric dynamics for the AE index.

Quantitative photopyroelectric out-of-phase spectroscopy of amorphous silicon thin films deposited on crystalline silicon

1991 ◽  
Vol 69 (3-4) ◽  
pp. 317-323 ◽  
Author(s):  
Constantinos Christofides ◽  
Andreas Mandelis ◽  
Albert Engel ◽  
Michel Bisson ◽  
Gord Harling
Keyword(s):  
Thin Films ◽  
Optical Absorption ◽  
Absorption Coefficient ◽  
Dynamic Range ◽  
Crystalline Silicon ◽  
Optical Absorption Coefficient ◽  
Si Substrates ◽  
Silicon Thin Films ◽  
Background Compensation

A photopyroelectric spectrometer with real-time and(or) self-normalization capability was used for both conventional transmission and thermal-wave spectroscopic measurements of amorphous Si thin films, deposited on crystalline Si substrates. Optical-absorption-coefficient spectra were obtained from these measurements and the superior dynamic range of the out-of-phase (quadrature) photopyroelectric signal was established as the preferred measurement method, owing to its zero-background compensation capability. An extension of a photopyroelectric theoretical model was established and successfully tested in the determination of the optical absorption coefficient and the thermal diffusivity of the sample under investigation. Instrumental sensitivity limits of βt ≈ 5 × 10−3 were demonstrated.

scholarly journals Demand forecasting in Small and Medium Enterprises (SMEs) ED Aluminium Yogyakarta using causal, time series, and combined causal-time series approaches

2018 ◽  
Vol 204 ◽  
pp. 01004 ◽  
Author(s):  
Wildanul Isnaini ◽  
Andi Sudiarso
Keyword(s):  
Time Series ◽  
Small And Medium Enterprises ◽  
Demand Forecasting ◽  
Actual Condition ◽  
Inventory Data ◽  
Inventory Cost ◽  
Forecasting Method ◽  
Medium Enterprises ◽  
Better Than

ED Aluminium is the biggest Small and Medium Enterprises (SMEs) in Daerah Istimewa Yogyakarta (DIY) with 90 number of workers and 1,5 ton ingot capacity for production (Isnaini, 2014). Inventory data in December 2015 indicates that some products are overstocked (9%) and stockout (83%). This condition can happend because that SMEs still using intuition to predict the number of demand. Inventory fluctuation causes the inventory cost increases while overstock happend and lost the opportunity cost during stockout. To avoid overstock and stockout, the determination of demand with exact method is needed and one of them can be solved by forecasting method. This study aims to find the best forecasting methods of demand in 2015 using causal, time series, and combined causal-time series approces that better than the actual condition. The results of this research is the best forecasting method used to predict the number of sales in January-November 2015, that are SARIMA (3,1,1)(0,1,1)12 for WB, SARIMA (1,1,1)(1,0,1)6 for WSD, SARIMA (1,1,1)(1,1,0)6 for DE, SARIMA (2,1,1)(1,1,0)6 for PE, and SARIMA (2,1,3)(0,1,0)12 for PT.

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