Descriptor based on spectraln peaks correlograms

We have presented in this report a new peak descriptor SD based on the lag plot and discussed its relationship to the NBD. The SD, defined as the slope of a linear regression model constructed through the data samples belonging to a time sequence, shows to be a good candidate for describing sinusoidal and noise peak classes. A proper choice of data set is crucial for discerning correctly between the peak classes. The SD makes use only of the spectral peak shape and consequently this information can be explained by just two parameters, namely the root mean square BWrms and absolute bandwidth L. This is similar to the way the shape factor explains the filter spectral shape in the circuit theory. We have shown that BWrms and L, initially defined in the spectrum domain, hold relationship to the time duration of the data set and its sampling rate respectivelyVI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Descriptor based on spectraln peaks correlograms

We have presented in this report a new peak descriptor SD based on the lag plot and discussed its relationship to the NBD. The SD, defined as the slope of a linear regression model constructed through the data samples belonging to a time sequence, shows to be a good candidate for describing sinusoidal and noise peak classes. A proper choice of data set is crucial for discerning correctly between the peak classes. The SD makes use only of the spectral peak shape and consequently this information can be explained by just two parameters, namely the root mean square BWrms and absolute bandwidth L. This is similar to the way the shape factor explains the filter spectral shape in the circuit theory. We have shown that BWrms and L, initially defined in the spectrum domain, hold relationship to the time duration of the data set and its sampling rate respectivelyVI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Data-driven generation of synthetic wind speeds: a comparative study

The increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.This work was partly supported by the Research Foundation Flanders (FWO) [grant number 74213/K231719N]

Descriptor based on spectraln peaks correlograms

We have presented in this report a new peak descriptor SD based on the lag plot and discussed its relationship to the NBD. The SD, defined as the slope of a linear regression model constructed through the data samples belonging to a time sequence, shows to be a good candidate for describing sinusoidal and noise peak classes. A proper choice of data set is crucial for discerning correctly between the peak classes. The SD makes use only of the spectral peak shape and consequently this information can be explained by just two parameters, namely the root mean square BWrms and absolute bandwidth L. This is similar to the way the shape factor explains the filter spectral shape in the circuit theory. We have shown that BWrms and L, initially defined in the spectrum domain, hold relationship to the time duration of the data set and its sampling rate respectivelyVI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Influence of Application of Hottels Zonal Model and Six-Flux Model of Thermal Radiation on Numerical Simulations Results of Pulverized Coal Fired Furnace

Difference of results of numerical simulation of pulverized coal fired furnace when mathematical models contain various radiation models has been described in the paper. Two sets of numerical simulations of pulverized coal fired furnace of 210 MWe power boiler have been performed. One numerical simulation has contained Hottels zonal model, whereas the other numerical simulation has contained six-flux model. Other details of numerical simulations have been identical. The influence of radiation models has been examined through comparison of selected variables (gas-phase temperature, oxygen concentration, and absorbed radiative heat rate of surface zones of rear and right furnace walls), selected global parameters of furnace operation (total absorbed heat rate by all furnace walls and furnace exit gas-phase temperature). Computation time has been compared as well. Spatially distributed variables have been compared through maximal local differences and mean differences. Maximal local difference of gas-phase temperature has been 8.44%. Maximal local difference of absorbed radiative heat rate of the surface zones has been almost 80.0%. Difference of global parameters of furnace operation has been expressed in percents of value obtained by mathematical model containing Hottels zonal model and has not been bigger than 7.0%. Computation time for calculation of 1000 iterations has been approximately the same. Comparison with other radiation models is necessary for assessment of differences

Litholog generation with the StratigrapheR package and signal decomposition for cyclostratigraphic purposes

To establish an astronomical time scale, it is useful to perform a visual inspection of the lithological evolution, together with proxies record. It allows to have a clear understanding of the expression of Milankovitch cyclicity. However, performing such an inspection can be challenging due to the large amount of data and high spatial resolution required to perform a sound cyclostratigraphic analysis. To address this problem we present the StratigrapheR package in the free software environment R (https://CRAN.R-project.org/package=StratigrapheR). This package is designed to generate lithologs and to deal with stratigraphical information. StratigrapheR takes advantage of the repetitive nature of sections used for cyclostratigraphic purposes to automate as much as possible the litholog generation while still allowing the visualisation of discrepancies (e.g. lateral variations of thickness and irregular stratification boundaries) and of any particular features (e.g. fossil content, sedimentary structures, stratigraphical intervals, etc.). The package furthermore allows to import vector graphics as SVG files, to export the lithologs in PDF and SVG form, to manipulate stratigraphic interval data and to visualise oriented palaeomagnetic data. The lithologs made in StratigrapheR can be plotted at high resolution directly along the results of time series filtering and/or decomposition methods. This is particularly useful for high-frequency components inspection. Empirical Mode Decomposition (EMD) in particular can be used for visual inspection. It allows to compute different components -also called modes- by iteratively subtracting from the signal the mean envelope curves, defined by local minima and maxima. In the isolated modes, each contiguous local extrema are separated by a zero-crossing. This property furthermore allows the determination of instantaneous frequency and amplitude, using for instance the Hilbert transform. EMD typically decomposes standard cyclostratigraphic time series in maximum 15 modes, which allows all the instantaneous ratios of the modes frequencies - taken two by two- to be calculated in a realistic computational time. These instantaneous ratios of frequencies can then be used to find the signature of Milankovitch cycles by identifying relatively higher ratios distributions at values characteristic of the orbital cycles. Specific ratios intervals can then be isolated and linked back to the parts of the signal that are at their source

EuReCa ONE—27 Nations, ONE Europe, ONE Registry A prospective one month analysis of out-of-hospital cardiac arrest outcomes in 27 countries in Europe

AbstractIntroductionThe aim of the EuReCa ONE study was to determine the incidence, process, and outcome for out of hospital cardiac arrest (OHCA) throughout Europe.MethodsThis was an international, prospective, multi-centre one-month study. Patients who suffered an OHCA during October 2014 who were attended and/or treated by an Emergency Medical Service (EMS) were eligible for inclusion in the study. Data were extracted from national, regional or local registries.ResultsData on 10,682 confirmed OHCAs from 248 regions in 27 countries, covering an estimated population of 174 million. In 7146 (66%) cases, CPR was started by a bystander or by the EMS. The incidence of CPR attempts ranged from 19.0 to 104.0 per 100,000 population per year. 1735 had ROSC on arrival at hospital (25.2%), Overall, 662/6414 (10.3%) in all cases with CPR attempted survived for at least 30 days or to hospital discharge.ConclusionThe results of EuReCa ONE highlight that OHCA is still a major public health problem accounting for a substantial number of deaths in Europe.EuReCa ONE very clearly demonstrates marked differences in the processes for data collection and reported outcomes following OHCA all over Europe. Using these data and analyses, different countries, regions, systems, and concepts can benchmark themselves and may learn from each other to further improve survival following one of our major health care events