Dynamics of the cardiovascular autonomic regulation during orthostatic challenge is more relaxed in women

2018 ◽  
Vol 63 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Sina Reulecke ◽  
Sonia Charleston-Villalobos ◽  
Andreas Voss ◽  
Ramón González-Camarena ◽  
Jesús González-Hermosillo ◽  
...  
Keyword(s):  
Time Series ◽  
Sympathetic Activity ◽  
Low Frequency ◽  
Short Term ◽  
Auto Correlation ◽  
Complete Test ◽  
Auto Correlation Function ◽  
Head Up Tilt ◽  
Cardiovascular Autonomic Regulation ◽  
Sustained Increase

AbstractLinear dynamic analysis of cardiovascular and respiratory time series was performed in healthy subjects with respect to gender by shifted short-term segments throughout a head-up tilt (HUT) test. Beat-to-beat intervals (BBI), systolic (SYS) and diastolic (DIA) blood pressure and respiratory interval (RESP) time series were acquired in 14 men and 15 women. In time domain (TD), the descending slope of the auto-correlation function (ACF) (BBI_a31cor) was more pronounced in women than in men (p<0.05) during the HUT test and considerably steeper (p<0.01) at the end of orthostatic phase (OP). The index SYS_meanNN was slightly but significantly lower (p<0.05) in women during the complete test, while higher respiratory frequency and variability (RESP_sdNN) were found in women (p<0.05), during 10–20 min after tilt-up. In frequency domain (FD), during baseline (BL), BBI-normalized low frequency (BBI_LFN) and BBI_LF/HF were slightly but significantly lower (p<0.05), while normalized high frequency (BBI_HFN) was significantly higher in women. These differences were highly significant from the first 5 min after tilt-up (p<0.01) and highly significant (p<0.001) during 10–14 min of OP. Findings revealed that men showed instantaneously a pronounced and sustained increase in sympathetic activity to compensate orthostatism. In women, sympathetic activity was just increased slightly with delayed onset without considerably affecting sympatho-vagal balance.

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

Analysis of Surface Topography Characteristics in Electrochemical Mechanical Finishing

2010 ◽  
Vol 126-128 ◽  
pp. 658-663 ◽  
Author(s):  
Ze Fei Wei ◽  
Wen Ji Xu ◽  
Gui Bing Pang ◽  
Xu Yue Wang
Keyword(s):  
Surface Topography ◽  
Low Frequency ◽  
Surface Profile ◽  
Frequency Component ◽  
Original Surface ◽  
Auto Correlation ◽  
Before And After ◽  
Auto Correlation Function ◽  
The Mean ◽  
Quality Material

In this paper, surface topography characteristics of electrochemical mechanical finishing (ECMF) for steel was investigated. The scanning electron microscopy (SEM) was used to observe the surface topography. And the microcosmic geometry parameters were measured by Talysurf SLI2000. Compared with original surface, the surface topography characteristics of the workpiece machined by ECMF have been analyzed with altitude density function (ADF) and auto correlation function (ACF). The results show that there exist periodicity component in surface profile before and after finishing. The auto correlation curves of ECMF surface have a smaller average period compared with grinding surface. The low-frequency component and the mean ripple peak distance of original surface profile are obviously decreased. Furthermore, the ripples and peak density are increased, and the surface roughness Ra is decreased from 0.231μm to 0.023μm. The results indicate that surface quality, material ratio of the profile and wear resistance machined by ECMF are improved obviously.

scholarly journals Information Decomposition in the Frequency Domain: a New Framework to Study Cardiovascular and Cardiorespiratory Oscillations

2020 ◽  
Author(s):  
Luca Faes ◽  
Riccardo Pernice ◽  
Gorana Mijatovic ◽  
Yuri Antonacci ◽  
Jana Cernanova Krohova ◽  
...  
Keyword(s):  
Time Series ◽  
Spectral Measures ◽  
Short Term ◽  
Information Theoretic ◽  
Frequency Bands ◽  
Information Measures ◽  
Head Up Tilt ◽  
Specific Frequency ◽  
Domain Information ◽  
New Framework

SummaryWhile cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows to retrieve amounts of information shared by the processes within specific frequency bands which are otherwise not detectable by time-domain information measures, as well as coupling features which are not detectable by spectral measures. Then, it is applied to the time series of heart period, systolic and diastolic arterial pressure and respiration variability measured in healthy subjects monitored in the resting supine position and during head-up tilt. We show that the spectral measures of unique, redundant and synergistic information shared by these variability series, integrated within specific frequency bands of physiological interest, reflect the mechanisms of short term regulation of cardiovascular and cardiorespiratory oscillations and their alterations induced by the postural stress.

Analysis of Surface Morphology Characteristics in Mechanical Finishing and Electrochemical Mechanical Finishing

2010 ◽  
Vol 44-47 ◽  
pp. 2681-2685
Author(s):  
Heng Zhou Zhen
Keyword(s):  
Surface Morphology ◽  
Low Frequency ◽  
Surface Profile ◽  
Frequency Component ◽  
Original Surface ◽  
Auto Correlation ◽  
Before And After ◽  
Auto Correlation Function ◽  
Quality Material ◽  
Morphology Characteristics

In this paper, surface morphology characteristics of mechanical finishing (MF) and electrochemical mechanical finishing (ECMF) for 45# steel was investigated. The microcosmic geometry parameters were measured by Surfcorder SE-3H. Compared with original surface, the surface morphology characteristics of the workpiece machined by MF and ECMF have been analyzed with auto correlation function (ACF). The results show that there exist periodicity component in surface profile before and after finishing. The auto correlation curves of ECMF surface have a smaller average period compared with MF surface. The low-frequency component and the mean ripple peak distance of original surface profile are obviously decreased. Furthermore, the ripples and peak density are increased, ECFM have a higher efficiency compared with MF, and the surface roughness Ra is decreased from 0.231μm to 0.032μm in 1 minute. The results indicate that surface quality, material ratio of the profile and wear resistance machined by ECMF are improved obviously.

scholarly journals Generalized Relational Tensors For Irregularly Sampled Time Series: Methods To Store, Re-generate, And Predict

2021 ◽  
Author(s):  
Vasilii Gromov ◽  
Anastasia Necheporenko ◽  
Andrei Gaisin ◽  
Ilya Volkov ◽  
Stanislav Diner
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Chaotic Time Series ◽  
Optimization Techniques ◽  
Initial Time ◽  
Auto Correlation ◽  
Initial Time Series ◽  
Auto Correlation Function ◽  
Estimate Quality

Abstract The paper deals with a generalized relational tensor, a novel discrete structure to store information about a time series, and algorithms (1) to fill the structure, (2) to generate a time series from the structure, and (3) to predict a time series, for both regularly and irregularly sampled time series. The algorithms combine the concept of generalized z-vectors with ant colony optimization techniques. In order to estimate quality of the storing/re-generating procedure, a difference between characteristics of the initial and regenerated time series is used. The structure allows working with a multivariate time series, with an irregularly sampled time series, and with a number of series as well. For chaotic time series, a difference between characteristics of the initial time series (the highest Lyapunov exponent, the auto-correlation function) and those of the time series re-generated from a structure is used to assess the effectiveness of the algorithms in question. The approach has shown fairly good results for periodic and benchmark chaotic time series and satisfactory results for real-world chaotic data.

The Correlation and Spectrum Research on Cylindrical Surface Lapping Machined with Abrasive Jet Finishing Restricted by Grinding Wheel

2008 ◽  
Vol 389-390 ◽  
pp. 320-325
Author(s):  
Feng Liu ◽  
Ya Dong Gong ◽  
Yu Qiao Shan ◽  
Guang Qi Cai
Keyword(s):  
Correlation Function ◽  
Cross Correlation ◽  
Low Frequency ◽  
Grinding Wheel ◽  
Small Range ◽  
Auto Correlation ◽  
Surface Contour ◽  
Power Spectral ◽  
Finishing Process ◽  
Auto Correlation Function

With the correlation in stochastic process applied to the experimental results, the surface during grinding and further lapping with abrasive jet finishing (AJF) restricted by grinding wheel was investigated with respect to auto correlation function (ACF) , cross correlation function (CCF) and power spectral density (PSD) analysis. The results indicated that AJF made the surface contour formed periodicity in a small range and removed fluctuation of the surface contour in low frequency greatly. The average spacing of the surface contour decreased and the machined surfaces changed from continuous parallel micro-groove and plough to randomly distributed discontinuous micro-pit with the increase of machining circles. The surface texture became fine and surface roughness was obviously improved. Furthermore, the isotropy surface and uniformity veins both parallel and perpendicular machining direction was attained by the finishing process to improve greatly the wearable capability of the workpiece.

scholarly journals EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Menglong Wu ◽  
Yicheng Ye ◽  
Nanyan Hu ◽  
Qihu Wang ◽  
Huimin Jiang ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Signal Sequence ◽  
Low Frequency ◽  
Arma Model ◽  
Short Term ◽  
Svm Model ◽  
Safety Production ◽  
Mining Safety ◽  
Production Situation

In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.

scholarly journals Usage of time series models in assessing the value of demand in the insurance market

2019 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Nermin Saad Mohamed Fahmy ◽  
Sharifah Alrajhi
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Financial Institutions ◽  
Insurance Market ◽  
Time Series Models ◽  
Moving Averages ◽  
Quarterly Data ◽  
Auto Correlation ◽  
Auto Correlation Function ◽  
Auto Regression

Insurance can affect savings and investment as insurance and reinsurance companies are among the most important financial institutions in the country's economy. The researcher will analyze, based on the partial auto correlation function and experimenting with several models of auto regression and moving averages, and testing the most appropriate model to describe the quarterly data on the number of polices.

scholarly journals Short-term prediction of ionospheric parameters based on auto-correlation analysis

1999 ◽  
Vol 42 (1) ◽  
Author(s):  
I. Kutiev ◽  
P. Muhtarov ◽  
L. R. Cander ◽  
M. F. Levy
Keyword(s):  
Prediction Method ◽  
Error Statistics ◽  
Short Term ◽  
Auto Correlation ◽  
Term Prediction ◽  
Auto Correlation Function ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Weighting Coefficients ◽  
Auto Regressive Model

A prediction method based on a simple auto-regressive model has been developed for short-term prediction of ionospheric characteristics. The method determines the auto-correlation function for the hourly values of the parameter of interest, using the time series from the previous 25 days. The resulting weighting coefficients can then be used to forecast future values of the parameter. The method has been applied to predict f0F2 up to 24 h ahead for stations Uppsala, Slough, Poitiers and Sofia. Error statistics are presented

scholarly journals TRAFFIC COLLISION TIME SERIES ANALYSIS (A CASE STUDY OF KARAJ–QAZVIN FREEWAY)

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 941-948 ◽  
Author(s):  
R. Sanayei ◽  
A. R. Vafainejad ◽  
J. Karami ◽  
H. Aghamohammadi
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Prediction Models ◽  
Ar Model ◽  
Periodic Pattern ◽  
Accident Prediction ◽  
Auto Correlation ◽  
Accident Data ◽  
Auto Correlation Function ◽  
Simulation Results

Abstract. The application of Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) in recent years has been improved in analyzing big traffic data, modelling traffic collisions and decreasing processing time in finding collision patterns. Accident prediction models for short and long time can help in designing and programming traffic plans and decreasing road accidents. Based on the above details, in this paper, the Karaj-Qazvin highway accident data (1097 samples) and its patterns from 2009 to 2013 have been analyzed using time series methods.In the first step, using auto correlation function (ACF) and partial auto correlation function (PACF), the rank of time series model supposed to be autoregressive (AR) model and in the second stage, its coefficients were found. In order to extract the accident data, ArcGIS software was run. Furthermore, MATLAB software was used to find the model rank and its coefficients. In addition, Stata SE software was used for statistical analysis. The simulation results showed that on the weekly scale, based on the trend and periodic pattern of data, the model type and rank, ACF and PACF values, an accurate weekly hybrid model (time series and PACF) of an accident can be created. Based on simulation results, the investigated model predicts the number of accident using two prior week data with the Root Mean Square Error (RMSE) equal to three.

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