Time-series differential pressure fluctuations of a flooding regime: A preliminary experimental results investigation on a 1/30 down-scaled PWR hot leg geometry

2021 ◽  
Author(s):  
Achilleus Hermawan Astyanto ◽  
Yusuf Rahman ◽  
Akbar Yuga Adhikara Medha ◽  
Deendarlianto ◽  
Indarto
Keyword(s):  
Time Series ◽  
Pressure Fluctuations ◽  
Differential Pressure ◽  
Experimental Results ◽  
Flooding Regime

DYNAMICAL ESTIMATION OF CALENDAR EFFECTS

2006 ◽  
Vol 06 (01) ◽  
pp. L7-L15
Author(s):  
ALEXANDROS LEONTITSIS
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Noise Level ◽  
Nonlinear Dynamical System ◽  
Main Tool ◽  
Experimental Results ◽  
Accurate Estimation ◽  
Nonlinear Dynamical ◽  
Correlation Integral ◽  
Calendar Effects

The paper introduces a method for estimation and reduction of calendar effects from time series, which their fluctuations are governed by a nonlinear dynamical system and additive normal noise. Calendar effects can be considered deviations of the distribution(s) of particular group(s) of observations that have a common characteristic related to the calendar. The concept of this method is the following: since the calendar effects are not related to the dynamics of the time series, the accurate estimation and reduction will result a time series with a smaller amount of noise level (i.e. more accurate dynamics). The main tool of this method is the correlation integral, due to its inherit capability of modeling both the dynamics and the additive normal noise. Experimental results are presented on the Nasdaq Cmp. index.

A Radiated-Noise Simulator for Underwater Target Based on Vector Time Series

2014 ◽  
Vol 644-650 ◽  
pp. 4023-4026
Author(s):  
Yang Ju ◽  
Xin Yong Wang
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Model ◽  
Experimental Results ◽  
Small Probability ◽  
Radiated Noise ◽  
True Value ◽  
Underwater Target ◽  
Vector Time Series

The vector time series model for simulating the underwater target radiated-noise is developed in this paper. Experimental results show that the true value lying outside the confidence interval would be a small probability event.

Using API Call Sequences for IoT Malware Classification Based on Convolutional Neural Networks

2021 ◽  
Vol 31 (04) ◽  
pp. 587-612
Author(s):  
Qianguang Lin ◽  
Ni Li ◽  
Qi Qi ◽  
Jiabin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Comprehensive Evaluation ◽  
Optimal Solution ◽  
Classification Problem ◽  
Experimental Results ◽  
Series Data ◽  
Chi Square ◽  
Malware Classification ◽  
Model Training

Internet of Things (IoT) devices built on different processor architectures have increasingly become targets of adversarial attacks. In this paper, we propose an algorithm for the malware classification problem of the IoT domain to deal with the increasingly severe IoT security threats. Application executions are represented by sequences of consecutive API calls. The time series of data is analyzed and filtered based on the improved information gains. It performs more effectively than chi-square statistics, in reducing the sequence lengths of input data meanwhile keeping the important information, according to the experimental results. We use a multi-layer convolutional neural network to classify various types of malwares, which is suitable for processing time series data. When the convolution window slides down the time sequence, it can obtain higher-level positions by collecting different sequence features, thereby understanding the characteristics of the corresponding sequence position. By comparing the iterative efficiency of different optimization algorithms in the model, we select an algorithm that can approximate the optimal solution to a small number of iterations to speed up the convergence of the model training. The experimental results from real world IoT malware sample show that the classification accuracy of this approach can reach more than 98%. Overall, our method has demonstrated practical suitability for IoT malware classification with high accuracies and low computational overheads by undergoing a comprehensive evaluation.

Chaos in blood flow control in genetic and renovascular hypertensive rats

1991 ◽  
Vol 261 (3) ◽  
pp. F400-F408 ◽  
Author(s):  
K. P. Yip ◽  
N. H. Holstein-Rathlou ◽  
D. J. Marsh
Keyword(s):  
Time Series ◽  
Blood Flow ◽  
Phase Space ◽  
Renal Artery ◽  
Initial Conditions ◽  
Pressure Fluctuations ◽  
Deterministic Chaos ◽  
Tubuloglomerular Feedback ◽  
Hypertensive Rats ◽  
Statistical Measures

Hydrostatic pressure and flow in renal proximal tubules oscillate at 30–40 mHz in normotensive rats anesthetized with halothane. The oscillations originate in tubuloglomerular feedback, a mechanism that provides local blood flow regulation. Instead of oscillations, spontaneously hypertensive rats (SHR) have aperiodic tubular pressure fluctuations; the pattern is suggestive of deterministic chaos. Normal rats made hypertensive by clipping one renal artery had similar aperiodic tubular pressure fluctuations in the unclipped kidney, and the fraction of rats with irregular fluctuations increased with time after the application of the renal artery clip. Statistical measures of deterministic chaos were applied to tubular pressure data. The correlation dimension, a measure of the dimension of the phase space attractor generating the time series, indicated the presence of a low-dimension strange attractor, and the largest Lyapunov exponent, a measure of the rate of divergence in phase space, was positive, indicating sensitivity to initial conditions. These time series therefore satisfy two criteria of deterministic chaos. The measures were the same in SHR as in rats with renovascular hypertension. Since two different models of hypertension displayed similar dynamics, we suggest that chaotic behavior is a common feature of renal vascular control in the natural history of the disease.

scholarly journals Explore Big Data and Forecasting Future Values using Univariate Arima Model in R

2018 ◽  
Vol 7 (2.7) ◽  
pp. 1107 ◽  
Author(s):  
S Sagar Imambi ◽  
P Vidyullatha ◽  
M V.B.T.Santhi ◽  
P Haran Babu
Keyword(s):  
Time Series ◽  
Big Data ◽  
Real Time ◽  
Arima Model ◽  
Electronic Equipment ◽  
Experimental Results ◽  
Business Organization ◽  
Huge Amount ◽  
Diagnostic Data ◽  
Using Data

Electronic equipment and sensors spontaneously create diagnostic data that needs to be stocked and processed in real time. It is not only difficult to keep up with huge amount of data but also reasonably more challenging to analyze it.  Big Data is providing many opportunities for organizations to evolve their processes they try to move beyond regular BI activities like using data to populate reports. Predicting future values is one of the requirements for any business organization. The experimental results shows that time series model with ARIMA (3,0,1)(1,0,0) is best fitted for predicting future values of the sales. 

Numerical Calculation of Pressure Fluctuations in the Volute of a Centrifugal Fan

2005 ◽  
Vol 128 (2) ◽  
pp. 359-369 ◽  
Author(s):  
Rafael Ballesteros-Tajadura ◽  
Sandra Velarde-Suárez ◽  
Juan Pablo Hurtado-Cruz ◽  
Carlos Santolaria-Morros
Keyword(s):  
Fluid Dynamics ◽  
Pressure Fluctuations ◽  
Three Dimensional ◽  
Power Spectra ◽  
Operating Conditions ◽  
Experimental Results ◽  
Time Dependent ◽  
Centrifugal Fan ◽  
Wall Pressure Fluctuations ◽  
Good Agreement

In this work, a numerical model has been applied in order to obtain the wall pressure fluctuations at the volute of an industrial centrifugal fan. The numerical results have been compared to experimental results obtained in the same machine. A three-dimensional numerical simulation of the complete unsteady flow on the whole impeller-volute configuration has been carried out using the computational fluid dynamics code FLUENT®. This code has been employed to calculate the time-dependent pressure both in the impeller and in the volute. In this way, the pressure fluctuations in some locations over the volute wall have been obtained. The power spectra of these fluctuations have been obtained, showing an important peak at the blade passing frequency. The amplitude of this peak presents the highest values near the volute tongue, but the spatial pattern over the volute extension is different depending on the operating conditions. A good agreement has been found between the numerical and the experimental results.

scholarly journals Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Sun-Hee Kim ◽  
Christos Faloutsos ◽  
Hyung-Jeong Yang
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Regression Model ◽  
Random Coefficients ◽  
Experimental Results ◽  
Ar Model ◽  
Forecasting Method ◽  
Eeg Data ◽  
Future Value ◽  
Auto Regression

Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with−1and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately.

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