scholarly journals Experimental studies on emergency processes in real time Impulse Power Conversion Systems

2016 ◽  
Vol 12 (2) ◽  
pp. 41-46 ◽  
Author(s):  
Evgeny A Godovnikov ◽  
Ruslan T Usmanov
Keyword(s):  
Time Series ◽  
Real Time ◽  
Power Conversion ◽  
Experimental Studies ◽  
Experimental System ◽  
Experimental Setup ◽  
Analysis Of Time Series

This article describes an experimental system for studying the dynamics of impulse power conversion systems. The peculiarity of this experimental setup is due to the implementation of the analysis of time series of synchronized in real-time through the use of microcontrollers STMicroelectronics and National Instruments firms.

scholarly journals Scale-space analysis of time series in circulatory research

2006 ◽  
Vol 291 (6) ◽  
pp. H3012-H3022 ◽  
Author(s):  
Kim Erlend Mortensen ◽  
Fred Godtliebsen ◽  
Arthur Revhaug
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Real Time ◽  
Repeated Measures ◽  
Multiple Scales ◽  
Visual Presentation ◽  
Scale Space ◽  
Statistical Tool ◽  
Data Points ◽  
Analysis Of Time Series

Statistical analysis of time series is still inadequate within circulation research. With the advent of increasing computational power and real-time recordings from hemodynamic studies, one is increasingly dealing with vast amounts of data in time series. This paper aims to illustrate how statistical analysis using the significant nonstationarities (SiNoS) method may complement traditional repeated-measures ANOVA and linear mixed models. We applied these methods on a dataset of local hepatic and systemic circulatory changes induced by aortoportal shunting and graded liver resection. We found SiNoS analysis more comprehensive when compared with traditional statistical analysis in the following four ways: 1) the method allows better signal-to-noise detection; 2) including all data points from real time recordings in a statistical analysis permits better detection of significant features in the data; 3) analysis with multiple scales of resolution facilitates a more differentiated observation of the material; and 4) the method affords excellent visual presentation by combining group differences, time trends, and multiscale statistical analysis allowing the observer to quickly view and evaluate the material. It is our opinion that SiNoS analysis of time series is a very powerful statistical tool that may be used to complement conventional statistical methods.

Some Preliminary Results of an Analysis of Coordinates Time Series of RTK Network LitPos

2017 ◽  
Author(s):  
Karolis Galinauskas ◽  
Rimvydas Baniulis ◽  
Eimuntas Paršeliunas ◽  
Marius Petniunas
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Positioning System ◽  
Post Processing ◽  
Coordinate Time ◽  
Preliminary Results ◽  
Coordinate Time Series ◽  
Series Analysis ◽  
Analysis Of Time Series

LitPOS (Lithuanian Positioning System), the network of permanent reference GNSS stations, became operational in July 2007. It provides data both for real-time and post-processing applications. Coordinate time series were obtained from LitPOS reprocessing data for years 2008–2014. The reprocessing was done using Bernese 5.2 software. The analysis of time series was done with Matlab routine “GITSA” (GPS Interactive Time Series Analysis). In this paper the review of obtained coordinate time series will be given, in addition error tracking and some of the results will be assessed. An analysis of discontinuities and outliers with possible problem solutions will be provided. As a result more consistent accumulated multi-year solution is presented.

Supervised and unsupervised machine-learning for automated quality control of environmental sensor data

2021 ◽  
Author(s):  
Julius Polz ◽  
Lennart Schmidt ◽  
Luca Glawion ◽  
Maximilian Graf ◽  
Christian Werner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Quality Control ◽  
Real Time ◽  
Experimental System ◽  
Training Data ◽  
Sensor Data ◽  
Small Scale ◽  
Environmental Sensor ◽  
Erroneous Data

<p>We can observe a global decrease of well maintained weather stations by meteorological services and governmental institutes. At the same time, environmental sensor data is increasing through the use of opportunistic or remote sensing approaches. Overall, the trend for environmental sensor networks is strongly going towards automated routines, especially for quality-control (QC) to provide usable data in near real-time. A common QC scenario is that data is being flagged manually using expert knowledge and visual inspection by humans. To reduce this tedious process and to enable near-real time data provision, machine-learning (ML) algorithms exhibit a high potential as they can be designed to imitate the experts actions. </p><p>Here we address these three common challenges when applying ML for QC: 1) Robustness to missing values in the input data. 2) Availability of training data, i.e. manual quality flags that mark erroneous data points. And 3) Generalization of the model regarding non-stationary behavior of one  experimental system or changes in the experimental setup when applied to a different study area. We approach the QC problem and the related issues both as a supervised and an unsupervised learning problem using deep neural networks on the one hand and dimensionality reduction combined with clustering algorithms on the other.</p><p>We compare the different ML algorithms on two time-series datasets to test their applicability across scales and domains. One dataset consists of signal levels of 4000 commercial microwave links distributed all over Germany that can be used to monitor precipitation. The second dataset contains time-series of soil moisture and temperature from 120 sensors deployed at a small-scale measurement plot at the TERENO site “Hohes Holz”.</p><p>First results show that supervised ML provides an optimized performance for QC for an experimental system not subject to change and at the cost of a laborious preparation of the training data. The unsupervised approach is also able to separate valid from erroneous data at reasonable accuracy. However, it provides the additional benefit that it does not require manual flags and can thus be retrained more easily in case the system is subject to significant changes. </p><p>In this presentation, we discuss the performance, advantages and drawbacks of the proposed ML routines to tackle the aforementioned challenges. Thus, we aim to provide a starting point for researchers in the promising field of ML application for automated QC of environmental sensor data.</p>

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

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