Новый подход к экспериментальному исследованию больших ансамблей радиотехнических генераторов со сложными связями

A new approach is proposed that allows one to investigate experimentally complex dynamics in large ensembles of coupled radio engineering generators. The approach is used for the construction of an analog-to-digital experimental setup for studying ensembles of generators with time-delayed feedback. A possibility of specifying an arbitrary architecture of couplings and different types of couplings between generators is implemented in the setup. It is shown that the complex topology of couplings and parameters of all generators can be reconstructed from their experimental time series.

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TIME SERIES ANALYSIS OF MULTIVARIATE TIME-DELAYED SYSTEMS WITH NOISE: APPLICATIONS TO LASER PHYSICS AND HUMAN MOVEMENT

Stochastics and Dynamics ◽

10.1142/s0219493705001456 ◽

2005 ◽

Vol 05 (02) ◽

pp. 297-306 ◽

Cited By ~ 5

Author(s):

T. D. FRANK ◽

R. FRIEDRICH ◽

P. J. BEEK

Keyword(s):

Time Series ◽

Stochastic Systems ◽

Evolution Equations ◽

Human Movement ◽

Analysis Method ◽

Noise Sources ◽

Delayed Systems ◽

Experimental Time Series ◽

Multivariate Systems ◽

Time Delayed Feedback

A data analysis method is proposed to reconstruct evolution equations of stochastic systems with time-delayed feedback from experimental time series. Multivariate systems involving noise sources with arbitrary correlation times are considered.

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The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

International Journal of Renewable Energy Development ◽

10.14710/ijred.7.2.139-150 ◽

2018 ◽

Vol 7 (2) ◽

pp. 139-150 ◽

Cited By ~ 1

Author(s):

Adekunlé Akim Salami ◽

Ayité Sénah Akoda Ajavon ◽

Mawugno Koffi Kodjo ◽

Seydou Ouedraogo ◽

Koffi-Sa Bédja

Keyword(s):

Time Series ◽

Wind Speed ◽

Time Series Data ◽

Empirical Method ◽

Likelihood Method ◽

Series Data ◽

New Approach ◽

Weibull Parameters ◽

Distribution Histogram ◽

Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

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A new approach for optimal offline time-series segmentation with error bound guarantee

Pattern Recognition ◽

10.1016/j.patcog.2021.107917 ◽

2021 ◽

Vol 115 ◽

pp. 107917

Author(s):

Ángel Carmona-Poyato ◽

Nicolás Luis Fernández-Garcia ◽

Francisco José Madrid-Cuevas ◽

Antonio Manuel Durán-Rosal

Keyword(s):

Time Series ◽

Error Bound ◽

New Approach ◽

Time Series Segmentation

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“Artificial Wood” Lignocellulosic Membranes: Influence of Kraft Lignin on the Properties and Gas Transport in Tunicate-Based Nanocellulose Composites

Membranes ◽

10.3390/membranes11030204 ◽

2021 ◽

Vol 11 (3) ◽

pp. 204

Author(s):

Ievgen Pylypchuk ◽

Roman Selyanchyn ◽

Tetyana Budnyak ◽

Yadong Zhao ◽

Mikael Lindström ◽

...

Keyword(s):

Knudsen Diffusion ◽

Cellulose Nanofibers ◽

Kraft Lignin ◽

Separation Processes ◽

New Approach ◽

Future Studies ◽

Different Types ◽

Molecular Sizes ◽

Fast Transport ◽

Cellulose Membranes

Nanocellulose membranes based on tunicate-derived cellulose nanofibers, starch, and ~5% wood-derived lignin were investigated using three different types of lignin. The addition of lignin into cellulose membranes increased the specific surface area (from 5 to ~50 m2/g), however the fine porous geometry of the nanocellulose with characteristic pores below 10 nm in diameter remained similar for all membranes. The permeation of H2, CO2, N2, and O2 through the membranes was investigated and a characteristic Knudsen diffusion through the membranes was observed at a rate proportional to the inverse of their molecular sizes. Permeability values, however, varied significantly between samples containing different lignins, ranging from several to thousands of barrers (10−10 cm3 (STP) cm cm−2 s−1 cmHg−1cm), and were related to the observed morphology and lignin distribution inside the membranes. Additionally, the addition of ~5% lignin resulted in a significant increase in tensile strength from 3 GPa to ~6–7 GPa, but did not change thermal properties (glass transition or thermal stability). Overall, the combination of plant-derived lignin as a filler or binder in cellulose–starch composites with a sea-animal derived nanocellulose presents an interesting new approach for the fabrication of membranes from abundant bio-derived materials. Future studies should focus on the optimization of these types of membranes for the selective and fast transport of gases needed for a variety of industrial separation processes.

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An investigation of acoustic interaction with the ocean bottom from experimental time series generated by explosive sources

The Journal of the Acoustical Society of America ◽

10.1121/1.2020973 ◽

1983 ◽

Vol 74 (S1) ◽

pp. S43-S43

Author(s):

David Knobles

Keyword(s):

Time Series ◽

Ocean Bottom ◽

Experimental Time ◽

Acoustic Interaction ◽

Experimental Time Series

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ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

The Scientific World JOURNAL ◽

10.1155/2015/205749 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Eva Volna ◽

Martin Kotyrba ◽

Hashim Habiballa

Keyword(s):

Neural Networks ◽

Time Series ◽

Ecg Signals ◽

Detection Systems ◽

Language Interpretation ◽

Different Types ◽

Time Courses ◽

Natural Language Interpretation ◽

Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

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