scholarly journals MODEL FOR DETERMINING CLASSIFICATION OF FILLING MATERIALS HARDENING

2020 ◽  
Vol 5 (443) ◽  
pp. 6-12
Author(s):  
Arslanov M.Z., ◽  
◽  
Mustafin S.A., ◽  
Zeinullin A.A., ◽  
Kulpeshov B.S., ◽  
...  
Keyword(s):  
Time Series ◽  
A Priori ◽  
The State ◽  
Filling Material ◽  
Filling Materials ◽  
Fundamental Possibility ◽  
Heterogeneous Features ◽  
Priori Information ◽  
Dynamic Time

This paper presents the model for solving the problem of classification of trajectories of development of states of filling material in the presence of a priori information on the trajectories of processes that have already passed the development of states of the processes. Consideration of the hardening process of stowage material as a chemical-technological process, which can be considered as a multi-parameter dynamic (time) series, allows us to determine the development class of the state of the material based on the classification of the state of the stowage. The proposed approach has established the fundamental possibility of using the proposed methodology to solve the problem of dividing given trajectories represented by time series into classes. It allows us to obtain a model that, according to formal rules, determines the classification of trajectories by sets of heterogeneous features of its state at certain time and improves the reliability of the classification.

scholarly journals Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

2018 ◽  
Vol 10 (10) ◽  
pp. 1647 ◽  
Author(s):  
Ramses Molijn ◽  
Lorenzo Iannini ◽  
Paco López Dekker ◽  
Paulo Magalhães ◽  
Ramon Hanssen
Keyword(s):  
Soil Type ◽  
A Priori ◽  
The State ◽  
Vegetation Types ◽  
A Priori Information ◽  
Vegetation Growth ◽  
Temporal Fluctuations ◽  
Priori Information ◽  
Precipitation Events

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

scholarly journals Classification of biosensor time series using dynamic time warping: applications in screening cancer cells with characteristic biomarkers

2016 ◽  
pp. 21 ◽  
Author(s):  
Balaji Panchapakesan ◽  
Patrick Trainor ◽  
Shesh Rai ◽  
Farhad Khosravi ◽  
Goetz Kloecker
Keyword(s):  
Time Series ◽  
Cancer Cells ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time

A Priori Information and Time Series Analysis. Essays in Economic Theory and Measurement

1963 ◽  
Vol 14 (2) ◽  
pp. 305
Author(s):  
Henri Guitton ◽  
F. M. Fisher ◽  
C. Kaysen
Keyword(s):  
Economic Theory ◽  
Time Series ◽  
Time Series Analysis ◽  
A Priori ◽  
A Priori Information ◽  
Series Analysis ◽  
Priori Information

Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series

2017 ◽  
Author(s):  
Hoang Anh Dau ◽  
Diego Furtado Silva ◽  
Francois Petitjean ◽  
Germain Forestier ◽  
Anthony Bagnall ◽  
...  
Keyword(s):  
Time Series ◽  
Window Width ◽  
Dynamic Time

scholarly journals Short-term forecast of fire in the premises based on modification of the Brown’s zero-order model

2021 ◽  
Vol 4 (10(112)) ◽  
pp. 52-58
Author(s):  
Boris Pospelov ◽  
Evgenіy Rybka ◽  
Olekcii Krainiukov ◽  
Oleksandr Yashchenko ◽  
Yuliia Bezuhla ◽  
...  
Keyword(s):  
Time Series ◽  
A Priori ◽  
Absolute Error ◽  
Zero Order ◽  
The State ◽  
Order Model ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Proposed Modification

This paper reports the rationale for the modification of Brown’s zero-order model, which ensures increased accuracy of the short-term fire forecast based on the use of the current measure of recurrence in the increments of the state of the air environment in the premises. A special feature of the proposed model modification is that the a priori model of the dynamics of the level of the time series of the measure of the current recurrence of increments in the air environment states determined by the dangerous factors of the fire has been modified. In this case, it is proposed that the new a priori model should take into consideration additionally the value of the current increments of the level of the studied time series. That makes it possible to negligibly reduce errors of the short-term forecast of fire in the premises without significantly complicating Brown’s zero-order model while retaining all its implementing advantages. The provided accuracy of the forecast for one step in advance on the basis of a time series of measures of the current recurrence of increments of the state of the air environment, determined from the experimental data during the ignition of alcohol and timber in a laboratory chamber, has been investigated. The considered quantitative indicators of forecast accuracy are the absolute and average errors exponentially smoothed with a parameter of 0.4. It has been established that for the proposed modification the value of the average absolute error does not exceed 0.02 %. That means that an error of the short-term forecast of a fire in the premises based on the proposed modification is an order of magnitude less than that in the case of using known Brown’s model at the smoothing parameter from an unclustered set. The results from the ignition of alcohol and timber in the laboratory chamber, in general, indicate significant advantages of using the proposed modification of Brown’s zero-order model for a short-term forecast of a fire in the premises.

The ILRS Contribution to ITRF2020

2021 ◽  
Author(s):  
Vincenza Luceri ◽  
Erricos C. Pavlis ◽  
Antonio Basoni ◽  
David Sarrocco ◽  
Magdalena Kuzmicz-Cieslak ◽  
...  
Keyword(s):  
Time Series ◽  
Tracking System ◽  
A Priori ◽  
Systematic Errors ◽  
Novel Approach ◽  
Priori Information ◽  
The Stability ◽  
The Individual ◽  
Stability And Consistency

<p>The International Laser Ranging Service (ILRS) contribution to ITRF2020 has been prepared after the re-analysis of the data from 1993 to 2020, based on an improved modeling of the data and a novel approach that ensures the results are free of systematic errors in the underlying data. This reanalysis incorporates an improved “target signature” model (CoM) that allows better separation of true systematic error of each tracking system from the errors in the model describing the target’s signature. The new approach was developed after the completion of ITRF2014, the ILRS Analysis Standing Committee (ASC) devoting almost entirely its efforts on this task. The robust estimation of persistent systematic errors at the millimeter level permitted the adoption of a consistent set of long-term mean corrections for data collected in past years, which are now applied a priori (information provided by the stations from their own engineering investigations are still taken into consideration). The reanalysis used these corrections, leading to improved results for the TRF attributes, reflected in the resulting new time series of the TRF origin and especially in the scale. Seven official ILRS Analysis Centers computed time series of weekly solutions, according to the guidelines defined by the ILRS ASC. These series were combined by the ILRS Combination Center to obtain the official ILRS product contribution to ITRF2020.</p><p>The presentation will provide an overview of the analysis procedures and models, and it will demonstrate the level of improvement with respect to the previous ILRS product series; the stability and consistency of the solution are discussed for the individual AC contributions and the combined SLR time series.</p>

A Priori Information and Time Series Analysis: Essays in Economic Theory and Measurement.

1964 ◽  
Vol 59 (306) ◽  
pp. 607
Author(s):  
Harry V. Roberts ◽  
Franklin M. Fisher
Keyword(s):  
Economic Theory ◽  
Time Series ◽  
Time Series Analysis ◽  
A Priori ◽  
A Priori Information ◽  
Series Analysis ◽  
Priori Information

scholarly journals An Algorithm for Estimating Fixed Points of Dynamical Systems from Time Series

1998 ◽  
Vol 08 (11) ◽  
pp. 2203-2213 ◽  
Author(s):  
Luis A. Aguirre ◽  
Álvaro V. P. Souza
Keyword(s):  
Time Series ◽  
Fixed Point ◽  
Dynamical Systems ◽  
Fixed Points ◽  
Prior Knowledge ◽  
A Priori ◽  
Real Data ◽  
A Priori Information ◽  
Chua's Circuit ◽  
Priori Information

This paper presents an algorithm for estimating fixed points of dynamical systems from time series. In some cases the new procedure can accurately estimate fixed points of which there is very little information in the data. Another advantage is that, although no prior knowledge is assumed, the new algorithm permits the user to employ a priori information about the system such as symmetry and the existence of a trivial fixed point. The new algorithm is tested on the Lorenz and Rössler systems and on real data taken from Chua's circuit.

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