A Method of Dynamic Model Checking by Fusion Entropy and Residual Correlation Analysis

2008 ◽  
Author(s):  
Qiang Zhu
Keyword(s):  
Model Checking ◽  
Correlation Analysis ◽  
Dynamic Model ◽  
Residual Correlation

scholarly journals COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning

2021 ◽  
Author(s):  
Antoni Torres-Signes ◽  
M. Pilar Frías ◽  
Maria Dolores Ruiz-Medina
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Correlation Analysis ◽  
Multivariate Time Series ◽  
Quadratic Loss ◽  
Probability Density Estimation ◽  
Soft Data ◽  
Residual Correlation ◽  
Data Framework ◽  
Trigonometric Regression

Abstract A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March, 8, 2020 until May, 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.

scholarly journals Correlation Analysis and Its Application on an Asymmetry Rotor Structure with Overhang

2021 ◽  
Vol 11 (23) ◽  
pp. 11501
Author(s):  
Shun Zhong ◽  
Suxia Zhang ◽  
Jie Yang ◽  
Yanan Zhang
Keyword(s):  
Correlation Analysis ◽  
Dynamic Model ◽  
Finite Element Method Simulation ◽  
Normal Structure ◽  
Measured Signal ◽  
Alternative Approach ◽  
Prediction And Control ◽  
Overhung Rotors ◽  
And Control ◽  
Rotor Structure

Overhung rotors are widely used in the industrial field. However, compared with normal structure rotors, the prediction and control of overhung rotors cannot achieve good performance. The work aims to investigate the dynamical behaviours of an overhung rotor by means of correlation analysis, and find its possible application. In this work, based on a real type of rotor, the dynamic model of the rotor with overhang is established by means of the finite element method. Simulation of the dynamic model with different input positions and support stiffnesses is conducted. Based on the methodology of correlation analysis, by introducing a correlation parameter of a proportion of amplitude of measured signal and imbalance mass, the position which has most effect on the vibration is found. Meanwhile, an experiment on the same type of overhung rotor is carried out to validate the results. The numerical results and corresponding experimental results prove that the overhung node has the most effect on the vibration amplitudes of the measured points. Choosing the overhung node to add trial weight, the overhung rotor can be easily balanced. The theory provides an alternative approach to modal analysis which needs more knowledge of the system.

Detection and Location of Pipeline Leak Based on Multiscale Correlation

2014 ◽  
Vol 494-495 ◽  
pp. 908-912
Author(s):  
Da Hui Li ◽  
Tao Jin
Keyword(s):  
Correlation Analysis ◽  
Dynamic System ◽  
Detection Method ◽  
Actual Data ◽  
System Model ◽  
Pipeline System ◽  
Scale Analysis ◽  
Residual Correlation ◽  
Multi Scale ◽  
Multi Scale Analysis

In the paper, firstly establishes the pipeline system model based on the principles of correlation analysis, and determines the correlation characteristics of pipeline flow and pressure. Secondly multi-scale analysis is introduced into dynamic system processing. A leak detection method is shown with the multiscale correlation analysis, and is compared to the residual correlation analysis; it overcomes two the mistakes with different nature, such as too much interference or loss of leakage signal details. Finally through the actual data verification, the methods suitable for mutation signal, but slowly changing signals need to find another method.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

Short-term volume and turgor regulation in yeast

2008 ◽  
Vol 45 ◽  
pp. 147-160 ◽  
Author(s):  
Jörg Schaber ◽  
Edda Klipp
Keyword(s):  
Dynamic Model ◽  
Volume Change ◽  
Volume Regulation ◽  
Time Course ◽  
Osmotic Shock ◽  
Intracellular Concentration ◽  
Short Term ◽  
Hydrodynamic Property ◽  
Turgor Regulation ◽  
Thermodynamic Framework

Volume is a highly regulated property of cells, because it critically affects intracellular concentration. In the present chapter, we focus on the short-term volume regulation in yeast as a consequence of a shift in extracellular osmotic conditions. We review a basic thermodynamic framework to model volume and solute flows. In addition, we try to select a model for turgor, which is an important hydrodynamic property, especially in walled cells. Finally, we demonstrate the validity of the presented approach by fitting the dynamic model to a time course of volume change upon osmotic shock in yeast.

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