MULTI-STAGE ESTIMATION METHOD FOR NOISE REDUCTION AND HEARING APPARATUS

2013 ◽  
Vol 133 (2) ◽  
pp. 1197
Author(s):  
Oliver Dreßler ◽  
Wolfgang Sörgel
Keyword(s):  
Noise Reduction ◽  
Estimation Method ◽  
Multi Stage ◽  
Stage Estimation

scholarly journals A Method for Systemic Risk Estimation Based on CDS Indices

2015 ◽  
Vol 8 (1) ◽  
pp. 103-124
Author(s):  
Gabriel Gaiduchevici
Keyword(s):  
Goodness Of Fit ◽  
Risk Estimation ◽  
Multivariate Time Series ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Parameter Estimates ◽  
Copula Models ◽  
Goodness Of Fit Tests ◽  
Multi Stage ◽  
Stage Estimation

AbstractThe copula-GARCH approach provides a flexible and versatile method for modeling multivariate time series. In this study we focus on describing the credit risk dependence pattern between real and financial sectors as it is described by two representative iTraxx indices. Multi-stage estimation is used for parametric ARMA-GARCH-copula models. We derive critical values for the parameter estimates using asymptotic, bootstrap and copula sampling methods. The results obtained indicate a positive symmetric dependence structure with statistically significant tail dependence coefficients. Goodness-of-Fit tests indicate which model provides the best fit to data.

scholarly journals Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method

Measurement ◽  
2019 ◽  
Vol 139 ◽  
pp. 226-235 ◽  
Author(s):  
Junchao Guo ◽  
Dong Zhen ◽  
Haiyang Li ◽  
Zhanqun Shi ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Noise Reduction ◽  
Reduction Method ◽  
Rolling Element Bearing ◽  
Multi Stage ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Feature Extraction

An Improved Noise Reduction Algorithm Based on Manifold Learning and Its Application to Signal Noise Reduction

2010 ◽  
Vol 26-28 ◽  
pp. 653-656 ◽  
Author(s):  
Guang Bin Wang ◽  
Liang Pei Huang
Keyword(s):  
Noise Reduction ◽  
Manifold Learning ◽  
Estimation Method ◽  
Vibration Signal ◽  
Reduction Algorithm ◽  
Intrinsic Dimension ◽  
Reduction Effect ◽  
Dimension Space ◽  
Space Data ◽  
Noise Reduction Algorithm

In the noise reduction algorithm based on manifold learning, phase space data may be distorted and reduction targets are chosen at random, it made efficiency and effect of noise reduction lower.To solve this problem, a improved noise reducation method (local tangent space mean reconstruction) was proposed.The process of global array by affine transformation will be replaced with mean reconstruction,and the intrinsic dimension was estimate as dimension of reduction targets by using maximum likehood estimation method, the data in addition to intrinsic dimension space will be eliminated.Noise reduction experiment to fan vibration signal with noise shows this method had better noise reduction effect.

scholarly journals Multistage noise reduction processing for vibration signal of hydropower units

2021 ◽  
Vol 2108 (1) ◽  
pp. 012008
Author(s):  
Yousong Shi ◽  
Jianzhong Zhou
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Field Testing ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Multi Stage ◽  
Actual Field ◽  
Stationary Vibration ◽  
Testing Environments

Abstract In actual field testing environments of hydropower units, unit vibration signals are often contaminated with noise. In order to obtain the real vibration signal, a multi-stage vibration signal denoise method SG-SVD-VMD is proposed for the guide bearing nonlinear and non-stationary vibration signals. And the root mean square error (RMSE) and signal to noise ratio (SNR) are used to evaluate the noise reduction ability of eight methods. The results show that the noise-canceling ability of this proposed method has improved to some extent. It can effectively suppress the noise of the hydropower units vibration signals. This method can effectively identify the shaft track and the running state of hydropower units.

scholarly journals A new estimation method for COVID-19 time-varying reproduction number using active cases

2020 ◽  
Author(s):  
A. Hasan ◽  
H. Susanto ◽  
V.R. Tjahjono ◽  
R. Kusdiantara ◽  
E.R.M. Putri ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Estimation Method ◽  
Virus Transmission ◽  
Estimation Procedure ◽  
Time Varying ◽  
State Variables ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Novel Coronavirus ◽  
Stage Estimation

AbstractWe propose a new method to estimate the time-varying effective (or instantaneous) reproduction number of the novel coronavirus disease (COVID-19). The method is based on a discrete-time stochastic augmented compartmental model that describes the virus transmission. A two-stage estimation method, which combines the Extended Kalman Filter (EKF) to estimate reported state variables (active and removed cases) and a low pass filter based on a rational transfer function to remove short term fluctuations of the reported cases, is used with case uncertainties that are assumed to follow a Gaussian distribution. Our method does not require information regarding serial intervals, which makes the estimation procedure simpler without reducing the quality of the estimate. We show that the proposed method is comparable to common approaches, e.g., age-structured and new cases based sequential Bayesian models. We also apply it to COVID-19 cases in the Scandinavian countries: Denmark, Sweden, and Norway, where we see a delay of about four days in predicting the epidemic peak.

scholarly journals Determinants of Child Height in Uganda: A Consideration of the Selection Bias Caused by Child Mortality

1995 ◽  
Vol 16 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Namkee Ahn ◽  
Abusaleh Shariff
Keyword(s):  
Child Mortality ◽  
Selection Bias ◽  
Estimation Method ◽  
Female Education ◽  
Health Transition ◽  
Maternal Characteristics ◽  
Child Height ◽  
Socio Economic Variables ◽  
Stage Estimation ◽  
Height For Age

This paper reports a methodology for analysis and presents the determinants of child height in Uganda. A two-stage estimation method that evaluated the effects of covariates on child height for age after controlling for the selection bias caused by child mortality was necessary. Important determinants of child health in Uganda are the child's and some maternal characteristics. Some environmental factors (at the levels of both community aggregate and household) have significance. The effects of mothers’ characteristics were relatively more sensitive to correction of the selection bias. In particular, mother's secondary education almost doubled its effect and became significant in determining the height of children. Overall results suggest that Uganda is facing a phase of health transition in which the effect of socio-economic variables (at both individual and community levels) are beginning to show up significantly. Although an all-round developmental effort is essential, selective interventions aiming to improve female education and, where that is difficult, extension of appropriate information through radio are likely to improve the survival and health of children.

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