Real-Time Solution of Time-Varying Yau Filtering Problems via Direct Method and Gaussian Approximation

2019 ◽  
Vol 64 (4) ◽  
pp. 1648-1654 ◽  
Author(s):  
Xiuqiong Chen ◽  
Ji Shi ◽  
Stephen S.-T. Yau
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Direct Method ◽  
Time Varying ◽  
Time Solution

Study on real-time thermal–mechanical–frictional coupling characteristics of ball bearings based on the inverse thermal network method

2021 ◽  
pp. 135065012199356
Author(s):  
Tie-Jun Li ◽  
Meng-Zhuo Wang ◽  
Chun-Yu Zhao
Keyword(s):  
Real Time ◽  
Thermal Contact Resistance ◽  
Thermal Contact ◽  
Time Varying ◽  
Ball Bearings ◽  
The Real ◽  
Thermal Network ◽  
Network Method ◽  
Thermal Network Method ◽  
Coupling Characteristics

The real-time thermal–mechanical–frictional coupling characteristics of bearings are critical to the accuracy, reliability, and life of entire machines. To obtain the real-time dynamic characteristics of ball bearings, a novel model to calculate point contact dynamic friction in mixed lubrication was firstly presented in this work. The model of time-varying thermal contact resistance under fit between the ring and the ball, between the ring and the housing, and between the ring and the shaft was established using the fractal theory and the heat transfer theory. Furthermore, an inverse thermal network method with time-varying thermal contact resistance was presented. Using these models, the real-time thermal–mechanical–frictional coupling characteristics of ball bearings were obtained. The effectiveness of the presented models was verified by experiment and comparison.

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

Direct Method for Time-Varying Nonlinear Filtering Problems

2017 ◽  
Vol 53 (2) ◽  
pp. 630-639 ◽  
Author(s):  
Xiuqiong Chen ◽  
Xue Luo ◽  
Stephen S.-T. Yau
Keyword(s):  
Nonlinear Filtering ◽  
Direct Method ◽  
Time Varying

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

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