scholarly journals Reliable Time Propagation Algorithms for PMF and RBPMF

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 261
Author(s):  
Chang-Ky Sung ◽  
Sang Jeong Lee
Keyword(s):  
Performance Improvement ◽  
Nonlinear Models ◽  
Linear Interpolation ◽  
Statistical Characteristics ◽  
Gaussian Kernel ◽  
Process Noise ◽  
Computational Load ◽  
Kernel Length ◽  
Time Propagation ◽  
Grid Interval

This paper addresses the reliable time propagation algorithms for Point Mass Filter (PMF) and Rao–Blackwellized PMF (RBPMF) for the nonlinear estimaton problem. The conventional PMF and RBPMF process the probability diffusion for the time propagation with the direct sampled-values of the process noise. However, if the grid interval is not dense enough, it fails to represent the statistical characteristics of the noise accurately so the performance might deteriorate. To overcome that problem, we propose time propagation convolution algorithms adopting Moment Matched Gaussian Kernel (MMGK) on regular grids through mass linear interpolation. To extend the dimension of the MMGK that can accurately describe the noise moments up to the kernel length, we propose the extended MMGK based on the outer tensor product. The proposed time propagation algorithms using one common kernel through the mass linear interpolation not only improve the performance of the filter but also significantly reduce the computational load. The performance improvement and the computational load reduction of the proposed algorithms are verified through numerical simulations for various nonlinear models.

scholarly journals Linear and nonlinear QSAR models of acute intravenous toxicity to mice for organic chemicals

2012 ◽  
Vol 58 (4) ◽  
pp. 357-371 ◽  
Author(s):  
O.A. Raevsky ◽  
E.A. Liplavskaya ◽  
A.V. Yarkov ◽  
O.E. Raevskaya ◽  
A.P. Worth
Keyword(s):  
Functional Group ◽  
Linear Models ◽  
Nonlinear Models ◽  
Statistical Characteristics ◽  
Organic Chemicals ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Linear And Nonlinear ◽  
Small Clusters

QSAR analysis of acute intravenous toxicity to mice for 68 monofunctional chemicals is presented. There compounds represents seven classes of organic chemicals: hydrocarbons (6 chemicals), alcohols (13), amides (22), amines (12), ethers (5), ketones (7), nitriles (3). Preliminary consideration of data for these chemicals showed that it is necessary to consider not only linear toxicity - descriptors relationships, but also nonlinear models. The linear and nonlinear QSAR models were considered for each from indicated classes of organic chemicals. Analogical models were constructed for whole subset of monofunctional chemicals. The statistical parameters and robustness of nonlinear models are essential better then statistics of linear models. Replacing a lipophilicity descriptor with molecular polarizability and H-bond ability in nonlinear models permits also to improve statistical characteristics. Clearly, if relationships between the intravenous toxicity of compounds bearing only a single functional group and lipophilicity are nonlinear, then similar relationships must be considered with compounds containing more than one functional group. To check up this idea whole set of small clusters containing structure relative compounds with few functional groups was examined from position of linear and nonlinear relationships between toxicity and lipophilicity. It was estimated in most causes advantages of nonlinear models.

scholarly journals Comparison of correlation analysis techniques for irregularly sampled time series

2011 ◽  
Vol 18 (3) ◽  
pp. 389-404 ◽  
Author(s):  
K. Rehfeld ◽  
N. Marwan ◽  
J. Heitzig ◽  
J. Kurths
Keyword(s):  
Time Series ◽  
Kernel Methods ◽  
Large Scale ◽  
Cross Correlation ◽  
Kernel Method ◽  
Linear Interpolation ◽  
Irregular Sampling ◽  
Gaussian Kernel ◽  
Robust Estimator ◽  
Mean Square Errors

Abstract. Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation) or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques. All methods have comparable root mean square errors (RMSEs) for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF) the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods. We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem δ18O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory) is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data.

IMAGE RESTORATION: THE WAVELET-BASED APPROACH

2003 ◽  
Vol 17 (01) ◽  
pp. 151-162 ◽  
Author(s):  
TERTULIEN NDJOUNTCHE ◽  
ROLF UNBEHAUEN
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Noise Reduction ◽  
Performance Improvement ◽  
Hidden Markov ◽  
Reduction Technique ◽  
Statistical Characteristics ◽  
Signal Distortion ◽  
Significant Performance ◽  
Noise Reduction Technique

Wavelet-based techniques are suitable for recovering a signal corrupted by noise. The time- and frequency-localization capabilities of wavelets provide better noise reduction and less signal distortion than conventional filtering methods. The noise reduction technique used in this paper is based on the hidden Markov model (HMM) structure, which can efficiently shape the statistical characteristics of practical data. As confirmed by numerical results, the HMM based approach provides a significant performance improvement over competing methods.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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