Application of on-line Adaptive Weighted Fusion in Mine Gas Measurement System

2012 ◽  
Vol 239-240 ◽  
pp. 1395-1398
Author(s):  
Yan Ju Wang ◽  
Li Kun Yang ◽  
Yu Tian Wang
Keyword(s):  
Measurement System ◽  
Mean Square ◽  
Least Mean Square ◽  
Fusion Algorithm ◽  
Important Indicator ◽  
Weighted Fusion ◽  
Environmental Monitoring System ◽  
The Mean ◽  
Mine Gas ◽  
Mean Square Errors

In mine environmental monitoring system, the concentration of mine gas is an important indicator. Aiming at the redundant information from multi-gas sensors in the measurement system, adaptive weighted fusion algorithm was presented. Using this algorithm, it was unnecessary to be aware of any pre-defined knowledge about these datas measured by the sensors. That the algorithm could adjust the fused sensor’s weight in time according to the variation in sensors’ variances makes the mean square error minimal. It was also proved theoretically that this fusion algorithm is linear and unbiased, in respect of the least mean square errors. Simulation results showed that this fusion algorithm is effective and the result of fused data is superior to the mean estimate algorithm in respect of accuracy and fault tolerance.

scholarly journals Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

2017 ◽  
Vol 4 (9) ◽  
pp. 160889 ◽  
Author(s):  
Liyan Xu ◽  
Fabing Duan ◽  
Xiao Gao ◽  
Derek Abbott ◽  
Mark D. McDonnell
Keyword(s):  
Stochastic Resonance ◽  
Recursive Algorithm ◽  
Signal Strength ◽  
Distinct Form ◽  
Mean Square ◽  
Least Mean Square ◽  
Complete Knowledge ◽  
The Mean ◽  
Suprathreshold Stochastic Resonance ◽  
Over Time

Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman–LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.

scholarly journals Realization of the Primary Terrestrial Reference Frame

1991 ◽  
Vol 127 ◽  
pp. 108-115
Author(s):  
W. Kosek ◽  
B. Kołaczek
Keyword(s):  
Reference Frame ◽  
Geographic Distribution ◽  
Terrestrial Reference Frame ◽  
Mean Square ◽  
Minimum Value ◽  
Collocation Points ◽  
Station Coordinates ◽  
The Mean ◽  
Transformation Parameters ◽  
Mean Square Errors

AbstractThe PTRF is based on 43 sites with 64 SSC collocation points with the optimum geographic distribution, which were selected from all stations of the ITRF89 according to the criterion of the minimum value of the errors of 7 parameters of transformation. The ITRF89 was computed by the IERS Terrestrial Frame Section in Institut Geographique National - IGN and contains 192 VLBI and SLR stations (points) with 119 collocation ones. The PTRF has been compared with the ITRF89. The errors of the 7 parameters of transformation between the PTRF and 18 individual SSC as well as the mean square errors of station coordinates are of the same order as those for the ITRF89. The transformation parameters between the ITRF89 and the PTRF are negligible and their errors are of the order of 3 mm.

Mathematical-Statistical Methods for the Analysis of Aerial Triangulation Adjustment Errors Using a Computer Program

1975 ◽  
Vol 29 (2) ◽  
pp. 175-188
Author(s):  
M. Mosaad Allam
Keyword(s):  
Computer Program ◽  
Frequency Interval ◽  
Mean Square ◽  
Fisher Criterion ◽  
Statistical Parameters ◽  
Sample Distribution ◽  
Development Section ◽  
The Mean ◽  
Kolmogorov Criterion ◽  
Mean Square Errors

In practice, photogrammetrists use a single statistic reliability interval criterion, based on the mean square errors, to judge the accuracy of adjustment of photogrammetric blocks. Even in some cases, if the practical and theoretical distributions of frequency interval agree, such a test does not make it possible to establish the closeness of their convergence nor the degree of their difference. In other words, to get a complete picture of the character of the distribution of errors in the adjusted photogrammetric blocks, it is insufficient to investigate any single statistic. In the Research and Development Section of the Topographical Survey Directorate, a computer program (SABA) has been designed to analyze the errors of photogrammetric block adjustments, compute various statistical parameters and check the sample distribution using Kolmogorov criterion. Based on the decision taken, the correspondence between the empirical and theoretical distribution series are checked using the criterion χ2. The program divides the adjusted block to make a comparative evaluation of accuracies in the different sub-blocks. In this case, in addition to Kolmogorov and χ2 tests, the program checks the reliability intervals of the means and mean square errors of the samples and uses Fisher criterion ‘F’ to check the hypothesis of the equality of dispersion. SABA is coded in Fortran IV and Compass for the CDC CYBER 74 and requires a central memory of 28K decimal works. SABA is the acronym for Statistical Analysis of Block Adjustment.

Extrapolation Problem for Continuous Time Periodically Correlated Isotropic Random Fields

2017 ◽  
Vol 19 ◽  
pp. 1-23
Author(s):  
Iryna Golichenko ◽  
Oleksand Masyutka ◽  
Mikhail Moklyachuk
Keyword(s):  
Random Field ◽  
Spectral Characteristics ◽  
Mean Square ◽  
Spectral Densities ◽  
Optimal Linear ◽  
Extrapolation Problem ◽  
Time Argument ◽  
The Mean ◽  
Mean Square Errors ◽  
Isotropic Random

The problem of optimal linear estimation of functionals depending on the unknown values of a random fieldζ(t,x), which is mean-square continuous periodically correlated with respect to time argumenttє R and isotropic on the unit sphere Sn with respect to spatial argumentxєSn. Estimates are based on observations of the fieldζ(t,x) +Θ(t,x) at points (t,x) :t< 0;xєSn, whereΘ(t,x) is an uncorrelated withζ(t,x) random field, which is mean-square continuous periodically correlated with respect to time argumenttє R and isotropic on the sphereSnwith respect to spatial argumentxєSn. Formulas for calculating the mean square errors and the spectral characteristics of the optimal linear estimate of functionals are derived in the case of spectral certainty where the spectral densities of the fields are exactly known. Formulas that determine the least favourable spectral densities and the minimax (robust) spectral characteristics are proposed in the case where the spectral densities are not exactly known while a class of admissible spectral densities is given.

Huit campagnes d’observations de Saturne a l’Astrolabe Danjon a San Fernando

1978 ◽  
Vol 48 ◽  
pp. 471-478
Author(s):  
M. Sanchez
Keyword(s):  
Theoretical Development ◽  
Mean Square ◽  
Mean Values ◽  
San Fernando ◽  
The Mean ◽  
Mean Square Errors ◽  
Right Ascension

Abstract:This paper contains an analysis of Saturn observations with Danjon astrolabe at San Fernando. These observations were obtained during eight winter campaigns (1970-1978). Table 1 gives the mean values for each of the quantities Δα and Δδ (astrolabe - American Ephemeris) and the mean square errors. Figure 1 to 8 shows the results (right ascension and declination) and the corresponding smoothing curves. The accuracy of these curves is also given in table 1. The analysis of the values Δα and Δδ seem to show that there are differences, between the theoretical development of ephemeris and the observations, of periodical character.

Realization of the Precise Point Positioning (PPP) technique and its accuracy

2017 ◽  
Vol 927 (9) ◽  
pp. 42-49
Author(s):  
A.V. Voytenko
Keyword(s):  
Precise Point Positioning ◽  
Russian Language ◽  
Convergence Time ◽  
Practical Implementation ◽  
Mean Square ◽  
Dual Frequency ◽  
Russian Scientist ◽  
The Mean ◽  
Point Positioning ◽  
Mean Square Errors

The article notes that the replacement of the English name «Precise Point Positioning» (PPP) in Russian-language sources is possible using the term «accurate differential positioning» (TDP) technique. The author proposes to use both terms. This article contains information about the practical implementation of the PPP in the on-line service. The author has analyzed the research on the accuracy of PPP foreign and domestic experts and scholars. The author analyzed the data about the convergence time for PPP solutions. These data belong to another Russian scientist. The results of evaluating the accuracy of the PPP of different scientists led to the next. The author of this article gave the mean square errors topocentric coordinates of the geodetic points. The coordinates of the points must be obtained by dual-frequency GPS-measurements for a period of 24 hours with the help of PPP. The author proposed a formula for the calculation of the mean square error of the spatial position of geodetic point, if its position is obtained in the processing of dual-frequency GPS-observations of less than 24 hours. The article written conclusions about the features, defects and PPP development.

scholarly journals Convergence Analysis for the SMC-MeMBer and SMC-CBMeMBer Filters

2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
Feng Lian ◽  
Chen Li ◽  
Chongzhao Han ◽  
Hui Chen
Keyword(s):  
Monte Carlo ◽  
Convergence Analysis ◽  
Sequential Monte Carlo ◽  
Mean Square ◽  
Convergence Results ◽  
Mean Square Convergence ◽  
The Mean ◽  
Mean Square Errors

The convergence for the sequential Monte Carlo (SMC) implementations of the multitarget multi-Bernoulli (MeMBer) filter and cardinality-balanced MeMBer (CBMeMBer) filters is studied here. This paper proves that the SMC-MeMBer and SMC-CBMeMBer filters, respectively, converge to the true MeMBer and CBMeMBer filters in the mean-square sense and the corresponding bounds for the mean-square errors are given. The significance of this paper is in theory to present the convergence results of the SMC-MeMBer and SMC-CBMeMBer filters and the conditions under which the two filters satisfy mean-square convergence.

scholarly journals Multichannel Active Noise Control Based on Filtered-x Affine Projection-Like and LMS Algorithms with Switching Filter Selection

2019 ◽  
Vol 9 (21) ◽  
pp. 4669 ◽  
Author(s):  
Ángel A. Vázquez ◽  
Eduardo Pichardo ◽  
Juan G. Avalos ◽  
Giovanny Sánchez ◽  
Hugo M. Martínez ◽  
...  
Keyword(s):  
Steady State ◽  
Computational Cost ◽  
Lms Algorithm ◽  
Alternative Methods ◽  
Mean Square ◽  
Least Mean Square ◽  
Affine Projection ◽  
Mean Square Errors ◽  
Switching Filter ◽  
Filter Selection

Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

Application of Adaptive Weighted Fusion in Robot Multi-Sensor Ranging System

2012 ◽  
Vol 443-444 ◽  
pp. 442-446
Author(s):  
De Qiang Jiao ◽  
Li Li Pang ◽  
Yu Juan Teng
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Experimental Results ◽  
Mean Square ◽  
Infrared Sensors ◽  
Fusion Algorithm ◽  
Weighted Fusion ◽  
Highly Effective

Ultrasonic seniors and infrared sensors are utilized in the robot to range and locate. The precision of the whole system is often impaired. In order to improve the system’s precision, the method of adaptive weighted fusion algorithm basing on minimum mean square error is employed in this paper. According to the experimental results, this method is highly effective in the further improvement of the system ranging precision.

scholarly journals A Novel Fast Image Stitching Method Based on the Combination of SURF and Cell

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qing An ◽  
Xijiang Chen ◽  
Shusen Wu
Keyword(s):  
Image Fusion ◽  
Power Function ◽  
Chromatic Aberration ◽  
Image Stitching ◽  
Feature Points ◽  
Matching Accuracy ◽  
Fusion Algorithm ◽  
Weighted Fusion ◽  
The Mean ◽  
Traditional Image

The traditional image stitching method has some shortcomings such as double shadow, chromatic aberration, and stitching. In view of this, this paper proposes a power function-weighted image stitching method that combines SURF optimization and improved cell acceleration. First, the method uses the cosine similarity to preliminarily judge the similarity of the feature points and then uses the two-way consistency mutual selection to filter the feature point pairs again. Simultaneously, some incorrect matching points in the reverse matching are eliminated. Finally, the method uses the MSAC algorithm to perform fine matching. Then, the power function-weighted fusion algorithm is used to calculate the weight of the center point. The power function weight of the accelerated cell is used to perform the final image fusion. The experimental results show that the matching accuracy rate of the proposed method is about 11 percentage points higher than the traditional SURF algorithm, and the time is reduced by about 1.6 s. In the image fusion stage, this paper first selects images with different brightness, angles, resolutions, and scales to verify the effectiveness of the proposed method. The results show that the proposed method effectively solves the ghosting and stitching seams. Comparing with the traditional fusion algorithm, the time consumption is reduced by at least 2 s, the mean square error is reduced by about 1.32%∼1.48%, and the information entropy is improved by about 0.98%∼1.70%. The proposed method has better performance in matching accuracy and fusion effect and has better stitching quality.

Sign in / Sign up

Export Citation Format

Share Document