Velocity and displacement of explosion-induced earth tremors derived from acceleration

1968 ◽  
Vol 58 (5) ◽  
pp. 1573-1582
Author(s):  
J. V. Poppitz
Keyword(s):  
Seismic Data ◽  
Nuclear Explosion ◽  
Mean Square ◽  
Least Mean Square ◽  
Time Zero ◽  
Acceleration Time Histories ◽  
Acceleration Data ◽  
Time Histories ◽  
Recorded Data ◽  
The Mean

Abstract Velocity and displacement time histories which are derived by integrating acceleration time histories of nuclear-explosion-induced earth tremors usually end in values that are obviously too large. It can be demonstrated that small errors, which are within the range of error one would expect from field-recorded data, could cause such unacceptable velocities and displacements. Assuming that such errors do exist but realizing that they cannot be determined exactly, two “correction” procedures are presented which adjust the acceleration data so that it is more rational. The two procedures are the least-mean-square-velocity technique (by G. V. Berg and G. W. Housner and used extensively with seismic data), which is based on the criterion of minimizing the mean of the velocity squared, and the end-time-zero technique, which depends on boundary conditions at the beginning and end of the tremor. The end-time-zero technique is recommended for use with nuclear-explosion-induced data.

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.

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 The Eigenfunction Method for Determining Displacement Time History in Structural Dynamic Analysis

2019 ◽  
Author(s):  
Bo Li ◽  
Mahesh M. Pandey ◽  
Yang Lu ◽  
Kao-Shan Dai
Keyword(s):  
Numerical Integration ◽  
Time History ◽  
Structural Dynamic ◽  
Acceleration Time ◽  
Acceleration Time Histories ◽  
Acceleration Data ◽  
Time Histories ◽  
Viable Approach ◽  
Eigenfunction Method ◽  
Displacement Information

In condition monitoring of structures, acceleration time histories are usually recorded due to ease of instrumentation. In cases where the information about a displacement time history is required, the acceleration data needs to be integrated to obtain the velocity and then the velocity needs to be integrated to obtain the displacements. However, the numerical integration of the acceleration data usually introduces an unrealistic drift component to the velocity as well as displacement. This paper presents an eigenfunction method to derive velocity and displacement time histories from a given acceleration time history. The paper analyzes displacements in two case studies using the numerical integration as well as the proposed eigenfunction method. It is concluded that the eigenfunction method is a viable approach to derive the displacement information from the acceleration data.

scholarly journals Stability Conditions for the Leaky LMS Algorithm Based on Control Theory Analysis

2016 ◽  
Vol 41 (4) ◽  
pp. 731-739 ◽  
Author(s):  
Dariusz Bismor ◽  
Marek Pawelczyk
Keyword(s):  
Point Of View ◽  
Lms Algorithm ◽  
Theoretic Approach ◽  
Mean Square ◽  
Least Mean Square ◽  
Theory Analysis ◽  
Step Size ◽  
Practical Applications ◽  
The Mean ◽  
Points Of View

AbstractThe Least Mean Square (LMS) algorithm and its variants are currently the most frequently used adaptation algorithms; therefore, it is desirable to understand them thoroughly from both theoretical and practical points of view. One of the main aspects studied in the literature is the influence of the step size on stability or convergence of LMS-based algorithms. Different publications provide different stability upper bounds, but a lower bound is always set to zero. However, they are mostly based on statistical analysis. In this paper we show, by means of control theoretic analysis confirmed by simulations, that for the leaky LMS algorithm, a small negative step size is allowed. Moreover, the control theoretic approach alows to minimize the number of assumptions necessary to prove the new condition. Thus, although a positive step size is fully justified for practical applications since it reduces the mean-square error, knowledge about an allowed small negative step size is important from a cognitive point of view.

scholarly journals Genetic Programming and Standardization in Water Temperature Modelling

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Maritza Arganis ◽  
Rafael Val ◽  
Jordi Prats ◽  
Katya Rodríguez ◽  
Ramón Domínguez ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Water Temperature ◽  
Mean Square Error ◽  
Performance Improvement ◽  
Explanatory Power ◽  
Temperature Behavior ◽  
Mean Square ◽  
Computational Tool ◽  
Recorded Data ◽  
The Mean

An application of Genetic Programming (an evolutionary computational tool) without and with standardization data is presented with the aim of modeling the behavior of the water temperature in a river in terms of meteorological variables that are easily measured, to explore their explanatory power and to emphasize the utility of the standardization of variables in order to reduce the effect of those with large variance. Recorded data corresponding to the water temperature behavior at the Ebro River, Spain, are used as analysis case, showing a performance improvement on the developed model when data are standardized. This improvement is reflected in a reduction of the mean square error. Finally, the models obtained in this document were applied to estimate the water temperature in 2004, in order to provide evidence about their applicability to forecasting purposes.

scholarly journals A NEW GENERALIZED LEAST MEAN-SQUARE ALGORITHM FOR PROCESSING NON-STATIONARY SEISMIC DATA

1987 ◽  
Vol 26 (3) ◽  
pp. 393-406
Author(s):  
A. H. COMINGUEZ
Keyword(s):  
Seismic Data ◽  
Mean Square ◽  
Least Mean Square ◽  
Least Mean Square Algorithm

Se presenta un algoritmo adaptable apropiado para deconvolver trazas, el cual está basado sobre una expresión generalizada de la técnica de mínimo error cuadrático medio. El uso del nuevo proceso se recomienda especialmente para elaborar sismogramas de reflexión sísmica que contengan reverberaciones variables en el tiempo. Mediante la aplicación del sistema adaptable los coeficientes del operador se recalculaban para cada tiempo de la señal de entrada. Tanto las características de convergencia del algoritmo como sus propiedades de estabilidad se analizan y comparan con las del algoritmo tradicional LMS. Para tal efecto se presentan ilustraciones con sismogramas sintéticos. La aplicabilidad del método expuesto parece promisoria para pruebas sísmicas en agues proco profundas.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

The mean-square generalized delayed decision feedback sequence detector

2003 ◽  
Vol 14 (3) ◽  
pp. 265-268 ◽  
Author(s):  
Maurizio Magarini ◽  
Arnaldo Spalvieri ◽  
Guido Tartara
Keyword(s):  
Decision Feedback ◽  
Mean Square ◽  
The Mean
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