AN EXAMPLE OF THE APPLICATION OF AUTO-CORRELATION AND SPECTRAL ANALYSIS IN MINING EXPLORATION *

1974 ◽  
Vol 22 (4) ◽  
pp. 747-760 ◽  
Author(s):  
D. D. SARMA
Keyword(s):  
Spectral Analysis ◽  
Auto Correlation ◽  
Correlation And Spectral Analysis ◽  
Mining Exploration

scholarly journals Application of time-series analyses to the hydrological functioning of an Alpine karstic system: the case of Bange-L’Eau-Morte

2004 ◽  
Vol 8 (6) ◽  
pp. 1051-1064 ◽  
Author(s):  
T. Mathevet ◽  
M. l. Lepiller ◽  
A. Mangin
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Analysis ◽  
Noise Analysis ◽  
Recession Curves ◽  
Time Series Analyses ◽  
Correlation And Spectral Analysis ◽  
Wavelet Analyses ◽  
Dependent Behaviour ◽  
Hydrological Functioning

Abstract. This paper analyses the hydrological functioning of the Bange-L’Eau-Morte karstic system using classical and original techniques, recession curves, correlation and spectral analyses, noise analysis and wavelet analyses. The main characteristics that can be deduced are the recession coefficients, the dynamic volume of storage, the response time of the system, the quickflow and baseflow components and the snowmelt characteristics. The non-stationary and timescale-dependent behaviour of the system is studied and particular features of the runoff are shown. The step-by-step use of these different techniques provides a general methodology applicable to different karstic systems to provide quantifiable and objective criteria for differentiation and comparison of karstic systems. Keywords: karstic hydrology, Bauges mountains, recession curves, correlation and spectral analysis, wavelet analysis, snowmelt

APPLICATION OF KOTELNIKOV’S THEOREM TO ANALYSIS OF RANDOM PROCESSES

2019 ◽  
pp. 28-34
Author(s):  
O. V. Goriunov ◽  
S. V. Slovtsov
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Random Process ◽  
Spectral Characteristics ◽  
Random Processes ◽  
Statistical Characteristics ◽  
Signal Spectrum ◽  
The Fourier Transform ◽  
The Stability ◽  
Correlation And Spectral Analysis

Analysis of many dynamic tasks arising in engineering applications is associated with the construction of spectral characteristics. However, the application of spectral analysis to random oscillations, which in most cases describe real processes (technical, technological, etc.), has a number of features and limitations associated, in particular, with the anconvergence of the Fourier transform. The substantiated metrological evaluation of the spectra associated with the reliability of the applied results is complicated by the absence of a rigorous mathematical model of a random process. The above remarks were solved on the basis of application of Kotelnikov's theorem at decomposition of a random process on known eigenfunctions. The obtained decomposition allowed us to obtain a number of results in the field of correlation and spectral analysis of random processes: the stability of the ACF and the relationship with the statistical characteristics of the implementation is proved, the orthogonal decomposition of the random process in the form of a continuous function is presented, which allows us to consider the evaluation and analyze the characteristics of the realizations without the use of a fast Fourier transform; the natural relationship between ACF and spectral density for a time-limited signal is shown, and the symmetric form of recording the signal spectrum is justified.

Comparison of Stochastic Identification Techniques for Dynamic Modelling of Plastics Extrusion Processes

1978 ◽  
Vol 192 (1) ◽  
pp. 299-309 ◽  
Author(s):  
A. K. Kochhar ◽  
J. Parnaby
Keyword(s):  
Spectral Analysis ◽  
Maximum Likelihood ◽  
Computer Control ◽  
Dynamic Modelling ◽  
Generalized Least Squares ◽  
Extrusion Process ◽  
Correlation And Spectral Analysis ◽  
Comparison Of The Results ◽  
Identification Techniques ◽  
Plastics Extrusion

The important plastics extrusion process is briefly described and the difficulties of modelling the process from physical considerations are outlined. A number of stochastic process identification techniques, i.e. correlation, spectral analysis, generalized least squares, instrumental variable, correlation matching, maximum likelihood and Box-Jenkins algorithms are briefly reviewed. The results of experimental work carried out on a laboratory plastics extruder, using random perturbations in screw speed, are presented. From a comparison of the results of different identification methods, it is suggested that although correlation and spectral analysis techniques can help in improving the understanding of the process mechanisms, the type of models best suited for high level feed-forward computer control are of the Box-Jenkins and maximum likelihood structural forms.

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