Analysis of Electrochemical Noise by Power Spectral Density Applied to Corrosion Studies: Maximum Entropy Method or Fast Fourier Transform?

1998 ◽  
Vol 145 (8) ◽  
pp. 2780-2786 ◽  
Author(s):  
U. Bertocci ◽  
J. Frydman ◽  
C. Gabrielli ◽  
F. Huet ◽  
M. Keddam
Keyword(s):  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Maximum Entropy ◽  
Power Spectral Density ◽  
Maximum Entropy Method ◽  
Electrochemical Noise ◽  
Entropy Method ◽  
Power Spectral ◽  
Corrosion Studies

scholarly journals Pewilayahan Siklus Suhu Permukaan Laut di Perairan Indonesia Menggunakan Metode Power Spectral Density (PSD)

PRISMA FISIKA ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 85
Author(s):  
Syarifah Resha Fadziella ◽  
Yoga Satria Putra ◽  
Arie Antasari Kushadiwijayanto
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Power Spectral Density ◽  
Power Spectral

Penelitian tentang siklus suhu permukaan laut (SPL) dominan terbesar pertama dan kedua di Perairan Indonesia telah dilakukan menggunakan metode Power Spectral Density (PSD) berdasarkan data time series SPL selama 40 tahun (1979-2018). Dari analisis yang dilakukan siklus dominan terbesar pertama adalah siklus 12.15 bulan (annual) dan siklus 6 bulan (semiannual). Siklus 12.15 bulan (annual) cenderung berada di perairan Utara dan perairan Selatan Indonesia sedangkan siklus 6 bulan (semiannual) cenderung berada di kawasan ekuator kecuali perairan Ekuatorial Samudra Hindia. Kemudian, siklus dominan terbesar kedua memiliki beragam periode seperti siklus setengah tahun (semiannual), tahunan (annual) dan siklus antar tahunan (interannual). Siklus setengah tahun (semiannual) berada di perairan Utara dan perairan Selatan Indonesia, siklus tahunan (annual) berada di kawasan ekuator, dan siklus antar tahunan (interannual) berada di perairan Barat Sumatera, Selat Makassar, Teluk Tomini, Laut Halmahera, dan di perairan Papua.Kata Kunci : Suhu permukaan laut, Perairan Indonesia, Power Spectral Density (PSD), dan Fast Fourier Transform (FFT).

scholarly journals An approach to spectral analysis of psychologically influenced speech

2017 ◽  
Vol 7 (1.2) ◽  
pp. 66
Author(s):  
Bhagyalaxmi Jena ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Information Content ◽  
Power Spectral Density ◽  
Speech Signal ◽  
Power Spectral ◽  
Normal Speech

The significant part of any speech signal lies in the information content and the emotions contents like stress or fatigue at a particular period of time. The classification of various types of stress and their effects are defined here. To analyze the changes in stressed speech than that of the normal speech, a database has been created which has investigated the stress among students during the examination in our college. In this paper, the spectral analysis of speech is done where emphasis has been given in the parameters like Fast Fourier Transform (FFT), spectrogram and Power Spectral Density (PSD). These parameters have been simulated using MATLAB codes. The comparison of the mentioned parameters is also done between a normal speech and a psychological stressed speech.

Computers and infrared spectroscopy: evolution and revolution

1991 ◽  
Vol 69 (11) ◽  
pp. 1781-1785 ◽  
Author(s):  
D. J. Moffatt ◽  
J. K. Kauppinen ◽  
H. H. Mantsch
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Key Words ◽  
Maximum Entropy ◽  
Input Data ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
History Of ◽  
The Fourier Transform ◽  
The Relationship

A brief history of the relationship between computer and infrared spectroscopist is given with emphasis on the use of the Fourier transform. Subsequently, an algorithm is developed that may be used to devise an objective Fourier self-deconvolution procedure which depends only on the input data and is not subject to the biases that are often introduced in such subjective techniques. Key words: deconvolution, Fourier transform, maximum entropy method.

Twenty-Four-Hour Power Spectral Analysis by Maximum Entropy Method of Blood Pressure in Primary Hyperaldosteronism

1993 ◽  
Vol 2 (3) ◽  
pp. 189-196 ◽  
Author(s):  
Franco Veglio ◽  
Giuliano Pinna ◽  
Remo Melchio ◽  
Franco Rabbia ◽  
Paola Molino ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Power Spectral Analysis ◽  
Entropy Method ◽  
Primary Hyperaldosteronism ◽  
Power Spectral

Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals

1997 ◽  
Vol 122 (1) ◽  
pp. 12-19 ◽  
Author(s):  
S. V. Kamarthi ◽  
S. R. T. Kumara ◽  
P. H. Cohen
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fourier Transform ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Flank Wear ◽  
Wavelet Representation ◽  
Power Spectral ◽  
Frequency Components ◽  
Ae Signals

This paper investigates a flank wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density of AE signals in turning is sensitive to gradually increasing flank wear. In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. To overcome some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated. This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals for flank wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing flank wear than the Fourier representation. [S1087-1357(00)71401-8]

Sign in / Sign up

Export Citation Format

Share Document