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.

Design Wind Speeds Using Fast Fourier Transform

2011 ◽  
pp. 1576-1591
Author(s):  
Z.. Ismail ◽  
N. H. Ramli ◽  
Z.. Ibrahim ◽  
T. A. Majid ◽  
G. Sundaraj ◽  
...  
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Time Series Data ◽  
Absolute Magnitude ◽  
Stationary Pattern ◽  
Wind Speeds ◽  
Hidden Periodicities ◽  
Using Data

In this chapter, a study on the effects of transforming wind speed data, from a time series domain into a frequency domain via Fast Fourier Transform (FFT), is presented. The wind data is first transformed into a stationary pattern from a non-stationary pattern of time series data using statistical software. This set of time series is then transformed using FFT for the main purpose of the chapter. The analysis is done through MATLAB software, which provides a very useful function in FFT algorithm. Parameters of engineering significance such as hidden periodicities, frequency components, absolute magnitude and phase of the transformed data, power spectral density and cross spectral density can be obtained. Results obtained using data from case studies involving thirty-one weather stations in Malaysia show great potential for application in verifying the current criteria used for design practices.

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]

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