Browsing by Author "Astuti, W."
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Item Adaptive Short Time Fourier Transform (STFT) Analysis of seismic electric signal (SES): A comparison of Hamming and rectangular window(IEEE, 2012-09-23) Astuti, W.; Sediono, W.; Aibinu, A. M.; Akmeliawati, R.; Salami, Momoh-Jimoh E.Seismic electric signal (SES) is one of features for predicting earthquakes (EQs) because of its significant changes in the amplitude of the signal prior to the earthquake. This paper presents detailed analysis of SES recorded prior to earthquake that occurred in Greece in the period from January 1, 2008 to June 30, 2008. During this period of time 5 earthquakes were recorded with magnitudes greater than 6R. In this analysis STFT involving adaptively sliding window technique is used, in which Hamming and rectangular window functions are applied and compared. The comparison shows that Hamming window gives better results in analyzing the first significantly changes of SES prior to the EQ. The application of Hamming window resulted in less rippled spectrum shape which is more suitable to be used in characterizing the SES.Item Animal sound activity detection using multi-class support vector machines(IEEE, 2005-05-17) Astuti, W.; Aibinu, A. M.; Salami, Momoh-Jimoh E.; Akmelawati, R.; Muthalif, Asan G. A.On March 11th 2011, the whole world was taken aback by another tragic experience of Tsunami triggered by a magnitude 9.8 earthquake in Japan. Just few days after that, on March 25th 2011, another earthquake of magnitude 6.8 hit Myanmar deaths and destructions. Despite the loss incurred on properties and human being, available data show that relatively few numbers of animals died during most natural disasters. Prior to the occurrence of these disasters, available reports shows that animals do migrate to higher level or leave the areas en masse ahead of the event. Other related account show that animal sometimes behaves in unusual ways prior to the occurrence of these natural disasters. These overwhelming evidences point to the fact that animals might have the ability to sense impending natural disaster precursor signals ahead of time. This paper discusses the preliminary results obtained from the use of support vector machine (SVM) and Mel-frequency cepstral coefficients (MFCC) in the development of animal sound activity detection (ASAD) which is an integral part in the development of earthquake and natural disaster prediction using unusual animal behavior. The use of MFCC has been proposed for the features extraction stage while SVM has been proposed for classification of the extracted features. Preliminary results obtained shows that the MFCC and SVM can be used for features extraction and features classification respectively.Item Investigation of the characteristics of geoelectric field signals prior to earthquakes using adaptive STFT techniques(Copernicus GmbH, 2013-06-28) Astuti, W.; Sediono, W.; Akmeliawati, R.; Aibinu, A. M.; Salami, Momoh-Jimoh E.An earthquake is one of the most destructive natural disasters that can occur, often killing many people and causing large material losses. Hence, the ability to predict earthquakes may reduce the catastrophic effects caused by this phenomenon. The geoelectric field is a feature that can be used to predict earthquakes (EQs) because of significant changes in the amplitude of the signal prior to an earthquake. This paper presents a detailed analysis of geoelectric field signals of earthquakes which occurred in 2008 in Greece. In 2008, 12 earthquakes occurred in Greece. Five of them were recorded with magnitudes greater than Ms = 5R (5R), while seven of them were recorded with magnitudes greater than Ms = 6R (6R). In the analysis, the 1st significant changes of the geoelectric field signal are detected. Then, the signal is segmented and windowed. The adaptive short-time Fourier transform (adaptive STFT) technique is then applied to the windowed signal, and the spectral analysis is performed thereafter. The results show that the 1st significant changes of the geoelectric field prior to an earthquake have a significant amplitude frequency spectrum compared to other conditions, i.e. normal days and the day of the earthquake, which can be used as input parameters for earthquake prediction.Item Singular Value Decomposition (SVD) Based Orthogonal Transform Approach for Earth's Electric Field Signal Processing(IEEE, 2014-09-23) Astuti, W.; Salami, Momoh-Jimoh E.; Akmeliawati, R.; Sediono, W.The Earth's electric field signal is generated from the released energy through a sudden dislocation of the segment in the earth's crust. Many researchers have reported the use of parametric modeling technique for earth's electric field signal processing. The existing earth's electric signal processing based on parametric modeling technique has suffered from the noise. Therefore, the effective earth's electric field signal processing is necessary in order to process the signal with better performance for the identification. Singular value decomposition (SVD) based parametric modeling technique is applied as feature extraction technique to the Earth's electric field signal. The projection of excitation signal on the right eigenvector of the LPC filter impulse response matrix is involved in this technique. The combination of SVD-based parametric modeling technique has perfectly classified the significant Earth's electric field data prior to the earthquake and the Earth's electric field data on the normal condition after the polynomial kernel function is applied.Item Time Domain Feature Extraction Technique for earth's electric field signal prior to the Earthquake(IOP Conference Series. Materials Science and Engineering (Online), 2013-12) Astuti, W.; Sediono, W.; Akmeliawati, R.; Salami, Momoh-Jimoh E.Earthquake is one of the most destructive of natural disasters that killed many people and destroyed a lot of properties. By considering these catastrophic effects, it is highly important of knowing ahead of earthquakes in order to reduce the number of victims and material losses. Earth's electric field is one of the features that can be used to predict earthquakes (EQs), since it has significant changes in the amplitude of the signal prior to the earthquake. This paper presents a detailed analysis of the earth’s electric field due to earthquakes which occurred in Greece, between January 1, 2008 and June 30, 2008. In that period of time, 13 earthquakes had occurred. 6 of them were recorded with magnitudes greater than Ms= 5R (5R), while 7 of them were recorded with magnitudes greater than Ms= 6R (6R). Time domain feature extraction technique is applied to analyze the 1st significant changes in the earth’s electric field prior to the earthquake. Two different time domain feature extraction techniques are applied in this work, namely Simple Square Integral (SSI) and Root Mean Square (RMS). The 1st significant change of the earth's electric field signal in each of monitoring sites is extracted using those two techniques. The feature extraction result can be used as input parameter for an earthquake prediction system.