scholarly journals Temporal Change of Spectra and Lyapunov Exponent Volcanic Tremor at Raung Volcano, Indonesia

2020 ◽  
Vol 9 (3S) ◽  
pp. 163-167
Keyword(s):  
Lyapunov Exponent ◽  
Fourier Transformation ◽  
Temporal Change ◽  
Volcanic Tremor ◽  
Fast Fourier Transformation ◽  
Maximal Lyapunov Exponent ◽  
Stretching Factor ◽  
Deterministic Processes ◽  
The Relationship ◽  
Generating System

Spectral Analyses and estimation maximal lyapunov exponent (MLE) of volcanic tremor recorded at Raung Volcano were carried out to investigate dynamical systems regarding to their generating system. Their results of both analyses can explain the temporal change in frequency and deterministic processes of the dynamical system. Spectral analysis of volcanic tremor was estimated by the average periodogram method which includes division, Fast Fourier Transformation and averaging. MLE was estimated by graphing the relationship between Stretching Factor (S) and the number of points in the tractor (N) diagram. Content frequency of volcanic tremor Raung Volcano is range from 2.68 to 3.7 Hz. Temporally, there is no significant change, which means that there is no change in the geometry of the Raung volcanic tremor source. This is also shown by the maximal lyapunov exponent which is temporarily constant and positive. That shows that the source process of Raung volcano is chaotic

scholarly journals Maximal Lyapunov exponent variations of volcanic tremor recorded during explosive and effusive activity at Mt Semeru volcano, Indonesia

2013 ◽  
Vol 20 (6) ◽  
pp. 1137-1145 ◽  
Author(s):  
K. I. Konstantinou ◽  
C. A. Perwita ◽  
S. Maryanto ◽  
A. Budianto ◽  
M. Hendrasto ◽  
...  
Keyword(s):  
Lyapunov Exponent ◽  
Chaotic Behavior ◽  
Fluid Pressure ◽  
Volcanic Tremor ◽  
Period Doubling ◽  
Generation Model ◽  
Maximal Lyapunov Exponent ◽  
Effusive Activity ◽  
Unsteady Fluid Flow ◽  
Unsteady Fluid

Abstract. We analyze 25 episodes of volcanic tremor recorded from 22 November until 31 December 2009 at Mt Semeru volcano in order to investigate their spectral and dynamical properties. The overtone frequencies for most of the tremor events indicate a pattern of period-doubling, which is one possible route that can lead a system to chaotic behavior. Exponential divergence of the phase space orbits is a strong indicator of chaos and was quantified by estimating the maximal Lyapunov exponent (MLE) for all tremor events. MLEs were found to vary linearly with the number of frequency overtones present in the tremor signals. This implies that the tremor source at Semeru fluctuates between a quasi-periodic state with few overtone frequencies (2–3) and small MLEs (~0.013), and a chaotic one with more overtones (up to 8) and larger MLEs (up to 0.039). These results agree well with the tremor generation model suggested previously by Julian (1994), which describes wall oscillations of a crack excited by unsteady fluid flow. In this model, as fluid pressure increases, a period-doubling cascade leads to numerous new frequencies and a chaotic tremor signal. The temporal variation of MLEs exhibited significant fluctuations from 23 until 31 December when the eruptive activity shifted from explosive to effusive. Such a situation may reflect variable fluid pressure conditions inside the conduit, where at first magma is accumulated and subsequently is erupted, releasing the buildup of pressure. Our results give further evidence for the role of nonlinear deterministic processes in generating volcanic tremor and call for similar investigations to be conducted in other volcanoes.

Study of Non-Smooth Gear Based on MÜLLER Algorithm

2014 ◽  
Vol 912-914 ◽  
pp. 769-773
Author(s):  
Miao Xin Xiao ◽  
Jian Jun Yang
Keyword(s):  
Lyapunov Exponent ◽  
Power Systems ◽  
Smooth Point ◽  
Gear System ◽  
Largest Lyapunov Exponent ◽  
Maximal Lyapunov Exponent ◽  
Detection Function ◽  
Unified Algorithm ◽  
The Relationship ◽  
System Phase

It is found that the relationship the detection function and collision function of non smooth point by using MÜLLER algorithm, and launched the conversion between the different states of gear system of the offset vector. So the unified algorithm of maximal Lyapunov exponent of non smooth gear system is obtained. An example is given to verified this method. The validities of algorithm for largest Lyapunov exponent of gear power systems is proved by comparing the the largest Lyapunov, system phase and Poincaré section.

scholarly journals Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Umer Sadiq Khan ◽  
Xingjun Zhang ◽  
Yuanqi Su
Keyword(s):  
Object Detection ◽  
Fourier Transformation ◽  
Active Contour ◽  
Active Contour Model ◽  
Salient Object Detection ◽  
Fast Fourier Transformation ◽  
Salient Object ◽  
Active Contour Models ◽  
Contour Model ◽  
Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

Hamiltonian Chaos in a Model Alveolus

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
F. S. Henry ◽  
F. E. Laine-Pearson ◽  
A. Tsuda
Keyword(s):  
Reynolds Number ◽  
Fixed Point ◽  
Lyapunov Exponent ◽  
Fine Particles ◽  
Hamiltonian Dynamics ◽  
Maximal Lyapunov Exponent ◽  
Poincaré Sections ◽  
Pulmonary Acinus ◽  
Hamiltonian Chaos ◽  
Poincare Sections

In the pulmonary acinus, the airflow Reynolds number is usually much less than unity and hence the flow might be expected to be reversible. However, this does not appear to be the case as a significant portion of the fine particles that reach the acinus remains there after exhalation. We believe that this irreversibility is at large a result of chaotic mixing in the alveoli of the acinar airways. To test this hypothesis, we solved numerically the equations for incompressible, pulsatile, flow in a rigid alveolated duct and tracked numerous fluid particles over many breathing cycles. The resulting Poincaré sections exhibit chains of islands on which particles travel. In the region between these chains of islands, some particles move chaotically. The presence of chaos is supported by the results of an estimate of the maximal Lyapunov exponent. It is shown that the streamfunction equation for this flow may be written in the form of a Hamiltonian system and that an expansion of this equation captures all the essential features of the Poincaré sections. Elements of Kolmogorov–Arnol’d–Moser theory, the Poincaré–Birkhoff fixed-point theorem, and associated Hamiltonian dynamics theory are then employed to confirm the existence of chaos in the flow in a rigid alveolated duct.

Generation of Wave-Field Time Histories by the Modulated Discrete Fourier Transformation

2003 ◽  
Author(s):  
Yousun Li
Keyword(s):  
Wave Field ◽  
Fourier Transformation ◽  
Time Instant ◽  
Discrete Fourier Transformation ◽  
Fast Fourier Transformation ◽  
Structural Members ◽  
Random Waves ◽  
Offshore Structure ◽  
Time Histories ◽  
The Time Domain

In the time domain simulation of the response of an offshore structure under random waves, the time histories of the wave field should be generated as the input to the dynamic equations. Herein the wave field is the wave surface elevation, the water particle velocities and accelerations at structural members. The generated time histories should be able to match the given wave-field spectral descriptions, to trace the structural member motions if it is a compliant offshore structure, and be numerically efficient. Most frequently used generation methods are the direct summation of a limited number of cosine functions, the Fast Fourier Transformation, and the digital filtering model. However, none of them can really satisfy all the above requirements. A novel technique, called the Modulated Discrete Fourier Transformation, has been developed. Under this method, the wave time histories at each time instant is a summation of a few time-varying complex functions. The simulated time histories have continuous spectral density functions, and the motions of the structural members are well included. This method seems to be superior to all the conventional methods in terms of the above mentioned three requirements.

Dynamic stability of running: The effects of speed and leg amputations on the maximal Lyapunov exponent

2013 ◽  
Vol 23 (4) ◽  
pp. 043131 ◽  
Author(s):  
Nicole Look ◽  
Christopher J. Arellano ◽  
Alena M. Grabowski ◽  
William J. McDermott ◽  
Rodger Kram ◽  
...  
Keyword(s):  
Lyapunov Exponent ◽  
Dynamic Stability ◽  
Maximal Lyapunov Exponent

Abstract 16924: Initial AMSA and Increased AMSA After CPR Predict Second Shock Success in Initially Shock-resistant VF During Out-of-hospital Cardiac Arrest

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Ulrich Herken ◽  
Weilun Quan
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Success Rate ◽  
Fourier Transformation ◽  
Amplitude Spectrum ◽  
Fast Fourier Transformation ◽  
Shock Delivery ◽  
Spectrum Area ◽  
Hospital Cardiac Arrest

Purpose: Amplitude spectrum area (AMSA), which is calculated from the ventricular fibrillation (VF) waveform using fast Fourier transformation, has been recognized as a predictor of successful defibrillation (DF) and as an index of myocardial perfusion and viability during resuscitation. In this study, we investigated whether a change in AMSA occurring during CPR would predict DF outcome for subsequent DF attempts after a failed DF. We hypothesized that a patient responding to CPR with an increase in AMSA would have an increased likelihood of DF success. Methods: This was a retrospective analysis of out-of-hospital cardiac arrest patients who received a second DF due to initially shock-resistant VF. A total of 193 patients with an unsuccessful first DF were identified in a manufacturer database of electrocardiographic defibrillator records. AMSA was calculated for the first DF (AMSA1) and the second DF (AMSA2) during a 2.1 sec window ending 0.5 sec prior to DF. A successful DF attempt was defined as the presence of an organized rhythm with a rate ≥ 40 / min starting within 60 sec from the DF and lasting for > 30 sec. After the failed first DF, all patients received CPR for 2 to 3 minutes before delivery of the second DF. Change in AMSA (dAMSA) was calculated as dAMSA = AMSA2 - AMSA1. Results: The overall second DF success rate was 14.5%. Multivariable logistic regression showed that both AMSA1 and dAMSA were independent predictors of second DF success with odds ratios of 1.24 (95% CI 1.12 - 1.38, p<0.001) and 1.27 (95% CI 1.16 - 1.41, p<0.001) for each mVHz change in AMSA or dAMSA, respectively. Conclusions: In initially DF-resistant VF, a high initial AMSA value predicted an increased likelihood of second shock success. An increase of AMSA in response to CPR also predicted a higher second shock success rate. Monitoring of AMSA during resuscitation therefore may be useful to guide CPR efforts, possibly including timing of second shock delivery. These findings also further support the value of AMSA as indicator of myocardial viability.

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