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.

scholarly journals Near Fault Effect on the Response of Single Hinged Compliant Offshore Tower

2018 ◽  
Vol 203 ◽  
pp. 01015
Author(s):  
Syed Yusuf Javed
Keyword(s):  
Offshore Structures ◽  
Dynamic Responses ◽  
Simultaneous Action ◽  
Integration Scheme ◽  
Random Waves ◽  
Sea Waves ◽  
Offshore Structure ◽  
Wave Condition ◽  
Near Fault ◽  
Time Histories

The response of compliant offshore structure under simultaneous action of random waves and earthquake loading has been analyzed. Since earthquake forces play a significant role in affecting the response of these offshore structures, comparative studies have been carried out considering near fault and far fault seismic excitations in the presence of moderate random sea waves. The offshore tower is modeled as an inverted pendulum with a cylindrical shaft connected by an articulated joint at the base. Seismic forces are evaluated by dividing the tower shaft into finite elements with masses lumped at the nodes. The nonlinearities associated with the system owing to variable submergence, drag force, variable buoyancy along with the geometry are considered in the analysis. The nonlinear dynamic equation of motion is formulated considering Lagrangian approach, which is solved in time domain by the Newmark-beta integration scheme. The sea state conditions, more precisely the water particle kinematics are evaluated using Airy’s wave theory along with the stretching modifications, as suggested by Chakrabarti. To minimize the dynamic responses, emphasis has to be given to the variations in height and position of the buoyancy chamber in extreme wave condition. The results are expressed in the form of time histories of deck displacement, hinge rotation, hinge shear and the bending moment. Parameters like maximum, minimum, mean and standard deviation are also determined by statistical analysis of response time histories of the dynamic responses at articulated joint.

The discrete Fourier transformation for seasonality and anomaly detection of an application to rare data

2020 ◽  
Vol 54 (2) ◽  
pp. 121-132
Author(s):  
Aryana Collins Jackson ◽  
Seán Lacey
Keyword(s):  
Time Domain ◽  
Fourier Transformation ◽  
Binary Data ◽  
Low Frequency ◽  
Discrete Fourier Transformation ◽  
Cycle Number ◽  
Content Type ◽  
Hard Drive ◽  
Period Detection ◽  
The Time Domain

PurposeThe discrete Fourier transformation (DFT) has been proven to be a successful method for determining whether a discrete time series is seasonal and, if so, for detecting the period. This paper deals exclusively with rare data, in which instances occur periodically at a low frequency.Design/methodology/approachData based on real-world situations is simulated for analysis.FindingsCycle number detection is done with spectral analysis, period detection is completed using DFT coefficients and signal shifts in the time domain are found using the convolution theorem. Additionally, a new method for detecting anomalies in binary, rare data is presented: the sum of distances. Using this method, expected events which have not occurred and unexpected events which have occurred at various sampling frequencies can be detected. Anomalies which are not considered outliers to be found.Research limitations/implicationsAliasing can contribute to extra frequencies which point to extra periods in the time domain. This can be reduced or removed with techniques such as windowing. In future work, this will be explored.Practical implicationsApplications include determining seasonality and thus investigating the underlying causes of hard drive failure, power outages and other undesired events. This work will also lend itself well to finding patterns among missing desired events, such as a scheduled hard drive backup or an employee's regular login to a server.Originality/valueThis paper has shown how seasonality and anomalies are successfully detected in seasonal, discrete, rare and binary data. Previously, the DFT has only been used for non-rare data.

scholarly journals An illustration of harmonic regression based on the results of the fast Fourier transformation

2002 ◽  
Vol 12 (2) ◽  
pp. 185-202 ◽  
Author(s):  
Imre Bertfai
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Fourier Transformation ◽  
Point Of View ◽  
Fast Fourier Transformation ◽  
Harmonic Model ◽  
Additional Advantage ◽  
Starting Point ◽  
Harmonic Regression ◽  
The Time Domain

The well-known methodology of the Fourier analysis is put against the background in the 2nd half of the century parallel to the development of the time-domain approach in the analysis of mainly economical time series. However, from the author's point of view, the former possesses some hidden analytical advantages which deserve to be re-introduced to the toolbox of analysts. This paper, through several case studies, reports research results for computer algorithm providing a harmonic model for time series. The starting point of the particular method is a harmonic analysis (Fourier-analysis or Lomb-periodogram). The results are optimized in a multifold manner resulting in a model which is easy to handle and able to forecast the underlying data. The results provided are particularly free from limitations characteristic for that methods. Furthermore, the calculated results are easy to interpret and use for further decisions. Nevertheless, the author intends to enhance the procedure in several ways. The method shown seems to be very effective and useful in modeling time series consisting of periodic terms. An additional advantage is the easy interpretation of the obtained parameters.

Damage Detection of Steel Domes Subjected to Earthquakes by Using Wavelet Transform

2010 ◽  
Vol 150-151 ◽  
pp. 1580-1583 ◽  
Author(s):  
Jin Zheng ◽  
Bo Chen ◽  
Chun Yang Ye
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Time Instant ◽  
Discrete Wavelet ◽  
Acceleration Response ◽  
Sudden Loss ◽  
Detection Approach ◽  
Time Histories ◽  
The Time Domain ◽  
Detection Quality

The degradation of a steel dome due to strong earthquake may lead to a sudden loss of structural stiffness in a structure and cause a discontinuity in acceleration response time histories recorded in the vicinity of damage location at damage time instant. A damage detection approach based on discrete wavelet transform is developed and implemented in this paper to detect the damage time instant and location of a steel dome due to sudden seismic damage. To examine the feasibility of the wavelet-based detection approach, parameter studies are carried out to investigate the detection quality for different noise intensities. The time instant and location of sudden damage event are detected through the distribution of discrete wavelet coefficients in the time domain and spatial distribution along the structural span.

Application in Information Technology by Digital Signal Processor

2014 ◽  
Vol 1056 ◽  
pp. 236-239
Author(s):  
Si Li Chen ◽  
Peng Cheng Li
Keyword(s):  
Information Technology ◽  
Digital Signal Processor ◽  
Fourier Transformation ◽  
Digital Signal ◽  
Discrete Fourier Transformation ◽  
Fast Fourier Transformation ◽  
Technical Application ◽  
Actual Case ◽  
Almost All ◽  
Signal Processor

During the progress of information technology and digital signal, digital signal processor has been an important discipline. The technical application in digital signal processor has already covered almost all scientific and technological fields. Because its advantages are small size, low energy consumption, high accuracy, reliable, flexible, integrated and multidimensional-using, digital signal processor take the place of analog signal system. Conception of digital signal processor is introduced, and its feature and application are summarized, also information technology has been presented. We have made solutions in information technology of digital signal processor by using DFT(Discrete Fourier Transformation), FFT(Fast Fourier Transformation) and digital filter. Actual case have been analysed. Finally we have made the conclusion.

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.

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