Wave Period Distribution in Mixed Sea-States

2004 ◽  
Vol 126 (1) ◽  
pp. 105-112 ◽  
Author(s):  
Germa´n Rodrı´guez ◽  
C. Guedes Soares ◽  
Mercedes Pacheco
Keyword(s):  
Probabilistic Models ◽  
Simulated Data ◽  
Peak Frequency ◽  
Relative Energy ◽  
Wave Period ◽  
Frequency Separation ◽  
Frequency Wave ◽  
Practical Applications ◽  
Period Distribution ◽  
Spectral Frequency

The statistical distribution of zero up-crossing wave periods in Gaussian mixed sea-states is examined by using numerically simulated data. Nine different kinds of bimodal spectra are used to analyze the effects of the relative energy ratio and the peak frequency separation between the low- and high-frequency wave fields on the wave period distribution. Observed results are compared with predictions given by probabilistic models suitable for practical applications. Numerical results reveal a different behavior for low- and high-period bands. Furthermore, comparisons of the empirical data with some probabilistic models often used in practice shows that none of these models is able to characterize adequately all the cases of combined sea-states examined. In fact, they might be used only in the case of wind-sea dominated sea-states with small separation between spectral frequency peaks. However, a model recently proposed by Myrhaug and Slaattelid [1] represents adequately the observed distributions of wave period.

Wave Height Distribution in Mixed Sea States

2001 ◽  
Vol 124 (1) ◽  
pp. 34-40 ◽  
Author(s):  
German Rodriguez ◽  
C. Guedes Soares ◽  
Mercedes Pacheco ◽  
E. Pe´rez-Martell
Keyword(s):  
Wave Height ◽  
Probabilistic Models ◽  
Simulated Data ◽  
Peak Frequency ◽  
Frequency Separation ◽  
Height Distribution ◽  
Zero Crossing ◽  
Frequency Wave ◽  
Wave Height Distribution ◽  
Wave Heights

The statistical distribution of zero-crossing wave heights in Gaussian mixed sea states is examined by analyzing numerically simulated data. Nine different kinds of bimodal scalar spectra are used to study the effects of the relative energy ratio and the peak frequency separation between the low and high frequency wave fields on the wave height distribution. Observed results are compared with predictions of probabilistic models adopted in practice. Comparisons of the empirical data with relevant probabilistic models reveals that the Rayleigh model systematically overestimates the number of observed wave heights larger than the mean wave height, except for one of the cases analyzed. None of the models used to predict the observed exceedance probabilities is able to characterize adequately all cases of bimodal sea states examined here.

Nonlinear Multiresponse Parameter Estimation Using Simplex Optimization Method

2015 ◽  
Vol 1105 ◽  
pp. 299-304
Author(s):  
A. Al Saleh Mohammad ◽  
A. Yussuf Abdirahman
Keyword(s):  
Probabilistic Models ◽  
Length Distribution ◽  
Simulated Data ◽  
Optimization Method ◽  
Sequence Length ◽  
Distribution Data ◽  
Multiple Site ◽  
First Order ◽  
Site Type ◽  
Market Requirements

Polyolefin molecular architectures are designed according to customer needs and demands. Hence, it is essential to determine the catalytic behavior that gives the polymer the characteristics it needs to meet the market requirements. Today most of the industrial polyolefin production depends on multiple-site-type catalysts such as Ziegler-Natta catalysts. In this work a methodology to estimate parameters for polyolefin multiple-site-type catalysts was presented. The sequence length distribution data were simulated using Zeroth-order and First-order Markovian models. These simulated data were used to test the robustness of the optimization method. The optimization method used was able to retrieve and comprehend the proper probabilistic models and provide acceptable polymerization parameters estimates.

A Method to Implement the Random Signals for Time Varying Offshore Wave Height During Storm Condition in an Intra-Wave Period Wave Model

2006 ◽  
Author(s):  
Yuliang Zhu ◽  
Shunqi Pan ◽  
Premanandan T. Fernando ◽  
Xiaoyan Zhou
Keyword(s):  
Wave Height ◽  
Wave Model ◽  
Peak Frequency ◽  
Wave Period ◽  
Storm Event ◽  
Time Varying ◽  
Wave Heights ◽  
Offshore Wave ◽  
Storm Condition ◽  
Period Wave

In this paper, a method to implement the surface elevation at the offshore boundary during storm conditions is presented in the intra-wave period wave model. At storm condition, the offshore incident significant wave height is time varying. In the case of time varying incident wave height, the JONSWAP energy spectrum can be manipulated as follows: H1/32s(f). s(f) is the energy density function for a unit wave height. During a storm event not only the offshore boundary significant wave heights but also the peak frequency varies. If we choose a mean peak frequency during a storm event, s(f) can be calculated for the mean peak frequency for the storm event. The amplitudes of the component waves for the random signals are calculated from the unit energy density function s(f), and the phase angle of the component wave, So we can numerically generate surface elevation time series for the time varying offshore wave heights. The method was verified in the intra-wave period wave model using field measurements at Sea Palling site Norfolk UK.

H 2, Mixed H2/H∞ and H2/L1 Optimally Tuned Passive Isolators and Absorbers

1998 ◽  
Vol 120 (2) ◽  
pp. 282-287 ◽  
Author(s):  
Wassim M. Haddad ◽  
Ali Razavi
Keyword(s):  
Frequency Response ◽  
Ad Hoc ◽  
Vibration Suppression ◽  
Peak Frequency ◽  
Optimization Method ◽  
Worst Case ◽  
Practical Applications ◽  
System Vibration ◽  
Response Performance ◽  
Optimization Schemes

In many practical applications, unbalanced rotating machinery cause vibrations that transmit large oscillatory forces to the system foundation. Using ad hoc optimization schemes tuned isolators and absorbers have traditionally been designed to suppress system vibration levels by attempting to minimize the peak frequency response of the force/displacement transmissibility system transfer function. In this paper, we formulate the classical isolator and absorber vibration suppression problems in terms of modern system theoretic criteria involving H2 (shock response), mixed H2/H∞ (worst-case peak frequency response), and mixed H2/L1 (worst-case peak amplitude response) performance measures. In particular, using a quasi-Newton optimization method we design H2, mixed H2/H∞ and mixed H2/L1 optimally tuned isolators and absorbers for multi-degree-of-freedom vibrational systems. Finally, we compare our results to the classical Snowdon and Den Hartog absorbers.

scholarly journals A probabilistic model for indel evolution: differentiating insertions from deletions

2020 ◽  
Author(s):  
Gil Loewenthal ◽  
Dana Rapoport ◽  
Oren Avram ◽  
Asher Moshe ◽  
Alon Itzkovitch ◽  
...  
Keyword(s):  
Model Selection ◽  
Probabilistic Models ◽  
Simulated Data ◽  
Selection Procedure ◽  
Biological Data ◽  
Effective Population ◽  
Selection Scheme ◽  
Insertions And Deletions ◽  
Deletion Rate ◽  
Approximate Bayesian

AbstractInsertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here we introduce several improvements to indel modeling: (1) while previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here, we propose a richer model that explicitly distinguishes between the two; (2) We introduce numerous summary statistics that allow Approximate Bayesian Computation (ABC) based parameter estimation; (3) We develop a neural-network model-selection scheme to test whether the richer model better fits biological data compared to the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed indel model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate. Finally, we demonstrate that indel rates are negatively correlated to the effective population size across various phylogenomic clades.

scholarly journals Scaling probabilistic models of genetic variation to millions of humans

2014 ◽  
Author(s):  
Prem Gopalan ◽  
Wei Hao ◽  
David M. Blei ◽  
John D. Storey
Keyword(s):  
Population Genetics ◽  
Population Structure ◽  
Genetic Variation ◽  
Probabilistic Models ◽  
Simulated Data ◽  
Genomic Data ◽  
Human Populations ◽  
Data Sets ◽  
Complex Population ◽  
Simulated Data Sets

One of the major goals of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. To this end, researchers have developed sophisticated statistical methods to capture the complex population structure that underlies observed genotypes in humans, and such methods have been effective for analyzing modestly sized genomic data sets. However, the number of genotyped humans has grown significantly in recent years, and it is accelerating. In aggregate about 1M individuals have been genotyped to date. Analyzing these data will bring us closer to a nearly complete picture of human genetic variation; but existing methods for population genetics analysis do not scale to data of this size. To solve this problem we developed TeraStructure. TeraStructure is a new algorithm to fit Bayesian models of genetic variation in human populations on tera-sample-sized data sets (1012observed genotypes, e.g., 1M individuals at 1M SNPs). It is a principled approach to Bayesian inference that iterates between subsampling locations of the genome and updating an estimate of the latent population structure of the individuals. On data sets of up to 2K individuals, TeraStructure matches the existing state of the art in terms of both speed and accuracy. On simulated data sets of up to 10K individuals, TeraStructure is twice as fast as existing methods and has higher accuracy in recovering the latent population structure. On genomic data simulated at the tera-sample-size scales, TeraStructure continues to be accurate and is the only method that can complete its analysis.

Genetic Algorithm for the Reconstruction of Bragg Grating Sensor Strain Distribution

Aerospace ◽  
2003 ◽  
Author(s):  
Apninder Gill ◽  
Kara Peters ◽  
Michel Studer
Keyword(s):  
Genetic Algorithm ◽  
Optical Fiber ◽  
Strain Distribution ◽  
Axial Strain ◽  
Simulated Data ◽  
Strain Sensors ◽  
Bragg Grating ◽  
Inversion Method ◽  
Period Distribution ◽  
Bragg Grating Sensor

Optical fiber Bragg gratings are unique among embedded strain sensors due to their potential to measure strain distributions with a spatial resolution of a few nanometers over gage lengths of a few centimeters. This article presents a genetic algorithm for the interrogation of optical fiber Bragg grating strain sensors. The method calculates the period distribution along the Bragg grating which can then be directly related to the axial strain distribution. The period distribution is determined from the output intensity spectrum of the grating via a T-matrix approach. The genetic algorithm inversion method presented requires only intensity information and reconstructs non-linear and discontinuous distributions well, including regions with significant gradients. The method is demonstrated through example reconstructions of Bragg grating sensor simulated data. The development of this algorithm will permit the use of Bragg grating sensors for damage identification in regions close to localized damages where strong strain non-linearities occur.

scholarly journals Insights Into the Internal Dynamics of Natural Lahars From Analysis of 3-Component Broadband Seismic Signals at Volcán de Colima, Mexico

2020 ◽  
Vol 8 ◽  
Author(s):  
Braden Walsh ◽  
Velio Coviello ◽  
Lucia Capra ◽  
Jonathan Procter ◽  
Victor Márquez-Ramirez
Keyword(s):  
Peak Frequency ◽  
Video Camera ◽  
Bedload Transport ◽  
Warning Systems ◽  
Volcán De Colima ◽  
Broadband Seismometer ◽  
Volcan De Colima ◽  
The Cross ◽  
Spectral Frequency ◽  
Three Components

Lahar monitoring on active volcanoes is challenging, and the ever changing environment leads to inconsistent results that hamper a warning systems ability to characterize the flow event properly. Therefore, more data, new methods, and the use of different sensors needs to be tested, which could lead to improvements in warning capabilities. Here, we present data from a 3-component broadband seismometer and video camera installed 3 m from the Lumbre channel on Volcán de Colima, Mexico to understand rheology differences within multiple lahar events that occurred in late 2016. We examine differences in frequency and directionality from each seismic component. Results indicate an increase in peak frequency above background in each component when a lahar nears the sensor, and a decrease in overall peak frequency when transitioning from a streamflow to a higher concentration flow. The seismic frequency distribution for the cross-channel component for the streamflow has a wider range compared with the lahar events. In contrast, the peak spectral frequency of the streamflow is narrower in comparison to the lahar events in the flow parallel and vertical directions. Estimated directionality ratios (cross-channel signal divided by flow parallel signal) yielded further evidence for a rheologic change between streamflow and lahars. Directionality ratios >1 were calculated for each lahar, and <1 for streamflow. Finally, we demonstrate from component analyses that channelization or freedom of movement in the cross-channel, bedload transport in the flow parallel, and bed composition in the vertical directions are possibly the main drivers in the peak spectral frequency output of lahars. The results described here indicate that using all three components may provide important information about lahar dynamics, which may be useful for automatic detection and warning systems, and using all three components should be encouraged.

Practical method for parametric array source evaluation at 120 kHz frequency range in the near-field region

2020 ◽  
Vol 68 (5) ◽  
pp. 389-398
Author(s):  
Hongmin Ahn ◽  
Yonghwan Hwang ◽  
Yub Je ◽  
Wonkyu Moon
Keyword(s):  
Near Field ◽  
Sound Field ◽  
Simulated Data ◽  
Practical Method ◽  
Difference Frequency ◽  
Water Tank ◽  
Small Aperture ◽  
Frequency Wave ◽  
Parametric Array ◽  
The Difference

A parametric array is a non-linear conversion process that can generate a narrow beam of low-frequency sound with a small aperture. One of the challenging issues with a parametric array is precise measurement of the sound field generated. In particular, near the transducer, it is not easy to measure the sound field generated by a parametric array precisely, because the amplitude of the difference frequency wave is much lower than the amplitude of the primary wave. In this study, the practical issues that should be considered in the design of near-distance experiments with a parametric array are examined. Limiting effects were examined, and their associated characteristics were identified by numerical simulations. Experiments were performed in a water tank (18 x 12 x 10 m) to assess these characteristics, using a custom-designed acoustic filter; the beam pattern and propagation curve of the difference frequency wave generated by the parametric array were measured and compared with simulated data.

Stochastic Local Interaction Models for Processing Spatiotemporal Datasets

2020 ◽  
Author(s):  
Dionissios Hristopulos ◽  
Vasiliki Agou ◽  
Andreas Pavlides ◽  
Panagiota Gkafa
Keyword(s):  
Markov Random Fields ◽  
Probabilistic Models ◽  
Interaction Model ◽  
Simulated Data ◽  
Space Time ◽  
Kernel Functions ◽  
Local Interaction ◽  
Model Parameters ◽  
Time Data ◽  
Precision Matrix

<p>We present recent advances related to Stochastic Local Interaction (SLI) models. These probabilistic models capture local correlations by means of suitably constructed precision matrices which are inferred from the available data. SLI models share features with Gaussian Markov random fields, and they can be used to complete spatial and spatiotemporal datasets with missing data.  SLI models are applicable to data sampled on both regular and irregular space-time grids.  The SLI models can also incorporate space-time trend functions. The degree of localization provided by SLI models is determined by means of kernel functions and appropriate bandwidths that adaptively determine local neighborhoods around each point of interest (including points in the sampling set and the map grid). The local neighborhoods lead to sparse precision (inverse covariance) matrices and also to explicit, semi-analytical relations for predictions, which are based on the conditional mean and the conditional variance.</p><p>We focus on a simple SLI model whose parameter set involves amplitude and rigidity coefficients as well as a characteristic length scale. The SLI precision matrix is expressed explicitly in terms of the model parameter and the kernel function. The parameter estimation is based on the method of maximum likelihood estimation (MLE). However, covariance matrix inversion is not required, since the precision matrix is known conditionally on the model parameters. In addition, the calculation of the precision matrix determinant can be efficiently performed computationally given the sparsity of the precision matrix.  Typical values of the sparsity index obtained by analyzing various environmental datasets are less than 1%. </p><p>We discuss the results of SLI predictive performance with both real and simulated data sets. We find that in terms of cross validation measures the performance of the method is similar to ordinary kriging while the computations are faster.  Overall, the SLI model takes advantage of sparse precision matrix structure to reduce the computational memory and time required for the processing of large spatiotemporal datasets.  </p><p><strong> </strong></p><p><strong>References</strong></p><ol><li>D. T. Hristopulos. Stochastic local interaction (SLI) model: Bridging machine learning and geostatistics. Computers and Geosciences, 85(Part B):26–37, December 2015. doi:10.1016/j.cageo.2015.05.018.</li> <li>D. T. Hristopulos and V. D. Agou. Stochastic local interaction model for space-time data. Spatial Statistics, page 100403, 2019. doi:10.1016/j.spasta.2019.100403.</li> <li>D. T. Hristopulos, A. Pavlides, V. D. Agou, P. Gkafa. Stochastic local interaction model for geostatistical analysis of big spatial datasets, 2019. arXiv:2001.02246</li> </ol>

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