scholarly journals STOCHASTIC ANALYSIS OF BEACH PROFILE DATA

1970 ◽  
Vol 1 (12) ◽  
pp. 83
Author(s):  
Choule Sonu ◽  
Myron H. Young
Keyword(s):  
Stochastic Analysis ◽  
Power Spectra ◽  
Simulated Data ◽  
Transition Model ◽  
Random Waves ◽  
Beach Profile ◽  
Sediment Storage ◽  
Beach Sediment ◽  
Beach Width ◽  
Comparison Of The Results

Stochastic prediction of beach changes by means of a linear leastsquares transfer function requires a knowledge of power spectra Since most field data are too short to ensure stable analysis, an attempt was made to generate data artifically by a Monte Carlo simulation A beach profile transition model which considers the beach profile as a dynamic system allows beach width, sediment storage, and surface configuration to be determined m successive profiles and simulates beach cycles associated with random waves which are in sufficient agreement with the actual observation The simulated data are amenable to standard stochastic analysis to yield power spectra, cross spectra, coherence functions , and phase lags Comparison of the results with those derived from actual data shows reasonable agreement It appears that the process of beach sediment storage involves a combination of classes of Markov Gaussian random processes, whereas that of beach width resembles a white noise Coupling between these two parameters occurs in the lower frequency range with periodicities longer than about 8 days Moreover, the beach width shows phase advance before sediment storage Although the beach profile transition model requires further refinement, especially m regard to quantitative response to waves of various magnitudes and characteristics, the basic concept of the model is sound and will probably explain beach changes in various types of world coasts.

scholarly journals Bayesian modeling and significant features exploration in wavelet power spectra

2007 ◽  
Vol 14 (1) ◽  
pp. 79-88 ◽  
Author(s):  
D. V. Divine ◽  
F. Godtliebsen
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Statistical Significance ◽  
Power Spectra ◽  
Simulated Data ◽  
Scale Space ◽  
Independent Random Variables ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

Study of Processes in Shaping High Floodland and Deep-Cut Channel in Xiaolangdi Reservoir

2012 ◽  
Vol 212-213 ◽  
pp. 407-412
Author(s):  
Kun Peng Li ◽  
Shu Kui Chen ◽  
Huai Bao Ma ◽  
Ting Wang
Keyword(s):  
Operation Mode ◽  
Storage Period ◽  
Cross Sectional ◽  
Sediment Storage ◽  
Beach Sediment ◽  
Main River ◽  
Flood Period ◽  
Topographic Condition ◽  
Xiaolangdi Reservoir

The simulation test on the late sediment storage period in shaping program of high floodland and deep-cut channel on the Xiaolangdi reservoir, and the evolution of high floodland and deep-cut channel, for the Xiaolangdi reservoir sediment retaining period reasonable reservoir operation mode, provide reference for reservoir extending service life.The study shows that:①Formation of High floodland and deep-cut channel evolution, which based on the beach sediment uplift and slot synchronization channel and then washed down the process, whether the erosion and scour quantity to whether can form high floodland and deep-cut channel topography is essential;②The tributary of the riverbed is equivalent to extending transversely, tributary river sedimentation process and topographic condition and tributary junction of the main river deposition pattern and process and other factors are closely related, tributary cross-sectional deposition morphology is mostly parallel to the raising tendency, department branch flow entrance there was a sandbar;③the reservoir during the flood period rainfall scouring camera, in favor of hyperconcentrated flow is generated, to a certain extent, recovery tank capacity, play long term use of reservoir.

Estimation of Ship Roll Parameters in Random Waves

1992 ◽  
Vol 114 (2) ◽  
pp. 114-121 ◽  
Author(s):  
J. B. Roberts ◽  
J. F. Dunne ◽  
A. Debonos
Keyword(s):  
Markov Property ◽  
Simulated Data ◽  
Wide Band ◽  
Equation Of Motion ◽  
Roll Motion ◽  
Random Waves ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Ship Roll ◽  
Stochastic Input

The problem of estimating the parameters in an equation of roll motion from roll measurements only, taken in an irregular sea, is discussed. A single degree of freedom equation of motion is assumed, with a wide-band stochastic input and with a linear-in-the-parameters representation of both the damping and restoration terms. A method based on the Markov property of the energy envelope process, associated with the roll motion, is developed which enables all the relevant parameters to be estimated. The method is validated by applying it to some simulated data, for which the true parameters are known.

scholarly journals APPLICABILITY OF SUB-SAND SYSTEM TO BEACH EROSION CONTROL

1986 ◽  
Vol 1 (20) ◽  
pp. 92 ◽  
Author(s):  
Yoshiaki Kawata ◽  
Yoshito Tsuchiya
Keyword(s):  
Normal Wave ◽  
Erosion Control ◽  
Beach Erosion ◽  
Beach Profile ◽  
Sand Filter ◽  
Beach Sediment ◽  
Filter System ◽  
Fluid Boundary ◽  
Wave Conditions ◽  
Control Work

We performed experiments in the laboratory under controlled conditions in order to determine the applicability of a sub-sand filter system to the beach erosion control work. The filter system is used to control a flow condition at the sediment-fluid boundary. In the foreshore, it increases the inflowing velocity into the beach and thus results in increasing the threshold of beach sediment movement. The sub-sand filter system accelerates accretion of much beach sediment in the foreshore through the development of a berm under normal wave conditions. When wave conditions change from normal to stormy , it is also applicable to stabilize the beach profile, and thus decrease loss of beach sediment from the foreshore to the offshore.

Random Short-Crested Wave Forces on a Pile

1989 ◽  
Vol 111 (3) ◽  
pp. 242-250 ◽  
Author(s):  
M. Isaacson ◽  
O. Nwogu
Keyword(s):  
Laboratory Tests ◽  
National Research Council ◽  
Simulated Data ◽  
Wave Forces ◽  
Random Waves ◽  
Random Forces ◽  
Wave Basin ◽  
Offshore Wave ◽  
Hydraulics Laboratory ◽  
The Vertical Distribution

Numerical simulations and laboratory tests have been carried out to investigate the effect of wave short-crestedness on the random forces on a vertical pile. Linearized expressions based on the Morison equation are derived for the spectra of the in-line and transverse forces in short-crested seas. Expressions are also derived for the probability distribution of the peak resultant force for narrow-banded short-crested seas. The experiments were conducted in the offshore wave basin of the Hydraulics Laboratory, National Research Council of Canada. The pile was segmented in order to provide data on the vertical distribution of forces. The pile was subjected to long and short-crested random waves. Comparisons are presented between experimental and numerically simulated data.

scholarly journals Unpacking Transient Event Dynamics in Electrophysiological Power Spectra

2019 ◽  
Vol 32 (6) ◽  
pp. 1020-1034 ◽  
Author(s):  
Andrew J. Quinn ◽  
Freek van Ede ◽  
Matthew J. Brookes ◽  
Simone G. Heideman ◽  
Magdalena Nowak ◽  
...  
Keyword(s):  
Markov Models ◽  
Spectral Power ◽  
A Priori ◽  
Power Spectra ◽  
Simulated Data ◽  
Burst Detection ◽  
Spectral Content ◽  
Transient Event ◽  
Electrophysiological Recordings ◽  
Event Dynamics

AbstractElectrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral ‘bursts’ or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.

Scattering of cerebral blood flow estimates using 133Xe intracarotid injection

1979 ◽  
Vol 236 (5) ◽  
pp. H680-H688
Author(s):  
P. Celsis ◽  
J. P. Marc-Vergnes ◽  
J. P. Charlet ◽  
A. Sevin
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Random Error ◽  
Stochastic Analysis ◽  
Relative Weight ◽  
Mean Flow ◽  
Empirical Formulas ◽  
Real Curves ◽  
Xenon 133 ◽  
Comparison Of The Results

Computer-generated and real curves were used to study the random error in the blood flow estimates calculated from xenon-133 cerebral clearance curves. The initial count rate affects the scattering under a threshold of 5,000 counts/s, but above this figure its influence is negligible. Flow values, relative weight of gray matter, and processes of analysis have also an effect. For the stochastic analysis, a theoretical standard deviation calculable from each experimental curve was proved. For other methods, empirical formulas were established. Using these formulas, intervals of uncertainty can be calculated allowing a useful comparison of the results obtained on the same patient, from different areas or from subsequent tests. Such intervals also allow the comparison of mean flow values obtained from a particular curve by the three usual processes of analysis. With high initial count rates, more than 75% of the intervals of uncertainty are disjointed. This suggests that the random error plays little part in the differences observed.

Hemodynamic fluctuations and baroreflex sensitivity in humans: a beat-to-beat model

1987 ◽  
Vol 253 (3) ◽  
pp. H680-H689 ◽  
Author(s):  
R. W. deBoer ◽  
J. M. Karemaker ◽  
J. Strackee
Keyword(s):  
Cardiovascular System ◽  
Peripheral Resistance ◽  
Power Spectra ◽  
Simulated Data ◽  
Equation Model ◽  
Resonance Phenomenon ◽  
Actual Data ◽  
Arterial Tree ◽  
Sinus Arrhythmia ◽  
Arterial Blood

A beat-to-beat model of the cardiovascular system is developed to study the spontaneous short-term variability in arterial blood pressure (BP) and heart rate (HR) data from humans at rest. The model consists of a set of difference equations representing the following mechanisms: 1) control of HR and peripheral resistance by the baroreflex, 2) Windkessel properties of the systemic arterial tree, 3) contractile properties of the myocardium (Starling's law and restitution), and 4) mechanical effects of respiration on BP. The model is tested by comparing power spectra and cross spectra of simulated data from the model with spectra of actual data from resting subjects. To make spectra from simulated data and from actual data tally, it must be assumed that respiratory sinus arrhythmia at rest is caused by the conversion of respiratory BP variability into HR variability by the fast, vagally mediated baroreflex. The so-called 10-s rhythm in HR and BP appears as a resonance phenomenon due to the delay in the sympathetic control loop of the baroreflex. The simulated response of the model to an imposed increase of BP is shown to correspond with the BP and HR response in patients after administration of a BP-increasing drug, such as phenylephrine. It is concluded that the model correctly describes a number of important features of the cardiovascular system. Mathematical properties of the difference-equation model are discussed.

scholarly journals Parameter inference and model comparison using theoretical predictions from noisy simulations

2019 ◽  
Vol 490 (4) ◽  
pp. 5749-5756 ◽  
Author(s):  
Niall Jeffrey ◽  
Filipe B Abdalla
Keyword(s):  
Model Comparison ◽  
Closed Form Solution ◽  
Power Spectra ◽  
Simulated Data ◽  
Analysis Data ◽  
Form Solution ◽  
Summary Statistic ◽  
Unknown Parameters ◽  
Light Curve Analysis ◽  
Theoretical Predictions

ABSTRACT When inferring unknown parameters or comparing different models, data must be compared to underlying theory. Even if a model has no closed-form solution to derive summary statistics, it is often still possible to simulate mock data in order to generate theoretical predictions. For realistic simulations of noisy data, this is identical to drawing realizations of the data from a likelihood distribution. Though the estimated summary statistic from simulated data vectors may be unbiased, the estimator has variance that should be accounted for. We show how to correct the likelihood in the presence of an estimated summary statistic by marginalizing over the true summary statistic in the framework of a Bayesian hierarchical model. For Gaussian likelihoods where the covariance must also be estimated from simulations, we present an alteration to the Sellentin–Heavens corrected likelihood. We show that excluding the proposed correction leads to an incorrect estimate of the Bayesian evidence with Joint Light-Curve Analysis data. The correction is highly relevant for cosmological inference that relies on simulated data for theory (e.g. weak lensing peak statistics and simulated power spectra) and can reduce the number of simulations required.

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