fMRI model diagnostics for the Double Gamma and temporal derivative

2012 ◽  
Author(s):  
Ben Cassidy ◽  
Victor Solo
Keyword(s):  
Model Diagnostics ◽  
Temporal Derivative

On Calculation Schemes of Spatial Gradient for Spatio-temporal Derivative Method

2003 ◽  
Vol 23 (Supplement1) ◽  
pp. 21-24
Author(s):  
Yasufumi YAMAMOTO ◽  
Yuya AKAMATSU ◽  
Noriyoshi YONEHARA ◽  
Tomomasa UEMURA
Keyword(s):  
Spatial Gradient ◽  
Derivative Method ◽  
Temporal Derivative ◽  
Spatio Temporal

scholarly journals Modeling Best Practice Life Expectancy Using Gumbel Autoregressive Models

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 51
Author(s):  
Anthony Medford
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Extreme Value Theory ◽  
Autoregressive Model ◽  
Best Practice ◽  
Gumbel Distribution ◽  
Value Theory ◽  
Model Diagnostics ◽  
Temporal Dependence ◽  
Simulation Results

Best practice life expectancy has recently been modeled using extreme value theory. In this paper we present the Gumbel autoregressive model of order one—Gumbel AR(1)—as an option for modeling best practice life expectancy. This class of model represents a neat and coherent framework for modeling time series extremes. The Gumbel distribution accounts for the extreme nature of best practice life expectancy, while the AR structure accounts for the temporal dependence in the time series. Model diagnostics and simulation results indicate that these models present a viable alternative to Gaussian AR(1) models when dealing with time series of extremes and merit further exploration.

scholarly journals Lattice Boltzmann kinetic modeling and simulation of thermal liquid–vapor system

2014 ◽  
Vol 25 (12) ◽  
pp. 1441002 ◽  
Author(s):  
Yanbiao Gan ◽  
Aiguo Xu ◽  
Guangcai Zhang ◽  
Junqi Wang ◽  
Xijun Yu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Lattice Boltzmann ◽  
Present Model ◽  
Velocity Model ◽  
Interfacial Stress ◽  
Benchmark Tests ◽  
Temporal Derivative ◽  
Spatial Derivatives ◽  
Discrete Velocity Model ◽  
Liquid Vapor

We present a highly efficient lattice Boltzmann (LB) kinetic model for thermal liquid–vapor system. Three key components are as below: (i) a discrete velocity model (DVM) by Kataoka et al. [Phys. Rev. E69, 035701(R) (2004)]; (ii) a forcing term Ii aiming to describe the interfacial stress and recover the van der Waals (VDW) equation of state (EOS) by Gonnella et al. [Phys. Rev. E76, 036703 (2007)] and (iii) a Windowed Fast Fourier Transform (WFFT) scheme and its inverse by our group [Phys. Rev. E84, 046715 (2011)] for solving the spatial derivatives, together with a second-order Runge–Kutta (RK) finite difference scheme for solving the temporal derivative in the LB equation. The model is verified and validated by well-known benchmark tests. The results recovered from the present model are well consistent with previous ones [Phys. Rev. E84, 046715 (2011)] or theoretical analysis. The usage of less discrete velocities, high-order RK algorithm and WFFT scheme with 16th-order in precision makes the model more efficient by about 10 times and more accurate than the original one.

TIME-TO-CONTACT INFORMATION ESTIMATION FOR MONOCULAR MOBILE ROBOTS

2008 ◽  
Vol 05 (03) ◽  
pp. 223-233 ◽  
Author(s):  
RONG LIU ◽  
MAX Q. H. MENG
Keyword(s):  
Mobile Robots ◽  
Optical Flow ◽  
Active Contour Model ◽  
Visual Navigation ◽  
Moving Object ◽  
Time To Contact ◽  
Temporal Derivative ◽  
Novel Method ◽  
Derivatives Of ◽  
Flow Experiments

Time-to-contact (TTC) provides vital information for obstacle avoidance and for the visual navigation of a robot. In this paper, we present a novel method to estimate the TTC information of a moving object for monocular mobile robots. In specific, the contour of the moving object is extracted first using an active contour model; then the height of the motion contour and its temporal derivative are evaluated to generate the desired TTC estimates. Compared with conventional techniques employing the first-order derivatives of optical flow, the proposed estimator is less prone to errors of optical flow. Experiments using real-world images are conducted and the results demonstrate that the developed method can successfully achieve TTC with an average relative error (ARVE) of 0.039 with a single calibrated camera.

scholarly journals Reciprocal Green's functions and the quick forecast of submarine landslide tsunamis

2020 ◽  
Vol 20 (3) ◽  
pp. 771-781 ◽  
Author(s):  
Guan-Yu Chen ◽  
Chin-Chih Liu ◽  
Janaka J. Wijetunge ◽  
Yi-Fung Wang
Keyword(s):  
Green's Functions ◽  
Green’S Functions ◽  
Submarine Landslide ◽  
Tsunami Source ◽  
Submarine Earthquakes ◽  
Temporal Derivative ◽  
Submarine Mass Failure ◽  
Tsunami Waveform ◽  
Tsunami Scenarios ◽  
Landslide Tsunamis

Abstract. Although tsunamis generated by submarine mass failure are not as common as those induced by submarine earthquakes, sometimes the generated tsunamis are higher than a seismic tsunami in the area close to the tsunami source, and the forecast is much more difficult. In the present study, reciprocal Green's functions (RGFs) are proposed as a useful tool in the forecast of submarine landslide tsunamis. The forcing in the continuity equation due to depth change in a landslide is represented by the temporal derivative of the water depth. After a convolution with reciprocal Green's function, the tsunami waveform can be obtained promptly. Thus, various tsunami scenarios can be considered once a submarine landslide happens, and a useful forecast can be formulated. When a submarine landslide occurs, the various possibilities for tsunami generation can be analyzed and a useful forecast can be devised.

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