Ambient noise imaging in warm shallow seas; second-order moment and model-based imaging algorithms

1999 ◽  
Vol 106 (6) ◽  
pp. 3201-3210 ◽  
Author(s):  
John R. Potter ◽  
Mandar Chitre
Keyword(s):  
Ambient Noise ◽  
Second Order ◽  
Order Moment ◽  
Model Based ◽  
Imaging Algorithms ◽  
Shallow Seas

scholarly journals Method for Measuring the Second-Order Moment of Atmospheric Turbulence

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 564
Author(s):  
Hong Shen ◽  
Longkun Yu ◽  
Xu Jing ◽  
Fengfu Tan
Keyword(s):  
Atmospheric Turbulence ◽  
Weighting Function ◽  
Structure Constant ◽  
Turbulence Models ◽  
Second Order ◽  
Index Structure ◽  
Order Moment ◽  
Site Testing ◽  
Optical Effects ◽  
Novel Method

The turbulence moment of order m (μm) is defined as the refractive index structure constant Cn2 integrated over the whole path z with path-weighting function zm. Optical effects of atmospheric turbulence are directly related to turbulence moments. To evaluate the optical effects of atmospheric turbulence, it is necessary to measure the turbulence moment. It is well known that zero-order moments of turbulence (μ0) and five-thirds-order moments of turbulence (μ5/3), which correspond to the seeing and the isoplanatic angles, respectively, have been monitored as routine parameters in astronomical site testing. However, the direct measurement of second-order moments of turbulence (μ2) of the whole layer atmosphere has not been reported. Using a star as the light source, it has been found that μ2 can be measured through the covariance of the irradiance in two receiver apertures with suitable aperture size and aperture separation. Numerical results show that the theoretical error of this novel method is negligible in all the typical turbulence models. This method enabled us to monitor μ2 as a routine parameter in astronomical site testing, which is helpful to understand the characteristics of atmospheric turbulence better combined with μ0 and μ5/3.

A second-order moment method applied to gas-solid risers

AIChE Journal ◽  
2012 ◽  
Vol 58 (12) ◽  
pp. 3653-3675 ◽  
Author(s):  
Juhui Chen ◽  
Shuyan Wang ◽  
Dan Sun ◽  
Huilin Lu ◽  
Dimitri Gidaspow ◽  
...  
Keyword(s):  
Moment Method ◽  
Second Order ◽  
Order Moment

Shape Interpretation of Second-Order Moment Invariants

2016 ◽  
Vol 56 (1) ◽  
pp. 125-136 ◽  
Author(s):  
Joviša Žunić ◽  
Dragiša Žunić
Keyword(s):  
Second Order ◽  
Order Moment ◽  
Moment Invariants

scholarly journals Ambient noise in shallow seas: Propagation issues

1993 ◽  
Vol 94 (3) ◽  
pp. 1824-1825
Author(s):  
Ira Dyer
Keyword(s):  
Ambient Noise ◽  
Shallow Seas

scholarly journals Distributionally robust joint chance constraints with second-order moment information

2011 ◽  
Vol 137 (1-2) ◽  
pp. 167-198 ◽  
Author(s):  
Steve Zymler ◽  
Daniel Kuhn ◽  
Berç Rustem
Keyword(s):  
Chance Constraints ◽  
Second Order ◽  
Order Moment ◽  
Distributionally Robust

Dynamic financial economic fluctuation model based on non-normal distribution

2020 ◽  
pp. 1-10
Author(s):  
Li Wang
Keyword(s):  
Normal Distribution ◽  
High Frequency ◽  
Extreme Value ◽  
High Order ◽  
High Frequency Data ◽  
Order Moment ◽  
Frequency Data ◽  
High Order Moment ◽  
Model Based ◽  
Skewness And Kurtosis

This paper discusses the modeling of financial volatility under the condition of non-normal distribution. In order to solve the problem that the traditional central moment cannot estimate the thick-tailed distribution, the L-moment which is widely used in the hydrological field is introduced, and the autoregressive conditional moment model is used for static and dynamic fitting based on the generalized Pareto distribution. In order to solve the dimension disaster of multidimensional conditional skewness and kurtosis modeling, the multidimensional skewness and kurtosis model based on distribution is established, and the high-order moment model is deduced. Finally, the problems existing in the traditional investment portfolio are discussed, and on this basis, the high-order moment portfolio is further studied. The results show that the key lies in the selection of the model and the assumption of asset probability distribution. Financial risk analysis can be effective only with a large sample. High-frequency data contain more information and can provide rich data resources. The conditional generalized extreme value distribution can well describe the time-varying characteristics of scale parameters and shape parameters and capture the conditional heteroscedasticity in the high-frequency extreme value time series. Better describe the persistence and aggregation of the extreme value of high frequency data as well as the peak and thick tail characteristics of its distribution.

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