UnitecDEAMP: Flow Feature Profiling for Malicious Events Identification in Darknet Space

Author(s):  
Ruibin Zhang ◽  
Chi Yang ◽  
Shaoning Pang ◽  
Hossein Sarrafzadeh
Keyword(s):  
2020 ◽  
Vol 224 (2) ◽  
pp. 961-972
Author(s):  
A G Semple ◽  
A Lenardic

SUMMARY Previous studies have shown that a low viscosity upper mantle can impact the wavelength of mantle flow and the balance of plate driving to resisting forces. Those studies assumed that mantle viscosity is independent of mantle flow. We explore the potential that mantle flow is not only influenced by viscosity but can also feedback and alter mantle viscosity structure owing to a non-Newtonian upper-mantle rheology. Our results indicate that the average viscosity of the upper mantle, and viscosity variations within it, are affected by the depth to which a non-Newtonian rheology holds. Changes in the wavelength of mantle flow, that occur when upper-mantle viscosity drops below a critical value, alter flow velocities which, in turn, alter mantle viscosity. Those changes also affect flow profiles in the mantle and the degree to which mantle flow drives the motion of a plate analogue above it. Enhanced upper-mantle flow, due to an increasing degree of non-Newtonian behaviour, decreases the ratio of upper- to lower-mantle viscosity. Whole layer mantle convection is maintained but upper- and lower-mantle flow take on different dynamic forms: fast and concentrated upper-mantle flow; slow and diffuse lower-mantle flow. Collectively, mantle viscosity, mantle flow wavelengths, upper- to lower-mantle velocities and the degree to which the mantle can drive plate motions become connected to one another through coupled feedback loops. Under this view of mantle dynamics, depth-variable mantle viscosity is an emergent flow feature that both affects and is affected by the configuration of mantle and plate flow.


2019 ◽  
Vol 19 (22) ◽  
pp. 14289-14310 ◽  
Author(s):  
Ping Zhu ◽  
Bryce Tyner ◽  
Jun A. Zhang ◽  
Eric Aligo ◽  
Sundararaman Gopalakrishnan ◽  
...  

Abstract. While turbulence is commonly regarded as a flow feature pertaining to the planetary boundary layer (PBL), intense turbulent mixing generated by cloud processes also exists above the PBL in the eyewall and rainbands of a tropical cyclone (TC). The in-cloud turbulence above the PBL is intimately involved in the development of convective elements in the eyewall and rainbands and consists of a part of asymmetric eddy forcing for the evolution of the primary and secondary circulations of a TC. In this study, we show that the Hurricane Weather Research and Forecasting (HWRF) model, one of the operational models used for TC prediction, is unable to generate appropriate sub-grid-scale (SGS) eddy forcing above the PBL due to a lack of consideration of intense turbulent mixing generated by the eyewall and rainband clouds. Incorporating an in-cloud turbulent-mixing parameterization in the vertical turbulent-mixing scheme notably improves the HWRF model's skills in predicting rapid changes in intensity for several past major hurricanes. While the analyses show that the SGS eddy forcing above the PBL is only about one-fifth of the model-resolved eddy forcing, the simulated TC vortex inner-core structure, secondary overturning circulation, and the model-resolved eddy forcing exhibit a substantial dependence on the parameterized SGS eddy processes. The results highlight the importance of eyewall and rainband SGS eddy forcing to numerical prediction of TC intensification, including rapid intensification at the current resolution of operational models.


2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shaoping Zhu ◽  
Limin Xia

A novel method based on hybrid feature is proposed for human action recognition in video image sequences, which includes two stages of feature extraction and action recognition. Firstly, we use adaptive background subtraction algorithm to extract global silhouette feature and optical flow model to extract local optical flow feature. Then we combine global silhouette feature vector and local optical flow feature vector to form a hybrid feature vector. Secondly, in order to improve the recognition accuracy, we use an optimized Multiple Instance Learning algorithm to recognize human actions, in which an Iterative Querying Heuristic (IQH) optimization algorithm is used to train the Multiple Instance Learning model. We demonstrate that our hybrid feature-based action representation can effectively classify novel actions on two different data sets. Experiments show that our results are comparable to, and significantly better than, the results of two state-of-the-art approaches on these data sets, which meets the requirements of stable, reliable, high precision, and anti-interference ability and so forth.


Author(s):  
Masahiro Nakashima ◽  
Hui Li ◽  
Takahide Tabata ◽  
Tsutomu Nozaki

The flow feature of the jet issuing from the circular pipe with the rotating inclined section has been investigated by the method of the flow visualization and the image processing. It has been found that the jet diffusion is affected by the inclined angle and the rotating speed. The coherent structure of the jet has been also studied by using the wavelet multi-resolution analysis. The multi-scale turbulent structures were visualized and the core and edge of the vortex were identified at different broad scales.


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