Cloud Model—A Bidirectional Cognition Model between Concept’s Extension and Intension

2012 ◽  
pp. 391-400 ◽  
Author(s):  
Guoyin Wang ◽  
Changlin Xu
Keyword(s):  
Cloud Model ◽  
Cognition Model

E-Government Public Cloud Model (EGPCM)

2018 ◽  
Vol 31 (1) ◽  
pp. 244
Author(s):  
Nada M. Al-Hakkak ◽  
Ban Salman Shukur ◽  
Atheel Sabih Shaker
Keyword(s):  
Cloud Computing ◽  
Cloud Model ◽  
Public Cloud ◽  
New Model ◽  
The World

   The concept of implementing e-government systems is growing widely all around the world and becoming an interest to all governments. However, governments are still seeking for effective ways to implement e-government systems properly and successfully. As services of e-government increased and citizens’ demands expand, the e-government systems become more costly to satisfy the growing needs. The cloud computing is a technique that has been discussed lately as a solution to overcome some problems that an e-government implementation or expansion is going through. This paper is a proposal of a  new model for e-government on basis of cloud computing. E-Government Public Cloud Model EGPCM, for e-government is related to public cloud computing.

Image scrambling algorithm based on cloud model

2013 ◽  
Vol 33 (9) ◽  
pp. 2497-2500
Author(s):  
Tiesheng FAN ◽  
Zhongqing ZHANG ◽  
Jing SUN ◽  
Xuechun LUO ◽  
Guiqiang LU ◽  
...  
Keyword(s):  
Cloud Model ◽  
Image Scrambling

Evaluation of Landslide Hazard Degree Based On the Normal Cloud Model

2017 ◽  
Vol 10 (01) ◽  
pp. 88-94
Author(s):  
CHEN DONGHUI ◽  
XU PEIHUA ◽  
ZHANG WEN ◽  
CHEN JIANPING ◽  
SONG SHENGYUAN ◽  
...  
Keyword(s):  
Cloud Model ◽  
Landslide Hazard ◽  
Hazard Degree

scholarly journals A Cloud Microphysics Parameterization for Shallow Cumulus Clouds Based on Lagrangian Cloud Model Simulations

2018 ◽  
Vol 75 (11) ◽  
pp. 4031-4047 ◽  
Author(s):  
Yign Noh ◽  
Donggun Oh ◽  
Fabian Hoffmann ◽  
Siegfried Raasch
Keyword(s):  
Cloud Model ◽  
Cloud Microphysics ◽  
Heaviside Function ◽  
Mixing Ratio ◽  
Cumulus Clouds ◽  
Cloud Droplets ◽  
Shallow Cumulus ◽  
Initial Stage ◽  
Accretion Rates ◽  
The Moment

Abstract Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at > 3.5 μm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.

scholarly journals Evaluation of freeze–thaw erosion in Tibet based on the cloud model

2021 ◽  
Author(s):  
Junfu Fan ◽  
Taoying Hu ◽  
Xiao Yu ◽  
Jiahao Chen ◽  
Liusheng Han ◽  
...  
Keyword(s):  
Cloud Model ◽  
Freeze Thaw

scholarly journals Quantitative Risk Assessment for Deep Tunnel Failure Based on Normal Cloud Model: A Case Study at the ASHELE Copper Mine, China

2021 ◽  
Vol 11 (11) ◽  
pp. 5208
Author(s):  
Jianpo Liu ◽  
Hongxu Shi ◽  
Ren Wang ◽  
Yingtao Si ◽  
Dengcheng Wei ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Large Scale ◽  
Cloud Model ◽  
Quantitative Risk Assessment ◽  
Geological Condition ◽  
Deep Tunnel ◽  
Entropy Weight ◽  
Copper Mine ◽  
Evaluation Indexes ◽  
Metal Mines

The spatial and temporal distribution of tunnel failure is very complex due to geologic heterogeneity and variability in both mining processes and tunnel arrangement in deep metal mines. In this paper, the quantitative risk assessment for deep tunnel failure was performed using a normal cloud model at the Ashele copper mine, China. This was completed by considering the evaluation indexes of geological condition, mining process, and microseismic data. A weighted distribution of evaluation indexes was determined by implementation of an entropy weight method to reveal the primary parameters controlling tunnel failure. Additionally, the damage levels of the tunnel were quantitatively assigned by computing the degree of membership that different damage levels had, based on the expectation normalization method. The methods of maximum membership principle, comprehensive evaluation value, and fuzzy entropy were considered to determine the tunnel damage levels and risk of occurrence. The application of this method at the Ashele copper mine demonstrates that it meets the requirement of risk assessment for deep tunnel failure and can provide a basis for large-scale regional tunnel failure control in deep metal mines.

scholarly journals Energy sustainability evaluation of 30 provinces in China using the improved entropy weight-cloud model

2021 ◽  
Vol 126 ◽  
pp. 107657
Author(s):  
Xiaoran Hou ◽  
Tao Lv ◽  
Jie Xu ◽  
Xu Deng ◽  
Feng Liu ◽  
...  
Keyword(s):  
Cloud Model ◽  
Energy Sustainability ◽  
Sustainability Evaluation ◽  
Entropy Weight
Sign in / Sign up

Export Citation Format

Share Document