Generating Optimal Thresholds in a Hysteresis Queue: Application to a Cloud Model

2019 ◽  
Author(s):  
Thomas Tournaire ◽  
Hind Castel-Taleb ◽  
Emmanuel Hyon ◽  
Toussaint Hoche
Keyword(s):  
Cloud Model ◽  
Optimal Thresholds

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 A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Guang Yang ◽  
Yuntao Ma ◽  
Jiaqi Hu
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extraction Methods ◽  
Average Error ◽  
Geographic Coordinate System ◽  
Optimal Thresholds ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

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.

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