Rotation-Assisted Separation Model of Constrained-Surface Stereolithography

2021 ◽  
Author(s):  
Min Hu ◽  
Haobo Cheng ◽  
Yunpeng Feng
Keyword(s):  
Separation Model

scholarly journals A cloud computing separation model based on information flow

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Wei Ma ◽  
Huanqin Li ◽  
Deden Witarsyah
Keyword(s):  
Cloud Computing ◽  
Information Flow ◽  
End Users ◽  
Separation Mechanism ◽  
Computing Systems ◽  
Cloud Computing Security ◽  
Model Based ◽  
Separation Model

Abstract Separation is the primary consideration in cloud computing security. A series of security and safety problems would arise if a separation mechanism is not deployed appropriately, thus affecting the confidence of cloud end-users. In this paper, together with characteristics of cloud computing, the separation issue in cloud computing has been analyzed from the perspective of information flow. The process of information flow in cloud computing systems is formalized to propose corresponding separation rules. These rules have been verified in this paper and it is shown that the rules conform to non-interference security, thus ensuring the security and practicability of the proposed rules.

scholarly journals Viscoelastic phase separation model for ternary polymer solutions

2021 ◽  
Vol 154 (10) ◽  
pp. 104903
Author(s):  
Kenji Yoshimoto ◽  
Takashi Taniguchi
Keyword(s):  
Phase Separation ◽  
Polymer Solutions ◽  
Ternary Polymer ◽  
Separation Model

On turbulent boundary-layer separation

1968 ◽  
Vol 32 (2) ◽  
pp. 293-304 ◽  
Author(s):  
V. A. Sandborn ◽  
C. Y. Liu
Keyword(s):  
Boundary Layer ◽  
Shear Stress ◽  
Turbulent Boundary Layer ◽  
Analytical Study ◽  
Boundary Layer Separation ◽  
Mean Velocity ◽  
Laminar Separation ◽  
Layer Separation ◽  
Turbulent Separation ◽  
Separation Model

An experimental and analytical study of the separation of a turbulent boundary layer is reported. The turbulent boundary-layer separation model proposed by Sandborn & Kline (1961) is demonstrated to predict the experimental results. Two distinct turbulent separation regions, an intermittent and a steady separation, with correspondingly different velocity distributions are confirmed. The true zero wall shear stress turbulent separation point is determined by electronic means. The associated mean velocity profile is shown to belong to the same family of profiles as found for laminar separation. The velocity distribution at the point of reattachment of a turbulent boundary layer behind a step is also shown to belong to the laminar separation family.Prediction of the location of steady turbulent boundary-layer separation is made using the technique employed by Stratford (1959) for intermittent separation.

Ultra Fast Speech Separation Model with Teacher Student Learning

2021 ◽  
Author(s):  
Sanyuan Chen ◽  
Yu Wu ◽  
Zhuo Chen ◽  
Jian Wu ◽  
Takuya Yoshioka ◽  
...  
Keyword(s):  
Student Learning ◽  
Speech Separation ◽  
Teacher Student ◽  
Separation Model

Blind Complex Source Separation Based on Cyclostationary Statistics

2014 ◽  
Vol 519-520 ◽  
pp. 1051-1056
Author(s):  
Jie Guo ◽  
An Quan Wei ◽  
Lei Tang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Performance ◽  
Separation Algorithm ◽  
Complex Signals ◽  
Cyclic Frequency ◽  
Complex Source ◽  
Simulation Results ◽  
Better Than ◽  
Separation Model

This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.

scholarly journals Ulpa particle separation model in a spiral classifier

2020 ◽  
pp. 31-35
Author(s):  
O.S Beshta ◽  
V.M Kuvaiev ◽  
I.K Mladetskyi ◽  
M.V. Kuvaiev
Keyword(s):  
Particle Separation ◽  
Spiral Classifier ◽  
Separation Model
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