Uncertainty in Position of Objects as an Influence Factor of the Area Value

With the development of the Intelligent Transportation System, various distributed sensors (including GPS, radar, infrared sensors) process massive data and make decisions for emergencies. Federated learning is a new distributed machine learning paradigm, in which system heterogeneity is the difficulty of fairness design. This paper designs a system heterogeneous fair federated learning algorithm (SHFF). SHFF introduces the equipment influence factor I into the optimization target and dynamically adjusts the equipment proportion with other performance. By changing the global fairness parameter θ, the algorithm can control fairness according to the actual needs. Experimental results show that, compared with the popular q-FedAvg algorithm, the SHFF algorithm proposed in this paper improves the average accuracy of the Worst 10% by 26% and reduces the variance by 61%.

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The Analysis on the Starting Friction Torque Increase of Magnetic Fluid Revolving Sealing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.275-277.429 ◽

2013 ◽

Vol 275-277 ◽

pp. 429-432 ◽

Cited By ~ 2

Author(s):

Yu Qiang Cai ◽

Na Xing

Keyword(s):

Magnetic Fluid ◽

Magnetic Particles ◽

Influence Factor ◽

Magnetic Field Gradient ◽

Friction Torque ◽

Key Factor ◽

Lower Temperature ◽

Change Of Microstructure ◽

Starting Torque ◽

Main Influence

Abstract. Magnetic fluid revolving sealing is widely used in modern industry. In the process of application, it is founded that the starting friction torque is very large, particularly at lower temperature. This problem has become a key factor restricting the application of magnetic fluid rotation sealing. In this paper, the mechanism of starting torque increase is analyzed, based on the change of microstructure and its viscosity. After analysis , such conclusion is obtained , which can be described: to a certain sealing structure, the type of magnetic fluid, size distribution of magnetic particles as well as the working condition concluding temperature, magnetic field gradient and the revolving velocity of shaft is the main influence factor of starting friction torque . It is very useful to reduce the starting friction torque.

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Nonlinear Analysis of Cable-Stayed Bridge

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.587-589.1558 ◽

2014 ◽

Vol 587-589 ◽

pp. 1558-1562

Author(s):

Hai Hong Mo

Keyword(s):

Quantitative Analysis ◽

Nonlinear Analysis ◽

Large Deformation ◽

Influence Factor ◽

Basic Theory ◽

Main Beam ◽

Deformation Effect ◽

Cable Stayed Bridge ◽

Cable Force

The nonlinear basic theory and nonlinear influence factor of cable-stayed bridge has been introduced. Quantitative analysis to the sag effect, beam-column effect and large deformation effect has been done based on a cable-stayed bridge. Analysis show that the sag effect, beam-column effect and large deformation effect of cable force is not obvious, but the sag effect should not been ignored in the calculation of the main beam.

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