Urban agglomeration ecological risk transfer model based on Bayesian and ecological network

2020 ◽  
Vol 161 ◽  
pp. 105006 ◽  
Author(s):  
Wen Zhang ◽  
Gengyuan Liu ◽  
Zhifeng Yang
2014 ◽  
Vol 131 ◽  
pp. 27-35 ◽  
Author(s):  
Hortense Serret ◽  
Richard Raymond ◽  
Jean-Christophe Foltête ◽  
Philippe Clergeau ◽  
Laurent Simon ◽  
...  

2020 ◽  
Vol 117 ◽  
pp. 106677 ◽  
Author(s):  
Zhuo Li ◽  
Weiguo Jiang ◽  
Wenjie Wang ◽  
Zheng Chen ◽  
Ziyan Ling ◽  
...  

Author(s):  
XinMei Shi ◽  
Daan M. Maijer ◽  
Guy Dumont

Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies for eliminating defects focus on the execution of a pre-set casting cycle, die structure design or the combination of both. To respond to process variability and mitigate its negative effects, advanced process control methodologies may be employed to dynamically adjust the operational parameters of the process. In this work, a finite element heat transfer model, validated by comparison with experimental data, has been developed to predict the evolution of temperatures and the volume of liquid encapsulation in an experimental casting process. A virtual process, made up of the heat transfer model and a wrapper script for communication, has been employed to simulate the continuous operation of the real process. A stochastic state-space model, based on data from measurements and the virtual process, has been developed to provide a reliable representation of this virtual process. The parameters of the deterministic portion result from system identification of the virtual process, whereas the parameters of the stochastic portion arise from the analysis and comparison of measurement data with virtual process data. The resulting state-space model, which can be extended to a multi-input multi-output model, will facilitate the design of a model-based controller for this process.


2005 ◽  
Vol 11 (3) ◽  
pp. 407-429 ◽  
Author(s):  
M. Elahinia ◽  
J. Koo ◽  
M. Ahmadian ◽  
C. Woolsey

This paper investigates a nonlinear controller designed to stabilize a single-degree-of-freedom rotary shape memory alloy (SMA) actuated robotic arm. To this end, a bias-type robotic arm was built using 150 pm Flexinol SMA wire. This robot is designed to lift and position lightweight objects. Upon complete phase transformation, the SMA wire actuates the robot to rotate up to 1350. A linear spring is used to extend the wire to its original length because the SMA wire can only apply force in one direction. To measure the angular position of the robotic arm, an optical rotary encoder was used. To stabilize the robot, a model-based controller was developed. The controller incorporates the SMA actuated robot model with nonlinear control techniques. The model consists of three parts: the dynamics/kinematics of the arm, the thermoruechanical behavior of SMA wire, and the heat transfer model of the wire. The model-based backstepping controller determines the applied voltage to the SMA wire for positioning the arm at the desired angle by first calculating the wire's stress to stabilize the arm. The voltage to the SMA wire is then calculated based on the desired stress and the SMA's thermomechanical and heat transfer models. A series of simulations were performed to investigate stabilizing performance of the controller. Moreover, other issues such as robustness of the control design was evaluated. The results show that the control algorithms is able to globally and asymptotically stabilize the robot. The results further indicate that the sliding mode control has better robustness properties.


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