scholarly journals Research and Application of Heavy-Equipment Parachute Rope Tension Sensor

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Min Yao ◽  
Yanhua Jin ◽  
Min Zhao ◽  
Shaohua Xu
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Test System ◽  
Simulation Software ◽  
Support Vector ◽  
Test Results ◽  
Maximum Weight ◽  
Swarm Optimization ◽  
Ansys Simulation ◽  
Heavy Equipment ◽  
Tension Sensor

Heavy-equipment airdrops are mainly used to deliver relief supplies and heavy weapons. Given the heavy weight of the goods, the tension of the extraction and brake ropes of the parachute significantly affects the safety of the aircraft. On the basis of the measurement and installation characteristics of the parachute rope, this study designed the structure of a nondestructive pressure-type parachute rope tension sensor and set the location of the strain gauge patch using the ANSYS simulation software to obtain a high sensor sensitivity. The temperature error of the tension sensor is compensated, and the precision is improved using the LSSVM-PSO (Least Squares Support Vector Machine-Particle Swarm Optimization) algorithm. The developed tension sensor is applied to the extraction parachute test system to measure the traction of 2 and 8 m2 parachutes. Test results show that the maximum weight of the platform these two parachutes can draw and the effect of parachute opening can be calculated.

scholarly journals Power load forecasting algorithm based on nonlinear inertial factor change pattern particle swarm optimization algorithm

2018 ◽  
Vol 173 ◽  
pp. 02016
Author(s):  
Jin Liang ◽  
Wang Yongzhi ◽  
Bao Xiaodong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Power Load ◽  
Selection Of

The common method of power load forecasting is the least squares support vector machine, but this method is very dependent on the selection of parameters. Particle swarm optimization algorithm is an algorithm suitable for optimizing the selection of support vector parameters, but it is easy to fall into the local optimum. In this paper, we propose a new particle swarm optimization algorithm, it uses non-linear inertial factor change that is used to optimize the algorithm least squares support vector machine to avoid falling into the local optimum. It aims to make the prediction accuracy of the algorithm reach the highest. The experimental results show this method is correct and effective.

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