scholarly journals An Adaptive Sampling Algorithm and Approximate-Model-Based Optimization Method

2018 ◽  
Vol 1060 ◽  
pp. 012080
Author(s):  
J L Tian ◽  
X Y Tong
Keyword(s):  
Adaptive Sampling ◽  
Optimization Method ◽  
Approximate Model ◽  
Sampling Algorithm ◽  
Model Based

Optimization of a Shock Absorber Design Using Model-Based Approach

2012 ◽  
Vol 452-453 ◽  
pp. 1351-1355 ◽  
Author(s):  
Grzegorz Wszołek ◽  
Piotr Czop ◽  
Dawid Jakubowski ◽  
Damian Slawik
Keyword(s):  
Automotive Industry ◽  
Shock Absorber ◽  
Flow Characteristic ◽  
Optimization Method ◽  
Design Parameters ◽  
Coupled Models ◽  
Model Based ◽  
Noise Evaluation ◽  
Optimal Values ◽  
Application Scope

The aim of this paper is to demonstrate a possibility to optimize a shock absorber design to minimize level of vibrations with the use of model-based approach. The paper introduces a proposal of an optimization method that allows to choose the optimal values of the design parameters using a shock absorber model to minimize the level of vibrations. A model-based approach is considered to obtain the optimal pressure-flow characteristic by simulations conducted with the use of coupled models, including the damper and the servo-hydraulic tester model. The presence of the tester model is required due to high non-linear coupling of the tested object (damper) and the tester itself to be used for noise evaluation. This kind of evaluation is used in the automotive industry to investigate dampers, as an alternative to vehicle-level tests. The paper provides numerical experimental case studies to show application scope of the proposed method

Optimization of the OOP Children Restraint System

2012 ◽  
Vol 155-156 ◽  
pp. 386-390
Author(s):  
Zhong Hao Bai ◽  
Jing Fei ◽  
Wei Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Optimization Method ◽  
Approximate Model ◽  
Parameters Optimization ◽  
Fe Model ◽  
Restraint System ◽  
Effective Protection ◽  
Multi Body

Based on the study of SAE J1980-2008 and FMVSS 208, MADYMO7.1 is used to establish a Multi-body and FE model for two OOP children, and the statistic test is implemented to verify the accuracy of the model. The airbag parameters impacting OOP children greatly and their ranges are selected to determine the objective function. With the Latin Hypercube Sampling method, the Kring approximate model is constructed, and multi-island genetic algorithm is used in subsequently parameters optimization. The results show that the proposed optimization method can provide effective protection for 6-year-old OOP children.

scholarly journals An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68324-68336 ◽  
Author(s):  
Yanlong Sun ◽  
Yazhou Yuan ◽  
Xiaolei Li ◽  
Qimin Xu ◽  
Xinping Guan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Adaptive Sampling ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Sampling Algorithm
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