A fully automatic group selection for form-finding process of truncated tetrahedral tensegrity structures via a double-loop genetic algorithm

2016 ◽  
Vol 106 ◽  
pp. 308-315 ◽  
Author(s):  
Seunghye Lee ◽  
Buntara Sthenly Gan ◽  
Jaehong Lee
Keyword(s):  
Genetic Algorithm ◽  
Group Selection ◽  
Double Loop ◽  
Form Finding ◽  
Tensegrity Structures ◽  
Automatic Group ◽  
Fully Automatic ◽  
Selection For

A Genetic Algorithm Approach for 2-D Tensegrity Form Finding

2014 ◽  
Vol 17 (11) ◽  
pp. 1669-1679 ◽  
Author(s):  
Shirko Faroughi ◽  
Mehdi Abdollahi Kamran ◽  
Jaehong Lee
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Stiffness Matrix ◽  
Equilibrium Configuration ◽  
Positive Definite ◽  
Connectivity Matrix ◽  
Form Finding ◽  
Numerical Examples ◽  
Tensegrity Structures ◽  
Genetic Algorithm Approach

This paper presents a novel and versatile method for finding 2-D tensegrity structures form finding. Using this method, different possibilities for the geometry of 2-D tensegrity structures can be found with little information about the structure. As opposed to most existing procedures this method only needs the number of each member prototype, the number of tensegrity nodes and connectivity at each node to be known. The form finding is done by minimizing objective function, which considers the rank deficiencies of the geometry, the prestress coefficients and the semi-positive definite condition of the stiffness matrix. Genetic algorithm as the global search is taken into account first for generating the connectivity matrix, initial prestress coefficients and also minimizing the objective function. Several numerical examples are given to demonstrate the competence and robustness of the current study in searching new different possibility self-equilibrium configuration of tensegrity structures.

Form-finding of free-form tensegrity structures by genetic algorithm–based total potential energy minimization

2016 ◽  
Vol 20 (5) ◽  
pp. 784-796 ◽  
Author(s):  
Fatih Uzun
Keyword(s):  
Genetic Algorithm ◽  
Potential Energy ◽  
Free Form ◽  
Stable Form ◽  
Equilibrium Form ◽  
Form Finding ◽  
Tensegrity Structures ◽  
Computational Performance ◽  
Stability Of Structures

Free-form tensegrities are composed of randomly connected cable and strut elements. The complexity of these structures causes determination of their self-equilibrium form to be a formidable task. There can be an infinite number of solutions with different forms, but it is difficult to identify the best form in terms of stability. Based on the fact that stability of structures is inversely proportional to potential energy, a genetic algorithm minimization process is developed to determine the self-equilibrium form of free-form regular tensegrity structures. The capability of the form-finding process on determination of the most stable form with minimum potential energy is investigated using two main parameters of free-form regular tensegrities which are cable–strut length ratio at rest and number of strut elements. The computational performance of the proposed method is also tested using free-form tensegrities with different number of structural elements.

Data-Driven Mode and Group Selection for Downlink MU-MIMO with Implementation in Commodity 802.11ac Network

2021 ◽  
pp. 1-1
Author(s):  
Shi Su ◽  
Wai-Tian Tan ◽  
Xiaoqing Zhu ◽  
Rob Liston ◽  
Behnaam Aazhang
Keyword(s):  
Group Selection ◽  
Data Driven ◽  
Selection For

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

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