Certain Words, Tilings, Their Non-Periodicity, and Substitutions of High Dimension

2002 ◽  
pp. 303-348
Author(s):  
Jun-ichi Tamura
Keyword(s):  
High Dimension

scholarly journals A hybrid Feature Selection Optimization Model for High Dimension Data Classification

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohammed Qaraad ◽  
Souad Amjad ◽  
Ibrahim I.M. Manhrawy ◽  
Hanaa Fathi ◽  
Bayoumi A. Hassan ◽  
...  
Keyword(s):  
Feature Selection ◽  
High Dimension ◽  
Optimization Model ◽  
Data Classification ◽  
High Dimension Data

A Secure High-dimension Consensus Mechanism against Adversaries

2021 ◽  
Author(s):  
Xiaoyu Luo ◽  
Chengcheng Zhao ◽  
Jianping He
Keyword(s):  
High Dimension

scholarly journals Testing covariates in high dimension linear regression with latent factors

2016 ◽  
Vol 144 ◽  
pp. 25-37 ◽  
Author(s):  
Wei Lan ◽  
Yue Ding ◽  
Zheng Fang ◽  
Kuangnan Fang
Keyword(s):  
Linear Regression ◽  
High Dimension ◽  
Latent Factors

Flutter of high-dimension nonlinear system for a FGM truncated conical shell

2017 ◽  
Vol 25 (1) ◽  
pp. 47-61 ◽  
Author(s):  
Y. X. Hao ◽  
S. W. Yang ◽  
W. Zhang ◽  
M. H. Yao ◽  
A. W. Wang
Keyword(s):  
Nonlinear System ◽  
High Dimension ◽  
Conical Shell ◽  
Truncated Conical Shell

scholarly journals GBUO: “The Good, the Bad, and the Ugly” Optimizer

2021 ◽  
Vol 11 (5) ◽  
pp. 2042
Author(s):  
Hadi Givi ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ruben Morales-Menendez ◽  
Ricardo A. Ramirez-Mendoza ◽  
...  
Keyword(s):  
High Dimension ◽  
Essential Role ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Stochastic Search ◽  
Objective Functions ◽  
Science And Engineering ◽  
Fixed Dimension ◽  
Dimension Functions ◽  
Simulation Results

Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms.

Strange effects in high dimension

2010 ◽  
Vol 53 (2) ◽  
pp. 96-96
Author(s):  
Sanjoy Dasgupta
Keyword(s):  
High Dimension

Configuration Robustness Analysis of the Optimal Design of Cable-Driven Manipulators

2016 ◽  
Vol 8 (6) ◽  
Author(s):  
Joshua T. Bryson ◽  
Xin Jin ◽  
Sunil K. Agrawal
Keyword(s):  
Optimal Design ◽  
Stochastic Optimization ◽  
High Dimension ◽  
Degrees Of Freedom ◽  
Robustness Analysis ◽  
Optimal Solution ◽  
Design Parameters ◽  
Robot Leg ◽  
Robot Performance

Designing an effective cable architecture for a cable-driven robot becomes challenging as the number of cables and degrees of freedom of the robot increase. A methodology has been previously developed to identify the optimal design of a cable-driven robot for a given task using stochastic optimization. This approach is effective in providing an optimal solution for robots with high-dimension design spaces, but does not provide insights into the robustness of the optimal solution to errors in the configuration parameters that arise in the implementation of a design. In this work, a methodology is developed to analyze the robustness of the performance of an optimal design to changes in the configuration parameters. This robustness analysis can be used to inform the implementation of the optimal design into a robot while taking into account the precision and tolerances of the implementation. An optimized cable-driven robot leg is used as a motivating example to illustrate the application of the configuration robustness analysis. Following the methodology, the effect on robot performance due to design variations is analyzed, and a modified design is developed which minimizes the potential performance degradations due to implementation errors in the design parameters. A robot leg is constructed and is used to validate the robustness analysis by demonstrating the predicted effects of variations in the design parameters on the performance of the robot.

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