Model Robust Optimization Method of Using Feature Pyramid

2022 ◽  
pp. 611-617
Author(s):  
Jiaze Sun ◽  
Yanmei Tang ◽  
Shuyan Wang
Keyword(s):  
Robust Optimization ◽  
Optimization Method ◽  
Feature Pyramid

scholarly journals EFFECTS ON OPTIMIZATION RESULTS BY THERMAL LOAD PREDICTION ERRORS AND PROPOSAL OF ROBUST OPTIMIZATION METHOD

2009 ◽  
Vol 74 (641) ◽  
pp. 829-836
Author(s):  
Daisuke SUMIYOSHI ◽  
Akifumi WADA ◽  
Yasunori AKASHI ◽  
Tetsuo HAYASHI
Keyword(s):  
Robust Optimization ◽  
Thermal Load ◽  
Optimization Method ◽  
Prediction Errors ◽  
Load Prediction

A Robust Optimization Method for Systems With Significant Nonlinear Effects

1993 ◽  
Author(s):  
Jyh-Cheng Yu ◽  
Kosuke Ishii
Keyword(s):  
Heat Treatment ◽  
Robust Optimization ◽  
Nonlinear Effects ◽  
Optimization Method ◽  
Post Heat Treatment ◽  
Worst Case ◽  
Estimation Scheme ◽  
Optimization Methodology ◽  
Major Factors ◽  
The Cost

Abstract This paper describes a robust optimization methodology for design involving either complex simulations or actual experiments. The proposed procedure optimizes the worst case response that consists of a weighted sum of expected mean and response variance. The estimation scheme for expected mean and variance adopts the modified 3-point Gauss quadrature integration to assure superior accuracy for systems with significant nonlinear effects. We apply the proposed method to the robust design of geometric parameters of heat treated parts to minimize the cost of post heat treatment operations. The paper investigates the major factors influencing geometric distortions due to heat treatment and the rules of thumb in design. The study focuses on relating dimensional distortion to the design of part geometry. To illustrate the utility of the proposed method, we present the formulation of a case study on allocation of dimensions of preheat treated (green) shafts to minimize the cost of post heat treatment operations. The final result is not presented yet pending the completion of further experiments.

A Robust Optimization Method on the Transmission System of the Cutting Unit of Shearer

2020 ◽  
Author(s):  
Hongbin Gao ◽  
Junjun Chen
Keyword(s):  
Robust Optimization ◽  
Design Method ◽  
Harmonic Balance Method ◽  
Optimization Method ◽  
Transmission System ◽  
Manufacturing Cost ◽  
Internal Excitation ◽  
Tooth Width ◽  
Solution Methods ◽  
Cutting Unit

Abstract The transmission system of the cutting unit of shearer is divided into three basic components: planetary reduction form, one gear on one shaft form and a double gears on one shaft. The dynamic differential equations of three basic components are established respectively, and the volume functions of each structure are obtained. The characteristics of the internal excitation of the transmission system are analyzed, and the solution methods of the motion parameters of each component are obtained based on the harmonic balance method. Taking the parameters such as tooth number, modulus and tooth width as optimized variables, and a robust optimization method with the minimum value of multi-parameter evaluation function weighted linearly by dimensionless volume and vibration for the transmission system of the cutting unit of shearer is presented. Taking a certain type of shearer as an example, the transmission system of the cutting unit is optimized by using the presented method. After the design, the size is reduced by 5.4%, the maximum torsional acceleration of the drum is reduced by 17.8%, and the maximum torsional acceleration of the first gear is reduced by 9.6%. The results show that the design method can reduce the manufacturing cost of shearer and reduce the failure rate of the cutting unit.

scholarly journals Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4642
Author(s):  
Li Dai ◽  
Dahai You ◽  
Xianggen Yin
Keyword(s):  
Robust Optimization ◽  
Dirichlet Process ◽  
Forecast Error ◽  
Optimization Methods ◽  
Optimization Method ◽  
Gaussian Mixture ◽  
Data Driven ◽  
Uncertainty Sets ◽  
Uncertainty Set ◽  
Simulation Results

Traditional robust optimization methods use box uncertainty sets or gamma uncertainty sets to describe wind power uncertainty. However, these uncertainty sets fail to utilize wind forecast error probability information and assume that the wind forecast error is symmetrical and independent. This assumption is not reasonable and makes the optimization results conservative. To avoid such conservative results from traditional robust optimization methods, in this paper a novel data driven optimization method based on the nonparametric Dirichlet process Gaussian mixture model (DPGMM) was proposed to solve energy and reserve dispatch problems. First, we combined the DPGMM and variation inference algorithm to extract the GMM parameter information embedded within historical data. Based on the parameter information, a data driven polyhedral uncertainty set was proposed. After constructing the uncertainty set, we solved the robust energy and reserve problem. Finally, a column and constraint generation method was employed to solve the proposed data driven optimization method. We used real historical wind power forecast error data to test the performance of the proposed uncertainty set. The simulation results indicated that the proposed uncertainty set had a smaller volume than other data driven uncertainty sets with the same predefined coverage rate. Furthermore, the simulation was carried on PJM 5-bus and IEEE-118 bus systems to test the data driven optimization method. The simulation results demonstrated that the proposed optimization method was less conservative than traditional data driven robust optimization methods and distributionally robust optimization methods.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

SU-E-T-701: Equivalence and Differences Between Robust Optimization Methods and PTV Based Optimization Method for IMRT and IMPT

2013 ◽  
Vol 40 (6Part22) ◽  
pp. 367-367
Author(s):  
S Ge ◽  
M Quan ◽  
L Zhang ◽  
W Liu ◽  
R Mohan
Keyword(s):  
Robust Optimization ◽  
Optimization Methods ◽  
Optimization Method
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