Hotspot detection on post-OPC layout using full-chip simulation-based verification tool: a case study with aerial image simulation

2003 ◽  
Author(s):  
Juhwan Kim ◽  
Minghui Fan
Keyword(s):  
Aerial Image ◽  
Image Simulation ◽  
Hotspot Detection ◽  
Verification Tool ◽  
Simulation Based

Dynamic Simulation-Based Case Study of Fusion on Small-Scale Electrical Grids

2015 ◽  
Vol 68 (2) ◽  
pp. 341-345 ◽  
Author(s):  
Shutaro Takeda ◽  
Satoshi Konishi ◽  
Yasushi Yamamoto ◽  
Ryuta Kasada ◽  
Shigeki Sakurai
Keyword(s):  
Dynamic Simulation ◽  
Small Scale ◽  
Electrical Grids ◽  
Simulation Based

Python parallel processing for hyperspectral image simulation: based on distance functions

2021 ◽  
Author(s):  
Veerendra Satya Sylesh Peddinti ◽  
Venkata Ravibabu Mandla ◽  
Shashi Mesapam ◽  
Suresh Kancherla
Keyword(s):  
Parallel Processing ◽  
Hyperspectral Image ◽  
Distance Functions ◽  
Image Simulation ◽  
Simulation Based

Reliability-Based Analysis and Optimization of the Gravity Balancing Performance of Spring-Articulated Serial Robots with Uncertainties

2021 ◽  
pp. 1-32
Author(s):  
Vu Linh Nguyen ◽  
Chin-Hsing Kuo ◽  
Po Ting Lin
Keyword(s):  
Pso Algorithm ◽  
Reliability Based Design Optimization ◽  
Swarm Optimization ◽  
Reliability Based Design ◽  
Serial Robots ◽  
Simulation Based ◽  
Serial Robot ◽  
Simulation Results ◽  
Uncertainty Level

Abstract This article proposes a method for analyzing the gravity balancing reliability of spring-articulated serial robots with uncertainties. Gravity balancing reliability is defined as the probability that the torque reduction ratio (the ratio of the balanced torque to the unbalanced torque) is less than a specified threshold. The reliability analysis is performed by exploiting a Monte Carlo simulation (MCS) with consideration of the uncertainties in the link dimensions, masses, and compliance parameters. The gravity balancing begins with a simulation-based analysis of the gravitational torques of a typical serial robot. Based on the simulation results, a gravity balancing design for the robot using mechanical springs is realized. A reliability-based design optimization (RBDO) method is also developed to seek a reliable and robust design for maximized balancing performance under a prescribed uncertainty level. The RBDO is formulated with consideration of a probabilistic reliability constraint and solved by using a particle swarm optimization (PSO) algorithm. A numerical example is provided to illustrate the gravity balancing performance and reliability of a robot with uncertainties. A sensitivity analysis of the balancing design is also performed. Lastly, the effectiveness of the RBDO method is demonstrated through a case study in which the balancing performance and reliability of a robot with uncertainties are improved with the proposed method.

Sign in / Sign up

Export Citation Format

Share Document