Wear particle classification in a fuzzy grey system

Wear ◽  
1999 ◽  
Vol 225-229 ◽  
pp. 1238-1247 ◽  
Author(s):  
Z. Peng ◽  
T.B. Kirk
Keyword(s):  
Wear Particle ◽  
Grey System ◽  
Particle Classification

Wear particle classification considering particle overlapping

Wear ◽  
2019 ◽  
Vol 422-423 ◽  
pp. 119-127 ◽  
Author(s):  
Peng Peng ◽  
Jiugen Wang
Keyword(s):  
Wear Particle ◽  
Particle Classification

A hybrid convolutional neural network for intelligent wear particle classification

2019 ◽  
Vol 138 ◽  
pp. 166-173 ◽  
Author(s):  
Yeping Peng ◽  
Junhao Cai ◽  
Tonghai Wu ◽  
Guangzhong Cao ◽  
Ngaiming Kwok ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wear Particle ◽  
Particle Classification

Wear particle classification using genetic programming evolved features

2018 ◽  
Vol 30 (5) ◽  
pp. 229-246 ◽  
Author(s):  
Bin Xu ◽  
Guangrui Wen ◽  
Zhifen Zhang ◽  
Feng Chen
Keyword(s):  
Genetic Programming ◽  
Wear Particle ◽  
Particle Classification

Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Insurance Market ◽  
Grey Model ◽  
Loss Ratio ◽  
Grey System ◽  
Mathematical Technique ◽  
Supervisory Authority ◽  
Error Ratio ◽  
Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

METHOD FOR LUBRICATED SLIDING WEAR PARTICLE CHARACTERIZATION

2019 ◽  
Author(s):  
Alessandro Augusto Olimpio Ferreira Vittorino ◽  
Túlio Alves Rodrigues ◽  
Marco Aurélio Freitas Santos Júnior ◽  
Washington Martins da Silva Jr.
Keyword(s):  
Sliding Wear ◽  
Wear Particle ◽  
Particle Characterization ◽  
Lubricated Sliding
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