Shape understanding system: the application of fuzzy sets, neural networks and statistical methods in the process of visual thinking

Author(s):  
Z. Les ◽  
M. Les
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hyunhee Lee ◽  
Gyeongmin Kim ◽  
Yuna Hur ◽  
Heuiseok Lim

2010 ◽  
Vol 163-167 ◽  
pp. 1854-1857
Author(s):  
Anuar Kasa ◽  
Zamri Chik ◽  
Taha Mohd Raihan

Prediction of internal stability for segmental retaining walls reinforced with geogrid and backfilled with residual soil was carried out using statistical methods and artificial neural networks (ANN). Prediction was based on data obtained from 234 segmental retaining wall designs using procedures developed by the National Concrete Masonry Association (NCMA). The study showed that prediction made using ANN was generally more accurate to the target compared with statistical methods using mathematical models of linear, pure quadratic, full quadratic and interactions.


2013 ◽  
Vol 18 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Yevgenia Chvertko ◽  
Mykola Shevchenko ◽  
Andriy Pirumov

Statistical methods of analysis are currently widely used to develop control and monitoring systems for different welding processes. These methods allow to obtain information about the process including effect of all factors on its results, which is often difficult to evaluate due to the complexity of the process. The authors made efforts to apply these methods to develop the system for monitoring the parameters of flash-butt welding in real-time mode. The paper gives brief information about the features of flash-butt welding of reinforcement bars and some basic limitation of this process application. The main reasons of formation of defects in welded joints are given as well as analysis of possibility of application of monitoring systems for their determination. The on-line monitoring system based on neural networks was developed for evaluation of process deviations. This system is believed to be adequate for determination of process violations resulting in disturbances of welding parameter and can be used for prediction of possible defects in the welded joints.


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