Solving heterogenous region for diffuse optical tomography with a convolutional forward calculation model and the inverse neural network

2020 ◽  
Author(s):  
Xiang Fang ◽  
Chenyang Gao ◽  
Yingxin Li ◽  
Ting Li
Keyword(s):  
Neural Network ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Calculation Model ◽  
Forward Calculation

Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography

2020 ◽  
Vol 59 (5) ◽  
pp. 1461 ◽  
Author(s):  
Sohail Sabir ◽  
Sanghoon Cho ◽  
Yejin Kim ◽  
Rizza Pua ◽  
Duchang Heo ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Convolutional Neural Network ◽  
Optical Tomography ◽  
Diffuse Optical Tomography

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Evaluation of Muscle Activities in Human Forearms under Exercises by Diffuse Optical Tomography

2012 ◽  
Vol 132 (3) ◽  
pp. 374-383 ◽  
Author(s):  
Yukari Tanikawa ◽  
Feng Gao ◽  
Michio Miyakawa ◽  
Toru Kiryu ◽  
Tomohiro Kizuka ◽  
...  
Keyword(s):  
Optical Tomography ◽  
Diffuse Optical Tomography

scholarly journals High Resolution, Deep Imaging Using Confocal Time-of-flight Diffuse Optical Tomography

2021 ◽  
pp. 1-1
Author(s):  
Yongyi Zhao ◽  
Ankit Raghuram ◽  
Hyun Kim ◽  
Andreas Hielscher ◽  
Jacob T Robinson ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Tomography ◽  
Time Of Flight ◽  
Diffuse Optical Tomography ◽  
Deep Imaging
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