scholarly journals Turning Process Monitoring with Deep Neural Network Trained by FEM Simulation

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 376-380
Author(s):  
Takashi Misaka ◽  
Jonny Herwan ◽  
Ichiro Ogura ◽  
Yoshiyuki Furukawa
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Deep Neural Network ◽  
Fem Simulation ◽  
Turning Process

scholarly journals Predictive Business Process Monitoring – Remaining Time Prediction using Deep Neural Network with Entity Embedding

2019 ◽  
Vol 161 ◽  
pp. 1080-1088
Author(s):  
Nur Ahmad Wahid ◽  
Taufik Nur Adi ◽  
Hyerim Bae ◽  
Yulim Choi
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Business Process ◽  
Deep Neural Network ◽  
Time Prediction ◽  
Business Process Monitoring ◽  
Remaining Time

Nonlinear Non-Gaussian and Multimode Process Monitoring-Based Multi-Subspace Vine Copula and Deep Neural Network

2020 ◽  
Vol 59 (32) ◽  
pp. 14385-14397
Author(s):  
Yang Zhou ◽  
Xiang Ren ◽  
Shaojun Li
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Deep Neural Network ◽  
Non Gaussian ◽  
Vine Copula

Deep neural network based recursive feature learning for nonlinear dynamic process monitoring

2019 ◽  
Vol 98 (4) ◽  
pp. 919-933 ◽  
Author(s):  
Jiazhen Zhu ◽  
Hongbo Shi ◽  
Bing Song ◽  
Shuai Tan ◽  
Yang Tao
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Dynamic Process ◽  
Nonlinear Dynamic ◽  
Deep Neural Network ◽  
Feature Learning

Study on Human Detection System Using Deep Neural Network and Alternative Learning for Autonomous Flying Drones

2019 ◽  
Vol 139 (2) ◽  
pp. 149-157
Author(s):  
Itaru Nagayama ◽  
Wakaki Uehara ◽  
Takaya Miyazato
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Detection System ◽  
Human Detection ◽  
Alternative Learning

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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