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

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 RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 529 ◽  
Author(s):  
Hui Zeng ◽  
Bin Yang ◽  
Xiuqing Wang ◽  
Jiwei Liu ◽  
Dongmei Fu
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Deep Neural Network ◽  
Low Cost ◽  
Evidence Theory ◽  
Feature Learning ◽  
Decision Fusion ◽  
Support Vector ◽  
Depth Sensors ◽  
Depth Images

With the development of low-cost RGB-D (Red Green Blue-Depth) sensors, RGB-D object recognition has attracted more and more researchers’ attention in recent years. The deep learning technique has become popular in the field of image analysis and has achieved competitive results. To make full use of the effective identification information in the RGB and depth images, we propose a multi-modal deep neural network and a DS (Dempster Shafer) evidence theory based RGB-D object recognition method. First, the RGB and depth images are preprocessed and two convolutional neural networks are trained, respectively. Next, we perform multi-modal feature learning using the proposed quadruplet samples based objective function to fine-tune the network parameters. Then, two probability classification results are obtained using two sigmoid SVMs (Support Vector Machines) with the learned RGB and depth features. Finally, the DS evidence theory based decision fusion method is used for integrating the two classification results. Compared with other RGB-D object recognition methods, our proposed method adopts two fusion strategies: Multi-modal feature learning and DS decision fusion. Both the discriminative information of each modality and the correlation information between the two modalities are exploited. Extensive experimental results have validated the effectiveness of the proposed method.

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
Sign in / Sign up

Export Citation Format

Share Document