Results 221 to 230 of about 3,351,616 (311)

FIGAN: A Missing Industrial Data Imputation Method Customized for Soft Sensor Application

IEEE Transactions on Automation Science and Engineering, 2022
Missing data is quite common in the industrial field, resulting in problems in downstream applications, as most data driven methods used in these applications rely on complete and high-quality dataset to build a high-quality model.
Zoujing Yao, Chunhui Zhao
semanticscholar   +1 more source

A Novel Soft Sensor Modeling Approach Based on Difference-LSTM for Complex Industrial Process

IEEE Transactions on Industrial Informatics, 2022
The main purpose of soft sensor modeling is to capture the dynamic nonlinear features between the easy-to-measure auxiliary variables and the difficult-to-measure key variables.
Jiayi Zhou, Xiaoli Wang, Chunhua Yang, Wei Xiong   +3 more
semanticscholar   +1 more source

An evolutionary deep learning soft sensor model based on random forest feature selection technique for penicillin fermentation process.

ISA transactions, 2022
Accurate and reliable measurement of key biological parameters during penicillin fermentation is of great significance for improving penicillin production.
Lei Hua, Chu Zhang, Wei Sun, Yiman Li, Jinlin Xiong, M. S. Nazir   +5 more
semanticscholar   +1 more source

Novel Transformer Based on Gated Convolutional Neural Network for Dynamic Soft Sensor Modeling of Industrial Processes

IEEE Transactions on Industrial Informatics, 2021
Industrial process data are usually time-series data collected by sensors, which have the characteristics of high nonlinearity, dynamics, and noises. Many existing soft sensor modeling methods usually focus on dominant variables and auxiliary variables ...
Zhiqiang Geng, Zhiwei Chen, Qingchao Meng, Yongming Han   +3 more
semanticscholar   +1 more source

A Deep Probabilistic Transfer Learning Framework for Soft Sensor Modeling With Missing Data

IEEE Transactions on Neural Networks and Learning Systems, 2021
Soft sensors have been extensively developed and applied in the process industry. One of the main challenges of the data-driven soft sensors is the lack of labeled data and the need to absorb the knowledge from a related source operating condition to ...
Zheng Chai, Chunhui Zhao, Biao Huang, Hongtian Chen   +3 more
semanticscholar   +1 more source

Soft microflow sensors

Lab on a Chip, 2009
We present a rapid prototyping method for integrating functional components in conventional PDMS microfluidic devices. We take advantage of stop-flow lithography (D. Dendukuri, S. S. Gu, D. C. Pregibon, T. A. Hatton and P. S. Doyle, Lab Chip, 2007, 7, 818)(1) to achieve the in situ fabrication of mobile and deformable elements with a controlled ...
Rafaele, Attia, Daniel C, Pregibon, Patrick S, Doyle, Jean-Louis, Viovy, Denis, Bartolo   +4 more
openaire   +2 more sources

Knowledge Automation Through Graph Mining, Convolution, and Explanation Framework: A Soft Sensor Practice

IEEE Transactions on Industrial Informatics, 2022
In industrial processes, data-driven soft sensors have played an important role for the effective process control, optimization, and monitoring. Deep learning technique has been widely used in soft sensor field in recent years for its excellent feature ...
Zhichao Chen, Zhiqiang Ge
semanticscholar   +1 more source

Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

IEEE Transactions on Industrial Informatics, 2020
Soft sensor has been extensively utilized in industrial processes for prediction of key quality variables. To build an accurate virtual sensor model, it is very significant to model the dynamic and nonlinear behaviors of process sequential data properly.
Xiaofeng Yuan, Lin Li, Yalin Wang
semanticscholar   +1 more source

Predictive Modeling With Multiresolution Pyramid VAE and Industrial Soft Sensor Applications

IEEE Transactions on Cybernetics, 2022
In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development.
Bingbing Shen, Le Yao, Zhiqiang Ge
semanticscholar   +1 more source

Disruptive, Soft, Wearable Sensors

Advanced Materials, 2019
AbstractThe wearable industry is on the rise, with a myriad of technical applications ranging from real‐time health monitoring, the Internet of Things, and robotics, to name but a few. However, there is a saying “wearable is not wearable” because the current market‐available wearable sensors are largely bulky and rigid, leading to uncomfortable wearing
Yunzhi Ling, Tiance An, Lim Wei Yap, Bowen Zhu, Shu Gong, Wenlong Cheng   +5 more
openaire   +2 more sources