Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling

Sampling-Interval-Aware LSTM for Industrial Process Soft Sensing of Dynamic Time Sequences With Irregular Sampling Measurements

(2021) Xiaofeng Yuan et al.   IEEE SENSORS JOURNAL

Uncertainty-aware soft sensor using Bayesian recurrent neural networks

(2021) Minjung Lee et al.   ADVANCED ENGINEERING INFORMATICS

Learning Deep Multimanifold Structure Feature Representation for Quality Prediction With an Industrial Application

(2021) Chenliang Liu et al.   IEEE Transactions on Industrial Informatics

Ensemble deep learning based semi-supervised soft sensor modeling method and its application on quality prediction for coal preparation process

(2020) Xianhui Yin et al.   ADVANCED ENGINEERING INFORMATICS

A Just-in-Time Fine-Tuning Framework for Deep Learning of SAE in Adaptive Data-Driven Modeling of Time-Varying Industrial Processes

(2020) Yijun Wu et al.   IEEE SENSORS JOURNAL

A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes

(2020) Yuchen Jiang et al.   IEEE SENSORS JOURNAL

High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity

(2019) Katherine Meacham-Hensold et al.   REMOTE SENSING OF ENVIRONMENT

Soft sensor based on stacked auto-encoder deep neural network for air preheater rotor deformation prediction

(2018) Xiao Wang et al.   ADVANCED ENGINEERING INFORMATICS

A Novel Sliding Window PCA-IPF Based Steady-State Detection Framework and Its Industrial Application

(2018) Yalin Wang et al.   IEEE Access

Quality variable prediction for chemical processes based on semisupervised Dirichlet process mixture of Gaussians

(2018) Weiming Shao et al.   CHEMICAL ENGINEERING SCIENCE

Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application

(2017) Zhiqiang Ge et al.   IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Deep learning

(2015) Yann LeCun et al.   NATURE

A Bayesian framework for real-time identification of locally weighted partial least squares

(2014) Ming Ma et al.   AICHE JOURNAL

Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants

(2014) Hiromasa Kaneko et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes

(2014) Xiaofeng Yuan et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Data-driven soft sensor development based on deep learning technique

(2014) Chao Shang et al.   JOURNAL OF PROCESS CONTROL

Application of online support vector regression for soft sensors

(2013) Hiromasa Kaneko et al.   AICHE JOURNAL

Online Mixture of Univariate Linear Regression Models for Adaptive Soft Sensors

(2013) Francisco Souza et al.   IEEE Transactions on Industrial Informatics

Review of Recent Research on Data-Based Process Monitoring

(2013) Zhiqiang Ge et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill

(2012) Steven X. Ding et al.   IEEE Transactions on Industrial Informatics

Review of adaptation mechanisms for data-driven soft sensors

(2010) Petr Kadlec et al.   COMPUTERS & CHEMICAL ENGINEERING

Soft sensor based composition estimation and controller design for an ideal reactive distillation column

(2010) S.R. Vijaya Raghavan et al.   ISA TRANSACTIONS

Data-driven Soft Sensors in the process industry

(2009) Petr Kadlec et al.   COMPUTERS & CHEMICAL ENGINEERING

Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares

(2009) Yingwei Zhang et al.   IEEE Transactions on Industrial Informatics