Special Issue "Advanced on Modeling and State Estimation for Industrial Processes"

Submission Deadline: 30 November 2021 (closed)
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Guest Editors
Prof. Shunyi Zhao, Jiangnan University, Wuxi, China
Prof. Xiaoli Luan, Jiangnan University, Wuxi, China
Prof. Jinfeng Liu, University of Alberta, Edmonton, Canada
Dr. Ruomu Tan, ABB Corporate Research Germany, Ladenburg, Germany


In the past few years, significant progress has been made in modeling and state estimation for industrial processes to improve control performance, reliable monitoring, quick and accurate fault detection, diagnosis,  high product quality,  fule and resource consumption, etc. However, with the fast development of information technology, numerous essential issues are facing in modeling and state estimation, which generates the new need for novel modeling and or state estimation methodologies and in-depth studies of them.


For example, due to many online sensors equipped, measurements are commonly collected with the characterizes of high volume, velocity, and variety. Therefore, feature selection or dimension reduction that extracts the signal interested and removes redundant information will be more critical during modeling than before, which will, in turn, affect the modeling and estimation algorithmic performance. Fast operation and high computational efficiency of an algorithm matter more since data often arrive rapidly, and the target values are usually required in an online manner. Besides, high accurate estimates of key parameters become more critical since reliable and precise modeling plays a vital role in many tasks, including control, detection, and monitoring. All these challenge the current statistical signal processing techniques from applicability, computational efficiency, and effectiveness. This special issue aims to bring the researchers in this area together with the engineers to break down barriers and develop innovative solutions and practical algorithms.


Potential topics include but are not limited to the following:


● Variational Bayesian Modeling Methods for Industrial Process

● Transfer Modeling for Industrial Process

● Unsupervised Modeling for Industrial Process

● First Principle Modeling for Industrial Process 

● Non-parametric Bayesian Modeling for Industrial Process

● Distributed Multi-Agent Modeling Algorithms and Its Industrial Applications

● Robust Modeling Methods for Industrial Process

● Supervised Modeling and Its Industrial Applications

● Filter-Aided Methods for Industrial Processes

● Nonlinear Modeling Methods and Its Industrial Applications

• First Principal Modeling
• Unsupervised Modeling
• Non-parametric Bayesian Modeling
• Robust Modeling
• Filter-Aided Methods
• Nonlinear Modeling Methods
• Industrial Process

Published Papers
  • Range-Only UWB SLAM for Indoor Robot Localization Employing Multi-Interval EFIR Rauch-Tung-Striebel Smoother
  • Abstract For improving the localization accuracy, a multi-interval extended finite impulse response (EFIR)-based RauchTung-Striebel (R-T-S) smoother is proposed for the range-only ultra wide band (UWB) simultaneous localization and mapping (SLAM) for robot localization. In this mode, the EFIR R-T-S (ERTS) smoother employs EFIR filter as the forward filter and the R-T-S smoothing method to smooth the EFIR filter’s output. When the east or the north position is considered as stance, the ERTS is used to smooth the position directly. Moreover, the estimation of the UWB Reference Nodes’ (RNs’) position is smoothed by the R-T-S smooth method in parallel. The test illustrates… More
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