Artificial neural networks for water quality soft-sensing in wastewater treatment: a review
This paper aims to present a comprehensive survey on water quality soft-sensing of a
wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We …
wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We …
Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …
PM₂. ₅ monitoring: use information abundance measurement and wide and deep learning
K Gu, H Liu, Z Xia, J Qiao, W Lin… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This article devises a photograph-based monitoring model to estimate the real-time PM 2.5
concentrations, overcoming currently popular electrochemical sensor-based PM 2.5 …
concentrations, overcoming currently popular electrochemical sensor-based PM 2.5 …
Deep learning-based model predictive control for continuous stirred-tank reactor system
A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment
processes. Its control is a challenging industrial-process-control problem due to great …
processes. Its control is a challenging industrial-process-control problem due to great …
Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network
The connection faults between the cells of a battery pack can increase contact resistance
and thus result in abnormal heating at the connections, which can seriously damage or even …
and thus result in abnormal heating at the connections, which can seriously damage or even …
Event-driven model predictive control with deep learning for wastewater treatment process
Wastewater treatment processes (WWTPs) have been considered as complex control
problems, because effluent water standard, stability and multioperational conditions need to …
problems, because effluent water standard, stability and multioperational conditions need to …
An efficient self-organizing deep fuzzy neural network for nonlinear system modeling
G Wang, J Qiao - IEEE Transactions on Fuzzy Systems, 2021 - ieeexplore.ieee.org
A fuzzy neural network (FNN) is an effective learning system that combines neural network
and fuzzy logic, which has achieved great success in nonlinear system modeling. However …
and fuzzy logic, which has achieved great success in nonlinear system modeling. However …
PM2. 5 concentration modeling and prediction by using temperature-based deep belief network
H Xing, G Wang, C Liu, M Suo - Neural Networks, 2021 - Elsevier
Air quality prediction is a global hot issue, and PM 2.5 is an important factor affecting air
quality. Due to complicated causes of formation, PM 2.5 prediction is a thorny and …
quality. Due to complicated causes of formation, PM 2.5 prediction is a thorny and …
An approach for brain tumor detection using optimal feature selection and optimized deep belief network
Abstract Nowadays, a Magnetic Resonance Image (MRI) scan acts as an efficient tool for
efficiently detecting the abnormal tissues present in the brain. It is a complex process for …
efficiently detecting the abnormal tissues present in the brain. It is a complex process for …
Adaptive assessment of power system transient stability based on active transfer learning with deep belief network
B Li, J Wu - IEEE Transactions on Automation Science and …, 2022 - ieeexplore.ieee.org
Transient stability assessment (TSA) based on deep learning has recently attracted broad
attention. The offline training of deep learning model typically requires a large number of …
attention. The offline training of deep learning model typically requires a large number of …