AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …
components and functionalities required for analyzing and operating buildings. However, in …
A survey on deep learning for data-driven soft sensors
Q Sun, Z Ge - IEEE Transactions on Industrial Informatics, 2021 - ieeexplore.ieee.org
Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …
prediction, and many other important applications. With the development of hardware and …
A review of deep learning applications for railway safety
Railways speedily transport many people and goods nationwide, so railway accidents can
pose immense damage. However, the infrastructure of railways is so complex that its …
pose immense damage. However, the infrastructure of railways is so complex that its …
Learning deep multimanifold structure feature representation for quality prediction with an industrial application
Due to the existence of complex disturbances and frequent switching of operational
conditions characteristics in the real industrial processes, the process data under different …
conditions characteristics in the real industrial processes, the process data under different …
A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data
X Yuan, S Qi, Y Wang, H Xia - Control Engineering Practice, 2020 - Elsevier
Hierarchical local nonlinear dynamic feature learning is of great importance for soft sensor
modeling in process industry. Convolutional neural network (CNN) is an excellent local …
modeling in process industry. Convolutional neural network (CNN) is an excellent local …
Data mode related interpretable transformer network for predictive modeling and key sample analysis in industrial processes
D Liu, Y Wang, C Liu, X Yuan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Accurate prediction of quality variables that are difficult to measure is crucial for industrial
process control and optimization. However, the fluctuations in raw material quality and …
process control and optimization. However, the fluctuations in raw material quality and …
A coupled computational fluid dynamics and back-propagation neural network-based particle swarm optimizer algorithm for predicting and optimizing indoor air …
In the modern era, people spend approximately 90% of their time in indoor settings, such as
offices and residential buildings. As prolonged exposure to indoor environments can …
offices and residential buildings. As prolonged exposure to indoor environments can …
A novel self-supervised deep LSTM network for industrial temperature prediction in aluminum processes application
This article studies the influence of pot temperature or electrolyte temperature in the
aluminum reduction production. Specifically, these indexes reflect the distribution of the …
aluminum reduction production. Specifically, these indexes reflect the distribution of the …
Prediction of material removal rate in chemical mechanical polishing via residual convolutional neural network
Chemical mechanical polishing (CMP) is one of the most powerful technologies to achieve
global planarization for precision machining of the wafer surface. CMP contributes to …
global planarization for precision machining of the wafer surface. CMP contributes to …
An improved stacking ensemble learning-based sensor fault detection method for building energy systems using fault-discrimination information
G Li, Y Zheng, J Liu, Z Zhou, C Xu, X Fang… - Journal of Building …, 2021 - Elsevier
Sensor fault detection is essential to maintain operations of heating, ventilation, and air
conditioning systems (HVACs) in buildings. Data-driven sensor fault detection methods are …
conditioning systems (HVACs) in buildings. Data-driven sensor fault detection methods are …