Knowledge-based fault diagnosis in industrial internet of things: a survey

Y Chi, Y Dong, ZJ Wang, FR Yu… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Industrial Internet of Things (IIoT) systems connect a plethora of smart devices, such as
sensors, actuators, and controllers, to enable efficient industrial productions in manners …

Machine learning in bioprocess development: from promise to practice

LM Helleckes, J Hemmerich, W Wiechert… - Trends in …, 2023 - cell.com
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess
development provides large amounts of heterogeneous experimental data, containing …

When deep learning-based soft sensors encounter reliability challenges: a practical knowledge-guided adversarial attack and its defense

R Guo, H Liu, D Liu - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …

Learning deep multimanifold structure feature representation for quality prediction with an industrial application

C Liu, K Wang, Y Wang, X Yuan - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Due to the existence of complex disturbances and frequent switching of operational
conditions characteristics in the real industrial processes, the process data under different …

A supervised bidirectional long short-term memory network for data-driven dynamic soft sensor modeling

CF Lui, Y Liu, M **e - IEEE Transactions on Instrumentation …, 2022 - ieeexplore.ieee.org
Data-driven soft sensors have been widely adopted in industrial processes to learn hidden
knowledge automatically from process data, then to monitor difficult-to-measure quality …

Quality-driven regularization for deep learning networks and its application to industrial soft sensors

C Ou, H Zhu, YAW Shardt, L Ye, X Yuan… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
The growth of data collection in industrial processes has led to a renewed emphasis on the
development of data-driven soft sensors. A key step in building an accurate, reliable soft …

A survey of emergencies management systems in smart cities

DG Costa, JPJ Peixoto, TC Jesus, P Portugal… - IEEE …, 2022 - ieeexplore.ieee.org
The rapid urbanization process in the last century has deeply changed the way we live and
interact with each other. As most people now live in urban areas, cities are experiencing …

On paradigm of industrial big data analytics: From evolution to revolution

Z Yang, Z Ge - IEEE Transactions on Industrial Informatics, 2022 - ieeexplore.ieee.org
The arrival of the intelligent manufacturing and industrial internet era brings more and more
opportunities and challenges to modern industry. Specifically, the revolution of the …

Detection of mulberry ripeness stages using deep learning models

SHM Ashtiani, S Javanmardi, M Jahanbanifard… - IEEE …, 2021 - ieeexplore.ieee.org
Ripeness classification is one of the most challenging tasks in the postharvest management
of mulberry fruit. The risks of microbial contamination and human error in manual sorting are …

[HTML][HTML] Adhesively bonded joints–a review on design, manufacturing, experiments, modeling and challenges

Y Wei, X **, Q Luo, Q Li, G Sun - Composites Part B: Engineering, 2024 - Elsevier
This paper provides a state-of-the-art review on adhesively bonded joints (ABJs) for
composite materials and structures, with a focus on the open literatures from 2016 to 2023 to …