Principal component analysis
Principal component analysis is a versatile statistical method for reducing a cases-by-
variables data table to its essential features, called principal components. Principal …
variables data table to its essential features, called principal components. Principal …
A review on soft sensors for monitoring, control, and optimization of industrial processes
Over the past twenty years, numerous research outcomes have been published, related to
the design and implementation of soft sensors. In modern industrial processes, various types …
the design and implementation of soft sensors. In modern industrial processes, various types …
The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management
Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …
people. Accurate flood forecasts and control are essential to lessen these effects and …
Changes in energy consumption according to building use type under COVID-19 pandemic in South Korea
An unprecedented global lockdown has been implemented for controlling the spread of
COVID-19 in many countries. These actions are reducing the number of coronics, but with …
COVID-19 in many countries. These actions are reducing the number of coronics, but with …
Parallel spatio-temporal attention-based TCN for multivariate time series prediction
J Fan, K Zhang, Y Huang, Y Zhu, B Chen - Neural Computing and …, 2023 - Springer
As industrial systems become more complex and monitoring sensors for everything from
surveillance to our health become more ubiquitous, multivariate time series prediction is …
surveillance to our health become more ubiquitous, multivariate time series prediction is …
Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions
Spatiotemporal forecasting (STF) extends traditional time series forecasting or spatial
interpolation problem to space and time dimensions. Here, we review the statistical, physical …
interpolation problem to space and time dimensions. Here, we review the statistical, physical …
Fault-tolerant soft sensors for dynamic systems
H Chen, B Huang - IEEE Transactions on Control Systems …, 2023 - ieeexplore.ieee.org
Unpredicted faults occurring in automation systems deteriorate the performance of soft
sensors and may even lead to incorrect results. To address the problem, this study develops …
sensors and may even lead to incorrect results. To address the problem, this study develops …
AIoT for sustainable manufacturing: Overview, challenges, and opportunities
The integration of IoT and AI has gained significant attention as an emerging means to
digitize manufacturing industries and drive sustainability in the context of Industry 4.0. In …
digitize manufacturing industries and drive sustainability in the context of Industry 4.0. In …
[HTML][HTML] Latent variable models in the era of industrial big data: Extension and beyond
A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …
technique in modern industry. Among various data-driven methods, latent variable models …
Scenario-based automated data preprocessing to predict severity of construction accidents
Occupational accidents are common in the construction industry, therefore develo**
prediction models to detect high severe accidents would be useful. However, existing …
prediction models to detect high severe accidents would be useful. However, existing …