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 supervised bidirectional long short-term memory network for data-driven dynamic soft sensor modeling
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 …
knowledge automatically from process data, then to monitor difficult-to-measure quality …
[HTML][HTML] Machine learning applications in off-road vehicles interaction with terrain: An overview
With the advent of artificial intelligence, the analysis of systems related to complex
processes has become possible or easier. The interaction of the traction factor of off-road …
processes has become possible or easier. The interaction of the traction factor of off-road …
Predicting host CPU utilization in the cloud using evolutionary neural networks
Abstract The Infrastructure as a Service (IaaS) platform in cloud computing provides
resources as a service from a pool of compute, network, and storage resources. One of the …
resources as a service from a pool of compute, network, and storage resources. One of the …
A novel virtual sample generation method based on a modified conditional Wasserstein GAN to address the small sample size problem in soft sensing
In the modern chemical industry process, soft sensing has been widely used. However, the
lack of valid and sufficient data has made it difficult to apply advanced soft sensor modeling …
lack of valid and sufficient data has made it difficult to apply advanced soft sensor modeling …
A SIA-LSTM based virtual metrology for quality variables in irregular sampled time sequence of industrial processes
In industrial processes, there are usually strongly dynamic temporal relationship between
process data sequence. Hence, dynamic modeling methods are popular for soft sensing of …
process data sequence. Hence, dynamic modeling methods are popular for soft sensing of …
A multi-objective neural network trained with differential evolution for dynamic economic emission dispatch
Multi-objective optimisation has received considerable attention in recent years as many
real world problems have multiple conflicting objectives. There is an additional layer of …
real world problems have multiple conflicting objectives. There is an additional layer of …
Instantaneous vehicle fuel consumption estimation using smartphones and recurrent neural networks
The high level of air pollution in urban areas, caused in no small extent by road transport,
requires the implementation of continuous and accurate monitoring techniques if emissions …
requires the implementation of continuous and accurate monitoring techniques if emissions …
Differential evolution with dynamic combination based mutation operator and two-level parameter adaptation strategy
Differential evolution (DE) is a simple yet effective algorithm for numerical optimization, and
its performance significantly depends on mutation operator and control parameters …
its performance significantly depends on mutation operator and control parameters …
Lights and shadows in evolutionary deep learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges
Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …
Learning models for several purposes: from the discovery of network topologies and …