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 …
[HTML][HTML] Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain
Wind power forecasting has supported operational decision-making for power system and
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
A survey on state estimation techniques and challenges in smart distribution systems
This paper presents a review of the literature on state estimation (SE) in power systems.
While covering works related to SE in transmission systems, the main focus of this paper is …
While covering works related to SE in transmission systems, the main focus of this paper is …
A comprehensive survey on imputation of missing data in internet of things
The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …
communication technologies, and Internet protocols with broad applications. Collecting data …
Hybrid recommender system based on autoencoders
A standard model for Recommender Systems is the Matrix Completion setting: given
partially known matrix of ratings given by users (rows) to items (columns), infer the unknown …
partially known matrix of ratings given by users (rows) to items (columns), infer the unknown …
A deep probabilistic transfer learning framework for soft sensor modeling with missing data
Soft sensors have been extensively developed and applied in the process industry. One of
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …
Missing modalities imputation via cascaded residual autoencoder
Affordable sensors lead to an increasing interest in acquiring and modeling data with
multiple modalities. Learning from multiple modalities has shown to significantly improve …
multiple modalities. Learning from multiple modalities has shown to significantly improve …
Physics-guided deep neural networks for power flow analysis
Solving power flow (PF) equations is the basis of power flow analysis, which is important in
determining the best operation of existing systems, performing security analysis, etc …
determining the best operation of existing systems, performing security analysis, etc …
Imputation of missing data with neural networks for classification
SJ Choudhury, NR Pal - Knowledge-Based Systems, 2019 - Elsevier
We propose a mechanism to use data with missing values for designing classifiers which is
different from predicting missing values for classification. Our imputation method uses an …
different from predicting missing values for classification. Our imputation method uses an …
Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments
Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic …
(SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic …