Robust imputation method with context-aware voting ensemble model for management of water-quality data
Water-quality monitoring and management are crucial for ensuring the safety and
sustainability of water resources. However, missing data is a frequent problem in water …
sustainability of water resources. However, missing data is a frequent problem in water …
Spatiotemporal generative adversarial imputation networks: An approach to address missing data for wind turbines
X Hu, Z Zhan, D Ma, S Zhang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Wind turbine data collection often suffers from missing data due to network blockage and
sensor failure. Existing data imputation methods require complete datasets for training and …
sensor failure. Existing data imputation methods require complete datasets for training and …
A missing manufacturing process data imputation framework for nonlinear dynamic soft sensor modeling and its application
L Ma, M Wang, K Peng - Expert Systems with Applications, 2024 - Elsevier
Data-driven soft sensors have been widely used in manufacturing processes for product
quality prediction. However, in engineering practice, traditional linear soft sensors are …
quality prediction. However, in engineering practice, traditional linear soft sensors are …
Machine learning-based ensemble classifiers for anomaly handling in smart home energy consumption data
PP Kasaraneni, Y Venkata Pavan Kumar, GLK Moganti… - Sensors, 2022 - mdpi.com
Addressing data anomalies (eg, garbage data, outliers, redundant data, and missing data)
plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on …
plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on …
Missing data imputation for industrial time series with adaptive median iteration based on generative adversarial networks
Time series in industrial processes often exhibits missing data caused by inevitable factors
such as equipment failures and sensor errors. These missing data include vital information …
such as equipment failures and sensor errors. These missing data include vital information …
Time Series Data and Recent Imputation Techniques for Missing Data: A Review
The development of multisensory systems and the ongoing application of data collection
technologies have both contributed to the explosion of time series data. However, due to …
technologies have both contributed to the explosion of time series data. However, due to …
A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers
Simple Summary Earthquakes are a common occurrence at The Geysers geothermal field in
California which, over the years, have led to general land sinking in the area. In our study …
California which, over the years, have led to general land sinking in the area. In our study …
Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning
In practice, time series data obtained is usually small and missing, which poses a great
challenge to data analysis in different domains, such as increasing the bias of model …
challenge to data analysis in different domains, such as increasing the bias of model …
Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes
K Purna Prakash, YVP Kumar… - Energy Exploration …, 2024 - journals.sagepub.com
Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems
to optimize energy consumption. However, these systems frequently encounter issues with …
to optimize energy consumption. However, these systems frequently encounter issues with …
Wasserstein adversarial learning for identification of power quality disturbances with incomplete data
PQDs have adverse impacts on the safe operation and reliability of the modern integrated
power system so it is of great necessity to identify them. Existence of missing measurement …
power system so it is of great necessity to identify them. Existence of missing measurement …