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Sensors | Free Full-Text | Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty - Vimarsana News

Sensors | Free Full-Text | Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty

In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detecte...

Source: mdpi.com
5 Data Mining Techniques You Should Know About - Vimarsana News

5 Data Mining Techniques You Should Know About

Data mining is the process of determining patterns to create insights and solutions for organizations.

The top tool of data clustering - Vimarsana News

The top tool of data clustering

Richard Steele describes how ‘clustering’ techniques can be a useful part of any data analysis toolkit

JST Press Release:Classification of 16 Adult Sleep Patterns Based on Large-scale Sleep Analysis - Vimarsana News

JST Press Release:Classification of 16 Adult Sleep Patterns Based on Large-scale Sleep Analysis

A research group of The University of Tokyo led by Professor Hiroki Ueda (also a Riken team leader) and Machiko Katori, and Assistant Professor Shoi Shi (RIKEN) used ACCEL, an original machine learning algorithm developed by their research laboratory, to determine sleep and waking states based on arm acceleration and converted the acceleration data of approximately 100,000 people in the UK Biobank into sleep data, which was then analyzed in detail. They found that the sleep patterns of these 100,000 people could be classified into 16 different types.

Source: jst.go.jp