Published September 23, 2021
| Version v1
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Dataset and Code for "Unsupervised anomaly detection based on minimum spanning tree approximated distance measures and its application to hydropower turbines"
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Description
This set of files provides the report for reproducing the results in the paper, Ahmed, Dagnino, and Ding, 2019, “Unsupervised anomaly detection based on minimum spanning tree approximated distance measures and its application to hydropower turbines,” IEEEE Transactions on Automation Science and Engineering, Vol. 16(2), pp. 654-667 .
Files
J67_code&data.zip
Files
(9.1 MB)
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md5:dc82f3a60325fecc5a6adc955b6510df
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md5:6bbb6e98fc0aaf177f682c6478acf7ea
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Additional details
References
- Ahmed, Dagnino, and Ding, 2019, "Unsupervised anomaly detection based on minimum spanning tree approximated distance measures and its application to hydropower turbines," IEEEE Transactions on Automation Science and Engineering, Vol. 16(2), pp. 654-667