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Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping
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DOI：10.3390/s25041183.png)
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摘要
En 中文
The Chalk River Laboratories (CRL) site in Ontario, Canada, has long been a hub for nuclear research, which has resulted in the accumulation of legacy nuclear waste, including radioactive materials such as uranium, plutonium, and other radionuclides. Effective management of this legacy requires precise contamination and risk assessments, with a particular focus on the concentration levels of fissile materials such as U235. These assessments are essential for maintaining nuclear criticality safety. This study estimates the upper bounds of U235 concentrations. We investigated the use of a hybrid parametric bootstrapping method and robust statistical techniques to analyze datasets with outliers, then compared these outcomes with those derived from nonparametric bootstrapping. This study underscores the significance of measuring U235 for ensuring safety, conducting environmental monitoring, and adhering to regulatory compliance requirements at nuclear legacy sites. We used publicly accessible U235 data from the Eastern Desert of Egypt to demonstrate the application of these statistical methods to small datasets, providing reliable upper limit estimates that are vital for remediation and decommissioning efforts. This method seeks to enhance the interpretation of sensor data, ultimately supporting safer nuclear waste management practices at legacy sites such as CRL.
Keyword:
hybrid parametric bootstrapping
small dataset
nonparametric bootstrapping
robust statistical method
upper confidence limit
density of granite from the eastern desert of Egypt
most frequent value
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
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