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Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
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Fusion Science and Technology
25th anniversary of failure (and a chance at opportunity)
Silver anniversaries are usually a cause for great celebration, but this one is a cause for regret and a renewed plea for action. January 31, 2023, marks the 25th anniversary of the Department of Energy’s failure under law and contract to start the removal of spent nuclear fuel (SNF) from the country’s 74 commercial nuclear power plant sites in 35 states for disposal per the Nuclear Waste Policy Act of 1982. I personally know the moral and legal responsibility associated with this failure, because in 1998, as the then responsible DOE official, I had to personally announce it. But much has changed since then. There are many new avenues to address these past problems like federal, state, and community partnerships that can be beneficial for all, if there is DOE leadership and congressional action for both disposal and storage.
Lázaro Emílio Makili, Jesús A. Vega Sánchez, Sebastián Dormido-Canto
Fusion Science and Technology | Volume 62 | Number 2 | October 2012 | Pages 347-355
Selected Paper from the Seventh Fusion Data Validation Workshop 2012 (Part 1) | doi.org/10.13182/FST12-A14626
Articles are hosted by Taylor and Francis Online.
This paper addresses the problem of finding a minimal and good enough training data set for classification purposes by using active learning and conformal predictors. Active learning means to have control in the selection process of training samples instead of choosing them in a random way. To this end, active learning methodologies look for establishing selection criteria in order to find out the samples that show better discrimination capabilities. In the present case, conformal predictors have been used for these purposes. Results will be presented in a five-class classification problem with images. The features are the vertical detail coefficients of the Haar wavelet transform at level four to diminish the sample dimensionality by reducing the spatial redundancy of the images. The active selection of training sets (through the reliability measures of a conformal predictor) allows the improvement of the classifiers. Here, the word "improvement" refers to obtaining higher generalization properties thereby avoiding overfitting. Support vector machines classifiers, in the one-versus-the-rest approach, have been used as the underlying classifiers.