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This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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Framatome signs contracts with Sizewell C
French nuclear developer Framatome is slated to deliver key equipment for Sizewell C Ltd.’s two large reactors planned for the United Kingdom’s Suffolk coast.
The agreement, reportedly worth multiple billions of euros, was announced this week and will involve Framatome from the design phase until commissioning. The company also agreed to a long-term fuel supply deal. Framatome is 80.5 percent owned by France’s EDF and 19.5 percent owned by Mitsubishi Heavy Industries.
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.