ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
Latest Magazine Issues
Apr 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
May 2024
Nuclear Technology
Fusion Science and Technology
Latest News
DOE issues final RFQ for WIPP clean energy initiative
The Department of Energy’s Office of Environmental Management has issued a request for qualifications for interested parties and prospective offerors looking to enter into a realty agreement for carbon-pollution-free electricity (CFE) projects at the department’s Waste Isolation Pilot Plant site in southeastern New Mexico.
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.