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Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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Las Vegas, NV|Mandalay Bay Resort and Casino
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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!
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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.
Miltiadis Alamaniotis, Sangkyu Lee, Tatjana Jevremovic
Nuclear Technology | Volume 191 | Number 1 | July 2015 | Pages 41-57
Technical Paper | Radiation Transport and Protection | doi.org/10.13182/NT14-75
Articles are hosted by Taylor and Francis Online.
Radioisotope identification from low-count-rate spectra or spectra obtained through low-resolution detectors constitutes a challenging environment for accurate spectral analysis. The use of intelligent processing algorithms is a significant step in analyzing spectra, conceivably increasing the accuracy of the nuclide identification in such scenarios. This paper introduces an intelligent methodology for automated processing of low-count gamma-ray spectra acquired with a scintillation detector aimed at identifying radioisotope patterns, and it evaluates the performance of this methodology against a set of experimentally acquired gamma-ray spectra. The novel methodology adopts tools from the “artificial intelligence library” to preprocess the spectrum and subsequently identify radioisotopes. In particular, in the preprocessing step, the measured spectrum is divided into equally long energy intervals, whose values are replaced with those computed by a support vector regressor equipped with a linear kernel function. In the next step, the fuzzy logic–based identifier matches spectral peaks with entries in the spectral library, aiming at identifying isotopic signatures in the spectrum. The proposed intelligent methodology is benchmarked against the multiple-linear-regression (MLR) spectrum-fitting algorithm. Assessment results demonstrate the effectiveness of the proposed methodology in identifying isotopes compared with the MLR algorithm by significantly reducing the number of false detections and improving correct detection performance. Furthermore, the proposed methodology exhibits an overall higher detection sensitivity (by 13.3%) and precision (by 46.8%) than those obtained with MLR.