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Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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2024 ANS Annual Conference
June 16–19, 2024
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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Latest News
X-energy receives federal tax credit for TRISO fuel facility
Advanced reactor company X-energy has been awarded $148.5 million in tax credits under the Inflation Reduction Act for construction of its TRISO-X fuel fabrication facility in Oak Ridge, Tenn.
Miltiadis Alamaniotis, Andreas Ikonomopoulos, Tatjana Jevremovic, Lefteri H. Tsoukalas
Nuclear Technology | Volume 175 | Number 2 | August 2011 | Pages 480-497
Technical Paper | Radiation Measurements and General Instrumentation | doi.org/10.13182/NT11-A12319
Articles are hosted by Taylor and Francis Online.
Nuclear resonance fluorescence (NRF) has been considered as a promising method for cargo inspection. Almost all isotopes existing in nature yield a unique NRF spectral signature. NRF signals obtained during cargo inspection are aggregates of various signatures from materials hidden inside. The challenge is to identify individual signatures embedded in this signature aggregation. Background noise and spectra overlap to further complicate the NRF signal analysis. This paper addresses these concerns through an intelligent methodology recognizing signature spectra and, subsequently, identifying cargo materials. The methodology relies on fuzzy logic for pattern identification and evaluation of the weighted options involved in decision making. The intelligent methodology is presented using different simulated NRF signal scenarios. The results obtained demonstrate that the algorithm is highly accurate in most spectra carrying a signal-to-noise ratio (SNR) >20 db. Misses and false alarms were observed for isotopes with only one NRF peak (lead) with SNR <35 db. Extensive parameter testing under different scenarios indicated the existence of parameter couples that maximize the accuracy even for SNR values <20 db. In all cases the algorithm execution time was <0.1 s and was significantly faster than that of the maximum likelihood algorithm.