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An Algorithm for Fast Fault Location and Classification Based on Mathematical Morphology and Machine Learning.
Abstract not provided.
Testing Machine Learned Fault Detection and Classification on a DC Microgrid.
Abstract not provided.
A Machine Learning-based Method using the Dynamic Mode Decomposition for Fault Location and Classification.
Abstract not provided.
A Keyword-Enhanced Approach to Handle Class Imbalance in Clinical Text Classification
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Attention Mechanisms in Clinical Text Classification: A Comparative Evaluation
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Classification of fNIRS Finger Tapping Data With Multi-Labeling and Deep Learning
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Automatic Particle Trajectory Classification in Plasma Simulations
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VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
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Reactor Facility Operational State Classification Using Gas Effluents and Radiation Measurements
Inferring the operational state of a reactor facility, using measurements from an independent in-situ monitoring system, is critical to the assessment of its compliance to agreements. We consider the problem of inferring the on/off operational state of a facility by using gas effluents and radiation measurements collected at the reactor's off-gas ventilation stack and within its surrounding area. Effluent release concentrations of three noble gases, namely, Ar-41, Cs-138, and Xe-138, are collected inside the ventilation stack and provided every four hours. Gamma spectra is recorded every second by a radiation detector located about 100 meters offset from the stack, which is affected by weather and nearby conditions. We study classifiers based on the correlation coefficient (CC) method [1] applied to individual modalities and their combinations. We implement CC classifiers using ground truth measurements collected over 2 operational cycles of High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) in year 2017.
A Multiresolution Technique for the Classification of Precipitation Echoes in Radar Data
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Classification and Reconstruction of High-Dimensional Signals From Low-Dimensional Features in the Presence of Side Information
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On/Off State Classification of a Reactor Facility Using Gas Effluence Measurements
Inferring on/off operational state of a reactor facility using measurements from an independent monitoring system is critical to the assessment of its compliance to agreements. In this paper, we consider the problem of inferring on/off state of a reactor facility using measurements of Ar-41, Cs-138, and Xe-138 gas effluence types collected at the facility’s off-gas stack. We present classifiers based on thresholding measurements of individual effluence types, and then present fusers that combine their outputs or measurements. We present five fusers based on simple majority rule, Chow’s pattern recognition function, Fisher’s combined p-value statistic, physics-based Poisson radiation counts model, and correlation coefficient method. In addition, we also test five machine learning methods based on non-linear classifiers, which are available as R packages. Finally, our results show that: (i) these gas effluence measurements are effective in inferring on/off state of a reactor facility; for example, best fusers achieve ~97% detection at ~1% false alarm rate, and (ii) fusers that combine all effluence types based on physics-based models, correlation coefficient and Fisher’s method outperform simple majority rule, Chow’s fusers and machine learning methods, as well as when they are applied to individual and pairs of effluence types.