James Ghawaly - Academia.edu (original) (raw)

Papers by James Ghawaly

This article presents an analysis of the method of construction and results for a classifier inte... more This article presents an analysis of the method of construction and results for a classifier intended to identify vehicles using low-frequency acoustic data collected by a distributed sensor network. This data is collected as part of a venture intended to explore data analytics and multisensor fusion techniques for the monitoring of activities at a test bed nuclear facility located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. We describe the associated target signature and design a classifier based on a multilayer perceptron, followed by an analysis of its results. We discuss how overall accuracy is not the only consideration in constructing this classifier, and how for this application, it is actually desirable to operate at a lower level of accuracy in exchange for a reduction in the false alarm rate, as well as how this relates to the actual deployment of the classifier in practical use.

Engineering Applications of Artificial Intelligence, May 1, 2022

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Feb 5, 2020

The detection, identification, and localization of illicit nuclear materials in urban environment... more The detection, identification, and localization of illicit nuclear materials in urban environments is of utmost importance for national security. Most often, the process of performing these operations consists of a team of trained individuals equipped with radiation detection devices that have built-in algorithms to alert the user to the presence nuclear material and, if possible, to identify the type of nuclear material present. To encourage the development of new detection, radioisotope identification, and source localization algorithms, a dataset consisting of realistic Monte Carlo-simulated radiation detection data from a 2 in. × 4 in. × 16 in. NaI(Tl) scintillation detector moving through a simulated urban environment based on Knoxville, Tennessee, was developed and made public in the form of a Topcoder competition. The methodology used to create this dataset has been verified using experimental data collected at the Fort Indiantown Gap National Guard facility. Realistic signals from special nuclear material and industrial and medical sources are included in the data for developing and testing algorithms in a dynamic real-world background.

Journal of Nuclear Engineering

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an... more The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ri...

Proceedings of the 2023 International Conference on Neuromorphic Systems

This work reports on new results and insights from the optimization of spiking neural networks de... more This work reports on new results and insights from the optimization of spiking neural networks developed for gamma-ray radiation anomaly detection. Our previous paper introduced the first known neuromorphic algorithm for this application, demonstrating promising results and insights into optimal hyperparameter selectionparticularly in the choice of data input encodings. Since the first paper, we have tested the algorithms on new datasets to investigate transferability from one background radiation environment to another. We have also performed a new hyperparameter optimization experiment with this new dataset to investigate the impact of new radiation data formatting techniques, the inclusion or neuronal temporality, and neuron charge leakage. This paper provides an overview and discussion of the results from this study. Of note, we report that the inclusion of neuronal temporality, or the process of maintaining synaptic state between sequences of input, improves recall by over 50% at an operationally-relevant false alarm rate of 1 hr −1. CCS CONCEPTS • Applied computing → Physics; • Computing methodologies → Genetic algorithms; Neural networks.

PLOS ONE

As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to... more As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that t...

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 1, 2022

Proceedings of the International Conference on Neuromorphic Systems 2022

Spiking neural networks (SNNs) have recently gained a lot of attention for use in low-power neuro... more Spiking neural networks (SNNs) have recently gained a lot of attention for use in low-power neuromorphic and edge computing. On their own, SNNs are difficult to train, owing to their lack of a differentiable activation function and their inherent tendency towards chaotic behavior. This work takes a strictly neuroscience-inspired approach to designing and training SNNs. We demonstrate that the use of neuromodulated synaptic time dependent plasticity (STDP) can be used to create a variety of different learning paradigms including unsupervised learning, semi-supervised learning, and reinforcement learning. In order to tackle the highly dynamic and potentially chaotic spiking behavior of SNNs both during training and testing, we discuss a variety of neuroscience-inspired hoemeostatic mechanisms for keeping the network\u27s activity in a healthy range. All of these concepts are brought together in the development of a SNN model that is trained and tested on the MNIST handwritten digits d...

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Aug 1, 2021

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 13, 2021

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Oct 1, 2021

Proposed for presentation at the INMM & ESARDA Joint Annual Meeting in , .

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

The detection of anomalous radioactive sources in environments characterized by a high level of v... more The detection of anomalous radioactive sources in environments characterized by a high level of variation in the background radiation is a challenging problem in nuclear security. A variety of natural and artificial sources contribute to background radiation dynamics including variations in the absolute and relative concentrations of naturally occurring radioisotopes in different materials, the wet-deposition of 222^{222}222Rn daughters during precipitation, and background suppression due to physical objects in the detector scene called ``clutter. This dissertation presents a new datacentric algorithm for radiation anomaly detection in dynamic background environments. The algorithm is based on a custom deep neural autoencoder architecture called the Autoencoder Radiation Anomaly Detection (ARAD) model. An autoencoder is a type of neural network that compresses data at its input through a series of computational layers into a dimensionally-constrained representation called the latent spa...

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 1, 2022

The Radiological Anomaly Detection and Identification (RADAI) software package is a python librar... more The Radiological Anomaly Detection and Identification (RADAI) software package is a python library for implementing, training, and storing algorithms that detect and identify anomalies in gamma-ray spectra. The library defines a general framework for implementing detection (binary) and identification (classification) algorithms, objects to encapsulate the results of analyses, a variety of temporal filtering tools that can be used in constructing algorithms, and conceptual design that allows easy reading and writing of algorithms (and their time dependent state). In addition to this framework, the library includes implementations of a variety of algorithms from the scientific literature including: gross-counts k-sigma, SPRT, N-SCRAD, Region of Interest, and Censored Energy Window. The implementation of these algorithms within the RADAI package was done to facilitate user-initiated training and configuration to by applied to different gamma-ray detector types. Finally, benchmarked and...