Autonomic Intelligent Cyber Sensor (AICS)
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97 MATHEMATICS AND COMPUTING↗
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Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications
There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.
97 MATHEMATICS AND COMPUTING↗
A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control
In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Robust Predictive Design of Field Measurements for Evapotranspiration Barriers Using Universal Multiple linear Regression
Surface barriers are normally installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, we used an existing data set, model-simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR) to optimize the required number of sensors in a 2-m thick surface barrier. To understand the usefulness of implementing predictive uMLR to accommodate multiple monitoring objectives, we compare several network designs, selected based on down-sampling of existing data, with a recommended sensor design based on model simulations performed without consideration of existing data. We further added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We discovered that uMLR, combined with robust decision-making, provides a simple, flexible, and high-quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.
58 GEOSCIENCES↗
Malcolm Deployment Guide for Solar Power Generation Plants
This guide provides detailed instructions for deploying Malcolm in Solar Power Generation systems. It covers the deployment process, from understanding the network architecture of these systems to configuring network switches and Switched Port Analyzer (SPAN) ports or mirror ports or TAPs. The guide also includes best practices for deploying Hedgehog sensors, another critical component in these systems. Following this guide, users can enhance network visibility, improve their system’s security, and effectively troubleshoot common issues.
14 SOLAR ENERGY↗
Addressing Data Veracity in Big Data Applications
Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. Such scenarios lead to partial data problem and raise the issue of data veracity in big data applications. We describe a novel solution to the problem of making short term predictions (up to a few hours ahead) in absence of real-time data from sensors in Smart Grid. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sensors. We thus reduce the communication complexity involved in transmitting sensory data in Smart Grids. We use real-world electricity consumption data from smart meters to empirically demonstrate the usefulness of our method. Our dataset consists of data collected at 15-min intervals from 170 smart meters in the USC Microgrid for 7 years, totaling 41,697,600 data points.
97 MATHEMATICS AND COMPUTING↗
Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NO x /NH 3 quantification
Sensors to detect mixtures of NO x /NH 3 are needed to monitor emissions of diesel automobiles where a selective catalytic reduction system uses an NH 3 mediated reaction to reduce NO x . We report on the application of a three electrode La 0.8 Sr 0.2 CrO 3 , Au 0.5 Pd 0.5 , Pt mixed potential sensor using yttria-stabilized-zirconia (YSZ) as a solid electrolyte to NO x /NH 3 sensing. Artificial neural networks were used to automatically decode the concentrations of NO x /NH 3 and errors of less than 15% are achieved. The optimal architecture for ANN decoding and the maximum density of training data points are also determined. The stability of the sensor was monitored by electrochemical impedance spectroscopy. The impedance associated with YSZ oxygen ion conduction and the electrochemical reactions at the three-phase interface are tracked for a period of over 100 days.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Sensor selection and tool wear prediction with data‐driven models for precision machining
Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.
Han, Seulki↗
Data Release Report for Source Physics Experiments 2 and 3 (SPE-2 and SPE-3) Nevada National Security Site
The second Source Physics Experiment shot (SPE-2) was conducted in Nevada on October 25, 2011, at 1900:00.011623 Greenwich Mean Time (GMT). The explosive source was 997 kilograms (kg) trinitrotoluene (TNT) equivalent of sensitized heavy ammonium fuel oil (SHANFO) detonated at a depth of 45.7 meters (m). The third Source Physics Experiment shot (SPE-3) was conducted in Nevada on July 24, 2012, at 1800:00.44835 GMT. The explosive source was 905 kg TNT equivalent of SHANFO detonated at a depth of 45.8 m. Both shots were recorded by an extensive set of instrumentation that includes sensors both at near-field (less than 100 m) and far-field (100 m or greater) distances. The near-field instruments consisted of three-component accelerometers deployed in boreholes at 15, 46, and 55 m depths around the shot and a set of single-component vertical accelerometers on the surface. The far-field network was composed of a variety of seismic and acoustic sensors, including short-period geophones, broadband seismometers, three-component accelerometers, and rotational seismometers at distances of 100 m to 25 kilometers. This report coincides with the release of these data for analysts and organizations that are not participants in this program. This report describes the second and third Source Physics Experiment shots and the various types of near-field and far-field data that are available.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Data Release Report for Source Physics Experiments 2 and 3 (SPE-2 and SPE-3) Nevada National Security Site
The second Source Physics Experiment shot (SPE-2) was conducted in Nevada on October 25, 2011, at 1900:00.011623 Greenwich Mean Time (GMT). The explosive source was 997 kilograms (kg) trinitrotoluene (TNT) equivalent of sensitized heavy ammonium fuel oil (SHANFO) detonated at a depth of 45.7 meters (m). The third Source Physics Experiment shot (SPE-3) was conducted in Nevada on July 24, 2012, at 1800:00.44835 GMT. The explosive source was 905 kg TNT equivalent of SHANFO detonated at a depth of 45.8 m. Both shots were recorded by an extensive set of instrumentation that includes sensors both at near-field (less than 100 m) and far-field (100 m or greater) distances. The near-field instruments consisted of three-component accelerometers deployed in boreholes at 15, 46, and 55 m depths around the shot and a set of single-component vertical accelerometers on the surface. The far-field network was composed of a variety of seismic and acoustic sensors, including short-period geophones, broadband seismometers, three-component accelerometers, and rotational seismometers at distances of 100 m to 25 kilometers. This report coincides with the release of these data for analysts and organizations that are not participants in this program. This report describes the second and third Source Physics Experiment shots and the various types of near-field and farfield data that are available.This revised document includes reports on baseline shift corrections for the SPE-2 and SPE-3 shots that were missing from the original January 2015 version.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators
Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations . These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.
Hossain, Raisa↗
Data Release Report for Source Physics Experiment 4Prime (SPE-4Prime) Nevada National Security Site (Revision 1)
The fourth test in the Source Physics Experiment series (SPE-4Prime) was conducted at a granite site in Nevada on May 21, 2015, at 18:35:44.000,003,989 ± 20 nanoseconds Coordinated Universal Time. The explosive source was 69.1 kilograms (kg) (including detonator assembly) of Composition C4 plastic explosive (89.14 kg TNT equivalent) detonated at a depth of 87.2 meters (m). The explosion was recorded by an extensive set of instrumentation that includes sensors both at near-field (less than 100 m) and far-field (100 m or greater) distances. The near-field instruments consisted of three-component accelerometers installed at various depths from 7 to 99 m in boreholes positioned around the source hole, and a set of single-component vertical accelerometers on the surface. The far-field network was composed of a variety of seismic and acoustic sensors, including short-period geophones, broadband seismometers, and three-component accelerometers at distances of 100 m to 25 kilometers. Infrasound data were collected from ground-based sensor arrays at distances out to 5 kilometers. This revised document includes a new Section 3.2.2, which is a discussion of clock timing issues discovered at four far-field stations.
58 GEOSCIENCES↗
Data Release Report for Source Physics Experiment 4Prime (SPE-4Prime) Nevada National Security Site
The fourth test in the Source Physics Experiment series (SPE-4Prime) was conducted at a granite site in Nevada on May 21, 2015, at 18:35:44.000,003,989 ± 20 nanoseconds Coordinated Universal Time. The explosive source was 69.1 kilograms (kg) (including detonator assembly) of Composition C4 plastic explosive (89.14 kg TNT equivalent) detonated at a depth of 87.2 meters (m). The explosion was recorded by an extensive set of instrumentation that includes sensors both at near-field (less than 100 m) and far-field (100 m or greater) distances. The near-field instruments consisted of three-component accelerometers installed at various depths from 7 to 99 m in boreholes positioned around the source hole, and a set of single-component vertical accelerometers on the surface. The far-field network was composed of a variety of seismic and acoustic sensors, including short-period geophones, broadband seismometers, and three-component accelerometers at distances of 100 m to 25 kilometers. Infrasound data were collected from ground-based sensor arrays at distances out to 5 kilometers.
58 GEOSCIENCES↗
Data Release Report Source Physics Experiments 5 and 6 (SPE-5 and SPE-6), Nevada National Security Site
The fifth Source Physics Experiment test (SPE-5) was conducted in Nevada on April 26, 2016, at 20:49:00 Coordinated Universal Time (UTC). The explosive source was 4,288.7 kilograms (kg) of polymer-bonded explosive (PBXN) –110, equivalent to 5,035 kg of trinitrotoluene (TNT) detonated at a depth of 76.5 meters (m). The sixth Source Physics Experiment test (SPE-6) was conducted in Nevada on October 12, 2016 at 18:36:00 UTC. The explosive source was 1,982.2 kg of PBXC-141, equivalent to 2,240.2 kg of TNT detonated at the depth of 31.4 m. Both tests were recorded by an extensive set of instrumentation that includes sensors both at near-field (less than 100 m) and far-field (100 m or greater) distances. The near-field instruments consisted of three-component accelerometers installed at various depths ranging from 4 to 99 m below ground surface in boreholes positioned around the shot, and a set of single-component vertical accelerometers on the surface. The far-field network was composed of a variety of seismic and acoustic sensors, including short-period geophones, broadband seismometers, and three-component accelerometers at distances of 100 m to 400 kilometers. In addition, both explosions were recorded in a temporary array of 996 geophones arranged in a densely spaced grid pattern (“large N”). This report coincides with the release of these data for analysts and organizations that are not participants in this program. This report describes the fifth and sixth Source Physics Experiment tests and the various types of near-field and far-field data that are available.
58 GEOSCIENCES↗
Designing an Intrusion Detection for an Adjustable Speed Drive System Controlling a Critical Process
In this article, we address the cyber-security problem of industrial control systems (ICSs) when their sensor measurements may be compromised due to an attacker who has intercepted those measurements via a network. We introduce a general-purpose method “Dynamic Watermarking (DW)” to detect potential cyber-intrusions on speed sensor measurements within industrial control systems, which deploy an adjustable speed drive (ASD) to control a critical process. The DW method is injecting a random private low-amplitude signal with a zero mean Gaussian distribution, “watermark”, into one of the input phase voltages powering the ASD system. The watermark signal propagates through the system including pulse width modulation (PWM) power conversion stage and motor, then ultimately appears in the speed sensor measurements. By deploying two statistical DW tests with two proper thresholds, the system can detect potential cyber-intrusions or unobservable cyber-attacks such as replay attacks and false data injection attacks (FDIA). The DW method tested on a laboratory-scale ASD system experimentally to protect the system against cyber-intrusions. This system, powered by a commercial PWM drive operating at 208 V, 3-phase, and 3.7 kW, served as our experimental platform.
42 ENGINEERING↗
Retina-inspired narrowband perovskite sensor array for panchromatic imaging
The retina is the essential part of the human visual system that receives light, converts it to neural signal, and transmits to brain for visual recognition. The red, green, and blue (R/G/B) cone retina cells are natural narrowband photodetectors (PDs) sensitive to R/G/B lights. Connecting with these cone cells, a multilayer neuro-network in the retina provides neuromorphic preprocessing before transmitting to brain. Inspired by this sophistication, we develop the narrowband (NB) imaging sensor combining R/G/B perovskite NB sensor array (mimicking the R/G/B photoreceptors) with a neuromorphic algorithm (mimicking the intermediate neural network) for high-fidelity panchromatic imaging. Compared to commercial sensors, we use perovskite “intrinsic” NB PD to exempt the complex optical filter array. In addition, we use an asymmetric device configuration to collect photocurrent without external bias, enabling a power-free photodetection feature. These results display a promising design for efficient and intelligent panchromatic imaging.
42 ENGINEERING↗
A multiscale anisotropic polymer network model coupled with phase field fracture
The study of polymers has continued to gain substantial attention due to their expanding range of applications, spanning essential engineering fields to emerging domains like stretchable electronics, soft robotics, and implantable sensors. These materials exhibit remarkable properties, primarily stemming from their intricate polymer chain network, which, in turn, increases the complexity of precisely modeling their behavior. Especially for modeling elastomers and their fracture behavior, accurately accounting for the deformations of the polymer chains is vital for predicting the rupture in highly stretched chains. Despite the importance, many robust multiscale continuum frameworks for modeling elastomer fracture tend to simplify network deformations by assuming uniform behavior among chains in all directions. Recognizing this limitation, our study proposes a multiscale fracture model that accounts for the anisotropic nature of elastomer network responses. At the microscale, damage in the chains is assumed to be driven by both the chain's entropy and the internal energy due to molecular bond distortions. In order to bridge the stretching in the chains to the macroscale deformation, we employ the maximal advance path constraint network model, inherently accommodating anisotropic network responses. As a result, chains oriented differently can be predicted to exhibit varying stretch and, consequently, different damage levels. To drive macroscale fracture based on damages in these chains, we utilize the micromorphic regularization theory, which involves the introduction of dual local-global damage variables at the macroscale. The macroscale local damage variable is obtained through the homogenization of the chain damage values, resulting in the prediction of an isotropic material response. The macroscale global damage variable is subjected to nonlocal effects and boundary conditions in a thermodynamically consistent phase field continuum formulation. Moreover, the total dissipation in the system is considered to be mainly due to the breaking of the molecular bonds at the microscale. To validate our model, we employ the double-edge notched tensile test as a benchmark, comparing simulation predictions with existing experimental data. Additionally, to enhance our understanding of the fracturing process, we conduct uniaxial tensile experiments on a square film made up of polydimethylsiloxane (PDMS) rubber embedded with a hole and notches and then compare our simulation predictions with the experimental observations. Furthermore, we visualize the evolution of stretch and damage values in chains oriented along different directions to assess the predictive capacity of the model. In conclusion, the results are also compared with another existing model to evaluate the utility of our model in accurately simulating the fracture behavior of rubber-like materials.
42 ENGINEERING↗
A gimbal platform stabilization for topographic applications
The aim of this work is the stabilization of a Gimbal platform for optical sensors acquisitions in topographic applications using mobile vehicles. The stabilization of the line of sight (LOS) consists in tracking the command velocity in presence of nonlinear noise due to the external environment. The hardware architecture is characterized by an Ardupilot platform that allows the control of both the mobile device and the Gimbal. Here we developed a new approach to stabilize the Gimbal platform, which is based on neural network. For the control system, we considered a plant that represents the transfer function of the servo system control model for an inertial stabilized Gimbal platform. The transductor used in the feed-back line control is characterized by the Rate Gyro transfer function installed onboard of Ardupilot. For the simulation and investigation of the system performance, we used the Simulink tool of Matlab. Results show that the hardware/software approach is efficient, reliable and cheap for direct photogrammetry, as well as for general purpose applications using mobile vehicles.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗