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DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2012

Design for the Structure and the Mechanics of Moballs

The moball is envisioned to be a round, self-powered, and wind-driven multifunctioning sensor used in the Gone with the Wind ON-Mars (GOWON) [http://www.lpi.usra.edu/meetings/ marsconcepts2012/pdf/4238.pdf]: A Wind-Driven Networked System of Mobile sensors on Mars. The moballs would have sensing, processing, and communication capabilities. The moballs would perform in situ detection of key environmental elements such as vaporized water, trace gases, wind, dust, clouds, light and UV exposure, temperature, as well as minerals of interest, possible biosignatures, surface magnetic and electric fields, etc. The embedded various low-power micro instruments could include a Multispectral Microscopic Imager (to detect various minerals), a compact curved focal plane array camera (UV/Vis/NIR) with a large field of view, a compact UV/Visible spectrometer, a micro-weather station, etc. The moballs could communicate with each other and an orbiter. Their wind- or gravity-driven rolling movement could be used to harvest and store electric energy. They could also generate and store energy using the sunlight, when available, and the diurnal temperature variations on Mars. The moballs would be self-aware of their (and their neighbors ) positions, energy storage, and memory availability; they would have processing power and could intelligently cooperate with neighboring moballs by distributing tasks, sharing data, and fusing information. The major advantages of using the wind-driven and spherical moball network over rovers or other fixed sensor webs to explore Mars would be: (1) moballs could explore a much larger expanse of Mars in a much faster fashion, (2) they could explore the difficult terrains such as steep slopes and sand dunes, and (3) they would be self-energy- generating and could work together and move around autonomously. The challenge in designing the structure and the mechanics of the moball would be that it should be sturdy enough to withstand the impact of its initial fall, as well as other impacts from obstacles in its way. A mechanism would be needed that could enable hundreds of moballs to be carried while they would be deflated and compact, then would inflate them just after deploying them to their drop site. Furthermore, the moballs should also be light enough to allow them to move easily over obstacles by force of the wind. They also should have some kind of maneuvering mechanism in place to help them avoid very hazardous sharp objects or events, and to enable them to get closer to the objects of interest. The structure of the moballs was designed so that they would have different layers. The outer layer should comprise a sturdy, yet light, polymer that could withstand both the impact of the initial drop, as well as the impact of the different obstacles it would encounter while traversing the surface of Mars. This polymer should not deteriorate with the 100 K daily temperature swings on Mars. The inner layer should consist of a very light gas such as nitrogen or helium. In terms of maneuvering, six very light weights placed at strategic locations would give moballs the ability to turn, or even hop, over hazardous (e.g., sharp) obstacles, or even initiate a movement (before getting more help from the wind to be carried around) when stuck. Maneuvering would be necessary in order to get closer to objects of interest. If the weights would be allowed to move freely, they could also be used to generate energy.

Davoodi, Faranak↗

DOE OSTI2022

Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

Here, we use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the 'mean estimation', whereas its standard deviation can be used to construct 'confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban flow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global field reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-field state estimation from sectional data with two-dimensional CNN. We find that SWAG can obtain physically-interpretable confidence-interval estimates from the perspective of epistemic uncertainty. This capability supports its use for a wide range of problems in science and engineering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Towards Secure Autonomous Vehicles: An Integrated Edge and Multi-Modal Machine Learning Framework for Intrusion Detection

Autonomous vehicles (AVs) are vulnerable to cyberattacks targeting both internal communication networks and external perception sensors. While edge-based intrusion de- tection for Controller Area Network (CAN) buses offers real-time protection, it cannot detect cross-modal threats. Conversely, multi-modal fusion approaches improve coverage but often lack efficiency for in-vehicle deployment. This thesis integrates two complemen- tary solutions: (1) a lightweight, edge-deployable machine learning framework for CAN bus intrusion detection, and (2) a late-fusion system combining CAN FD and LiDAR data. Together, they form a hierarchical defense capable of handling single-modality and coordi- nated attacks. Simulations show that CAN-only models reach 93% accuracy on simulated DoS, spoofing, replay, and fuzzy attacks, while the fusion system achieves 0.87 AUC and 0.82 F1-score at 2 ms latency. This unified framework establishes a scalable, explainable, and field-ready strategy for AV cybersecurity.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

An Authentication Vulnerability Assessment of Connected Lighting Systems

Emerging connected lighting systems (CLS) that incorporate distributed intelligence, network interfaces, and sensors can become data-collection platforms that enable a wide range of valuable new capabilities as well as greater energy savings in buildings and cities. However, CLS technology is currently at an early stage of development, and its increased connectivity introduces cybersecurity risks that are new to the lighting industry and that must be addressed for successful integration with other systems. While a number of existing frameworks, guidelines, and tests for evaluating cybersecurity vulnerability may apply to CLS in whole or in part, there is currently no mandatory requirement for cybersecurity testing or certification. The lighting industry, including technology developers and specification organizations, is currently evaluating the suitability of existing frameworks and guidelines for CLS. To support these efforts, Pacific Northwest National Laboratory (PNNL) is conducting a series of studies intended to educate lighting industry stakeholders on specific cybersecurity practices and characterize their implementation in commercially available CLS with varying system architectures, network-communication technologies, and degrees of maturity. This study demonstrates that tests for authentication vulnerabilities can be developed with objective pass/fail criteria, therby facilitating comparisons between CLS. Based on the limited results of this study, it appears that the CLS that are being brought to market have varying levels of authentication vulnerability. It is hoped that these evaluations will support and perhaps accelerate industry discussions on the risks of specific security vulnerabilities, what vulnerabilities should be addressed by in-development of future lighting-specific best practices, and whether any such practices should be included in voluntary lighting standards.

42 ENGINEERING↗

DOE OSTI2021

Relocating microseismicity from downhole monitoring of the Decatur CCS site using a modified double-difference algorithm

SUMMARY The injection of CO2 at the Decatur carbon capture and storage site has generated significant microseismic activity, which occurs in distinct spatial clusters up to approximately 2.2 km from the primary injection well. Accurate and precise event locations are vital for the characterization of the microseismicity to help understand the reservoir response to the CO2 injection, whilst enabling the identification of minor faults and fractures below the resolution of conventional active seismic imaging. However, microseismic monitoring of fluid injection sites, such as Decatur, is often performed using a network of borehole sensors often from a single well. While these downhole sensors have excellent detection capabilities, their poor azimuthal coverage limits the ability to precisely determine event locations. We have developed a modified double-difference relocation algorithm suitable for both 1-D and 3-D velocity models, and which incorporates differential back azimuth observations to allow the benefits of the original double-difference algorithm to be applicable to a downhole microseismic monitoring setting. Applying the modified double-difference algorithm to the microseismicity at Decatur, we have successfully relocated 4293 events. The relocation included over 59 million observations for 757 285 event pairs, split across seven geographic regions. Despite the majority of observations being recorded in only two boreholes, with an almost identical azimuthal coverage, the results have shown to be reliable with significantly reduced residuals and low uncertainties associated with the final locations. We have analysed the residuals in terms of their association with each geographic region, data type, station and individual events, to fully appreciate their influence in the inversion and the fit of the data to the final set of event locations. For each region, the relocated seismicity has become less diffuse with improved clustering, and with newly visible linear features often orientated in a NE–SW direction. These results show the potential improvements that can be made to microseismic event locations recorded by a borehole network with a limited and variable azimuthal distribution.

58 GEOSCIENCES↗

DOE OSTI2024

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

Implementation of Sensor and Control Designs for Bioregenerative Systems

The goal of the Spring 1990 EGM 4001 Design class was to design, fabricate, and test sensors and control systems for a closed loop life support system (CLLSS). The designs investigated were to contribute to the development of NASA's Controlled Ecological Life Support System (CELSS) at Kennedy Space Center (KSC). Designs included a seed moisture content sensor, a porous medium wetness sensor, a plant health sensor, and a neural network control system. The seed group focused on the design and implementation of a sensor that could detect the moisture content of a seed batch. The porous medium wetness group concentrated on the development of a sensor to monitor the amount of nutrient solution within a porous plate incorporating either infrared reflectance or thermal conductance properties. The plant health group examined the possibility of remotely monitoring the health of the plants within the Biomass Production Chamber (BPC) using infrared reflectance properties. Finally, the neural network group concentrated on the ability to use parallel processing in order to control a robot arm and analyze the data from the health sensor to detect regions of a plant.

Rodriguez, Pedro R.↗

DOE OSTI2018

Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

With recent technology advance in Internet of Things (IoT) that involves human, sensors, and mobile devices, networks are not only growing much larger but more complex and dynamic in nature. The spatiotemporal dynamics of networks are represented by both topological changes and temporal shifts of attribute information associated with network components. Understanding the pattern and trend of dynamic networks is increasingly important. While data mining approaches are generally useful in analyzing the statistical properties of networks, there has been recent trend to consider bringing human into the loop, and to examine how feedback from visualizations of large-scale dynamic networks can further improve data mining and machine learning. Traditional visualization methods based on animation and sequences of snapshot graphs are also limited by human cognitive capability. Here, we present a dynamic network analysis and visualization (DNAV) tool which explores the spatiotemporal dimensions of graph components. In particular, nodes and edges are augmented with spatiotemporal segmentation based on both topological and attribute dynamics (e.g., time and locations of connectivity). To further facilitate analysis of large dynamic networks, DNAV includes statistical dynamic overviews alongside graph views, as well as data filtering modules for scalable analysis. Using case studies on public datasets, we demonstrate the effectiveness of DNAV in understanding and analyzing anomalies in dynamic networks such as computer communication networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗

DOE OSTI2025

Real-Time FPGA Implementation For Frequency Sweep Interferometry In The LBNF Complex

Rossel, A. Jacob [Fermilab; Unlisted]↗

DOE OSTI2024

The contributions of microclimatic information in advancing ecosystem science

Drawing upon over 100 years of scholarly work on microclimate, we first present an overview of the history, key references, and critical issues surrounding the collection and utilization of microclimate records in ecosystem studies. We place particular emphasis on addressing specific and pressing issues related to the applications of microclimate at the community-ecosystem-landscape level, excluding those of controlled experiment such as growth chambers and greenhouses. Specifically, we: (1) highlight some key issues concerning the collection, quality assurance/quality control (QA/QC), and utilization of microclimatic data in ecosystem studies; (2) revisit microclimatic responses to the structural changes of ecosystems and landscapes; and (3) emphasize the significance of microclimate in understanding major ecosystem/landscape processes and functions. Vapor pressure deficit (VPD) is particularly emphasized for its calculation and use because of its burgeoning applications in the literature. Case studies for each of the three thematic topics are provided with selected references to demonstrate challenges and solutions. As the scientific community gears up to enhance microclimatic stations, we envision significant increases in the use of smart sensors, wireless access, networking, open databases, and computational capabilities. Understanding and addressing some of the issues raised in this synthesis paper may help advance microclimate research and foster collaboration with other relevant disciplines, such as ecosystem science.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. Here, this study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

The Spatiotemporal Evolution of Granular Microslip Precursors to Laboratory Earthquakes

Laboratory earthquake experiments provide important observational constraints for our understanding of earthquake physics. Here we leverage continuous waveform data from a network of piezoceramic sensors to study the spatial and temporal evolution of microslip activity during a shear experiment with synthetic fault gouge. We combine machine learning techniques with ray theoretical seismology to detect, associate, and locate tens of thousands of microslip events within the gouge layer. Microslip activity is concentrated near the center of the system but is highly variable in space and time. While microslip activity rate increases as failure approaches, the spatiotemporal evolution can differ substantially between stick-slip cycles. These results illustrate that even within a single, well-constrained laboratory experiment, the dynamics of earthquake nucleation can be highly complex.

58 GEOSCIENCES↗

DOE OSTI2022

Resilient Operating Constraints for Power Distribution Systems under Setpoint Attacks

Integration and operation of distributed generation (DG) and energy storage (ES) in power distribution systems are enabled by communication networks and embedded sensor and control devices that increase the vulnerability of the systems to cyber-threats, broadening the attack surface and making adversary actions more unpredictable. This paper proposes a methodology that uses ellipsoidal approximations to quantify the potential damage caused by successful attacks that affect, directly or indirectly, the desired operation setpoints and may drive the power distribution operation to unsafe states by violating the limits of voltage or line flows. More specifically, a new methodology is introduced to find the optimal non-symmetric operating constraints that can be imposed to each DG and ES in order to guarantee that the power distribution system is resilient to any malicious setpoints. The proposed method takes as inputs the system topology, DG and ES capabilities, and load limits to solve a convex optimization problem formulated using linear matrix inequalities (LMIs) and the power flow equations. The proposed solution is agnostic to the attacker's action or load profile and it does not require any assumption about the location or means of the attack. The numerical results on a test distribution feeder with several DG and ES illustrate how the proposed resilient operating constraints guarantee the security of the power distribution system under setpoint attacks.

Giraldo, Jairo↗

DOE OSTI2023

Distributed Grid-Sensing Service Layer: A System Architecture Blueprint

In this report, a scalable, decentralized, sensor-oriented communication blueprint is introduced. The proposed architecture has been specifically designed to support the mass deployment of sensors and also enable data consumers to access the underlying data streams using a distributed systems approach. With the proposed approach, sensors and data clients can be decoupled from the physical communication infrastructure and migrated into a modern, software-defined infrastructure ecosystem that can be configured to suit the end application’s requirements. In its current iteration, this blueprint defines the interactions, processes, and expected outcomes that each individual component within the architecture must support the overall ecosystem’s needs. Within this work, such an assembly of services is called the data bus; it represents the heart of the distributed grid sensing services architecture, a multiyear project that aims to design, implement, and test a reference sensor-data dissemination network that can support the needs of the extended grid state. We expect this blueprint to guide our future implementation efforts

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

A Graphics Processing Unit (GPU) Approach to Large Eddy Simulation (LES) for Transport and Contaminant Dispersion

Recent advances in the development of large eddy simulation (LES) atmospheric models with corresponding atmospheric transport and dispersion (AT&D) modeling capabilities have made it possible to simulate short, time-averaged, single realizations of pollutant dispersion at the spatial and temporal resolution necessary for common atmospheric dispersion needs, such as designing air sampling networks, assessing pollutant sensor system performance, and characterizing the impact of airborne materials on human health. The high computational burden required to form an ensemble of single-realization dispersion solutions using an LES and coupled AT&D model has, until recently, limited its use to a few proof-of-concept studies. An example of an LES model that can meet the temporal and spatial resolution and computational requirements of these applications is the joint outdoor-indoor urban large eddy simulation (JOULES). A key enabling element within JOULES is the computationally efficient graphics processing unit (GPU)-based LES, which is on the order of 150 times faster than if the LES contaminant dispersion simulations were executed on a central processing unit (CPU) computing platform. JOULES is capable of resolving the turbulence components at a suitable scale for both open terrain and urban landscapes, e.g., owing to varying environmental conditions and a diverse building topology. In this paper, we describe the JOULES modeling system, prior efforts to validate the accuracy of its meteorological simulations, and current results from an evaluation that uses ensembles of dispersion solutions for unstable, neutral, and stable static stability conditions in an open terrain environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure

In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗