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The Commercial Routing Assistance (CRA) tool merges coordinated and vetted data streams, plots multiple automated or custom routing options, and visualizes the wide variety of state regulations and actions that a driver would encounter along a route. With CRA, commercial operators can plan vehicle movements across multiple states quickly, particularly during emergencies.
SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
A simple and scalable framework for longitudinal analysis of Twitter data is developed that combines latent topic models with computational geometric methods. Dimensionality reduction tools from computational geometry are applied to learn the intrinsic manifold on which the latent, temporal topics reside. Then shortest path distances on the manifold are used to link together these topics. The proposed framework permits visualization of the low-dimensional embedding which provides clear interpretation of the complex, high-dimensional trajectories that may exist among latent topics. Practical application of the proposed framework is demonstrated through its ability to capture and effectively visualize natural progression of latent COVID-19 related topics learned from Twitter data. Interpretability of the trajectories is achieved by comparing to real-world events. In addition, the framework permits study of spatial variation in Twitter behavior for learned topics. The analysis demonstrates that the proposed framework is able to capture granular-level impact of COVID-19 on public discussions. We end by arguing that Twitter data, when analyzed within the proposed framework, can serve as a valuable supplementary data stream for COVID-related studies.
The science of mountainous hydrology spans the atmosphere through the bedrock and inherently crosses physical and disciplinary boundaries: land-atmosphere interactions in complex terrain enhance clouds and precipitation, while watersheds retain and release water over a large range of spatial and temporal scales. Limited observations in complex terrain challenge efforts to improve predictive models of the hydrology in the face of rapid changes. The Upper Colorado River exemplifies these challenges, especially with ongoing mismatches between precipitation, snowpack, and discharge. Consequently, the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility has deployed an observatory to the East River Watershed near Crested Butte, Colorado between September 2021 and June 2023 to measure the main atmospheric drivers of water resources, including precipitation, clouds, winds, aerosols, radiation, temperature and humidity. This effort, called the Surface Atmosphere Integrated Field Laboratory (SAIL), is also working in tandem with DOE-sponsored surface and subsurface hydrologists and other federal, state, and local partners. SAIL data can be benchmarks for model development by producing a wide range of observational information on precipitation and its associated processes, including those processes that impact snowpack sublimation and redistribution, aerosol direct radiative effects in the atmosphere and in the snowpack, aerosol impacts on clouds and precipitation, and processes controlling surface fluxes of energy and mass. Preliminary data from SAIL’s first year showcase the rich information content in SAIL’s many data-streams and support testing hypotheses that will ultimately improve scientific understanding and predictability of Upper Colorado River hydrology in 2023 and beyond.
With the ever-increasing deluge of data and demand for warfighters to make decisions upon its analysis, U.S. defense strategy has prioritized the development of artificial intelligence (AI)/machine learning (ML) systems that can analyze multi-source data streams and suggest courses of action. However, there is a history of systems that have failed to be adopted by the warfighter due not only to unsolved technical challenges, but also a lack of usability or contributions to mission effectiveness, perceived or otherwise. To avoid this, research into human-AI teaming shows promise for developing AI systems that work with frontline operators. A recent National Academies of Sciences, Engineering, and Medicine report (NASEM, 2022) presented 57 research objectives in this area; however, the U.S. Navy requires a more-focused set of priorities, as it is impossible to tackle every priority. A workshop involving 23 human factors scientists, computer scientists, and active-duty sailors was organized at the Naval Information Warfare Center Pacific, resulting in a set of five research priorities spanning near-, mid-, and far-term time frames. This panel will summarize the results of this workshop, with a focus on the big questions both going into this workshop and coming out of it. The panel participants come from government, academia, and industry, providing perspective from the different kinds of organizations required to accomplish these research goals.
Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract
Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.
This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024
We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.
U.S. military influenza surveillance uses electronic reporting of clinical diagnoses to monitor health of military personnel and detect naturally occurring and bioterrorism-related epidemics. While accurate, these systems lack in timeliness. More recently, researchers have used novel data sources to detect influenza in real time and capture nontraditional populations. With data-mining techniques, military social media users are identified and influenza-related discourse is integrated along with medical data into a comprehensive disease model. By leveraging heterogeneous data streams and developing dashboard biosurveillance analytics, the researchers hope to increase the speed at which outbreaks are detected and provide accurate disease forecasting among military personnel.
The world is at an inflection point where our ability to collect data now far outpaces our ability to make use of it. LANL has a number of efforts to help us pull more meaningful insights out of our data and target resources to where they will be most impactful. We are exploring an approach to recognizing activity patterns across disparate data streams for a more holistic view of nuclear facility monitoring.
The idea of acausality for control of a wave energy converter (WEC) is a concept that has been popular since the birth of modern wave energy research in the 1970s. This concept has led to considerable research into wave prediction and feedforward WEC control algorithms. However, the findings in this report mostly negate the need for wave prediction to improve WEC energy absorption, and favor instead feedback driven control strategies. Feedback control is shown to provide performance that rivals a prediction-based controller, which has been unrealistically assumed to have perfect prediction. It is well known in classical control engineering that perfect knowledge of past and future events will always lead to higher performing systems. However, it is also well known that the underlying system must be well-designed; control cannot fix a bad design. Additionally, one must consider the practical application of a control design, which relies on measurements and actuation systems. There are major implications to cost and reliability when relying on remote sensors requiring real-time data-streaming (e.g., remote wave buoys). This report shows that for a well-designed WEC, in which closed loop dynamics is considered since early stages of design, a suboptimal controller using no prediction can achieve more than 90% of the theoretical maximum. A predictionless feedback resonating (FBR) controller performs within 0.1% percent of a controller with perfect future knowledge (something which is not practically attainable). Given the major challenges with accurate and robust wave prediction, this result provides a major argument and incentive for utilizing feedback for WEC control. Implementation of these feedback strategies is readily attainable, while the strategy requiring perfect wave prediction will demand an unknown number of additional years to research and develop, all in the service of a marginal 1% benefit.
During FY18, BNL continued helping FLIR to integrate the instrument prototype, improving the readout software and testing electronics. BNL and LLNL carried out more measurements with the 4x4 arrays in laboratory conditions using several Cs sources and reanalyzed the raw data from the field measurements. LLNL processed the old recalibrated data and new data collected in the lab. These efforts allowed us to understand the cause of the discrepancies observed during the measurements at the Westinghouse Fuel Fabrication Facility (WFFF). The problem was traced back to the original ASIC and DAQ firmware provided by the ASIC developers. A program error and some other deficiencies were identified in the original firmware. These problems, which caused missing some detected events from the data stream, never showed up in the lab environment. So, they were not spotted before the field test. The ASIC designers are currently working to address these problems and will provide the updated firmware for the next field measurements.
This project pulls inspiration from modern smart home technologies and commercial IoT sensing and applies it to the challenge of monitoring and verification of nuclear facilities. Building off the idea that the whole is greater than the sum of its parts, the goal is to develop methods for combining disparate data representative of typical IAEA data streams to improve detection of undeclared activities at safeguarded facilities.
During FY18, BNL continued helping FLIR to integrate the instrument prototype, improving the readout software and testing electronics. BNL and LLNL carried out more measurements with the 4x4 arrays in laboratory conditions using several Cs sources and reanalyzed the raw data from the field measurements. LLNL processed the old recalibrated data and new data collected in the lab. These efforts allowed us to understand the cause of the discrepancies observed during the measurements at the Westinghouse Fuel Fabrication Facility (WFFF). The problem was traced back to the original ASIC and DAQ firmware provided by the ASIC developers. A program error and some other deficiencies were identified in the original firmware. These problems, which caused missing some detected events from the data stream, never showed up in the lab environment. So, they were not spotted before the field test. The ASIC designers are currently working to address these problems and will provide the updated firmware for the next field measurements.
Smart Labs are: 1) Laboratories that operate at the highest level of safety and energy performance; 2) Laboratories that use a building-specific data stream to continuously monitor and adjust facility conditions.
Data provided by the instrumentation located at the National Wind Technology Center (NWTC) is compiled below for the purposes of characterizing the atmospheric operating conditions at the National Renewable Energy Laboratories (NREL) wind site. Conditions are presented conditioned on month of the year to show seasonal variation, and based on characteristic quantities seen in the atmospheric flow including, bulk wind speed, direction, and atmospheric stability. All data reported and analyzed in the following document were collected from instrumentation mounted on the 135 m-tall masts erected at the NWTC near Boulder, Colorado. Each of the two met-masts (named M4 and M5) were sited specifically to measure the inflow wind to several of the utility-scale turbines at the NWTC. The construction, operation, and maintenance of the masts has been supported since their commissioning in 2011 by the Department of Energy, Office of Energy Efficiency and Renewable Energy, Wind and Water Power Program. Data from the masts are publicly available for download or basic visualization, hosted on NREL's website (https://nwtc.nrel.gov/135mData). Data from the masts have been used in the past for a wide range of projects including wind turbine power performance measurements, high fidelity model development and validation, and atmospheric science research. An interactive visualization platform has been developed on NREL's Open Energy Information platform and can be used to reproduce many of the graphics included in the current work. This report describes in detail the atmospheric conditions recorded by M5, the inflow mast for the DOE-owned GE 1.5 MW wind turbine. The report focuses on data recorded from August 2012 to May 2018 and includes: an introduction to the wind climate at the NWTC; a physical description of the met masts; a review of installed instrumentation; and data collection, processing, and quality control methods. In addition to a concise description of the instrumentation from the met masts and the data channels they provide, this report characterizes the behavior of the atmospheric resource first in a cumulative sense, discussing mainly the distributions wind speeds, flow directions and thermodynamic properties at several heights along the towers. Periodicity in atmospheric conditions is then detailed through analysis of diurnal and annual cycles of the data streams. Periods of both low turbulence and extreme atmospheric events that are of interest in terms of wind turbine operation and wind energy research are detailed in the final chapter of this report. Low turbulence events are somewhat rare at the NWTC and are of interest from the perspective of validating the operation of new wind turbine and blade designs. Similarly, the NTWC is known for a relatively higher rate of occurrence of extreme weather events.
The team of Southwest Research Institute® (SwRI®), ITEA, Electric Power Research Institute, Inc. (EPRI), GE Global Research (GE), Jacobs Engineering (Jacobs), and Peter Reineck Associates (PRA) are advancing Flameless Pressurized Oxy-Combustion (FPO), a novel coal technology. This effort seeks to complete the preliminary engineering and planning of a 50-MWth Pilot Plant that produces electricity and steam for district heating. The technology, pioneered by ITEA at the 5-MWth scale, must be brought to a higher technology readiness level (TRL) to be viable at the commercial scale. This project builds upon extensive evaluation of the techno-economic performance of the FPO technology already performed by ITEA. This includes system improvements, such as the addition of a turbo-expander to the flue gas stream. Some of the performance-enhancing components are not as well defined as others. The primary goal of this project was to bring all of the core cycle components to the same level of design maturity. Extensive cycle modeling and evaluation explored various configurations of the process. Different coals, operating conditions, and heat recovery devices were considered and modeled using Aspen Plus. The results of the cycle modeling informed the pilot plant configuration and the planned configuration at the commercial scale. The system pressurization is maintained in the flue gas clean-up and water removal. This allows for efficient and cost-effective carbon dioxide compression and sequestration package. Once the cycle was optimized, preliminary drawings, layouts, and plans defined the pilot facility. Process flow diagrams (PFD) were used to describe the pilot configuration in greater detail. A heat and mass balance with stream data was created. A master equipment list specified the operating conditions for major pieces of equipment within the pilot using this heat and mass balance. A detailed cost estimate for the planned configuration was completed with an Association for the Advancement of Cost Engineering (AACE) Class IV level of cost analysis. A site selection effort evaluated a variety of potential candidate sites. The selection effort focused on three sites for an in-depth evaluation of the merits and viability of hosting a pilot demonstration. The ability of the host site to support the project with cost sharing was also assessed. The selection process chose the University of Wyoming (UW) as the host site, and cost estimates for the pilot capital estimate were made using local data. A preliminary test plan and operation plan, which creates figures of merit, control, and instrumentation was produced. An effort that outlines methods of pilot cost reduction was completed near the end of the project. A techno-economic assessment (TEA) evaluated the FPO technology at the commercial scale in comparison to the NETL baseline cases. The reference plants were used in combination with proprietary equipment estimates to build a plant capital cost and cost of electricity evaluation. FPO performed better than the sub-bituminous post-combustion capture cases. Further preliminary estimates of improvements to the FPO cycle show even further gains when compared to conventional carbon capture methods.