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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Hazard Contribution Modes of Machine Learning Components

Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs)—i.e., software components that use machine learning (ML)—are to analyze and understand the contribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the otion of hazard contribution modes (HCMs)—a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing LEC safety analysis towards eventually automating it in a tool.

Smith, Colin↗
DOE OSTI2020

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗
DOE OSTI2021

Development of Automated Pipeline for Time-Resolved Link-Wise Vehicular Energy Consumption in the Chattanooga, TN Road Network

The Department of Energy (DOE) has shown strong interest in detecting energy inefficiencies in regional road networks, so as to derive energy consumed at a high spatial temporal resolution. We have developed a workflow to automate the estimation of time-resolved vehicular energy consumption over each link in a road network of interest. The road network used in the current work is centered around the city of Chattanooga, Tennessee and its bordering regions. Utilizing the most mature road network for the Chattanooga, TN region, vehicle speed & count data from TomTom in conjunction with machine learning methods, we have developed an automated pipeline to estimate energy consumption for every link in the network. The first step in the pipeline is ingesting vehicle probe counts and speed estimates from TomTom API. In the next step, the probe counts, speed profiles and other exogenous data (i.e. road types, weather data, ground-truth volume counts and more) were used as input to a supervised learning algorithm to estimate the number of vehicles throughout the entire region for each road segment. These volume estimates were then mapped to a unified road network that contained additional important information such as percentage change in gradient across a link, number of lanes and link lengths that are features in pre-trained single vehicle energy-consumption models available with the RouteE software developed at NREL. The per vehicle energy consumption on each road link predicted using appropriate RouteE vehicular models were multiplied by the volume estimate for the corresponding link over a given time period to predict energy consumed per link for the time interval of interest. Currently, work is underway to improve both the RouteE per vehicle energy estimate and the volume estimates derived from TomTom probe counts. We have also explored the correlation of the link-wise energy estimates with the features of the pre-trained RouteE machine learning model in order to gain insight into what factors contribute most to the link-wise energy consumption.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2021

DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers

Modern scientific workflows are increasing in complexity with growth in computation power, incorporation of non-traditional computation methods, and advances in technologies enabling data streaming to support on-the-fly computation. These workflows have unpredictable runtime behaviors, and a fixed, predetermined resource assignment on supercomputers can be inefficient for overall performance and throughput. Inability to change resource assignments further limits the scientists to avail of science-driven opportunities or respond to failures.We introduce DYFLOW, a flexible framework that orchestrates scientific workflows on supercomputers based on user-designed policies. DYFLOW compartmentalizes orchestration stages into simplified constructs, and end-users can program and reuse them according to their workflow requirements through an easy-to-use interface. These constructs hide the intricacies involved in runtime management from end-users, for instance, procurement of information to understand the workflow state, assessment, and supervision of the runtime changes. DYFLOW is designed to work alongside existing workflow management systems and reuse the available (static) support for workflow management. We have integrated DYFLOW with an existing workflow management tool as a demonstration. With experiments performed on use cases from three types of scientific workflows and two different parallel architectures, we show that DYFLOW achieves the desired orchestration incurring a small cost to carry out the runtime changes.

Singhal, Swati↗
DOE OSTI2022

Engaging a Diverse Workforce in the Building Sciences Through the JUMP Into STEM Program: Impact Study; Preprint

Ensuring inclusivity in building science professions requires engaging people from underrepresented demographics; unfortunately, many races, ethnicities, and genders continue to be underrepresented. The JUMP into STEM program works to rectify this, encouraging creative ideation and highlighting the unique perspectives of a diverse array of students. The top students are awarded internships that provide opportunities to work in building science research with close supervision from mentors in the field. In the research presented here, we investigate the impact of participation in JUMP into STEM on students' careers, including the impact on students from diverse backgrounds. Furthermore, we research students' awareness of career opportunities in building science, students' perception of their ability to meaningfully contribute to this field, and the career building opportunities that JUMP into STEM provides for students. Through participant interviews and data analysis, we measure program diversity, inclusion, and effectiveness at achieving goals. We find that among the hundreds of students who have participated in this program, more than 25 different majors and/or educational degree programs are represented and more than 45% of students attend minority serving institutions or historically black colleges and universities. The JUMP into STEM experience had a significant effect on career choices or career development for more than 80% of participants. Our results also indicate that the JUMP into STEM program is effective at promoting diversity in the building sciences and is successfully contributing to the larger effort within the building science industry to promote and ensure equity in technological progress and innovation.

building energy science↗
DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗
DOE OSTI2022

Solar forecasting using machine learned cloudiness classification

Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Hamann, Hendrik F.↗
DOE OSTI2022

Multi-Sensor Data Acquisition System for Process Monitoring

Idaho National Laboratory is building a new nuclear fuel cycle test bed. This fuel cycle test bed named “Beartooth” will give researchers the opportunity to study the nuclear fuel cycle process. The process operations include the use of centrifugal contactors and process flow for the purification of special nuclear material recovery in used fuel. This new research facility has the need to use non-traditional measurement sensors to determine the state-of-health of the solvent extraction process. These non-traditional sensors can also enhance nuclear nonproliferation supervision and other activities. The non-traditional sensors include accelerometers, acoustic, current, flow, colorimetric, temperature, pH and conductivity. The challenge behind deploying all these sensors, is the need for fast and reliable data collection. The data acquisition system (DAQ) needs to be capable of recording up to 12 accelerometers and/or acoustic microphones simultaneously at rates up to 12.8 kSamples/second/channel. This requires the need for a strong architecture and data collection solution. This summary identifies the system architecture, DAQ, and sensors needed to support non-traditional measurements in a solvent extraction process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Label Assist: Personalized Travel Models for Longitudinal Data Collection

Understanding travel behavior is crucial to transportation decarbonization. OpenPATH is an open-source mobility platform which collects and analyzes human travel behavior at the individual level. The mobile application passively senses trips and prompts users to label them. However, users find the labeling process burdensome; less than half the trips are typically labeled, making much of the data unusable in aggregate analyses of mobility patterns. Prior work has addressed the response fatigue challenge through automated mode inference using sensor data, but sensors cannot capture all aspects of travel behavior. We explore an alternative approach in which we leverage prior user input to predict travel choices in novel trips. We first explore trip clustering methods and develop a novel two-step pipeline using DBSCAN and SVMs to extract realistic geospatial clusters. We then propose two strategies to predict trip labels: (i) clustering trips and extrapolating labels for similar trips, and (ii) random forest classification. The random forest approach is able to achieve - $70-80% accuracy (purpose: 72%, mode: 79%, replaced mode: 81%). These novel approaches to trip classification allow us to increase the rate of user labeling by suggesting predicted labels to be verified by the user. Unlabeled trips can also contribute to aggregate analyses, using label predictions and their associated confidences as a substitute. While there exist other travel survey apps with the ability to infer travel choices, to our knowledge, this is the first paper to describe such a supervised system and rigorously evaluate it.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2021

Unleashing the Power of Industrial Big Data through Scalable Manual Labeling

Big Data plays a central role in the remarkable results achieved by Machine Learning (ML) and especially Deep Learning (DL) in the recent years. However, the difficulty in obtaining a reasonable amount of labeled samples limits ML/DL application in various domains, including industrial equipment and system monitoring. In this paper the need for methods that turn manual labeling into a scalable process is highlighted. A real world problem is analyzed for which weak supervision methods, successfully employed in other domains, did not produce acceptable results. An alternative approach based on clustering ensembles is described and tested, achieving good performance.

Paes Leao, Bruno↗
DOE OSTI2023

HIGH-LOW FIDELITY THERMAL HYDRAULIC COUPLING USING AI/MACHINE LEARNING ALGORITHMS

The primary goal of the US Department of Energy (DOE) office of Nuclear Energy Integrated Energy Systems (IES) program is to develop the tools and framework for coupling multi-scale and multi-physical thermal and electrical energy usage and storage systems. High- and low-fidelity (high–low) coupling is a key feature of multi-scale, multi-component systems and has been an important focus of research in the nuclear energy community for the past two decades. An essential feature of demonstrating the capability to couple high-fidelity and low-fidelity systems for real-time applications are surrogate/reduced order models (ROM). For the purposes of this study, surrogate models are essentially Blackbox models, typically developed using supervised Machine learning (ML) algorithms. The surrogate models can be used to mimic the response of high-fidelity models to represent large historical datasets and coupled with more general low-fidelity system models distributed as Functional Mock-up Interface (FMI) or Functional Mock-up Units (FMU) modules. The example is demonstrated with Spallation Neutron Source (SNS) First Target Station flow loop data. The flow loop is a liquid mercury loop with a pump, piping, heat exchange, and internal heat generation in the target window. This work elucidates some of the potential benefits and future needs of developing tools for high–low system coupling of energy systems.

Williams, Wesley↗
DOE OSTI2023

An Agenda for Multimodal Foundation Models for Earth Observation

Archives of remote sensing (RS) data are increasing swiftly as new sensing modalities with enhanced spatiotemporal resolution become operational. While promising new breakthroughs, the sheer volume of RS archives stretches the limits of human analysts and existing AI tools, as most models are: i) limited to single data modalities; ii) task-specific; iii) heavily reliant on labeled data. The emerging Foundation Models (FMs) have the potential to address these limitations. Trained on vast unlabeled datasets through self-supervised learning, FMs enable generic feature extraction that facilitate specialization to a wide variety of downstream tasks. This paper describes a vision towards an FM for multimodal Earth Observation data (FM4EO), discussing key building blocks and open challenges. We put particular emphasis on multimodal reasoning, a topic underexplored in EO. Our ultimate goal is a practical path toward FM4EO with capacity to unlock breakthroughs in few-shot learning scenarios, multimodal geographic knowledge integration, synthesis, and hypothesis generation.

Ambrozio Dias, Philipe↗
DOE OSTI2023

LDRD 22A1059-068FP Tailoring the Properties of Multi-Phase Materials Through the Use of Correlative Microscopy and Machine Learning - Poster

High strength alloys with good ductility, hardness, and toughness are needed to meet stringent design requirements for extreme environments. One complication in this pursuit is the evidence that metals rarely exhibit both high strength and good fracture toughness as the underlying mechanisms work in opposition. An exception to this behavior is found in multiphase alloys that form complex microstructures of mixed phases with variable grain sizes and shapes that provide increased fracture toughness by the arrangement of their constituent elements. We propose to explore this phenomenon using state-of-the-art machine learning (ML) techniques in a new and novel manner to identify and correlate the critical microstructural features in a Titanium-10Vanadium-2Iron-3Aluminum (Ti-10V-2Fe-3Al) alloy that is reported to exhibit high strength and fracture toughness. Additionally, we will employ multiple, complementary characterization techniques such as optical microscopy, electron backscatter diffraction (EBSD), energy dispersive spectroscopy (EDS) and scanning electron microscopy to provide multi-layer, quantitative ground truth measures of the microstructures. This data will be used to train a Convolutional Neural Network (CNN) in a semi-supervised environment to identify key microstructural features such as ? platelet dimensions and locations and ?/? phase boundaries and correlate those features with the strength and toughness. Here the ? and ? nomenclature refers to hexagonal close pack (hcp) and body center cubic (bcc) crystal structures, respectively. Previous work has focused on popular alloys and typically used one characterization technique. This research is focused on a promising titanium alloy, uses multiple complimentary characterization tools to provide precise microstructural information and correlates to improved fracture toughness. The resulting ML tool can be trained for additional microstructural features, different alloy(s), and or target mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

On the Road to Achieving Safety Excellence at the Idaho Cleanup Project: The Initial Challenges, Solutions, and Positive Results of Striving to be the Best and Safest in the DOE Complex - 20518

When Fluor Idaho, LLC (Fluor Idaho) assumed the Idaho Cleanup Project Core contract (ICP Core) in mid-2016, it inherited the successful multi-faceted, employee-owned safety programs of two prior contractors supporting cleanup operations at the Idaho National Laboratory Site. Fluor Idaho incorporated the best of both safety programs along with Fluor Corporate's 106-year legacy of safety initiatives and successes. Fluor Idaho integrated the separate programs and cultures under a One Fluor approach - one project, one mission, One Fluor. Job changes, management changes, and procedures, processes, and system changes presented multiple challenges. Those challenges combined with the severe Winter of 2016/2017 resulted in numerous slips, trips, and falls first aids and reportable injuries. The road to safety excellence started off bumpy and stayed that way for some time. Since then, Fluor Idaho has successfully implemented several key initiatives including employee engagement; management engagement and time in the field; communications improvements; safety culture sustainment; injury/illness prevention; organizational learning; and DOE Voluntary Protection Program (VPP) readiness and preparation. Management became more engaged by making more workplace visits and increasing their supervision of 'life critical work' or complex activities. Additional training became mandatory for first-line supervisors and front-line workers. Most recently, Fluor Idaho increased its injury and illness prevention initiatives extensively. In the last year, things have turned around in a dramatic way. This shift is directly attributable to the significant number of initiatives launched in late 2017 and 2018. These initiatives addressed safety performance in the five key areas of the DOE VPP and promoted sustainability of the company's safety culture. The five key areas are: management leadership, employee involvement, work-site analysis, hazard prevention and control, and safety and health training. Employees and managers alike recommitted to making safety the highest value. The result of this laser-like focus on safety and accident and injury prevention is that injuries and first aids are a fraction of what they were. During FY 2019, employees worked more than 4.2 million hours without a serious injury or lost work-day and 1 million hours without a recordable injury. Fluor Idaho is proud of its safety trend. The company invested significant resources in improving its safety record, and that investment is paying off. Other contractors in the DOE Complex can learn and benefit from this discussion of the challenges Fluor Idaho faced, as well as its innovative safety initiatives that are producing results that are being recognized by our U.S. Department of Energy (DOE) customer. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗