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DOE OSTI2021

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

DOE OSTI2021

HUMAN-AUTOMATED VEHICLE INTERACTIONS

Guo, Longxiang↗

NASA NTRS1988

Spacelab Life Sciences 1 - The stepping stone

The Spacelab Life Sciences (SLS-1) mission scheduled for launch in March 1990 will study the effects of microgravity on physiological parameters of humans and animals. The data obtained will guide equipment design, performance of activities involving the use of animals, and prediction of human physiological responses during long-term microgravity exposure. The experiments planned for the SLS-1 mission include a particulate-containment demonstration test, integrated rodent experiments, jellyfish experiments, and validation of the small-mass measuring instrument. The design and operation of the Research Animal Holding Facility, General-Purpose Work Station, General-Purpose Transfer Unit, and Animal Enclosure Module are discussed and illustrated with drawings and diagrams.

Dalton, B. P.↗

DOE OSTI2022

Report on PTT Imaging of Defects in AM Metallic Materials-Part 2

36 MATERIALS SCIENCE↗

DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1992

Energy requirements for space flight

Both the United States and the Soviet Union perform human space research. This paper reviews data available on energy metabolism in the microgravity of space flight. The level of energy utilization in space seems to be similar to that on earth, as does energy availability. However, despite adequate intake of energy and protein and in-flight exercise, lean body mass was catabolized, as indicated by negative nitrogen balance. Metabolic studies during simulated microgravity (bed rest) and true microgravity in flight have shown changes in blood glucose, fatty acids and insulin concentrations, suggesting that energy metabolism may be altered during space flight. Future research should focus on the interactions of lean body mass, diet and exercise in space, and their roles in energy metabolism during space flight.

Lane, Helen W.↗

NASA NTRS1997

Design and Operational Evaluation of the Traffic Management Advisor at the Ft. Worth Air Route Traffic Control Center

NASA and the FAA have designed and developed and an automation tool known as the Traffic Management Advisor (TMA). The system was operationally evaluated at the Ft. Worth Air Route Traffic Control Center (ARTCC). The TMA is a time-based strategic planning tool that provides Traffic Management Coordinators and En Route Air Traffic Controllers the ability to efficiently optimize the capacity of a demand impacted airport. The TMA consists of trajectory prediction, constraint-based runway scheduling, traffic flow visualization and controllers advisories. The TMA was used and operationally evaluated for forty-one rush traffic periods during a one month period in the Summer of 1996. The evaluations included all shifts of air traffic operations as well as periods of inclement weather. Performance data was collected for engineering and human factor analysis and compared with similar operations without the TMA. The engineering data indicates that the operations with the TMA show a one to two minute per aircraft delay reduction during rush periods. The human factor data indicate a perceived reduction in en route controller workload as well as an increase in job satisfaction. Upon completion of the evaluation, the TMA has become part of the normal operations at the Ft. Worth ARTCC.

Swenson, Harry N.↗

DOE OSTI2024

SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions

Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving the LLMs to align existing foundation models with scientific disciplines, concepts and goals. In this work, we present SciTune as a tuning framework to improve the ability of LLMs to follow scientific multimodal instructions. To test our methodology, we use a human-generated scientific instruction tuning dataset and train a large multimodal model LLaMA-SciTune that connects a vision encoder and LLM for science-focused visual and language understanding. LLaMA-SciTune significantly outperforms the state-of-the-art models in the generated figure types and captions in multiple scientific multimodal benchmarks. In comparison to the models that are fine-tuned with machine generated data only, LLaMA-SciTune surpasses human performance on average and in many sub-categories on the ScienceQA benchmark.

• Artificial intelligence (AI) / machine learning ↗

NASA NTRS2013

Future Challenges in Managing Human Health and Performance Risks for Space Flight

The global economy forces many nations to consider their national investments and make difficult decisions regarding their investment in future exploration. To enable safe, reliable, and productive human space exploration, we must pool global resources to understand and mitigate human health & performance risks prior to embarking on human exploration of deep space destinations. Consensus on the largest risks to humans during exploration is required to develop an integrated approach to mitigating risks. International collaboration in human space flight research will focus research on characterizing the effects of spaceflight on humans and the development of countermeasures or systems. Sharing existing data internationally will facilitate high quality research and sufficient power to make sound recommendations. Efficient utilization of ISS and unique ground-based analog facilities allows greater progress. Finally, a means to share results of human research in time to influence decisions for follow-on research, system design, new countermeasures and medical practices should be developed. Although formidable barriers to overcome, International working groups are working to define the risks, establish international research opportunities, share data among partners, share flight hardware and unique analog facilities, and establish forums for timely exchange of results. Representatives from the ISS partnership research and medical communities developed a list of the top ten human health & performance risks and their impact on exploration missions. They also drafted a multilateral data sharing plan to establish guidelines and principles for sharing human spaceflight data. Other working groups are also developing methods to promote international research solicitations. Collaborative use of analog facilities and shared development of space flight research and medical hardware continues. Establishing a forum for exchange of results between researchers, aerospace physicians and program managers takes careful consideration of researcher concerns and decision maker needs. Active participation by researchers in the development of this forum is essential, and the benefit can be tremendous. The ability to rapidly respond to research results without compromising publication rights and intellectual property will facilitate timely reduction in human health and performance risks in support of international exploration missions.

Corbin, Barbara J.↗

NASA NTRS1990

Quantitative assessment of human motion using video motion analysis

In the study of the dynamics and kinematics of the human body, a wide variety of technologies was developed. Photogrammetric techniques are well documented and are known to provide reliable positional data from recorded images. Often these techniques are used in conjunction with cinematography and videography for analysis of planar motion, and to a lesser degree three-dimensional motion. Cinematography has been the most widely used medium for movement analysis. Excessive operating costs and the lag time required for film development coupled with recent advances in video technology have allowed video based motion analysis systems to emerge as a cost effective method of collecting and analyzing human movement. The Anthropometric and Biomechanics Lab at Johnson Space Center utilizes the video based Ariel Performance Analysis System to develop data on shirt-sleeved and space-suited human performance in order to plan efficient on orbit intravehicular and extravehicular activities. The system is described.

Probe, John D.↗

DOE OSTI2016

A model-based scatter artifacts correction for cone beam CT

Purpose: Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x-ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. Methods: The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated. Results: Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four-components segmentation yields the best results, while the results of three-components segmentation are still acceptable. The parameters (iteration number K and weight β) affect the accuracy of the scatter correction and the results get improved as K and β increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from −21.8 to −0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the in vivo human images is greatly improved after correction. Conclusions: The software-based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT-based interventional procedures and adaptive radiation therapy.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

Integrating Human System Information with the Systems Platform for Aggregating and Relating Capabilities (SPARC)

Within Human Health and Performance, there exists a wealth of human system information that’s used a regular basis in support of NASA human exploration objectives, but the challenge is that all of this information was stored in multiple different locations and organized for specific uses, limiting its effectiveness and straining communication across multiple groups. To address this challenge, our project, the Systems Platform for Aggregating and Relating Capabilities (SPARC) was tasked with developing a new NASA internal web application that aggregates and relates multiple programs’ human system products, such as technical standards, program requirements and verifications, human system risks, research and evidence, and exploration capabilities, into one centralized platform that addresses the needs of human health and performance from multiple different perspectives. Using agile development methodologies, user-experience (UX) driven design principles, data visualization, and a strong emphasis on continuous improvement though consistent stakeholder engagement, the SPARC project released a beta version in less than 4 months, broadly launched version 1.0.0 Agency-wide three months after the beta, and has over 180 users in the first year of development. Our second year of development will see us moving from our initial capabilities to increasingly robust and complex integrations and visualizations, including Directed Acyclical Graphs (DAGs), natural language processing (NLP) for dynamic generation of relationships between the sources of truth, and an expansion into hierarchical levels of system design in support of the human exploration programs.

Data science↗

NASA NTRS2017

Mild Normobaric Hypoxia Exposure for Human-Autonomy System Testing

An experiment investigated the impact of normobaric hypoxia induction on aircraft pilot performance to specifically evaluate the use of hypoxia as a method to induce mild cognitive impairment to explore human-autonomous systems integration opportunities. Results of this exploratory study show that the effect of 15,000 feet simulated altitude did not induce cognitive deficits as indicated by performance on written, computer-based, or simulated flight tasks. However, the subjective data demonstrated increased effort by the human test subject pilots to maintain equivalent performance in a flight simulation task. This study represents current research intended to add to the current knowledge of performance decrement and pilot workload assessment to improve automation support and increase aviation safety.

Stephens, Chad L.↗

NASA NTRS2010

Human Factors Research for Space Exploration: Measurement, Modeling, and Mitigation

As part of NASA's Human Research Program, the Space Human Factors Engineering Project serves as the bridge between Human Factors research and Human Spaceflight applications. Our goal is to be responsive to the operational community while addressing issues at a sufficient level of abstraction to ensure that our tools and solutions generalize beyond the point design. In this panel, representatives from four of our research domains will discuss the challenges they face in solving current problems while also enabling future capabilities. Historically, engineering-dominated organizations have tended to view good Human Factors (HF) as a desire rather than a requirement in system design and development. Our field has made significant gains in the past decade, however; the Department of Defense, for example, now recognizes Human-System Integration (HSI), of which HF is a component, as an integral part of their divisions hardware acquisition processes. And our own agency was far more accepting of HF/HSI requirements during the most recent vehicle systems definition than in any prior cycle. Nonetheless, HF subject matter experts at NASA often find themselves in catch up mode... coping with legacy systems (hardware and software) and procedures that were designed with little regard for the human element, and too often with an attitude of we can deal with any operator issues during training. Our challenge, then, is to segregate the true knowledge gaps in Space Human Factors from the prior failures to incorporate best (or even good) HF design principles. Further, we strive to extract the overarching core HF issues from the point-design-specific concerns that capture the operators (and managers) attention. Generally, our approach embraces a 3M approach to Human Factors: Measurement, Modeling, and Mitigation. Our first step is to measure human performance, to move from subjective anecdotes to objective, quantified data. Next we model the phenomenon, using appropriate methods in our field, modifying them to suit the unique aspects of the space environment. Finally, we develop technologies, tools, and procedures to mitigate the decrements in human performance and capabilities that occur in space environments. When successful, we decrease risks to crew safety and to mission success. When extremely successful (or lucky), we devise generalizable solutions that advance the state of our practice. Our panel is composed of researchers from diverse domains of our project... from different boxes, if you will, of the Human Factors Analysis and Classification System (HFACS).

Kaiser, Mary K.↗

DOE OSTI2022

Instantiation of HCML Demonstrating Bayesian Predictive Modeling for Attentional Control

The research team developed models of Attentional Control (AC) that are unique to existing modeling approaches in the literature. The goal was to enable the research team to (1) make predictions about AC and human performance in real-world scenarios and (2) to make predictions about individual characteristics based on human data. First, the team developed a proof-of-concept approach for representing an experimental design and human subjects data in a Bayesian model, then demonstrated an ability to draw inferences about conditions of interest relevant to real-world scenarios. Ultimately, this effort was successful, and we were able to make reasonable (meaning supported by behavioral data) inferences about conditions of interest to develop a risk model for AC (where risk is defined as a mismatch between AC and attentional demand). The team additionally defined a path forward for a human-constrained machine learning (HCML) approach to make predictions about an individual's state based on performance data. The effort represents a successful first step in both modeling efforts and serves as a basis for future work activities. Numerous opportunities for future work have been defined.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Automating Anomaly Detection for Target systems at Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory, produces the world’s most intense pulse neutrons beams. An accelerated proton beam is directed into a mercury target to generate neutrons via spallation. The target system accounted for over 40% of the overall downtime of the facility in 2022. Thus, early detection in anomalies in the target systems can enable taking corrective actions to avoid failures and reduce downtime. Fault prognostics and anomaly detection in accelerators, both at SNS and outside, has largely focused on the beam side. This paper presents one the first studies exploring leveraging machine learning to automate the detection of anomalies in the target system. The target system consists of over 30 different interconnected subsystems, and the present work focuses on the mercury process system as a use case. Analyzing data from 28 process variables from 2022 and 2023, tree-based and reconstruction-based algorithms are employed to detect anomalies in archived data. The algorithms detected previously unreported anomalies, several of which were deemed alert worthy by human experts, particularly those found by reconstruction-based algorithms. Using data from each production run in the accelerator increased the generalizability of the models in time. Efforts are now underway to implement a workflow for incorporating human feedback to update the models and evaluating performance on unseen data. The models will eventually be integrated into the existing System Tracking and Reliability system with a web interface for automated anomaly detection and reporting along with a pathway for incorporating human feedback for model updates.

Raj, Anant [ORNL] (ORCID:0000000306711244)↗

DOE OSTI2023

An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator

Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.

99 GENERAL AND MISCELLANEOUS↗