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Larson, Steve

Publications and source records attributed to Larson, Steve.

10 records
All sourcesNASA NTRSDOE OSTI
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 OSTI2019

Autonomous Radiological Survey Systems for Locating Fired Depleted Uranium Munitions - 19212

An ensemble of autonomous radiological survey platforms that utilize a suite of scintillation detectors that can be used to conduct radiological site characterization, assist with remediation efforts, or conduct site clearance in accordance with the Multi-Agency Radiologic Survey and Site Investigation Manual are described. The overall systems are composed of inorganic scintillators, light detection and ranging, global positioning, inertial guidance, wireless telemetry, and signal processing systems. Two primary designs are discussed: a three-meter wide platform and multiple half-meter-wide systems. The survey systems are used to conduct radiological surveys over large areas autonomously, without human involvement. The three-meter system has been designed to cover large, open areas free of obstacles. Smaller robotic platforms are designed to handle a large range of terrain types and to conduct surveys in areas that are plagued with obstacles, such as bushes and trees. The primary design criteria for these systems were accurate locating of fired depleted uranium (DU) munitions. Several U.S. Army facilities have remnant DU material on active firing ranges where DU rounds have been used in performance evaluations. Many ranges also have unexploded ordinance (UXO) challenges which are problematic for conducting walkover surveys. The combination of having active DU impacted firing ranges with UXO make conducting site characterization surveys very difficult and hazardous. The autonomous surveying systems allow radiological scanning data collection to be scheduled around range activities and reduces human exposure to ionizing radiation and UXO. All of the robotic platforms act independently, and are controlled by a centralized computer system via a wireless telemetry system. The central computer system is responsible for setting up survey units, survey paths, and scheduling which platform is to complete the scheduled survey. Unit selection and scheduling are completed by technicians, and the work is conducted autonomously by these survey platforms. The survey areas and survey paths are transmitted to each platform through the telemetry system. Radiological and positional data are collected by the systems and transmitted back to the central computer system in real-time. Collected data is saved to a database for post-survey analysis or can be analyzed in real-time. Analysis of the radiological data is used to construct radiation intensity and estimated DU concentration maps. Automatically generated reports for each survey unit will provide range managers with: survey coverage data maps, estimated uranium concentration maps, and a detailed list of areas that are above a user defined concentration. This paper describes the components of each of the surveying systems, their capabilities, and limitations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Genome mapping of quantitative trait loci (QTL) controlling domestication traits of intermediate wheatgrass ( Thinopyrum intermedium )

Allohexaploid (2n = 6x = 42) intermediate wheatgrass (Thinopyrum intermedium), abbreviated IWG, is an outcrossing perennial grass belonging to the tertiary gene pool of wheat. Perenniality would be valuable option for grain production, but attempts to introgress this complex trait from wheat-Thinopyrum hybrids have not been commercially successful. Efforts to breed IWG itself as a dual-purpose forage and grain crop have demonstrated useful progress and applications, but grain yields are significantly less than wheat. Therefore, genetic and physical maps have been developed to accelerate domestication of IWG. Herein, these maps were used to identify quantitative trait loci (QTLs) and candidate genes associated with IWG grain production traits in a family of 266 full-sib progenies derived from two heterozygous parents, M26 and M35. Transgressive segregation was observed for 17 traits related to seed size, shattering, threshing, inflorescence capacity, fertility, stem size, and flowering time. A total of 111 QTLs were detected in 36 different regions using 3826 genotype-by-sequence markers in 21 linkage groups. The most prominent QTL had a LOD score of 15 with synergistic effects of 29% and 22% over the family means for seed retention and percentage of naked seeds, respectively. Many QTLs aligned with one or more IWG gene models corresponding to 42 possible domestication orthogenes including the wheat Q and RHT genes. A cluster of seed-size and fertility QTLs showed possible alignment to a putative Z self-incompatibility gene, which could have detrimental grain-yield effects when genetic variability is low. Finally, these findings elucidate pathways and possible hurdles in the domestication of IWG.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Depleted Uranium Discrimination Using Spectral Ratios from Data-Starved Spectra - 18619

The Institute for Clean Energy Technology (ICET) has developed mobile surveying platforms for the retrieval of fired depleted uranium (DU) munitions. ICET surveying platforms are fitted with differential global positioning system (GPS), scintillation detectors, and central processing units (CPUs). The surveying technology has historically been used to assist facilities where DU contamination is a concern to locate, retrieve, and remediate DU-contaminated areas. Residence times over DU sources for mobile systems normally do not produce enough data to use gamma ray spectroscopy to verify the source type. Discussed is a potential method used to discriminate DU signatures in data-starved gamma ray spectral data collected during field surveys. Laboratory tests have shown that data-starved gamma ray spectra for zero-valence, oxide, and uranium ores can be distinguished from each other and from background by spectral ratioing. Zero-valence DU and DU oxide gamma ray signatures differ due to the attenuation of low-energy gammas emitted from U-238 daughters. DU signatures can be differentiated from naturally occurring uranium spectra due to the presence of daughters below U-234, primarily bismuth (Bi)-214, in the uranium decay series. Three primary regions of interest (ROI) within the analyzed test spectra are used to roughly characterize the shape of each test spectrum and compare them to background. The three ROIs are focused around the gamma-emitting daughters of U-238: thorium (Th)-234, protactinium (Pa)-234 metastable (m), and Bi-214. One of the three regions is considered the base region. This region is used to estimate what the counts in the other two regions should be by multiplying the counts in the ROIs by a ratio of background counts from an area previously found to be impacted. Using this spectral ratioing technique, a non-zero difference in spectral ratio values indicates a shift in the test spectra away from background. Calculating ratios between the base region and the other two regions allows for a graphical representation of the data in a two-dimensional spectral ratioing space, which is used to differentiate between the types of uranium sources previously mentioned. Surveying processes that are based on gross counts from scintillation counters can result in a large number of false positives as a result of shifting background signals or the presence of naturally occurring uranium. Application of the spectral ratioing technique can further assist in site characterization by analyzing all of the data-starved survey spectra to identify false positives or the need for additional data collection. Accurate surveys of DU testing and training ranges reduces the costs associated with range clean-up by discriminating clean areas from those requiring remediation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2012

Analysis of Phoenix Anomalies and IV and V Findings Applied to the GRAIL Mission

Analysis of patterns in IV&V findings and their correlation with post-launch anomalies allowed GRAIL to make more efficient use of IV&V services . Fewer issues. . Higher fix rate. . Better communication. . Increased volume of potential issues vetted, at lower cost. . Hard to make predictions of post-launch performance based on IV&V findings . Phoenix made sound fix/use as-is decisions . Things that were fixed eliminated some problems, but hard to quantify. . Broad predictive success in one area, but inverse relationship in others.

Gravity Recovery and Interior Laboratory (GRAIL)↗
NASA NTRS2012

Cognitive Bias in Systems Verification

Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

Cognitive bias↗
NASA NTRS2012

Analysis of Phoenix Anomalies and IV & V Findings Applied to the GRAIL Mission

NASA IV&V was established in 1993 to improve safety and cost-effectiveness of mission critical software. Since its inception the tools and strategies employed by IV&V have evolved. This paper examines how lessons learned from the Phoenix project were developed and applied to the GRAIL project. Shortly after selection, the GRAIL project initiated a review of the issues documented by IV&V for Phoenix. The motivation was twofold: the learn as much as possible about the types of issues that arose from the flight software product line slated for use on GRAIL, and to identify opportunities for improving the effectiveness of IV&V on GRAIL. The IV&V Facility provided a database dump containing 893 issues. These were categorized into 16 bins, and then analyzed according to whether the project responded by changing the affected artifacts or using as-is. The results of this analysis were compared to a similar assessment of post-launch anomalies documented by the project. Results of the analysis were discussed with the IV&V team assigned to GRAIL. These discussions led to changes in the way both the project and IV&V approached the IV&V task, and improved the efficiency of the activity.

Larson, Steve↗
NASA NTRS2012

Cognitive Bias in the Verification and Validation of Space Flight Systems

Cognitive bias is generally recognized as playing a significant role in virtually all domains of human decision making. Insight into this role is informally built into many of the system engineering practices employed in the aerospace industry. The review process, for example, typically has features that help to counteract the effect of bias. This paper presents a discussion of how commonly recognized biases may affect the verification and validation process. Verifying and validating a system is arguably more challenging than development, both technically and cognitively. Whereas there may be a relatively limited number of options available for the design of a particular aspect of a system, there is a virtually unlimited number of potential verification scenarios that may be explored. The probability of any particular scenario occurring in operations is typically very difficult to estimate, which increases reliance on judgment that may be affected by bias. Implementing a verification activity often presents technical challenges that, if they can be overcome at all, often result in a departure from actual flight conditions (e.g., 1-g testing, simulation, time compression, artificial fault injection) that may raise additional questions about the meaningfulness of the results, and create opportunities for the introduction of additional biases. In addition to mitigating the biases it can introduce directly, the verification and validation process must also overcome the cumulative effect of biases introduced during all previous stages of development. A variety of cognitive biases will be described, with research results for illustration. A handful of case studies will be presented that show how cognitive bias may have affected the verification and validation process on recent JPL flight projects, identify areas of strength and weakness, and identify potential changes or additions to commonly used techniques that could provide a more robust verification and validation of future systems.

Larson, Steve↗
NASA NTRS1992

AVIRIS ground data processing system

During the last year and a half, Feb. 1991 to Jun. 1992, a major upgrade of the Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) ground data processing system took place. Both the hardware and software components were changed significantly to improve the processing capacity and performance and to structure a data facility capable of handling the projected work load into the near future. A summary report of these changes and some projections for the future are provided. The objectives of the AVIRIS data facility are to decommutate and archive AVIRIS data and to provide raw or radiometrically calibrated data products to the science investigator. These primary objectives have not changed from the initial concepts. The upgrade effort has greatly improved the processing system. These objectives can now be accomplished in a more timely fashion at a reasonable cost and there is sufficient capacity to manage the current processing load and provide for future growth. The method of implementation added the flexibility to provide better service to the investigator and allow for future changes.

Hansen, Earl G.↗