Results for “Image Analysis” — Page 38

DOE OSTI2017

Direct evidence of atomic-scale structural fluctuations in catalyst nanoparticles

Rational catalyst design requires an atomic scale mechanistic understanding of the chemical pathways involved in the catalytic process. A heterogeneous catalyst typically works by adsorbing reactants onto its surface, where the energies for specific bonds to dissociate and/or combine with other species (to form desired intermediate or final products) are lower. Here, using the catalytic growth of single-walled carbon nanotubes (SWCNTs) as a prototype reaction, we show that the chemical pathway may in-fact involve the entire catalyst particle, and can proceed via the fluctuations in the formation and decomposition of metastable phases in the particle interior. We record in situ and at atomic resolution, the dynamic phase transformations occurring in a Cobalt catalyst nanoparticle during SWCNT growth, using a state-of-the-art environmental transmission electron microscope (ETEM). The fluctuations in catalyst carbon content are quantified by the automated, atomic-scale structural analysis of the time-resolved ETEM images and correlated with the SWCNT growth rate. We find the fluctuations in the carbon concentration in the catalyst nanoparticle and the fluctuations in nanotube growth rates to be of complementary character. These findings are successfully explained by reactive molecular dynamics (RMD) simulations that track the spatial and temporal evolution of the distribution of carbon atoms within and on the surface of the catalyst particle. We anticipate that our approach combining real-time, atomic-resolution image analysis and molecular dynamics simulations will facilitate catalyst design, improving reaction efficiencies and selectivity towards the growth of desired structure.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

An image classification deep-learning algorithm for shrapnel detection from ultrasound images

Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Morphology of Nano and Micro Fiber Structures in Ultrafine Particles Filtration

Selected procedures permitting to prepare homogeneous nanofibre structures of the desired morphology by employing a suitable combination of variables during the electrospinning process are presented. A comparison (at the same pressure drop) was made of filtration capabilities of planar polyurethane nanostructures formed exclusively by nanofibres, space polycarbonate nanostructures having bead spacers, structures formed by a combination of polymethyl methacrylate micro- and nanofibres and polypropylene meltblown microstructures, through which ultrafine particles of ammonium sulphate 20-400 nm in size were filtered. The structures studied were described using a new digital image analysis technique based on black and white images obtained by scanning electron microscopy. More voluminous structures modified with distance microspheres and having a greater thickness and mass per square area of the material, i.e. structures possessing better mechanical properties, demanded so much in nanostructures, enable preparation of filters having approximately the same free volume fraction as flat nanofibre filters but an increased effective fibre surface area, changed pore size morphology and, consequently, a higher filter quality.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Mapping Benthic Habitat Conditions and Seafloor Deposits using Sediment Profile Imaging and a Semiautomated Image Processing System

Objectives/Scope: Sediment profile imaging (SPI) technology characterizes in situ physical, geochemical, and biological seafloor features. SPI was required by Mexico’s Agency for Safety, Energy, and Environment in 2017 for environmental baseline surveys (EBS) of oil lease blocks in the southern Gulf of Mexico. Because of its ability to provide information on benthic community health and the distribution of very thin (down to the centimeter scale) layers of deposited materials, SPI technology is highly effective for mapping drill cuttings or drilling muds released around wellheads during exploration or production and documenting their ecological impacts. SPI technology has not been widely used in the oil and gas industry for EBS or other monitoring activities. A primary objective of this work is to improve the transparency and consistency of the SPI data generation and data management process. Methods, Procedures, Process: The SPI camera works like an inverted periscope and obtains an undisturbed 21x15-cm cross-sectional image of the upper sediment column. The camera is internally powered and can be deployed rapidly from a standard winch in depths to 4,000 m. Many stations can be sampled in a single day by “pogo-sticking” across a survey area. Sediment grain size, penetration depth, surface boundary roughness, natural and anthropogenic depositional layers, depth of the oxidized surface sediment layer, maximum biogenic mixing depth, and infaunal successional stage can be directly measured at sea or immediately following the cruise. Final SPI data sets can be provided within a few weeks of the survey. Results, Observations, Conclusions: Details on the features measured in SPI images and the underlying interpretive paradigms are presented. To standardize the SPI data generation process, Integral Consulting Inc. has developed 1) a semiautomated image analysis platform, and 2) a SPI data-specific database architecture that allows both numerical and non-numerical metrics to be incorporated into a standard database structure. An integrated, software-based SPI analysis platform has been developed that imports image files and metadata and provides a graphical user interface. The software automatically stores the data, which can then be reviewed for quality assurance, plotted, statistically analyzed, and mapped or exported to other platforms (e.g., Esri ArcGIS©) for further evaluation. Image processing algorithms have been developed using a combination of open-source and commercially available software packages (e.g., MATLAB® and OpenCV) to automatically quantify key parameters. Novel/Additive Information: SPI technology’s underutilization in the oil and gas industry may be in part due to a lack of standardization in the measurement of basic features in SPI images. A primary objective of this work is to develop a streamlined, standardized, and transparent process for generating and managing SPI data.

02 PETROLEUM↗

NASA NTRS1973

Application of an electronic image analyzer to dimensional measurements from neutron radiographs

Means of obtaining improved dimensional measurements from neutron radiographs of nuclear fuel elements are discussed. The use of video-electronic image analysis relative to edge definition in radiographic images is described. Based on this study, an edge definition criterion is proposed for overcoming image unsharpness effects in taking accurate diametral measurements from radiographs. An electronic density slicing method for automatic edge definition is described. Results of measurements made with video micrometry are compared with scanning microdensitometer and micrometric physical measurements. An image quality indicator for estimating photographic and geometric unsharpness is described.

Vary, A.↗

NASA NTRS1984

Tracking Visible Targets Automatically

Report summarizes techniques for automatic pointing of scientific instruments by reference to visible targets. Applications foreseen in industrial robotics. Measurement done by image analysis based on gradient edge location, image-centroid location and/or outline matching.

Armstrong, R. W.↗

NASA NTRS1983

Atomization and combustion performance of antimisting kerosene and jet fuel

Combustion performance of antimisting kerosene (AMK) containing FM-9 polymer was investigated at various levels of degradation (restoration of AMK for normal use in a gas turbine engine). To establish the relationship of degradation and atomization to performance in an aircraft gas turbine combustor, sprays formed by the nozzle of a JT8-D combustor with Jet A and AMK at 1 atmosphere (atm) (14.1 lb/square in absolute) pressure and 22 C at several degradation levels were analyzed. A new spray characterization technique based on digital image analysis of high resolution, wide field spray images formed under pulsed ruby laser sheet illumination was developed. Combustion tests were performed for these fuels in a JT8-D single can combustor facility to measure combustion efficiency and the lean extinction limit. Correlation of combustion performance under simulated engine operating conditions with nozzle spray Sauter mean diameter (SMD) measured at 1 atm and 22 C were observed. Fuel spray SMD and hence the combustion efficiency are strongly influenced by fuel degradation level. Use of even the most highly degraded AMK tested (filter ratio = 1.2) resulted in an increase in fuel consumption of 0.08% to 0.20% at engine cruise conditions.

Fleeter, R.↗

NASA NTRS1986

SOT: A rapid prototype using TAE windows

The development of the window interface extension feature of the Transportable Applications Executive (TAE) is discussed. This feature is being used to prototype a space station payload interface in order to demonstrate and assess the benefits of using windows on a bit mapped display and also to convey the concept of telescience, the control and operation of space station payloads from remote sites. The prototype version of the TAE with windows operates on a DEC VAXstation 100. This workstation has a high resolution 19 inch bit mapped display, a keyboard and a three-button mouse. The VAXstation 100 is not a stand-alone workstation, but is controlled by software executing on a VAX/8600. A short scenario was developed utilizing the Solar Optical Telescope (SOT) as an example payload. In the scenario the end-user station includes the VAXstation 100 plus an image analysis terminal used to display the CCD images. The layout and use of the prototype elements, i.e., the root menu, payload status window, and target acquisition menu is described.

Stephens, Mark↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface. In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha-Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRSDate not supplied

Autonomous Navigation of a Lunar Relay Using GNSS and Other Measurements

Many of the highest priority destinations at the Moon lack a continuous view of Earth, such as the lunar poles or lunar far side. Exploration of these sites will require spacecraft in cislunar space to relay communications and provide position, navigation, and timing (PNT) services. Accurate knowledge of relay position, velocity, and time is essential to these services. This paper describes a concept for a PNT Instrument being developed for the Lunar Communications Relay and Navigation Systems (LCRNS) Project. The instrument is intended as a payload that would enable autonomous, on-board, real-time navigation and timing using Global Navigation Satellite System (GNSS), optical navigation, and one-way measurements from Earth-based ground stations. Hardware-in-the-loop simulations using flight software are used to realistically characterize performance on hardware platforms with a path to flight. These results provide preliminary validation of the proposed PNT Instrument, demonstrate the benefits of augmenting GNSS with other measurements, and serve as an insightful reference for the design of future lunar missions, including those that will operate within the LunaNet framework of standards. This instrument concept relies on several technologies developed at NASA Goddard Space Flight Center (GSFC). For GNSS observables, the instrument relies on the high-altitude NavCube 3 mini (NC3m) GNSS receiver specifically designed for cislunar applications. The autoNGC system, which consists of flight software and a hardware platform, is responsible for fusing the observables using its extended Kalman filter, the Goddard Enhanced Onboard Navigation System (GEONS). Optical navigation observables are processed within autoNGC (“autonomous Navigation, Guidance, and Control”) using the Goddard Image Analysis & Navigation Tool (GIANT) which is also responsible for simulating high-fidelity images for test and analysis. In addition to describing the PNT Instrument and its components, the paper will present predicted performance based on simulation results. As a baseline, it will present GNSS-only hardware-in-the loop results using a NC3m test unit to process Spirent-simulated GPS signals in a potential lunar relay trajectory: a 12-hour elliptical frozen lunar orbit (ELFO). GEONS then processes the GPS pseudorange and time differenced carrier phase measurements to estimate and propagate the relay state (position, velocity, and time). These results extend previously published work that showed preliminary ELFO performance. Previous work has shown the importance of other measurement types, so additional simulations are performed which augment GNSS with ground station observables and several methods of optical navigation, including celestial navigation, limb-finding (e.g., observations of the lunar horizon), and terrain relative navigation (TRN). TRN involves correlating simulated predicted images of the lunar surface with actual imagery; misalignments of landmarks identified in each image are translated into relay state updates. TRN is valuable as a measurement of the relay’s state relative to the Moon, especially during GNSS outages or after maneuvers. One-way Pseudorange and Doppler measurements from Earth-based ground stations are also simulated. The full set of observables is processed using autoNGC. These simulations make use of autoNGC and NC3m test units, a lab atomic clock, and a pulse-per-second (PPS) generation and distribution system. This combination of subsystems, and the hardware platforms used in this analysis, represents a PNT Instrument that could be flown on a lunar relay. Results from the hardware-in-the-loop simulations presented in this paper provide a preliminary assessment of the achievable navigation performance of this instrument concept. PNT Instrument performance is compared to the GPS-only performance, and a discussion is provided on the apparent merits and challenges of each measurement type.

Ben Ashman↗

DOE OSTI2015

SU-E-T-11: A Cloud Based CT and LINAC QA Data Management System

Purpose: The current status quo of QA data management consists of a mixture of paper-based forms and spreadsheets for recording the results of daily, monthly, and yearly QA tests for both CT scanners and LINACs. Unfortunately, such systems suffer from a host of problems as, (1) records can be easily lost or destroyed, (2) data is difficult to access — one must physically hunt down records, (3) poor or no means of historical data analysis, and (4) no remote monitoring of machine performance off-site. To address these issues, a cloud based QA data management system was developed and implemented. Methods: A responsive tablet interface that optimizes clinic workflow with an easy-to-navigate interface accessible from any web browser was implemented in HTML/javascript/CSS to allow user mobility when entering QA data. Automated image QA was performed using a phantom QA kit developed in Python that is applicable to any phantom and is currently being used with the Gammex ACR, Las Vegas, Leeds, and Catphan phantoms for performing automated CT, MV, kV, and CBCT QAs, respectively. A Python based resource management system was used to distribute and manage intensive CPU tasks such as QA phantom image analysis or LaTeX-to-PDF QA report generation to independent process threads or different servers such that website performance is not affected. Results: To date the cloud QA system has performed approximately 185 QA procedures. Approximately 200 QA parameters are being actively tracked by the system on a monthly basis. Electronic access to historical QA parameter information was successful in proactively identifying a Linac CBCT scanner’s performance degradation. Conclusion: A fully comprehensive cloud based QA data management system was successfully implemented for the first time. Potential machine performance issues were proactively identified that would have been otherwise missed by a paper or spreadsheet based QA system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Fine precipitation scenarios of AlZnMg(Cu) alloys revealed by advanced atomic-resolution electron microscopy study Part I: Structure determination of the precipitates in AlZnMg(Cu) alloys

Although they are among the most important precipitation-hardened materials for industry applications, the high-strength AlZnMg(Cu) alloys have thus far not yet been understood adequately about their underlying precipitation scenarios in relation with the properties. This is partly due to the fact that the structures of a number of different precipitates involved in electron microscopy in association with quantitative image simulations have to be employed; a systematic study of these hardening precipitates in different alloys is also necessary. In Part I of the present study, it is shown that there are five types of structurally different precipitates including the equilibrium η-phase precipitate. Using two state-of-the-art atomic-resolution imaging techniques in electron microscopy in association with quantitative image simulations, we have determined and clarified all the unknown precipitate structures. It is demonstrated that atomic-resolution imaging can directly suggest approximate structure models, whereas quantitative image analysis can refine the structure details that are much smaller than the resolution of the microscope. This combination is crucially important for solving the difficult structure problems of the strengthening precipitates in AlZnMg(Cu) alloys. - Highlights: Part I: • We determine and verify all the key precipitate structures in AlMgZn(Cu) alloys. • We employ aberration-corrected scanning transmission electron microscopy (STEM). • We use aberration-corrected high-resolution TEM (HRTEM) for the investigations. • We obtain atomic-resolution images of the precipitates and model their structures. • We refine all precipitate structures with quantitative image simulation analysis. Part II: • The hardening precipitates in AlZnMg alloys shall be classified into two groups. • Two precipitation scenarios coexist in the alloys. • The precipitation behavior of such an alloy depends on the alloy's composition. • Very detailed phase/structure transformations among the precipitates are revealed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Tracking Robot Location for Non-Destructive Evaluation of Double-Shell Tanks

(1) Background: Non-destructive evaluation of double-shell nuclear-waste storage tanks at the U.S. Department of Energy’s Hanford site requires a robot to navigate a network of air slots in the confined space between primary and secondary tanks. Situational awareness, data collection, and data interpretation require continuous tracking of the robot’s location. (2) Methods: Robot location is continuously monitored using video image analysis for short distances and laser ranging for absolute location. (3) Results: The technique was demonstrated in our laboratory using a mockup of air slot and robot. (4) Conclusions: Location tracking and display provide decision support to inspectors and lay the groundwork for automated data collection.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

User-Driven Sampling Strategies in Image Exploitation

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Void formation during cyclic oxidation of oxide dispersion strengthened FeCrAl alloys

Here, oxide dispersion strengthened FeCrAl alloys are considered for various high-temperature applications due to their excellent mechanical properties and oxidation resistance, but void formation is a concern for component durability. Cyclic oxidation testing was conducted on alloy MA956 and PM2000 at 1100–1200ºC for durations of up to 10,000 h and the void content was determined from optical cross-sections by image analysis. At 1100ºC, 100 h cycle, the low void concentrations measured were consistent with two competing mechanisms proposed in the literature: coalescence of process-induced nano-voids and alloy sintering. At 1200ºC using 1 h cycles, the void concentration was much higher, reaching ~3% to 6% for specimens tested to breakaway oxidation. The void growth was attributed to the high oxidation rates and the annihilation of oxidation-induced vacancies at the surface of the process-induced voids.

36 MATERIALS SCIENCE↗