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At least 1,117 records · Page 62

Machine-learning-based, online estimation of ceramic’s microstructure upon the laser spot brightness during laser sintering

The ceramic microstructure strongly influences its properties. During manufacturing, the online monitoring of microstructure is critical to ensure the desired material properties. So far, the microstructure on the relevant scale is usually characterized offline using scanning electron microscopy (SEM), which is time and cost-consuming. In this work, we demonstrate a cost-effective, machine learning (ML)-based approach to simulate the SEM micrographs in real-time from the laser spot brightness. We experimentally observed a strong correlation between the laser spot brightness and the corresponding microstructure at the exact locations. The brightness values obtained from thermal emission images and the corresponding SEM micrographs were used in the training datasets. The ML algorithm was a style-based conditional generative adversarial network (CGAN). After training, the ML model could generate high-fidelity microstructure images within 0.1 seconds based on in-situ captured brightness at the laser sintering spot. We used the average grain sizes as the metric to evaluate the accuracy of the ML-predicted micrographs. Here, the ML-predicted microstructures were in good agreement, with less than 5% in difference from the real SEM images. In conclusion, we demonstrate the cost-effective, online microstructure estimation during laser sintering with a simple setup (a camera, a regular computer, and the ML model).

08 HYDROGEN↗

InFOCuS: A Balloon-borne Hard X-ray Imaging Telescope

InFOCuS is a new generation balloon-borne hard X-ray telescope with focusing optics and spectroscopy. After several successful flights in recent years it is being refurbished for a 2010 flight from Australia to map the hard X-ray emission from the galactic center region. in this poster, we present the status of the mirror, detectors and attitude control system. The grazing incidence optics consists of a depth-graded platinum-carbon multilayer mirror with an 8-meter focal length. It has an effective area of 78 cm(sup 2) at 30 keV. an angular resolution of 2.0 arcmin (HPD), and a field of view of 10 arcmin. The detector is a CdZnTe solid-state device capable of imaging spectroscopy. The detector is surrounded by a 3-cm thick CsI anti-coincidence shield to reduce background from particles and photons not incident along the mirror focal direction. The gondola is being reconfigured in a floating ball configuration to improve pointing control and allow the telescope to be pointed vertically. Tracking will be accomplished with a suite of on-axis and off-axis star cameras. The payload will be ready to fly from Alice Springs, Australia in Spring 2010. In this flight, InFOCuS will have the angular resolution and sensitivity to determine whether Sgr A* is the source of the hard X-rays detected by Swift/BAT and INTEGRAL and determine if there are other nearby hard X-ray sources.

Krimm, Hans A.↗

Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems

Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.

42 ENGINEERING↗

TM digital image products for applications

The image characteristics of digital data generated by LANDSAT 4 thematic mapper (TM) are discussed. Digital data from the TM resides in tape files at various stages of image processing. Within each image data file, the image lines are blocked by a factor of either 5 for a computer compatible tape CCT-BT, or 4 for a CCT-AT and CCT-PT; in each format, the image file has a different format. Nominal geometric corrections which provide proper geodetic relationships between different parts of the image are available only for the CCT-PT. It is concluded that detector 3 of band 5 on the TM does not respond; this channel of data needs replacement. The empty bin phenomenon in CCT-AT images results from integer truncations of mixed-mode arithmetric operations.

Barker, J. L.↗

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Hybrid Lyot Coronagraph for WFIRST: High Contrast Testbed Demonstration in Flight-like Low Flux Environment

In order to validate required operation of the proposed Wide-Field InfraRed Survey Telescope (WFIRST) coronagraph instrument, we have built a testbed in Jet Propulsion Laboratory (JPL), which is analogous to the baseline WFIRST coronagraph instrument architecture. Since its birth in 2016, this testbed, named as Occulting Mask Coronagraph (OMC) testbed, has demonstrated several crucial technological milestones: Broadband high contrast demonstration in both Hybrid Lyot Coronagraph (HLC) and Shape Pupil Coronagraph (SPC) modes while the Low Order Wavefront Sensing and Control (LOWFS/C) subsystem senses and corrects the dynamic flight-like wavefront disturbances. In this paper, we present up-to-date progress of HLC mode demonstration in the OMC testbed. While injecting the flight-like low photon flux starlight with expected Line of Sight (LoS) and Wavefront Error (WFE) perturbation to the OMC testbed, we demonstrate generating high contrast dark hole images. We first study the expected photon flux in actual flight environment, and estimate detection noise and estimation accuracy of the complex electric field if the wavefront sensing algorithm is used based on the pair-wise difference imaging. Then, we introduce our improved scheme to mitigate this photon-starved flight-like low flux environment. As a result, we generate a dark hole that meets the WFIRST raw contrast requirements using the 2nd magnitude star light. We establish the key ideas, describe test setups, and demonstrate test results with data analysis.

Wilson, Daniel↗

Recorder/processor apparatus

Laser beam is intensity modulated in response to incoming video signals. Latent image is recorded on rotating drum which generates raster in conjunction with incrementally-driven lens carriage. Image is fed automatically to thermal processor; actual image is developed by controlled application of heat onto medium containing latent image.

Shim, I. H.↗

Range Imaging without Moving Parts

Range-imaging instruments of a type now under development are intended to generate the equivalent of three-dimensional images from measurements of the round-trip times of flight of laser pulses along known directions. These instruments could also provide information on characteristics of targets, including roughnesses and reflectivities of surfaces and optical densities of such semi-solid objects as trees and clouds. Unlike in prior range-imaging instruments based on times of flight along known directions, there would be no moving parts; aiming of the laser beams along the known directions would not be accomplished by mechanical scanning of mirrors, prisms, or other optical components. Instead, aiming would be accomplished by using solid-state devices to switch input and output beams along different fiber-optic paths. Because of the lack of moving parts, these instruments could be extraordinarily reliable, rugged, and long-lasting. An instrument of this type would include an optical transmitter that would send out a laser pulse along a chosen direction to a target. An optical receiver coaligned with the transmitter would measure the temporally varying intensity of laser light reflected from the target to determine the distance and surface characteristics of the target. The transmitter would be a combination of devices for generating precise directional laser illumination. It would include a pulsed laser, the output of which would be coupled into a fiber-optic cable with a fan-out and solid-state optical switches that would enable switching of the laser beam onto one or more optical fibers terminated at known locations in an array on a face at the focal plane of a telescope. The array would be imaged by the telescope onto the target space. The receiver optical system could share the aforementioned telescope with the transmitter or could include a separate telescope aimed in the same direction as that of the transmitting telescope. In either case, light reflected from the target would be focused by the receiver optical system onto an array of optical fibers matching the array in the transmitter. These optical fibers would couple the received light to one or more photodetector( s). Optionally, the receiver could include solid-state optical switches for choosing which optical fiber(s) would couple light to the photodetector(s). This instrument architecture is flexible and can be optimized for a wide variety of applications and levels of performance. For example, it is scalable to any number of pixels and pixel resolutions and is compatible with a variety of ranging and photodetection methodologies, including, for example, ranging by use of modulated (including pulsed and encoded) light signals. The use of fixed arrays of optical fibers to generate controlled illumination patterns would eliminate the mechanical complexity and much of the bulk of optomechanical scanning assemblies. Furthermore, digital control of the selection of the fiber-optic pathways for the transmitted beams could afford capabilities not seen in previous three-dimensional range-imaging systems. Instruments of this type could be specialized for use as, for example, proximity detectors, three-dimensional robotic vision systems, airborne terrain-mapping systems, and inspection systems.

Blair, J. Bryan↗

Digital Mammography with a Mosaic of CCD-Arrays

The present invention relates generally to a mammography device and method and more particularly to a novel digital mammography device and method to detect microcalcifications of precancerous tissue. A digital mammography device uses a mosaic of electronic digital imaging arrays to scan an x-ray image. The mosaic of arrays is repositioned several times to expose different portions of the image, until the entire image is scanned. The data generated by the arrays during each exposure is stored in a computer. After the final exposure, the computer combines data of the several partial images to produce a composite of the original x-ray image. An aperture plate is used to reduce scatter and the overall exposure of the patient to x-rays. The novelty of this invention is that it provides a digital mammography device with large field coverage, high spatial resolution, scatter rejection, excellent contrast characteristics and lesion detectability under clinical conditions. This device also shields the patient from excessive radiation, can detect extremely small calcifications and allows manipulation and storage of the image.

Jalink, Antony, Jr.↗

Digital Mammography with a Mosaic of CCD Arrays

A digital mammography device uses a mosaic of electronic digital imaging arrays to scan an x-ray image is discussed. The mosaic of arrays is repositioned several times to expose different portions of the image, until the entire image is scanned. The data generated by the arrays during each exposure is stored in a computer. After the final exposure, the computer combines data of the several partial images to produce a composite of the original x-ray image. An aperture plate is used to reduce scatter and the overall exposure of the patient to x-rays.

Jalink, Antony, Jr.↗

Reflective Solar Band Striping Mitigation Method for the GOES-R Series Advanced Baseline Imager Using Special Scans

The large focal plane arrays used in the new generation of the Geostationary Operational Environmental Satellites (GOES) advanced baseline imager (ABI) introduce new calibration challenges compared with the heritage GOES imagers. The increased number of detectors allows for increased spatial, spectral, and temporal performance, but as a trade-off, it has an increased risk of image striping. We detail the development of a new postlaunch relative calibration capability for ABI reflective solar bands that utilizes ABI special scans to generate a set of relative gains that can be applied to improve image quality and reduce image striping. Results demonstrate that the method reduces image striping in the ABI solar reflective bands over varying scene content and time, both diurnally and over an extended period. This methodology ensures a calibration strategy that is consistent with heritage approaches yet adapts to the new postlaunch validation challenges presented by the new class of operational imagers in the GOES-R series. The developed approach is ready for operational use, as needed, and can be easily implemented into operations to support the operational production of geostationary imagery of the Earth.

Monica Cook↗

Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation

In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LIDAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA’s Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNNbased algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the statedependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver

Rahmani, Amir↗

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).

14 SOLAR ENERGY↗

Foil Mirrors and Multilayer Developments at Goddard

This paper summarizes progress at Goddard toward incorporating refinements into the foil mirror technology that extend its utilization in astronomical research well into the future. The Japan/US collaborative mission ASCA has demonstrated both the strengths of first generation foil mirrors but also the areas where improvements will be needed. A radical change in the way we smooth the foil surface has already been implemented and represents an important first step that has considerably improved the imaging capability of our second generation mirrors. Additional breakthroughs are still needed, however, if foil mirrors are to play a major role in the Next Generation X ray Observatory era. Foil mirrors offer, additionally, a number of attractive attributes as regards the use of multilayers to extend imaging spectroscopy to tens of keV. Preliminary results of multilayers deposited on our new foil reflectors are very encouraging.

Serlemitsos, Peter J.↗

Building Maps for Terrain Relative Navigation Using Blender: An Open-Source Approach

A persistent challenge for vision-based navigation systems that compare imagery to a reference map is generating high quality maps with similar lighting conditions. Image rendering software can be used to apply variable lighting to reference maps or to generate synthetic imagery for test trajectories. While many image rendering software packages are available, with several developed specifically for spaceflight applications, there are often limitations due to cost, image fidelity, or flexibility. In this paper, we demonstrate the use of an open-source image rendering software, Blender, for use in Terrain Relative Navigation (TRN) applications. A scene in Blender was generated based on elevation data and satellite imagery of the region of West Texas used by Blue Origin for the operation of their New Shepard suborbital rocket. The Blender scene was validated by reproducing imagery collected during a flight of New Shepard in October 2020 and was further used to generate reference maps for use by a TRN algorithm on a subsequent New Shepard flight in August 2021. The work was performed under the NASA Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project, which is focused on technology advancement for precision landing and hazard avoidance. This work aims to lower the cost of entry and generally promote the adoption and advancement of vision-based navigation technologies.

Kyle W Smith↗

Chasing ghosts: characterization of artifact generation in coded aperture decoding due to experimental implementation

Coded aperture imaging is a form of lensless aperture imaging that projects multiple overlapping images of the source onto the detector, enhancing signal strength, which is advantageous for low-flux sources or high-resolution imaging. This technique requires decoding of the detector signal to reconstruct the original source, which involves convolving the detector data with the aperture pattern. When the signal is from a centered point source, the reconstructed source image is known as the point spread function (PSF). A clean PSF without artifacts is a Dirac delta function [Appl. Opt. 20, 1858 (1981)]. This paper examines the robustness of the decoding process against variations in experimental tolerances by analyzing artifact growth in the reconstructed PSF. We illustrate the effects of incorrect magnification, rotation, and detector size and find that aperture–detector rotational misalignment about the imaging axis is the most sensitive parameter, with significant artifact generation occurring with angular offsets of less than one degree. We discuss compensation methods for imperfect aperture placement, finding that small detector sizes produce uncompensatable artifact generation, and compare theoretical predictions with experimental PSF measurements of a rank , 6.8 mm thick (less than one mean free path) coded aperture with a 3.5 mm cell size, conducted at the MegaJOuLe Neutron Imaging Radiography dense plasma focus [IEEE Trans. Plasma Sci. 49, 3299 (2021)] using a 2.45 MeV neutron source. Based on our findings, we recommend using magnified coded apertures in the under-sampled regime, which allows for the inclusion of fiducial markers to characterize aperture–detector rotational offsets and the addition of mechanical coupling, where possible, to constrain rotational and magnification offsets.

Selwood, M. P. [Lawrence Livermore National Labora↗

Self-supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.

79 ASTRONOMY AND ASTROPHYSICS↗