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DOE OSTI2021

Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall–magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this letter, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

42 ENGINEERING↗

NASA NTRS1990

Processing and analysis of radiometer measurements for airborne reconnaissance

This paper describes selected results of airborne, radiometric imaging measurements at 90 GHz and 140 GHz relevant for the application in reconnaissance. Using a temperature resolution below 0.5 K and an angular resolution of about 1-degree high-quality images show the capability of discriminating between many brightness temperature classes within our natural environment and man-made objects. Measurement examples are given for cloud and fog penetration at 90 GHz, for the detection of vehicles on roads, and for the detection and classification of airports and airplanes. The application of different contour enhancement methods (Marr-Hildreth and Canny) shows the possibility of extracting lines and shapes precisely in order to improve automatic target recognition. The registration of the passive images with corresponding X-band synthetic aperture images from the same area is carried out and the high degree of correlation is discussed.

Suess, Helmut↗

DOE OSTI2024

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts

In metal additive manufacturing (AM), understanding the process-structure-performance relationships requires a combination of multi-scale characterization techniques that allows for the measurement of the melt pool shape and boundary and classifying various defects and flaws in the AM parts. Such approaches can be destructive, only 2D in nature, or have a small field of view and can be complex to co-register and analyze. Here, in this work, we present a non-destructive 3D inspection technique that employs dual-energy X-ray computed tomography (XCT) along with a model-based iterative reconstruction (MBIR) and a new segmentation algorithm. The proposed approach and algorithm are not only capable of classifying and quantifying flaws such as pores, cracks, and inclusions, but they also allow for the extraction of microstructural features such as melt pool boundaries (MPB) and melt pool regions (MPR), that can help understand process-structure-performance relationships for alloys under study. As an exemplar application, we employed the method for characterization of an additively manufactured aluminum alloy crept under tensile stress at 300 °C for 1064 h. Our results demonstrate high quality segmentation and classification of various flaws and MPB and MPR, for the first time, using 3D X-ray CT inspection. The delineated MPB and MPR in the crept samples reveal the preferential growth paths of cracks that formed during creep deformation. The technique was used for successfully quantifying the characteristics (number of defects, their density, volume fraction, etc.) of the manufacturing-induced pores and creep-induced cracks, which is necessary to better understand the creep failure mechanisms of the material.

36 MATERIALS SCIENCE↗

DOE OSTI2019

In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach

Additive manufacturing (AM) has attracted considerable attention in recent years. This technology overcomes the geometrical limits of workpieces produced with the traditional subtractive methods and so gives the opportunity to manufacture highly complex shapes. Unfortunately, the repeatability of the manufacturing process and the monitoring of quality are not reliable enough to be utilized in mass production. The quality monitoring of AM processes in commercial equipment has been largely based on temperature measurements of the process zone or high-resolution imaging of the layers. However, both techniques lack information about the physical phenomena taking place in the depth of the materials medium and this limits their reliability in real-life applications. To overcome those restrictions, we propose to combine acoustic emission and reinforcement learning. The former captures the information about the subsurface dynamics of the process. The latter is a branch of machine learning that allows interpreting the received data in terms of quality. The combination of both is an original method for in situ and real-time quality monitoring. Acoustic data were collected during a real process using a commercial AM machine. The process parameters were selected to achieve three levels of quality in terms of porosity concentration while manufacturing a stainless steel 316L cuboid shape. Using our method, we demonstrated that each level of quality produced unique acoustic signatures during the build that were recognized by the classifier. The classification accuracy reached in this work proves that the proposed method has high potential to be used as in situ and real-time monitoring of AM quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2015

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.

Statistics↗

DOE OSTI2018

A new analysis approach based on Haralick texture features for the characterization of microstructure on the example of low-alloy steels

Highlights: • Microstructures were characterized based on orientation-invariant image-texture features. • A new parameter called “amplitude”, was formulated and is based on image rotation and the Haralick parameters: texture contrast, correlation, homogeneity and energy. • The proposed method is insensitive to changing image resolution and image contrast/brightness settings. - Abstract: Microstructures were analyzed by an improved texture-based method using gray level co-occurrence matrices (GLCM). This method is based on a new parameter calculated from the stepwise rotation of images and thereby, calculating the values independent of the original texture orientation. The proposed method was applied on a database of etched and scanning electron microscopy (SEM)-imaged low-carbon steel microstructures that are currently extensively used for automated microstructure classification. The results on the microstructures consisting of pearlitic, lath martensitic and lower bainitic constituents revealed that the method allows a significant separation of various types of microstructures in the ideal case of square-shaped cutouts. For complete grains of the corresponding second phases, the results imply that the application of a classifier is advantageous to distinguish them with a sufficient accuracy. The robustness and workability of the method was further demonstrated by discussing the effect of varying the image resolution and contrast/brightness settings during image acquisition. It was shown that such user-dependent setting parameters do not impair the separability of the steel constituents by using the proposed method.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗

NASA NTRS1984

Distribution and evolution of asteroid rotation rates

Data on the rotational characteristics of more than 300 asteroids are currently available, and it is now clear that the distribution of the rotation rates is nonrandom. A plot of rotation rate against asteroid diameter shows large dispersion but is distinctly V-shaped. The minimum of this curve at about 120 km may separate primordial asteroids from their collision products. There is also evidence that rotation rate depends on type classification, and weak evidence that it may also depend on family membership. Recent bias-free observations suggest that the marked rise of rotation rate with decreasing diameter D for those asteroids with D less than 120 km cannot be completely accounted for by observational-selection effects. A significantly large subset of the small asteroids have exceptionally long rotation periods suggestive of either a different nature and origin or a peculiar history. Models that have been proposed to account for these results are discussed.

Dermott, S. F.↗

NASA NTRSDate not supplied

Infusing Statistical Thinking into the NASA Quesst Community Test Campaign

Statistical thinking permeates many important decisions as NASA plans its Quesst mission, which will culminate in a series of community overflights using the X-59 aircraft to demonstrate low-noise supersonic flight. Month-long longitudinal surveys will be deployed to assess human perception and annoyance to this new acoustic phenomenon. NASA works with a large contractor team to develop systems and methodologies to estimate noise doses, to test and field socio-acoustic surveys, and to study the relationship between the two quantities, dose and response, through appropriate choices of statistical models. This latter dose-response relationship will serve as an important tool as national and international noise regulators debate whether overland supersonic flights could be permitted once again within permissible noise limits. In this presentation we highlight several areas where statistical thinking has come into play, including issues of sampling, classification and data fusion, and analysis of longitudinal survey data that are subject to rare events and the consequences of measurement error. We note several operational constraints that shape the appeal or feasibility of some decisions on statistical approaches, and we identify several important remaining questions to be addressed.

Bayesian model↗

DOE OSTI2016

MO-DE-207A-02: A Feature-Preserving Image Reconstruction Method for Improved Pancreaticlesion Classification in Diagnostic CT Imaging

Purpose: To develop a feature-preserving model based image reconstruction (MBIR) method that improves performance in pancreatic lesion classification at equal or reduced radiation dose. Methods: A set of pancreatic lesion models was created with both benign and premalignant lesion types. These two classes of lesions are distinguished by their fine internal structures; their delineation is therefore crucial to the task of pancreatic lesion classification. To reduce image noise while preserving the features of the lesions, we developed a MBIR method with curvature-based regularization. The novel regularization encourages formation of smooth surfaces that model both the exterior shape and the internal features of pancreatic lesions. Given that the curvature depends on the unknown image, image reconstruction or denoising becomes a non-convex optimization problem; to address this issue an iterative-reweighting scheme was used to calculate and update the curvature using the image from the previous iteration. Evaluation was carried out with insertion of the lesion models into the pancreas of a patient CT image. Results: Visual inspection was used to compare conventional TV regularization with our curvature-based regularization. Several penalty-strengths were considered for TV regularization, all of which resulted in erasing portions of the septation (thin partition) in a premalignant lesion. At matched noise variance (50% noise reduction in the patient stomach region), the connectivity of the septation was well preserved using the proposed curvature-based method. Conclusion: The curvature-based regularization is able to reduce image noise while simultaneously preserving the lesion features. This method could potentially improve task performance for pancreatic lesion classification at equal or reduced radiation dose. The result is of high significance for longitudinal surveillance studies of patients with pancreatic cysts, which may develop into pancreatic cancer. The Senior Author receives financial support from Siemens GmbH Healthcare.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1993

Classification of the LCVF AVIRIS test site with a Kohonen artificial neural network

We present a classification of an AVIRIS spectral image of the Lunar Crater Volcanic Field (LCVF). Geologic mapping from such data is made possible by distinctive mineral signatures: absorption features and the shape of the spectral continuum. The subtle spectral shape differences between some of the geological units in this scene along with the high dimensionality of the spectral presents a challenging pattern recognition task. We found an artificial neural network powerful in separating 13 geological units based on the full spectral resolution. The LCVF, in northern Nye County, Nevada, was the primary focus of the NASA-sponsored Geologic Remote Sensing Field Experiment in the summer of 1989. It consists of over 100 square miles of Quaternary basaltic pyroclastic and flow deposits. These deposits lie atop ignimbrites and silicic lava flows of Tertiary age and in turn are overlain by Quaternary alluvial and playa deposits. This AVIRIS image was collected on September 29, 1989 at 11:44 at 11:44 PDT. The 256-by-256 pixel subsection in this study contains oxidized basaltic cinder deposits, the southern half of the Lunar Lake playa, and outcrops of the Rhyollite of Big Sand Spring Valley. Vegetation in LCVF is sparse, but locally abundant within washes and near springs.

Merenyi, Erzsebet↗

NASA NTRS2020

Fast Neutron Spectroscopy with Organic Scintillation Detectors in a High-Radiation Environment

Organic scintillators with pulse shape discrimination capability are widely used in both research and practical applications of neutron detection. The neutron and gamma-ray identification performance of the detector depends on the classification algorithms, noise filters and pileup rejection criteria in a high-flux bremsstrahlung radiation environment. In this paper, a technique has been developed and implemented for the neutron detection with multiple filter and discrimination steps, which to a high confidence level eliminates counting of gamma-ray pulses. Such a technique is merited when making measurements in a high flux bremsstrahlung and secondary fluorescence environment. The EJ-309 and stilbene detectors coupled to the digital data acquisition system were used for the calibration assessments with standard gamma-ray and neutron sources such as 137Cs, 60Co, 252Cf, and Am-Be. The MCNPX-PoliMi and GEANT4 toolkits were used to simulate the light output and the optical photon transport in the scintillators and create detector response functions for each type of detector. The neutron spectrum unfolding algorithm, GRAVELW, was used to recreate and calibrate with the Am-Be as final step before applying the neutron detection system to extract fusion neutron spectra generated in an intense bremsstrahlung radiation environment. This new technique described offers the user the ability to measure neutron spectra in a high-flux gamma-ray field and tune the parameters to meet required filtering needs.

Neutron Detector, Organic Scintillator, PulseShape↗

DOE OSTI2015

NuSTAR SPECTROSCOPY OF MULTI-COMPONENT X-RAY REFLECTION FROM NGC 1068

We report on high-energy X-ray observations of the Compton-thick Seyfert 2 galaxy NGC 1068 with NuSTAR, which provide the best constraints to date on its >10 keV spectral shape. The NuSTAR data are consistent with those from past and current instruments to within cross-calibration uncertainties, and we find no strong continuum or line variability over the past two decades, which is in line with its X-ray classification as a reflection-dominated Compton-thick active galactic nucleus. The combined NuSTAR, Chandra, XMM-Newton, and Swift BAT spectral data set offers new insights into the complex secondary emission seen instead of the completely obscured transmitted nuclear continuum. The critical combination of the high signal-to-noise NuSTAR data and the decomposition of the nuclear and extranuclear emission with Chandra allow us to break several model degeneracies and greatly aid physical interpretation. When modeled as a monolithic (i.e., a single N{sub H}) reflector, none of the common Compton reflection models are able to match the neutral fluorescence lines and broad spectral shape of the Compton reflection hump without requiring unrealistic physical parameters (e.g., large Fe overabundances, inconsistent viewing angles, or poor fits to the spatially resolved spectra). A multi-component reflector with three distinct column densities (e.g., with best-fit values of N{sub H} of 1.4 × 10{sup 23}, 5.0 × 10{sup 24}, and 10{sup 25} cm{sup −2}) provides a more reasonable fit to the spectral lines and Compton hump, with near-solar Fe abundances. In this model, the higher N{sub H} component provides the bulk of the flux to the Compton hump, while the lower N{sub H} component produces much of the line emission, effectively decoupling two key features of Compton reflection. We find that ≈30% of the neutral Fe Kα line flux arises from >2″ (≈140 pc) and is clearly extended, implying that a significant fraction (and perhaps most) of the <10 keV reflected component arises from regions well outside a parsec-scale torus. These results likely have ramifications for the interpretation of Compton-thick spectra from observations with poorer signal-to-noise and/or more distant objects.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

SN REFSDAL: CLASSIFICATION AS A LUMINOUS AND BLUE SN 1987A-LIKE TYPE II SUPERNOVA

We have acquired Hubble Space Telescope (HST) and Very Large Telescope near-infrared spectra and images of supernova (SN) Refsdal after its discovery as an Einstein cross in fall 2014. The HST light curve of SN Refsdal has a shape consistent with the distinctive, slowly rising light curves of SN 1987A-like SNe, and we find strong evidence for a broad H α P-Cygni profile and Na I D absorption in the HST grism spectrum at the redshift ( z = 1.49) of the spiral host galaxy. SNe IIn, largely powered by circumstellar interaction, could provide a good match to the light curve of SN Refsdal, but the spectrum of a SN IIn would not show broad and strong H α and Na I D absorption. From the grism spectrum, we measure an H α expansion velocity consistent with those of SN 1987A-like SNe at a similar phase. The luminosity, evolution, and Gaussian profile of the H α emission of the WFC3 and X-shooter spectra, separated by ∼2.5 months in the rest frame, provide additional evidence that supports the SN 1987A-like classification. In comparison with other examples of SN 1987A-like SNe, photometry of SN Refsdal favors bluer B - V and V - R colors and one of the largest luminosities for the assumed range of potential magnifications. The evolution of the light curve at late times will provide additional evidence about the potential existence of any substantial circumstellar material. Using MOSFIRE and X-shooter spectra, we estimate a subsolar host-galaxy metallicity (8.3 ± 0.1 dex and <8.4 dex, respectively) near the explosion site.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Artificial neural network algorithms for pulse shape discrimination and recovery of piled-up pulses in organic scintillators

We developed two neural-network (NN)-based algorithms (fully-connected neural network (Fc-NN) and recurrent neural network (RNN)) to perform pulse shape discrimination (PSD) and identification of piled-up pulses produced by organic scintillators, upon interaction with neutrons and gamma rays. We tested the algorithms on measured and verification sets of data and compared their classification performances to standard approaches. At a high acquisition count rate (100,000 counts per second, cps), in the presence of a gamma-to-neutron ratio of approximately 400–1, the proposed NN-based algorithm achieves a fraction of misclassified neutron, gamma, and piled-up pulses of approximately 1%, 1.8%, and 0.6%, respectively. Compared to the traditional approach, it exhibits 3×, 14×, and 11× improved (lower) miscalculation rates for neutron, gamma, and piled-up pulses, respectively. Here, we also demonstrate the capability of NN-based algorithms of successfully recovering and identifying neutron and gamma ray compositions from piled-up pulses in challenging, high pulse count rate conditions.

42 ENGINEERING↗

NASA NTRS1987

Determine precipitation rates from visible and infrared satellite images of clouds by pattern recognition technique

A more advanced cloud pattern analysis algorithm was subsequently developed to take the shape and brightness of the various clouds into account in a manner that is more consistent with the human analyst's perception of GOES cloud imagery. The results of that classification scheme were compared with precipitation probabilities observed from ships of opportunity off the U.S. east coast to derive empirical regressions between cloud types and precipitation probability. The cloud morphology was then quantitatively and objectively used to map precipitation probabilities during two winter months during which severe cold air outbreaks were observed over the northwest Atlantic. Precipitation probabilities associated with various cloud types are summarized. Maps of precipitation probability derived from the cloud morphology analysis program for two months and the precipitation probability derived from thirty years of ship observation were observed.

Weinman, James A.↗

NASA NTRS2005

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rai, Man Mohan↗

NASA NTRS2007

Hybrid Neural Network and Support Vector Machine Method for Optimization

Rai, Man Mohan↗