Results for “Clustered patterns” — Page 28

DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Rainfall↗

DOE OSTI2016

Isotopomer-selective spectra of a single intact H 2 O molecule in the Cs + (D 2 O) 5 H 2 O isotopologue: Going beyond pattern recognition to harvest the structural information encoded in vibrational spectra

We report the vibrational signatures of a single H 2 O water molecule occupying distinct sites of the hydration network in the Cs + (H 2 O) 6 cluster. This is accomplished using isotopomer selective IR-IR hole-burning on the Cs + (D 2 O) 5 (H 2 O) clusters formed by gas-phase exchange of a single, intact H 2 O molecule for D 2 O in the Cs + (D 2 O) 6 ion. The OH stretching pattern of the Cs + (H 2 O) 6 isotopologue is accurately recovered by superposition of the isotopomer spectra, thus establishing that the H 2 O incorporation is random and that the OH stretching manifold is largely due to contributions from decoupled water molecules. This behavior enables a powerful new way to extract structural information from vibrational spectra of size-selected clusters by explicitly identifying the local environments responsible for specific infrared features. The Cs + (H 2 O) 6 structure was unambiguously assigned to the 4.1.1 isomer (a homodromic water tetramer with two additional flanking water molecules) from the fact that its computed IR spectrum matches the observed overall pattern and recovers the embedded correlations in the two OH stretching bands of the water molecule in the Cs + (D 2 O) 5 (H 2 O) isotopomers. The 4.1.1 isomer is the lowest in energy among other candidate networks at advanced (e.g., CCSD(T)) levels of theoretical treatment after corrections for (anharmonic) zero-point energy (ZPE). With the structure in hand, we then explore the mechanical origin of the various band locations using a local electric field formalism. Furthermore, this approach promises to provide a transferrable scheme for the prediction of the OH stretching fundamentals displayed by water networks in close proximity to solute ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

Large-scale Nanostructure Simulations from X-ray Scattering Data On Graphics Processor Clusters

X-ray scattering is a valuable tool for measuring the structural properties of materialsused in the design and fabrication of energy-relevant nanodevices (e.g., photovoltaic, energy storage, battery, fuel, and carbon capture andsequestration devices) that are key to the reduction of carbon emissions. Although today's ultra-fast X-ray scattering detectors can provide tremendousinformation on the structural properties of materials, a primary challenge remains in the analyses of the resulting data. We are developing novelhigh-performance computing algorithms, codes, and software tools for the analyses of X-ray scattering data. In this paper we describe two such HPCalgorithm advances. Firstly, we have implemented a flexible and highly efficient Grazing Incidence Small Angle Scattering (GISAXS) simulation code based on theDistorted Wave Born Approximation (DWBA) theory with C++/CUDA/MPI on a cluster of GPUs. Our code can compute the scattered light intensity from any givensample in all directions of space; thus allowing full construction of the GISAXS pattern. Preliminary tests on a single GPU show speedups over 125x compared tothe sequential code, and almost linear speedup when executing across a GPU cluster with 42 nodes, resulting in an additional 40x speedup compared to usingone GPU node. Secondly, for the structural fitting problems in inverse modeling, we have implemented a Reverse Monte Carlo simulation algorithm with C++/CUDAusing one GPU. Since there are large numbers of parameters for fitting in the in X-ray scattering simulation model, the earlier single CPU code required weeks ofruntime. Deploying the AccelerEyes Jacket/Matlab wrapper to use GPU gave around 100x speedup over the pure CPU code. Our further C++/CUDA optimization deliveredan additional 9x speedup.

36 MATERIALS SCIENCE↗

DOE OSTI2012

Identifying monomer phases and cluster phases in lysozyme solutions by studying the temperature dependence of the short-time dynamics

Recently experiments that combine both small angle neutron scattering (SANS) and Neutron Spin Echo (NSE) have demonstrated that dynamic clusters can form in concentrated lysozyme solutions when there is a right combination of a short-ranged attraction and a long-ranged electrostatic repulsion. In this paper, we study the temperature effect on the dynamic cluster formation and try to pinpoint the transition concentration from a monomer phase to a cluster phase. Interestingly at even a relatively high concentration (10 % mass fraction), despite the significant change of the SANS patterns that are associated with the change of the short-ranged attraction among proteins, the normalized short-time self-diffusion coefficient is not affected. This is interpreted due to the fact that there is no cluster formation at this condition. However, at larger concentrations such as 17.5 % and 22.5 % mass fraction, we show that the average hydrodynamic radius increase significantly and causes a large decrease of the normalized self-diffusion coefficient when the temperature is changed from 25 oC to 5 oC indicating the formation of dynamic clusters in solution.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

Extensive gene flow over Europe and possible speciation

Biogeographical patterns and large-scale genetic structure have been little studied in ectomycorrhizal (EM) fungi, despite the ecological and economic importance of EM symbioses. We coupled population genetics and phylogenetic approaches to understand spatial structure in fungal populations on a continental scale. Using nine microsatellite markers, we characterized gene flow among 16 populations of the widespread EM basidiomycete Laccaria amethystina over Europe (i.e. over 2900 km). We also widened our scope to two additional populations from Japan (104 km away) and compared them with European populations through microsatellite markers and multilocus phylogenies, using three nuclear genes (NAR, G6PD and ribosomal DNA) and two mitochondrial ribosomal genes. European L. amethystina populations displayed limited differentiation (average FST = 0.041) and very weak isolation by distance (IBD). This panmictic European pattern may result from effective aerial dispersal of spores, high genetic diversity in populations and mutualistic interactions with multiple hosts that all facilitate migration. The multilocus phylogeny based on nuclear genes confirmed that Japanese and European specimens were closely related but clustered on a geographical basis. By using microsatellite markers, we found that Japanese populations were strongly differentiated from the European populations (FST = 0.416), more than expected by extrapolating the European pattern of IBD. Population structure analyses clearly separated the populations into two clusters, i.e. European and Japanese clusters. We discuss the possibility of IBD in a continuous population (considering some evidence for a ring species over the Northern Hemisphere) vs. an allopatric speciation over Eurasia, making L. amethystina a promising model of intercontinental species for future studies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Sensitivity to Thermonuclear Reaction Rates in Modeling the Abundance Anomalies of NGC 2419

Abundance anomalies in globular clusters provide strong evidence for multiple stellar populations within each cluster. These populations are usually interpreted as distinct generations, with the currently observed second-generation stars having formed in part from the ejecta of massive, first-generation “polluter” stars, giving rise to the anomalous abundance patterns. The precise nature of the polluters and their enrichment mechanism are still unclear. Even so, the chemical abundances measured in second-generation stars within the globular cluster NGC 2419 provide insight into this puzzling process. Previous work used Monte Carlo nuclear reaction network calculations to constrain the temperature–density conditions that could reproduce the observed abundances, thereby placing robust limits on the origins of the polluter material. The effect of individual reaction rates on these conditions has not been studied, however. Thus, we perform an exhaustive sensitivity study on the nuclear physics input to determine which reactions have the greatest impact on these predictions. We find that the {sup 30}Si(p,γ){sup 31}P, {sup 37}Ar(p,γ){sup 38}K, {sup 38}Ar(p,γ){sup 39}K, and {sup 39}K(p,γ){sup 40}Ca reactions are all critical in determining the temperature–density conditions, and ultimately, the origins of the polluter material. We conclude with recommendations for future experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

traffic flow management↗

NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

weather↗

DOE OSTI2014

Field-assisted patterned dissolution of silver nanoparticles in phosphate glass

Phosphate glass samples doped with silver ions through a Na⁺-Ag⁺ ion-exchange process were treated in a hydrogen atmosphere at temperatures near 430°C for durations ranging from 4 to 5 h. Such treatment causes metallic silver precipitation at the surface as well as nanoclustering of silver atoms under the surface under conditions very similar to those used for silicate glasses. The presence of silver clusters resulted in a characteristic coloring of the glass and was verified by the observation of a plasmon resonance peak near 410–420 nm in the absorption spectra. Applying a DC voltage between 1.4 and 2 kV at temperatures between 120 and 130°C led to dissolution of the clusters in the area under the positive electrode, thereby bleaching the glass color. The use of a patterned doped-silicon electrode further led to the formation of a 300 nm thick surface relief on the glass surface and of a volume complex permittivity grating extending at least 4 μm under the surface. Such volume complex refractive index gratings may find applications in passive or active (laser) photonic devices in rare-earth doped phosphate glasses, where conventional bulk grating formation techniques have limited applicability.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2017

Coordination of the Ser2056 and Thr2609 Clusters of DNA-PKcs in Regulating Gamma Rays and Extremely Low Fluencies of Alpha-Particle Irradiation to G 0 /G 1 Phase Cells

The catalytic subunit of DNA dependent protein kinase (DNA-PKcs) and its kinase activity are critical for mediation of non-homologous end-joining (NHEJ) of DNA double-strand breaks (DSB) in mammalian cells after gamma-ray irradiation. Additionally, DNA-PKcs phosphorylations at the T2609 cluster and the S2056 cluster also affect DSB repair and cellular sensitivity to gamma radiation. Previously we reported that phosphorylations within these two regions affect not only NHEJ but also homologous recombination repair (HRR) dependent DSB repair. In this study, we further examine phenotypic effects on cells bearing various combinations of mutations within either or both regions. Effects studied included cell killing as well as chromosomal aberration induction after 0.5–8 Gy gamma-ray irradiation delivered to synchronized cells during the G0/G1 phase of the cell cycle. Blocking phosphorylation within the T2609 cluster was most critical regarding sensitization and depended on the number of available phosphorylation sites. It was also especially interesting that only one substitution of alanine in each of the two clusters separately abolished the restoration of wild-type sensitivity by DNA-PKcs. Similar patterns were seen for induction of chromosomal aberrations, reflecting their connection to cell killing. To study possible change in coordination between HRR and NHEJ directed repair in these DNA-PKcs mutant cell lines, we compared the induction of sister chromatid exchanges (SCEs) by very low fluencies of alpha particles with mutant cells defective in the HRR pathway that is required for induction of SCEs. Finally, levels of true SCEs induced by very low fluence of alpha-particle irradiation normally seen in wild-type cells were only slightly decreased in the S2056 cluster mutants, but were completely abolished in the T2609 cluster mutants and were indistinguishable from levels seen in HRR deficient cells. Again, a single substitution in the S2056 together with a single substitution in the T2609 cluster abolished SCE formation and thus also effectively interferes with HRR.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

Observation and simulation of hard x ray photoelectron diffraction to determine polarity of polycrystalline zinc oxide films with rotation domains

X ray photoelectron diffraction (XPD) patterns of polar zinc oxide (ZnO) surfaces were investigated experimentally using hard x rays and monochromatized Cr K{alpha} radiation and theoretically using a cluster model approach and a dynamical Bloch wave approach. We focused on photoelectrons emitted from the Zn 2p{sub 3/2} and O 1s orbitals in the analysis. The obtained XPD patterns for the (0001) and (0001) surfaces of a ZnO single crystal were distinct for a given emitter and polarity. Polarity determination of c-axis-textured polycrystalline ZnO thin films was also achieved with the concept of XPD, even though the in-plane orientation of the columnar ZnO grains was random.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

A novel data-driven approach is proposed for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO 3 , multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern-by-pattern crystal indexing process. Here, this work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms.

42 ENGINEERING↗

DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗

NASA NTRS1994

The Large Impact Process Inferred from the Geology of Lunar Multiring Basins

The study of the geology of multiring impact basins on the Moon over the past ten years has given us a rudimentary understanding of how these large structures have formed and evolved on the Moon and other bodies. Two-ring basins on the Moon begin to form at diameters of about 300 km; the transition diameter at which more than two rings appear is uncertain, but it appears to be between 400 and 500 km in diameter. Inner rings tend to be made up of clusters or aligned segments of massifs and are arranged into a crudely concentric pattern; scarp-like elements may or may not be present. Outer rings are much more scarp-like and massifs are rare to absent. Basins display textured deposits, interpreted as ejecta, extending roughly an apparent basin radius exterior to the main topographic rim. Ejecta may have various morphologies, ranging from wormy and hummocky deposits to knobby surfaces; the causes of these variations are not known, but may be related to the energy regime in which the ejecta are deposited. Outside the limits of the textured ejecta are found both fields of satellitic craters (secondaries) and light plains deposits. Impact melt sheets are observed on the floors of relatively unflooded basins. Samples of impact melts from lunar basins have basaltic major-element chemistry, characterized by K, rare-earth elements (REE), P, and other trace elements of varying concentration (KREEP); ages are between 3.8 and 3.9 Ga. These lithologies cannot be produced through the fusion of known pristine (plutonic) rock types, suggesting the occurrence of unknown lithologies within the Moon. These melts were probably generated at middle to lower crustal levels. Ejecta compositions, preservation of pre-basin topography, and deposit morphologies all indicate that the excavation cavity of multiring basins is between about 0.4 and 0.6 times the diameter of the apparent crater diameter. Basin depths of excavation can be inferred from the composition of basin ejecta. A variety of mechanisms has been proposed to account for the formation of basin rings but none of them are entirely plausible. Mechanisms can be divided into two broad groups: (1) forcible uplift due to fluidization of the target; (2) concentric, brittle, fracturing and failure of the target, on regional (megaterraces) to global scales (lithospheric fracturing). Most basin rings are spaced at a constant factor on all planets. Evidence supports divergent ringforming models, so it may be that the ring-locating mechanism differs from the ring-forming mechanism. Thus, large-scale crustal foundering (megaterracing) could occur along concentric zones of weakness created by some type of resonant wave mechanism (fluidization and uplift); such immediate crustal adjustment could then be followed by long-term adjustment of the fractured lithosphere.

Spudis, Paul D.↗

NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗

NASA NTRS2019

Cluster Analysis of Spectroscopic Line Profiles and EUV Emission in RMHD Simulations and Observations of the Solar Atmosphere

Spatially-resolved observations from the IRIS, SDO/AIA, and other space mission and ground-based telescopes, coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar atmosphere. To better understand the dynamical and thermodynamic properties in the simulation data and their connection to observations, it is essential to determine similarities in the behaviors of the synthesized and observed emission. However, the complexity of observational data and physical processes makes comparison of observations and modeling results difficult. In this work, we show the initial results of application of K-Means clustering (unsupervised machine learning) algorithm to two different problems: 1) recognition of the typical spectroscopic line profiles observed by IRIS during solar flares and their typical dynamic behavior; 2) recognition of shocks and heating events in synthetic AIA emission data obtained from StellarBox quiet-Sun simulations. The average silhouette width technique for the KMeans algorithm is utilized in different ways to obtain optimal numbers of clusters. We discuss application of the emission clustering to visualizations of the computational volume, understanding its evolutionary trends and behavior patterns, and inversion (reconstruction) of physical properties of the solar atmosphere from synthesizes emission data.

Sadykov, Viacheslav↗

DOE OSTI2020

Big Data and AI at DoE's Legacy Sites - 20546

More than 30 years have passed since DOE started the decommissioning of nuclear weapon complexes and the clean-up of soil and groundwater. All the sites have been collecting and archiving soil and groundwater monitoring datasets; particularly contaminant concentration time-series. These datasets provide unparalleled opportunities to understand the system behavior (including more fundamental hydrological and geochemical processes, the response to various perturbations, the long-term trend and environmental decay rate towards the regulatory limit). This understanding is critical for providing multiple lines of evidences that can support site closure. In this study, we explore the machine learning (ML) and artificial intelligence (AI) applications to the long-term soil and groundwater management at DoE's legacy sites. ML can improve our understanding of the subsurface systems, which is critical for long-term monitoring and management of the sites, while AI can automate or support some of decision-making processes (e.g., anomaly detection, monitoring well placements). The particular focuses are to develop general algorithms to: (1) to identify distinct spatiotemporal patterns and to identify several groups that have similar temporal behaviors, using unsupervised clustering methods, (2) identify the different temporal scales of hydrological responses to climate perturbations by time-series analysis, and (3) reduce the number of monitoring wells by identifying the minimum sufficient number of wells to capture the heterogeneity of the groundwater contaminant plume and concentration distribution, using the Gaussian Process model. We demonstrate our methodology at the Savannah River Site F-Area. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗