Selection from multivariate normal populations.
Selection probability from multivariable normal populations in terms of noncentrality parameters
POPULATION↗
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The effects of microstructure on deformation twinning in Mg WE43
The interplay between microstructure and deformation twinning in a WE43-T6 Mg alloy under uniaxial compression was investigated using a combination of scanning electron microscopy with digital image correlation (SEM-DIC) and crystal plasticity finite element (CPFE) simulation. To improve understanding of the statistical characteristics of deformation twin formation, microstructural effects were characterized in over 1000 grains through metrics including the nominal Schmid Factor, grain size, geometric compatibility factor (m'), residual Burgers vector, and the strain accommodated by neighboring grains. There was a strong correlation between the nominal Schmid Factor and both twin activation and variant selection, but this was not fully deterministic. Deformation twinning also exhibited a strong dependence on existing slip and twinning in the neighboring grain and on the m' value. Within the range of grain sizes present in this condition, grain size was determined to have minimal effect on deformation twinning. Finally, statistical analysis of CPFE simulations was used to further investigate microstructural effects on twinning, and qualitatively captured the effect of the nominal Schmid Factor.
36 MATERIALS SCIENCE↗
An Efficient Pipeline for Abdomen Segmentation in CT Images
Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient’s diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline’s optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Bayesian operator inference for data-driven reduced-order modeling
This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by the structure of the governing equations, the task of learning a reduced-order model from data is posed as a Bayesian inverse problem with Gaussian prior and likelihood. The resulting posterior distribution characterizes the operators defining the reduced-order model, hence the predictions subsequently issued by the reduced-order model are endowed with uncertainty. The statistical moments of these predictions are estimated via a Monte Carlo sampling of the posterior distribution. Since the reduced models are fast to solve, this sampling is computationally efficient. Furthermore, the proposed Bayesian framework provides a statistical interpretation of the regularization term that is present in the deterministic operator inference problem, and the empirical Bayes approach of maximum marginal likelihood suggests a selection algorithm for the regularization hyperparameters. The proposed method is demonstrated on two examples: the compressible Euler equations with noise-corrupted observations, and a single-injector combustion process.
97 MATHEMATICS AND COMPUTING↗
Machine Learning Algorithms for Automated NIF Capsule Mandrel Selection
Small shells, approximately 2 mm in diameter, made from Poly(α-methylstyrene) (PAMS) are used as mandrels in the production of glow discharge polymer capsules located at the center of inertial confinement fusion experiments. The visual inspection process of microscope images of these shell mandrels, including detection of micron-sized defects on the shell surface as well as the handling and sorting, is a very labor-intensive, repetitive, and highly subjective process that stands to benefit greatly from automation. As part of an effort to decrease the number of labor hours spent in capsule handling, inspection, and metrology, the development of robotic systems was presented in a paper by Carlson et al., “Automation in Target Fabrication” [Fusion Sci. Technol., Vol. 70, p. 274 (2016)]. The current work expands the automated image acquisition systems developed previously and adds the use of convolutional neural networks to select capsules best suited for use in the downstream production process. Through the use of these machine learning algorithms, the selection process becomes robust, repeatable, and operator independent. Finally, as an added benefit the system developed as part of this work is able to provide defect statistics on entire shell batches and feed this information upstream to the production team.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 Theory Manual
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
97 MATHEMATICS AND COMPUTING↗
DECOVALEX2019 Task C: Results of Step 2 modeling
The work for Step 1 performed at Sandia National Laboratories and reported in Section 7 has been updated to incorporate new data and to conduct new simulations using a new larger base case domain. The new simulations also include statistical analysis for different fracture realizations. A sensitivity analysis was also conducted to the study of the effect of domain size. A much larger mesh was selected to minimize boundary effects. The DFN model was upscaled to the new base case domain and the much larger domain to generate relevant permeability and porosity fields for each case. The calculations updated for Step 2 are described in Section 12.1. New calculations have also been conducted to model the flooding of the CTD and the resulting pressure recovery. The modeling includes matching of pressure and chloride experimental data at the six observation locations in Well 12M133. The modeling was done for the 10 fracture realizations. The Step 2 recovery simulations are described in Section 12.2. The Step 2 work is summarized in Section 12.3.
42 ENGINEERING↗
Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
97 MATHEMATICS AND COMPUTING↗
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
97 MATHEMATICS AND COMPUTING↗
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Theory Manual (V.6.15)
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
97 MATHEMATICS AND COMPUTING↗
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 Theory Manual
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota’s iterative analysis capabilities.
97 MATHEMATICS AND COMPUTING↗
A DECam Search for Explosive Optical Transients Associated with IceCube Neutrino Alerts
To facilitate multimessenger studies with TeV and PeV astrophysical neutrinos, the IceCube Collaboration has developed a realtime alert system for the highest confidence and best localized neutrino events. In this work we investigate the likelihood of association between realtime high-energy neutrino alerts and explosive optical transients, with a focus on core-collapse supernovae (CC SNe) as candidate neutrino sources. We report results from triggered optical follow-up observations of two IceCube alerts, IC170922A and IC171106A, with Blanco/DECam ($gri$ to 24th magnitude in $\sim6$ epochs). Based on a suite of simulated supernova light curves, we develop and validate selection criteria for CC SNe exploding in coincidence with neutrino alerts. The DECam observations are sensitive to CC SNe at redshifts $z \lesssim 0.3$. At redshifts $z \lesssim 0.1$, our selection criteria reduce background SNe contamination to a level below the predicted signal. For the IC170922A (IC171106A) follow-up observations, we expect that 12.1% (9.5%) of coincident CC SNe at $z \lesssim 0.3$ are recovered, and that on average, 0.23 (0.07) unassociated SNe in the 90% containment regions also pass our selection criteria. We find two total candidate CC SNe that are temporally coincident with the neutrino alerts, but none in the 90% containment regions, which is statistically consistent with expected rates of background CC SNe for these observations. Here, given the signal efficiencies and background rates derived from this pilot study, we estimate that to determine whether CC SNe are the dominant contribution to the total TeV-PeV energy IceCube neutrino flux at the $3\sigma$ confidence level, DECam observations similar to those of this work would be needed for $\sim200$ neutrino alerts, though this number falls to $\sim60$ neutrino alerts if redshift information is available for all candidates.
79 ASTRONOMY AND ASTROPHYSICS↗
The response of numerical weather prediction analysis systems to FGGE 2b data
An intercomparison of analyses of the main PGGE Level IIb data set is presented with three advanced analysis systems. The aims of the work are to estimate the extent and magnitude of the differences between the analyses, to identify the reasons for the differences, and finally to estimate the significance of the differences. Extratropical analyses only are considered. Objective evaluations of analysis quality, such as fit to observations, statistics of analysis differences, and mean fields are discussed. In addition, substantial emphasis is placed on subjective evaluation of a series of case studies that were selected to illustrate the importance of different aspects of the analysis procedures, such as quality control, data selection, resolution, dynamical balance, and the role of the assimilating forecast model. In some cases, the forecast models are used as selective amplifiers of analysis differences to assist in deciding which analysis was more nearly correct in the treatment of particular data.
Hollingsworth, A.↗
Sensor data validation and reconstruction. Phase 1: System architecture study
The sensor validation and data reconstruction task reviewed relevant literature and selected applicable validation and reconstruction techniques for further study; analyzed the selected techniques and emphasized those which could be used for both validation and reconstruction; analyzed Space Shuttle Main Engine (SSME) hot fire test data to determine statistical and physical relationships between various parameters; developed statistical and empirical correlations between parameters to perform validation and reconstruction tasks, using a computer aided engineering (CAE) package; and conceptually designed an expert system based knowledge fusion tool, which allows the user to relate diverse types of information when validating sensor data. The host hardware for the system is intended to be a Sun SPARCstation, but could be any RISC workstation with a UNIX operating system and a windowing/graphics system such as Motif or Dataviews. The information fusion tool is intended to be developed using the NEXPERT Object expert system shell, and the C programming language.
Source record↗
A model study of the response of mesospheric ozone to short-term solar ultraviolet flux variations
An investigation is conducted in order to determine the relative importance of several modeled processes in controlling the magnitude and phase of the mesospheric ozone response. A detailed one-dimensional modeling study of the mesospheric ozone response to solar UV flux variations is conducted to remove some of the deficiencies in previous studies. This study is also used to examine specifically the importance of solar zenith angle, self-consistent calculation of water vapor abundance, and temperature feedback with a nonlocal thermodynamic equilibrium radiation model. The photochemical model is described, and the assumptions made for the purpose of comparing model results with the observed ozone response obtained from a statistical analysis of Solar Mesosphere Explorer data (Keating et al., 1987) are discussed. The numerical results for the theoretical ozone response are presented. The results of selected time-dependent calculations are considered to illustrate the degree to which a relatively simple model of the mesosphere is able to capture the major characteristics of the observed response.
Summers, M. E.↗
A data fusion algorithm for multi-sensor microburst hazard assessment
A recursive model-based data fusion algorithm for multi-sensor microburst hazard assessment is described. An analytical microburst model is used to approximate the actual windfield, and a set of 'best' model parameters are estimated from measured winds. The winds corresponding to the best parameter set can then be used to compute alerting factors such as microburst position, extent, and intensity. The estimation algorithm is based on an iterated extended Kalman filter which uses the microburst model parameters as state variables. Microburst state dynamic and process noise parameters are chosen based on measured microburst statistics. The estimation method is applied to data from a time-varying computational simulation of a historical microburst event to demonstrate its capabilities and limitations. Selection of filter parameters and initial conditions is discussed. Computational requirements and datalink bandwidth considerations are also addressed.
Wanke, Craig R.↗
A data fusion algorithm for multi-sensor microburst hazard assessment
A recursive model-based data fusion algorithm for multi-sensor microburst hazard assessment is described. An analytical microburst model is used to approximate the actual windfield, and a set of 'best' model parameters are estimated from measured winds. The winds corresponding to the best parameter set can then be used to compute alerting factors such as microburst position, extent, and intensity. The estimation algorithm is based on an iterated extended Kalman filter which uses the microburst model parameters as state variables. Microburst state dynamic and process noise parameters are chosen based on measured microburst statistics. The estimation method is applied to data from a time-varying computational simulation of a historical microburst event to demonstrate its capabilities and limitations. Selection of filter parameters and initial conditions is discussed. Computational requirements and datalink bandwidth considerations are also addressed.
Wanke, Craig R.↗
Statistical studies of impulsive events at high latitudes
A statistical study has been made of the high-latitude impulsive events that were observed during the 1985-1986 South Pole Balloon Campaign. The events were selected by searching for unipolar pulses greater than or equal to 10 nT above background in the vertical component of the magnetic field on the ground and/or pedestal or 'W' shaped horizontal electric field perturbations greater than or equal to 10 mV/m in amplitude and accompanied by perturbations in the vertical electric field at balloon altitude. A main event list comprising 112 events was compiled from the 468 hours of data available. Three aspects of the events were examined: the solar wind conditions prior to the event, local time of observation, and intrinsic properties of the events. The local time distribution was obtained from the 112 entry main event list and was found to be nearly uniform across the dayside, with no midday gap. The event rate found using our low-amplitude selection criteria was 0.7 event/hr, comparable to expectations based on in situ studies of the magnetopause. A total of 42 events were found for which data were available from Interplanetary Monitoring Platform (IMP) 8. Of these events, 12 occurred when the Z(sub GSM) component (B(sub Z)) of the interplanetary magnetic field (IMF) was northward and 30 occurred when B(sub Z) was southward or fluctuating. Only three of the B(sub Z) northward cases and only five of the B(sub Z) southward cases were preceded by pressure pulses greater than 0.4 nPa in amplitude. Ten of the events were studied in detail by means of a model-fitting method discussed elsewhere. This method infers values of several parameters, including the total current flowing in a coaxial or monopole system and a two-dimensional dipole system. The intrinsic properties of the events showed that only approximately 10% of the total current contributed to momentum transfer to the high-latitude ionosphere, that the direction of the motion depended more on local time of observation than IMF B(sub y), and that events were usually several hundred kilometers in size. The observed B(sub z) control found in the 42 event list and the prevalence of coaxial current dominated events are inconsistent with the predictions of the pressure pulse model.
Lin, Z. M.↗