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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Adaptive Tracking Algorithm for Convection in Simulated and Remote Sensing Data

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The report highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A parallel hierarchical blocked adaptive cross approximation algorithm

This article presents a low-rank decomposition algorithm based on subsampling of matrix entries. The proposed algorithm first computes rank-revealing decompositions of submatrices with a blocked adaptive cross approximation (BACA) algorithm, and then applies a hierarchical merge operation via truncated singular value decompositions (H-BACA). The proposed algorithm significantly improves the convergence of the baseline ACA algorithm and achieves reduced computational complexity compared to the traditional decompositions such as rank-revealing QR. Numerical results demonstrate the efficiency, accuracy, and parallel scalability of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Determining the Effectiveness of Incorporating Geographic Information Into Vehicle Performance Algorithms

This thesis presents a research study using one year of driving data obtained from plug-in hybrid electric vehicles (PHEV) located in Sacramento and San Francisco, California to determine the effectiveness of incorporating geographic information into vehicle performance algorithms. Sacramento and San Francisco were chosen because of the availability of high resolution (1/9 arc second) digital elevation data. First, I present a method for obtaining instantaneous road slope, given a latitude and longitude, and introduce its use into common driving intensity algorithms. I show that for trips characterized by >40m of net elevation change (from key on to key off), the use of instantaneous road slope significantly changes the results of driving intensity calculations. For trips exhibiting elevation loss, algorithms ignoring road slope overestimated driving intensity by as much as 211 Wh/mile, while for trips exhibiting elevation gain these algorithms underestimated driving intensity by as much as 333 Wh/mile. Second, I describe and test an algorithm that incorporates vehicle route type into computations of city and highway fuel economy. Route type was determined by intersecting trip GPS points with ESRI StreetMap road types and assigning each trip as either city or highway route type according to whichever road type comprised the largest distance traveled. The fuel economy results produced by the geographic classification were compared to the fuel economy results produced by algorithms that assign route type based on average speed or driving style. Most results were within 1 mile per gallon ({approx}3%) of one another; the largest difference was 1.4 miles per gallon for charge depleting highway trips. The methods for acquiring and using geographic data introduced in this thesis will enable other vehicle technology researchers to incorporate geographic data into their research problems.

58 GEOSCIENCES↗
DOE OSTI2016

Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes

Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space, however convergence is not guaranteed. This article demonstrates the novelty of hybrid genetic algorithm which combines both stochastic and deterministic routine for improved optimization results. A new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in foods, biofuels, and biotechnology processes. For each case study the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing the hybrid genetic predicted 5% higher anthocyanin yield. Optimization of bio-oil production using HGA resulted in a 4.73 % higher yield. In enzyme production process HGA predicted 2.18 IU/gds of higher xylanase yield. In conclusion, hybridization of genetic algorithm with a deterministic algorithm resulted in maximum optimum compared to the statistical methods.

09 BIOMASS FUELS↗
DOE OSTI2022

A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

14 SOLAR ENERGY↗
DOE OSTI2011

GX-Means: A model-based divide and merge algorithm for geospatial image clustering

One of the practical issues in clustering is the specification of the appropriate number of clusters, which is not obvious when analyzing geospatial datasets, partly because they are huge (both in size and spatial extent) and high dimensional. In this paper we present a computationally efficient model-based split and merge clustering algorithm that incrementally finds model parameters and the number of clusters. Additionally, we attempt to provide insights into this problem and other data mining challenges that are encountered when clustering geospatial data. The basic algorithm we present is similar to the G-means and X-means algorithms; however, our proposed approach avoids certain limitations of these well-known clustering algorithms that are pertinent when dealing with geospatial data. We compare the performance of our approach with the G-means and X-means algorithms. Experimental evaluation on simulated data and on multispectral and hyperspectral remotely sensed image data demonstrates the effectiveness of our algorithm.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2010

An adaptive grid-based all hexahedral meshing algorithm based on 2-refinement.

Most adaptive mesh generation algorithms employ a 3-refinement method. This method, although easy to employ, provides a mesh that is often too coarse in some areas and over refined in other areas. Because this method generates 27 new hexes in place of a single hex, there is little control on mesh density. This paper presents an adaptive all-hexahedral grid-based meshing algorithm that employs a 2-refinement method. 2-refinement is based on dividing the hex to be refined into eight new hexes. This method allows a greater control on mesh density when compared to a 3-refinement procedure. This adaptive all-hexahedral meshing algorithm provides a mesh that is efficient for analysis by providing a high element density in specific locations and a reduced mesh density in other areas. In addition, this tool can be effectively used for inside-out hexahedral grid based schemes, using Cartesian structured grids for the base mesh, which have shown great promise in accommodating automatic all-hexahedral algorithms. This adaptive all-hexahedral grid-based meshing algorithm employs a 2-refinement insertion method. This allows greater control on mesh density when compared to 3-refinement methods. This algorithm uses a two layer transition zone to increase element quality and keeps transitions from lower to higher mesh densities smooth. Templates were introduced to allow both convex and concave refinement.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

A NEW ALGORITHM FOR RADIOISOTOPE IDENTIFICATION OF SHIELDED AND MASKED SNM/RDD MATERIALS

Detection and identification of shielded and masked nuclear materials is crucial to national security, but vast borders and high volumes of traffic impose stringent requirements for practical detection systems. Such tools must be be mobile, and hence low power, provide a low false alarm rate, and be sufficiently robust to be operable by non-technical personnel. Currently fielded systems have not achieved all of these requirements simultaneously. Transport modeling such as that done in GADRAS is able to predict observed spectra to a high degree of fidelity; our research is focusing on a radionuclide identification algorithm that inverts this modeling within the constraints imposed by a handheld device. Key components of this work include incorporation of uncertainty as a function of both the background radiation estimate and the hypothesized sources, dimensionality reduction, and nonnegative matrix factorization. We have partially evaluated performance of our algorithm on a third-party data collection made with two different sodium iodide detection devices. Initial results indicate, with caveats, that our algorithm performs as good as or better than the on-board identification algorithms. The system developed was based on a probabilistic approach with an improved approach to variance modeling relative to past work. This system was chosen based on technical innovation and system performance over algorithms developed at two competing research institutions. One key outcome of this probabilistic approach was the development of an intuitive measure of confidence which was indeed useful enough that a classification algorithm was developed based around alarming on high confidence targets. This paper will present and discuss results of this novel approach to accurately identifying shielded or masked radioisotopes with radiation detection systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Analysis of Community Detection Algorithms for Large Scale Cyber Networks

The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.

algorithms↗
DOE OSTI2021

Parallel Algorithms for Efficient Computation of High-Order Line Graphs of Hypergraphs

This paper considers structures of systems beyond dyadic (pairwise) interactions and investigates mathematical modeling of multi-way interactions and connections as hypergraphs, where captured relationships among system entities are set-valued. To date, in most situations, entities in a hypergraph are considered connected as long as there is at least one common ``neighbor''. However, minimal commonality sometimes discards the ``strength'' of connections and interactions among groups. To this end, considering the ``width'' of a connection, referred to as the \emph{$s$-overlap} of neighbors, provides more meaningful insights into how closely the communities or entities interact with each other. In addition, $s$-overlap computation is the fundamental kernel to construct the line graph of a hypergraph, a low-order approximation of the hypergraph which can carry significant information about the original hypergraph. Subsequent stages of a data analytics pipeline then can apply highly-tuned graph algorithms on the line graph to reveal important features. Given a hypergraph, computing the $s$-overlaps by exhaustively considering all pairwise entities can be computationally prohibitive. To tackle this challenge, we develop efficient algorithms to compute $s$-overlaps and the corresponding line graph of a hypergraph. We propose several heuristics to avoid execution of redundant work and improve performance of the $s$-overlap computation. Our parallel algorithm, combined with these heuristics, is orders of magnitude (more than $10\times$) faster than the naive algorithm in all cases and the SpGEMM algorithm with filtration in most cases (especially with large $s$ value).

hypergraph algorithms, graph algorithms, parallel ↗
DOE OSTI2014

New algorithms for iterative matrix‐free eigensolvers in quantum chemistry

New algorithms for iterative diagonalization procedures that solve for a small set of eigen‐states of a large matrix are described. The performance of the algorithms is illustrated by calculations of low and high‐lying ionized and electronically excited states using equation‐of‐motion coupled‐cluster methods with single and double substitutions (EOM‐IP‐CCSD and EOM‐EE‐CCSD). We present two algorithms suitable for calculating excited states that are close to a specified energy shift (interior eigenvalues). One solver is based on the Davidson algorithm, a diagonalization procedure commonly used in quantum‐chemical calculations. The second is a recently developed solver, called the “Generalized Preconditioned Locally Harmonic Residual (GPLHR) method.” We also present a modification of the Davidson procedure that allows one to solve for a specific transition. The details of the algorithms, their computational scaling, and memory requirements are described. The new algorithms are implemented within the EOM‐CC suite of methods in the Q‐Chem electronic structure program. © 2014 Wiley Periodicals, Inc.

Zuev, Dmitry↗
DOE OSTI2014

A nonlinearly preconditioned conjugate gradient algorithm for rank‐ R canonical tensor approximation

Summary Alternating least squares (ALS) is often considered the workhorse algorithm for computing the rank‐ R canonical tensor approximation, but for certain problems, its convergence can be very slow. The nonlinear conjugate gradient (NCG) method was recently proposed as an alternative to ALS, but the results indicated that NCG is usually not faster than ALS. To improve the convergence speed of NCG, we consider a nonlinearly preconditioned NCG (PNCG) algorithm for computing the rank‐ R canonical tensor decomposition. Our approach uses ALS as a nonlinear preconditioner in the NCG algorithm. Alternatively, NCG can be viewed as an acceleration process for ALS. We demonstrate numerically that the convergence acceleration mechanism in PNCG often leads to important pay‐offs for difficult tensor decomposition problems, with convergence that is significantly faster and more robust than for the stand‐alone NCG or ALS algorithms. We consider several approaches for incorporating the nonlinear preconditioner into the NCG algorithm that have been described in the literature previously and have met with success in certain application areas. However, it appears that the nonlinearly PNCG approach has received relatively little attention in the broader community and remains underexplored both theoretically and experimentally. Thus, this paper serves several additional functions, by providing in one place a concise overview of several PNCG variants and their properties that have only been described in a few places scattered throughout the literature, by systematically comparing the performance of these PNCG variants for the tensor decomposition problem, and by drawing further attention to the usefulness of nonlinearly PNCG as a general tool. In addition, we briefly discuss the convergence of the PNCG algorithm. In particular, we obtain a new convergence result for one of the PNCG variants under suitable conditions, building on known convergence results for non‐preconditioned NCG. Copyright © 2014 John Wiley & Sons, Ltd.

Sterck, Hans De↗
DOE OSTI2016

Incremental k-core decomposition: Algorithms and evaluation

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. Furthermore, for a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs

We develop an adaptive, multivariate partitioning algorithm for solving nonconvex, Mixed-Integer Nonlinear Programs (MINLPs) with polynomial functions to global optimality. In particular, we present an iterative algorithm that exploits piecewise, convex relaxation approaches via disjunctive formulations to solve MINLPs that is different than conventional spatial branch-and-bound approaches. The algorithm partitions the domains of variables in an adaptive and non-uniform manner at every iteration to focus on productive areas of the search space. Furthermore, domain reduction techniques based on sequential, optimization-based bound-tightening and piecewise relaxation techniques, as a part of a presolve step, are integrated into the main algorithm. Finally, we demonstrate the effectiveness of the algorithm on well-known benchmark problems (including Pooling and Blending instances) from MINLPLib and compare our algorithm with state-of-the-art global optimization solvers. With our novel approach, we solve several large-scale instances, some of which are not solvable by state-of-the-art solvers. We also succeed in reducing the best known optimality gap for a hard, generalized pooling problem instance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multistart algorithm for identifying all optima of nonconvex stochastic functions

Here, we propose a multistart algorithm to identify all local minima of a constrained, nonconvex stochastic optimization problem. The algorithm uniformly samples points in the domain and then starts a local stochastic optimization run from any point that is the "probabilistically best" point in its neighborhood. Under certain conditions, our algorithm is shown to asymptotically identify all local optima with high probability; this holds even though our algorithm is shown to almost surely start only finitely many local stochastic optimization runs. We demonstrate the performance of an implementation of our algorithm on nonconvex stochastic optimization problems, including identifying optimal variational parameters for the quantum approximate optimization algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A spectral, quasi-cylindrical and dispersion-free Particle-In-Cell algorithm

We propose a spectral Particle-In-Cell (PIC) algorithm that is based on the combination of a Hankel transform and a Fourier transform. For physical problems that have close-to-cylindrical symmetry, this algorithm can be much faster than full 3D PIC algorithms. In addition, unlike standard finite-difference PIC codes, the proposed algorithm is free of spurious numerical dispersion, in vacuum. This algorithm is benchmarked in several situations that are of interest for laser-plasma interactions. These benchmarks show that it avoids a number of numerical artifacts, that would otherwise affect the physics in a standard PIC algorithm - including the zero-order numerical Cherenkov effect.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Comparison of Dose Distributions With TG-43 and Collapsed Cone Convolution Algorithms Applied to Accelerated Partial Breast Irradiation Patient Plans

Purpose: To compare the treatment plans for accelerated partial breast irradiation calculated by the new commercially available collapsed cone convolution (CCC) and current standard TG-43–based algorithms for 50 patients treated at our institution with either a Strut-Adjusted Volume Implant (SAVI) or Contura device. Methods and Materials: We recalculated target coverage, volume of highly dosed normal tissue, and dose to organs at risk (ribs, skin, and lung) with each algorithm. For 1 case an artificial air pocket was added to simulate 10% nonconformance. We performed a Wilcoxon signed rank test to determine the median differences in the clinical indices V90, V95, V100, V150, V200, and highest-dosed 0.1 cm{sup 3} and 1.0 cm{sup 3} of rib, skin, and lung between the two algorithms. Results: The CCC algorithm calculated lower values on average for all dose-volume histogram parameters. Across the entire patient cohort, the median difference in the clinical indices calculated by the 2 algorithms was <10% for dose to organs at risk, <5% for target volume coverage (V90, V95, and V100), and <4 cm{sup 3} for dose to normal breast tissue (V150 and V200). No discernable difference was seen in the nonconformance case. Conclusions: We found that on average over our patient population CCC calculated (<10%) lower doses than TG-43. These results should inform clinicians as they prepare for the transition to heterogeneous dose calculation algorithms and determine whether clinical tolerance limits warrant modification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗