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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Multivariate network traffic analysis using clustered patterns

Traffic analysis is a core element in network operations and management for various purposes including change detection, traffic prediction, and anomaly detection. In this paper, we introduce a new approach to online traffic analysis based on a pattern-based representation for high-level summarization of the traffic measurement data. Unlike the past online analysis techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the observed patterns from previous time windows enabling intuitive analysis. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility. Our extensive experimental results with public traces and collected monitoring measurements from ESnet traffic traces show that our pattern-based approach is effective for multivariate analysis of online network traffic with visual and quantitative tools.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Spatial Analysis of Great Lakes Regional Icing Cloud Liquid Water Content

Abstract Clustering of cloud microphysical conditions, such as liquid water content (LWC) and drop size, can affect the rate and shape of ice accretion and the airworthiness of aircraft. Clustering may also degrade the accuracy of cloud LWC measurements from radars and microwave radiometers being developed by the government for remotely mapping icing conditions ahead of aircraft in flight. This paper evaluates spatial clustering of LWC in icing clouds using measurements collected during NASA research flights in the Great Lakes region. We used graphical and analytical approaches to describe clustering. The analytical approach involves determining the average size of clusters and computing a clustering intensity parameter. We analyzed flight data composed of 1-s-frequency LWC measurements for 12 periods ranging from 17.4 minutes (73 km) to 45.3 minutes (190 km) in duration. Graphically some flight segments showed evidence of consistency with regard to clustering patterns. Cluster intensity varied from 0.06, indicating little clustering, to a high of 2.42. Cluster lengths ranged from 0.1 minutes (0.6 km) to 4.1 minutes (17.3 km). Additional analyses will allow us to determine if clustering climatologies can be developed to characterize cluster conditions by region, time period, or weather condition. Introduction

Ryerson, Charles C.↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
NASA NTRS1993

Clustering of galaxies near damped Lyman-alpha systems with (z) = 2.6

The galaxy two-point correlation function, xi, at (z) = 2.6 is determined by comparing the number of Ly-alpha-emitting galaxies in narrowband CCD fields selected for the presence of damped L-alpha absorption to their number in randomly selected control fields. Comparisons between the presented determination of (xi), a density-weighted volume average of xi, and model predictions for (xi) at large redshifts show that models in which the clustering pattern is fixed in proper coordinates are highly unlikely, while better agreement is obtained if the clustering pattern is fixed in comoving coordinates. Therefore, clustering of Ly-alpha-emitting galaxies around damped Ly-alpha systems at large redshifts is strong. It is concluded that the faint blue galaxies are drawn from a parent population different from normal galaxies, the presumed offspring of damped Ly-alpha systems.

Wolfe, A. M↗
NASA NTRS1989

Infall patterns around rich clusters of galaxies

The pattern of infall velocities induced by a rich cluster of galaxies is considered, using an infall model based on the Friedmann solution to determine the exact implicit dependence of the peculiar velocity on the density enhancment and the mean cosmological mass density, Omega(0). An analytic model for the distribution of galaxies around a cluster core in redshift space is developed. The high-density caustics in redshift space are shown to appear as envelopes around rich clusters. Assuming that the galaxies trace the matter distribution, low Omega(0) models can explain observational data obtained for four clusters. The present results support the prediction that light traces mass in the infall region.

Regos, Eniko↗
DOE OSTI2019

A New Approach to Multivariate Network Traffic Analysis

© 2019, Springer Science+Business Media, LLC & Science Press, China. Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers.

Kim, Jinoh↗
DOE OSTI2019

A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation

The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. Here, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data imply social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Unsupervised Clustering-Based Short-Term Solar Forecasting

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.

14 SOLAR ENERGY↗
NASA NTRS1973

Cluster Seeking Techniques in Pattern Recognition

A cluster seeking technique is defined as a method of dividing data into subsets, called clusters. These clusters contain data points that are similar to each other and different from the elements of other clusters. Various cluster seeking techniques were broken down into seven categories: (1) probabilistic, (2) signal detection, (3) clustering, (4) clumping, (5) eigenvalue, (6) minimal mode seeking, and (7) miscellaneous. Each category is described and one or more algorithms of that type are presented.

Barr, B. J.↗
DOE OSTI2021

A Taxonomic Classification Approach for Global Spatio-temporal Data

The World Bank, World Health Organization, and other major vendors collectively provide thousands of global time series datasets that focus on issues of the environment, public health, economics, violence, education, and national security. Sorting these data into meaningful information requires the use of data mining techniques to cluster trends into an orderly and manageable number of cases. The World SpatioTemporal Analytics and Mapping (WSTAMP) project database (wstamp.ornl.gov) was developed to spatiotemporally harmonize global vendor data (23,300+ attributes, 200+ locations, 50+ years). Within the WSTAMP analytical environment, Dynamic Time Warping (DTW) has been a highly effective data-driven approach for clustering and mapping these time series into national spatiotemporal behavior maps. Two significant properties have surfaced from this work. First, several recognizable cluster patterns have emerged and persist across a range of locations, attributes, and time frames (e.g., increasing, decreasing, rebounding, peak, oscillating). Secondly, practitioners engaging WSTAMP have noted the explanatory and anticipatory value of these patterns and articulated particular interest in detecting them within the spatiotemporal cube. This need was addressed by shifting DTW-based clustering from an open ended, data-driven implementation to a taxonomic pattern matching approach. This paper presents the method including implementation strategies for visualization and human computer interaction and applies the approach to a sample data set and concludes with next steps.

Stewart, Robert↗
NASA NTRS1985

On percolation as a cosmological test

Difficulties in the use of percolation as a complementary statistic for the galaxy clustering pattern are pointed out by studying simple toy models and dynamical N-body models that represent the competing clustering scenarios. The percolation properties are found not to be very sensitive to the presence of pancakes and strings once they are clumpy, and hence they do not distinguish properly between models that are very different. In the case of very smooth pancakes, the ability to percolate depends on sampling parameters, such as the mean number density and the volume, in a way which is unknown a priori because it depends on the same properties that the test ought to measure. This problem could, in principle, be eased by using volume-limited samples of high mean number density (an order of magnitude denser than the CfA redshift survey volume limited at 4000 km/s) and by comparing to models of identical number density and volume. An alternative approach, based on the sampling effects themselves, may provide a qualitative test for pancakes in samples of lower densities.

Dekel, A.↗
DOE OSTI2025

The Tianlai-WIYN North Celestial Cap Redshift Survey

We present the results of a small, low redshift spectroscopic survey of galaxies within 3 degrees of the North Celestial Pole (NCP) selected using V-band photometry obtained from the North Celestial Cap Survey (NCCS) (Gorbikov & Brosch 2014). The purpose of the current survey is to create a redshift space template for 21 cm emission from neutral hydrogen with which to correlate radio line intensity observations by the Tianlai dish and cylinder interferometers. A total of 898 redshifts were obtained from the 2102 extended objects in the NCCS with m_V < 19 in the survey area. After accounting for extinction, the survey geometry and selection effects, the number density and clustering pattern of galaxies in the redshift catalog are consistent with other low redshift surveys. We were also able to identify 11 galaxy cluster candidates from this redshift catalog.

Ansari, Reza [AIM, Saclay]↗
NASA NTRS1990

The Cortex Transform as an image preprocessor for sparse distributed memory: An initial study

An experiment is described which was designed to evaluate the use of the Cortex Transform as an image processor for Sparse Distributed Memory (SDM). In the experiment, a set of images were injected with Gaussian noise, preprocessed with the Cortex Transform, and then encoded into bit patterns. The various spatial frequency bands of the Cortex Transform were encoded separately so that they could be evaluated based on their ability to properly cluster patterns belonging to the same class. The results of this study indicate that by simply encoding the low pass band of the Cortex Transform, a very suitable input representation for the SDM can be achieved.

Olshausen, Bruno↗
DOE OSTI2019

The distinction of CPR bacteria from other bacteria based on protein family content

Abstract Candidate phyla radiation (CPR) bacteria separate phylogenetically from other bacteria, but the organismal distribution of their protein families remains unclear. Here, we leveraged sequences from thousands of uncultivated organisms and identified protein families that co-occur in genomes, thus are likely foundational for lineage capacities. Protein family presence/absence patterns cluster CPR bacteria together, and away from all other bacteria and archaea, partly due to proteins without recognizable homology to proteins in other bacteria. Some are likely involved in cell-cell interactions and potentially important for episymbiotic lifestyles. The diversity of protein family combinations in CPR may exceed that of all other bacteria. Over the bacterial tree, protein family presence/absence patterns broadly recapitulate phylogenetic structure, suggesting persistence of core sets of proteins since lineage divergence. The CPR could have arisen in an episode of dramatic but heterogeneous genome reduction or from a protogenote community and co-evolved with other bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multi-physics modeling of injected nanoparticles effect on remediation of CO 2 leakage through cracks

One potential risk in CO 2 sequestration is the leakage of carbon dioxide, which can result in contamination of underground water, creating potential threats to existing ecosystems. The common leakage pathway is through the pre-existing fractures or discontinuities within cement in the wellbore, incurred by the environmental conditions imposed on the cement. Injecting nanoparticles into pre-existing cracks is one of the most recently proposed ideas for mitigating fracture propagation in cement CO 2 sequestration. To demonstrate the feasibility of this new technology, a numerical approach was taken in this work, as it is challenging to investigate it in a laboratory setting. We proposed a coupled ALE (Arbitrary Lagrangian–Eulerian)–DEM (Discrete Element Method)–peridynamic modeling strategy within the LS-Dyna package to investigate the intertwined interaction among the CO 2 fluid flow, native fluid (e.g., brine), particle clusters, and cracks within the cement. The numerical results demonstrate that injected nanoparticles can effectively reduce the pressure exerted on the crack surface. Accordingly, the potential fracture propagation at the crack tip would be reduced compared to corresponding cases without nanoparticels as pressurized by fluid flow. This result verifies the effectiveness of proposed nanoparticle injection technology. Finally, using this established modeling strategy, the effect of filling particles on the fracture mitigation for different crack geometries (e.g. particle cluster patterns, aspect ratio of crack aperture and length) and CO 2 reservoir pressure are examined. The result shows that injected particles successfully reduce the fracture propagation in these scenarios.

42 ENGINEERING↗
DOE OSTI2024

Graph Analytics on Jellyfish topology

Because large unstructured datasets is important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately, obtaining scaling and performance for irregular communication is challenging because contemporary network interconnects are primarily designed to maximize bandwidths of fixed-neighborhoods large-message exchanges (e.g., stencils). Although there is no consensus on the “best” network topologies for irregular communication, unstructured graph-based interconnects can be more suitable. We analyze three popular graph workloads – clustering, pattern enumeration, and traversal — on comparable networks (in terms of resources and costs) constructed from Jellyfish Random Regular, Dragonfly and Fat tree topologies, varying the routing algorithms. Using packet-level simulations, we demonstrate up to 60% improvement in communication time with Jellyfish due to diversity of the short paths between arbitrary endpoints, which can reduce overall network stalls and congestion.

Graph Analytics, network topology, interconnect, H↗