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At least 1,135 records · Page 63

Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.

97 MATHEMATICS AND COMPUTING↗

Multilevel Probit Regression for 3-Alternative Forced Choice Audibility Testing

A recent psychoacoustic test at NASA Langley generated a dataset of 3-alternative forced-choice responses for 40 subjects that measured the audibility of a tone complex in a shaped broadband masker. The task was completed by 4 subjects at a time in a small theatre-like environment using predetermined stimuli levels. These data were subject to 4 forms of probit regression: a “complete pooling” analysis in which all data from the test was fit with one curve, two forms of “no pooling” analyses in which subjects’ data were treated individually (using both packaged and custom software), and a “partial pooling” analysis in which multilevel-regression software fit both individual curves as well as population-level parameters at the same time. The results of the analyses are compared in terms of both individual- and population-level parameters. Partial pooling appears to give the most consistent results at both levels, as well as provide the most robustness among the packaged approaches (albeit with added complexity over single-level regression). These results are also contained in a recent NASA technical memorandum entitled “Comparisons of Analysis Methods Applied to Alternative Forced Choice Audibility Data.”

Psychoacoustics↗

Adaptive grid methods for RLV environment assessment and nozzle analysis

Rapid access to highly accurate data about complex configurations is needed for multi-disciplinary optimization and design. In order to efficiently meet these requirements a closer coupling between the analysis algorithms and the discretization process is needed. In some cases, such as free surface, temporally varying geometries, and fluid structure interaction, the need is unavoidable. In other cases the need is to rapidly generate and modify high quality grids. Techniques such as unstructured and/or solution-adaptive methods can be used to speed the grid generation process and to automatically cluster mesh points in regions of interest. Global features of the flow can be significantly affected by isolated regions of inadequately resolved flow. These regions may not exhibit high gradients and can be difficult to detect. Thus excessive resolution in certain regions does not necessarily increase the accuracy of the overall solution. Several approaches have been employed for both structured and unstructured grid adaption. The most widely used involve grid point redistribution, local grid point enrichment/derefinement or local modification of the actual flow solver. However, the success of any one of these methods ultimately depends on the feature detection algorithm used to determine solution domain regions which require a fine mesh for their accurate representation. Typically, weight functions are constructed to mimic the local truncation error and may require substantial user input. Most problems of engineering interest involve multi-block grids and widely disparate length scales. Hence, it is desirable that the adaptive grid feature detection algorithm be developed to recognize flow structures of different type as well as differing intensity, and adequately address scaling and normalization across blocks. These weight functions can then be used to construct blending functions for algebraic redistribution, interpolation functions for unstructured grid generation, forcing functions to attract/repel points in an elliptic system, or to trigger local refinement, based upon application of an equidistribution principle. The popularity of solution-adaptive techniques is growing in tandem with unstructured methods. The difficultly of precisely controlling mesh densities and orientations with current unstructured grid generation systems has driven the use of solution-adaptive meshing. Use of derivatives of density or pressure are widely used for construction of such weight functions, and have been proven very successful for inviscid flows with shocks. However, less success has been realized for flowfields with viscous layers, vortices or shocks of disparate strength. It is difficult to maintain the appropriate mesh point spacing in the various regions which require a fine spacing for adequate resolution. Mesh points often migrate from important regions due to refinement of dominant features. An example of this is the well know tendency of adaptive methods to increase the resolution of shocks in the flowfield around airfoils, but in the incorrect location due to inadequate resolution of the stagnation region. This problem has been the motivation for this research.

Thornburg, Hugh J.↗

Evolution of International Space Station Program Safety Review Processes and Tools

The International Space Station Program at NASA is constantly seeking to improve the processes and systems that support safe space operations. To that end, the ISS Program decided to upgrade their Safety and Hazard data systems with 3 goals: make safety and hazard data more accessible; better support the interconnection of different types of safety data; and increase the efficiency (and compliance) of safety-related processes. These goals are accomplished by moving data into a web-based structured data system that includes strong process support and supports integration with other information systems. Along with the data systems, ISS is evolving its submission requirements and safety process requirements to support the improved model. In contrast to existing operations (where paper processes and electronic file repositories are used for safety data management) the web-based solution provides the program with dramatically faster access to records, the ability to search for and reference specific data within records, reduced workload for hazard updates and approval, and process support including digital signatures and controlled record workflow. In addition, integration with other key data systems provides assistance with assessments of flight readiness, more efficient review and approval of operational controls and better tracking of international safety certifications. This approach will also provide new opportunities to streamline the sharing of data with ISS international partners while maintaining compliance with applicable laws and respecting restrictions on proprietary data. One goal of this paper is to outline the approach taken by the ISS Progrm to determine requirements for the new system and to devise a practical and efficient implementation strategy. From conception through implementation, ISS and NASA partners utilized a user-centered software development approach focused on user research and iterative design methods. The user-centered approach used on the new ISS hazard system utilized focused user research and iterative design methods employed by the Human Computer Interaction Group at NASA Ames Research Center. Particularly, the approach emphasized the reduction of workload associated with document and data management activities so more resources can be allocated to the operational use of data in problem solving, safety analysis, and recurrence control. The methods and techniques used to understand existing processes and systems, to recognize opportunities for improvement, and to design and review improvements are described with the intent that similar techniques can be employed elsewhere in safety operations. A second goal of this paper is to provide and overview of the web-based data system implemented by ISS. The software selected for the ISS hazard systemMission Assurance System (MAS)is a NASA-customized vairant of the open source software project Bugzilla. The origin and history of MAS as a NASA software project and the rationale for (and advantages of) using open-source software are documented elsewhere (Green, et al., 2009).

Ratterman, Christian D.↗

Rapid Detection of Anomalies in Battery Energy Storage System Data

Data analytics is pivotal in assessing the technical characteristics and performance of Battery Energy Storage Systems (BESS), underpinning BESS modeling, optimization, and control. However, raw datasets frequently harbor anomalies from measurement errors and equipment malfunctions, impacting BESS reliability and analysis accuracy To address the challenge, this paper presents a novel methodology for the rapid detection of anomalous charge or discharge cycles within BESS operational data, expediting the cleaning process while ensuring data integrity. We’ve collected diverse and comprehensive real-world BESS operational datasets in collaboration with the Electric Power Research Institute and multiple Washington State utilities. These datasets serve dual roles: enabling comprehensive data exploration and analysis for understanding underlying challenges and method development, while also acting as a vital validation resource, demonstrating practical effectiveness. The proposed method detects anomalies and aids in their resolution, improving system performance characterization precision. It also reveals recurring data anomaly sources, offering insights for data collection and handling enhancement. Practitioners can gain valuable insights from the identified anomalous cycles in the real-world datasets along with the investigative process for root cause analyses and essential data cleaning steps.

Crawford, Aladsair J.↗

Model averaging approaches to data subset selection

Model averaging is a useful and robust method for dealing with model uncertainty in statistical analysis. Often, it is useful to consider data subset selection at the same time, in which model selection criteria are used to compare models across different subsets of the data. Two different criteria have been proposed in the literature for how the data subsets should be weighted. We compare the two criteria closely in a unified treatment based on the Kullback-Leibler divergence and conclude that one of them is subtly flawed and will tend to yield larger uncertainties due to loss of information. Here, analytical and numerical examples are provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

R-Matrix Analysis of the Neutron-Induced Cross Sections on 143 Nd and 147,149 Sm Measured at LANSCE [Slides]

This presentation covers how an established used of R-Matrix codes is being implemented at LANSCE. This presentation displays how there are unique features accessible with detectors at LANSCE that can be used to perform more complete R-Matrix analysis. Additionally discussed is how spin separation is a powerful technique that can be applied in DANCE measurements. Unique method to measure transmission with DICER are discussed. Finally, transmission and capture data are being analyzed in this work. The study can be extended to other channels. The ongoing analysis of the 143 Nd data is presented in this work. The same method is being applied to analyze the 147,149 Sm data. The 143 Nd and 147,149 Sm work started in FY-23 funded by NCSP.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Open Rotor Computational Aeroacoustic Analysis with an Immersed Boundary Method

Reliable noise prediction capabilities are essential to enable novel fuel efficient open rotor designs that can meet the community and cabin noise standards. Toward this end, immersed boundary methods have reached a level of maturity where more and more complex flow problems can be tackled with this approach. This paper demonstrates that our higher-order immersed boundary method provides the ability for aeroacoustic analysis of wake-dominated flow fields generated by a contra-rotating open rotor. This is the first of a kind aeroacoustic simulation of an open rotor propulsion system employing an immersed boundary method. In addition to discussing the methodologies of how to apply the immersed boundary method to this moving boundary problem, we will provide a detailed validation of the aeroacoustic analysis approach employing the Launch Ascent and Vehicle Aerodynamics (LAVA) solver. Two free-stream Mach numbers with M=0.2 and M=0.78 are considered in this analysis that are based on the nominally take-off and cruise flow conditions. The simulation data is compared to available experimental data and other computational results employing more conventional CFD methods. Spectral analysis is used to determine the dominant wave propagation pattern in the acoustic near-field.

Analysis↗

GEOS-2 C-band radar system project. Spectral analysis as related to C-band radar data analysis

Work performed on spectral analysis of data from the C-band radars tracking GEOS-2 and on the development of a data compaction method for the GEOS-2 C-band radar data is described. The purposes of the spectral analysis study were to determine the optimum data recording and sampling rates for C-band radar data and to determine the optimum method of filtering and smoothing the data. The optimum data recording and sampling rate is defined as the rate which includes an optimum compromise between serial correlation and the effects of frequency folding. The goal in development of a data compaction method was to reduce to a minimum the amount of data stored, while maintaining all of the statistical information content of the non-compacted data. A digital computer program for computing estimates of the power spectral density function of sampled data was used to perform the spectral analysis study.

Source record↗

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

Background: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

60 APPLIED LIFE SCIENCES↗

A multi-dimensional search for new heavy resonances decaying to boosted $\text{W}$ $\text{W}$, $\text{W}$ $\text{Z}$, or $\text{Z}$ $\text{Z}$ boson pairs in the dijet final state at 13 $\text {Te}\text{V}$

A search in an all-jet final state for new massive resonances decaying to $\text{ W }{}{}\text{ W }{}{}$, $\text{ W }{}{}\text{ Z }{}{}$, or $\text{ Z }{}{}\text{ Z }{}{}$ boson pairs using a novel analysis method is presented. The analysis is performed on data corresponding to an integrated luminosity of 77.3 $\,\text {fb}^{-1}$ recorded with the CMS experiment at the LHC at a centre-of-mass energy of 13 $\text {Te}\text {V}$. The search is focussed on potential narrow-width resonances with masses above 1.2 $\text {Te}\text {V}$, where the decay products of each $\text{ W }{}{}$ or $\text{ Z }{}{}$ boson are expected to be collimated into a single, large-radius jet. The signal is extracted using a three-dimensional maximum likelihood fit of the two jet masses and the dijet invariant mass, yielding an improvement in sensitivity of up to 30% relative to previous search methods. No excess is observed above the estimated standard model background. In a heavy vector triplet model, spin-1 ${\text {Z}}^{\prime }$ and ${\text {W}}^{\prime }$ resonances with masses below 3.5 and 3.8 $\text {Te}\text {V}$, respectively, are excluded at 95% confidence level. In a bulk graviton model, upper limits on cross sections are set between 27 and 0.2 $\,\text {fb}$ for resonance masses between 1.2 and 5.2 $\text {Te}\text {V}$, respectively. The limits presented in this paper are the best to date in the dijet final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗

TopFusion: Using Topological Feature Space for Fusion and Imputation in Multi-Modal Data

We present a novel multi-modal data fusion technique using topological features. The method, TopFusion, leverages the flexibility of topological data analysis tools (namely persistent homology and persistence images) to map multi-modal datasets into a common feature space by forming a new multi-channel persistence image. Each channel in the image is representative of a view of the data from a modality-dependent filtration. We demonstrate that the topological perspective we take allows for more effective data reconstruction, i.e. imputation. In particular, by performing imputation in topological feature space we are able to outperform the same imputation techniques applied to raw data or alternatively derived features. We show that TopFusion representations can be used as input to downstream deep learning-based computer vision models and doing so achieves comparable performance to other fusion methods for classification on two multi-modal datasets.

Myers, Audun D.↗

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗

Analysis of variable-head viscometer data

Nonnewtonian mathematical model and plotting method for analyzing variable head viscometer data obtained on pseudo-plastic polymer solutions - Rheology

MATHEMATICAL MODEL↗