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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.

3-D↗

DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗

DOE OSTI2025

Balancing Trade-offs: Adaptive Differential Privacy in Interpretable Machine Learning Models

In the advancing field of machine learning, balancing accuracy, interpretability, and privacy represents a significant challenge. The problem is exacerbated by the widespread deployment of pre-trained models locally in diverse applications, which could lead to various amounts of privacy leakage. Conventional Differential Privacy strategies, in which uniform noises are applied to model gradients, guarantee data privacy at the expense of accuracy and interpretability. This paper introduces a Feature-Sensitive Adaptive Differential Privacy (FADP) framework with a unique noise-adding strategy. Noises are adaptively added based on feature importance clustering, where important features are considered for interpretability. By employing a unique masking technique, FADP selectively preserves crucial features with minimal noise interference, maintaining accuracy while enhancing interpretability. The FADP framework addresses the limitations of traditional DP methods by preserving critical channels and improving interpretability — a vital requirement in machine learning applications that demand transparency in model decisions. Through comprehensive testing, FADP is shown to balance the trade-offs among accuracy, privacy, and interpretability, marking a substantial advancement in the field of privacy-preserving machine learning.

Farhad Riya, Farhin [University of Tennessee, Knox↗

NASA NTRS1984

A geographic comparison of selected large-scale planetary surface features

Photographic and cartographic comparisons of geographic features on Mercury, the Moon, Earth, Mars, Ganymede, Callisto, Mimas, and Tethys are presented. Planetary structures caused by impacts, volcanism, tectonics, and other natural forces are included. Each feature is discussed individually and then those of similar origin are compared at the same scale.

Meszaros, S. P.↗

NASA NTRS1974

OMS engine shutoff valve and actuation system design and evaluation

Shutoff valve and actuation system concepts that are most suitable for the Orbital Maneuvering Systems engine application were determined. Emphasis was placed on the ten year and 100 mission life requirement, propellant and propellant residue compatibility and weight. It was found that poppet or ball valves utilizing electric or electropneumatic actuation were most applicable. Preliminary design layouts of a number of valve and actuation concepts were prepared and analyzed to make the optimum concept selection. Pneumatic actuation systems were required to feature their own pneumatic supply so that for the quad redundant valve, it was necessary to include two pneumatic supply systems, one for each of the series legs of the quad redundant package. The requirement for the pneumatic package placed heavy reliability, weight, and maintenance penalties upon electropneumatic actuation systems. The two valve and actuation systems concepts selected featured electric torque motor operation and a poppet as well as a ball valve concept with a retractable seal.

Wichmann, H.↗

DOE OSTI2023

Kinetic Control via Binding Sites within the Confined Space of Metal Metalloporphyrin-Frameworks for Enhanced Shape-Selectivity Catalysis

One striking feature of enzyme is its controllable ability to trap substrates via synergistic or cooperative binding in the enzymatic pocket, which renders the shape-selectivity of product by the confined spatial environment. The success of shape-selective catalysis relies on the ability of enzyme to tune the thermodynamics and kinetics for chemical reactions. In emulation of enzyme's ability, we showcase herein a targeting strategy with the substrate being anchored on the internal pore wall of metal-organic frameworks (MOFs), taking full advantage of the sterically kinetic control to achieve shape-selectivity for the reactions. For this purpose, a series of binding site-accessible metal metalloporphyrin-frameworks (MMPFs) have been investigated to shed light on the nature of enzyme-mimic catalysis. They exhibit a different density of binding sites that are well arranged into the nanospace with corresponding distances of opposite binding sites. Furthermore, such a structural specificity results in a facile switch in selectivity from an exclusive formation of the thermodynamically stable product to the kinetic product. Thus, the proposed targeting strategy, based on the combination of porous materials and binding events, paves a new way to develop highly efficient heterogeneous catalysts for shifting selectivity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1975

Space station interior design: Results of the NASA/AIA space station interior national design competition

The results of the NASA/AIA space station interior national design competition held during 1971 are presented in order to make available to those who work in the architectural, engineering, and interior design fields the results of this design activity in which the interiors of several space shuttle size modules were designed for optimal habitability. Each design entry also includes a final configuration of all modules into a complete space station. A brief history of the competition is presented with the competition guidelines and constraints. The first place award entry is presented in detail, and specific features from other selected designs are discussed. This is followed by a discussion of how some of these design features might be applied to terrestrial as well as space situations.

Haines, R. F.↗

NASA NTRS2006

FuzzObserver

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Howard, Ayanna↗

NASA NTRS1978

V/STOL AND digital avionics system for UH-1H

A hardware and software system for the Bell UH-1H helicopter was developed that provides sophisticated navigation, guidance, control, display, and data acquisition capabilities for performing terminal area navigation, guidance and control research. Two Sperry 1819B general purpose digital computers were used. One contains the development software that performs all the specified system flight computations. The second computer is available to NASA for experimental programs that run simultaneously with the other computer programs and which may, at the push of a button, replace selected computer computations. Other features that provide research flexibility include keyboard selectable gains and parameters and software generated alphanumeric and CRT displays.

Liden, S.↗

DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2006

Ground-Based Localization of Mars Rovers

The document discusses a procedure for localizing the Mars rovers in site frame, a locally defined reference frame on the Martian surface. MER onboard position within a site frame is estimated onboard and is based on wheel odometry. Odometry estimation of rover position is only reliable over relatively short distances assuming no wheel slip, sinkage, etc. As the rover traverses, its onboard estimate of position in the current site frame accumulates errors and will need to be corrected on occasions via relocalization on the ground (mission operations). The procedure provides a systematic process for ground operators to localize the rover. The method focuses on analysis of acquired images used to declare a site frame and images acquired post-drive. Target selection is performed using two main steps. In the first step, the user identifies features of interest from the images used to declare the current site. Each of the selected target s position in site frame is recorded. In the second step, post-traverse measurements of the selected features positions are recorded again, this time in rover frame, using images acquired post-traverse. In the third step, we transform the post-traverse target s positions to local level frame. In the fourth step, we compute the delta differences in the pre- and post-traverse target s position. In the fifth step, we analyze the delta differences with techniques that compute their statistics to determine the rover s position in the site frame.

Trebi-Ollennu, Ashitey↗

DOE OSTI2024

Detecting Important Drivers of Gridded Population Modeling With Machine Learning

High-resolution population datasets have been lever-aged across a broad swath of domains, such as climate change, public policy, humanitarian aid, and rescue operations, among others. Machine learning methods were adopted to generate high-resolution or gridded population estimates by using various geospatial input features such as buildings, roads, and nighttime lights. In this study, we evaluate the importance of population features using Random Forest models across three levels of analysis, utilizing permutation measures. Our research aims to address key questions to enhance our understanding of high-resolution population modeling, such as: Are certain features globally (10 countries collectively) more important than others? Do optimal features vary by country? Within each country, do feature importance differ across administrative units? What similarities exist in feature importance at the global, country, and administrative unit levels? To answer these questions, we leverage the Kneedle algorithm to automate the selection of optimum features. We find that there are patterns displayed by features across spatial boundaries, evidenced by the same feature being the most important indicator of population across 7 of the 10 countries modeled. Our findings indicate that while important features may vary across geographies, certain features consistently hold greater importance than others agnostic of geography.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗

DOE OSTI2025

Improving galaxy cluster selection with the outskirt stellar mass of galaxies

The number density and redshift evolution of optically selected galaxy clusters offer an independent measurement of the amplitude of matter fluctuations, 𝑆 8 . However, recent results have shown that clusters chosen by the redMaPPer algorithm show richness-dependent biases that affect the weak lensing signals and number densities of clusters, increasing uncertainty in the cluster mass calibration and reducing their constraining power. Here, in this work, we evaluate an alternative cluster proxy, outskirt stellar mass, 𝑀 out , defined as the total stellar mass within a [50, 100] kpc envelope centered on a massive galaxy. This proxy exhibits scatter comparable to redMaPPer richness, 𝜆, but is less likely to be subject to projection effects. We compare the Dark Energy Survey Year 3 redMaPPer cluster catalog with a 𝑀 out selected cluster sample from the Hyper-Suprime Camera survey. We use weak lensing measurements to quantify and compare the scatter of 𝑀 out and 𝜆 with halo mass. Our results show 𝑀 out has a scatter consistent with 𝜆, with a similar halo mass dependence, and that both proxies contain unique information about the underlying halo mass. We find 𝜆-selected samples introduce features into the measured Δ⁢Σ signal that are not well fit by a log-normal scatter only model, absent in 𝑀 out selected samples. Our findings suggest that 𝑀 out offers an alternative for cluster selection with more easily calibrated selection biases, at least at the generally lower richnesses probed here. Combining both proxies may yield a mass proxy with a lower scatter and more tractable selection biases, enabling the use of lower mass clusters in cosmology. Finally, we find the scatter and slope in the 𝜆 −𝑀 out scaling relation to be 0.49 ±0.02 and 0.38 ±0.09.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2019

Scaling Evaluation of Ice-Crystal Icing on a Modern Turbofan Engine in PSL Using the COMDES-MELT Code

This paper presents preliminary ice-crystal icing (ICI) altitude scaling evaluation results of a Honeywell Uncertified Research Engine (HURE) that was tested in the NASA Glenn Research Center Propulsion Systems Laboratory (PSL) during January of 2018. This engine geometry features a hidden core design to keep the core less exposed. The engine was fitted with internal video cameras to observe various ice buildup processes at multiple selected locations within the engine core flow path covering the fan stator, the splitter-lip/shroud/strut, and the high pressure compressor (HPC) variable inlet guide vane (IGV) regions. The potential ice accretion risk was pre-determined to occur by using NASA’s in-house 1D Engine Icing Risk assessment code, COMDES-MELT. The code was successful in predicting the risk of ice accretion in adiabatic regions like the fan-stator of the HURE at specific engine operating points. However at several operating points during the test, liquid water was observed running along the shroud toward the variable IGV of the HPC regions with an air temperature well below freezing, thus no particle melting could have occurred due to heating from the air alone. It was reasoned that other sources of heat were present in that region. To account for these heat sources the inlet total temperature was adjusted to give a wet bulb temperature of 24 F below the standard minimum wet bulb temperature of 492 R to allow ice to accrete in the splitter-lip/shroud/strut region, which was determined from a reference case where hard ice was observed in that region. With that adjustment the COMDES-MELT code was successful in providing operating points where there was a risk of ice accretion during the test campaign. In addition to calculating possible conditions at different selected lower altitudes, simulations were run to determine potential inlet conditions that could lead to ice-crystal accretion along the prescribed stations where the cameras were available. From there, scaled test conditions were determined by best matching the following three icing related parameters of the reference condition: (1) the local air total wet bulb temperature, (2) the local ice crystal cloud melt ratio and (3) the engine fan face ice/water to air mass flux ratio of the ice crystal cloud. Instantaneous images taken from the time-lapsed movies of ice buildup were used along with the relevant thermodynamic data of air, water vapor and local icing condition to help evaluate how closely the proposed altitude scaling method could be used in ground based test facility to duplicate selected reference ICI features observed at specific location inside this engine at different scale altitudes. Discussions on observed limitation for engine icing scaling application from this test campaign and needed improvement are provided. A scaling test procedure to help identify potential ICI risk conditions and possible ice accretion locations of a new turbofan engine is evaluated in PSL.

Engine Testing in PSL↗

DOE OSTI2024

Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data—time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

machine learning, event detection, PMU↗

DOE OSTI2024

Anion Selectivities in Zwitterion Grafted Nanopores: Effect of Zwitterion Architecture

The separation of ions of similar charge is a crucial challenge in many applications, from water treatment to precious metal recovery. Membranes with cross-linked zwitterionic amphiphilic copolymer (ZAC-X) selective layers, which feature self-assembled, zwitterion-lined nanodomains for permeation, offer unique permselectivity between monovalent anions (e.g. $\mathrm{Cl^-}$/$\mathrm{F^-}$). This has motivated studies on the mechanisms of transport and selectivity in this family of materials. In this study, we conducted molecular dynamics simulations of aqueous salt solutions within zwitterion-functionalized nanopores to elucidate the influence of dipole orientation of the ZI ligands on anion diffusivities, partitioning, and permeabilities. Here, our model compares systems with contrasting ZI organization: surface-cation-anion ($\mathrm{S\text{-}ZI^{+}\text{-}ZI^{-}}$, Motif A) and surface-anion-cation ($\mathrm{S\text{-}ZI^{-}\text{-}ZI^{+}}$, Motif B). Our results reveal that Motif A exhibits less pronounced ion pairing due to a spatial separation in the radial profiles of cations and anions. Motif B demonstrates prominent ion pairing for smaller anions owing to their overlap with cation distributions. Further, our potential of mean force profiles reveal that anion partitioning increases with anion size in both ligand motifs, whereas Motif B exhibits significantly higher partitioning selectivity towards larger anions compared to Motif A. Our results for ion diffusivities show that the self-diffusivities of both anions and cations are lower for Motif B compared to Motif A. Such trends in anion partitioning and diffusivities can be explained by differences in the interactions and steric hindrance experienced by the anionic species in Motifs A and B. Finally, our results for anion permselectivity, obtained by combining partitioning and diffusivity, indicate that partitioning trends dominate over diffusivity trends. Consequently, anion permeability increases with anion size, and ligand Motif B yields much higher permselectivity towards larger anions compared to ligand Motif A.

anions↗

DOE OSTI2020

Improving five-year survival prediction via multitask learning across HPV-related cancers

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Furthermore, data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1996

ASpec: A New Spectrum and Line Analysis Package for IRAF

ASpec is a new spectrum and line analysis package developed at STScI. ASpec is designed as an add-on package for IRAF and incorporates a variety of analysis techniques for astronomical spectra. ASpec operates on spectra from a wide variety of ground-based and space-based instruments. ASpec allows simultaneous handling of spectra from different wavelength regimes. ASpec accommodates non-linear dispersion relations. ASpec provides a variety of functions, individually or in combination, with which to fit spectral features and the continuum. ASpec permits the masking of known bad data. ASpec provides a powerful, intuitive graphical user interface implemented using the new IRAF Object Manager and customized to handle: data input/output (I/O); on-line 'help'; selection of relevant features for analysis; plotting and graphical interaction; and data base management.

Hulbert, Stephen J.↗