Search NASA⌕ Search

SEARCH · Search NASA

Results for “Detection approach”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33

Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques

This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-

Srivastava, Ashok, N.↗

Development of IR Contrast Data Analysis Application for Characterizing Delaminations in Graphite-Epoxy Structures

Objective: Develop infrared (IR) flash thermography application based on use of a calibration standard for inspecting graphite-epoxy laminated/honeycomb structures. Background: Graphite/Epoxy composites (laminated and honeycomb) are widely used on NASA programs. Composite materials are susceptible for impact damage that is not readily detected by visual inspection. IR inspection can provide required sensitivity to detect surface damage in composites during manufacturing and during service. IR contrast analysis can provide characterization of depth, size and gap thickness of impact damage. Benefits/Payoffs: The research provides an empirical method of calibrating the flash thermography response in nondestructive evaluation. A physical calibration standard with artificial flaws such as flat bottom holes with desired diameter and depth values in a desired material is used in calibration. The research devises several probability of detection (POD) analysis approaches to enable cost effective POD study to meet program requirements.

Havican, Marie↗

Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

Active Learning↗

Ultrasonic NDE Simulation for Composite Manufacturing Defects

The increased use of composites in aerospace components is expected to continue into the future. The large scale use of composites in aerospace necessitates the development of composite-appropriate nondestructive evaluation (NDE) methods to quantitatively characterize defects in as-manufactured parts and damage incurred during or post manufacturing. Ultrasonic techniques are one of the most common approaches for defect/damage detection in composite materials. One key technical challenge area included in NASA's Advanced Composite's Project is to develop optimized rapid inspection methods for composite materials. Common manufacturing defects in carbon fiber reinforced polymer (CFRP) composites include fiber waviness (in-plane and out-of-plane), porosity, and disbonds; among others. This paper is an overview of ongoing work to develop ultrasonic wavefield based methods for characterizing manufacturing waviness defects. The paper describes the development and implementation of a custom ultrasound simulation tool that is used to model ultrasonic wave interaction with in-plane fiber waviness (also known as marcelling). Wavefield data processing methods are applied to the simulation data to explore possible routes for quantitative defect characterization.

Leckey, Cara A. C.↗

All-Sky Medium-Energy Gamma-Ray Observatory (AMEGO)

The gamma-ray energy range from a few hundred keV (kiloelectronvolts) to a few hundred MeV (megaelectronvolts) has remained largely unexplored since the pioneering but limited observations by COMPTEL (The Imaging Compton Telescope) on the CGRO (Compton Gamma Ray Observatory) (1991-2000). Fundamental astrophysics questions can be addressed by a mission in the MeV range, from astrophysical jets and extreme physics of compact objects to a large population of unidentified objects. Such a mission will also provide critical inputs for multimessenger astrophysics by identifying and exploring the astrophysical objects that produce gravitational waves and neutrinos. To address these questions, we are developing AMEGO: All-sky Medium Energy Gamma-ray Observatory, as a NASA probe-class mission, to investigate the energy range from 200 keV to greater than10 GeV with good energy (ranging from less than 1 percent at the low end to approximately 10 percent at the high end) and angular resolution (from 2 to 6 degrees depending on energy) and with sensitivity a factor of 20-50 better than previous instruments. Measurements at these energies are challenging, mainly due to the fact that two photon interaction processes, Compton scattering and pair production, compete. These interaction processes require different approaches in both detection and data analysis, and consequently in the instrument concept. AMEGO will be capable of measuring both Compton-scattering events at lower energies and pair-production events at higher energies. AMEGO will also have sensitivity to linear polarization of detected radiation at a level of 20 percent minimum detectable polarization from a source 1 percent of the Crab intensity, observed for 106 seconds. AMEGO will be operating mainly in scanning (discovery) mode with a field-of-view of 2.5 sr (Special Relativity) (20 percent of the sky observation any time), with the capability to be pointed to particular regions of interest..

Moiseev, Alexander↗

Upgrades to the International Space Station Urine Processor Assembly

Since November 2008, the ISS Urine Processor Assembly (UPA) has successfully generated distillate from crew urine. Several rounds of modifications and upgrades have also been implemented to improve overall system performance throughout the years. The Distillation Assembly (DA) is the latest upgraded unit available, installed in September 2020. This unit has shown immediate performance improvements and redesign success. Most significant upgrades have been a redesigned drive train to eliminate belt slips, improved sealing to reduce leak paths and improve wear, and a redesigned liquid level sensor. The Purge Pump and Separator Assembly (PPSA) technology demonstration is the next available upgraded unit and has finished ground assembly and testing and is targeted for ISS delivery within the coming year. Most recent ground studies have identified areas to focus future UPA upgrade considerations with exploration missions in mind, specifically for better fault detection and isolations approaches as well as potential hardware redesigns to facilitate successful dormancy operations.

Urine Processor Assembly↗

Changes in Satellite Retrievals of Atmospheric Composition over Eastern China During the 2020 COVID-19 Lockdowns

We examined daily level-3 satellite retrievals of Atmospheric Infrared Sounder (AIRS) CO, Ozone Monitoring Instrument (OMI) SO2 and NO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) over eastern China to understand how COVID-19 lockdowns affected atmospheric composition. Changes in 2020 were strongly dependent on the choice of background period since 2005 and whether trends in atmospheric composition were accounted for. Over central east China during the 23 January–8 April lockdown window, CO in 2020 was between 3 % and 12 % lower than average depending on the background period. The 2020 CO was not consistently less than expected from trends beginning between 2005 and 2016 and ending in 2019 but was 3 %–4 % lower than the background mean during the 2017–2019 period when CO changes had flattened. Similarly for AOD, 2020 was between 14 % and 30 % lower than averages beginning in 2005 and 14 %–17 % lower compared to different background means beginning in 2016. NO2 in 2020 was between 30 % and 43 % lower than the mean over different background periods and between 17 % and 33 % lower than what would be expected for trends beginning later than 2011. Relative to the 2016–2019 period when NO2 had flattened, 2020 was 30 %–33 % lower. Over southern China, 2020 NO2 was between 23 % and 27 % lower than different background means beginning in 2013, the beginning of a period of persistently lower NO2. CO over southern China was significantly higher in 2020 than what would be expected, which we suggest was partly because of an active fire season in neighboring countries. Over central east and southern China, 2020 SO2 was higher than expected, but this depended strongly on how daily regional values were calculated from individual retrievals and reflects background values approaching the retrieval detection limit. Future work over China, or other regions, needs to take into account the sensitivity of differences in 2020 to different background periods and trends in order to separate the effects of COVID-19 on air quality from previously occurring changes or from variability in other sources.

atmospheric composition↗

Northeast US Ecological Forecasting: Modeling Invasive Plant Habitat Suitability to Support Management Efforts in the American Northeast

Invasive plant species threaten environmental and economic interests when they spread into new areas, outcompete native species, and disrupt ecosystem services. If the spread is not controlled early, species can become well-established and increasingly difficult to manage. The National Park Service (NPS) Invasive Plant Management Teams (IPMTs) strive for an “early detection, rapid response” approach to reducing invasive species spread. Management teams can better prioritize their work with the help of species distribution models (SDMs), which map habitat suitability by combining species occurrences with environmental predictor variables. Scarce invaded range data for newly arrived invasive species presents a particular challenge for producing accurate models. To improve future modeling efforts, this project compared SDM methods using different spatial scales to model two plant species invasive to the Northeast US: the well-established Japanese stiltgrass (Microstegium vimineum) and newer invasive species wavyleaf basketgrass (Oplismenus undulatifolius). The team used NASA Earth observations and climate datasets to model occurrence data and predictor layers at a US-specific extent (90m2 spatial resolution) and global extent (1 km2 spatial resolution). Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) provided data for US Normalized Difference Moisture Indices (NDMI), while global NDMI and topographic predictor layers were derived from Shuttle Radar Topography Mission (SRTM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting models indicated important predictor variables for each species and explored the benefits and tradeoffs of using global data to model habitat suitability for new-arrival invasive species.

Rebecca Ohman↗

A Machine Learning-Based Approach to Time-Series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multi-branch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of one year of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning-based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

Samuel Fordin↗

Classification of Dissolution Events Using Fusion of Effluents Measurements and Classifiers

Classifiers for dissolution events at a radiochemical processing facility are studied using gamma spectra measurements of effluents collected by a high purity germanium detector located at its off-gas stack. Data sets collected at the Oak Ridge National Laboratory’s Radiochemical Engineering Development Center under a Pu dissolution campaign spanning a three months period are utilized. Features corresponding to the activity levels of 15 radionuclides, including isotopes of iodine, krypton, and xenon, that are indicated by the target decay chains, are computed from the spectra at 1 hour intervals. A conceptualization diagram is developed to reflect the steps from the source to measurement to feature computation that depend on fission products indicated by decay chains, chemical processing, and effluents transport to the off-gas stack. A diverse set of eight classifiers based on different design principles are trained using the ground truth data for this campaign, and the outputs of top three classifiers, namely, classification trees, Ensemble of Trees (EOT), and k-nearest neighbor, are combined using EOT classifier-fuser. Our results show that for 5-fold cross validation, features associated with isotopes of xenon provide the lowest classification error among the different elements across the classifiers; the classification error is furthered improved when all 15 isotope features are used by each classifier, and it is again improved by the fusion of three classifiers. Further reduction in classification error is achieved by using a measurement window of 1-2 days which is identified based on half-life time estimates of the isotopes; it is long enough for the stabilization of feature estimates while being short enough not to be affected by the follow on dissolution events. As a net result of feature and classifier fusion, combined with the incorporation of decay chain and isotope half-life information, this approach achieves 98% detection rate while maintaining a false alarm rate under 2% for this data set.

Rao, Nageswara↗

Shedding Light on the Deactivation Pathways of a CrOx/Al2O3 Dehydrogenation Catalyst with Emission Spectroscopy

In comparison to traditional methods that focus on signal loss and disappearance of the Cr2O3 spectral features, this work presents a niche approach to direct detection of the onset and acceleration of Cr3+ solid state diffusion; a critical physical deactivation pathway that limits the lifetime of the CrOx/Al2O3 propane dehydrogenation catalyst used in the commercial Catofin process.

25 ENERGY STORAGE↗

Benzenesulfonamide Derivatives as Complexants and Extractants for Addressing the Mercury Problem at the Savannah River Site

Mercury (Hg) is a major global pollutant arising from both natural and anthropogenic sources. Its widespread use in medicinal and industrial applications makes it a common chemical exposure and environmental pollutant. It can exist in several forms which include: Metallic mercury (Hg{sup 0}), mercurous (Hg{sub 2}{sup 2+}), mercuric salts (Hg{sup 2+}), and organic mercury (e.g. CH{sub 3}Hg{sup +}), with the latter being the most toxic of all the species. Due to mercury's high toxicity, new approaches towards its detection has received significant attention in the scientific community. Mercury exposure at the Savannah River Site (SRS) has been a recent concern especially with increasing amounts of organic mercury in the saltstone. It originates mainly from its use as an acidic dissolution catalyst of aluminum cladding from target fuels within the uranium and plutonium processing operations [1]. It is present to an amount of about 60 metric tons in the high-level waste (HLW) tanks. Organic Mercury species have been found in low activity waste (LAW) at the site that eventually ends up in the saltstone. Therefore, there is a need for: i) Converting organic mercury to other less toxic forms and ii) Complexation and removal of Hg prior of disposal of LAW in saltstone. Various methods have been developed for selective sensing of mercury in the presence of other toxic metals. These methods include using ligands that can form organo-soluble metal complexes with different optical and spectroscopic properties that can be used for toxic metal sensing. In 2005, our group pioneered an ion-exchange extraction method, in which o-phenylenediamine-derived disulfonamides were used to complex and selectively extract and sense Pb{sup 2+} from aqueous solutions into an organic phase [2,3]. Herein, a disulfonamide and a bis-dansylamide have been shown to extract, complex and sense Hg(II). Ligand 1: The crystal structure of the disulfonamide-Hg complex confirms the complexation of Hg(II) with the ligand. Complexation was corroborated by the {sup 1}H-NMR spectra obtained after contacting solutions of various concentrations of Hg{sup 2+} with 2 mM ligand in chloroform. Distinct resonances are observed at Hg/L ratio of 0.5 that are also observed for the isolated 1:2 complex. In the presence of excess mercury, new resonances, as well as the movement of Et{sub 3}N resonances indicate the formation of a different Hg-sulfonamide-triethylamine complex, presumably having 1:1 Hg:L stoichiometry. The electronic spectra of aqueous phases after extraction show that there was no free ligand absorption at 0.5 eq of Hg, indicating a complete complexation. Complexation was also confirmed by the UV-visible titrations with Hg{sup 2+} at constant ligand concentration. pH-dependent extraction carried out shows that extraction of Hg(II) by ligand 1 was over 90% for most alkaline pHs. Ligand 2: The crystal structure of the Ligand 2 complex formed with Hg(OAc){sub 2} shows a remarkable coordination pattern with 4:2 metal:ligand stoichiometry. The fluorescent bis-dansyl disulfonamide derivative was found to complex and sense HgCl{sub 2} and Hg(OAc){sub 2} by demonstrating fluorescence quenching upon Hg(II) addition in comparison with other metals (Zn(II), Cd(II), Pb(II)). No were observed for Cu(II), Ag(I) and Co(II). We have shown the complexation of Hg(II) by a disulfonamide and a bis-dansyl disulfonamide ligand using several spectroscopic methods. Ligand 1 was able to extract mercury into chloroform and form a complex in the presence of excess mercury by synergistic complexation with triethylamine acting as a co-ligand. X-ray and NMR both confirm a 1:2 HgL{sub 2} stoichiometry. Ligand 2 can be used for sensing of Hg(II) as fluorescence quenching was observed after addition of Hg(II)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Advanced Building Technologies for Energy Savings and Decarbonization

The building sector is a major consumer of energy, making it essential to explore innovative strategies for reducing its environmental impact. As we shift our focus toward decarbonization and electrification, the need for advanced building operation techniques and equipment becomes increasingly urgent. These advancements are crucial for maintaining or enhancing indoor environmental quality while simultaneously minimizing energy consumption. This Special Issue aims to showcase cutting-edge technologies in building energy management, alongside effective measurement and verification methods. It will also address fault detection and diagnosis approaches, leveraging both simulation and experimental studies. The ultimate goal is to highlight solutions that not only reduce CO₂ emissions but also improve indoor environmental quality, creating healthier and more sustainable living and working spaces. By presenting a diverse array of research contributions, this Special Issue will provide valuable insights into the latest advancements in building energy technologies. It will drive the discussion around effective strategies for energy efficiency and environmental sustainability in the building sector. These efforts can help transform buildings from energy consumers into more efficient spaces that contribute to reducing our overall carbon footprint.

Im, Piljae↗

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

Bilton, Kyle J. (ORCID:0000000184553689)↗

Grid Edge Waveform Analytics Framework for Event Detection and Classification

This paper provides a grid edge waveform analytics framework for power system event detection and classification in the local as well as in the wide area. This framework overviews data excellence for event detection and classification. The data excellence describes the data acquisition process and requirements, data processing, data quality, and data integrity. Power system event detection in the local area based on different features such as energy-based, cyclostationary approach, template matching, and wavelet transform are also discussed. Furthermore, local area event detection and classification using approaches such as statistical, signal processing, artificial intelligence, and hybrid are also discussed. Moreover, an overview of wide-area event detection and classification along with several other aspects such as wide-area events, wide-area event detection approaches, event location and system performance, event pattern recognition, inter-area oscillation, and wide-area frequency response under variable deployment of inverter-based resources are also provided. The proposed framework is the first step toward the goal of developing appropriate tools and methodologies to detect and classify local as well as wide-area events using waveform analytics. The appropriate event detection and classification framework development is especially important now as more and more grid edge devices with communication capabilities are being deployed in the modern power grid than ever before.

Bhusal, Narayan↗

On Anomaly Detection for Transactive Energy Systems with Competitive Market

Two anomaly-detection criteria are proposed for transactive energy systems with competitive markets. Participants of transactive energy systems seek an optimal power allocation through hybrid economic-control methods to facilitate the integration of various types of distributed energy resources to power distribution systems. In transactive energy systems, every participant is assumed to be a rational entity, and consumers have diminishing marginal utility and suppliers have increasing marginal cost. With the first proposed anomaly-detection criterion, the monotonicity of marginal cost and marginal utility are examined. The impact of line flow constraints is also taken into consideration. Then, the second anomaly-detection criterion is proposed for TESs with marginal cost and marginal utility which change faster than a certain rate. The second criterion is more accurate than the first one for TES with marginal cost and marginal utility which change faster than a certain rate, but it requires the knowledge of that rate. Neither criteria requires more data than those necessary to find the optimal power allocation and the market-clear price in a transactive energy system. Therefore, the proposed criteria do not disclose any more data than necessary. As the monotonicity of marginal cost and marginal utility in a TES with competitive markets results in convex objective functions in an optimization problem and strongly convex ones when marginal cost and marginal utility changes faster than a certain rate, the two detection criteria are also applicable to anomaly detection of general convex optimization problems. Simulations are carried out to show the efficacy of the proposed criteria to detect anomalies caused by cyberattacks.

Wang, Peng↗

Machine Learning Enabled Position Detection for 6.78 MHz UAV Wireless Power Transfer System

This paper presents a novel supervised machine learning (SML) approach for accurate position detection of the receiver coil in wireless power transfer (WPT) systems using only secondary-side electrical measurements, with applications in autonomous unmanned aerial vehicle (UAV) charging. The proposed method trains a supervised learning model to map measured secondary-side voltage and current features to the receiver’s spatial position with high precision. This enables an autonomous UAV to determine its location relative to the primary coil center, the optimal position for maximizing wireless charging efficiency. The sensing method is fully integrated into a standard WPT system, utilizing the same primary and secondary coils for both power transfer and position detection, thereby eliminating additional sensing hardware. The use of a 6.78 MHz operating frequency enhances positional sensitivity, as high-frequency near-field electromagnetic fields respond strongly to small spatial variations. Experimental validation is performed on a 30 W scaled prototype featuring a 210 mm × 140 mm primary coil, a 50 mm × 80 mm receiver coil, and a 15 mm air gap. Results demonstrate reliable position estimation and a strong correlation between predicted position and optimal coil alignment. This integrated framework unifying position detection and wireless charging offers a promising foundation for future autonomous electric vertical takeoff and landing (eVTOL) systems, enabling compact, hardware-efficient, and high-accuracy charging solutions.

Colak, Kerim [New York University]↗

Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition

We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗