Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “statistical change detection”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physics-based Induced Earthquake Forecasting: Process Understanding, and Hazards Mitigation

Disposal of saltwater co-produced with oil and gas is linked to elevated seismicity in the Central and Midwest US. There is a concern that these events may lead to widespread damage and an overall increase in seismicity. Thus an improved understanding of the spatially and temporally variable deformation and stress field associated with fluid injection operation is critically necessary for evaluating time-varying seismic hazards. Despite the improvements in seismic monitoring capacity and the resulting decrease in the magnitude detection threshold, estimates of induced earthquake probability remain elusive due to insufficient models incapable of accounting for the complex physics governing the process of induced seismicity. The proposed research effort will comprehensively analyze, integrate, and interpret geodetic, injection, and seismic data in the vicinity of the injection sites in Oklahoma to resolve the 4-dimensional distribution of pore pressure and stress in the shallow crust. This project, in particular, is focused on exploring the statistical relation between injection operation and increased earthquake hazard. The amplitude of and the extent to which pore pressure changes are determined by some factors, in particular, the hydrogeological properties of the rocks, such as diffusivity. Thus the available deformation data will be used to constrain hydrogeological properties of the medium, to accurately resolve the evolution of crustal stresses due to fluid injection. Having the time-varying models of stress changes, a statistical framework will be implemented to estimate the time-dependent probability of large earthquakes on the nearby fault systems. These data and models help to improve seismic hazard estimates and aid in constructing operational-induced earthquake forecast models. This information can also be integrated into the updated U.S. National Seismic Hazard Map, which local communities and authorities use in their earthquake risk estimates and mitigation efforts.

58 GEOSCIENCES↗
NASA NTRS2010

Modeling of LEO Orbital Debris Populations in Centimeter and Millimeter Size Regimes

The building of the NASA Orbital Debris Engineering Model, whether ORDEM2000 or its recently updated version ORDEM2010, uses as its foundation a number of model debris populations, each truncated at a minimum object-size ranging from 10 micron to 1 m. This paper discusses the development of the ORDEM2010 model debris populations in LEO (low Earth orbit), focusing on centimeter (smaller than 10 cm) and millimeter size regimes. Primary data sets used in the statistical derivation of the cm- and mm-size model populations are from the Haystack radar operated in a staring mode. Unlike cataloged objects of sizes greater than approximately 10 cm, ground-based radars monitor smaller-size debris only in a statistical manner instead of tracking every piece. The mono-static Haystack radar can detect debris as small as approximately 5 mm at moderate LEO altitudes. Estimation of millimeter debris populations (for objects smaller than approximately 6 mm) rests largely on Goldstone radar measurements. The bi-static Goldstone radar can detect 2- to 3-mm objects. The modeling of the cm- and mm-debris populations follows the general approach to developing other ORDEM2010-required model populations for various components and types of debris. It relies on appropriate reference populations to provide necessary prior information on the orbital structures and other important characteristics of the debris objects. NASA's LEO-to-GEO Environment Debris (LEGEND) model is capable of furnishing such reference populations in the desired size range. A Bayesian statistical inference process, commonly adopted in ORDEM2010 model-population derivations, changes a priori distribution into a posteriori distribution and thus refines the reference populations in terms of data. This paper describes key elements and major steps in the statistical derivations of the cm- and mm-size debris populations and presents results. Due to lack of data for near 1-mm sizes, the model populations of 1- to 3.16-mm objects are an empirical extension from larger debris. The extension takes into account the results of micro-debris (from 10 micron to 1 mm) population modeling that is based on shuttle impact data, in the hope of making a smooth transition between micron and millimeter size regimes. This paper also includes a brief discussion on issues and potential future work concerning the analysis and interpretation of Goldstone radar data.

Xu, Y.-L.↗
NASA NTRS2016

Measuring Small Debris - What You Can't See Can Hurt You

While modeling gives us a tool to better understand the Earth orbit debris environment, it is measurements that give us "ground truth" about what is happening in space. Assets that can detect orbital debris remotely from the surface of the Earth, such as radars and telescopes, give us a statistical view of how debris are distributed in space, how they are being created, and how they are evolving over time. In addition, in situ detectors in space are giving us a better picture of how the small particle environment is actually damaging spacecraft today. IN addition, simulation experiments on the ground help us to understand what we are seeing in orbit. This talk will summarize the history of space debris measurements, how it has changed our view of the Earth orbit environment, and how we are designing the experiments of tomorrow.

Matney, Mark↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1974

Estimate of winter wheat yield from ERTS-1

A model for estimating wheat yield per acre has been applied to acreage estimates derived from ERTS-1 imagery to project the 1973 wheat yields for a ten county area in southwest Kansas. The results (41.04 million bushels) are within 3 per cent of the preharvest estimates for the same area prepared by the USDA Statistical Reporting Service (39.91 million bushels). The projection from ERTS data is based on a visual enumeration of all detectable wheat fields in the study area and was completed while the harvest was in progress. Visual identification of winter wheat is readily achieved by using a temporal sequence of images (band 5 for Sept.-Oct.; band 5 for Dec.-Jan.; and band 5 and 7 for March-April). Identification can be improved by stratifying the project area into subregions having more or less homogeneous agricultural practices and crop mixes. By doing this, small changes in the spectral appearance of wheat related to soil type, irrigation, etc. can be accounted for. The interpretation rules developed by visual analysis can be automated for rapid computer surveys.

Morain, S. A.↗
NASA NTRS1973

Opportunities for space surveys of moisture anomalies

Review of a combination of infrared scanning, unsupervised spectroscopic classification, change detection, and perturbation spectroscopy for monitoring anomalous moisture exchange from space. Infrared sensors have been developed which can monitor the solar reflectance from droplet distributions in vegetation and clouds, the absorption of thermal radiation by water vapor, and the microwave attenuation by atmospheric water. Unsupervised spectroscopic classification techniques are emerging which have the potential to subdivide strip maps from infrared scanners into different moisture targets. Intersecting and parallel spacecraft orbits provide opportunities to study the change of recognized targets between two scans and to recognize anomalous moisture exchange from those changes which significantly exceed the statistical uncertainty of the spectroscopic target signature. The new concept of perturbation spectroscopy shows a potential to quantitatively determine the change of integrated water vapor pressure with a minimum of 'atmospheric truth' and with low spectroscopic resolution that is typical of simple interference filters.

Krause, F. R.↗
NASA NTRS2004

Co-Seismic Mass Displacement and its Effect on Earth's Rotation and Gravity

Mantle processes often involve large-scale mass transport, ranging from mantle convection, tectonic motions, glacial isostatic adjustment, to tides, atmospheric and oceanic loadings, volcanism and seismicity. On very short time scale of less than an hour, co-seismic event, apart from the "shaking" that is the earthquake, leaves behind permanent (step-function-like) displacements in the crust and mantle. This redistribution of mass changes the Earth's inertia tensor (and hence Earth's rotation in both length-of-day and polar motion), and the gravity field. The question is whether these effects are large enough to be of any significance. In this paper we report updated calculation results based on Chao & Gross. The calculation uses the normal mode summation scheme, applied to over twenty thousand major earthquakes that occurred during 1976-2002, according to source mechanism solutions given by the Harvard Centroid Moment Tensor catalog. Compared to the truly large ones earlier in the century, the earthquakes we study are individually all too small to have left any discernible signature in geodetic records of Earth rotation or global gravity field. However, their collective effects continue to exhibit an extremely strong statistical tendencies, conspiring to decrease J2 and J22 while shortening LOD, resulting in a rounder and more compact Earth. Strong tendency is also seen in the earthquakes trying to "nudge" the Earth rotation pole towards approx. 140 deg.E, roughly opposite to the observed polar drift direction. Currently, the Gravity Recovery And Climate Experiment (GRACE) is measuring the time-variable gravity to high degree and order with unprecedented accuracy. Our results show that great earthquakes such as the 1960 Chilean or 1964 Alaskan events cause gravitational field changes that are large enough to be detected by GRACE.

Chao, B. F.↗
NASA NTRS1997

Evaluation of heart rate changes: electrocardiographic versus photoplethysmographic methods

The heart rate (HR) variation to forced deep breathing (HRDB) and to the Valsalva maneuver (Valsalva ratio; VR) are the two most widely used tests of cardiovagal function in human subjects. The HR is derived from a continuously running electrocardiographic (ECG) recording. Recently, HR derived from the arterial waveform became available on the Finapres device (FinapHR), but its ability to detect rapid changes in HR remains uncertain. We therefore evaluated HRDB and VR derived from FinapHR using ECG-derived HR (ECGHR) recordings as the standard. We also compared the averaged HR on Finapres (Finapav) with beat-to-beat Finapres (FinapBB) values. Studies were undertaken in 12 subjects with large HR variations: age, 34.5 +/- 9.3 (SD) years; six males and six females. FinapBB values were superimposable upon ECGHR for both HRDB and VR. In contrast, Finapav failed to follow ECGHR for HRDB and followed HRECG with a lag for the VR. To evaluate statistically how closely FinapHR approximated ECGHR, we undertook regression analysis, using mean values for each subject. To compare the two methods, we evaluated the significance of the difference between test and standard values. For HRDB, FinapBB reproducibly recorded HR (R2 = 0.998), and was significantly (p = 0.001) better than Finapav (R2 = 0.616; p < 0.001). For VR, HRBB generated a VR that was not significantly different from the correct values, while HRav generated a value that was slightly but consistently lower than the correct values (p < 0.001). We conclude that FinapHR reliably records HR variations in the beat-to-beat mode for cardiovascular HR tests.

Non-NASA Center↗
DOE OSTI2026

A New Window into the Baryon Cycle at Cosmic Noon with Line Intensity Mapping: Forecasts for auto- and cross-correlations in [CII]-158$μ$m, HI 21 cm, CO$_{J+1\rightarrow J}$, and H$α$ galaxies

Across the peak of cosmic star formation at $z\sim1-2$, inflow, processing, and feedback drive rapid changes in the spatial distribution and chemical composition of baryons in galaxies and surrounding reservoirs; this baryon cycle can be tomographically mapped by line intensity mapping (LIM) of atomic hydrogen, ionized carbon, and carbon monoxide. We present a simulation-based forecasting framework for detecting auto- and cross-power spectra between spectroscopic surveys of four such tracers at $z\sim0.5-1.7$ mapping the same deep field - TIM, EoRSpec/FYST, MeerKAT, & Euclid. We forward-model 3-D distributions for these tracers from magnetohydrodynamic simulations, directly capturing the two-halo, one-halo, and shot statistics without relying on analytical decompositions. We further detail a signal-to-noise formalism, tailored to LIM surveys with highly anisotropic geometries and Fourier-space coverage. We demonstrate that galaxy cross-correlations will be the dominant discovery channel for current-generation surveys. These instruments will detect the auto-spectra for CO and HI 21 cm and the CO $\times$ 21 cm cross-spectrum at modest S/N $\sim 1-10$, while placing upper limits on the [CII]-158$μ$m signals. [CII], CO, and HI LIM will be $\sim3-30\times$ ($0.5-1.5$ dex) more sensitive to cross-correlation with the Euclid survey, however, than their respective auto-correlations, constraining all three models of line emission at high significance (S/N $\sim 10-40$) within this decade. Finally, we formulate a staged instrumental trajectory with planned or reasonable improvements, including the as-proposed SKA-Mid. We forecast advancing the per-$k$-mode sensitivities of each auto-, galaxy-line, and line-line spectrum by several orders of magnitude, enabling new percent- and sub-percent level constraints on cosmology and the redshift evolution of star formation and the baryon cycle.

Agrawal, Shubh [Pennsylvania U., Dept. Math.]↗
NASA NTRS2021

Moisture and Temperature Influences on Nonlinear Vegetation Trends in Serengeti National Park

While long-term vegetation greening trends have appeared across large land areas over the late 20th century, uncertainty remains in identifying and attributing finer-scale vegetation changes and trends, particularly across protected areas. Serengeti National Park (SNP) is a critical East African protected area, where seasonal vegetation cycles support vast populations of grazing herbivores and a host of ecosystem dynamics. Previous work has shown how non-climate drivers (e.g. land use) shape the SNP ecosystem, but it is still unclear to what extent changing climate conditions influence SNP vegetation, particularly at finer spatial and temporal scales. We fill this research gap by evaluating long-term (1982–2016) changes in SNP leaf area index (LAI) in relation to both temperature and moisture availability using Ensemble Empirical Mode Decomposition and Principal Component Analysis with regression techniques. We find that SNP LAI trends are nonlinear, display high sub-seasonal variation, and are influenced by lagged changes in both moisture and temperature variables and their interactions. LAI during the long rains (e.g. March) exhibits a greening-to-browning trend reversal starting in the early 2000s, partly due to antecedent precipitation declines. In contrast, LAI during the short rains (e.g. November, December) displays browning-to-greening alongside increasing moisture availability. Rising temperature trends also have important, secondary interactions with moisture variables to shape these SNP vegetation trends. Our findings show complex vegetation-climate interactions occurring at important temporal and spatial scales of the SNP, and our rigorous statistical approaches detect these complex climate-vegetation trends and interactions, while guarding against spurious vegetation signals.

Moisture↗
NASA NTRS2005

Structural Health Monitoring of Composite Plates Under Ambient and Cryogenic Conditions

Methods for structural health monitoring are now being assessed, especially in high-performance, extreme environment, safety-critical applications. One such application is for composite cryogenic fuel tanks. The work presented here attempts to characterize and investigate the feasibility of using imbedded piezoelectric sensors to detect cracks and delaminations under cryogenic and ambient conditions. Different types of excitation and response signals and different sensors are employed in composite plate samples to aid in determining an optimal algorithm, sensor placement strategy, and type of imbedded sensor to use. Variations of frequency and high frequency chirps of the sensors are employed and compared. Statistical and analytic techniques are then used to determine which method is most desirable for a specific type of damage and operating environment. These results are furthermore compared with previous work using externally mounted sensors. More work is needed to accurately account for changes in temperature seen in these environments and be statistically significant. Sensor development and placement strategy are other areas of further work to make structural health monitoring more robust. Results from this and other work might then be incorporated into a larger composite structure to validate and assess its structural health. This could prove to be important in the development and qualification of any 2nd generation reusable launch vehicle using composites as a structural element.

Engberg, Robert C.↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRS2007

High-frequency Electrocardiogram Analysis in the Ability to Predict Reversible Perfusion Defects during Adenosine Myocardial Perfusion Imaging

Background: A previous study has shown that analysis of high-frequency QRS components (HF-QRS) is highly sensitive and reasonably specific for detecting reversible perfusion defects on myocardial perfusion imaging (MPI) scans during adenosine. The purpose of the present study was to try to reproduce those findings. Methods: 12-lead high-resolution electrocardiogram recordings were obtained from 100 patients before (baseline) and during adenosine Tc-99m-tetrofosmin MPI tests. HF-QRS were analyzed regarding morphology and changes in root mean square (RMS) voltages from before the adenosine infusion to peak infusion. Results: The best area under the curve (AUC) was found in supine patients (AUC=0.736) in a combination of morphology and RMS changes. None of the measurements, however, were statistically better than tossing a coin (AUC=0.5). Conclusion: Analysis of HF-QRS was not significantly better than tossing a coin for determining reversible perfusion defects on MPI scans.

Tragardh, Elin↗
NASA NTRSDate not supplied

Formation and Propagation of Atmospheric River and Its Impact on Extreme Precipitation Events in the North Pacific and the Western North America

Seasonal and interannual evolution patterns of atmospheric rivers (AR) in the North Pacific are examined as a function of the formation region where an AR is first detected using 43-year MERRA-2 reanalysis data. Integrated water vapor transport (IVT) is used to detect AR with latitude dependent thresholds of IVT to better detect AR-like features in the high latitudes. Based on 3-hourly AR statistics, three main AR genesis regions in the North Pacific (i.e., South China Sea (SCS), Western North Pacific (WNP) and Central Pacific Ocean and Hawaiian Islands (CPO) are identified. WNP is the main source of AR with 1475 ARs detected for 43 cold seasons (NDJFM). Over 70% of all AR formed in the WNP has longer than 2-day. On the other hand, AR from CPO tends to have shorter lifetime than those from WNP and SCS. While propagation patterns of AR from SCS and WNP are similar, AR from WNP tends to reach mature phase quicker and shows higher change of impacting west coast of North America. Longevity and strength of AR are also examined based on three large-scale circulation modes (e.g. ENSO, WP, and EAJS) over the East Asia identified from eigen analysis of upper-level zonal winds. During El Nino, the number of AR formed in the western Pacific increased by 20%. First two days, the average size and intensity shows little difference compared with ARs in La Nina years. AR appears to grow in size in El Nino vs. La Nina years. Positive phase of WP correlated with less, but larger and stronger AR formation over WNP and SCS regions. EAJS has little impact in the numbers of AR and its size, but ARs in a stronger EAJS tends to grow larger. Atmospheric circulation associated with the initial formation and propagation of AR from the different regions in the North Pacific as well as its impact on extreme precipitation events in the North Pacific and the west coast of North America will be also discussed.

Integrated water vapor transport↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗
NASA NTRSDate not supplied

Correlated Changes in Retinal Thickness and Visuomotor Function in Asymptomatic Plaquenil Toxicity Patients

We have previously shown that a 5-minute eye-movement test can capture a number of largely independent “oculometric” parameters of human visual/visuomotor function (Liston & Stone, 2014). This test has been successfully used to characterize mild neural impairment due to traumatic brain injury, sleep and circadian disruption, and alcohol consumption (Liston et al., 2017; Stone et al, 2019; Tyson et al, 2021). Here, we extend this approach to study patients at risk of progressive posterior-segment illness to see how oculometrics relate to standard clinical ophthalmological measures. Five female patients (<20:40 BCVA, 34-60 years old) undergoing hydroxychloroquine treatment (2-7mg/kg/day for 10-23 years for rheumatologic illness) have participated thus far. Under monocular viewing conditions in randomized order, they tracked a moving spot undergoing radial step-ramp motion in one of 90 randomized directions, at one of 5 randomized speeds, starting at random times (for details, see Stone et al, 2019). We computed nine independent oculometrics (latency, initial acceleration, steady-state gain, proportion smooth, saccadic rate, saccadic amplitude, direction noise, speed responsiveness, and speed noise). Spectral-domain optical coherence tomography (Zeiss, Dublin, CA) was used to measure the average thickness of the nine Early Treatment Diabetic Retinopathy Study macular sectors from the internal limiting membrane to the retinal pigmented epithelium. We used Pearson’s correlation between retinal thickness and our oculometrics with t-statistics to test separate one-sided hypotheses across 10 retinae. Our preliminary analysis found significant changes in several different aspects of visuomotor performance. Most notably, latency increased, initial acceleration decreased, and steady-state pursuit gain decreased as retinal thickness decreased (r2 = 0.364, 0.579, and 0.692; one-tailed P = 0.032, 0.005, and 0.001, respectively). Speed responsiveness appeared to decrease, but this effect was marginal for our current sample (r2 = 0.253, P = 0.069). Lastly, direction discrimination and saccadic behavior appeared unchanged. We conclude that oculometrics may provide sensitive measures of changes in retinal thickness. More importantly, oculometrics have the potential to detect impairment before it becomes evident with standard clinical imaging tools, while also providing insight into the behavioral implications of any resulting deficits in visual/visuomotor function.

eye movements↗
DOE OSTI2020

Detection of defects in atomic-resolution images of materials using cycle analysis

The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

36 MATERIALS SCIENCE↗