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Cluster-Based Flight Trajectory Outlier Detection
Given a set of flight trajectories, can we classify the trajectories that do not follow a normal path? By identifying abnormal trajectories, further analysis can be done to determine the reasoning for these actions. Addressing these scenarios can bring possible solutions for holding and rerouting problems when its time to incorporate UAM in the airspace.
Beyond Ultra-diffuse Galaxies. I. Mass–Size Outliers among the Satellites of Milky Way Analogs
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Outliers: multicolour photometry guiding the search for evolved binary systems in the globular cluster 47 Tucanae
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Outlier Detection and Removal in Multivariate Time Series for a More Robust Machine Learning–based Solar Flare Prediction
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Mechanisms of Oxide Formation in the Self-Reacting Friction Stir Weld Process
The initial objective of this study was to evaluate the effect of an extended delay time between preweld cleaning and the completion of a self-reacting friction stir welding (SRFSW) process on the resulting quality of various thickness panels of AA2219-T87. The current NASA standard specifies no more than a 48 hour delay between preweld cleaning and actual welding. The concern is whether increasing the cleaning delay time results in development of the residual oxide defect (ROD) in SRFSW. This concern emanates from the possibility of increased time correlating with increased oxide layer thickness on the faying surfaces. Oxide content on the faying surfaces has been reported to correlate with the occurrence of the ROD which reduces mechanical properties. When the SRFSW process was first adopted by the NASA Marshall Space Flight Center (MSFC), unexpected low tensile values that resulted were attributed to oxides within the weld that appeared to follow the former faying surface contours. Mitigation of the ROD was achieved through a combination of modifications to the processing parameters, tool designs, and incorporation of a weld seam offset. Two operations are involved in preweld cleaning: the first is removal of oil and grease, and the second is removal of surface oxides. In arc welding, improper cleaning of the faying surfaces of aluminum welded joints can increase the sensitivity toward development of defects. As the aluminum is locally melted, these contaminants contribute toward the development of porosity, inclusions, entrapped oxides, and other discontinuities which can degrade the strength of the weld joint. For weldment of large structures, the weld joint is typically cleaned, fit-up, and tack welded prior to the final full penetration welding pass. Because of the stringent joint fit-up requirements for mismatch and peaking for launch vehicle structures, the joint fit-up can sometimes contribute to lengthy delays between cleaning and tack welding, especially for circumferential weld joints on large diameter components. When the conventional friction stir welding (CFSW) process was introduced at the NASA MSFC, there was no procedure for cleaning prior to the solid-state joining process. As the process expanded to include SRFSW, preparation of the faying, crown, and root surfaces were implemented to overcome the ROD. Although the solid-state process is not expected to reach temperatures high enough for dissociation of the native oxide layer, concern remained regarding the redeposition of the native oxide layer within the stir zone. NASA has previously established the allowable time at 48 hours between preweld cleaning and a SRFSW process. The effect of potential 2 contamination resulting from an extended delay to 188 hours was subsequently evaluated for SRFSWs using tensile testing and metallographic imaging. Tensile specimens were tested at room temperature (RT), and at cryogenic conditions of liquid nitrogen (LN2) and liquid hydrogen. No detrimental effect on weld quality, as determined by weld strength, was reported for cleaning delays of 48, 120, 168, 240 or 288 hours. While no trends were established in this study, which extended the delay from 48 to 188 hours, there were a few outliers in terms of ultimate tensile strength (UTS). According to M. Fisher's 2014 Boeing Company Memo no. EYBF-MAF-14-029, all outliers were above the minimum acceptance criteria, but out of family with respect to the average values. As the robustness and reliability of any process ultimately depends on the average values as well as the outliers, an understanding of the cause of these outliers will ultimately improve the process. This report examines those outliers and their possible causes.
Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals
Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.
FY12 End of Year Report for NEPP DDR2 Reliability
This document reports the status of the NASA Electronic Parts and Packaging (NEPP) Double Data Rate 2 (DDR2) Reliability effort for FY2012. The task expanded the focus of evaluating reliability effects targeted for device examination. FY11 work highlighted the need to test many more parts and to examine more operating conditions, in order to provide useful recommendations for NASA users of these devices. This year's efforts focused on development of test capabilities, particularly focusing on those that can be used to determine overall lot quality and identify outlier devices, and test methods that can be employed on components for flight use. Flight acceptance of components potentially includes considerable time for up-screening (though this time may not currently be used for much reliability testing). Manufacturers are much more knowledgeable about the relevant reliability mechanisms for each of their devices. We are not in a position to know what the appropriate reliability tests are for any given device, so although reliability testing could be focused for a given device, we are forced to perform a large campaign of reliability tests to identify devices with degraded reliability. With the available up-screening time for NASA parts, it is possible to run many device performance studies. This includes verification of basic datasheet characteristics. Furthermore, it is possible to perform significant pattern sensitivity studies. By doing these studies we can establish higher reliability of flight components. In order to develop these approaches, it is necessary to develop test capability that can identify reliability outliers. To do this we must test many devices to ensure outliers are in the sample, and we must develop characterization capability to measure many different parameters. For FY12 we increased capability for reliability characterization and sample size. We increased sample size this year by moving from loose devices to dual inline memory modules (DIMMs) with an approximate reduction of 20 to 50 times in terms of per device under test (DUT) cost. By increasing sample size we have improved our ability to characterize devices that may be considered reliability outliers. This report provides an update on the effort to improve DDR2 testing capability. Although focused on DDR2, the methods being used can be extended to DDR and DDR3 with relative ease.
Using Machine Learning to Identify Novel Hydroclimate States
Anthropogenic climate change is expected to alter drought risk in the future. However, droughts are not uncommon or unprecedented, as documented in tree-ring-based reconstructions of the summer average Palmer drought severity index (PDSI). Using an unsupervised machine-learning method trained on these reconstructions of pre-industrial climate, we identify outliers: years in which the spatial pattern of PDSI is unusual relative to ‘normal' variability. We show that in many regions, outliers are more frequently identified in the twentieth and twenty-first centuries. This trend is more pronounced when the regional drought atlases are combined into a single global dataset. By definition, outlier patterns at the 10% level are expected to occur once per decade, but from 1950 to 2000 more than 6 years per decade are identified as outliers in the global drought atlas (GDA). Extending the GDA through 2020 using an observational dataset suggests that anomalous global drought conditions are present in 80% of years in the twenty-first century. Our results indicate, without recourse to climate models, that the world is more frequently experiencing drought conditions that are highly unusual in the context of past natural climate variability.
Clustering of noisy image data using an adaptive neuro-fuzzy system
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
Groundspeed filtering for CTAS
Ground speed is one of the radar observables which is obtained along with position and heading from NASA Ames Center radar. Within the Center TRACON Automation System (CTAS), groundspeed is converted into airspeed using the wind speeds which CTAS obtains from the NOAA weather grid. This airspeed is then used in the trajectory synthesis logic which computes the trajectory for each individual aircraft. The time history of the typical radar groundspeed data is generally quite noisy, with high frequency variations on the order of five knots, and occasional 'outliers' which can be significantly different from the probable true speed. To try to smooth out these speeds and make the ETA estimate less erratic, filtering of the ground speed is done within CTAS. In its base form, the CTAS filter is a 'moving average' filter which averages the last ten radar values. In addition, there is separate logic to detect and correct for 'outliers', and acceleration logic which limits the groundspeed change in adjacent time samples. As will be shown, these additional modifications do cause significant changes in the actual groundspeed filter output. The conclusion is that the current ground speed filter logic is unable to track accurately the speed variations observed on many aircraft. The Kalman filter logic however, appears to be an improvement to the current algorithm used to smooth ground speed variations, while being simpler and more efficient to implement. Additional logic which can test for true 'outliers' can easily be added by looking at the difference in the a priori and post priori Kalman estimates, and not updating if the difference in these quantities is too large.
Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences
This paper addresses the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. The approach taken uses unsupervised clustering of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by detailed analysis of outliers to detect anomalies. As the LCS measure is expensive to compute, the first part of the paper discusses existing algorithms, such as the Hunt-Szymanski algorithm, that have low time-complexity. We then discuss why these algorithms often do not work well in practice and present a new hybrid algorithm for computing the LCS that, in our tests, outperforms the Hunt-Szymanski algorithm by a factor of five. The second part of the paper presents new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. The algorithms provide a coherent description to an analyst of the anomalies in the sequence, compared to more normal sequences. The algorithms we present are general and domain-independent, so we discuss applications in related areas such as anomaly detection.
FY12 End of Year Report for NEPP DDR2 Reliability
This document reports the status of the NEPP Double Data Rate 2 (DDR2) Reliability effort for FY2012. The task expanded the focus of evaluating reliability effects targeted for device examination. FY11 work highlighted the need to test many more parts and to examine more operating conditions, in order to provide useful recommendations for NASA users of these devices. In order to develop these approaches, it is necessary to develop test capability that can identify reliability outliers. To do this we must test many devices to ensure outliers are in the sample, and we must develop characterization capability to measure many different parameters. For FY12 we increased capability for reliability characterization and sample size. We increased sample size by moving from loose devices to DIMMs with an approximate reduction of 20 to 50 times in terms of per DUT cost. By increasing sample size we have improved our ability to characterize devices that may be considered reliability outliers. This report provides an update on the effort to improve DDR2 testing capability. Although focused on DDR2, the methods being used can be extended to DDR and DDR3 with relative ease.
Exact intervals and tests for median when one sample value possibly an outliner
Available are independent observations (continuous data) that are believed to be a random sample. Desired are distribution-free confidence intervals and significance tests for the population median. However, there is the possibility that either the smallest or the largest observation is an outlier. Then, use of a procedure for rejection of an outlying observation might seem appropriate. Such a procedure would consider that two alternative situations are possible and would select one of them. Either (1) the n observations are truly a random sample, or (2) an outlier exists and its removal leaves a random sample of size n-1. For either situation, confidence intervals and tests are desired for the median of the population yielding the random sample. Unfortunately, satisfactory rejection procedures of a distribution-free nature do not seem to be available. Moreover, all rejection procedures impose undesirable conditional effects on the observations, and also, can select the wrong one of the two above situations. It is found that two-sided intervals and tests based on two symmetrically located order statistics (not the largest and smallest) of the n observations have this property.
Evaluation of the MV (CAPON) Coherent Doppler Lidar Velocity Estimator
The performance of the CAPON velocity estimator for coherent Doppler lidar is determined for typical space-based and ground-based parameter regimes. Optimal input parameters for the algorithm were determined for each regime. For weak signals, performance is described by the standard deviation of the good estimates and the fraction of outliers. For strong signals, the fraction of outliers is zero. Numerical effort was also determined.
Combinations of Earth Orientation Measurements: SPACE94, COMB94, and POLE94
A Kalman filter has been used to combine independent measurements of the Earth's orientation taken by the space-geodetic observing techniques of lunar laser ranging, satellite laser ranging, very long baseline interferometry, and the Global Positioning System. Prior to their combination, the data series were adjusted to have the same bias and rate, the stated uncertainties of the measurements were adjusted, and data points considered to be outliers were deleted. The resulting combination, SPACE94, consists of smoothed, interpolated polar motion and UT1-UTC values spanning October 6, 1976, to January 27, 1995, at 1-day intervals. The Kalman filter was then used to combine the space-geodetic series comprising SPACE94 with two different, independent series of Earth orientation measurements taken by the technique of optical astrometry. Prior to their combination with SPACE94, the bias, rate and annual term of the optical astrometric series were corrected, the stated uncertainties of the measurements were adjusted, and data points considered to be outliers were deleted. The adjusted optical astrometric series were then combined with SPACE94 in two steps: (1) the Bureau International de l'Heure (BIH) optical astrometric series was combined with SPACE94 to form COMB94, a combined series of smoothed, interpolated polar motion and UT1-UTC values spanning January 20, 1962, to January 27, 1995, at 5-day intervals, and (2) the International Latitude Service (ILS) optical astrometric series was combined with COMB94 to form POLE94, a combined series of smoothed, interpolated polar motion values spanning January 20, 1900, to January 21, 1995, at 30.4375-day intervals.
Original Size and Shape of the Sudbury Structure
This paper presents new evidence bearing on the original size and shape of the Sudbury impact structure. Current opinion is almost unanimous that the structure is a multiring basin with an original diameter of about 200 km and a circular shape that has since been shortened in a northwest-southeast direction by Penokean deformation Evidence for this interpretation, collected chiefly from north of the Sudbury Igneous Complex (SIC), includes supposed outer rings on Landsat imagery, distant occurrences of "Sudbury breccia" (generally defined as pseudotachylite), shatter cone occurrences, and outliers of Huronian sedimentary rock thought to be down-faulted rings. New data from imaging radar and field work north of the SIC, however, contradict this evidence. Radar imagery shows no signs of the supposed outer rings mapped by earlier workers on Landsat images. The most prominent ring has been found to be a chance alignment of two independent fracture sets. Radar imagery from the CCRS Convair 580, with look direction almost normal to the north rim of the SIC, shows no evidence of the rings despite strong look azimuth highlighting. Radar imagery has shown many unmapped diabase dikes north of the SIC. Several exposures of supposed Sudbury breccia are associated with these dikes or with Nipissing diabase intrusions, in some cases actually inside the dikes or directly continuous with them. They appear to be igneous intrusion breccias with no relation to impact. Shock-wave interaction at lithologic contacts cannot be invoked for most of these, because they are part of a northwest trending swarm cutting the SIC in the North Range, and hence too young for an impact origin. Similar diabase-related breccias and pseudotachylite-like veins have been found far outside the Sudbury area between Chapleau and Thessalon. Shatter cones north of the SIC are few and poorly developed, perhaps due to the coarse-grained Footwall rock, and cannot be considered a continuous zone analogous to their occurrence on the South Range in Huronian rocks. Supposed down-faulted outliers of Huronian rocks north of the SIC show no consistent relation to faulting, and the Huronian/Archean contact is locally erosional. Radar imagery and field-checking confirm Rousell's conclusion that the North Range has undergone little or no Penokean deformation. T'his implies that the plan view outline of the crater (floor of the SIC) is original. Extrapolation of the North Range as part of a circular arc leads to an impossibly great diameter. It is concluded that although Penokean deformation largely accounts for the structure's shape, the original crater was not circular and was much smaller than 200 km across.