Simulations of the Hazard Detection System for Approach Trajectories of the Morpheus Lunar Lander
No abstract available
Monte Carlo↗
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.
Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts
Cell abundances of Prochlorococcus, Synechococcus, and autotrophic picoeukaryotes were estimated in surface waters using principal component analysis (PCA) of hyperspectral and multispectral remote-sensing reflectance data. This involved the development of models that employed multilinear correlations between cell abundances across the Atlantic Ocean and a combination of PCA scores and sea surface temperatures. The models retrieve high Prochlorococcus abundances in the Equatorial Convergence Zone and show their numerical dominance in oceanic gyres, with decreases in Prochlorococcus abundances towards temperate waters where Synechococcus flourishes, and an emergence of picoeukaryotes in temperate waters. Fine-scale in-situ sampling across ocean fronts provided a large dynamic range of measurements for the training dataset, which resulted in the successful detection of fine-scale Synechococcus patches. Satellite implementation of the models showed good performance (R(exp 2) > 0.50) when validated against in-situ data from six Atlantic Meridional Transect cruises. The improved relative performance of the hyperspectral models highlights the importance of future high spectral resolution satellite instruments, such as the NASA PACE mission’s Ocean Color Instrument, to extend our spatiotemporal knowledge about ecologically relevant phytoplankton assemblages.
Priscila Kienteca Lange↗
Physically-Based Machine Learning Approach to Dust Detection in NASA/NOAA Geostationary Satellite Imagery
Explore the source record for details and available documents.
Dust↗
Life detection systems.
Some promising newer approaches for detecting microorganisms are discussed, giving particular attention to the integration of different methods into a single instrument. Life detection methods may be divided into biological, chemical, and cytological methods. Biological methods are based on the biological properties of assimilation, metabolism, and growth. Devices for the detection of organic materials are considered, taking into account an instrument which volatilizes, separates, and analyzes a sample sequentially. Other instrumental systems described make use of a microscope and the cytochemical staining principle.
Mitz, M. A.↗
Life detection on Mars: technical/strategic approach
Explore the source record for details and available documents.
life detection Mars exploration↗
Stratospheric ClO - In-situ detection with a new approach
Stratospheric chlorine monoxide concentrations were measured in situ using a new and improved observational method in which the detection package is lowered on a tether from a balloon-borne platform floating near 40 km altitude and then reeled back up. Two reel down and reel up sequences covering 38 to 31 km produced (ClO) profiles which agree with each other to + or - 12 percent. The measured (ClO) profile falls within the envelope of nine previous fast parachute drop results, but exhibits a different shape, peaking near 32 km as opposed to approximately 39 km for the average of the previous profiles. These results are lower than but consistent with the profiles derived from remote sensing methods, and indicate the need for simultaneous measurements of relevant species.
Brune, Wm. H.↗
Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach
The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.
Risk detection↗
Evaluating Novel Approaches for In Situ Analysis in the Search for Extraterrestrial Life
Universal approaches to detect extraterrestrial life are required to determine whether life exists outside of earth. A significant limitation of current detection strategies is the absence of a reliable, universal approach. They do not provide mechanisms to detect life that has evolved distinctly from life on Earth. We hypothesize that the most basic, essential features of life that can be detected are that they decrease their internal entropy at the expense of free energy obtained from their surroundings and that they produce heat associated with metabolic reactions. We propose that by using a thermodynamic analysis of the energy associated with these fundamental components of life, one can readily quantify the degree of free energy difference between life forms or their remnants and their abiotic surroundings. Analysis of the free energy entropy term (and thus structural complexity) and enthalpy term (as a manifestation of the energy release caused by metabolic reactions) may provide definitive detection of the presence of life. As a part of our analysis we review data available in the literature about the thermodynamics of microbial growth to assess the extent microbial metabolism and growth may contribute to negative entropy of maintenance (metabolism) and structural complexity. Additionally, we evaluate the different mechanisms by which structural complexity can be measured using calorimetry. An experimental framework is considered using concepts from previous work that could be used to further validate the calorimetric methods for life detection by potentially differentiating between life forms or their remnants from an abiotic surrounding. Results obtained demonstrate the applications of this approach and indicate that modification of a microcalorimetric technique has potential for incorporation into a space exploration platform as a life-detection payload.
Life Detection, Calorimetry↗
Remote structural damage detection via substructuring
An approach to structural damage detection which utilizes the concept of a substructure transfer function matrix is proposed. The major advantage of this approach is that structural changes localized within a small volume can be detected by studying the changes in the transfer function for a relatively small substructure containing the damaged portion.
Walton, William B.↗
A New All Solid State Approach to Gaseous Pollutant Detection
Recent efforts in our laboratories have concentrated on the development of an all solid state gas sensor, by combining solid electrolyte (ion exchange membrane) technology with advanced thin film deposition processes. With the proper bias magnitude and polarity these miniature electro-chemical,cells show remarkable current responses for many common pollution gases. Current activity is now focused on complementing a multiple array (matrix) of these solid state sensors, with a digital electronic scanner device possessing "scan-compare-identify-alarm: capability. This innovative approach to multi-component pollutant gas analysis may indeed be the advanced prototype for the "third generation" class of pollution analysis instrumentation so urgently needed in the decade ahead.
Brown, V.↗
Fault detection and diagnosis using neural network approaches
Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.
Kramer, Mark A.↗
A Bispectral Composite Threshold Approach for Automatic Cloud Detection in VIIRS Imagery
The detection of clouds in satellite imagery has a number of important applications in weather and climate studies. The presence of clouds can alter the energy budget of the Earth‐atmosphere system through scattering and absorption of shortwave radiation and the absorption and re‐emission of infrared radiation at longer wavelengths. The scattering and absorption characteristics of clouds vary with the microphysical properties of clouds, hence the cloud type. Thus, detecting the presence of clouds over a region in satellite imagery is important in order to derive atmospheric or surface parameters that give insight into weather and climate processes. For many applications however, clouds are a contaminant whose presence interferes with retrieving atmosphere or surface information. In these cases, is important to isolate cloud‐free pixels, used to retrieve atmospheric thermodynamic information or surface geophysical parameters, from cloudy ones. This abstract describes an application of a two‐channel bispectral composite threshold (BCT) approach applied to VIIRS imagery. The simplified BCT approach uses only the 10.76 and 3.75 micrometer spectral channels from VIIRS in two spectral tests; a straight‐forward infrared threshold test with the longwave channel and a shortwave - longwave channel difference test. The key to the success of this approach as demonstrated in past applications to GOES and MODIS data is the generation of temporally and spatially dependent thresholds used in the tests from a previous number of days at similar observations to the current data. The paper and subsequent presentation will present an overview of the approach and intercomparison results with other satellites, methods, and against verification data.
LaFontaine Frank J.↗
A Coupled Approach for Structural Damage Detection with Incomplete Measurements
This historical work couples model order reduction, damage detection, dynamic residual/mode shape expansion, and damage extent estimation to overcome the incomplete measurements problem by using an appropriate undamaged structural model. A contribution of this work is the development of a process to estimate the full dynamic residuals using the columns of a spring connectivity matrix obtained by disassembling the structural stiffness matrix. Another contribution is the extension of an eigenvector filtering procedure to produce full-order mode shapes that more closely match the measured active partition of the mode shapes using a set of modified Ritz vectors. The full dynamic residuals and full mode shapes are used as inputs to the minimum rank perturbation theory to provide an estimate of damage location and extent. The issues associated with this process are also discussed as drivers of near-term development activities to understand and improve this approach.
James, George↗
An approach to the remote detection of earth resources in sub-arid lands
Nimbus 2 high resolution infrared radiometer data on earth resources of arid and subarid lands
Pouquet, J.↗
Multitemporal and geobotanical approach in the remote detection of Greisenization areas in the Serra da Pedra Branca Granite, Goias State, Brazil
A multiseasonal analysis of LANDSAT multispectral images in CCT format permitted the mapping of lithologic facies in the Pedra Branca Granite, using geobotanical associations, which occur in the form of variations in the density of the cerrado vegetation, as well as the predominance of certain distinct vegetation species. Dry season images did not show very good results in lithological differentiation due to anomalous illumination conditions related to the low solar elevation and the homogeneity in the vegetation cover, specially the grass that becomes dry during this season. Rainy season images, on the other hand, allowed the separation of the lithological types, a fact that can be attributed to a greater differentiation among the geobotanical associations. The muscovite-granite facies with greisenization zones within the Serra da Pedra Branca were mapped. This methodology can be successfully applied to similar known granite bodies elsewhere in the Tin Province of Goias.
Parada, N. D. J.↗
A stopping theoretic approach to minimal time detection of system parameter change
The problem of minimal time detection of abrupt parameter changes in linear stochastic systems considered. The problem is posed as an optimal stopping problem for the detection in change of the induced probability measure. Under the assumption of a prior distribution for the time of change (or disruption) a stopping rule is given which minimizes the average detection delay when there is knowledge of the new measure after the change. When the new induced measure is unknown, a stopping rule is given, based only on the noisy observations and is shown to be better than the a priori knowlege of the disruption time.
Mazumdar, Ravi R.↗
Eigenstructure assignment approach for structural damage detection
In this work, a methodology for incorporating measured modal data into an existing refined finite element model is examined with the objective of detecting and locating structural damage. The algorithm is based on the partial inverse problem, in that only partial spectral information is required. The technique utilizes a symmetric eigenstructure assignment algorithm to perform the partial spectral assignment. Algorithms to enhance mode shape assignability and to preserve sparsity in the damaged FEM are developed. The sparsity preservation is of particular importance when considering damage detection in trusslike structures. Several examples are presented to highlight the key points made within the paper.
Zimmerman, David C.↗
An inverse problem approach for structural damage detection - Finite element model refinement
In this work, a methodology for incorporating measured modal data into an existing refined finite element model is examined with the objective of detecting and locating structural damage. This same algorithm is also useful in terms of finite element model refinement. The algorithm is based on the partial inverse problem, in that only partial spectral information is required. The technique utilizes a symmetric eigenstructure assignment algorithm to perform the partial spectral assignment. Algorithms to enhance mode shape assignability and to preserve sparsity in the updated model are developed. The sparsity preservation is of particular importance when considering damage detection in truss-like structures. Several examples are presented which highlight the key points made within the paper.
Zimmerman, D. C.↗