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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.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

State Identification for Planetary Rovers: Learning and Recognition

A planetary rover must be able to identify states where it should stop or change its plan. With limited and infrequent communication from ground, the rover must recognize states accurately. However, the sensor data is inherently noisy, so identifying the temporal patterns of data that correspond to interesting or important states becomes a complex problem. In this paper, we present an approach to state identification using second-order Hidden Markov Models. Models are trained automatically on a set of labeled training data; the rover uses those models to identify its state from the observed data. The approach is demonstrated on data from a planetary rover platform.

Aycard, Olivier↗
DOE OSTI2022

LaSDI: Parametric Latent Space Dynamics Identification

Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty quantification, and other various decision-making applications. This paper presents a data-driven framework for parametric latent space dynamics identification procedure that enables fast and accurate simulations. The parametric model is achieved by building a set of local latent space model and designing an interaction among them. An individual local latent space dynamics model achieves accurate solution in a trust region. By letting the set of trust region to cover the whole parameter space, our model shows an increase in accuracy with an increase in training data. Herein we introduce two different types of interaction mechanisms, i.e., point-wise and region-based approach. Both linear and nonlinear data compression techniques are used. We illustrate the framework of Latent Space Dynamics Identification (LaSDI) enable a fast and accurate solution process on various partial differential equations, i.e., Burgers’ equations, radial advection problem, and nonlinear heat conduction problem, achieving 0 (100)x speed-up and 0 (1)% relative error with respect to the corresponding full order models.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Identification of Fixations in Noisy Eye Movements via Recursive Subdivision

When solving problems, multi-person airline crews can choose whether to work together, or to address different aspects of a situation with a divide and conquer strategy. Knowing which of these strategies is most effective may help airlines develop better procedures and training. This paper concentrates on joint attention as a measure of crew coordination. We report results obtained by applying cross recurrence analysis to eye movement data from two-person crews, collected in a flight simulator experiment. The analysis shows that crews exhibit coordinated gaze roughly one sixth of the time, with a tendency for the captain to lead the first officers visual attention. The degree to which crews coordinate their gaze is not significantly correlated with performance ratings assigned by instructors; further research questions and approaches are discussed.

signal processing↗
NASA NTRS2006

NASA Taxonomies for Searching Problem Reports and FMEAs

Many types of hazard and risk analyses are used during the life cycle of complex systems, including Failure Modes and Effects Analysis (FMEA), Hazard Analysis, Fault Tree and Event Tree Analysis, Probabilistic Risk Assessment, Reliability Analysis and analysis of Problem Reporting and Corrective Action (PRACA) databases. The success of these methods depends on the availability of input data and the analysts knowledge. Standard nomenclature can increase the reusability of hazard, risk and problem data. When nomenclature in the source texts is not standard, taxonomies with mapping words (sets of rough synonyms) can be combined with semantic search to identify items and tag them with metadata based on a rich standard nomenclature. Semantic search uses word meanings in the context of parsed phrases to find matches. The NASA taxonomies provide the word meanings. Spacecraft taxonomies and ontologies (generalization hierarchies with attributes and relationships, based on terms meanings) are being developed for types of subsystems, functions, entities, hazards and failures. The ontologies are broad and general, covering hardware, software and human systems. Semantic search of Space Station texts was used to validate and extend the taxonomies. The taxonomies have also been used to extract system connectivity (interaction) models and functions from requirements text. Now the Reconciler semantic search tool and the taxonomies are being applied to improve search in the Space Shuttle PRACA database, to discover recurring patterns of failure. Usual methods of string search and keyword search fall short because the entries are terse and have numerous shortcuts (irregular abbreviations, nonstandard acronyms, cryptic codes) and modifier words cannot be used in sentence context to refine the search. The limited and fixed FMEA categories associated with the entries do not make the fine distinctions needed in the search. The approach assigns PRACA report titles to problem classes in the taxonomy. Each ontology class includes mapping words - near-synonyms naming different manifestations of that problem class. The mapping words for Problems, Entities and Functions are converted to a canonical form plus any of a small set of modifier words (e.g. non-uniformity NOT + UNIFORM.) The report titles are parsed as sentences if possible, or treated as a flat sequence of word tokens if parsing fails. When canonical forms in the title match mapping words, the PRACA entry is associated with the corresponding Problem, Entity or Function in the ontology. The user can search for types of failures associated with types of equipment, clustering by type of problem (e.g., all bearings found with problems of being uneven: rough, irregular, gritty ). The results could also be used for tagging PRACA report entries with rich metadata. This approach could also be applied to searching and tagging failure modes, failure effects and mitigations in FMEAs. In the pilot work, parsing 52K+ truncated titles (the test cases that were available), has resulted in identification of both a type of equipment and type of problem in about 75% of the cases. The results are displayed in a manner analogous to Google search results. The effort has also led to the enrichment of the taxonomy, adding some new categories and many new mapping words. Further work would make enhancements that have been identified for improving the clustering and further reducing the false alarm rate. (In searching for recurring problems, good clustering is more important than reducing false alarms). Searching complete PRACA reports should lead to immediate improvement.

Malin, Jane T.↗
NASA NTRS1971

The topology of the regularized integral surfaces of the 3-body problem

Momentum, angular momentum, and energy of integral surfaces in the planar three-body problem are considered. The end points of orbits which cross an isolating block are identified. It is shown that this identification has a unique extension to an identification which pairs the end points of orbits entering the block and which end in a binary collision with the end points of orbits leaving the block and which come from a binary collision. The problem of regularization is that of showing that the identification of the end points of crossing orbits has a continuous, unique extension. The regularized phase space for the three-body problem was obtained, as were regularized integral surfaces for the problem on which the three-body equations of motion induce flows. Finally the topology of these surfaces is described.

Easton, R.↗
NASA NTRS2005

A least-squares parameter estimation algorithm for switched hammerstein systems with applications to the VOR

A "Multimode" or "switched" system is one that switches between various modes of operation. When a switch occurs from one mode to another, a discontinuity may result followed by a smooth evolution under the new regime. Characterizing the switching behavior of these systems is not well understood and, therefore, identification of multimode systems typically requires a preprocessing step to classify the observed data according to a mode of operation. A further consequence of the switched nature of these systems is that data available for parameter estimation of any subsystem may be inadequate. As such, identification and parameter estimation of multimode systems remains an unresolved problem. In this paper, we 1) show that the NARMAX model structure can be used to describe the impulsive-smooth behavior of switched systems, 2) propose a modified extended least squares (MELS) algorithm to estimate the coefficients of such models, and 3) demonstrate its applicability to simulated and real data from the Vestibulo-Ocular Reflex (VOR). The approach will also allow the identification of other nonlinear bio-systems, suspected of containing "hard" nonlinearities.

Evaluation Studies↗
NASA NTRSDate not supplied

Differential Mobility Spectrometry on the ISS: Successes and Current Concerns

With the construction of the International Space Station (ISS), crew members moved from the relatively short flights of the Space Shuttle (~ 2 weeks) to missions lasting months to a year. As such, environmental monitoring needs also changed, transitioning from a reliance on archival sampling to a combination of archival sampling and in-flight data collection. In the case of trace volatile organic compound (VOC) monitoring, this in-flight monitoring was performed early on by the Volatile Organic Analyzer (VOA), a gas chromatograph-ion mobility spectrometer, which operated on the ISS for 7 years. This large, fixed-position analyzer was replaced by Air Quality Monitors (AQMs), a pair of small gas chromatograph-differential mobility spectrometers, in the spring of 2013. During their initial half decade of use, and covering multiple sets of units, the AQMs performed well, meeting all of their validation criteria and generally showing excellent accuracy when compared to the now-reduced archival samples. These instruments were also used to perform a survey of the US Operating Segment (US Lab Module, European Columbus Module, and Japanese Pressurized Module), confirming that the atmosphere in this segment is well-mixed. The AQMs were also able to provide important information in contingency situations, such as confirming that no ammonia was present on the ISS following alarms suggesting that the coolant loops had leaked in January 2015. As the fleet of AQMs has aged, though, issues of concern have arisen. These have ranged from pervasive electronics board problems to loss of sensitivity due to carbon dioxide to incorrect identification of compounds. Some of these issues are unavoidable due to the nature of the AQM’s use and preparation prior to flight. However, with the extension of the ISS to 2030 (well past its original 2024 lifetime), the fleet of AQMs will be asked to perform longer, requiring solutions to some of the inherent problems. In this presentation, we will discuss the history of the AQM on the ISS with a focus on more recent results. These results show both the continued usefulness of the AQM for atmospheric monitoring and also the current concerns, including: 1) the incorrect identification of benzene, 2) reporting of elevated isopropanol concentrations, and 3) the loss of the reactant ion peak (RIP) that must be resolved in order for the instrument to continue to serve as the primary trace VOC monitor on the ISS.

W T Wallace↗
NASA NTRSDate not supplied

Differential Mobility Spectrometry on the ISS: Successes and Current Concerns

With the construction of the International Space Station (ISS), crew members moved from the relatively short flights of the Space Shuttle (~ 2 weeks) to missions lasting months to a year. As such, environmental monitoring needs also changed, transitioning from a reliance on archival sampling to a combination of archival sampling and in-flight data collection. In the case of trace volatile organic compound (VOC) monitoring, this in-flight monitoring was performed early on by the Volatile Organic Analyzer (VOA), a gas chromatograph-ion mobility spectrometer, which operated on the ISS for 7 years. This large, fixed-position analyzer was replaced by Air Quality Monitors (AQMs), a pair of small gas chromatograph-differential mobility spectrometers, in the spring of 2013. During their initial half decade of use, and covering multiple sets of units, the AQMs performed well, meeting all of their validation criteria and generally showing excellent accuracy when compared to the now-reduced archival samples. These instruments were also used to perform a survey of the US Operating Segment (US Lab Module, European Columbus Module, and Japanese Pressurized Module), confirming that the atmosphere in this segment is well-mixed. The AQMs were also able to provide important information in contingency situations, such as confirming that no ammonia was present on the ISS following alarms suggesting that the coolant loops had leaked in January 2015. As the fleet of AQMs has aged, though, issues of concern have arisen. These have ranged from pervasive electronics board problems to loss of sensitivity due to carbon dioxide to incorrect identification of compounds. Some of these issues are unavoidable due to the nature of the AQM’s use and preparation prior to flight. However, with the extension of the ISS to 2030 (well past its original 2024 lifetime), the fleet of AQMs will be asked to perform longer, requiring solutions to some of the inherent problems. In this presentation, we will discuss the history of the AQM on the ISS with a focus on more recent results. These results show both the continued usefulness of the AQM for atmospheric monitoring and also the current concerns, including: 1) the incorrect identification of benzene, 2) reporting of elevated isopropanol concentrations, and 3) the loss of the reactant ion peak (RIP) that must be resolved in order for the instrument to continue to serve as the primary trace VOC monitor on the ISS.

William T. Wallace↗
NASA NTRS2024

Harnessing Collaborative Learning Automata to Guide Multi-objective Optimization based Inverse Analysis for Structural Damage Identification

Structural damage identification based on physical models is often transformed into an optimization problem that minimizes the difference between measurement information of structure being monitored and the model prediction in the parametric space. However, the objective function in this context often exhibits multimodality, involving high-dimensional variables due to the reliance on finite element models for damage identification. These features pose challenges to optimization algorithms, where entrapment in local solutions can lead to false positives and false negatives in damage identification. In this research, we propose a reinforcement learning based multi-swarm optimizer to tackle such challenges in pursuit of a small yet diverse solution set that can capture the true damage scenario as one of the solutions. The proposed method leverages the flexibility of the particle swarm optimizer and incorporates novel strategies of metaheuristics to realize targeted improvement. To enable the particle swarm to adaptively select the appropriate search strategy based on the current environment, we adopt the learning automata technique, which sidesteps the need for reward strategy selection that is usually ad hoc at each step of the search. The integration harnesses the automatic learning and self-adaptation capabilities of learning automata, enabling the particles to navigate based on environmental signals. This leads to accumulated probabilities tied to advantageous movements, fostering an adaptive exploration of particles in the search space. The proposed approach is first validated through implementing into benchmark test cases with comparisons. It is then applied to structural damage identification with piezoelectric admittance experimental signals. `The results highlight the capability of the algorithm to identify a small solution set with high accuracy to match the actual damage scenario.

Yang Zhang↗
NASA NTRS1982

An approximation theory for nonlinear partial differential equations with applications to identification and control

Approximation results from linear semigroup theory are used to develop a general framework for convergence of approximation schemes in parameter estimation and optimal control problems for nonlinear partial differential equations. These ideas are used to establish theoretical convergence results for parameter identification using modal (eigenfunction) approximation techniques. Results from numerical investigations of these schemes for both hyperbolic and parabolic systems are given.

Banks, H. T.↗
NASA NTRS1991

Target Detection Using Fractal Geometry

The concepts and theory of fractal geometry were applied to the problem of segmenting a 256 x 256 pixel image so that manmade objects could be extracted from natural backgrounds. The two most important measurements necessary to extract these manmade objects were fractal dimension and lacunarity. Provision was made to pass the manmade portion to a lookup table for subsequent identification. A computer program was written to construct cloud backgrounds of fractal dimensions which were allowed to vary between 2.2 and 2.8. Images of three model space targets were combined with these backgrounds to provide a data set for testing the validity of the approach. Once the data set was constructed, computer programs were written to extract estimates of the fractal dimension and lacunarity on 4 x 4 pixel subsets of the image. It was shown that for clouds of fractal dimension 2.7 or less, appropriate thresholding on fractal dimension and lacunarity yielded a 64 x 64 edge-detected image with all or most of the cloud background removed. These images were enhanced by an erosion and dilation to provide the final image passed to the lookup table. While the ultimate goal was to pass the final image to a neural network for identification, this work shows the applicability of fractal geometry to the problems of image segmentation, edge detection and separating a target of interest from a natural background.

Fuller, J. Joseph↗
DOE OSTI2021

A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems

In this note, we present a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing stochastic distribution control.

42 ENGINEERING↗
DOE OSTI2022

Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments

Here, we present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems; (b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsity-enforcing penalty functions such as $\ell_0$, without needing to utilize a continuation procedure or apply a rounding technique; (c) exhibits much lower computational cost than traditional gradient-based relaxation approaches; and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Detailed interpretation and analysis of selected corn blight watch data sets

A detailed interpretation and analysis of selected corn blight data set was undertaken in order to better define the present capabilities and limitations of agricultural remote multispectral sensing and automatic processing techniques and to establish the areas of investigation needing futher attention in the development of operational survey systems. While the emphasis of this effort was directed toward the detection of various corn blight levels, problems related to the more general task of crop identification were also investigated. Since the analog recognition computer (SPARC) was fully committed to the more routine aspects of processing and since the detailed interpretation and analysis required more in the way of quantitative information, the CDC 1604 digital computer was employed.

Nalepka, R. F.↗
NASA NTRS1972

System identification computational considerations.

System identification methods may be accurately characterized as inverse computational problems. Three approaches to the computational problem are considered. Equation error methods are based on regression analysis techniques. Attention is given to the maximum conditional likelihood estimate and the maximum unconditional likelihood (Bayesian) estimate. Gradient dependent algorithms for solving the minimization problems are discussed, taking into account Gaussian methods, quadratic model methods, and the homogeneous function model method of Jacobson and Oksman. Some advanced methods are also examined.

Bowles, R. L.↗
NASA NTRS1980

Solar limb emission lines near Ca II H & K and their spatial intensity variations

The paper employs solar observations of high spatial and spectral resolution to identify emission lines seen in the extended wings of Ca II H & K near the solar limb. Emission lines in the wings of H & K represent valuable diagnostics of the atmospheres of cool stars, with a varying information content which depends on their particular formation mechanism. In solar spectrograms different emission line formation mechanisms can be distinguished by the character of the spatial intensity variation (SIV) apparent in the lines. Various classes of H & K emission features, their spatial intensity variations and their formation mechanisms (of which some pose further problems) are discussed. A new extended list of line identifications is compiled based on their formation class and compared with other line lists. Evidence is found that stellar luminosity-sensitive lines tend to show large spatial intensity variation on the sun.

Rutten, R. J.↗
NASA NTRS1982

NASA/Army supported noise source/noise reduction programs at Langley

The helicopter noise research related to noise source mechanism identification and reduction includes many of the critical noise problems experienced by the helicopter. These include blade vortex interaction (BVI) noise, broadband turbulence ingestion noise, rotor blade self noise including trailing edge effects, model scale effects evaluations, and to some degree main rotor/tail rotor interaction noise. Issues that arise from this evaluation are; (1) Broadband noise can be a significant contribution to the overall noise problem. (2) Scale model investigations are an effective means of conducting helicopter noise research; however, more model/flight correlation studies are required to develop a high degree of confidence of the use of scale model results in the design process of helicopters. (3) More detailed investigations identifying critical factors affecting the main rotor/tail rotor noise mechanism are required.

Hoad, D. R.↗