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This brief presentation provides a timeline for the steps taken by an unmanned aerial system (UAS) pilot when maneuvering to avoid a conflicting aircraft. This talk also provides time estimates for each step in the timeline utilizing 'measured response' data from previous Human Systems Integration Division simulation research.
Description of a variable step incremental procedure for the solution of nonlinear equations in finite element structural analysis. The proposed procedure is effective in improving the accuracy of the basic incremental technique and in providing, in addition, an accurate estimate of the discretization error. The proposed approach is highly appropriate for solving problems for which the user has no a priori estimate of the step size to use.
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The El Nino Southern Oscillation is the dominant year-to-year mode of global climate variability. El Nino effects on terrestrial carbon cycling are mediated by associated climate anomalies, primarily drought, influencing fire emissions and biotic net ecosystem exchange (NEE). Here we evaluate whether El Nino produces a consistent response from the global carbon cycle. We apply a novel bottom-up approach to estimating global NEE anomalies based on FLUXNET data using land cover maps and weather reanalysis. We analyze 13 years (1997-2009) of globally gridded observational NEE anomalies derived from eddy covariance flux data, remotely-sensed fire emissions at the monthly time step, and NEE estimated from an atmospheric transport inversion. We evaluate the overall consistency of biospheric response to El Nino and, more generally, the link between global CO2 flux anomalies and El Nino-induced drought. Our findings, which are robust relative to uncertainty in both methods and time-lags in response, indicate that each event has a different spatial signature with only limited spatial coherence in Amazonia, Australia and southern Africa. For most regions, the sign of response changed across El Nino events. Biotic NEE anomalies, across 5 El Nino events, ranged from -1.34 to +0.98 Pg Cyr(exp -1, whereas fire emissions anomalies were generally smaller in magnitude (ranging from -0.49 to +0.53 Pg C yr(exp -1). Overall drought does not appear to impose consistent terrestrial CO2 flux anomalies during El Ninos, finding large variation in globally integrated responses from 11.15 to +0.49 Pg Cyr(exp -1). Despite the significant correlation between the CO2 flux and El Nino indices, we find that El Nino events have, when globally integrated, both enhanced and weakened terrestrial sink strength, with no consistent response across events
This work presents a methodology for estimating seasonal snow water equivalent (SWE) from the use of remotely sensed Visible and Near Infrared observations from the Landsat mission. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data over the Landsat 5-8 record (1985-2016). The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlations with in situ SWE observations of greater than 0.95. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (32 years), and accuracy provide a unique dataset for investigating snow processes. In particular, this presentation illustrates how the reanalysis dataset was used to provide climatology of the seasonal snowfall accumulation rates, distributions, and variability over the last three decades.
A new Robust Delta Modulator configuration is presented. The system is optimum in the sense of minimizing the mean square error. Under certain conditions it is shown that the optimum system reduces to an enhanced Abate scheme. The optimum system, obtained by Song, uses the polarity of the past two samples and the signal estimates, to obtain the appropriate step size. Since the signal statistics are estimated by the system they are not needed a priori. The system has been constructed and tested. Experimental pictures and voice tapes are presented.
The encoding of video signals using the Song Adaptive Delta Modulator (Song ADM) is discussed. The video signals are characterized as a sequence of pulses having arbitrary height and width. Although the ADM is suited to tracking signals having fast rise times, it was found that the DM algorithm (which permits an exponential rise for estimating an input step) results in a large overshoot and an underdamped response to the step. An overshoot suppression algorithm which significantly reduces the ringing while not affecting the rise time is presented along with formuli for the rise time and the settling time. Channel errors and their effect on the DM encoded bit stream were investigated.
The processing stage in which the restored values of the physical parameters are received is described. The following main steps are discussed: estimation of the state of the telemetry data, processing of the calibration data, and determination of the errors in the data; data decommutation and analysis of the structure of measurement cycles for each instrument; decoding, estimates of the reliability of the restored data, and their agreement with the models adopted for the measurement process; and analysis of errors due to deterministic and random factors. A block diagram of the method is presented.
Likelihood equations determined by the two types of samples which are necessary conditions for a maximum-likelihood estimate are considered. These equations, suggest certain successive-approximations iterative procedures for obtaining maximum-likelihood estimates. These are generalized steepest ascent (deflected gradient) procedures. It is shown that, with probability 1 as N sub 0 approaches infinity (regardless of the relative sizes of N sub 0 and N sub 1, i=1,...,m), these procedures converge locally to the strongly consistent maximum-likelihood estimates whenever the step size is between 0 and 2. Furthermore, the value of the step size which yields optimal local convergence rates is bounded from below by a number which always lies between 1 and 2.
A class of Runge-Kutta formulas is examined which permit the calculation of an accurate solution anywhere in the interval of integration. This is used in a code which seldom has to reject a step; rather it takes a reduced step if the estimated error is too large. The absolute stability implications of this are examined.
Geologists in exploration need to be able to determine the mineral composition of a given outcrop, and then proceed to another in order to carry out the process of geologic mapping. Since April 1984 researchers have been developing a portable microcomputer-based imaging system (with a grey-scale of 16 shades of amber), which were demonstrated during the November 1984 GSA field trip in the field at Yerington, NV. A color-version of the same technology was recently demonstrated. The portable computer selected is a COLBY 10-Megabyte, hard disk-equipped repackaged-IBM/XT, which operates on either 110/220 VAC or on 12VDC from the cigarette lighter in a field vehicle. A COMPAQ PLUS or an IBM Portable will also work on modified software. The underlying concept is that the atmospheric transmission and surface albedo/slope terms are multiplicative, relating the spectral irradiance to the spectral color of the surface materials. Thus, the spectral color of a pixel remains after averaged log-albedo and log-irradiance have been estimated. All these steps can be carried out on the COLBY microcomputer, using 80 image lines of the 128-channel, 12-bit imagery. Results are shown for such an 80-line segment, showing the identification of an O-H bearing mineral group (of slightly varying specific characters) on the flight line.
During this period investigations were carried out in three areas. Optimum energies and structures were estimated for single step ledges formed in the (111) surface of diamond. Binding energies of carbon atoms adsorbed on a (2x1) reconstructed Si(100) surface were calculated as a function of sub-monolayer coverages. Calculations were conducted to analyze the strain dependence of the binding energy of a carbon atom deposited on a (2x1) dimerized Si(100) surface. Results are discussed.
Optimum energies and structures were estimated for single step ledges formed on the diamond (111) surface. Binding energies of carbon atoms adsorbed on a (2x1) reconstructed Si(100) surface were calculated as a function of sub-monolayer coverages. Calculations were conducted to analyze the strain dependence of the binding energy of a carbon adatom deposited on a (2x1) dimerized Si(100) surface. Details and results of these studies are given as appendices.
In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.
This work presents a state-of-the art methodology for constructing snow water equivalent (SWE) reanalysis. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data from the Landsat 5-8 record (1985-2016). The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlation greater than 0.95 compared with in situ SWE observations. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes. This presentation illustrates how the reanalysis dataset was used to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years and ultimately improve real-time streamflow predictions.
NASA's future direct imaging flagship mission will require telescope mirror segmentation to survey a statistically significant population of nearby stellar systems by increasing aperture size beyond that achieved by traditional space-borne monolithic apertures thus far. Segmentation is a path for the size expansion, but it entails a challenge due to the introduction of temporal pupil discontinuities in terms of both phase and amplitude. Modeling of the integrated performance of the coronagraph with the expected dynamical profile of the telescope is an important step toward realistic estimates of the mission's science yields. We present results of a study using the hybrid Lyot coronagraph with a 15-m on-axis, segmented optical telescope assembly (OTA), akin to that of the Large UV / Optical /Infrared (LUVOIR) mission. We have developed a high-fidelity, end-to-end model as part of the Segmented Coronagraph Design and Analysis (SCDA) study that includes diffractive optical propagations at the 1e-10 contrast level across the optical train including the OTA and intermediate re-imaging optics inside the coronagraph instrument. We perform a sensitivity analysis to assess the contrast sensitivity to wavefront perturbations for a set of key telescope terms including segment-level low-order and mid-spatial frequency aberrations and evaluate expected exoplanet yields obtained in the presence of these sets of aberrations. Industry-provided segment-level wavefront aberration datasets involving the following mechanisms are input to the end-to-end model: (1) thermal gradients and accelerations, and (2) telescope dynamics including line-of-sight and segment jitter. Dynamically averaged PSFs in the presence of aberrations are fed into AYO (Altruistic Yield Optimization), a mission design reference simulator, that then assesses the scientific yield sensitivities.
When developing the aerodynamic databases for use in trajectory simulations, it is important to develop a system of metrics to qualify which aerodynamic models are best to use. Since aerodynamics are just one input into trajectory simulations, the results of these simulations do not reflect on the quality of the aerodynamic database used. This means that aerodynamic database comparisons must be done offline. While traditional metrics that focus on mean/nominal predictions are a good first step, more robust estimates of the prediction interval become important as more focused uncertainty models are developed. We explore the limitations of evaluating aerodynamic models based purely on nominal-centered response surfaces. Before elaborating and evaluating metrics based on distributed models, the value of evaluating prediction interval and confidence interval are discussed to conclude that prediction intervals are more relevant to the use of trajectory analysis. Several metrics to evaluate the prediction interval are introduced with a focus on the standard calibration metric. Finally, we compare candidate models using both mean and distributed metrics. A finalized candidate model developed using state of the art machine learning methods is compared to a baseline model developed using traditional aerodynamic database modeling techniques.
A method for estimating the generalized modal coordinates of an aircraft during flight has been developed. The Fiber-optic Sensing System (FOSS) offers an efficient and cost-effective method of measuring the strain at thousands of points along the wings. The estimation of modal coordinates was implemented as a two-step process. First, a maximum likelihood method is used to estimate the statistical properties of the sensors and generalized modal coordinates. Second, the strain mode shapes from the finite element model are used along with the statistical properties from the first step to estimate the generalized modal coordinates over time. Using simulated data from the X-56A Multi-Utility Technology Testbed (MUTT), different methods of modal coordinate estimation were compared to demonstrate the benefits and weaknesses of each. These were compared against the exact solution and an ordinary least squares (a more traditional method) estimate. Modal coordinate estimation methods were then applied to flight-test data from the X-56A aircraft to show that the method continues to work well with actual test data. The new estimation method provides insights unavailable from more classical approaches.