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At least 415 records · Page 23

Satellite remote sensing of primary production in the Sahel using NOAA AVHRR 1981-1988

Field measurements of primary production in semiarid grasslands in three Sahelian countries over a period of 8 years are analyzed in conjunction with multitemporal sums of vegetation indices derived from the NOAA spacecraft's Advanced Very High Resolution Radiometer. The results demonstrate that there is a strong linear relationship between the satellite observations of vegetation indices and the primary production in the range 0-3,000 kg/ha. The confidence intervals of estimation of production were in the range + or - 61-161 kg/ha.

Prince, Stephen D.↗

Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems

A simple numerical procedure for estimating the stochastic robustness of a linear time-invariant system is described. Monte Carlo evaluations of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This analysis approach treats not only Gaussian parameter uncertainties but non-Gaussian cases, including uncertain-but-bounded variation. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. Trivial extensions of the procedure admit consideration of alternate discriminants; thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions can also be estimated. Results are particularly amenable to graphical presentation.

Stengel, Robert F.↗

Review and statistical analysis of the use of ultrasonic velocity for estimating the porosity fraction in polycrystalline materials

A review and statistical analysis of the ultrasonic velocity method for estimating the porosity fraction in polycrystalline materials is presented. Initially, a semiempirical model is developed showing the origin of the linear relationship between ultrasonic velocity and porosity fraction. Then, from a compilation of data produced by many researchers, scatter plots of velocity versus percent porosity data are shown for Al2O3, MgO, porcelain-based ceramics, PZT, SiC, Si3N4, steel, tungsten, UO2,(U0.30Pu0.70)C, and YBa2Cu3O(7-x). Linear regression analysis produces predicted slope, intercept, correlation coefficient, level of significance, and confidence interval statistics for the data. Velocity values predicted from regression analysis of fully-dense materials are in good agreement with those calculated from elastic properties.

Roth, D. J.↗

Variance and bias computations for improved modal identification using ERA/DC

Variance and bias confidence criteria were recently developed for the eigensystem realization algorithm (ERA) identification technique. These criteria are extended for the modified version of ERA based on data correlation, ERA/DC, and also for the Q-Markov cover algorithm. The importance and usefulness of the variance and bias information are demonstrated in numerical studies. The criteria are shown to be very effective not only by indicating the accuracy of the identification results, especially in terms of confidence intervals, but also by helping the ERA user to obtain better results by seeing the effect of changing the sample time, adjusting the Hankel matrix dimension, choosing how many singular values to retain, deciding the model order, etc.

Longman, Richard W.↗

A sensitive method for measuring atmospheric concentrations of sulfur dioxide

A new method for measuring tropospheric sulfur dioxide concentrations is proposed which is based on the mist chamber sampling method. At the present stage of development, the detection limit of the method is approximately 20 parts per trillion for a 45-min sampling time, with lower concentrations detectable with lower precision. The overall reproducibility of the method (+/-95 percent confidence intervals) is estimated at +/-10 percent. The technique is relatively simple, inexpensive, and lightweight, making it ideally suited for numerous field applications in atmospheric chemistry and biogeochemical studies from both ground-based and airborne platforms.

Klemm, O.↗

A statistical model for evaluating GOPEX uplink performance

This article describes a statistical model to analyze the signal intensity received at the solid-state imaging (SSI) camera of the Galileo optical communications system from an Earth-based transmitter (GOPEX) demonstration. The analytical model assumes that the optical beam possesses a Gaussian profile and the communication channel has a log-normal scattering characteristic. The atmospheric-induced jitter is modelled as two independent zero mean Gaussian random variables. By modelling the system parameters as a set of independent and identically distributed (iid) random variables, the combined impact of uncertainties due to system parameters and the turbulent atmosphere can be approximated by a log-normal distributed signal intensity at the spacecraft. A Monte-Carlo software simulation package was also developed to compute the confidence interval probabilities for general optical beam profiles. Numerical results show that the approximation is valid for a wide range of operation scenarios.

Kiesaleh, K.↗

The dynamics of Abell 2634

We have amassed a large sample of velocity data for the cluster of galaxies Abell 2634 which contains the wide-angle tail (WAT) radio source 3C 465. Robust indicators of location and scale and their confidence intervals are used to determine if the cD galaxy, containing the WAT, has a significant peculiar motion. We find a cD peculiar radial velocity of 219 plus or minus 98 km s(exp -1). Further dynamical analyses, including substructure and normality tests, suggest that A 2634 is an unrelaxed cluster whose radio source structure may be bent by the turbulent gas of a recent cluster-subcluster merger.

Pinkney, J.↗

Experimental analysis of computer system dependability

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance.

Iyer, Ravishankar, K.↗

Time to detection of circulating microbubbles as a risk factor for symptoms of altitude decompression sickness

This study investigated the association between time at onset of circulating microbubbles (CMB) and symptoms of altitude decompression sickness (DCS), using Cox proportional hazard regression models. The study population consisted of 125 individuals who participated in direct ascent, simulated extravehicular activities profiles. Using individual CMB status as a time-dependent variable, we found that the hazard for symptoms increased significantly (at the end of 180 min at altitude) in the presence of CMB (Hazard Ratio = 29.59; 95 percent confidence interval (95 percent CI) = 7.66-114.27), compared to no CMB. Further examination was conducted on the subgroup of individuals who developed microbubbles during the test (n = 49), by using Cox regression. Individuals with late onset of CMB (greater than 60 min at altitude) showed a significantly reduced risk of symptoms (hazard ratio = 0.92; 95 percent CI = 0.89-0.95), compared to those with early onset (equal to or less than 60 min), while controlling for other risk factors. We conclude that time to detection of circulating microbubbles is an independent determinant of symptoms of DCS.

Kumar, K. V.↗

Analysis of upper and lower bounds of the frame noise in linear detector arrays

This paper estimates the upper and lower bounds of the frame noise of a linear detector array that uses a one-dimensional scan pattern. Using chi-square distribution, it is analytically shown why it is necessary to use the average of the variances and not the average of the standard deviations to estimate these bounds. Also, a criteria for determining whether any excessively noisy lines exist among the detectors is derived from these bounds. Using a Gaussian standard random variable generator, these bounds are demonstrated to be accurate within the specified confidence interval. A silicon detector array is then used for actual dark current measurements. The criterion developed for determination of noisy detectors is checked on the experimentally obtained data.

Jaggi, S.↗

Confidence bounds on structural reliability

Different approaches for quantifying physical, statistical, and model uncertainties associated with the distribution parameters which are aimed at determining structural reliability are described. Confidence intervals on the distribution parameters of the input random variables are estimated using four algorithms to evaluate uncertainty of the response. Design intervals are evaluated using either Monte Carlo simulation or an iterative approach. A first order approach can be used to compute a first approximation of the design interval, but its accuracy is not satisfactory. The regression approach which combines the iterative approach with Monte Carlo simulation is capable of providing good results if the performance function can be accurately represented using regression analysis. It is concluded that the design interval-based approach seems to be quite general and takes into account distribution and model uncertainties.

Mehta, S. R.↗

A function approximation approach to anomaly detection in propulsion system test data

Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

Whitehead, Bruce A.↗

Risk of decompression sickness in the presence of circulating microbubbles

In this study, we examined the association between microbubbles formed in the circulation from a free gas phase and symptoms of altitude decompression sickness (DCS). In a subgroup of 59 males of mean (S.D) age 31.2 (5.8) years who developed microbubbles during exposure to 26.59 kPa (4.3 psi) under simulated extravehicular activities (EVA), symptoms of DCS occurred in 24 (41 percent) individuals. Spencer grade 1 microbubbles occurred in 4 (7 percent), grade 2 in 9 (15 percent), grade 3 in 15 (25 percent), and grade 4 in 31 (53 percent) of subjects. Survival analysis using Cox proportional hazards regression showed that individuals with less than grade 3 CMB showed 2.46 times (95 percent confidence interval = 1.26 to 5.34) higher risk of symptoms. This information is crucial for defining the risk of DCS for inflight Doppler monitoring under space EVA. Altitude decompression sickness (DCS) occurs when there is acute reduction in ambient pressure. The symptoms of DCS are due to the formation of a free gas phase (in the form of gas microbubbles) in tissues during decompression. Musculo-skeletal pain of bends is the commonest form of DCS in altitude exposures. In the space flight environment, there is a risk of DCS when astronauts decompress from the normobaric shuttle pressure into the hypobaric space suit pressure (currently about 29.65 kPa (4.3 psi) for extra-vehicular activities (EVA). This risk is counterbalanced by a judicious combination of prior denitrogenation and staged decompression. Studies of DCS are limited by the duration of the test at reduced pressure. Since only a proportion of subjects tested develop symptoms, the information on DCS is generally incomplete or 'censored'. Many studies employ Doppler ultrasound monitoring of the precordial area for detecting circulating microbubbles (CMB). Although the association between CMB and bends pain is not causal, CMB are frequently monitored during decompression. In this paper, we examine the association between CMB and symptoms of DCS under simulated EVA profiles.

Kumar, K. Vasantha↗

Optimal experimental designs for the estimation of thermal properties of composite materials

Reliable estimation of thermal properties is extremely important in the utilization of new advanced materials, such as composite materials. The accuracy of these estimates can be increased if the experiments are designed carefully. The objectives of this study are to design optimal experiments to be used in the prediction of these thermal properties and to then utilize these designs in the development of an estimation procedure to determine the effective thermal properties (thermal conductivity and volumetric heat capacity). The experiments were optimized by choosing experimental parameters that maximize the temperature derivatives with respect to all of the unknown thermal properties. This procedure has the effect of minimizing the confidence intervals of the resulting thermal property estimates. Both one-dimensional and two-dimensional experimental designs were optimized. A heat flux boundary condition is required in both analyses for the simultaneous estimation of the thermal properties. For the one-dimensional experiment, the parameters optimized were the heating time of the applied heat flux, the temperature sensor location, and the experimental time. In addition to these parameters, the optimal location of the heat flux was also determined for the two-dimensional experiments. Utilizing the optimal one-dimensional experiment, the effective thermal conductivity perpendicular to the fibers and the effective volumetric heat capacity were then estimated for an IM7-Bismaleimide composite material. The estimation procedure used is based on the minimization of a least squares function which incorporates both calculated and measured temperatures and allows for the parameters to be estimated simultaneously.

Scott, Elaine P.↗

The effect of differences in time to detection of circulating microbubbles on the risk of decompression sickness

Circulating microbubbles (CMB) are frequently detected prior to the appearance of symptoms of Decompression Sickness (DCS). It is difficult to analyze the effect of CMB on symptoms due to differences in the time to detection of CMB. This paper uses survival analysis models to evaluate the risk of symptoms in the presence of CMB. Methods: Information on 81 exposures to an altitude of 6,400 m (6.5 psi) for a period of three hours, with simulated extravehicular activities, was examined. The presence or absence of CMB was included as a time dependent covariate of the Cox proportional hazards regression model. Using this technique, the subgroup of exposures with CMB was analyzed further. Mean (S.D.) time in minutes to onset of CMB and symptoms were 125 (63) and 165 (33) respectively, following the three hours exposure. The risk of symptoms (17/81) increased 14 times in the presence of CMB, after controlling for variations in time to detection of CMB. Further, the risk was lower when time to detection of CMB was greater than 60 minutes (risk ratio = 0.96; 95 percent confidence intervals = 0.94 - 0.99 0.99 P less than 0.01) compared to CMB before 60 minutes at altitude. Conclusions: Survival analysis showed that individual risk of DCS changes significantly due to variations in time to detection of CMB. This information is important in evaluating the risk of DCS in the presence of CMB.

Kumar, K. V.↗

A ruggedness evaluation of procedures for damage threshold testing optical materials

A ruggedness evaluation of approaches to damage threshold testing was performed to determine the influence of three procedural variables on damage threshold data. The differences between the number of test sites evaluated at an applied fluence level (1 site versus 10 sites), the number of laser pulses at each test site (1 pulse versus 200 pulses), and the beam diameter (0.35 mm versus 0.70 mm) were all found to significantly influence the damage threshold data over a 99-percent confidence interval.

Hooker, Matthew W.↗

Reliability analysis in the Office of Safety, Environmental, and Mission Assurance (OSEMA)

The technical personnel in the SEMA office are working to provide the highest degree of value-added activities to their support of the NASA Langley Research Center mission. Management perceives that reliability analysis tools and an understanding of a comprehensive systems approach to reliability will be a foundation of this change process. Since the office is involved in a broad range of activities supporting space mission projects and operating activities (such as wind tunnels and facilities), it was not clear what reliability tools the office should be familiar with and how these tools could serve as a flexible knowledge base for organizational growth. Interviews and discussions with the office personnel (both technicians and engineers) revealed that job responsibilities ranged from incoming inspection to component or system analysis to safety and risk. It was apparent that a broad base in applied probability and reliability along with tools for practical application was required by the office. A series of ten class sessions with a duration of two hours each was organized and scheduled. Hand-out materials were developed and practical examples based on the type of work performed by the office personnel were included. Topics covered were: Reliability Systems - a broad system oriented approach to reliability; Probability Distributions - discrete and continuous distributions; Sampling and Confidence Intervals - random sampling and sampling plans; Data Analysis and Estimation - Model selection and parameter estimates; and Reliability Tools - block diagrams, fault trees, event trees, FMEA. In the future, this information will be used to review and assess existing equipment and processes from a reliability system perspective. An analysis of incoming materials sampling plans was also completed. This study looked at the issues associated with Mil Std 105 and changes for a zero defect acceptance sampling plan.

Kauffmann, Paul J.↗

Cosmic temperature fluctuations from two years of COBE differential microwave radiometers observations

The first two years of Cosmic Background Explorer (COBE) Differential Microwave Radiometers (DMR) observations of the cosmic microwave background (CMB) anisotropy are analyzed and compared with our previously published first year results. The results are consistent, but the addition of the second year of data increases the precision and accuracy detected CMB temperature fluctuations. The 2 yr 53 GHz data are characterized by rms temperature fluctuations of (delta-T)(sub rms) (7 deg) = 44 +/- 7 micro-K and (delta-T)(sub rms) (10 deg) = 30.5 +/- 2.7 micro-K at 7 deg and 10 deg angular resolution, respectively. The 53 x 90 GHz cross-correlation amplitude at zero lag is C(0)(sup 1/2) = 36 +/- 5 micro-K (68% CL) for the unsmoothed (7 deg resolution) DMR data. We perform a likelihood analysis of the cross-correlation function, with Monte Carlo simulations to infer biases of the method, for a power-law model of initial density fluctuations, P(k) proportional to R(exp n). The Monte Carlo simulations indicate that derived estimates of n are biased by +0.11 +/- 0.01, while the subset of simulations with a low quadrupole (as observed) indicate a bias of +0.31+/- 0.04. Derived values for 68% confidence intervals are given corrected (and not corrected) for our estimated biases. Including the quadrupole anisotropy, the most likely quadrupole-normalized amplitude is Q(sub rms-PS) = 14.3(sup + 5.2 sub -3.3) micro-K (12.8(sup + 5.2 sub -3.3) micro-K0 with a spectral index n = 1.42(sup + 0.49 sub -0.55)(n = 1.53(sup + 0.49 sub -0.55). With n fixed to 1.0 the most likely amplitude is 18.2 +/- 11.5 micro-K (17.4 +/- 1.5 micro-K). The marginal likelihood of n is 1.42 +/- 0.37 (1.53 +/- 0.37). Excluding the quadrupole anisotropy, the most likely quadrupole-normalized amplitude is Q(sub rms-PS) = 17.4(sup + 7.5 sub -5.2) micro-K (15.8(sup + 7.5 sub -5.2) micro-K) with a spectral index n = 1.11(sup + 0.60 sub -0.55) (n = 1.22(sup + 0.60 sub -0.55). With n fixed to 1.0 the most likely amplitude is 18.6 +/- 1.6 micro-K (18.2 +/- 1.6 micro-K). The marginal likelihood of n is 1.11 +/- 0.40 (1.22 +/- 0.40). Our best estimate of the dipole from the 2 yr DMR data is 3.363 +/- 0.024 mK toward Galactic coordinates (l, b) = (264.4 deg +/- 0.2 deg, 48.1 deg +/- 0.4 deg), and our best estimate of the rms quadrupole amplitude in our sky is 6 +/- 3 micro-K (68% CL).

Bennett, C. L.↗