Approximate calculation of cumulative probability from a moment-generating function
Numerical method for calculating cumulative distribution of positive random variable
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Numerical method for calculating cumulative distribution of positive random variable
Reliable and accurate Delta technique, a nondestructive ultrasonics method, uses redirection of energy to detect randomly oriented imperfections in fusion butt welds. Data on flaws can be read from either an oscilloscope or a printout.
The joint distribution (as n tends to infinity) of the maxima of a sample of n independent observations of a bivariate random variable (X,Y) is studied. A method is developed for deriving the asymptotic distribution of the maxima, assuming that X and Y possess asymptotic extreme-value distributions and that the probability element dF(x,y) can be expanded in a canonical series. Applied both to the bivariate normal distribution and to the bivariate gamma and compound correlated bivariate Poisson distributions, the method shows that maxima from all these distributions are asymptotically uncorrelated.
Critical assessment of two versions of a procedure for calculating the pitch angle diffusion coefficient for cosmic rays in a static random magnetic field using the 'resonance integral' method of Hasselmann and Wiberenz (1968) and Jokipii (1972). One of these versions is shown to represent the physics of the situation more accurately than the other.
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.
A high frequency random vibration analysis, (statistical energy analysis (SEA) method) is examined. The SEA method accomplishes high frequency prediction of arbitrary structural configurations. A general SEA computer program is described. A summary of SEA theory, example problems of SEA program application, and complete program listing are presented.
The vortical evolution of mixing layers subject to various types of forcing is numerically simulated using pseudospectral methods. The effect of harmonic forcing and random noise in the initial conditions is examined with some results compared to experimental data. Spanwise forcing is found to enhance streamwise vorticity in a nonlinear process leading to a slow, secondary growth of the shear layer. The effect of forcing on a chemical reaction is favorably compared with experimental data at low Reynolds numbers. Combining harmonic and subharmonic forcing is shown to both augment and later destroy streamwise vorticity.
Repetition increases throughput and decreases delay. Paper discusses theoretical performance of communication system for land-mobile stations with satellite relay using ALOHA random-access protocol modified for repeated transimssions. Methods and conclusions contribute to general understanding of packet communications in fading channels.
The objective of research in aeroservoelasticity at the NASA Langley Research Center is to enhance the modeling, analysis, and multidisciplinary design methodologies for obtaining multifunction digital control systems for application to flexible flight vehicles. Recent accomplishments are discussed, and a status report on current activities within the Aeroservoelasticity Branch is presented. In the area of modeling, improvements to the Minimum-State Method of approximating unsteady aerodynamics are shown to provide precise, low-order aeroservoelastic models for design and simulation activities. Analytical methods based on Matched Filter Theory and Random Process Theory to provide efficient and direct predictions of the critical gust profile and the time-correlated gust loads for linear structural design considerations are also discussed. Two research projects leading towards improved design methodology are summarized. The first program is developing an integrated structure/control design capability based on hierarchical problem decomposition, multilevel optimization and analytical sensitivities. The second program provides procedures for obtaining low-order, robust digital control laws for aeroelastic applications. In terms of methodology validation and application the current activities associated with the Active Flexible Wing project are reviewed.
The vortical evolution of mixing layers subject to various types of forcing is numerically simulated using pseudospectral methods. The effect of harmonic forcing and random noise in the initial conditions is examined with some results compared to experimental data. Spanwise forcing is found to enhance streamwise vorticity in a nonlinear process leading to a slow, secondary growth of the shear layer. The effect of forcing on a chemical reaction is favorably compared with experimental data at low Reynolds numbers. Combining harmonic and subharmonic forcing is shown to both augment and later destroy streamwise vorticity.
The objective of research in aeroservoelasticity at the NASA Langley Research Center is to enhance the modeling, analysis, and multidisciplinary design methodologies for obtaining multifunction digital control systems for application to flexible flight vehicles. Recent accomplishments are discussed, and a status report on current activities within the Aeroservoelasticity Branch is presented. In the area of modeling, improvements to the Minimum-State Method of approximating unsteady aerodynamics are shown to provide precise, low-order aeroservoelastic models for design and simulation activities. Analytical methods based on Matched Filter Theory and Random Process Theory to provide efficient and direct predictions of the critical gust profile and the time-correlated gust loads for linear structural design considerations are also discussed. Two research projects leading towards improved design methodology are summarized. The first program is developing an integrated structure/control design capability based on hierarchical problem decomposition, multilevel optimization and analytical sensitivities. The second program provides procedures for obtaining low-order, robust digital control laws for aeroelastic applications. In terms of methodology validation and application the current activities associated with the Active Flexible Wing project are reviewed.
BACKGROUND: The extent to which sleep loss may predispose astronauts to a state of altered immunity during extended space travel prompts evaluation with ground-based models. OBJECTIVE: We sought to measure plasma levels of selected cytokines and their receptors, including the putative sleep-regulation proteins soluble TNF-alpha receptor (sTNF-alpha R) I and IL-6, in human subjects undergoing 2 types of sleep deprivation during environmental confinement with performance demands. METHODS: Healthy adult men (n = 42) were randomized to schedules that varied in severity of sleep loss: 4 days (88 hours) of partial sleep deprivation (PSD) involving two 2-hour naps per day or 4 days of total sleep deprivation (TSD). Plasma samples were obtained every 6 hours across 5 days and analyzed by using enzyme-linked immunoassays for sTNF-alpha RI, sTNF-alpha RII, IL-6, soluble IL-2 receptor, IL-10, and TNF-alpha. RESULTS: Interactions between the effects of time and sleep deprivation level were detected for sTNF-alpha RI and IL-6 but not for sTNF-alpha RII, soluble IL-2 receptor, IL-10, and TNF-alpha. Relative to the PSD condition, subjects in the TSD condition had elevated plasma levels of sTNF-alpha RI on day 2 (P =.04), day 3 (P =.01), and across days 2 to 4 of sleep loss (P =.01) and elevated levels of IL-6 on day 4 (P =.04). CONCLUSIONS: Total sleep loss produced significant increases in plasma levels of sTNF-alpha RI and IL-6, messengers that connect the nervous, endocrine, and immune systems. These changes appeared to reflect elevations of the homeostatic drive for sleep because they occurred in TSD but not PSD, suggesting that naps may serve as the basis for a countermeasures approach to prolonged spaceflight.
The gap in explicit resolution of phenomena between global climate models and cloud resolving models is shrinking at a steady pace with global integrations of several km in resolution now practical for at least some time scales. In moving toward finer resolution the long standing problem of convective parameterization is being examined along with the assumption of convective quasi-equilibrium and how this can be reconciled with the stochastic and intermittent nature of convection. In this context we examine the nature of parameterized convection in the NASA Goddard Earth Observing System (GEOS-5) Atmospheric General Circulation Model. Our analysis uses both coarse (2.5 degree) and fine scale 0.25 degree spatial resolution integrations. Two basic formulations are compared: the default option is the Relaxed Arakawa-Schubert (RAS) scheme which invokes a sequence of linearly entraining plumes and quasi-equilibrium closure. An optional modification of this method (the "Stochastic Tokioka" constraint) places a random lower limit on plume entrainment. An alternative representation is the Kain-Fritsch parameterization which was originally developed for mesoscale numerical modeling strategies. Here entrainment is determined by a crude buoyancy sorting approach that allows the plume spectrum to be more responsive to ambient vertical stratification of moisture. Diagnostics of the model behavior are referenced to recent observational evidence of continuous phase transition behavior. In particular we examine the relationship between column water vapor and probablility of convective presence and intensity. Sensitivity of the statistics of convective behavior to parcel mixing/entrainment formulations and parcel initial thermodynamics are considered. Observational statistics from A-Train and TRMM sensors provide validation of the model integrations.
In this paper we have analyzed circumstances under which a rigid particulate sample can behave optically as a true discrete random medium consisting of particles randomly moving relative to each other during measurement. To this end, we applied the numerically exact superposition T-matrix method to model far-field scattering characteristics of fully ordered and quasi-randomly arranged rigid multiparticle groups in fixed and random orientations. We have shown that, in and of itself, averaging optical observables over movements of a rigid sample as a whole is insufficient unless it is combined with a quasi-random arrangement of the constituent particles in the sample. Otherwise, certain scattering effects typical of discrete random media (including some manifestations of coherent backscattering) may not be accurately replicated.
We propose a generative quantum learning algorithm using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum Rényi divergence of order two, an unbounded function that mitigates barren plateaus which inhibit training variational circuits. We benchmark this method against other state-of-the-art adaptive algorithms by learning random two-local thermal states. We perform numerical experiments of up to 12 qubits comparing our method learning algorithms that use linear objective functions and show that Rényi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal Rényi divergence loss function.
Space payloads, such as those which fly on the Space Shuttle in Spacelab, are designed to withstand dynamic loads which consist of combined acoustic random loads and quasi-static acceleration loads. Methods for computing the payload stresses due to these loads are well known and appear in texts and NASA documents, but typically involve approximations such as the Miles' equation, as well as possible adjustments based on "modal participation factors." Alternatively, an existing capability in MSC/NASTRAN may be used to output exact root mean square [rms] stresses due to the random loads for any specified elements in the Finite Element Model. However, it is time consuming to use this methodology to obtain the rms stresses for the complete structural model and then combine them with the quasi-static loading induced stresses. Special processing was developed as described here to perform the stress analysis of all elements in the model using existing MSC/NASTRAN and MSC/PATRAN and UNIX utilities. Fail-safe and buckling analyses applications are also described.
Flowgraph techniques for closed systems, discussing properties, approximation method, topology equation, frequency response, constraints, oscillatory and stochastic processes, etc
A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.