Techniques for the Fast Simulation of Models of Highly dependable Systems

With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary Simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and nonMarkov models of highly dependable system

Numerical Computing and Graphics for the Power Method Transformation Using Mathematica

This paper provides the requisite information and description of software that perform numerical computations and graphics for the power method polynomial transformation. The software developed is written in the Mathematica 5.2 package PowerMethod.m and is associated with fifth-order polynomials that are used for simulating univariate and multivariate non-normal distributions. The package is flexible enough to allow a user the choice to model theoretical pdfs, empirical data, or a user's own selected distribution(s). The primary functions perform the following (a) compute standardized cumulants and polynomial coefficients, (b) ensure that polynomial transformations yield valid pdfs, and (c) graph power method pdfs and cdfs. Other functions compute cumulative probabilities, modes, trimmed means, intermediate correlations, or perform the graphics associated with fitting power method pdfs to either empirical or theoretical distributions. Numerical examples and Monte Carlo results are provided to demonstrate and validate the use of the software package. The notebook Demo.nb is also provided as a guide for user of the power method.

A Versatile workbench simulator: Five-phase inverter and PMa-SynRM performance evaluation

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Thispaperpresents the design and structure of aversatileworkbench simulator forevaluating the performance of a five-phase inverter andPermanent Magnet assisted Synchronous Reluctance Motor(PMa-SynRM). The simulatorallows for adding variations tothe modulationtechniques, changingthe inverter structure’s semiconductordevice, and calculatingtheinverter’spower losses. Itcanalso facilitate observingthe current, voltage,andthe jointtemperature ofthe semiconductors devices. Furthermore,wecanobtain a perform that is close to anactualPMa-SynRM, dependingon the desired conditionsof speed and torque. The workbench simulator wasdevelopedby combining three software: Matlab/Simulink, PLECSand Altair Flux.Postprint (author's final draft

Luminosity Distributions within Rich Clusters - II: Demonstration and Verification via Simulation

We present detailed simulations of long exposure CCD images. The simulations are used to explore the validity of the statistical method for reconstructing the luminosity distribution of galaxies within a rich cluster i.e. by the subtraction of field number-counts from those of a sight-line through the cluster. In particular we use the simulations to establish the reliability of our observational data presented in Paper 3. Based on our intended CCD field-of-view (6.5 by 6.5 arcmins) and a 1-sigma detection limit of 26 mags per sq arcsecond, we conclude that the luminosity distribution can be robustly determined over a wide range of absolute magnitude (-23 < M_{R} < -16) provided: (a) the cluster has an Abell richness 1.5 or greater, (b) the cluster's redshift lies in the range 0.1 < z < 0.3, (c) the seeing is better than FWHM 1.25'' and (d) the photometric zero points are accurate to within Delta m = \pm 0.12. If these conditions are not met then the recovered luminosity distribution is unreliable and potentially grossly miss-leading. Finally although the method clearly has limitations, within these limitations the technique represents an extremely promising probe of galaxy evolution and environmental dependencies.Comment: 24 pages, 8 figures accepted for publication in MNRAS also available from http://star-www.st-and.ac.uk/~spd3/bib.htm

Episodic future thinking in generalized anxiety disorder

Research on future-oriented cognition in generalized anxiety disorder (GAD) has primarily focused on worry, while less is known about the role of episodic future thinking (EFT), an imagery-based cognitive process. To characterize EFT in this disorder, we used the experimental recombination procedure, in which 21 GAD and 19 healthy participants simulated positive, neutral and negative novel future events either once or repeatedly, and rated their phenomenological experience of EFT. Results showed that healthy controls spontaneously generated more detailed EFT over repeated simulations. Both groups found EFT easier to generate after repeated simulations, except when GAD participants simulated positive events. They also perceived higher plausibility of negative-not positive or neutral-future events than did controls. These results demonstrate a negativity bias in GAD individuals' episodic future cognition, and suggest their relative deficit in generating vivid EFT. We discuss implications for the theory and treatment of GAD.R01 MH060941 - NIMH NIH HHS; R01 MH078308 - NIMH NIH HHS; R01AG08441 - NIA NIH HHS; R01 AT007257 - NCCIH NIH HHS; R01MH60941 - NIMH NIH HHS; R01 AG008441 - NIA NIH HHS; R34 MH099311 - NIMH NIH HHS; R21MH102646 - NIMH NIH HHS; R01AT007257 - NCCIH NIH HHS; R21 MH102646 - NIMH NIH HHS; R34MH078308 - NIMH NIH HH

The Transmuted Weibull-Pareto Distribution

A new generalization of the Weibull-Pareto distribution called the transmuted Weibull-Pareto distribution is proposed and studied. Various mathematical properties of this distribution including ordinary and incomplete moments, quantile and generating functions, Bonferroni and Lorenz curves and order statistics are derived. The method of maximum likelihood is used for estimating the model parameters. The flexibility of the new lifetime model is illustrated by means of an application to a real data set

Probabilistic computing with future deep sub-micrometer devices: a modelling approach

An approach is described that investigates the potential of probabilistic "neural" architectures for computation with deep sub-micrometer (DSM) MOSFETs. Initially, noisy MOSFET models are based upon those for a 0.35 /spl mu/m MOS technology with an exaggerated 1/f characteristic. We explore the manifestation of the 1/f characteristic at the output of a 2-quadrant multiplier when the key n-channel MOSFETs are replaced by "noisy" MOSFETs. The stochastic behavior of this noisy multiplier has been mapped on to a software (Matlab) model of a continuous restricted Boltzmann machine (CRBM) - an analogue-input stochastic computing structure. Simulation of this DSM CRBM implementation shows little degradation from that of a "perfect" CRBM. This paper thus introduces a methodology for a form of "technology-downstreaming" and highlights the potential of probabilistic architectures for DSM computation

Aeronautical Engineering: A special bibliography, supplement 60

This bibliography lists 284 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1975