Search NASA⌕ Search

SEARCH · Search NASA

Results for “RANDOM PROCESS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34

Exponential order statistic models of software reliability growth

Failure times of a software reliability growth process are modeled as order statistics of independent, nonidentically distributed exponential random variables. The Jelinsky-Moranda, Goel-Okumoto, Littlewood, Musa-Okumoto Logarithmic, and Power Law models are all special cases of Exponential Order Statistic Models, but there are many additional examples also. Various characterizations, properties and examples of this class of models are developed and presented.

Miller, D. R.↗

A nonlinear filtering process diagnostic system for the Space Station

A nonlinear filtering process diagnostic system, terrestrial simulation and real time implementation studies is presented. Possible applications to Space Station subsystem elements are discussed. A process diagnostic system using model based nonlinear filtering for systems with random structure was shown to provide improvements in stability, robustness, and overall performance in comparison to linear filter based systems. A suboptimal version of the nonlinear filter (zero order approximation filter, or ZOA filter) was used in simulation studies, initially, with a pressurized water reactor model and then with water/steam heat exchanger models. Finally, a real time implementation for leak detection in a water/steam heat exchanger was conducted using the ZOA filter and heat exchanger models.

Yoel, Raymond R.↗

Minimizing distortion in truss structures - A Hopfield network solution

Distortions in truss structures can result from random errors in element lengths that are typical of a manufacturing process. These distortions may be minimized by an optimal selection of elements from those available for placement between the prescribed nodes - a combinatorial optimization problem requiring significant investment of computational resource for all but the smallest problems. The present paper describes a formulation in which near-optimal element assignments are obtained as minimum-energy stable states, of an analogous Hopfield neural network. This requires mapping of the optimization problem into an energy function of the appropriate Liapunov form. The computational architecture is ideally suited to a parallel processor implementation and offers significant savings in computational effort. A numerical implementation of the approach is discussed with reference to planar truss problems.

Fu, B.↗

Fuzzy self-learning control for magnetic servo system

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Tarn, J. H.↗

Investigation of Allan variance for determining noise spectral forms with application to microwave radiometry

An investigation of the Allan variance method as a possible means for characterizing fluctuations in radiometric noise diodes has been performed. The goal is to separate fluctuation components into white noise, flicker noise, and random-walk noise. The primary means is by discrete-time processing, and the study focused primarily on the digital processes involved. Noise satisfying the requirements was generated by direct convolution, fast Fourier transformation (FFT) processing in the time domain, and FFT processing in the frequency domain. Some of the numerous results obtained are presented along with the programs used in the study.

Stanley, William D.↗

Minimizing distortion in truss structures -- a Hopfield network solution

Distortions in truss structures can result from random errors in elemental lengths that are typical of a manufacturing process. These distortions may be minimized by an optimal selection of elements from those available for placement between the prescribed nodes -- a combinatorial optimization problem requiring significant investment of computational resource for all but the smallest problems. The present paper describes a formulation in which near-optimal element assignments are obtained as minimum energy, stable states, of an analogous Hopfield neural network. This requires mapping of the optimization problem into an energy function of the appropriate Lyapunov form. The computational architecture is ideally suited to a parallel processor implementation and offers significant savings in computational effort. A numerical implementation of the approach is discussed with reference to planar truss problems.

Fu, B.↗

Uncertainty and Sensitivity Analyses of a Two-Parameter Impedance Prediction Model

This paper presents comparisons of predicted impedance uncertainty limits derived from Monte-Carlo-type simulations with a Two-Parameter (TP) impedance prediction model and measured impedance uncertainty limits based on multiple tests acquired in NASA Langley test rigs. These predicted and measured impedance uncertainty limits are used to evaluate the effects of simultaneous randomization of each input parameter for the impedance prediction and measurement processes. A sensitivity analysis is then used to further evaluate the TP prediction model by varying its input parameters on an individual basis. The variation imposed on the input parameters is based on measurements conducted with multiple tests in the NASA Langley normal incidence and grazing incidence impedance tubes; thus, the input parameters are assigned uncertainties commensurate with those of the measured data. These same measured data are used with the NASA Langley impedance measurement (eduction) processes to determine the corresponding measured impedance uncertainty limits, such that the predicted and measured impedance uncertainty limits (95% confidence intervals) can be compared. The measured reactance 95% confidence intervals encompass the corresponding predicted reactance confidence intervals over the frequency range of interest. The same is true for the confidence intervals of the measured and predicted resistance at near-resonance frequencies, but the predicted resistance confidence intervals are lower than the measured resistance confidence intervals (no overlap) at frequencies away from resonance. A sensitivity analysis indicates the discharge coefficient uncertainty is the major contributor to uncertainty in the predicted impedances for the perforate-over-honeycomb liner used in this study. This insight regarding the relative importance of each input parameter will be used to guide the design of experiments with test rigs currently being brought on-line at NASA Langley.

Jones, M. G.↗

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗

Software reliability report

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Unfortunately, the models appear to be unable to account for the random nature of the data. If the same code is debugged multiple times and one of the models is used to make predictions, intolerable variance is observed in the resulting reliability predictions. It is believed that data replication can remove this variance in lab type situations and that it is less than scientific to talk about validating a software reliability model without considering replication. It is also believed that data replication may prove to be cost effective in the real world, thus the research centered on verification of the need for replication and on methodologies for generating replicated data in a cost effective manner. The context of the debugging graph was pursued by simulation and experimentation. Simulation was done for the Basic model and the Log-Poisson model. Reasonable values of the parameters were assigned and used to generate simulated data which is then processed by the models in order to determine limitations on their accuracy. These experiments exploit the existing software and program specimens which are in AIR-LAB to measure the performance of reliability models.

Wilson, Larry↗

Proceedings of the First NASA Formal Methods Symposium

Topics covered include: Model Checking - My 27-Year Quest to Overcome the State Explosion Problem; Applying Formal Methods to NASA Projects: Transition from Research to Practice; TLA+: Whence, Wherefore, and Whither; Formal Methods Applications in Air Transportation; Theorem Proving in Intel Hardware Design; Building a Formal Model of a Human-Interactive System: Insights into the Integration of Formal Methods and Human Factors Engineering; Model Checking for Autonomic Systems Specified with ASSL; A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process; Software Model Checking Without Source Code; Generalized Abstract Symbolic Summaries; A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing; Component-Oriented Behavior Extraction for Autonomic System Design; Automated Verification of Design Patterns with LePUS3; A Module Language for Typing by Contracts; From Goal-Oriented Requirements to Event-B Specifications; Introduction of Virtualization Technology to Multi-Process Model Checking; Comparing Techniques for Certified Static Analysis; Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder; jFuzz: A Concolic Whitebox Fuzzer for Java; Machine-Checkable Timed CSP; Stochastic Formal Correctness of Numerical Algorithms; Deductive Verification of Cryptographic Software; Coloured Petri Net Refinement Specification and Correctness Proof with Coq; Modeling Guidelines for Code Generation in the Railway Signaling Context; Tactical Synthesis Of Efficient Global Search Algorithms; Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems; and Formal Methods for Automated Diagnosis of Autosub 6000.

Denney, Ewen↗

Accurate Magnetometer/Gyroscope Attitudes Using a Filter with Correlated Sensor Noise

Magnetometers and gyroscopes have been shown to provide very accurate attitudes for a variety of spacecraft. These results have been obtained, however, using a batch-least-squares algorithm and long periods of data. For use in onboard applications, attitudes are best determined using sequential estimators such as the Kalman filter. When a filter is used to determine attitudes using magnetometer and gyroscope data for input, the resulting accuracy is limited by both the sensor accuracies and errors inherent in the Earth magnetic field model. The Kalman filter accounts for the random component by modeling the magnetometer and gyroscope errors as white noise processes. However, even when these tuning parameters are physically realistic, the rate biases (included in the state vector) have been found to show systematic oscillations. These are attributed to the field model errors. If the gyroscope noise is sufficiently small, the tuned filter 'memory' will be long compared to the orbital period. In this case, the variations in the rate bias induced by field model errors are substantially reduced. Mistuning the filter to have a short memory time leads to strongly oscillating rate biases and increased attitude errors. To reduce the effect of the magnetic field model errors, these errors are estimated within the filter and used to correct the reference model. An exponentially-correlated noise model is used to represent the filter estimate of the systematic error. Results from several test cases using in-flight data from the Compton Gamma Ray Observatory are presented. These tests emphasize magnetometer errors, but the method is generally applicable to any sensor subject to a combination of random and systematic noise.

Sedlak, J.↗

Real-time onboard geometric image correction

A system to perform real time onboard geometric connection of LANDSAT D resolution satellite imagery is described. System requirements, algorithms, sensors, and other hardware components are defined. Feasibility of implementing the correction process is demonstrated using Kalman filter techniques to incorporate information from onboard ephemeris, attitude control, and ground control points. Random access sensor systems, such as charge injected devices and charge coupled devices are used to obtain pixel values at desired ground location, thus greatly reducing the data processing requirements.

Discenza, W.↗

Seed classification with random forest models

Premise: To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators. Methods and Results: Seeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species-classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species. Conclusions: This protocol is an adaptable tool for efficient and consistent identification of seed species or potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large-scale monitoring projects in conservation biology.

59 BASIC BIOLOGICAL SCIENCES↗

Statistical modeling of software reliability

This working paper discusses the statistical simulation part of a controlled software development experiment being conducted under the direction of the System Validation Methods Branch, Information Systems Division, NASA Langley Research Center. The experiment uses guidance and control software (GCS) aboard a fictitious planetary landing spacecraft: real-time control software operating on a transient mission. Software execution is simulated to study the statistical aspects of reliability and other failure characteristics of the software during development, testing, and random usage. Quantification of software reliability is a major goal. Various reliability concepts are discussed. Experiments are described for performing simulations and collecting appropriate simulated software performance and failure data. This data is then used to make statistical inferences about the quality of the software development and verification processes as well as inferences about the reliability of software versions and reliability growth under random testing and debugging.

Miller, Douglas R.↗

Process Produces Low-Secondary-Electron-Emission Surfaces

Textured carbon layer applied to copper by sputtering. Carbon surface characterized by dense, random array of needle-like spires or peaks that extend perpendicularly from local copper surface. Spires approximately 7 micrometers in height and spaced approximately 3 micrometers apart, on average. Copper substrate essentially completely covered by carbon layer, is tenacious and not damaged by vibration loadings representative of multistage depressed collector (MDC) applications. Process developed primarily to provide extremely low-secondary-electron-emission surface for copper for use as highefficiency electrodes in MDC's for microwave amplifier traveling-wave tubes (TWT's). Tubes widely used in space communications, aircraft, and terrestrial applications.

Curren, A. N.↗

Non-Genomic Origins of Proteins and Metabolism

It is proposed that evolution of inanimate matter to cells endowed with a nucleic acid- based coding of genetic information was preceded by an evolutionary phase, in which peptides not coded by nucleic acids were able to self-organize into networks capable of evolution towards increasing metabolic complexity. Recent findings that truly different, simple peptides (Keefe and Szostak, 2001) can perform the same function (such as ATP binding) provide experimental support for this mechanism of early protobiological evolution. The central concept underlying this mechanism is that the reproduction of cellular functions alone was sufficient for self-maintenance of protocells, and that self- replication of macromolecules was not required at this stage of evolution. The precise transfer of information between successive generations of the earliest protocells was unnecessary and, possibly, undesirable. The key requirement in the initial stage of protocellular evolution was an ability to rapidly explore a large number of protein sequences in order to discover a set of molecules capable of supporting self- maintenance and growth of protocells. Undoubtedly, the essential protocellular functions were carried out by molecules not nearly as efficient or as specific as contemporary proteins. Many, potentially unrelated sequences could have performed each of these functions at an evolutionarily acceptable level. As evolution progressed, however proteins must have performed their functions with increasing efficiency and specificity. This, in turn, put additional constraints on protein sequences and the fraction of proteins capable of performing their functions at the required level decreased. At some point, the likelihood of generating a sufficiently efficient set of proteins through a non-coded synthesis was so small that further evolution was not possible without storing information about the sequences of these proteins. Beyond this point, further evolution required coupling between proteins and informational polymers that is characteristic to all known forms of life. The emergence of such coupling must be postulated in any scenario of the origin of life, no matter whether it starts with RNA or proteins. To examine the evolutionary potential of non-genomic systems, a simple, computationally tractable model, which is still capable of capturing the essential features of the real system, has been studied computationally. Both constructive and destructive processes have been introduced into the model in a stochastic manner. Instead of assuming random reaction sets, only a suite of protobiologically plausible reactions has been considered. Peptides have been explicitly considered as protoenzymes and their catalytic efficiencies have been assigned on the basis of biochemical principles and experimental estimates. Simulations have been carried out using a novel approach (The Next Reaction Method) that is appropriate even for very low concentrations of reactants. Studies have focused on global autocatalytic processes and their diversity.

Pohorille, Andrew↗

Random field estimation approach to robot dynamics

The difference equations of Kalman filtering and smoothing recursively factor and invert the covariance of the output of a linear state-space system driven by a white-noise process. Here it is shown that similar recursive techniques factor and invert the inertia matrix of a multibody robot system. The random field models are based on the assumption that all of the inertial (D'Alembert) forces in the system are represented by a spatially distributed white-noise model. They are easier to describe than the models based on classical mechanics, which typically require extensive derivation and manipulation of equations of motion for complex mechanical systems. With the spatially random models, more primitive locally specified computations result in a global collective system behavior equivalent to that obtained with deterministic models. The primary goal of applying random field estimation is to provide a concise analytical foundation for solving robot control and motion planning problems.

Rodriguez, Guillermo↗

Tough, processable semi-interpenetrating polymer networks from monomer reactants

A high temperature semi-interpenetrating polymer network (semi-IPN) was developed which had significantly improved processability, damage tolerance, and mechanical performance, when compared to the commercial Thermid materials. This simultaneous semi-IPN was prepared by mixing the monomer precursors of Thermid AL-600 (a thermoset) and NR-150B2 (a thermoplastic) and allowing the monomers to react randomly upon heating. This reaction occurs at a rate which decreases the flow and broadens the processing window. Upon heating at a higher temperature, there is an increase in flow. Because of the improved flow properties, broadened processing window and enhanced toughness, high strength polymer matrix composites, adhesives and molded articles can now be prepared from the acetylene end-capped polyimides which were previously inherently brittle and difficult to process.

Pater, Ruth H.↗