309 Matching Results

Search Results

Advanced search parameters have been applied.

Pose and Motion Estimation Using Dual Quaternion-Based Extended Kalman Filtering

Description: A solution to the remote three-dimensional (3-D) measurement problem is presented for a dynamic system given a sequence of two-dimensional (2-D) intensity images of a moving object. The 3-D transformation is modeled as a nonlinear stochastic system with the state estimate providing the six-degree-of-freedom motion and position values as well as structure. The stochastic model uses the iterated extended Kalman filter (IEKF) as a nonlinear estimator and a screw representation of the 3-D transformation based on dual quaternions. Dual quaternions, whose elements are dual numbers, provide a means to represent both rotation and translation in a unified notation. Linear object features, represented as dual vectors, are transformed using the dual quaternion transformation and are then projected to linear features in the image plane. The method has been implemented and tested with both simulated and actual experimental data. Simulation results are provided, along with comparisons to a point-based IEKF method using rotation and translation, to show the relative advantages of this method. Experimental results from testing using a camera mounted on the end effector of a robot arm are also given.
Date: June 1, 1998
Creator: Goddard, J.S. & Abidi, M.A.
Partner: UNT Libraries Government Documents Department

A data-fitting procedure for chaotic time series

Description: In this paper the authors introduce data characterizations for fitting chaotic data to linear combinations of one-dimensional maps (say, of the unit interval) for use in subgrid-scale turbulence models. They test the efficacy of these characterizations on data generated by a chaotically-forced Burgers` equation and demonstrate very satisfactory results in terms of modeled time series, power spectra and delay maps.
Date: October 1, 1998
Creator: McDonough, J.M.; Mukerji, S. & Chung, S.
Partner: UNT Libraries Government Documents Department

Evaluating uncertainty in stochastic simulation models

Description: This paper discusses fundamental concepts of uncertainty analysis relevant to both stochastic simulation models and deterministic models. A stochastic simulation model, called a simulation model, is a stochastic mathematical model that incorporates random numbers in the calculation of the model prediction. Queuing models are familiar simulation models in which random numbers are used for sampling interarrival and service times. Another example of simulation models is found in probabilistic risk assessments where atmospheric dispersion submodels are used to calculate movement of material. For these models, randomness comes not from the sampling of times but from the sampling of weather conditions, which are described by a frequency distribution of atmospheric variables like wind speed and direction as a function of height above ground. A common characteristic of simulation models is that single predictions, based on one interarrival time or one weather condition, for example, are not nearly as informative as the probability distribution of possible predictions induced by sampling the simulation variables like time and weather condition. The language of model analysis is often general and vague, with terms having mostly intuitive meaning. The definition and motivations for some of the commonly used terms and phrases offered in this paper lead to an analysis procedure based on prediction variance. In the following mathematical abstraction the authors present a setting for model analysis, relate practical objectives to mathematical terms, and show how two reasonable premises lead to a viable analysis strategy.
Date: February 1, 1998
Creator: McKay, M.D.
Partner: UNT Libraries Government Documents Department

New techniques for the scientific visualization of three-dimensional multi-variate and vector fields

Description: Volume rendering allows us to represent a density cloud with ideal properties (single scattering, no self-shadowing, etc.). Scientific visualization utilizes this technique by mapping an abstract variable or property in a computer simulation to a synthetic density cloud. This thesis extends volume rendering from its limitation of isotropic density clouds to anisotropic and/or noisy density clouds. Design aspects of these techniques are discussed that aid in the comprehension of scientific information. Anisotropic volume rendering is used to represent vector based quantities in scientific visualization. Velocity and vorticity in a fluid flow, electric and magnetic waves in an electromagnetic simulation, and blood flow within the body are examples of vector based information within a computer simulation or gathered from instrumentation. Understand these fields can be crucial to understanding the overall physics or physiology. Three techniques for representing three-dimensional vector fields are presented: Line Bundles, Textured Splats and Hair Splats. These techniques are aimed at providing a high-level (qualitative) overview of the flows, offering the user a substantial amount of information with a single image or animation. Non-homogenous volume rendering is used to represent multiple variables. Computer simulations can typically have over thirty variables, which describe properties whose understanding are useful to the scientist. Trying to understand each of these separately can be time consuming. Trying to understand any cause and effect relationships between different variables can be impossible. NoiseSplats is introduced to represent two or more properties in a single volume rendering of the data. This technique is also aimed at providing a qualitative overview of the flows.
Date: October 1, 1995
Creator: Crawfis, R.A.
Partner: UNT Libraries Government Documents Department

Physical modeling of traffic with stochastic cellular automata

Description: A new type of probabilistic cellular automaton for the physical description of single and multilane traffic is presented. In this model space, time and the velocity of the cars are represented by integer numbers (as usual in cellular automata) with local update rules for the velocity. The model is very efficient for both numerical simulations and analytical investigations. The numerical results from extensive simulations reproduce very well data taken from real traffic (e.g. fundamental diagrams). Several analytical results for the model are presented as well as new approximation schemes for stationary traffic. In addition the relation to continuum hydrodynamic theory (Lighthill-Whitham) and the follow-the-leader models is discussed. The model is part of an interdisciplinary research program in Northrhine-Westfalia (``NRW Forschungsverbund Verkehrssimulation``) for the construction of a large scale microsimulation model for network traffic, supported by the government of NRW.
Date: September 1, 1995
Creator: Schreckenberg, M. & Nagel, K.
Partner: UNT Libraries Government Documents Department

Collisional stochastic ripple diffusion of alpha particles and beam ions on TFTR

Description: Predictions for ripple loss of fast ions from TFTR are investigated with a guiding center code including both collisional and ripple effects. A synergistic enhancement of fast ion diffusion is found for toroidal field ripple with collisions. The total loss is calculated to be roughly twice the sum of ripple and collisional losses calculated separately. Discrepancies between measurements and calculations of plasma beta at low current and large major radius are resolved when both effects are included for neutral beam ions. A 20--30% reduction in alpha particle heating is predicted for q{sub a} = 6--14, R = 2.6 m DT plasmas on TFTR due to first orbit and collisional stochastic ripple diffusion.
Date: July 1, 1995
Creator: Redi, M.H.; Zarnstorff, M.C.; White, R.B.; Budny, R.V.; Janos, A.C.; Owens, D.K. et al.
Partner: UNT Libraries Government Documents Department

Bayesian approximation of solutions to linear ordinary differential equations

Description: An approach to numerically solving linear ordinary differential equations, based on statistical Bayesian prediction, is described. Preliminary results on the details of choice of correlation parameters and experimental design are given, using first- and second-order example problems. 6 refs., 7 figs.
Date: November 1, 1990
Creator: Herzog, K.J.; Morris, M.D. & Mitchell, T.J.
Partner: UNT Libraries Government Documents Department

Renormalization and plasma physics

Description: A review is given of modern theories of statistical dynamics as applied to problems in plasma physics. The derivation of consistent renormalized kinetic equations is discussed, first heuristically, later in terms of powerful functional techniques. The equations are illustrated with models of various degrees of idealization, including the exactly soluble stochastic oscillator, a prototype for several important applications. The direct-interaction approximation is described in detail. Applications discussed include test particle diffusion and the justification of quasilinear theory, convective cells, E vector x B vector turbulence, the renormalized dielectric function, phase space granulation, and stochastic magnetic fields.
Date: February 1, 1980
Creator: Krommes, J.A.
Partner: UNT Libraries Government Documents Department

The effects of internal fluctuations on a class of nonequilibrium statistical field theories

Description: A class of models with applications to swarm behavior as well as many other types of spatially extended complex biological and physical systems is studied. Internal fluctuations can play an active role in the organization of the phase structure of such systems. In particular, for the class of models studied here the effect of internal fluctuations due to finite size is a renormalized decrease in the temperature near the point of spontaneous symmetry breaking.
Date: January 1, 1993
Creator: Millonas, M.M. (Los Alamos National Lab., NM (United States) Santa Fe Inst., NM (United States))
Partner: UNT Libraries Government Documents Department

Method for determining a stochastic transition

Description: A number of problems in physics can be reduced to the study of a measure-preserving mapping of a plane onto itself. One example is a Hamiltonian system with two degrees of freedom, i.e., two coupled nonlinear oscillators. These are among the simplest deterministic system that can have chaotic solutions. According to a theorem of Kolmogorov, Arnol'd, and Moser, these systems may also have more ordered orbits lying on curves that divide the plane. The existence of each of these orbit types depends sensitively on both the parameters of the problem, and on the initial conditions. The problem addressed in this paper is that of finding when given KAM orbits exist. The guiding hypothesis is that the disappearance of a KAM surface is associated with a sudden change from stability to instability of nearby periodic orbits. The relation between KAM surfaces and periodic orbits have been explored extensively here by the numerical computation of a particular mapping.
Date: November 1, 1978
Creator: Greene, J.M.
Partner: UNT Libraries Government Documents Department

Modeling and generating input processes

Description: This tutorial paper provides information relevant to the selection and generation of stochastic inputs to simulation studies. The primary area considered is multivariate but much of the philosophy at least is relevant to univariate inputs as well. 14 refs.
Date: January 1, 1987
Creator: Johnson, M.E.
Partner: UNT Libraries Government Documents Department

Nonlinear adaptive networks: A little theory, a few applications

Description: We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We than present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series tidal prediction in Venice Lagoon, sonar transient detection, control of nonlinear processes, balancing a double inverted pendulum and design advice for free electron lasers. 26 refs., 23 figs.
Date: January 1, 1990
Creator: Jones, R.D.; Qian, S.; Barnes, C.W.; Bisset, K.R.; Bruce, G.M.; Lee, K. et al.
Partner: UNT Libraries Government Documents Department

Research progress in dynamic security assessment

Description: Areas discussed are power system modeling, state estimation, structure decomposition, state forecasting, clustering and security measure development. A detailed dynamic model of a multi-machine power system has been developed. A process state estimator was developed to estimate the long-term dynamic behavior of the power system. The algorithm is identical to the extended Kalman filter but has a modified process noise driving term. A two-stage structure estimation technique was proposed for identifying the power system network configuration. Two approaches to structure decomposition were investigated. A time-scale decomposition of the system equations, based on a singular perturbation approach, was evaluated using a detailed model of a generating system. Spatial decomposition was examined by applying an optimal network decomposition technique to a 39-bus test system. Stochastic approximation based approaches to estimator simplification were examined. Explicit expressions were obtained for the evolution of the first and second moments of the system state. Research into security measures proceeded in three directions. The first area involves viewing the security assessment problem as a hyperplane crossing problem for a stochastic process. The second approach examined the stability of an unforced linear system where the system coefficients are subject to future jumps. The third area of research has led to the formulation of a security measure suitable for on-line assessment of transient stability.
Date: December 1, 1982
Partner: UNT Libraries Government Documents Department

The behavior of matter under nonequilibrium conditions: Fundamental aspects and applications. Progress Report for period April 15, 1990 - April 14, 1991

Description: Our report contains a brief summary of what has been achieved over the period of the contract. We have studied the behavior of matter under equilibrium conditions on three levels: (1) on the microscopic level in the frame of classical mechanics or of quantum theory; (2) on the stochastic level, which includes fluctuations; and (3) on the phenomenological, macroscopic level described by nonlinear equations. We first report on the level (1), then report on the levels (2) and (3).
Date: December 1, 1990
Creator: Prigogine, I.
Partner: UNT Libraries Government Documents Department

Some non-linear physics in crystallographic structures

Description: A summary of studies on simple but strongly nonlinear crystallographic models that make use of some methods in stochasticity is presented. Two one-dimensional models are described; one has been studied to understand some aspects of the nonlinear dynamics in crystals when close to the transition temperature, the other is for commensurability and incommensurability problems. Periodic orbits and the dynamics of a one-dimensional coupled double-well chain are considered, along with lattice locking and stochasticity. (RWR)
Date: October 1, 1977
Creator: Aubry, S.
Partner: UNT Libraries Government Documents Department

Ray and wave optics of integrable and stochastic systems

Description: The generalization of WKB methods to more than one dimension is discussed in terms of the integrability or non-integrability of the geometrical optics (ray Hamiltonian) system derived in the short-wave approximation. In the two-dimensional case the ray trajectories are either regular or stochastic, and the qualitative differences between these types of motion are manifested in the characteristics of the spectra and eigenfunctions. These are examined for a model system which may be integrable or stochastic, depending on a single parameter.
Date: July 1, 1979
Creator: McDonald, S.W. & Kaufman, A.N.
Partner: UNT Libraries Government Documents Department

Stochastic motion due to a single wave in a magnetoplasma

Description: A single electrostatic wave in a magnetoplasma causes stochastic ion motion in several physically different situations. Various magnetic fields (uniform, tokamak, and mirror) and various propagation angles with respect to the field have been studied. A brief review of this work shows that all situations can be understood using the concept of overlapping resonances. Analytical calculations of the wave amplitude necessary for stochasticity have been carried out in some cases and compared with computer and laboratory experiments. In the case of an axisymmetric mirror field the calculations predict stochastic motion of ions with energy below a threshold that depends weakly on the wave amplitude and on the scale lengths of the magnetic field. Studies with an azimuthally asymmetric field show that the asymmetry causes substantial changes in the motion of some ions.
Date: June 5, 1979
Creator: Smith, G.R.
Partner: UNT Libraries Government Documents Department

Role of statistical linearization in the solution of nonlinear stochastic equations

Description: The solution of a generalized Langevin equation is referred to as a stochastic process. If the external forcing function is Gaussian white noise, the forward Kolmogarov equation yields the transition probability density function. Nonlinear problems must be handled by approximation procedures e.g., perturbation theories, eigenfunction expansions, and nonlinear optimization procedures. After some comments on the first two of these, attention is directed to the third, and the method of statistical linearization is used to demonstrate a relation to the former two. Nonlinear stochastic systems exhibiting sustained or forced oscillations and the centered nonlinear Schroedinger equation in the presence of Gaussian white noise excitation are considered as examples. 5 figures, 2 tables. (RWR)
Date: August 31, 1977
Creator: Budgor, A.B.
Partner: UNT Libraries Government Documents Department

Representative element modeling of fracture systems based on stochastic analysis

Description: An important task associated with reservoir simulation is the development of a technique to model a large number of fractures with a single description. Representative elements must be developed before reservoir scale simulations can adequately address the effects of intersecting fracture systems on fluid migration. An effective element model will sharply reduce the cost and complexity of large scale simulations to bring these to manageable levels. Stochastic analysis is a powerful tool which can determine the hydraulic and transport characteristics of intersecting sets of statistically defined fractures. Hydraulic and transport characteristics are required to develop representative elements. Given an assumption of fully developed laminar flow, the net fracture conductivities and hence flow velocities can be determined from descriptive statistics of fracture spacing, orientation, aperture, and extent. The distribution of physical characteristics about their mean leads to a distribution of the associated conductivities. The variance of hydraulic conductivity induces dispersion into the transport process. The simplest of fracture systems, a single set of parallel fractures, is treated to demonstrate the usefulness of stochastic analysis. Explicit equations for conductivity of an element are developed and the dispersion characteristics are shown. The analysis reveals the dependence of the representative element properties on the various parameters used to describe the fracture system. 10 refs., 3 figs.
Date: January 1, 1986
Creator: Clemo, T.M.
Partner: UNT Libraries Government Documents Department

Uranium accountability for ATR fuel fabrication, a computer simulation

Description: A stochastic computer model has been designed to simulate the material control system used during the production of fuel plates for the Advanced Test Reactor. The model is designed so that manufacturing process and measurement parameters are fed in as input. Changes in the manufacturing process and measurement procedures are easily incorporated. Individual operations in the plant are described by program subroutines. By using this model values for Inventory Difference (ID) and Limit of Error on Inventory Difference (LEID) may be calculated for predetermined plant operating conditions. Furthermore the effect on ID and LEID produced by changing plant operating procedures and measurement technique may also be examined.
Date: January 1, 1979
Creator: Nieschmidt, E B; Dolan, C S; Vegors, Jr, S H & Wagner, Jr, E P
Partner: UNT Libraries Government Documents Department

Multilevel crossing rates for automated signal classification

Description: An investigation was made of multilevel crossing rates as a means of time series analysis of random signals. Pattern recognition techniques based on the Mahalanobis distance were implemented as a means of evaluating the discriminating power of level crossings. Measurement of multilevel crossing rates was found to be an easily implementable means for detection of changes in general frequency content. Level crossing analysis was also shown to be applicable for the study of conductivity measurements of two-phase flow of air and water, where knowledge of the relationship between amplitude and frequency was beneficial in characterizing the process.
Date: January 1, 1978
Creator: Mitchell, R.J. & Gonzalez, R.C.
Partner: UNT Libraries Government Documents Department

A stochastic learning algorithm for layered neural networks

Description: The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.
Date: December 31, 1992
Creator: Bartlett, E. B. & Uhrig, R. E.
Partner: UNT Libraries Government Documents Department

Limit theorem for the maximum of an exponential autoregressive process. Technical report No. 14

Description: The asymptotic behavior of the maximum of a particular autoregressive process is discussed. The process was introduced by Gaver and Lewis in 1975 as a generalization of the Poisson process which allows for some dependence in the successive interarrival times. The exact distribution of the maximum of the first two terms and the first three terms in the sequence (denoted by M/sub 1/ and M/sub 2/, respectively) is calculated, and upper and lower bounds are obtained for M/sub n/, the maximum of the first n + 1 terms in the sequence. Loynes in 1965 gave conditions under which a stationary stochastic process has a maximum which behaves in the limit just as for independent identically distributed variables with the marginal distribution of the stationary process. Such conditions are often very difficult to verify in practice. However, for this particular example the joint distribution of the process at time n and n + j + 1 is determined, and the conditions to obtain the limit theorem are verified by use of the Markov property.
Date: September 16, 1977
Creator: Chernick, M
Partner: UNT Libraries Government Documents Department