Equivalence and Differences between Structural Equation Modeling and State-Space Modeling Techniques

ERIC Educational Resources Information Center

Chow, Sy-Miin; Ho, Moon-ho R.; Hamaker, Ellen L.; Dolan, Conor V.

2010-01-01

State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and differences through analytic comparisons and…

Simulation analysis of photometric data for attitude estimation of unresolved space objects

NASA Astrophysics Data System (ADS)

Du, Xiaoping; Gou, Ruixin; Liu, Hao; Hu, Heng; Wang, Yang

2017-10-01

The attitude information acquisition of unresolved space objects, such as micro-nano satellites and GEO objects under the way of ground-based optical observations, is a challenge to space surveillance. In this paper, a useful method is proposed to estimate the SO attitude state according to the simulation analysis of photometric data in different attitude states. The object shape model was established and the parameters of the BRDF model were determined, then the space object photometric model was established. Furthermore, the photometric data of space objects in different states are analyzed by simulation and the regular characteristics of the photometric curves are summarized. The simulation results show that the photometric characteristics are useful for attitude inversion in a unique way. Thus, a new idea is provided for space object identification in this paper.

A model of head-related transfer functions based on a state-space analysis

NASA Astrophysics Data System (ADS)

Adams, Norman Herkamp

This dissertation develops and validates a novel state-space method for binaural auditory display. Binaural displays seek to immerse a listener in a 3D virtual auditory scene with a pair of headphones. The challenge for any binaural display is to compute the two signals to supply to the headphones. The present work considers a general framework capable of synthesizing a wide variety of auditory scenes. The framework models collections of head-related transfer functions (HRTFs) simultaneously. This framework improves the flexibility of contemporary displays, but it also compounds the steep computational cost of the display. The cost is reduced dramatically by formulating the collection of HRTFs in the state-space and employing order-reduction techniques to design efficient approximants. Order-reduction techniques based on the Hankel-operator are found to yield accurate low-cost approximants. However, the inter-aural time difference (ITD) of the HRTFs degrades the time-domain response of the approximants. Fortunately, this problem can be circumvented by employing a state-space architecture that allows the ITD to be modeled outside of the state-space. Accordingly, three state-space architectures are considered. Overall, a multiple-input, single-output (MISO) architecture yields the best compromise between performance and flexibility. The state-space approximants are evaluated both empirically and psychoacoustically. An array of truncated FIR filters is used as a pragmatic reference system for comparison. For a fixed cost bound, the state-space systems yield lower approximation error than FIR arrays for D>10, where D is the number of directions in the HRTF collection. A series of headphone listening tests are also performed to validate the state-space approach, and to estimate the minimum order N of indiscriminable approximants. For D = 50, the state-space systems yield order thresholds less than half those of the FIR arrays. Depending upon the stimulus uncertainty, a minimum state-space order of 7≤N≤23 appears to be adequate. In conclusion, the proposed state-space method enables a more flexible and immersive binaural display with low computational cost.

Performance and state-space analyses of systems using Petri nets

NASA Technical Reports Server (NTRS)

Watson, James Francis, III

1992-01-01

The goal of any modeling methodology is to develop a mathematical description of a system that is accurate in its representation and also permits analysis of structural and/or performance properties. Inherently, trade-offs exist between the level detail in the model and the ease with which analysis can be performed. Petri nets (PN's), a highly graphical modeling methodology for Discrete Event Dynamic Systems, permit representation of shared resources, finite capacities, conflict, synchronization, concurrency, and timing between state changes. By restricting the state transition time delays to the family of exponential density functions, Markov chain analysis of performance problems is possible. One major drawback of PN's is the tendency for the state-space to grow rapidly (exponential complexity) compared to increases in the PN constructs. It is the state space, or the Markov chain obtained from it, that is needed in the solution of many problems. The theory of state-space size estimation for PN's is introduced. The problem of state-space size estimation is defined, its complexities are examined, and estimation algorithms are developed. Both top-down and bottom-up approaches are pursued, and the advantages and disadvantages of each are described. Additionally, the author's research in non-exponential transition modeling for PN's is discussed. An algorithm for approximating non-exponential transitions is developed. Since only basic PN constructs are used in the approximation, theory already developed for PN's remains applicable. Comparison to results from entropy theory show the transition performance is close to the theoretic optimum. Inclusion of non-exponential transition approximations improves performance results at the expense of increased state-space size. The state-space size estimation theory provides insight and algorithms for evaluating this trade-off.

Phase space flow of particles in squeezed states

NASA Technical Reports Server (NTRS)

Ceperley, Peter H.

1994-01-01

The manipulation of noise and uncertainty in squeezed states is governed by the wave nature of the quantum mechanical particles in these states. This paper uses a deterministic model of quantum mechanics in which real guiding waves control the flow of localized particles. This model will be used to examine the phase space flow of particles in typical squeezed states.

Discrete-state representation of ion permeation coupled to fast gating in a model of ClC chloride channels: comparison to multi-ion continuous space Brownian dynamics simulations.

PubMed

Coalson, Rob D; Cheng, Mary Hongying

2010-01-28

A discrete-state model of chloride ion motion in a ClC chloride channel is constructed, following a previously developed multi-ion continuous space model of the same system (Cheng, M. H.; Mamonov, A. B.; Dukes, J. W.; Coalson, R. D. J. Phys. Chem. B 2007, 111, 5956) that included a simplistic representation of the fast gate in this channel. The reducibility of the many-body continuous space to the eight discrete-state model considered in the present work is examined in detail by performing three-dimensional Brownian dynamics simulations of each allowed state-to-state transition in order to extract the appropriate rate constant for this process, and then inserting the pairwise rate constants thereby obtained into an appropriate set of kinetic master equations. Experimental properties of interest, including the rate of Cl(-) ion permeation through the open channel and the average rate of closing of the fast gate as a function of bulk Cl(-) ion concentrations in the intracellular and extracellular electrolyte reservoirs are computed. Good agreement is found between the results obtained via the eight discrete-state model versus the multi-ion continuous space model, thereby encouraging continued development of the discrete-state model to include more complex behaviors observed experimentally in these channels.

Modeling Hubble Space Telescope flight data by Q-Markov cover identification

NASA Technical Reports Server (NTRS)

Liu, K.; Skelton, R. E.; Sharkey, J. P.

1992-01-01

A state space model for the Hubble Space Telescope under the influence of unknown disturbances in orbit is presented. This model was obtained from flight data by applying the Q-Markov covariance equivalent realization identification algorithm. This state space model guarantees the match of the first Q-Markov parameters and covariance parameters of the Hubble system. The flight data were partitioned into high- and low-frequency components for more efficient Q-Markov cover modeling, to reduce some computational difficulties of the Q-Markov cover algorithm. This identification revealed more than 20 lightly damped modes within the bandwidth of the attitude control system. Comparisons with the analytical (TREETOPS) model are also included.

(3 + 1)-dimensional topological phases and self-dual quantum geometries encoded on Heegaard surfaces

NASA Astrophysics Data System (ADS)

Dittrich, Bianca

2017-05-01

We apply the recently suggested strategy to lift state spaces and operators for (2 + 1)-dimensional topological quantum field theories to state spaces and operators for a (3 + 1)-dimensional TQFT with defects. We start from the (2 + 1)-dimensional TuraevViro theory and obtain a state space, consistent with the state space expected from the Crane-Yetter model with line defects.

The promise of the state space approach to time series analysis for nursing research.

PubMed

Levy, Janet A; Elser, Heather E; Knobel, Robin B

2012-01-01

Nursing research, particularly related to physiological development, often depends on the collection of time series data. The state space approach to time series analysis has great potential to answer exploratory questions relevant to physiological development but has not been used extensively in nursing. The aim of the study was to introduce the state space approach to time series analysis and demonstrate potential applicability to neonatal monitoring and physiology. We present a set of univariate state space models; each one describing a process that generates a variable of interest over time. Each model is presented algebraically and a realization of the process is presented graphically from simulated data. This is followed by a discussion of how the model has been or may be used in two nursing projects on neonatal physiological development. The defining feature of the state space approach is the decomposition of the series into components that are functions of time; specifically, slowly varying level, faster varying periodic, and irregular components. State space models potentially simulate developmental processes where a phenomenon emerges and disappears before stabilizing, where the periodic component may become more regular with time, or where the developmental trajectory of a phenomenon is irregular. The ultimate contribution of this approach to nursing science will require close collaboration and cross-disciplinary education between nurses and statisticians.

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

A Hierarchical Framework for State-Space Matrix Inference and Clustering.

PubMed

Zuo, Chandler; Chen, Kailei; Hewitt, Kyle J; Bresnick, Emery H; Keleş, Sündüz

2016-09-01

In recent years, a large number of genomic and epigenomic studies have been focusing on the integrative analysis of multiple experimental datasets measured over a large number of observational units. The objectives of such studies include not only inferring a hidden state of activity for each unit over individual experiments, but also detecting highly associated clusters of units based on their inferred states. Although there are a number of methods tailored for specific datasets, there is currently no state-of-the-art modeling framework for this general class of problems. In this paper, we develop the MBASIC ( M atrix B ased A nalysis for S tate-space I nference and C lustering) framework. MBASIC consists of two parts: state-space mapping and state-space clustering. In state-space mapping, it maps observations onto a finite state-space, representing the activation states of units across conditions. In state-space clustering, MBASIC incorporates a finite mixture model to cluster the units based on their inferred state-space profiles across all conditions. Both the state-space mapping and clustering can be simultaneously estimated through an Expectation-Maximization algorithm. MBASIC flexibly adapts to a large number of parametric distributions for the observed data, as well as the heterogeneity in replicate experiments. It allows for imposing structural assumptions on each cluster, and enables model selection using information criterion. In our data-driven simulation studies, MBASIC showed significant accuracy in recovering both the underlying state-space variables and clustering structures. We applied MBASIC to two genome research problems using large numbers of datasets from the ENCODE project. The first application grouped genes based on transcription factor occupancy profiles of their promoter regions in two different cell types. The second application focused on identifying groups of loci that are similar to a GATA2 binding site that is functional at its endogenous locus by utilizing transcription factor occupancy data and illustrated applicability of MBASIC in a wide variety of problems. In both studies, MBASIC showed higher levels of raw data fidelity than analyzing these data with a two-step approach using ENCODE results on transcription factor occupancy data.

State space truncation with quantified errors for accurate solutions to discrete Chemical Master Equation

PubMed Central

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-01-01

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEG), we truncate the state space by limiting the total molecular copy numbers in each MEG. We further describe a theoretical framework for analysis of the truncation error in the steady state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of 1) the birth and death model, 2) the single gene expression model, 3) the genetic toggle switch model, and 4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate out theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large number of stochastic networks. PMID:27105653

State Space Truncation with Quantified Errors for Accurate Solutions to Discrete Chemical Master Equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Youfang; Terebus, Anna; Liang, Jie

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEGs), we truncate the state space by limiting the total molecular copy numbers in each MEG. Wemore » further describe a theoretical framework for analysis of the truncation error in the steady-state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of (1) the birth and death model, (2) the single gene expression model, (3) the genetic toggle switch model, and (4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady-state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate our theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large number of stochastic networks.« less

State Space Truncation with Quantified Errors for Accurate Solutions to Discrete Chemical Master Equation

DOE PAGES

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-04-22

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEGs), we truncate the state space by limiting the total molecular copy numbers in each MEG. Wemore » further describe a theoretical framework for analysis of the truncation error in the steady-state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of (1) the birth and death model, (2) the single gene expression model, (3) the genetic toggle switch model, and (4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady-state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate our theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large number of stochastic networks.« less

Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)

NASA Astrophysics Data System (ADS)

Santos, Léonard; Thirel, Guillaume; Perrin, Charles

2018-04-01

In many conceptual rainfall-runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called operator splitting. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall-runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall-runoff models and make the resolution of the representation difficult, are first replaced by a so-called Nash cascade and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

Assessing performance of Bayesian state-space models fit to Argos satellite telemetry locations processed with Kalman filtering.

PubMed

Silva, Mónica A; Jonsen, Ian; Russell, Deborah J F; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F

2014-01-01

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6 ± 5.6 km) was nearly half that of LS estimates (11.6 ± 8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.

Assessing Performance of Bayesian State-Space Models Fit to Argos Satellite Telemetry Locations Processed with Kalman Filtering

PubMed Central

Silva, Mónica A.; Jonsen, Ian; Russell, Deborah J. F.; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F.

2014-01-01

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates. PMID:24651252

Robustness of reduced-order multivariable state-space self-tuning controller

NASA Technical Reports Server (NTRS)

Yuan, Zhuzhi; Chen, Zengqiang

1994-01-01

In this paper, we present a quantitative analysis of the robustness of a reduced-order pole-assignment state-space self-tuning controller for a multivariable adaptive control system whose order of the real process is higher than that of the model used in the controller design. The result of stability analysis shows that, under a specific bounded modelling error, the adaptively controlled closed-loop real system via the reduced-order state-space self-tuner is BIBO stable in the presence of unmodelled dynamics.

Development of a preprototype trace contaminant control system. [for space stations

NASA Technical Reports Server (NTRS)

1977-01-01

The steady state contaminant load model based on shuttle equipment and material test programs, and on the current space station studies was revised. An emergency upset contaminant load model based on anticipated emergency upsets that could occur in an operational space station was defined. Control methods for the contaminants generated by the emergency upsets were established by test. Preliminary designs of both steady state and emergency contaminant control systems for the space station application are presented.

SpaceNet: Modeling and Simulating Space Logistics

NASA Technical Reports Server (NTRS)

Lee, Gene; Jordan, Elizabeth; Shishko, Robert; de Weck, Olivier; Armar, Nii; Siddiqi, Afreen

2008-01-01

This paper summarizes the current state of the art in interplanetary supply chain modeling and discusses SpaceNet as one particular method and tool to address space logistics modeling and simulation challenges. Fundamental upgrades to the interplanetary supply chain framework such as process groups, nested elements, and cargo sharing, enabled SpaceNet to model an integrated set of missions as a campaign. The capabilities and uses of SpaceNet are demonstrated by a step-by-step modeling and simulation of a lunar campaign.

Linear system identification via backward-time observer models

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1993-01-01

This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.

Enriching mission planning approach with state transition graph heuristics for deep space exploration

NASA Astrophysics Data System (ADS)

Jin, Hao; Xu, Rui; Xu, Wenming; Cui, Pingyuan; Zhu, Shengying

2017-10-01

As to support the mission of Mars exploration in China, automated mission planning is required to enhance security and robustness of deep space probe. Deep space mission planning requires modeling of complex operations constraints and focus on the temporal state transitions of involved subsystems. Also, state transitions are ubiquitous in physical systems, but have been elusive for knowledge description. We introduce a modeling approach to cope with these difficulties that takes state transitions into consideration. The key technique we build on is the notion of extended states and state transition graphs. Furthermore, a heuristics that based on state transition graphs is proposed to avoid redundant work. Finally, we run comprehensive experiments on selected domains and our techniques present an excellent performance.

Modeling individual effects in the Cormack-Jolly-Seber Model: A state-space formulation

USGS Publications Warehouse

Royle, J. Andrew

2008-01-01

In population and evolutionary biology, there exists considerable interest in individual heterogeneity in parameters of demographic models for open populations. However, flexible and practical solutions to the development of such models have proven to be elusive. In this article, I provide a state-space formulation of open population capture-recapture models with individual effects. The state-space formulation provides a generic and flexible framework for modeling and inference in models with individual effects, and it yields a practical means of estimation in these complex problems via contemporary methods of Markov chain Monte Carlo. A straightforward implementation can be achieved in the software package WinBUGS. I provide an analysis of a simple model with constant parameter detection and survival probability parameters. A second example is based on data from a 7-year study of European dippers, in which a model with year and individual effects is fitted.

A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model.

PubMed

Feeney, Daniel F; Meyer, François G; Noone, Nicholas; Enoka, Roger M

2017-10-01

Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions. NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal transmission. This is the first application of a deterministic state-space model to represent the discharge characteristics of motor units during voluntary contractions. Copyright © 2017 the American Physiological Society.

Effect of Clustering Algorithm on Establishing Markov State Model for Molecular Dynamics Simulations.

PubMed

Li, Yan; Dong, Zigang

2016-06-27

Recently, the Markov state model has been applied for kinetic analysis of molecular dynamics simulations. However, discretization of the conformational space remains a primary challenge in model building, and it is not clear how the space decomposition by distinct clustering strategies exerts influence on the model output. In this work, different clustering algorithms are employed to partition the conformational space sampled in opening and closing of fatty acid binding protein 4 as well as inactivation and activation of the epidermal growth factor receptor. Various classifications are achieved, and Markov models are set up accordingly. On the basis of the models, the total net flux and transition rate are calculated between two distinct states. Our results indicate that geometric and kinetic clustering perform equally well. The construction and outcome of Markov models are heavily dependent on the data traits. Compared to other methods, a combination of Bayesian and hierarchical clustering is feasible in identification of metastable states.

Personalized State-space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose, and Meal Intake: An Extended Kalman Filter Approach.

PubMed

Wang, Qian; Molenaar, Peter; Harsh, Saurabh; Freeman, Kenneth; Xie, Jinyu; Gold, Carol; Rovine, Mike; Ulbrecht, Jan

2014-03-01

An essential component of any artificial pancreas is on the prediction of blood glucose levels as a function of exogenous and endogenous perturbations such as insulin dose, meal intake, and physical activity and emotional tone under natural living conditions. In this article, we present a new data-driven state-space dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of glucose level, insulin dose, and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman filter (EKF) to estimate time-varying coefficients of the patient-specific state-space model. We evaluate our empirical modeling using (1) the FDA-approved UVa/Padova simulator with 30 virtual patients and (2) clinical data of 5 type 1 diabetic patients under natural living conditions. Compared to a forgetting-factor-based recursive ARX model of the same order, the EKF model predictions have higher fit, and significantly better temporal gain and J index and thus are superior in early detection of upward and downward trends in glucose. The EKF based state-space model developed in this article is particularly suitable for model-based state-feedback control designs since the Kalman filter estimates the state variable of the glucose dynamics based on the measured glucose time series. In addition, since the model parameters are estimated in real time, this model is also suitable for adaptive control. © 2014 Diabetes Technology Society.

Combining Static Analysis and Model Checking for Software Analysis

NASA Technical Reports Server (NTRS)

Brat, Guillaume; Visser, Willem; Clancy, Daniel (Technical Monitor)

2003-01-01

We present an iterative technique in which model checking and static analysis are combined to verify large software systems. The role of the static analysis is to compute partial order information which the model checker uses to reduce the state space. During exploration, the model checker also computes aliasing information that it gives to the static analyzer which can then refine its analysis. The result of this refined analysis is then fed back to the model checker which updates its partial order reduction. At each step of this iterative process, the static analysis computes optimistic information which results in an unsafe reduction of the state space. However we show that the process converges to a fired point at which time the partial order information is safe and the whole state space is explored.

Simple Deterministically Constructed Recurrent Neural Networks

NASA Astrophysics Data System (ADS)

Rodan, Ali; Tiňo, Peter

A large number of models for time series processing, forecasting or modeling follows a state-space formulation. Models in the specific class of state-space approaches, referred to as Reservoir Computing, fix their state-transition function. The state space with the associated state transition structure forms a reservoir, which is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be potentially exploited by the reservoir-to-output readout mapping. The largely "black box" character of reservoirs prevents us from performing a deeper theoretical investigation of the dynamical properties of successful reservoirs. Reservoir construction is largely driven by a series of (more-or-less) ad-hoc randomized model building stages, with both the researchers and practitioners having to rely on a series of trials and errors. We show that a very simple deterministically constructed reservoir with simple cycle topology gives performances comparable to those of the Echo State Network (ESN) on a number of time series benchmarks. Moreover, we argue that the memory capacity of such a model can be made arbitrarily close to the proved theoretical limit.

Parallel State Space Construction for a Model Checking Based on Maximality Semantics

NASA Astrophysics Data System (ADS)

El Abidine Bouneb, Zine; Saīdouni, Djamel Eddine

2009-03-01

The main limiting factor of the model checker integrated in the concurrency verification environment FOCOVE [1, 2], which use the maximality based labeled transition system (noted MLTS) as a true concurrency model[3, 4], is currently the amount of available physical memory. Many techniques have been developed to reduce the size of a state space. An interesting technique among them is the alpha equivalence reduction. Distributed memory execution environment offers yet another choice. The main contribution of the paper is to show that the parallel state space construction algorithm proposed in [5], which is based on interleaving semantics using LTS as semantic model, may be adapted easily to the distributed implementation of the alpha equivalence reduction for the maximality based labeled transition systems.

Space Weather Forecasting and Supporting Research in the USA

NASA Astrophysics Data System (ADS)

Pevtsov, A. A.

2017-12-01

In the United State, scientific research in space weather is funded by several Government Agencies including the National Science Foundation (NSF) and the National Aeronautics and Space Agency (NASA). For civilian and commercial purposes, space weather forecast is done by the Space Weather Prediction Center (SWPC) of the National Oceanic and Atmospheric Administration (NOAA). Observational data for modeling come from the network of groundbased observatories funded via various sources, as well as from the instruments on spacecraft. Numerical models used in forecast are developed in framework of individual research projects. The article provides a brief review of current state of space weather-related research and forecasting in the USA.

State-Space Modeling of Dynamic Psychological Processes via the Kalman Smoother Algorithm: Rationale, Finite Sample Properties, and Applications

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2009-01-01

This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a…

Modeling T-cell activation using gene expression profiling and state-space models.

PubMed

Rangel, Claudia; Angus, John; Ghahramani, Zoubin; Lioumi, Maria; Sotheran, Elizabeth; Gaiba, Alessia; Wild, David L; Falciani, Francesco

2004-06-12

We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. Supplementary data and Matlab computer source code will be made available on the web at the URL given below. http://public.kgi.edu/~wild/LDS/index.htm

Glovebox Integrated Microgravity Isolation Technology (g-LIMIT): A Linearized State-Space Model

NASA Technical Reports Server (NTRS)

Hampton, R. David; Calhoun, Philip C.; Whorton, Mark S.

2001-01-01

Vibration acceleration levels on large space platforms exceed the requirements of many space experiments. The Glovebox Integrated Microgravity Isolation Technology (g-LIMIT) is being built by the NASA Marshall Space Flight Center to attenuate these disturbances to acceptable levels. G-LIMIT uses Lorentz (voice-coil) magnetic actuators to levitate and isolate payloads at the individual experiment/sub-experiment (versus rack) level. Payload acceleration, relative position, and relative orientation measurements are fed to a state-space controller. The controller, in turn, determines the actuator Currents needed for effective experiment isolation. This paper presents the development of an algebraic, state-space model of g-LIMIT, in a form suitable for optimal controller design. The equations are first derived using Newton's Second Law directly, then simplified to a linear form for the purpose of controller design.

Spike-Threshold Variability Originated from Separatrix-Crossing in Neuronal Dynamics

PubMed Central

Wang, Longfei; Wang, Hengtong; Yu, Lianchun; Chen, Yong

2016-01-01

The threshold voltage for action potential generation is a key regulator of neuronal signal processing, yet the mechanism of its dynamic variation is still not well described. In this paper, we propose that threshold phenomena can be classified as parameter thresholds and state thresholds. Voltage thresholds which belong to the state threshold are determined by the ‘general separatrix’ in state space. We demonstrate that the separatrix generally exists in the state space of neuron models. The general form of separatrix was assumed as the function of both states and stimuli and the previously assumed threshold evolving equation versus time is naturally deduced from the separatrix. In terms of neuronal dynamics, the threshold voltage variation, which is affected by different stimuli, is determined by crossing the separatrix at different points in state space. We suggest that the separatrix-crossing mechanism in state space is the intrinsic dynamic mechanism for threshold voltages and post-stimulus threshold phenomena. These proposals are also systematically verified in example models, three of which have analytic separatrices and one is the classic Hodgkin-Huxley model. The separatrix-crossing framework provides an overview of the neuronal threshold and will facilitate understanding of the nature of threshold variability. PMID:27546614

Spike-Threshold Variability Originated from Separatrix-Crossing in Neuronal Dynamics.

PubMed

Wang, Longfei; Wang, Hengtong; Yu, Lianchun; Chen, Yong

2016-08-22

The threshold voltage for action potential generation is a key regulator of neuronal signal processing, yet the mechanism of its dynamic variation is still not well described. In this paper, we propose that threshold phenomena can be classified as parameter thresholds and state thresholds. Voltage thresholds which belong to the state threshold are determined by the 'general separatrix' in state space. We demonstrate that the separatrix generally exists in the state space of neuron models. The general form of separatrix was assumed as the function of both states and stimuli and the previously assumed threshold evolving equation versus time is naturally deduced from the separatrix. In terms of neuronal dynamics, the threshold voltage variation, which is affected by different stimuli, is determined by crossing the separatrix at different points in state space. We suggest that the separatrix-crossing mechanism in state space is the intrinsic dynamic mechanism for threshold voltages and post-stimulus threshold phenomena. These proposals are also systematically verified in example models, three of which have analytic separatrices and one is the classic Hodgkin-Huxley model. The separatrix-crossing framework provides an overview of the neuronal threshold and will facilitate understanding of the nature of threshold variability.

[Application of State Space model in the evaluation of the prevention and control for mumps].

PubMed

Luo, C; Li, R Z; Xu, Q Q; Xiong, P; Liu, Y X; Xue, F Z; Xu, Q; Li, X J

2017-09-10

Objective: To analyze the epidemiological characteristics of mumps in 2012 and 2014, and to explore the preventive effect of the second dose of mumps-containing vaccine (MuCV) in mumps in Shandong province. Methods: On the basis of certain model assumptions, a Space State model was formulated. Iterated Filter was applied to the epidemic model to estimate the parameters. Results: The basic reproduction number ( R (0)) for children in schools was 4.49 (95 %CI : 4.30-4.67) and 2.50 (95 %CI : 2.38-2.61) respectively for the year of 2012 and 2014. Conclusions: Space State model seems suitable for mumps prevalence description. The policy of 2-dose MuCV can effectively reduce the number of total patients. Children in schools are the key to reduce the mumps.

Computing the modal mass from the state space model in combined experimental-operational modal analysis

NASA Astrophysics Data System (ADS)

Cara, Javier

2016-05-01

Modal parameters comprise natural frequencies, damping ratios, modal vectors and modal masses. In a theoretic framework, these parameters are the basis for the solution of vibration problems using the theory of modal superposition. In practice, they can be computed from input-output vibration data: the usual procedure is to estimate a mathematical model from the data and then to compute the modal parameters from the estimated model. The most popular models for input-output data are based on the frequency response function, but in recent years the state space model in the time domain has become popular among researchers and practitioners of modal analysis with experimental data. In this work, the equations to compute the modal parameters from the state space model when input and output data are available (like in combined experimental-operational modal analysis) are derived in detail using invariants of the state space model: the equations needed to compute natural frequencies, damping ratios and modal vectors are well known in the operational modal analysis framework, but the equation needed to compute the modal masses has not generated much interest in technical literature. These equations are applied to both a numerical simulation and an experimental study in the last part of the work.

State-space prediction of spring discharge in a karst catchment in southwest China

NASA Astrophysics Data System (ADS)

Li, Zhenwei; Xu, Xianli; Liu, Meixian; Li, Xuezhang; Zhang, Rongfei; Wang, Kelin; Xu, Chaohao

2017-06-01

Southwest China represents one of the largest continuous karst regions in the world. It is estimated that around 1.7 million people are heavily dependent on water derived from karst springs in southwest China. However, there is a limited amount of water supply in this region. Moreover, there is not enough information on temporal patterns of spring discharge in the area. In this context, it is essential to accurately predict spring discharge, as well as understand karst hydrological processes in a thorough manner, so that water shortages in this area could be predicted and managed efficiently. The objectives of this study were to determine the primary factors that govern spring discharge patterns and to develop a state-space model to predict spring discharge. Spring discharge, precipitation (PT), relative humidity (RD), water temperature (WD), and electrical conductivity (EC) were the variables analyzed in the present work, and they were monitored at two different locations (referred to as karst springs A and B, respectively, in this paper) in a karst catchment area in southwest China from May to November 2015. Results showed that a state-space model using any combinations of variables outperformed a classical linear regression, a back-propagation artificial neural network model, and a least square support vector machine in modeling spring discharge time series for karst spring A. The best state-space model was obtained by using PT and RD, which accounted for 99.9% of the total variation in spring discharge. This model was then applied to an independent data set obtained from karst spring B, and it provided accurate spring discharge estimates. Therefore, state-space modeling was a useful tool for predicting spring discharge in karst regions in southwest China, and this modeling procedure may help researchers to obtain accurate results in other karst regions.

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

NASA Astrophysics Data System (ADS)

Cui, Tiangang; Marzouk, Youssef; Willcox, Karen

2016-06-01

Two major bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. Yet incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting-both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We present and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.

Slicing AADL Specifications for Model Checking

NASA Technical Reports Server (NTRS)

Odenbrett, Maximilian; Nguyen, Viet Yen; Noll, Thomas

2010-01-01

To combat the state-space explosion problem in model checking larger systems, abstraction techniques can be employed. Here, methods that operate on the system specification before constructing its state space are preferable to those that try to minimize the resulting transition system as they generally reduce peak memory requirements. We sketch a slicing algorithm for system specifications written in (a variant of) the Architecture Analysis and Design Language (AADL). Given a specification and a property to be verified, it automatically removes those parts of the specification that are irrelevant for model checking the property, thus reducing the size of the corresponding transition system. The applicability and effectiveness of our approach is demonstrated by analyzing the state-space reduction for an example, employing a translator from AADL to Promela, the input language of the SPIN model checker.

Joint estimation over multiple individuals improves behavioural state inference from animal movement data.

PubMed

Jonsen, Ian

2016-02-08

State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone.

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

NASA Astrophysics Data System (ADS)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Efficient Calibration of Distributed Catchment Models Using Perceptual Understanding and Hydrologic Signatures

NASA Astrophysics Data System (ADS)

Hutton, C.; Wagener, T.; Freer, J. E.; Duffy, C.; Han, D.

2015-12-01

Distributed models offer the potential to resolve catchment systems in more detail, and therefore simulate the hydrological impacts of spatial changes in catchment forcing (e.g. landscape change). Such models may contain a large number of model parameters which are computationally expensive to calibrate. Even when calibration is possible, insufficient data can result in model parameter and structural equifinality. In order to help reduce the space of feasible models and supplement traditional outlet discharge calibration data, semi-quantitative information (e.g. knowledge of relative groundwater levels), may also be used to identify behavioural models when applied to constrain spatially distributed predictions of states and fluxes. The challenge is to combine these different sources of information together to identify a behavioural region of state-space, and efficiently search a large, complex parameter space to identify behavioural parameter sets that produce predictions that fall within this behavioural region. Here we present a methodology to incorporate different sources of data to efficiently calibrate distributed catchment models. Metrics of model performance may be derived from multiple sources of data (e.g. perceptual understanding and measured or regionalised hydrologic signatures). For each metric, an interval or inequality is used to define the behaviour of the catchment system, accounting for data uncertainties. These intervals are then combined to produce a hyper-volume in state space. The state space is then recast as a multi-objective optimisation problem, and the Borg MOEA is applied to first find, and then populate the hyper-volume, thereby identifying acceptable model parameter sets. We apply the methodology to calibrate the PIHM model at Plynlimon, UK by incorporating perceptual and hydrologic data into the calibration problem. Furthermore, we explore how to improve calibration efficiency through search initialisation from shorter model runs.

Brownian motion with adaptive drift for remaining useful life prediction: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2018-01-01

Linear Brownian motion with constant drift is widely used in remaining useful life predictions because its first hitting time follows the inverse Gaussian distribution. State space modelling of linear Brownian motion was proposed to make the drift coefficient adaptive and incorporate on-line measurements into the first hitting time distribution. Here, the drift coefficient followed the Gaussian distribution, and it was iteratively estimated by using Kalman filtering once a new measurement was available. Then, to model nonlinear degradation, linear Brownian motion with adaptive drift was extended to nonlinear Brownian motion with adaptive drift. However, in previous studies, an underlying assumption used in the state space modelling was that in the update phase of Kalman filtering, the predicted drift coefficient at the current time exactly equalled the posterior drift coefficient estimated at the previous time, which caused a contradiction with the predicted drift coefficient evolution driven by an additive Gaussian process noise. In this paper, to alleviate such an underlying assumption, a new state space model is constructed. As a result, in the update phase of Kalman filtering, the predicted drift coefficient at the current time evolves from the posterior drift coefficient at the previous time. Moreover, the optimal Kalman filtering gain for iteratively estimating the posterior drift coefficient at any time is mathematically derived. A discussion that theoretically explains the main reasons why the constructed state space model can result in high remaining useful life prediction accuracies is provided. Finally, the proposed state space model and its associated Kalman filtering gain are applied to battery prognostics.

Floquet resonant states and validity of the Floquet-Magnus expansion in the periodically driven Friedrichs models

NASA Astrophysics Data System (ADS)

Mori, Takashi

2015-02-01

The Floquet eigenvalue problem is analyzed for periodically driven Friedrichs models on discrete and continuous space. In the high-frequency regime, there exists a Floquet bound state consistent with the Floquet-Magnus expansion in the discrete Friedrichs model, while it is not the case in the continuous model. In the latter case, however, the bound state predicted by the Floquet-Magnus expansion appears as a metastable state whose lifetime diverges in the limit of large frequencies. We obtain the lifetime by evaluating the imaginary part of the quasienergy of the Floquet resonant state. In the low-frequency regime, there is no Floquet bound state and instead the Floquet resonant state with exponentially small imaginary part of the quasienergy appears, which is understood as the quantum tunneling in the energy space.

Space Weather Products at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Hesse, Michael; Kuznetsova, M.; Pulkkinen, A.; Maddox, M.; Rastaetter, L.; Berrios, D.; MacNeice, P.

2010-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of space weather forecasting tools. Owing to the pace of development in the science community, new model capabilities emerge frequently. Consequently, space weather products and tools involve not only increased validity, but often entirely new capabilities. This presentation will review the present state of space weather tools as well as point out emerging future capabilities.

A Bayesian model for visual space perception

NASA Technical Reports Server (NTRS)

Curry, R. E.

1972-01-01

A model for visual space perception is proposed that contains desirable features in the theories of Gibson and Brunswik. This model is a Bayesian processor of proximal stimuli which contains three important elements: an internal model of the Markov process describing the knowledge of the distal world, the a priori distribution of the state of the Markov process, and an internal model relating state to proximal stimuli. The universality of the model is discussed and it is compared with signal detection theory models. Experimental results of Kinchla are used as a special case.

A Generalized Timeline Representation, Services, and Interface for Automating Space Mission Operations

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Johnston, Mark; Frank, Jeremy; Giuliano, Mark; Kavelaars, Alicia; Lenzen, Christoph; Policella, Nicola

2012-01-01

Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.

Prediction of the dollar to the ruble rate. A system-theoretic approach

NASA Astrophysics Data System (ADS)

Borodachev, Sergey M.

2017-07-01

Proposed a simple state-space model of dollar rate formation based on changes in oil prices and some mechanisms of money transfer between monetary and stock markets. Comparison of predictions by means of input-output model and state-space model is made. It concludes that with proper use of statistical data (Kalman filter) the second approach provides more adequate predictions of the dollar rate.

Using Innovative Outliers to Detect Discrete Shifts in Dynamics in Group-Based State-Space Models

ERIC Educational Resources Information Center

Chow, Sy-Miin; Hamaker, Ellen L.; Allaire, Jason C.

2009-01-01

Outliers are typically regarded as data anomalies that should be discarded. However, dynamic or "innovative" outliers can be appropriately utilized to capture unusual but substantively meaningful shifts in a system's dynamics. We extend De Jong and Penzer's 1998 approach for representing outliers in single-subject state-space models to a…

Path Flow Estimation Using Time Varying Coefficient State Space Model

NASA Astrophysics Data System (ADS)

Jou, Yow-Jen; Lan, Chien-Lun

2009-08-01

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

Modelling, analyses and design of switching converters

NASA Technical Reports Server (NTRS)

Cuk, S. M.; Middlebrook, R. D.

1978-01-01

A state-space averaging method for modelling switching dc-to-dc converters for both continuous and discontinuous conduction mode is developed. In each case the starting point is the unified state-space representation, and the end result is a complete linear circuit model, for each conduction mode, which correctly represents all essential features, namely, the input, output, and transfer properties (static dc as well as dynamic ac small-signal). While the method is generally applicable to any switching converter, it is extensively illustrated for the three common power stages (buck, boost, and buck-boost). The results for these converters are then easily tabulated owing to the fixed equivalent circuit topology of their canonical circuit model. The insights that emerge from the general state-space modelling approach lead to the design of new converter topologies through the study of generic properties of the cascade connection of basic buck and boost converters.

Dynamics of Entropy in Quantum-like Model of Decision Making

NASA Astrophysics Data System (ADS)

Basieva, Irina; Khrennikov, Andrei; Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu

2011-03-01

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices. By using this equilibrium point Alice determines her mixed (i.e., probabilistic) strategy with respect to Bob. Thus our model is a model of thinking through decoherence of initially pure mental state. Decoherence is induced by interaction with memory and external environment. In this paper we study (numerically) dynamics of quantum entropy of Alice's state in the process of decision making. Our analysis demonstrates that this dynamics depends nontrivially on the initial state of Alice's mind on her own actions and her prediction state (for possible actions of Bob.)

The Microgravity Isolation Mount: A Linearized State-Space Model a la Newton and Kane

NASA Technical Reports Server (NTRS)

Hampton, R. David; Tryggvason, Bjarni V.; DeCarufel, Jean; Townsend, Miles A.; Wagar, William O.

1999-01-01

Vibration acceleration levels on large space platforms exceed the requirements of many space experiments. The Microgravity Vibration Isolation Mount (MIM) was built by the Canadian Space Agency to attenuate these disturbances to acceptable levels, and has been operational on the Russian Space Station Mir since May 1996. It has demonstrated good isolation performance and has supported several materials science experiments. The MIM uses Lorentz (voice-coil) magnetic actuators to levitate and isolate payloads at the individual experiment/sub-experiment (versus rack) level. Payload acceleration, relative position, and relative orientation (Euler-parameter) measurements are fed to a state-space controller. The controller, in turn, determines the actuator currents needed for effective experiment isolation. This paper presents the development of an algebraic, state-space model of the MIM, in a form suitable for optimal controller design. The equations are first derived using Newton's Second Law directly; then a second derivation (i.e., validation) of the same equations is provided, using Kane's approach.

Identification of nonlinear normal modes of engineering structures under broadband forcing

NASA Astrophysics Data System (ADS)

Noël, Jean-Philippe; Renson, L.; Grappasonni, C.; Kerschen, G.

2016-06-01

The objective of the present paper is to develop a two-step methodology integrating system identification and numerical continuation for the experimental extraction of nonlinear normal modes (NNMs) under broadband forcing. The first step processes acquired input and output data to derive an experimental state-space model of the structure. The second step converts this state-space model into a model in modal space from which NNMs are computed using shooting and pseudo-arclength continuation. The method is demonstrated using noisy synthetic data simulated on a cantilever beam with a hardening-softening nonlinearity at its free end.

Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistant States

NASA Technical Reports Server (NTRS)

Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter M.; Brenner, Martin J.

2017-01-01

This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and weak fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design.

A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.

PubMed

Chang, Chia-Wen; Tao, Chin-Wang

2017-09-01

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

A Financial Market Model Incorporating Herd Behaviour.

PubMed

Wray, Christopher M; Bishop, Steven R

2016-01-01

Herd behaviour in financial markets is a recurring phenomenon that exacerbates asset price volatility, and is considered a possible contributor to market fragility. While numerous studies investigate herd behaviour in financial markets, it is often considered without reference to the pricing of financial instruments or other market dynamics. Here, a trader interaction model based upon informational cascades in the presence of information thresholds is used to construct a new model of asset price returns that allows for both quiescent and herd-like regimes. Agent interaction is modelled using a stochastic pulse-coupled network, parametrised by information thresholds and a network coupling probability. Agents may possess either one or two information thresholds that, in each case, determine the number of distinct states an agent may occupy before trading takes place. In the case where agents possess two thresholds (labelled as the finite state-space model, corresponding to agents' accumulating information over a bounded state-space), and where coupling strength is maximal, an asymptotic expression for the cascade-size probability is derived and shown to follow a power law when a critical value of network coupling probability is attained. For a range of model parameters, a mixture of negative binomial distributions is used to approximate the cascade-size distribution. This approximation is subsequently used to express the volatility of model price returns in terms of the model parameter which controls the network coupling probability. In the case where agents possess a single pulse-coupling threshold (labelled as the semi-infinite state-space model corresponding to agents' accumulating information over an unbounded state-space), numerical evidence is presented that demonstrates volatility clustering and long-memory patterns in the volatility of asset returns. Finally, output from the model is compared to both the distribution of historical stock returns and the market price of an equity index option.

ψ-Epistemic Models are Exponentially Bad at Explaining the Distinguishability of Quantum States

NASA Astrophysics Data System (ADS)

Leifer, M. S.

2014-04-01

The status of the quantum state is perhaps the most controversial issue in the foundations of quantum theory. Is it an epistemic state (state of knowledge) or an ontic state (state of reality)? In realist models of quantum theory, the epistemic view asserts that nonorthogonal quantum states correspond to overlapping probability measures over the true ontic states. This naturally accounts for a large number of otherwise puzzling quantum phenomena. For example, the indistinguishability of nonorthogonal states is explained by the fact that the ontic state sometimes lies in the overlap region, in which case there is nothing in reality that could distinguish the two states. For this to work, the amount of overlap of the probability measures should be comparable to the indistinguishability of the quantum states. In this Letter, I exhibit a family of states for which the ratio of these two quantities must be ≤2de-cd in Hilbert spaces of dimension d that are divisible by 4. This implies that, for large Hilbert space dimension, the epistemic explanation of indistinguishability becomes implausible at an exponential rate as the Hilbert space dimension increases.

Saddle point localization of molecular wavefunctions.

PubMed

Mellau, Georg Ch; Kyuberis, Alexandra A; Polyansky, Oleg L; Zobov, Nikolai; Field, Robert W

2016-09-15

The quantum mechanical description of isomerization is based on bound eigenstates of the molecular potential energy surface. For the near-minimum regions there is a textbook-based relationship between the potential and eigenenergies. Here we show how the saddle point region that connects the two minima is encoded in the eigenstates of the model quartic potential and in the energy levels of the [H, C, N] potential energy surface. We model the spacing of the eigenenergies with the energy dependent classical oscillation frequency decreasing to zero at the saddle point. The eigenstates with the smallest spacing are localized at the saddle point. The analysis of the HCN ↔ HNC isomerization states shows that the eigenstates with small energy spacing relative to the effective (v1, v3, ℓ) bending potentials are highly localized in the bending coordinate at the transition state. These spectroscopically detectable states represent a chemical marker of the transition state in the eigenenergy spectrum. The method developed here provides a basis for modeling characteristic patterns in the eigenenergy spectrum of bound states.

Optimized decoy state QKD for underwater free space communication

NASA Astrophysics Data System (ADS)

Lopes, Minal; Sarwade, Nisha

Quantum cryptography (QC) is envisioned as a solution for global key distribution through fiber optic, free space and underwater optical communication due to its unconditional security. In view of this, this paper investigates underwater free space quantum key distribution (QKD) model for enhanced transmission distance, secret key rates and security. It is reported that secure underwater free space QKD is feasible in the clearest ocean water with the sifted key rates up to 207kbps. This paper extends this work by testing performance of optimized decoy state QKD protocol with underwater free space communication model. The attenuation of photons, quantum bit error rate and the sifted key generation rate of underwater quantum communication is obtained with vector radiative transfer theory and Monte Carlo method. It is observed from the simulations that optimized decoy state QKD evidently enhances the underwater secret key transmission distance as well as secret key rates.

Flatness-based control in successive loops for stabilization of heart's electrical activity

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Melkikh, Alexey

2016-12-01

The article proposes a new flatness-based control method implemented in successive loops which allows for stabilization of the heart's electrical activity. Heart's pacemaking function is modeled as a set of coupled oscillators which potentially can exhibit chaotic behavior. It is shown that this model satisfies differential flatness properties. Next, the control and stabilization of this model is performed with the use of flatness-based control implemented in cascading loops. By applying a per-row decomposition of the state-space model of the coupled oscillators a set of nonlinear differential equations is obtained. Differential flatness properties are shown to hold for the subsystems associated with the each one of the aforementioned differential equations and next a local flatness-based controller is designed for each subsystem. For the i-th subsystem, state variable xi is chosen to be the flat output and state variable xi+1 is taken to be a virtual control input. Then the value of the virtual control input which eliminates the output tracking error for the i-th subsystem becomes reference setpoint for the i + 1-th subsystem. In this manner the control of the entire state-space model is performed by successive flatness-based control loops. By arriving at the n-th row of the state-space model one computes the control input that can be actually exerted on the aforementioned biosystem. This real control input of the coupled oscillators' system, contains recursively all virtual control inputs associated with the previous n - 1 rows of the state-space model. This control approach achieves asymptotically the elimination of the chaotic oscillation effects and the stabilization of the heart's pulsation rhythm. The stability of the proposed control scheme is proven with the use of Lyapunov analysis.

Spatio-temporal statistical models for river monitoring networks.

PubMed

Clement, L; Thas, O; Vanrolleghem, P A; Ottoy, J P

2006-01-01

When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

An Investigation of State-Space Model Fidelity for SSME Data

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2008-01-01

In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.

Frequency-Weighting Filter Selection, for H2 Control of Microgravity Isolation Systems: A Consideration of the "Implicit Frequency Weighting" Problem

NASA Technical Reports Server (NTRS)

Hampton, Roy David; Whorton, Mark S.

1999-01-01

Many space-science experiments need an active isolation system to provide them with the requisite microgravity environment. The isolation systems planned for use with the International Space Station (ISS) have been appropriately modeled using relative position, relative velocity, and acceleration states. In theory, frequency-weighting design filters can be applied to these state-space models, in order to develop optimal H2 or mixed-norm controllers with desired stability and performance characteristics. In practice, however, since there is a kinematic relationship among the various states, any frequency weighting applied to one state will implicitly weight other states. These implicit frequency-weighting effects must be considered, for intelligent frequency-weighting filter assignment. This paper suggests a rational approach to the assignment of frequency-weighting design filters, in the presence of the kinematic coupling among states that exists in the microgravity vibration isolation problem.

Modeling volatility using state space models.

PubMed

Timmer, J; Weigend, A S

1997-08-01

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used: Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).

A canonical state-space representation for SISO systems using multipoint Jordan CFE. [Continued-Fraction Expansion

NASA Technical Reports Server (NTRS)

Hwang, Chyi; Guo, Tong-Yi; Shieh, Leang-San

1991-01-01

A canonical state-space realization based on the multipoint Jordan continued-fraction expansion (CFE) is presented for single-input-single-output (SISO) systems. The similarity transformation matrix which relates the new canonical form to the phase-variable canonical form is also derived. The presented canonical state-space representation is particularly attractive for the application of SISO system theory in which a reduced-dimensional time-domain model is necessary.

Recent Enhancements to the Development of CFD-Based Aeroelastic Reduced-Order Models

NASA Technical Reports Server (NTRS)

Silva, Walter A.

2007-01-01

Recent enhancements to the development of CFD-based unsteady aerodynamic and aeroelastic reduced-order models (ROMs) are presented. These enhancements include the simultaneous application of structural modes as CFD input, static aeroelastic analysis using a ROM, and matched-point solutions using a ROM. The simultaneous application of structural modes as CFD input enables the computation of the unsteady aerodynamic state-space matrices with a single CFD execution, independent of the number of structural modes. The responses obtained from a simultaneous excitation of the CFD-based unsteady aerodynamic system are processed using system identification techniques in order to generate an unsteady aerodynamic state-space ROM. Once the unsteady aerodynamic state-space ROM is generated, a method for computing the static aeroelastic response using this unsteady aerodynamic ROM and a state-space model of the structure, is presented. Finally, a method is presented that enables the computation of matchedpoint solutions using a single ROM that is applicable over a range of dynamic pressures and velocities for a given Mach number. These enhancements represent a significant advancement of unsteady aerodynamic and aeroelastic ROM technology.

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks.

PubMed

Meng, X Flora; Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M

2017-05-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. © 2017 The Author(s).

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks

PubMed Central

Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M.

2017-01-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. PMID:28566513

A model for rotorcraft flying qualities studies

NASA Technical Reports Server (NTRS)

Mittal, Manoj; Costello, Mark F.

1993-01-01

This paper outlines the development of a mathematical model that is expected to be useful for rotorcraft flying qualities research. A computer model is presented that can be applied to a range of different rotorcraft configurations. The algorithm computes vehicle trim and a linear state-space model of the aircraft. The trim algorithm uses non linear optimization theory to solve the nonlinear algebraic trim equations. The linear aircraft equations consist of an airframe model and a flight control system dynamic model. The airframe model includes coupled rotor and fuselage rigid body dynamics and aerodynamics. The aerodynamic model for the rotors utilizes blade element theory and a three state dynamic inflow model. Aerodynamics of the fuselage and fuselage empennages are included. The linear state-space description for the flight control system is developed using standard block diagram data.

An Airborne Radar Model For Non-Uniformly Spaced Antenna Arrays

DTIC Science & Technology

2006-03-01

Department of Defense, or the United States Government . AFIT-GE-ENG-06-58 An Airborne Radar Model For Non-Uniformly Spaced Antenna Arrays THESIS Presented...different circular arrays, one containing 24 elements and one containing 15 elements. The circular array per- formance is compared to that of a 6 × 6...model and compared to the radar model of [5, 6, 13]. The two models are mathematically equivalent when the uniformly spaced array is linear. The two

Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach.

PubMed

Efthymiou, Evdokia; Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa; Imbach, Lukas L

2017-10-01

The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (p<0.005) and correlated with independently identified visual EEG patterns such as generalized periodic discharges (p<0.02). Receiver operating characteristic (ROC) analysis confirmed the predictive value of lower state space velocity for poor clinical outcome after cardiac arrest (AUC 80.8, 70% sensitivity, 15% false positive rate). Model-based quantitative EEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy. Copyright © 2017 Elsevier B.V. All rights reserved.

Space strategy and governance of ESA small member states

NASA Astrophysics Data System (ADS)

Sagath, Daniel; Papadimitriou, Angeliki; Adriaensen, Maarten; Giannopapa, Christina

2018-01-01

The European Space Agency (ESA) has twenty-two Member States with a variety of governance structures and strategic priorities regarding their space activities. The objective of this paper is to provide an up-to date overview and a holistic assessment of the national space governance structures and strategic priorities of the eleven smaller Member States (based on annual ESA contributions). A link is made between the governance structure and the main strategic objectives. The specific needs and interests of small and new Member States in the frame of European Space Integration are addressed. The first part of the paper focuses on the national space governance structures in the eleven smaller ESA Member States. The governance models of these Member States are identified including the responsible ministries and the entities entrusted with the implementation of space strategy/policy and programmes of the country. The second part of this paper focuses on the content and analysis of the national space strategies and indicates the main priorities and trends in the eleven smaller ESA Member States. The priorities are categorised with regards to technology domains, the role of space in the areas of sustainability and the motivators for space investments. In a third and final part, attention is given to the specific needs and interests of the smaller Member States in the frame of European space integration. ESA instruments are tailored to facilitate the needs and interests of the eleven smaller and/or new Member States.

Finite element dynamic analysis of soft tissues using state-space model.

PubMed

Iorga, Lucian N; Shan, Baoxiang; Pelegri, Assimina A

2009-04-01

A finite element (FE) model is employed to investigate the dynamic response of soft tissues under external excitations, particularly corresponding to the case of harmonic motion imaging. A solid 3D mixed 'u-p' element S8P0 is implemented to capture the near-incompressibility inherent in soft tissues. Two important aspects in structural modelling of these tissues are studied; these are the influence of viscous damping on the dynamic response and, following FE-modelling, a developed state-space formulation that valuates the efficiency of several order reduction methods. It is illustrated that the order of the mathematical model can be significantly reduced, while preserving the accuracy of the observed system dynamics. Thus, the reduced-order state-space representation of soft tissues for general dynamic analysis significantly reduces the computational cost and provides a unitary framework for the 'forward' simulation and 'inverse' estimation of soft tissues. Moreover, the results suggest that damping in soft-tissue is significant, effectively cancelling the contribution of all but the first few vibration modes.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

NASA Astrophysics Data System (ADS)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-09-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlapmore » with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.« less

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

PubMed Central

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-01-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space. PMID:25240340

Realtime Knowledge Management (RKM): From an International Space Station (ISS) Point of View

NASA Technical Reports Server (NTRS)

Robinson, Peter I.; McDermott, William; Alena, Richard L.

2004-01-01

We are developing automated methods to provide realtime access to spacecraft domain knowledge relevant a spacecraft's current operational state. The method is based upon analyzing state-transition signatures in the telemetry stream. A key insight is that documentation relevant to a specific failure mode or operational state is related to the structure and function of spacecraft systems. This means that diagnostic dependency and state models can provide a roadmap for effective documentation navigation and presentation. Diagnostic models consume the telemetry and derive a high-level state description of the spacecraft. Each potential spacecraft state description is matched against the predictions of models that were developed from information found in the pages and sections in the relevant International Space Station (ISS) documentation and reference materials. By annotating each model fragment with the domain knowledge sources from which it was derived we can develop a system that automatically selects those documents representing the domain knowledge encapsulated by the models that compute the current spacecraft state. In this manner, when the spacecraft state changes, the relevant documentation context and presentation will also change.

On dynamical systems approaches and methods in f ( R ) cosmology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alho, Artur; Carloni, Sante; Uggla, Claes, E-mail: aalho@math.ist.utl.pt, E-mail: sante.carloni@tecnico.ulisboa.pt, E-mail: claes.uggla@kau.se

We discuss dynamical systems approaches and methods applied to flat Robertson-Walker models in f ( R )-gravity. We argue that a complete description of the solution space of a model requires a global state space analysis that motivates globally covering state space adapted variables. This is shown explicitly by an illustrative example, f ( R ) = R + α R {sup 2}, α > 0, for which we introduce new regular dynamical systems on global compactly extended state spaces for the Jordan and Einstein frames. This example also allows us to illustrate several local and global dynamical systems techniquesmore » involving, e.g., blow ups of nilpotent fixed points, center manifold analysis, averaging, and use of monotone functions. As a result of applying dynamical systems methods to globally state space adapted dynamical systems formulations, we obtain pictures of the entire solution spaces in both the Jordan and the Einstein frames. This shows, e.g., that due to the domain of the conformal transformation between the Jordan and Einstein frames, not all the solutions in the Jordan frame are completely contained in the Einstein frame. We also make comparisons with previous dynamical systems approaches to f ( R ) cosmology and discuss their advantages and disadvantages.« less

Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor.

PubMed

Prakash, J; Srinivasan, K

2009-07-01

In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blume-Kohout, Robin J; Scholten, Travis L.

Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) shouldmore » not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.« less

A boundary PDE feedback control approach for the stabilization of mortgage price dynamics

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Sarno, D.

2017-11-01

Several transactions taking place in financial markets are dependent on the pricing of mortgages (loans for the purchase of residences, land or farms). In this article, a method for stabilization of mortgage price dynamics is developed. It is considered that mortgage prices follow a PDE model which is equivalent to a multi-asset Black-Scholes PDE. Actually it is a diffusion process evolving in a 2D assets space, where the first asset is the house price and the second asset is the interest rate. By applying semi-discretization and a finite differences scheme this multi-asset PDE is transformed into a state-space model consisting of ordinary nonlinear differential equations. For the local subsystems, into which the mortgage PDE is decomposed, it becomes possible to apply boundary-based feedback control. The controller design proceeds by showing that the state-space model of the mortgage price PDE stands for a differentially flat system. Next, for each subsystem which is related to a nonlinear ODE, a virtual control input is computed, that can invert the subsystem's dynamics and can eliminate the subsystem's tracking error. From the last row of the state-space description, the control input (boundary condition) that is actually applied to the multi-factor mortgage price PDE system is found. This control input contains recursively all virtual control inputs which were computed for the individual ODE subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the mortgage price PDE system so as to assure that all its state variables will converge to the desirable setpoints. By showing the feasibility of such a control method it is also proven that through selected modification of the PDE boundary conditions the price of the mortgage can be made to converge and stabilize at specific reference values.

Micromagnetic Modeling: a Tool for Studying Remanence in Magnetite

NASA Astrophysics Data System (ADS)

ter Maat, G. W.; Fabian, K.; Church, N. S.; McEnroe, S. A.

2017-12-01

Micromagnetic modeling is a useful tool in understanding magnetic particle behavior. The domain state of, and interaction between, particles is influenced by their shape, size and spacing. Rocks contain a collection of grains with varying geometries. This study presents models of true geometries obtained by dual-beam focused ion beam scanning electron microscopy (FIB-SEM). Using focused ion beam nanotomography (FIB-nT) the shape and size of individual grains and their spacing are accurately determined. The particle assemblages discussed here are basalts from the Stardalur volcano in Iceland. The main carrier of the magnetization is oxy-exsolved magnetite which contains extensive microstructures from the micron to nanometer scale. The complex morphologies vary in shape from spherical to elongated to sheet-like shapes with SD to PSD domain states. We investigate large oxy-exsolved magnetite grains as well as smaller oxy-exsolved dendritic grains. The obtained 3D volumes are modeled using finite element micromagnetics software MERRILL, to calculate magnetization structures. By modeling a full hysteresis loop we can observe the complete switching process and visualize the mechanism of the reversal of the magnetization. Micromagnetic simulation of hysteresis loops of grains with varying geometry and spacing shows the magnetization state of, and magnetostatic interaction between, different grains. From the simulations the remanence state of the modeled reconstructed geometry is obtained. Modeling the behavior of separate individual grains is compared with modeling assemblages of grains with varying spacing to study the effect of interaction. The use of realistic geometries of oxy-exsolved magnetite in micromagnetic models allows the examination of the influence of shape, size and spacing on the magnetic properties of single particles, and magnetostatic interactions between them.These parameters are varied and tested to find if there is an increase in remanence-carrying capacity. The use of modeling of the realistic representation of the widespread microstructures allow us to test proposed enhancement of remanence, and more stable paleomagnetic recorders.

The Microgravity Vibration Isolation Mount: A Dynamic Model for Optimal Controller Design

NASA Technical Reports Server (NTRS)

Hampton, R. David; Tryggvason, Bjarni V.; DeCarufel, Jean; Townsend, Miles A.; Wagar, William O.

1997-01-01

Vibration acceleration levels on large space platforms exceed the requirements of many space experiments. The Microgravity Vibration Isolation Mount (MIM) was built by the Canadian Space Agency to attenuate these disturbances to acceptable levels, and has been operational on the Russian Space Station Mir since May 1996. It has demonstrated good isolation performance and has supported several materials science experiments. The MIM uses Lorentz (voice-coil) magnetic actuators to levitate and isolate payloads at the individual experiment/sub-experiment (versus rack) level. Payload acceleration, relative position, and relative orientation (Euler-parameter) measurements are fed to a state-space controller. The controller, in turn, determines the actuator currents needed for effective experiment isolation. This paper presents the development of an algebraic, state-space model of the MIM, in a form suitable for optimal controller design.

A Sequential Ensemble Prediction System at Convection Permitting Scales

NASA Astrophysics Data System (ADS)

Milan, M.; Simmer, C.

2012-04-01

A Sequential Assimilation Method (SAM) following some aspects of particle filtering with resampling, also called SIR (Sequential Importance Resampling), is introduced and applied in the framework of an Ensemble Prediction System (EPS) for weather forecasting on convection permitting scales, with focus to precipitation forecast. At this scale and beyond, the atmosphere increasingly exhibits chaotic behaviour and non linear state space evolution due to convectively driven processes. One way to take full account of non linear state developments are particle filter methods, their basic idea is the representation of the model probability density function by a number of ensemble members weighted by their likelihood with the observations. In particular particle filter with resampling abandons ensemble members (particles) with low weights restoring the original number of particles adding multiple copies of the members with high weights. In our SIR-like implementation we substitute the likelihood way to define weights and introduce a metric which quantifies the "distance" between the observed atmospheric state and the states simulated by the ensemble members. We also introduce a methodology to counteract filter degeneracy, i.e. the collapse of the simulated state space. To this goal we propose a combination of resampling taking account of simulated state space clustering and nudging. By keeping cluster representatives during resampling and filtering, the method maintains the potential for non linear system state development. We assume that a particle cluster with initially low likelihood may evolve in a state space with higher likelihood in a subsequent filter time thus mimicking non linear system state developments (e.g. sudden convection initiation) and remedies timing errors for convection due to model errors and/or imperfect initial condition. We apply a simplified version of the resampling, the particles with highest weights in each cluster are duplicated; for the model evolution for each particle pair one particle evolves using the forward model; the second particle, however, is nudged to the radar and satellite observation during its evolution based on the forward model.

Statistical physics of the symmetric group.

PubMed

Williams, Mobolaji

2017-04-01

Ordered chains (such as chains of amino acids) are ubiquitous in biological cells, and these chains perform specific functions contingent on the sequence of their components. Using the existence and general properties of such sequences as a theoretical motivation, we study the statistical physics of systems whose state space is defined by the possible permutations of an ordered list, i.e., the symmetric group, and whose energy is a function of how certain permutations deviate from some chosen correct ordering. Such a nonfactorizable state space is quite different from the state spaces typically considered in statistical physics systems and consequently has novel behavior in systems with interacting and even noninteracting Hamiltonians. Various parameter choices of a mean-field model reveal the system to contain five different physical regimes defined by two transition temperatures, a triple point, and a quadruple point. Finally, we conclude by discussing how the general analysis can be extended to state spaces with more complex combinatorial properties and to other standard questions of statistical mechanics models.

Statistical physics of the symmetric group

NASA Astrophysics Data System (ADS)

Williams, Mobolaji

2017-04-01

Ordered chains (such as chains of amino acids) are ubiquitous in biological cells, and these chains perform specific functions contingent on the sequence of their components. Using the existence and general properties of such sequences as a theoretical motivation, we study the statistical physics of systems whose state space is defined by the possible permutations of an ordered list, i.e., the symmetric group, and whose energy is a function of how certain permutations deviate from some chosen correct ordering. Such a nonfactorizable state space is quite different from the state spaces typically considered in statistical physics systems and consequently has novel behavior in systems with interacting and even noninteracting Hamiltonians. Various parameter choices of a mean-field model reveal the system to contain five different physical regimes defined by two transition temperatures, a triple point, and a quadruple point. Finally, we conclude by discussing how the general analysis can be extended to state spaces with more complex combinatorial properties and to other standard questions of statistical mechanics models.

A generalized analysis of solar space heating in the United States

NASA Astrophysics Data System (ADS)

Clark, J. A.

A life-cycle model is developed for solar space heating within the United States that is based on the solar design data from the Los Alamos Scientific Laboratory. The model consists of an analytical relationship among five dimensionless parameters that include all pertinent technical, climatological, solar, operating and economic factors that influence the performance of a Solar Space Heating System. An important optimum condition presented is the 'Breakeven' metered cost of conventional fuel at which the cost of the solar system is equal to that of a conventional heating system. The effect of Federal (1980) and State (1979) income tax credits on these costs is determined. A parameter that includes both solar availability and solar system utilization is derived and plotted on a map of the U.S. This parameter shows the most favorable present locations for solar space heating application to be in the Central and Mountain States. The data employed are related to the rehabilitated solar data recently made available by the National Climatic Center (SOLMET).

Pure state consciousness and its local reduction to neuronal space

NASA Astrophysics Data System (ADS)

Duggins, A. J.

2013-01-01

The single neuronal state can be represented as a vector in a complex space, spanned by an orthonormal basis of integer spike counts. In this model a scalar element of experience is associated with the instantaneous firing rate of a single sensory neuron over repeated stimulus presentations. Here the model is extended to composite neural systems that are tensor products of single neuronal vector spaces. Depiction of the mental state as a vector on this tensor product space is intended to capture the unity of consciousness. The density operator is introduced as its local reduction to the single neuron level, from which the firing rate can again be derived as the objective correlate of a subjective element. However, the relational structure of perceptual experience only emerges when the non-local mental state is considered. A metric of phenomenal proximity between neuronal elements of experience is proposed, based on the cross-correlation function of neurophysiology, but constrained by the association of theoretical extremes of correlation/anticorrelation in inseparable 2-neuron states with identical and opponent elements respectively.

Advancing Models and Theories for Digital Behavior Change Interventions.

PubMed

Hekler, Eric B; Michie, Susan; Pavel, Misha; Rivera, Daniel E; Collins, Linda M; Jimison, Holly B; Garnett, Claire; Parral, Skye; Spruijt-Metz, Donna

2016-11-01

To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The "state" is that of the individual based on multiple variables that define the "space" when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Method of performing computational aeroelastic analyses

NASA Technical Reports Server (NTRS)

Silva, Walter A. (Inventor)

2011-01-01

Computational aeroelastic analyses typically use a mathematical model for the structural modes of a flexible structure and a nonlinear aerodynamic model that can generate a plurality of unsteady aerodynamic responses based on the structural modes for conditions defining an aerodynamic condition of the flexible structure. In the present invention, a linear state-space model is generated using a single execution of the nonlinear aerodynamic model for all of the structural modes where a family of orthogonal functions is used as the inputs. Then, static and dynamic aeroelastic solutions are generated using computational interaction between the mathematical model and the linear state-space model for a plurality of periodic points in time.

Behavior of the maximum likelihood in quantum state tomography

NASA Astrophysics Data System (ADS)

Scholten, Travis L.; Blume-Kohout, Robin

2018-02-01

Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) should not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.

Behavior of the maximum likelihood in quantum state tomography

DOE PAGES

Blume-Kohout, Robin J; Scholten, Travis L.

2018-02-22

Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) shouldmore » not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.« less

Behavior of the maximum likelihood in quantum state tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blume-Kohout, Robin J; Scholten, Travis L.

Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) shouldmore » not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.« less

Numerical modeling of space-time wave extremes using WAVEWATCH III

NASA Astrophysics Data System (ADS)

Barbariol, Francesco; Alves, Jose-Henrique G. M.; Benetazzo, Alvise; Bergamasco, Filippo; Bertotti, Luciana; Carniel, Sandro; Cavaleri, Luigi; Y. Chao, Yung; Chawla, Arun; Ricchi, Antonio; Sclavo, Mauro; Tolman, Hendrik

2017-04-01

A novel implementation of parameters estimating the space-time wave extremes within the spectral wave model WAVEWATCH III (WW3) is presented. The new output parameters, available in WW3 version 5.16, rely on the theoretical model of Fedele (J Phys Oceanogr 42(9):1601-1615, 2012) extended by Benetazzo et al. (J Phys Oceanogr 45(9):2261-2275, 2015) to estimate the maximum second-order nonlinear crest height over a given space-time region. In order to assess the wave height associated to the maximum crest height and the maximum wave height (generally different in a broad-band stormy sea state), the linear quasi-determinism theory of Boccotti (2000) is considered. The new WW3 implementation is tested by simulating sea states and space-time extremes over the Mediterranean Sea (forced by the wind fields produced by the COSMO-ME atmospheric model). Model simulations are compared to space-time wave maxima observed on March 10th, 2014, in the northern Adriatic Sea (Italy), by a stereo camera system installed on-board the "Acqua Alta" oceanographic tower. Results show that modeled space-time extremes are in general agreement with observations. Differences are mostly ascribed to the accuracy of the wind forcing and, to a lesser extent, to the approximations introduced in the space-time extremes parameterizations. Model estimates are expected to be even more accurate over areas larger than the mean wavelength (for instance, the model grid size).

Decentralized control of large flexible structures by joint decoupling

NASA Technical Reports Server (NTRS)

Su, Tzu-Jeng; Juang, Jer-Nan

1992-01-01

A decentralized control design method is presented for large complex flexible structures by using the idea of joint decoupling. The derivation is based on a coupled substructure state-space model, which is obtained from enforcing conditions of interface compatibility and equilibrium to the substructure state-space models. It is shown that by restricting the control law to be localized state feedback and by setting the joint actuator input commands to decouple joint 'degrees of freedom' (dof) from interior dof, the global structure control design problem can be decomposed into several substructure control design problems. The substructure control gains and substructure observers are designed based on modified substructure state-space models. The controllers produced by the proposed method can operate successfully at the individual substructure level as well as at the global structure level. Therefore, not only control design but also control implementation is decentralized. Stability and performance requirement of the closed-loop system can be achieved by using any existing state feedback control design method. A two-component mass-spring damper system and a three-truss structure are used as examples to demonstrate the proposed method.

Mediterranean space-time extremes of wind wave sea states

NASA Astrophysics Data System (ADS)

Barbariol, Francesco; Carniel, Sandro; Sclavo, Mauro; Marcello Falcieri, Francesco; Bonaldo, Davide; Bergamasco, Andrea; Benetazzo, Alvise

2014-05-01

Traditionally, wind wave sea states during storms have been observed, modeled, and predicted mostly in the time domain, i.e. at a fixed point. In fact, the standard statistical models used in ocean waves analysis rely on the implicit assumption of long-crested waves. Nevertheless, waves in storms are mainly short-crested. Hence, spatio-temporal features of the wave field are crucial to accurately model the sea state characteristics and to provide reliable predictions, particurly of wave extremes. Indeed, the experimental evidence provided by novel instrumentations, e.g. WASS (Wave Acquisition Stereo System), showed that the maximum sea surface elevation gathered in time over an area, i.e. the space-time extreme, is larger than that one measured in time at a point, i.e. the time extreme. Recently, stochastic models used to estimate maxima of multidimensional Gaussian random fields have been applied to ocean waves statistics. These models are based either on Piterbarg's theorem or Adler and Taylor's Euler Characteristics approach. Besides a probability of exceedance of a certain threshold, they can provide the expected space-time extreme of a sea state, as long as space-time wave features (i.e. some parameters of the directional variance density spectrum) are known. These models have been recently validated against WASS observation from fixed and moving platforms. In this context, our focus was modeling and predicting extremes of wind waves during storms. Thus, to intensively gather space-time extremes data over the Mediterranean region, we used directional spectra provided by the numerical wave model SWAN (Simulating WAves Nearshore). Therefore, we set up a 6x6 km2 resolution grid entailing most of the Mediterranean Sea and we forced it with COSMO-I7 high resolution (7x7 km2) hourly wind fields, within 2007-2013 period. To obtain the space-time features, i.e. the spectral parameters, at each grid node and over the 6 simulated years, we developed a modified version of the SWAN model, the SWAN Space-Time (SWAN-ST). SWAN-ST results were post-processed to obtain the expected space-time extremes over the model domain. To this end, we applied the stochastic model of Fedele, developed starting from Adler and Taylor's approach, which we found to be more accurate and versatile with respect to Piterbarg's theorem. Results we obtained provide an alternative sight on Mediterranean extreme wave climate, which could represent the first step towards operationl forecasting of space-time wave extremes, on the one hand, and the basis for a novel statistical standard wave model, on the other. These results may benefit marine designers, seafarers and other subjects operating at sea and exposed to the frequent and severe hazard represented by extreme wave conditions.

Highway traffic estimation of improved precision using the derivative-free nonlinear Kalman Filter

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Zervos, Nikolaos; Melkikh, Alexey

2015-12-01

The paper proves that the PDE dynamic model of the highway traffic is a differentially flat one and by applying spatial discretization its shows that the model's transformation into an equivalent linear canonical state-space form is possible. For the latter representation of the traffic's dynamics, state estimation is performed with the use of the Derivative-free nonlinear Kalman Filter. The proposed filter consists of the Kalman Filter recursion applied on the transformed state-space model of the highway traffic. Moreover, it makes use of an inverse transformation, based again on differential flatness theory which enables to obtain estimates of the state variables of the initial nonlinear PDE model. By avoiding approximate linearizations and the truncation of nonlinear terms from the PDE model of the traffic's dynamics the proposed filtering methods outperforms, in terms of accuracy, other nonlinear estimators such as the Extended Kalman Filter. The article's theoretical findings are confirmed through simulation experiments.

A "Kanes's Dynamics" Model for the Active Rack Isolation System

NASA Technical Reports Server (NTRS)

Hampton, R. David; Beech, Geoffrey

1999-01-01

Many microgravity space-science experiments require vibratory acceleration levels unachievable without active isolation. The Boeing Corporation's Active Rack Isolation System (ARIS) employs a novel combination of magnetic actuation and mechanical linkages, to address these isolation requirements on the International Space Station (ISS). ARIS provides isolation at the rack (international Standard Payload Rack, or ISPR) level. Effective model-based vibration isolation requires (1) an appropriate isolation device, (2) an adequate dynamic (i.e., mathematical) model of that isolator, and (3) a suitable, corresponding controller. ARIS provides the ISS response to the first requirement. This paper presents one response to the second, in a state-space framework intended to facilitate an optimal-controls approach to the third. The authors use "Kane's Dynamics" to develop an state-space, analytical (algebraic) set of linearized equations of motion for ARIS.

A "Kane's Dynamics" Model for the Active Rack Isolation System

NASA Technical Reports Server (NTRS)

Hampton, R. D.; Beech, G. S.; Rao, N. N. S.; Rupert, J. K.; Kim, Y. K.

2001-01-01

Many microgravity space science experiments require vibratory acceleration levels unachievable without active isolation. The Boeing Corporation's Active Rack Isolation System (ARIS) employs a novel combination of magnetic actuation and mechanical linkages to address these isolation requirements on the International Space Station (ISS). ARIS provides isolation at the rack (International Standard Payload Rack (ISPR)) level. Effective model-based vibration isolation requires: (1) an appropriate isolation device, (2) an adequate dynamic (i.e., mathematical) model of that isolator, and (3) a suitable, corresponding controller. ARIS provides the ISS response to the first requirement. This paper presents one response to the second, in a state space framework intended to facilitate an optimal-controls approach to the third. The authors use "Kane's Dynamics" to develop a state-space, analytical (algebraic) set of linearized equations of motion for ARIS.

A generalized analysis of solar space heating

NASA Astrophysics Data System (ADS)

Clark, J. A.

A life-cycle model is developed for solar space heating within the United States. The model consists of an analytical relationship among five dimensionless parameters that include all pertinent technical, climatological, solar, operating and economic factors that influence the performance of a solar space heating system. An important optimum condition presented is the break-even metered cost of conventional fuel at which the cost of the solar system is equal to that of a conventional heating system. The effect of Federal (1980) and State (1979) income tax credits on these costs is determined. A parameter that includes both solar availability and solar system utilization is derived and plotted on a map of the U.S. This parameter shows the most favorable present locations for solar space heating application to be in the Central and Mountain States. The data employed are related to the rehabilitated solar data recently made available by the National Climatic Center.

A Financial Market Model Incorporating Herd Behaviour

PubMed Central

2016-01-01

Herd behaviour in financial markets is a recurring phenomenon that exacerbates asset price volatility, and is considered a possible contributor to market fragility. While numerous studies investigate herd behaviour in financial markets, it is often considered without reference to the pricing of financial instruments or other market dynamics. Here, a trader interaction model based upon informational cascades in the presence of information thresholds is used to construct a new model of asset price returns that allows for both quiescent and herd-like regimes. Agent interaction is modelled using a stochastic pulse-coupled network, parametrised by information thresholds and a network coupling probability. Agents may possess either one or two information thresholds that, in each case, determine the number of distinct states an agent may occupy before trading takes place. In the case where agents possess two thresholds (labelled as the finite state-space model, corresponding to agents’ accumulating information over a bounded state-space), and where coupling strength is maximal, an asymptotic expression for the cascade-size probability is derived and shown to follow a power law when a critical value of network coupling probability is attained. For a range of model parameters, a mixture of negative binomial distributions is used to approximate the cascade-size distribution. This approximation is subsequently used to express the volatility of model price returns in terms of the model parameter which controls the network coupling probability. In the case where agents possess a single pulse-coupling threshold (labelled as the semi-infinite state-space model corresponding to agents’ accumulating information over an unbounded state-space), numerical evidence is presented that demonstrates volatility clustering and long-memory patterns in the volatility of asset returns. Finally, output from the model is compared to both the distribution of historical stock returns and the market price of an equity index option. PMID:27007236

Operational Models Supporting Manned Space Flight

NASA Astrophysics Data System (ADS)

Johnson, A. S.; Weyland, M. D.; Lin, T. C.; Zapp, E. N.

2006-12-01

The Space Radiation Analysis Group (SRAG) at Johnson Space Center (JSC) has the primary responsibility to provide real-time radiation health operational support for manned space flight. Forecasts from NOAA SEC, real-time space environment data and radiation models are used to infer changes in the radiation environment due to space weather. Unlike current operations in low earth orbit which are afforded substantial protection from the geomagnetic field, exploration missions will have little protection and require improved operational tools for mission support. The current state of operational models and their limitations will be presented as well as an examination of needed tools to support exploration missions.

Dopant Segregation in Earth- and Space-Grown InP Crystals

NASA Astrophysics Data System (ADS)

Danilewsky, Andreas Nikolaus; Okamoto, Yusuke; Benz, Klaus Werner; Nishinaga, Tatau

1992-07-01

Macro- and microsegregation of sulphur in InP crystals grown from In solution by the travelling heater method under microgravity and normal gravity are analyzed using spatially resolved photoluminescence. Whereas the macrosegregation in earth- as well as space-grown crystals is explained by conventional steady-state models based on the theory of Burton, Prim and Slichter (BPS), the microsegregation can only be understood in terms of the non-steady-state step exchange model.

Application of a passivity based control methodology for flexible joint robots to a simplified Space Shuttle RMS

NASA Technical Reports Server (NTRS)

Sicard, Pierre; Wen, John T.

1992-01-01

A passivity approach for the control design of flexible joint robots is applied to the rate control of a three-link arm modeled after the shoulder yaw joint of the Space Shuttle Remote Manipulator System (RMS). The system model includes friction and elastic joint couplings modeled as nonlinear springs. The basic structure of the proposed controller is the sum of a model-based feedforward and a model-independent feedback. A regulator approach with link state feedback is employed to define the desired motor state. Passivity theory is used to design a motor state-based controller to stabilize the error system formed by the feedforward. Simulation results show that greatly improved performance was obtained by using the proposed controller over the existing RMS controller.

Analysing grouping of nucleotides in DNA sequences using lumped processes constructed from Markov chains.

PubMed

Guédon, Yann; d'Aubenton-Carafa, Yves; Thermes, Claude

2006-03-01

The most commonly used models for analysing local dependencies in DNA sequences are (high-order) Markov chains. Incorporating knowledge relative to the possible grouping of the nucleotides enables to define dedicated sub-classes of Markov chains. The problem of formulating lumpability hypotheses for a Markov chain is therefore addressed. In the classical approach to lumpability, this problem can be formulated as the determination of an appropriate state space (smaller than the original state space) such that the lumped chain defined on this state space retains the Markov property. We propose a different perspective on lumpability where the state space is fixed and the partitioning of this state space is represented by a one-to-many probabilistic function within a two-level stochastic process. Three nested classes of lumped processes can be defined in this way as sub-classes of first-order Markov chains. These lumped processes enable parsimonious reparameterizations of Markov chains that help to reveal relevant partitions of the state space. Characterizations of the lumped processes on the original transition probability matrix are derived. Different model selection methods relying either on hypothesis testing or on penalized log-likelihood criteria are presented as well as extensions to lumped processes constructed from high-order Markov chains. The relevance of the proposed approach to lumpability is illustrated by the analysis of DNA sequences. In particular, the use of lumped processes enables to highlight differences between intronic sequences and gene untranslated region sequences.

An optical flow-based state-space model of the vocal folds.

PubMed

Granados, Alba; Brunskog, Jonas

2017-06-01

High-speed movies of the vocal fold vibration are valuable data to reveal vocal fold features for voice pathology diagnosis. This work presents a suitable Bayesian model and a purely theoretical discussion for further development of a framework for continuum biomechanical features estimation. A linear and Gaussian nonstationary state-space model is proposed and thoroughly discussed. The evolution model is based on a self-sustained three-dimensional finite element model of the vocal folds, and the observation model involves a dense optical flow algorithm. The results show that the method is able to capture different deformation patterns between the computed optical flow and the finite element deformation, controlled by the choice of the model tissue parameters.

The MFA ground states for the extended Bose-Hubbard model with a three-body constraint

NASA Astrophysics Data System (ADS)

Panov, Yu. D.; Moskvin, A. S.; Vasinovich, E. V.; Konev, V. V.

2018-05-01

We address the intensively studied extended bosonic Hubbard model (EBHM) with truncation of the on-site Hilbert space to the three lowest occupation states n = 0 , 1 , 2 in frames of the S = 1 pseudospin formalism. Similar model was recently proposed to describe the charge degree of freedom in a model high-T c cuprate with the on-site Hilbert space reduced to the three effective valence centers, nominally Cu1+;2+;3+. With small corrections the model becomes equivalent to a strongly anisotropic S = 1 quantum magnet in an external magnetic field. We have applied a generalized mean-field approach and quantum Monte-Carlo technique for the model 2D S = 1 system with a two-particle transport to find the ground state phase with its evolution under deviation from half-filling.

Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Kuznetsova, M.; Maddox, M.; Pulkkinen, A.; Hesse, M.; Rastaetter, L.; Macneice, P.; Taktakishvili, A.; Berrios, D.; Chulaki, A.; Zheng, Y.;

Space-time least-squares Petrov-Galerkin projection in nonlinear model reduction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choi, Youngsoo; Carlberg, Kevin Thomas

Our work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model-reduction methods that first apply Petrov-Galerkin projection in the spatial dimension and subsequently apply time integration to numerically resolve the resulting low-dimensional dynamical system, the proposed method applies projection in space and time simultaneously. To accomplish this, the method first introduces a low-dimensional space-time trial subspace, which can be obtained by computing tensor decompositions of state-snapshot data. The method then computes discrete-optimal approximations in this space-time trial subspace by minimizing the residual arising after time discretization over allmore » space and time in a weighted ℓ 2-norm. This norm can be de ned to enable complexity reduction (i.e., hyper-reduction) in time, which leads to space-time collocation and space-time GNAT variants of the ST-LSPG method. Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and (3) error bounds that exhibit slower growth in time. Numerical examples performed on model problems in fluid dynamics demonstrate the ability of the method to generate orders-of-magnitude computational savings relative to spatial-projection-based reduced-order models without sacrificing accuracy.« less

Application of model reference adaptive control to a flexible remote manipulator arm

NASA Technical Reports Server (NTRS)

Meldrum, D. R.; Balas, M. J.

1986-01-01

An exact modal state-space representation is derived in detail for a single-link, flexible remote manipulator with a noncollocated sensor and actuator. A direct model following adaptive controller is designed to control the torque at the pinned end of the arm so as to command the free end to track a prescribed sinusoidal motion. Conditions that must be satisfied in order for the controller to work are stated. Simulation results to date are discussed along with the potential of the model following adaptive control scheme in robotics and space environments.

Direct use of linear time-domain aerodynamics in aeroservoelastic analysis: Aerodynamic model

NASA Technical Reports Server (NTRS)

Woods, J. A.; Gilbert, Michael G.

1990-01-01

The work presented here is the first part of a continuing effort to expand existing capabilities in aeroelasticity by developing the methodology which is necessary to utilize unsteady time-domain aerodynamics directly in aeroservoelastic design and analysis. The ultimate objective is to define a fully integrated state-space model of an aeroelastic vehicle's aerodynamics, structure and controls which may be used to efficiently determine the vehicle's aeroservoelastic stability. Here, the current status of developing a state-space model for linear or near-linear time-domain indicial aerodynamic forces is presented.

Modal space three-state feedback control for electro-hydraulic servo plane redundant driving mechanism with eccentric load decoupling.

PubMed

Zhao, Jinsong; Wang, Zhipeng; Zhang, Chuanbi; Yang, Chifu; Bai, Wenjie; Zhao, Zining

2018-06-01

The shaking table based on electro-hydraulic servo parallel mechanism has the advantage of strong carrying capacity. However, the strong coupling caused by the eccentric load not only affects the degree of freedom space control precision, but also brings trouble to the system control. A novel decoupling control strategy is proposed, which is based on modal space to solve the coupling problem for parallel mechanism with eccentric load. The phenomenon of strong dynamic coupling among degree of freedom space is described by experiments, and its influence on control design is discussed. Considering the particularity of plane motion, the dynamic model is built by Lagrangian method to avoid complex calculations. The dynamic equations of the coupling physical space are transformed into the dynamic equations of the decoupling modal space by using the weighted orthogonality of the modal main mode with respect to mass matrix and stiffness matrix. In the modal space, the adjustments of the modal channels are independent of each other. Moreover, the paper discusses identical closed-loop dynamic characteristics of modal channels, which will realize decoupling for degree of freedom space, thus a modal space three-state feedback control is proposed to expand the frequency bandwidth of each modal channel for ensuring their near-identical responses in a larger frequency range. Experimental results show that the concept of modal space three-state feedback control proposed in this paper can effectively reduce the strong coupling problem of degree of freedom space channels, which verify the effectiveness of the proposed model space state feedback control strategy for improving the control performance of the electro-hydraulic servo plane redundant driving mechanism. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Models of Solar Wind Structures and Their Interaction with the Earth's Space Environment

NASA Astrophysics Data System (ADS)

Watermann, J.; Wintoft, P.; Sanahuja, B.; Saiz, E.; Poedts, S.; Palmroth, M.; Milillo, A.; Metallinou, F.-A.; Jacobs, C.; Ganushkina, N. Y.; Daglis, I. A.; Cid, C.; Cerrato, Y.; Balasis, G.; Aylward, A. D.; Aran, A.

2009-11-01

The discipline of “Space Weather” is built on the scientific foundation of solar-terrestrial physics but with a strong orientation toward applied research. Models describing the solar-terrestrial environment are therefore at the heart of this discipline, for both physical understanding of the processes involved and establishing predictive capabilities of the consequences of these processes. Depending on the requirements, purely physical models, semi-empirical or empirical models are considered to be the most appropriate. This review focuses on the interaction of solar wind disturbances with geospace. We cover interplanetary space, the Earth’s magnetosphere (with the exception of radiation belt physics), the ionosphere (with the exception of radio science), the neutral atmosphere and the ground (via electromagnetic induction fields). Space weather relevant state-of-the-art physical and semi-empirical models of the various regions are reviewed. They include models for interplanetary space, its quiet state and the evolution of recurrent and transient solar perturbations (corotating interaction regions, coronal mass ejections, their interplanetary remnants, and solar energetic particle fluxes). Models of coupled large-scale solar wind-magnetosphere-ionosphere processes (global magnetohydrodynamic descriptions) and of inner magnetosphere processes (ring current dynamics) are discussed. Achievements in modeling the coupling between magnetospheric processes and the neutral and ionized upper and middle atmospheres are described. Finally we mention efforts to compile comprehensive and flexible models from selections of existing modules applicable to particular regions and conditions in interplanetary space and geospace.

Cycle-Averaged Phase-Space States for the Harmonic and the Morse Oscillators, and the Corresponding Uncertainty Relations

ERIC Educational Resources Information Center

Nicolaides, Cleanthes A.; Constantoudis, Vasilios

2009-01-01

In Planck's model of the harmonic oscillator (HO) a century ago, both the energy and the phase space were quantized according to epsilon[subscript n] = nhv, n = 0, 1, 2..., and [double integral]dp[subscript x] dx = h. By referring to just these two relations, we show how the adoption of "cycle-averaged phase-space states" (CAPSSs) leads to the…

Cosmic clustering

DOE PAGES

Anninos, Dionysios; Denef, Frederik

2016-06-30

We show that the late time Hartle-Hawking wave function for a free massless scalar in a fixed de Sitter background encodes a sharp ultrametric structure for the standard Euclidean distance on the space of field configurations. This implies a hierarchical, tree-like organization of the state space, reflecting its genesis as a branched diffusion process. In conclusion, an equivalent mathematical structure organizes the state space of the Sherrington-Kirkpatrick model of a spin glass.

Parametric State Space Structuring

NASA Technical Reports Server (NTRS)

Ciardo, Gianfranco; Tilgner, Marco

1997-01-01

Structured approaches based on Kronecker operators for the description and solution of the infinitesimal generator of a continuous-time Markov chains are receiving increasing interest. However, their main advantage, a substantial reduction in the memory requirements during the numerical solution, comes at a price. Methods based on the "potential state space" allocate a probability vector that might be much larger than actually needed. Methods based on the "actual state space", instead, have an additional logarithmic overhead. We present an approach that realizes the advantages of both methods with none of their disadvantages, by partitioning the local state spaces of each submodel. We apply our results to a model of software rendezvous, and show how they reduce memory requirements while, at the same time, improving the efficiency of the computation.

Transient modeling of the thermohydraulic behavior of high temperature heat pipes for space reactor applications

NASA Technical Reports Server (NTRS)

Hall, Michael L.; Doster, Joseph M.

1986-01-01

Many proposed space reactor designs employ heat pipes as a means of conveying heat. Previous researchers have been concerned with steady state operation, but the transient operation is of interest in space reactor applications due to the necessity of remote startup and shutdown. A model is being developed to study the dynamic behavior of high temperature heat pipes during startup, shutdown and normal operation under space environments. Model development and preliminary results for a hypothetical design of the system are presented.

Identification of the focal plane wavefront control system using E-M algorithm

NASA Astrophysics Data System (ADS)

Sun, He; Kasdin, N. Jeremy; Vanderbei, Robert

2017-09-01

In a typical focal plane wavefront control (FPWC) system, such as the adaptive optics system of NASA's WFIRST mission, the efficient controllers and estimators in use are usually model-based. As a result, the modeling accuracy of the system influences the ultimate performance of the control and estimation. Currently, a linear state space model is used and calculated based on lab measurements using Fourier optics. Although the physical model is clearly defined, it is usually biased due to incorrect distance measurements, imperfect diagnoses of the optical aberrations, and our lack of knowledge of the deformable mirrors (actuator gains and influence functions). In this paper, we present a new approach for measuring/estimating the linear state space model of a FPWC system using the expectation-maximization (E-M) algorithm. Simulation and lab results in the Princeton's High Contrast Imaging Lab (HCIL) show that the E-M algorithm can well handle both the amplitude and phase errors and accurately recover the system. Using the recovered state space model, the controller creates dark holes with faster speed. The final accuracy of the model depends on the amount of data used for learning.

Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling✩

PubMed Central

Akram, Sahar; Presacco, Alessandro; Simon, Jonathan Z.; Shamma, Shihab A.; Babadi, Behtash

2015-01-01

The underlying mechanism of how the human brain solves the cocktail party problem is largely unknown. Recent neuroimaging studies, however, suggest salient temporal correlations between the auditory neural response and the attended auditory object. Using magnetoencephalography (MEG) recordings of the neural responses of human subjects, we propose a decoding approach for tracking the attentional state while subjects are selectively listening to one of the two speech streams embedded in a competing-speaker environment. We develop a biophysically-inspired state-space model to account for the modulation of the neural response with respect to the attentional state of the listener. The constructed decoder is based on a maximum a posteriori (MAP) estimate of the state parameters via the Expectation Maximization (EM) algorithm. Using only the envelope of the two speech streams as covariates, the proposed decoder enables us to track the attentional state of the listener with a temporal resolution of the order of seconds, together with statistical confidence intervals. We evaluate the performance of the proposed model using numerical simulations and experimentally measured evoked MEG responses from the human brain. Our analysis reveals considerable performance gains provided by the state-space model in terms of temporal resolution, computational complexity and decoding accuracy. PMID:26436490

Towards the Next Generation of Space Environment Prediction Capabilities.

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.

2015-12-01

Since its establishment more than 15 years ago, the Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) is serving as an assess point to expanding collection of state-of-the-art space environment models and frameworks as well as a hub for collaborative development of next generation space weather forecasting systems. In partnership with model developers and international research and operational communities the CCMC integrates new data streams and models from diverse sources into end-to-end space weather impacts predictive systems, identifies week links in data-model & model-model coupling and leads community efforts to fill those gaps. The presentation will highlight latest developments, progress in CCMC-led community-wide projects on testing, prototyping, and validation of models, forecasting techniques and procedures and outline ideas on accelerating implementation of new capabilities in space weather operations.

A Comprehensive Study of Three Delay Compensation Algorithms for Flight Simulators

NASA Technical Reports Server (NTRS)

Guo, Liwen; Cardullo, Frank M.; Houck, Jacob A.; Kelly, Lon C.; Wolters, Thomas E.

2005-01-01

This paper summarizes a comprehensive study of three predictors used for compensating the transport delay in a flight simulator; The McFarland, Adaptive and State Space Predictors. The paper presents proof that the stochastic approximation algorithm can achieve the best compensation among all four adaptive predictors, and intensively investigates the relationship between the state space predictor s compensation quality and its reference model. Piloted simulation tests show that the adaptive predictor and state space predictor can achieve better compensation of transport delay than the McFarland predictor.

Community Coordinated Modeling Center (CCMC): Using innovative tools and services to support worldwide space weather scientific communities and networks

NASA Astrophysics Data System (ADS)

Mendoza, A. M.; Bakshi, S.; Berrios, D.; Chulaki, A.; Evans, R. M.; Kuznetsova, M. M.; Lee, H.; MacNeice, P. J.; Maddox, M. M.; Mays, M. L.; Mullinix, R. E.; Ngwira, C. M.; Patel, K.; Pulkkinen, A.; Rastaetter, L.; Shim, J.; Taktakishvili, A.; Zheng, Y.

2012-12-01

Community Coordinated Modeling Center (CCMC) was established to enhance basic solar terrestrial research and to aid in the development of models for specifying and forecasting conditions in the space environment. In achieving this goal, CCMC has developed and provides a set of innovative tools varying from: Integrated Space Weather Analysis (iSWA) web -based dissemination system for space weather information, Runs-On-Request System providing access to unique collection of state-of-the-art solar and space physics models (unmatched anywhere in the world), Advanced Online Visualization and Analysis tools for more accurate interpretation of model results, Standard Data formats for Simulation Data downloads, and recently Mobile apps (iPhone/Android) to view space weather data anywhere to the scientific community. The number of runs requested and the number of resulting scientific publications and presentations from the research community has not only been an indication of the broad scientific usage of the CCMC and effective participation by space scientists and researchers, but also guarantees active collaboration and coordination amongst the space weather research community. Arising from the course of CCMC activities, CCMC also supports community-wide model validation challenges and research focus group projects for a broad range of programs such as the multi-agency National Space Weather Program, NSF's CEDAR (Coupling, Energetics and Dynamics of Atmospheric Regions), GEM (Geospace Environment Modeling) and Shine (Solar Heliospheric and INterplanetary Environment) programs. In addition to performing research and model development, CCMC also supports space science education by hosting summer students through local universities; through the provision of simulations in support of classroom programs such as Heliophysics Summer School (with student research contest) and CCMC Workshops; training next generation of junior scientists in space weather forecasting; and educating the general public about the importance and impacts of space weather effects. Although CCMC is organizationally comprised of United States federal agencies, CCMC services are open to members of the international science community and encourages interagency and international collaboration. In this poster, we provide an overview of using Community Coordinated Modeling Center (CCMC) tools and services to support worldwide space weather scientific communities and networks.;

Monthly sediment discharge changes and estimates in a typical karst catchment of southwest China

NASA Astrophysics Data System (ADS)

Li, Zhenwei; Xu, Xianli; Xu, Chaohao; Liu, Meixian; Wang, Kelin; Yi, Ruzhou

2017-12-01

As one of the largest karst regions in the world, southwest China is experiencing severe soil erosion due to its special geological conditions, inappropriate land use, and lower soil loss tolerance. Knowledge and accurate estimations of changes in sediment discharge rates is important for finding potential measures to effectively control sediment delivery. This study investigated temporal variation in monthly sediment discharge (SD), and developed sediment rating curves and state-space model to estimate SD. Monthly water discharge, SD, precipitation, potential evapotranspiration, and normalized differential vegetation index during 2003-2015 collected from a typical karst catchment of Yujiang River were analyzed in present study. A Mann-Kendal test and Morlet wavelet analysis were employed to detect the changes in SD. Results indicated that a decreasing trend was observed in sediment discharge at monthly and annual scale. The water and sediment discharge both had a significant 1-year period, implying that water discharge has substantial influence on SD. The best state-space model using water discharge was a simple but effective model, accounting for 99% of the variation in SD. The sediment rating curves, however, represented only 78% of the variation in SD. This study provides an insight into the possibility of accurate estimation of SD only using water discharge with state-space model approach. State-space model is recommended as an effective approach for quantifying the temporal relationships between SD and its driving factors in karst regions of southwest China.

Models and applications for space weather forecasting and analysis at the Community Coordinated Modeling Center.

NASA Astrophysics Data System (ADS)

Kuznetsova, Maria

The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) was established at the dawn of the new millennium as a long-term flexible solution to the problem of transition of progress in space environment modeling to operational space weather forecasting. CCMC hosts an expanding collection of state-of-the-art space weather models developed by the international space science community. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment and developing and maintaining custom displays and powerful web-based systems and tools ready to be used by researchers, space weather service providers and decision makers. In support of space weather needs of NASA users CCMC is developing highly-tailored applications and services that target specific orbits or locations in space and partnering with NASA mission specialists on linking CCMC space environment modeling with impacts on biological and technological systems in space. Confidence assessment of model predictions is an essential element of space environment modeling. CCMC facilitates interaction between model owners and users in defining physical parameters and metrics formats relevant to specific applications and leads community efforts to quantify models ability to simulate and predict space environment events. Interactive on-line model validation systems developed at CCMC make validation a seamless part of model development circle. The talk will showcase innovative solutions for space weather research, validation, anomaly analysis and forecasting and review on-going community-wide model validation initiatives enabled by CCMC applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Znojil, Miloslav

For many quantum models an apparent non-Hermiticity of observables just corresponds to their hidden Hermiticity in another, physical Hilbert space. For these models we show that the existence of observables which are manifestly time-dependent may require the use of a manifestly time-dependent representation of the physical Hilbert space of states.

State Estimation of International Space Station Centrifuge Rotor With Incomplete Knowledge of Disturbance Inputs

NASA Technical Reports Server (NTRS)

Sullivan, Michael J.

2005-01-01

This thesis develops a state estimation algorithm for the Centrifuge Rotor (CR) system where only relative measurements are available with limited knowledge of both rotor imbalance disturbances and International Space Station (ISS) thruster disturbances. A Kalman filter is applied to a plant model augmented with sinusoidal disturbance states used to model both the effect of the rotor imbalance and the 155 thrusters on the CR relative motion measurement. The sinusoidal disturbance states compensate for the lack of the availability of plant inputs for use in the Kalman filter. Testing confirms that complete disturbance modeling is necessary to ensure reliable estimation. Further testing goes on to show that increased estimator operational bandwidth can be achieved through the expansion of the disturbance model within the filter dynamics. In addition, Monte Carlo analysis shows the varying levels of robustness against defined plant/filter uncertainty variations.

Spatial perspectives in state-and-transition models: A missing link to land management?

USDA-ARS?s Scientific Manuscript database

Conceptual models of alternative states and thresholds are based largely on observations of ecosystem processes at a few points in space. Because the distribution of alternative states in spatially-structured ecosystems is the result of variations in pattern-process interactions at different scales,...

Properties of heuristic search strategies

NASA Technical Reports Server (NTRS)

Vanderbrug, G. J.

1973-01-01

A directed graph is used to model the search space of a state space representation with single input operators, an AND/OR is used for problem reduction representations, and a theorem proving graph is used for state space representations with multiple input operators. These three graph models and heuristic strategies for searching them are surveyed. The completeness, admissibility, and optimality properties of search strategies which use the evaluation function f = (1 - omega)g = omega(h) are presented and interpreted using a representation of the search process in the plane. The use of multiple output operators to imply dependent successors, and thus obtain a formalism which includes all three types of representations, is discussed.

A Comparison of Ionospheric Model Performance for International Space Station Orbits

DTIC Science & Technology

2013-03-01

Huang, C. Y., F. A . Marcos, P. A . Roddy, M . R. Hairston, W. R. Coley, C. Roth, S . Bruinsma, and D. E. Hunton (2009), Broad plasma decreases in the... A COMPARISON OF IONOSPHERIC MODEL PERFORMANCE FOR INTERNATIONAL SPACE STATION ORBITS THESIS David J. Broadwater, Captain, USAF AFIT-ENP-13- M -04...not subject to copyright protection in the United States. AFIT-ENP-13- M -04 A COMPARISON OF IONOSPHERIC MODEL PERFORMANCE FOR INTERNATIONAL SPACE

Multi input single output model predictive control of non-linear bio-polymerization process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arumugasamy, Senthil Kumar; Ahmad, Z.

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state spacemore » model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.« less

Highlights of Space Weather Services/Capabilities at NASA/GSFC Space Weather Center

NASA Technical Reports Server (NTRS)

Fok, Mei-Ching; Zheng, Yihua; Hesse, Michael; Kuznetsova, Maria; Pulkkinen, Antti; Taktakishvili, Aleksandre; Mays, Leila; Chulaki, Anna; Lee, Hyesook

2012-01-01

The importance of space weather has been recognized world-wide. Our society depends increasingly on technological infrastructure, including the power grid as well as satellites used for communication and navigation. Such technologies, however, are vulnerable to space weather effects caused by the Sun's variability. NASA GSFC's Space Weather Center (SWC) (http://science.gsfc.nasa.gov//674/swx services/swx services.html) has developed space weather products/capabilities/services that not only respond to NASA's needs but also address broader interests by leveraging the latest scientific research results and state-of-the-art models hosted at the Community Coordinated Modeling Center (CCMC: http://ccmc.gsfc.nasa.gov). By combining forefront space weather science and models, employing an innovative and configurable dissemination system (iSWA.gsfc.nasa.gov), taking advantage of scientific expertise both in-house and from the broader community as well as fostering and actively participating in multilateral collaborations both nationally and internationally, NASA/GSFC space weather Center, as a sibling organization to CCMC, is poised to address NASA's space weather needs (and needs of various partners) and to help enhancing space weather forecasting capabilities collaboratively. With a large number of state-of-the-art physics-based models running in real-time covering the whole space weather domain, it offers predictive capabilities and a comprehensive view of space weather events throughout the solar system. In this paper, we will provide some highlights of our service products/capabilities. In particular, we will take the 23 January and the 27 January space weather events as examples to illustrate how we can use the iSWA system to track them in the interplanetary space and forecast their impacts.

Dynamical Cognitive Models of Social Issues in Russia

NASA Astrophysics Data System (ADS)

Mitina, Olga; Abraham, Fred; Petrenko, Victor

We examine and model dynamics in three areas of social cognition: (1) political transformations within Russia, (2) evaluation of political trends in other countries by Russians, and (3) evaluation of Russian stereotypes concerning women. We try to represent consciousness as vectorfields and trajectories in a cognitive state space. We use psychosemantic techniques that allow definition of the state space and the systematic construction of these vectorfields and trajectories and their portrait from research data. Then we construct models to fit them, using multiple regression methods to obtain linear differential equations. These dynamical models of social cognition fit the data quite well. (1) The political transformations were modeled by a spiral repellor in a two-dimensional space of a democratic-totalitarian factor and social depression-optimism factor. (2) The evaluation of alien political trends included a flow away from a saddle toward more stable and moderate political regimes in a 2D space, of democratic-totalitarian and unstable-stable cognitive dimensions. (3) The gender study showed expectations (attractors) for more liberated, emancipated roles for women in the future.

Formulation d'un modele mathematique par des techniques d'estimation de parametres a partir de donnees de vol pour l'helicoptere Bell 427 et l'avion F/A-18 servant a la recherches en aeroservoelasticite

NASA Astrophysics Data System (ADS)

Nadeau-Beaulieu, Michel

In this thesis, three mathematical models are built from flight test data for different aircraft design applications: a ground dynamics model for the Bell 427 helicopter, a prediction model for the rotor and engine parameters for the same helicopter type and a simulation model for the aeroelastic deflections of the F/A-18. In the ground dynamics application, the model structure is derived from physics where the normal force between the helicopter and the ground is modelled as a vertical spring and the frictional force is modelled with static and dynamic friction coefficients. The ground dynamics model coefficients are optimized to ensure that the model matches the landing data within the FAA (Federal Aviation Administration) tolerance bands for a level D flight simulator. In the rotor and engine application, rotors torques (main and tail), the engine torque and main rotor speed are estimated using a state-space model. The model inputs are nonlinear terms derived from the pilot control inputs and the helicopter states. The model parameters are identified using the subspace method and are further optimised with the Levenberg-Marquardt minimisation algorithm. The model built with the subspace method provides an excellent estimate of the outputs within the FAA tolerance bands. The F/A-18 aeroelastic state-space model is built from flight test. The research concerning this model is divided in two parts. Firstly, the deflection of a given structural surface on the aircraft following a differential ailerons control input is represented by a Multiple Inputs Single Outputs linear model whose inputs are the ailerons positions and the structural surfaces deflections. Secondly, a single state-space model is used to represent the deflection of the aircraft wings and trailing edge flaps following any control input. In this case the model is made non-linear by multiplying model inputs into higher order terms and using these terms as the inputs of the state-space equations. In both cases, the identification method is the subspace method. Most fit coefficients between the estimated and the measured signals are above 73% and most correlation coefficient are higher than 90%.

Overview of NASA Heliophysics and the Science of Space Weather

NASA Astrophysics Data System (ADS)

Talaat, E. R.

2017-12-01

In this paper, an overview is presented on the various activities within NASA that address space weather-related observations, model development, and research to operations. Specific to space weather, NASA formulates and implements, through the Heliophysics division, a national research program for understanding the Sun and its interactions with the Earth and the Solar System and how these phenomena impact life and society. NASA researches and prototypes new mission and instrument capabilities in this area, providing new physics-based algorithms to advance the state of solar, space physics, and space weather modeling.

The Shock Response of Space Bears: The Ability of Life to Survive Some of the Most Extreme Environments Known to Man

NASA Astrophysics Data System (ADS)

Painter, Jonathon; Leighs, James; Appleby-Thomas, Gareth; Hazael, Rachael; McMillan, Paul; Kristensen, Reinhardt

2013-06-01

There have been many recent discoveries of life forms living in environments previously thought to be completely uninhabitable. One particularly interesting discovery of this na- ture is the space bear or tardigrade. The name space bear is a colloquialism applied to the tardigrades because of a recent investigation which saw them being exposed to the vacuum of space and intense solar radiation, and surviving. Tardigrades have the ability to dehy- drate themselves, entering a state called cryptobiosis. This state enables them to survive in the vacuum of space. A single stage gas gun has been employed to uniaxially shock load and subsequently recover tardigrades in both regular and cryptobiotic states. Loading histories were calculated via hydrocode modelling. Survival data is presented comparing shocked and control samples for tardigrades both in normal and cryptobiotic states.

State-space decoding of primary afferent neuron firing rates

NASA Astrophysics Data System (ADS)

Wagenaar, J. B.; Ventura, V.; Weber, D. J.

2011-02-01

Kinematic state feedback is important for neuroprostheses to generate stable and adaptive movements of an extremity. State information, represented in the firing rates of populations of primary afferent (PA) neurons, can be recorded at the level of the dorsal root ganglia (DRG). Previous work in cats showed the feasibility of using DRG recordings to predict the kinematic state of the hind limb using reverse regression. Although accurate decoding results were attained, reverse regression does not make efficient use of the information embedded in the firing rates of the neural population. In this paper, we present decoding results based on state-space modeling, and show that it is a more principled and more efficient method for decoding the firing rates in an ensemble of PA neurons. In particular, we show that we can extract confounded information from neurons that respond to multiple kinematic parameters, and that including velocity components in the firing rate models significantly increases the accuracy of the decoded trajectory. We show that, on average, state-space decoding is twice as efficient as reverse regression for decoding joint and endpoint kinematics.

Inverse problem for multispecies ferromagneticlike mean-field models in phase space with many states

NASA Astrophysics Data System (ADS)

Fedele, Micaela; Vernia, Cecilia

2017-10-01

In this paper we solve the inverse problem for the Curie-Weiss model and its multispecies version when multiple thermodynamic states are present as in the low temperature phase where the phase space is clustered. The inverse problem consists of reconstructing the model parameters starting from configuration data generated according to the distribution of the model. We demonstrate that, without taking into account the presence of many states, the application of the inversion procedure produces very poor inference results. To overcome this problem, we use the clustering algorithm. When the system has two symmetric states of positive and negative magnetizations, the parameter reconstruction can also be obtained with smaller computational effort simply by flipping the sign of the magnetizations from positive to negative (or vice versa). The parameter reconstruction fails when the system undergoes a phase transition: In that case we give the correct inversion formulas for the Curie-Weiss model and we show that they can be used to measure how close the system gets to being critical.

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Multi-Agent Strategic Modeling in a Specific Environment

NASA Astrophysics Data System (ADS)

Gams, Matjaz; Bezek, Andraz

Multi-agent modeling in ambient intelligence (AmI) is concerned with the following task [19]: How can external observations of multi-agent systems in the ambient be used to analyze, model, and direct agent behavior? The main purpose is to obtain knowledge about acts in the environment thus enabling proper actions of the AmI systems [1]. Analysis of such systems must thus capture complex world state representation and asynchronous agent activities. Instead of studying basic numerical data, researchers often use more complex data structures, such as rules and decision trees. Some methods are extremely useful when characterizing state space, but lack the ability to clearly represent temporal state changes occurred by agent actions. To comprehend simultaneous agent actions and complex changes of state space, most often a combination of graphical and symbolical representation performs better in terms of human understanding and performance.

System modelling for LISA Pathfinder

NASA Astrophysics Data System (ADS)

Diaz-Aguiló, Marc; Grynagier, Adrien; Rais, Boutheina

LISA Pathfinder is the technology demonstrator for LISA, a space-borne gravitational waves observatory. The goal of the mission is to characterise the dynamics of the LISA Technology Package (LTP) to prove that on-board experimental conditions are compatible with the de-tection of gravitational waves. The LTP is a drag-free dynamics experiment which includes a control loop with sensors (interferometric and capacitive), actuators (capacitive actuators and thrusters), controlled disturbances (magnetic coils, heaters) and which is subject to various endogenous or exogenous noise sources such as infrared pressure or solar wind. The LTP experiment features new hardware which was never flown in space. The mission has a tight operation timeline as it is constrained to about 100 days. It is therefore vital to have efficient and precise means of investigation and diagnostics to be used during the on-orbit operations. These will be conducted using the LTP Data Analysis toolbox (LTPDA) which allows for simulation, parameter identification and various analyses (covariance analysis, state estimation) given an experimental model. The LTPDA toolbox therefore contains a series of models which are state-space representations of each component in the LTP. The State-Space Models (SSM) are objects of a state-space class within the LTPDA toolbox especially designed to address all the requirements of this tool. The user has access to a set of linear models which represent every satellite subsystem; the models are available in different forms representing 1D, 2D and 3D systems, each with settable symbolic and numeric parameters. To limit the possible errors, the models can be automatically linked to produce composite systems and closed-loops of the LTP. Finally, for the sake of completeness, accuracy and maintainability of the tool, the models contain all the physical information they mimic (i.e. variable units, description of parameters, description of inputs/outputs, etc). Models developed for this work include the fixed-point linearized equations of motion for the LTP and the linear models for sensors and actuators with their noise modelling blocks issued from the analysis of the individual actuators. The drag-free controller model includes the dis-crete delays expected in the hardware. In this work we briefly describe the software architecture, in order to concentrate then on the physical description of the models. This is supported by an overview of different user scenarios and some examples of model analysis that highlight the advantages of this high-level programming engineering toolbox for space mission data analysis and calibration.

Quantum-like model of brain's functioning: decision making from decoherence.

PubMed

Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu; Basieva, Irina; Khrennikov, Andrei

2011-07-21

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in a complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices (representing mental states). This equilibrium state determines Alice's mixed (i.e., probabilistic) strategy. We use a master equation in which quantum physics describes the process of decoherence as the result of interaction with environment. Thus our model is a model of thinking through decoherence of the initially pure mental state. Decoherence is induced by the interaction with memory and the external mental environment. We study (numerically) the dynamics of quantum entropy of Alice's mental state in the process of decision making. We also consider classical entropy corresponding to Alice's choices. We introduce a measure of Alice's diffidence as the difference between classical and quantum entropies of Alice's mental state. We see that (at least in our model example) diffidence decreases (approaching zero) in the process of decision making. Finally, we discuss the problem of neuronal realization of quantum-like dynamics in the brain; especially roles played by lateral prefrontal cortex or/and orbitofrontal cortex. Copyright © 2011 Elsevier Ltd. All rights reserved.

A unified theoretical framework for mapping models for the multi-state Hamiltonian.

PubMed

Liu, Jian

2016-11-28

We propose a new unified theoretical framework to construct equivalent representations of the multi-state Hamiltonian operator and present several approaches for the mapping onto the Cartesian phase space. After mapping an F-dimensional Hamiltonian onto an F+1 dimensional space, creation and annihilation operators are defined such that the F+1 dimensional space is complete for any combined excitation. Commutation and anti-commutation relations are then naturally derived, which show that the underlying degrees of freedom are neither bosons nor fermions. This sets the scene for developing equivalent expressions of the Hamiltonian operator in quantum mechanics and their classical/semiclassical counterparts. Six mapping models are presented as examples. The framework also offers a novel way to derive such as the well-known Meyer-Miller model.

On the physical Hilbert space of loop quantum cosmology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Noui, Karim; Perez, Alejandro; Vandersloot, Kevin

2005-02-15

In this paper we present a model of Riemannian loop quantum cosmology with a self-adjoint quantum scalar constraint. The physical Hilbert space is constructed using refined algebraic quantization. When matter is included in the form of a cosmological constant, the model is exactly solvable and we show explicitly that the physical Hilbert space is separable, consisting of a single physical state. We extend the model to the Lorentzian sector and discuss important implications for standard loop quantum cosmology.

ψ -ontology result without the Cartesian product assumption

NASA Astrophysics Data System (ADS)

Myrvold, Wayne C.

2018-05-01

We introduce a weakening of the preparation independence postulate of Pusey et al. [Nat. Phys. 8, 475 (2012), 10.1038/nphys2309] that does not presuppose that the space of ontic states resulting from a product-state preparation can be represented by the Cartesian product of subsystem state spaces. On the basis of this weakened assumption, it is shown that, in any model that reproduces the quantum probabilities, any pair of pure quantum states |ψ >,|ϕ > with <ϕ |ψ > ≤1 /√{2 } must be ontologically distinct.

Space-time modeling of timber prices

Treesearch

Mo Zhou; Joseph Buongriorno

2006-01-01

A space-time econometric model was developed for pine sawtimber timber prices of 21 geographically contiguous regions in the southern United States. The correlations between prices in neighboring regions helped predict future prices. The impulse response analysis showed that although southern pine sawtimber markets were not globally integrated, local supply and demand...

Stable quantum systems in anti-de Sitter space: Causality, independence, and spectral properties

NASA Astrophysics Data System (ADS)

Buchholz, Detlev; Summers, Stephen J.

2004-12-01

If a state is passive for uniformly accelerated observers in n-dimensional (n⩾2) anti-de Sitter (Ads) space-time (i.e., cannot be used by them to operate a perpetuum mobile), they will (a) register a universal value of the Unruh temperature, (b) discover a PCT symmetry, and (c) find that observables in complementary wedge-shaped regions necessarily commute with each other in this state. The stability properties of such a passive state induce a "geodesic causal structure" on AdS and concommitant locality relations. It is shown that observables in these complementary wedge-shaped regions fulfill strong additional independence conditions. In two-dimensional AdS these even suffice to enable the derivation of a nontrivial, local, covariant net indexed by bounded space-time regions. All these results are model-independent and hold in any theory which is compatible with a weak notion of space-time localization. Examples are provided of models satisfying the hypotheses of these theorems.

Spectral functions of a time-periodically driven Falicov-Kimball model: Real-space Floquet dynamical mean-field theory study

NASA Astrophysics Data System (ADS)

Qin, Tao; Hofstetter, Walter

2017-08-01

We present a systematic study of the spectral functions of a time-periodically driven Falicov-Kimball Hamiltonian. In the high-frequency limit, this system can be effectively described as a Harper-Hofstadter-Falicov-Kimball model. Using real-space Floquet dynamical mean-field theory (DMFT), we take into account the interaction effects and contributions from higher Floquet bands in a nonperturbative way. Our calculations show a high degree of similarity between the interacting driven system and its effective static counterpart with respect to spectral properties. However, as also illustrated by our results, one should bear in mind that Floquet DMFT describes a nonequilibrium steady state, while an effective static Hamiltonian describes an equilibrium state. We further demonstrate the possibility of using real-space Floquet DMFT to study edge states on a cylinder geometry.

Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method

NASA Astrophysics Data System (ADS)

Wang, Dong; Tse, Peter W.

2015-05-01

Slurry pumps are commonly used in oil-sand mining for pumping mixtures of abrasive liquids and solids. These operations cause constant wear of slurry pump impellers, which results in the breakdown of the slurry pumps. This paper develops a prognostic method for estimating remaining useful life of slurry pump impellers. First, a moving-average wear degradation index is proposed to assess the performance degradation of the slurry pump impeller. Secondly, the state space model of the proposed health index is constructed. A general sequential Monte Carlo method is employed to derive the parameters of the state space model. The remaining useful life of the slurry pump impeller is estimated by extrapolating the established state space model to a specified alert threshold. Data collected from an industrial oil sand pump were used to validate the developed method. The results show that the accuracy of the developed method improves as more data become available.

State-space based analysis and forecasting of macroscopic road safety trends in Greece.

PubMed

Antoniou, Constantinos; Yannis, George

2013-11-01

In this paper, macroscopic road safety trends in Greece are analyzed using state-space models and data for 52 years (1960-2011). Seemingly unrelated time series equations (SUTSE) models are developed first, followed by richer latent risk time-series (LRT) models. As reliable estimates of vehicle-kilometers are not available for Greece, the number of vehicles in circulation is used as a proxy to the exposure. Alternative considered models are presented and discussed, including diagnostics for the assessment of their model quality and recommendations for further enrichment of this model. Important interventions were incorporated in the models developed (1986 financial crisis, 1991 old-car exchange scheme, 1996 new road fatality definition) and found statistically significant. Furthermore, the forecasting results using data up to 2008 were compared with final actual data (2009-2011) indicating that the models perform properly, even in unusual situations, like the current strong financial crisis in Greece. Forecasting results up to 2020 are also presented and compared with the forecasts of a model that explicitly considers the currently on-going recession. Modeling the recession, and assuming that it will end by 2013, results in more reasonable estimates of risk and vehicle-kilometers for the 2020 horizon. This research demonstrates the benefits of using advanced state-space modeling techniques for modeling macroscopic road safety trends, such as allowing the explicit modeling of interventions. The challenges associated with the application of such state-of-the-art models for macroscopic phenomena, such as traffic fatalities in a region or country, are also highlighted. Furthermore, it is demonstrated that it is possible to apply such complex models using the relatively short time-series that are available in macroscopic road safety analysis. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

PubMed

Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

2014-01-01

Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

Effect of ion concentration changes in the limited extracellular spaces on sarcolemmal ion transport and Ca2+ turnover in a model of human ventricular cardiomyocyte.

PubMed

Hrabcová, Dana; Pásek, Michal; Šimurda, Jiří; Christé, Georges

2013-12-13

We have developed a computer model of human cardiac ventricular myocyte (CVM), including t-tubular and cleft spaces with the aim of evaluating the impact of accumulation-depletion of ions in restricted extracellular spaces on transmembrane ion transport and ionic homeostasis in human CVM. The model was based on available data from human CVMs. Under steady state, the effect of ion concentration changes in extracellular spaces on [Ca2+]i-transient was explored as a function of critical fractions of ion transporters in t-tubular membrane (not documented for human CVM). Depletion of Ca2+ and accumulation of K+ occurring in extracellular spaces slightly affected the transmembrane Ca2+ flux, but not the action potential duration (APD90). The [Ca2+]i-transient was reduced (by 2%-9%), depending on the stimulation frequency, the rate of ion exchange between t-tubules and clefts and fractions of ion-transfer proteins in the t-tubular membrane. Under non-steady state, the responses of the model to changes of stimulation frequency were analyzed. A sudden increase of frequency (1-2.5 Hz) caused a temporal decrease of [Ca2+] in both extracellular spaces, a reduction of [Ca2+]i-transient (by 15%) and APD90 (by 13 ms). The results reveal different effects of activity-related ion concentration changes in human cardiac t-tubules (steady-state effects) and intercellular clefts (transient effects) in the modulation of membrane ion transport and Ca2+ turnover.

Three-body effects in the Hoyle-state decay

NASA Astrophysics Data System (ADS)

Refsgaard, J.; Fynbo, H. O. U.; Kirsebom, O. S.; Riisager, K.

2018-04-01

We use a sequential R-matrix model to describe the breakup of the Hoyle state into three α particles via the ground state of 8Be. It is shown that even in a sequential picture, features resembling a direct breakup branch appear in the phase-space distribution of the α particles. We construct a toy model to describe the Coulomb interaction in the three-body final state and its effects on the decay spectrum are investigated. The framework is also used to predict the phase-space distribution of the α particles emitted in a direct breakup of the Hoyle state and the possibility of interference between a direct and sequential branch is discussed. Our numerical results are compared to the current upper limit on the direct decay branch determined in recent experiments.

Large-basis ab initio no-core shell model and its application to {sup 12}C

DOE Office of Scientific and Technical Information (OSTI.GOV)

Navratil, P.; Vary, J. P.; Barrett, B. R.

2000-11-01

We present the framework for the ab initio no-core nuclear shell model and apply it to obtain properties of {sup 12}C. We derive two-body effective interactions microscopically for specific model spaces from the realistic CD-Bonn and the Argonne V8' nucleon-nucleon (NN) potentials. We then evaluate binding energies, excitation spectra, radii, and electromagnetic transitions in the 0{Dirac_h}{Omega}, 2{Dirac_h}{Omega}, and 4{Dirac_h}{Omega} model spaces for the positive-parity states and the 1{Dirac_h}{Omega}, 3{Dirac_h}{Omega}, and 5{Dirac_h}{Omega} model spaces for the negative-parity states. Dependence on the model-space size, on the harmonic-oscillator frequency, and on the type of the NN potential, used for the effective interaction derivation,more » are studied. In addition, electromagnetic and weak neutral elastic charge form factors are calculated in the impulse approximation. Sensitivity of the form-factor ratios to the strangeness one-body form-factor parameters and to the influence of isospin-symmetry violation is evaluated and discussed. Agreement between theory and experiment is favorable for many observables, while others require yet larger model spaces and/or three-body forces. The limitations of the present results are easily understood by virtue of the trends established and previous phenomenological results.« less

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Noise effect in metabolic networks

NASA Astrophysics Data System (ADS)

Li, Zheng-Yan; Xie, Zheng-Wei; Chen, Tong; Ouyang, Qi

2009-12-01

Constraint-based models such as flux balance analysis (FBA) are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term 'noise', and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries

NASA Astrophysics Data System (ADS)

Wang, Dong; Zhao, Yang; Yang, Fangfang; Tsui, Kwok-Leung

2017-09-01

Brownian motion with adaptive drift has attracted much attention in prognostics because its first hitting time is highly relevant to remaining useful life prediction and it follows the inverse Gaussian distribution. Besides linear degradation modeling, nonlinear-drifted Brownian motion has been developed to model nonlinear degradation. Moreover, the first hitting time distribution of the nonlinear-drifted Brownian motion has been approximated by time-space transformation. In the previous studies, the drift coefficient is the only hidden state used in state space modeling of the nonlinear-drifted Brownian motion. Besides the drift coefficient, parameters of a nonlinear function used in the nonlinear-drifted Brownian motion should be treated as additional hidden states of state space modeling to make the nonlinear-drifted Brownian motion more flexible. In this paper, a prognostic method based on nonlinear-drifted Brownian motion with multiple hidden states is proposed and then it is applied to predict remaining useful life of rechargeable batteries. 26 sets of rechargeable battery degradation samples are analyzed to validate the effectiveness of the proposed prognostic method. Moreover, some comparisons with a standard particle filter based prognostic method, a spherical cubature particle filter based prognostic method and two classic Bayesian prognostic methods are conducted to highlight the superiority of the proposed prognostic method. Results show that the proposed prognostic method has lower average prediction errors than the particle filter based prognostic methods and the classic Bayesian prognostic methods for battery remaining useful life prediction.

Space Weather Model Testing And Validation At The Community Coordinated Modeling Center

NASA Astrophysics Data System (ADS)

Hesse, M.; Kuznetsova, M.; Rastaetter, L.; Falasca, A.; Keller, K.; Reitan, P.

The Community Coordinated Modeling Center (CCMC) is a multi-agency partner- ship aimed at the creation of next generation space weather models. The goal of the CCMC is to undertake the research and developmental work necessary to substantially increase the present-day modeling capability for space weather purposes, and to pro- vide models for transition to the rapid prototyping centers at the space weather forecast centers. This goal requires close collaborations with and substantial involvement of the research community. The physical regions to be addressed by CCMC-related activities range from the solar atmosphere to the Earth's upper atmosphere. The CCMC is an integral part of NASA's Living With aStar initiative, of the National Space Weather Program Implementation Plan, and of the Department of Defense Space Weather Tran- sition Plan. CCMC includes a facility at NASA Goddard Space Flight Center, as well as distributed computing facilities provided by the Air Force. CCMC also provides, to the research community, access to state-of-the-art space research models. In this paper we will provide updates on CCMC status, on current plans, research and devel- opment accomplishments and goals, and on the model testing and validation process undertaken as part of the CCMC mandate.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Priorities in national space strategies and governance of the member states of the European Space Agency

NASA Astrophysics Data System (ADS)

Adriaensen, Maarten; Giannopapa, Christina; Sagath, Daniel; Papastefanou, Anastasia

2015-12-01

The European Space Agency (ESA) has twenty Member States with a variety of strategic priorities and governance structures regarding their space activities. A number of countries engage in space activities exclusively though ESA, while others have also their own national space programme. Some consider ESA as their prime space agency and others have additionally their own national agency with respective programmes. The main objective of this paper is to provide an up-to date overview and a holistic assessment of strategic priorities and the national space governance structures in 20 ESA Member States. This analysis and assessment has been conducted by analysing the Member States public documents, information provided at ESA workshop on this topic and though unstructured interviews. The paper is structured to include two main elements: priorities and trends in national space strategies and space governance in ESA Member States. The first part of this paper focuses on the content and analysis of the national space strategies and indicates the main priorities and trends in Member States. The priorities are categorised with regards to technology domains, the role of space in the areas of sustainability and the motivators that boost engagement in space. These vary from one Member State to another and include with different levels of engagement in technology domains amongst others: science and exploration, navigation, Earth observation, human space flight, launchers, telecommunications, and integrated applications. Member States allocate a different role of space as enabling tool adding to the advancement of sustainability areas including: security, resources, environment and climate change, transport and communication, energy, and knowledge and education. The motivators motivating reasoning which enhances or hinders space engagement also differs. The motivators identified are industrial competitiveness, job creation, technology development and transfer, social benefits, international cooperation, and European non-dependence. The second part of the paper provides a categorisation of national space governance structures in ESA Member States. Different governance models are identified depending on the responsible ministries for space for a number of space related organisations and ESA. In the case of ESA, these can typically vary from the more traditional ministry of science and/or education, the ministry of industry and/or innovation to the more recent ones being the ministry of economy and the ministry of transport. Recognising the transverse nature of space and its potential as a tool for a number of policies like agriculture, environment, maritime, disaster management, etc., other ministries are more and more getting involved in space activities. The development and implementation of the space strategy and policy of a Member State is realised though the engagement of an implementing entity. The type, role and activity vary from Member State to Member State.

Analysis of biological time-lapse microscopic experiment from the point of view of the information theory.

PubMed

Štys, Dalibor; Urban, Jan; Vaněk, Jan; Císař, Petr

2011-06-01

We report objective analysis of information in the microscopic image of the cell monolayer. The process of transfer of information about the cell by the microscope is analyzed in terms of the classical Shannon information transfer scheme. The information source is the biological object, the information transfer channel is the whole microscope including the camera chip. The destination is the model of biological system. The information contribution is analyzed as information carried by a point to overall information in the image. Subsequently we obtain information reflection of the biological object. This is transformed in the biological model which, in information terminology, is the destination. This, we propose, should be constructed as state transitions in individual cells modulated by information bonds between the cells. We show examples of detected cell states in multidimensional state space. This space is reflected as colour channel intensity phenomenological state space. We have also observed information bonds and show examples of them.

Analysis of biological time-lapse microscopic experiment from the point of view of the information theory.

PubMed

Stys, Dalibor; Urban, Jan; Vanek, Jan; Císar, Petr

2010-07-01

We report objective analysis of information in the microscopic image of the cell monolayer. The process of transfer of information about the cell by the microscope is analyzed in terms of the classical Shannon information transfer scheme. The information source is the biological object, the information transfer channel is the whole microscope including the camera chip. The destination is the model of biological system. The information contribution is analyzed as information carried by a point to overall information in the image. Subsequently we obtain information reflection of the biological object. This is transformed in the biological model which, in information terminology, is the destination. This, we propose, should be constructed as state transitions in individual cells modulated by information bonds between the cells. We show examples of detected cell states in multidimensional state space reflected in space an colour channel intensity phenomenological state space. We have also observed information bonds and show examples of them. Copyright 2010 Elsevier Ltd. All rights reserved.

Techniques for modeling the reliability of fault-tolerant systems with the Markov state-space approach

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Johnson, Sally C.

1995-01-01

This paper presents a step-by-step tutorial of the methods and the tools that were used for the reliability analysis of fault-tolerant systems. The approach used in this paper is the Markov (or semi-Markov) state-space method. The paper is intended for design engineers with a basic understanding of computer architecture and fault tolerance, but little knowledge of reliability modeling. The representation of architectural features in mathematical models is emphasized. This paper does not present details of the mathematical solution of complex reliability models. Instead, it describes the use of several recently developed computer programs SURE, ASSIST, STEM, and PAWS that automate the generation and the solution of these models.

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

NASA Astrophysics Data System (ADS)

Chemali, Jessica; Ching, ShiNung; Purdon, Patrick L.; Solt, Ken; Brown, Emery N.

2013-10-01

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.

An Advanced Hierarchical Hybrid Environment for Reliability and Performance Modeling

NASA Technical Reports Server (NTRS)

Ciardo, Gianfranco

2003-01-01

The key issue we intended to address in our proposed research project was the ability to model and study logical and probabilistic aspects of large computer systems. In particular, we wanted to focus mostly on automatic solution algorithms based on a state-space exploration as their first step, in addition to the more traditional discrete-event simulation approaches commonly employed in industry. One explicitly-stated goal was to extend by several orders of magnitude the size of models that can be solved exactly, using a combination of techniques: 1) Efficient exploration and storage of the state space using new data structures that require an amount of memory sublinear in the number states; and 2) Exploitation of the existing symmetries in the matrices describing the system behavior using Kronecker operators. Not only we have been successful in achieving the above goals, but we exceeded them in many respects.

Thermal Analysis and Design of an Advanced Space Suit

NASA Technical Reports Server (NTRS)

Lin, Chin H.; Campbell, Anthony B.; French, Jonathan D.; French, D.; Nair, Satish S.; Miles, John B.

2000-01-01

The thermal dynamics and design of an Advanced Space Suit are considered. A transient model of the Advanced Space Suit has been developed and implemented using MATLAB/Simulink to help with sizing, with design evaluation, and with the development of an automatic thermal comfort control strategy. The model is described and the thermal characteristics of the Advanced Space suit are investigated including various parametric design studies. The steady state performance envelope for the Advanced Space Suit is defined in terms of the thermal environment and human metabolic rate and the transient response of the human-suit-MPLSS system is analyzed.

Multiverse Space-Antispace Dual Calabi-Yau `Exciplex-Zitterbewegung' Particle Creation

NASA Astrophysics Data System (ADS)

Amoroso, Richard L.

Modeling the `creation/emergence' of matter from spacetime is as old as modern cosmology itself and not without controversy within each model such as Static, Steady-state, Big Bang or Multiverse Continuous-State. In this paper we present only a brief primitive introduction to a new form of `Exciplex-Zitterbewegung' dual space-antispace vacuum Particle Creation applicable especially to Big Bang alternatives which are well-known but ignored; Hubble discovered `Redshift' not a Doppler expansion of the universe which remains the currently popular interpretation. Holographic Anthropic Multiverse cosmology provides viable alternatives to all seemingly sacrosanct pillars of the Big Bang. A model for Multiverse Space-Antispace Dual Calabi-Yau `Exciplex-Zitterbewegung' particle creation has only become possible by incorporating the additional degrees of freedom provided by the capacity complex dimensional extended Yang-Mills Kaluza-Klein correspondence provides.

Application of an unsteady-state model for predicting vertical temperature distribution to an existing atrium

DOE Office of Scientific and Technical Information (OSTI.GOV)

Takemasa, Yuichi; Togari, Satoshi; Arai, Yoshinobu

1996-11-01

Vertical temperature differences tend to be great in a large indoor space such as an atrium, and it is important to predict variations of vertical temperature distribution in the early stage of the design. The authors previously developed and reported on a new simplified unsteady-state calculation model for predicting vertical temperature distribution in a large space. In this paper, this model is applied to predicting the vertical temperature distribution in an existing low-rise atrium that has a skylight and is affected by transmitted solar radiation. Detailed calculation procedures that use the model are presented with all the boundary conditions, andmore » analytical simulations are carried out for the cooling condition. Calculated values are compared with measured results. The results of the comparison demonstrate that the calculation model can be applied to the design of a large space. The effects of occupied-zone cooling are also discussed and compared with those of all-zone cooling.« less

Closed Loop System Identification with Genetic Algorithms

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

A black box optimization approach to parameter estimation in a model for long/short term variations dynamics of commodity prices

NASA Astrophysics Data System (ADS)

De Santis, Alberto; Dellepiane, Umberto; Lucidi, Stefano

2012-11-01

In this paper we investigate the estimation problem for a model of the commodity prices. This model is a stochastic state space dynamical model and the problem unknowns are the state variables and the system parameters. Data are represented by the commodity spot prices, very seldom time series of Futures contracts are available for free. Both the system joint likelihood function (state variables and parameters) and the system marginal likelihood (the state variables are eliminated) function are addressed.

On Markov parameters in system identification

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Longman, Richard W.

1991-01-01

A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.

Loop-quantum-gravity vertex amplitude.

PubMed

Engle, Jonathan; Pereira, Roberto; Rovelli, Carlo

2007-10-19

Spin foam models are hoped to provide the dynamics of loop-quantum gravity. However, the most popular of these, the Barrett-Crane model, does not have the good boundary state space and there are indications that it fails to yield good low-energy n-point functions. We present an alternative dynamics that can be derived as a quantization of a Regge discretization of Euclidean general relativity, where second class constraints are imposed weakly. Its state space matches the SO(3) loop gravity one and it yields an SO(4)-covariant vertex amplitude for Euclidean loop gravity.

Approximations of thermoelastic and viscoelastic control systems

NASA Technical Reports Server (NTRS)

Burns, J. A.; Liu, Z. Y.; Miller, R. E.

1990-01-01

Well-posed models and computational algorithms are developed and analyzed for control of a class of partial differential equations that describe the motions of thermo-viscoelastic structures. An abstract (state space) framework and a general well-posedness result are presented that can be applied to a large class of thermo-elastic and thermo-viscoelastic models. This state space framework is used in the development of a computational scheme to be used in the solution of a linear quadratic regulator (LQR) control problem. A detailed convergence proof is provided for the viscoelastic model and several numerical results are presented to illustrate the theory and to analyze problems for which the theory is incomplete.

Program Model Checking: A Practitioner's Guide

NASA Technical Reports Server (NTRS)

Pressburger, Thomas T.; Mansouri-Samani, Masoud; Mehlitz, Peter C.; Pasareanu, Corina S.; Markosian, Lawrence Z.; Penix, John J.; Brat, Guillaume P.; Visser, Willem C.

2008-01-01

Program model checking is a verification technology that uses state-space exploration to evaluate large numbers of potential program executions. Program model checking provides improved coverage over testing by systematically evaluating all possible test inputs and all possible interleavings of threads in a multithreaded system. Model-checking algorithms use several classes of optimizations to reduce the time and memory requirements for analysis, as well as heuristics for meaningful analysis of partial areas of the state space Our goal in this guidebook is to assemble, distill, and demonstrate emerging best practices for applying program model checking. We offer it as a starting point and introduction for those who want to apply model checking to software verification and validation. The guidebook will not discuss any specific tool in great detail, but we provide references for specific tools.

Homoclinic accretion solutions in the Schwarzschild-anti-de Sitter space-time

NASA Astrophysics Data System (ADS)

Mach, Patryk

2015-04-01

The aim of this paper is to clarify the distinction between homoclinic and standard (global) Bondi-type accretion solutions in the Schwarzschild-anti-de Sitter space-time. The homoclinic solutions have recently been discovered numerically for polytropic equations of state. Here I show that they exist also for certain isothermal (linear) equations of state, and an analytic solution of this type is obtained. It is argued that the existence of such solutions is generic, although for sufficiently relativistic matter models (photon gas, ultrahard equation of state) there exist global solutions that can be continued to infinity, similarly to standard Michel's solutions in the Schwarzschild space-time. In contrast to that global solutions should not exist for matter models with a nonvanishing rest-mass component, and this is demonstrated for polytropes. For homoclinic isothermal solutions I derive an upper bound on the mass of the black hole for which stationary transonic accretion is allowed.

Online technique for detecting state of onboard fiber optic gyroscope

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miao, Zhiyong; He, Kunpeng, E-mail: pengkhe@126.com; Pang, Shuwan

2015-02-15

Although angle random walk (ARW) of fiber optic gyroscope (FOG) has been well modeled and identified before being integrated into the high-accuracy attitude control system of satellite, aging and unexpected failures can affect the performance of FOG after launch, resulting in the variation of ARW coefficient. Therefore, the ARW coefficient can be regarded as an indicator of “state of health” for FOG diagnosis in some sense. The Allan variance method can be used to estimate ARW coefficient of FOG, however, it requires a large amount of data to be stored. Moreover, the procedure of drawing slope lines for estimation ismore » painful. To overcome the barriers, a weighted state-space model that directly models the ARW to obtain a nonlinear state-space model was established for FOG. Then, a neural extended-Kalman filter algorithm was implemented to estimate and track the variation of ARW in real time. The results of experiment show that the proposed approach is valid to detect the state of FOG. Moreover, the proposed technique effectively avoids the storage of data.« less

Latest Community Coordinated Modeling Center (CCMC) services and innovative tools supporting the space weather research and operational communities.

NASA Astrophysics Data System (ADS)

Mendoza, A. M. M.; Rastaetter, L.; Kuznetsova, M. M.; Mays, M. L.; Chulaki, A.; Shim, J. S.; MacNeice, P. J.; Taktakishvili, A.; Collado-Vega, Y. M.; Weigand, C.; Zheng, Y.; Mullinix, R.; Patel, K.; Pembroke, A. D.; Pulkkinen, A. A.; Boblitt, J. M.; Bakshi, S. S.; Tsui, T.

2017-12-01

The Community Coordinated Modeling Center (CCMC), with the fundamental goal of aiding the transition of modern space science models into space weather forecasting while supporting space science research, has been serving as an integral hub for over 15 years, providing invaluable resources to both space weather scientific and operational communities. CCMC has developed and provided innovative web-based point of access tools varying from: Runs-On-Request System - providing unprecedented global access to the largest collection of state-of-the-art solar and space physics models, Integrated Space Weather Analysis (iSWA) - a powerful dissemination system for space weather information, Advanced Online Visualization and Analysis tools for more accurate interpretation of model results, Standard Data formats for Simulation Data downloads, and Mobile apps to view space weather data anywhere to the scientific community. In addition to supporting research and performing model evaluations, CCMC also supports space science education by hosting summer students through local universities. In this poster, we will showcase CCMC's latest innovative tools and services, and CCMC's tools that revolutionized the way we do research and improve our operational space weather capabilities. CCMC's free tools and resources are all publicly available online (http://ccmc.gsfc.nasa.gov).

Genetic Algorithm-Guided, Adaptive Model Order Reduction of Flexible Aircrafts

NASA Technical Reports Server (NTRS)

Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter; Brenner, Martin J.

2017-01-01

This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM) for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design.

Tethered Satellites as Enabling Platforms for an Operational Space Weather Monitoring System

NASA Technical Reports Server (NTRS)

Krause, L. Habash; Gilchrist, B. E.; Bilen, S.; Owens, J.; Voronka, N.; Furhop, K.

2013-01-01

Space weather nowcasting and forecasting models require assimilation of near-real time (NRT) space environment data to improve the precision and accuracy of operational products. Typically, these models begin with a climatological model to provide "most probable distributions" of environmental parameters as a function of time and space. The process of NRT data assimilation gently pulls the climate model closer toward the observed state (e.g. via Kalman smoothing) for nowcasting, and forecasting is achieved through a set of iterative physics-based forward-prediction calculations. The issue of required space weather observatories to meet the spatial and temporal requirements of these models is a complex one, and we do not address that with this poster. Instead, we present some examples of how tethered satellites can be used to address the shortfalls in our ability to measure critical environmental parameters necessary to drive these space weather models. Examples include very long baseline electric field measurements, magnetized ionospheric conductivity measurements, and the ability to separate temporal from spatial irregularities in environmental parameters. Tethered satellite functional requirements will be presented for each space weather parameter considered in this study.

Complexity of Kronecker Operations on Sparse Matrices with Applications to the Solution of Markov Models

NASA Technical Reports Server (NTRS)

Buchholz, Peter; Ciardo, Gianfranco; Donatelli, Susanna; Kemper, Peter

1997-01-01

We present a systematic discussion of algorithms to multiply a vector by a matrix expressed as the Kronecker product of sparse matrices, extending previous work in a unified notational framework. Then, we use our results to define new algorithms for the solution of large structured Markov models. In addition to a comprehensive overview of existing approaches, we give new results with respect to: (1) managing certain types of state-dependent behavior without incurring extra cost; (2) supporting both Jacobi-style and Gauss-Seidel-style methods by appropriate multiplication algorithms; (3) speeding up algorithms that consider probability vectors of size equal to the "actual" state space instead of the "potential" state space.

Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models

NASA Astrophysics Data System (ADS)

Vakilzadeh, Majid K.; Huang, Yong; Beck, James L.; Abrahamsson, Thomas

2017-02-01

A new multi-level Markov Chain Monte Carlo algorithm for Approximate Bayesian Computation, ABC-SubSim, has recently appeared that exploits the Subset Simulation method for efficient rare-event simulation. ABC-SubSim adaptively creates a nested decreasing sequence of data-approximating regions in the output space that correspond to increasingly closer approximations of the observed output vector in this output space. At each level, multiple samples of the model parameter vector are generated by a component-wise Metropolis algorithm so that the predicted output corresponding to each parameter value falls in the current data-approximating region. Theoretically, if continued to the limit, the sequence of data-approximating regions would converge on to the observed output vector and the approximate posterior distributions, which are conditional on the data-approximation region, would become exact, but this is not practically feasible. In this paper we study the performance of the ABC-SubSim algorithm for Bayesian updating of the parameters of dynamical systems using a general hierarchical state-space model. We note that the ABC methodology gives an approximate posterior distribution that actually corresponds to an exact posterior where a uniformly distributed combined measurement and modeling error is added. We also note that ABC algorithms have a problem with learning the uncertain error variances in a stochastic state-space model and so we treat them as nuisance parameters and analytically integrate them out of the posterior distribution. In addition, the statistical efficiency of the original ABC-SubSim algorithm is improved by developing a novel strategy to regulate the proposal variance for the component-wise Metropolis algorithm at each level. We demonstrate that Self-regulated ABC-SubSim is well suited for Bayesian system identification by first applying it successfully to model updating of a two degree-of-freedom linear structure for three cases: globally, locally and un-identifiable model classes, and then to model updating of a two degree-of-freedom nonlinear structure with Duffing nonlinearities in its interstory force-deflection relationship.

Space Weather Modeling Services at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Hesse, Michael

2006-01-01

The Community Coordinated Modeling Center (CCMC) is a multi-agency partnership, which aims at the creation of next generation space weather models. The goal of the CCMC is to support the research and developmental work necessary to substantially increase the present-day modeling capability for space weather purposes, and to provide models for transition to the Rapid Prototyping Centers at the space weather forecast centers. This goal requires close collaborations with and substantial involvement of the research community. The physical regions to be addressed by CCMC-related activities range from the solar atmosphere to the Earth's upper atmosphere. The CCMC is an integral part of the National Space Weather Program Implementation Plan, of NASA's Living With a Star (LWS) initiative, and of the Department of Defense Space Weather Transition Plan. CCMC includes a facility at NASA Goddard Space Flight Center. CCMC also provides, to the research community, access to state-of-the-art space research models. In this paper we will provide a description of the current CCMC status, discuss current plans, research and development accomplishments and goals, and describe the model testing and validation process undertaken as part of the CCMC mandate. Special emphasis will be on solar and heliospheric models currently residing at CCMC, and on plans for validation and verification.

Space Weather Modeling at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Hesse M.

2005-01-01

The Community Coordinated Modeling Center (CCMC) is a multi-agency partnership, which aims at the creation of next generation space weather models. The goal of the CCMC is to support the research and developmental work necessary to substantially increase the present-day modeling capability for space weather purposes, and to provide models for transition to the rapid prototyping centers at the space weather forecast centers. This goal requires dose collaborations with and substantial involvement of the research community. The physical regions to be addressed by CCMC-related activities range from the solar atmosphere to the Earth's upper atmosphere. The CCMC is an integral part of the National Space Weather Program Implementation Plan, of NASA's Living With a Star (LWS) initiative, and of the Department of Defense Space Weather Transition Plan. CCMC includes a facility at NASA Goddard Space Flight Center, as well as distributed computing facilities provided by the US Air Force. CCMC also provides, to the research community, access to state-of-the-art space research models. In this paper we will provide updates on CCMC status, on current plans, research and development accomplishments and goals, and on the model testing and validation process undertaken as part of the CCMC mandate. Special emphasis will be on solar and heliospheric models currently residing at CCMC, and on plans for validation and verification.

Wanted dead or alive: A state-space mark-recapture-recovery model incorporating multiple recovery types and state uncertainty

USGS Publications Warehouse

Hostetter, Nathan; Gardner, Beth; Evans, Allen F.; Cramer, Bradley M.; Payton, Quinn; Collis, Ken; Roby, Daniel D.

2017-01-01

We developed a state-space mark-recapture-recovery model that incorporates multiple recovery types and state uncertainty to estimate survival of an anadromous fish species. We apply the model to a dataset of out-migrating juvenile steelhead trout (Oncorhynchus mykiss) tagged with passive integrated transponders, recaptured during outmigration, and recovered on bird colonies in the Columbia River basin (2008-2014). Recoveries on bird colonies are often ignored in survival studies because the river reach of mortality is often unknown, which we model as a form of state uncertainty. Median outmigration survival from release to the lower river (river kilometer 729 to 75) ranged from 0.27 to 0.35, depending on year. Recovery probabilities were frequently >0.20 in the first river reach following tagging, indicating that one out of five fish that died in that reach was recovered on a bird colony. Integrating dead recovery data provided increased parameter precision, estimation of where birds consumed fish, and survival estimates across larger spatial scales. More generally, these modeling approaches provide a flexible framework to integrate multiple sources of tag recovery data into mark-recapture studies.

The consciousness state space (CSS)—a unifying model for consciousness and self

PubMed Central

Berkovich-Ohana, Aviva; Glicksohn, Joseph

2014-01-01

Every experience, those we are aware of and those we are not, is embedded in a subjective timeline, is tinged with emotion, and inevitably evokes a certain sense of self. Here, we present a phenomenological model for consciousness and selfhood which relates time, awareness, and emotion within one framework. The consciousness state space (CSS) model is a theoretical one. It relies on a broad range of literature, hence has high explanatory and integrative strength, and helps in visualizing the relationship between different aspects of experience. Briefly, it is suggested that all phenomenological states fall into two categories of consciousness, core and extended (CC and EC, respectively). CC supports minimal selfhood that is short of temporal extension, its scope being the here and now. EC supports narrative selfhood, which involves personal identity and continuity across time, as well as memory, imagination and conceptual thought. The CSS is a phenomenological space, created by three dimensions: time, awareness and emotion. Each of the three dimensions is shown to have a dual phenomenological composition, falling within CC and EC. The neural spaces supporting each of these dimensions, as well as CC and EC, are laid out based on the neuroscientific literature. The CSS dynamics include two simultaneous trajectories, one in CC and one in EC, typically antagonistic in normal experiences. However, this characteristic behavior is altered in states in which a person experiences an altered sense of self. Two examples are laid out, flow and meditation. The CSS model creates a broad theoretical framework with explanatory and unificatory power. It constructs a detailed map of the consciousness and selfhood phenomenology, which offers constraints for the science of consciousness. We conclude by outlining several testable predictions raised by the CSS model. PMID:24808870

Lotka-Volterra competition models for sessile organisms.

PubMed

Spencer, Matthew; Tanner, Jason E

2008-04-01

Markov models are widely used to describe the dynamics of communities of sessile organisms, because they are easily fitted to field data and provide a rich set of analytical tools. In typical ecological applications, at any point in time, each point in space is in one of a finite set of states (e.g., species, empty space). The models aim to describe the probabilities of transitions between states. In most Markov models for communities, these transition probabilities are assumed to be independent of state abundances. This assumption is often suspected to be false and is rarely justified explicitly. Here, we start with simple assumptions about the interactions among sessile organisms and derive a model in which transition probabilities depend on the abundance of destination states. This model is formulated in continuous time and is equivalent to a Lotka-Volterra competition model. We fit this model and a variety of alternatives in which transition probabilities do not depend on state abundances to a long-term coral reef data set. The Lotka-Volterra model describes the data much better than all models we consider other than a saturated model (a model with a separate parameter for each transition at each time interval, which by definition fits the data perfectly). Our approach provides a basis for further development of stochastic models of sessile communities, and many of the methods we use are relevant to other types of community. We discuss possible extensions to spatially explicit models.

Microgravity vibration isolation: Optimal preview and feedback control

NASA Technical Reports Server (NTRS)

Hampton, R. D.; Knospe, C. R.; Grodsinsky, C. M.; Allaire, P. E.; Lewis, D. W.

1992-01-01

In order to achieve adequate low-frequency vibration isolation for certain space experiments an active control is needed, due to inherent passive-isolator limitations. Proposed here are five possible state-space models for a one-dimensional vibration isolation system with a quadratic performance index. The five models are subsets of a general set of nonhomogeneous state space equations which includes disturbance terms. An optimal control is determined, using a differential equations approach, for this class of problems. This control is expressed in terms of constant, Linear Quadratic Regulator (LQR) feedback gains and constant feedforward (preview) gains. The gains can be easily determined numerically. They result in a robust controller and offers substantial improvements over a control that uses standard LQR feedback alone.

A new state space model for the NASA/JPL 70-meter antenna servo controls

NASA Technical Reports Server (NTRS)

Hill, R. E.

1987-01-01

A control axis referenced model of the NASA/JPL 70-m antenna structure is combined with the dynamic equations of servo components to produce a comprehansive state variable (matrix) model of the coupled system. An interactive Fortran program for generating the linear system model and computing its salient parameters is described. Results are produced in a state variable, block diagram, and in factored transfer function forms to facilitate design and analysis by classical as well as modern control methods.

The Space Academy: Going beyond "Inspiration"--A Pioneering Model for Science Education

ERIC Educational Resources Information Center

Ojha, Anu; Hill, Sarah

2012-01-01

This article outlines the Space Academy programme led by the National Space Centre from 2008 to 2011 with the stated goals of harnessing the inspirational contexts of space and climate change to support GCSE, A-level and vocational students in their curriculum studies as well as to enhance STEM teacher effectiveness and increase the awareness of…

Importance of Nuclear Physics to NASA's Space Missions

NASA Technical Reports Server (NTRS)

Tripathi, R. K.; Wilson, J. W.; Cucinotta, F. A.

2001-01-01

We show that nuclear physics is extremely important for accurate risk assessments for space missions. Due to paucity of experimental input radiation interaction information it is imperative to develop reliable accurate models for the interaction of radiation with matter. State-of-the-art nuclear cross sections models have been developed at the NASA Langley Research center and are discussed.

A solution for two-dimensional mazes with use of chaotic dynamics in a recurrent neural network model.

PubMed

Suemitsu, Yoshikazu; Nara, Shigetoshi

2004-09-01

Chaotic dynamics introduced into a neural network model is applied to solving two-dimensional mazes, which are ill-posed problems. A moving object moves from the position at t to t + 1 by simply defined motion function calculated from firing patterns of the neural network model at each time step t. We have embedded several prototype attractors that correspond to the simple motion of the object orienting toward several directions in two-dimensional space in our neural network model. Introducing chaotic dynamics into the network gives outputs sampled from intermediate state points between embedded attractors in a state space, and these dynamics enable the object to move in various directions. System parameter switching between a chaotic and an attractor regime in the state space of the neural network enables the object to move to a set target in a two-dimensional maze. Results of computer simulations show that the success rate for this method over 300 trials is higher than that of random walk. To investigate why the proposed method gives better performance, we calculate and discuss statistical data with respect to dynamical structure.

A High Performance Bayesian Computing Framework for Spatiotemporal Uncertainty Modeling

NASA Astrophysics Data System (ADS)

Cao, G.

2015-12-01

All types of spatiotemporal measurements are subject to uncertainty. With spatiotemporal data becomes increasingly involved in scientific research and decision making, it is important to appropriately model the impact of uncertainty. Quantitatively modeling spatiotemporal uncertainty, however, is a challenging problem considering the complex dependence and dataheterogeneities.State-space models provide a unifying and intuitive framework for dynamic systems modeling. In this paper, we aim to extend the conventional state-space models for uncertainty modeling in space-time contexts while accounting for spatiotemporal effects and data heterogeneities. Gaussian Markov Random Field (GMRF) models, also known as conditional autoregressive models, are arguably the most commonly used methods for modeling of spatially dependent data. GMRF models basically assume that a geo-referenced variable primarily depends on its neighborhood (Markov property), and the spatial dependence structure is described via a precision matrix. Recent study has shown that GMRFs are efficient approximation to the commonly used Gaussian fields (e.g., Kriging), and compared with Gaussian fields, GMRFs enjoy a series of appealing features, such as fast computation and easily accounting for heterogeneities in spatial data (e.g, point and areal). This paper represents each spatial dataset as a GMRF and integrates them into a state-space form to statistically model the temporal dynamics. Different types of spatial measurements (e.g., categorical, count or continuous), can be accounted for by according link functions. A fast alternative to MCMC framework, so-called Integrated Nested Laplace Approximation (INLA), was adopted for model inference.Preliminary case studies will be conducted to showcase the advantages of the described framework. In the first case, we apply the proposed method for modeling the water table elevation of Ogallala aquifer over the past decades. In the second case, we analyze the drought impacts in Texas counties in the past years, where the spatiotemporal dynamics are represented in areal data.

Modeling of Two-Wheeled Self-Balancing Robot Driven by DC Gearmotors

NASA Astrophysics Data System (ADS)

Frankovský, P.; Dominik, L.; Gmiterko, A.; Virgala, I.; Kurylo, P.; Perminova, O.

2017-08-01

This paper is aimed at modelling a two-wheeled self-balancing robot driven by the geared DC motors. A mathematical model consists of two main parts, the model of robot's mechanical structure and the model of the actuator. Linearized equations of motion are derived and the overall model of the two-wheeled self-balancing robot is represented in state-space realization for the purpose of state feedback controller design.

Deviation diagnosis and analysis of hull flat block assembly based on a state space model

NASA Astrophysics Data System (ADS)

Zhang, Zhiying; Dai, Yinfang; Li, Zhen

2012-09-01

Dimensional control is one of the most important challenges in the shipbuilding industry. In order to predict assembly dimensional variation in hull flat block construction, a variation stream model based on state space was presented in this paper which can be further applied to accuracy control in shipbuilding. Part accumulative error, locating error, and welding deformation were taken into consideration in this model, and variation propagation mechanisms and the accumulative rule in the assembly process were analyzed. Then, a model was developed to describe the variation propagation throughout the assembly process. Finally, an example of flat block construction from an actual shipyard was given. The result shows that this method is effective and useful.

An Analysis of USSPACECOM’s Space Surveillance Network (SSN) Sensor Tasking Methodology

DTIC Science & Technology

1992-12-01

2-6 2.3.2 Collateral Sensors .......................... 2- 7 2.3.3 Contributing Sensors ........................ 2-8 2.4 Space Surveillance Network...3I 3.1.1 T"hr State, Solution . ...... . ................... 3.:1 Page 3.1.2 The State-Transition Matrix... ............ 3- 7 3.2 Differential...Execution ........................... 4- 7 4.3.3 Model Verification ......................... 4-10 4.41 Differential Corrector

Rapid Computation of Thermodynamic Properties over Multidimensional Nonbonded Parameter Spaces Using Adaptive Multistate Reweighting.

PubMed

Naden, Levi N; Shirts, Michael R

2016-04-12

We show how thermodynamic properties of molecular models can be computed over a large, multidimensional parameter space by combining multistate reweighting analysis with a linear basis function approach. This approach reduces the computational cost to estimate thermodynamic properties from molecular simulations for over 130,000 tested parameter combinations from over 1000 CPU years to tens of CPU days. This speed increase is achieved primarily by computing the potential energy as a linear combination of basis functions, computed from either modified simulation code or as the difference of energy between two reference states, which can be done without any simulation code modification. The thermodynamic properties are then estimated with the Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model parameters without the need to define a priori how the states are connected by a pathway. Instead, we adaptively sample a set of points in parameter space to create mutual configuration space overlap. The existence of regions of poor configuration space overlap are detected by analyzing the eigenvalues of the sampled states' overlap matrix. The configuration space overlap to sampled states is monitored alongside the mean and maximum uncertainty to determine convergence, as neither the uncertainty or the configuration space overlap alone is a sufficient metric of convergence. This adaptive sampling scheme is demonstrated by estimating with high precision the solvation free energies of charged particles of Lennard-Jones plus Coulomb functional form with charges between -2 and +2 and generally physical values of σij and ϵij in TIP3P water. We also compute entropy, enthalpy, and radial distribution functions of arbitrary unsampled parameter combinations using only the data from these sampled states and use the estimates of free energies over the entire space to examine the deviation of atomistic simulations from the Born approximation to the solvation free energy.

Primary Dendrite Arm Spacings in Al-7Si Alloy Directionally Solidified on the International Space Station

NASA Technical Reports Server (NTRS)

Angart, Samuel; Lauer, Mark; Poirier, David; Tewari, Surendra; Rajamure, Ravi; Grugel, Richard

2015-01-01

Samples from directionally solidified Al- 7 wt. % Si have been analyzed for primary dendrite arm spacing (lambda) and radial macrosegregation. The alloy was directionally solidified (DS) aboard the ISS to determine the effect of mitigating convection on lambda and macrosegregation. Samples from terrestrial DS-experiments thermal histories are discussed for comparison. In some experiments, lambda was measured in microstructures that developed during the transition from one speed to another. To represent DS in the presence of no convection, the Hunt-Lu model was used to represent diffusion controlled growth under steady-state conditions. By sectioning cross-sections throughout the entire length of a solidified sample, lambda was measured and calculated using the model. During steady-state, there was reasonable agreement between the measured and calculated lambda's in the space-grown samples. In terrestrial samples, the differences between measured and calculated lambda's indicated that the dendritic growth was influenced by convection.

Practical Application of Model-based Programming and State-based Architecture to Space Missions

NASA Technical Reports Server (NTRS)

Horvath, Gregory; Ingham, Michel; Chung, Seung; Martin, Oliver; Williams, Brian

2006-01-01

A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps

Maximum efficiency of state-space models of nanoscale energy conversion devices

NASA Astrophysics Data System (ADS)

Einax, Mario; Nitzan, Abraham

2016-07-01

The performance of nano-scale energy conversion devices is studied in the framework of state-space models where a device is described by a graph comprising states and transitions between them represented by nodes and links, respectively. Particular segments of this network represent input (driving) and output processes whose properly chosen flux ratio provides the energy conversion efficiency. Simple cyclical graphs yield Carnot efficiency for the maximum conversion yield. We give general proof that opening a link that separate between the two driving segments always leads to reduced efficiency. We illustrate these general result with simple models of a thermoelectric nanodevice and an organic photovoltaic cell. In the latter an intersecting link of the above type corresponds to non-radiative carriers recombination and the reduced maximum efficiency is manifested as a smaller open-circuit voltage.

Maximum efficiency of state-space models of nanoscale energy conversion devices.

PubMed

Einax, Mario; Nitzan, Abraham

2016-07-07

The performance of nano-scale energy conversion devices is studied in the framework of state-space models where a device is described by a graph comprising states and transitions between them represented by nodes and links, respectively. Particular segments of this network represent input (driving) and output processes whose properly chosen flux ratio provides the energy conversion efficiency. Simple cyclical graphs yield Carnot efficiency for the maximum conversion yield. We give general proof that opening a link that separate between the two driving segments always leads to reduced efficiency. We illustrate these general result with simple models of a thermoelectric nanodevice and an organic photovoltaic cell. In the latter an intersecting link of the above type corresponds to non-radiative carriers recombination and the reduced maximum efficiency is manifested as a smaller open-circuit voltage.

Random Testing and Model Checking: Building a Common Framework for Nondeterministic Exploration

NASA Technical Reports Server (NTRS)

Groce, Alex; Joshi, Rajeev

2008-01-01

Two popular forms of dynamic analysis, random testing and explicit-state software model checking, are perhaps best viewed as search strategies for exploring the state spaces introduced by nondeterminism in program inputs. We present an approach that enables this nondeterminism to be expressed in the SPIN model checker's PROMELA language, and then lets users generate either model checkers or random testers from a single harness for a tested C program. Our approach makes it easy to compare model checking and random testing for models with precisely the same input ranges and probabilities and allows us to mix random testing with model checking's exhaustive exploration of non-determinism. The PROMELA language, as intended in its design, serves as a convenient notation for expressing nondeterminism and mixing random choices with nondeterministic choices. We present and discuss a comparison of random testing and model checking. The results derive from using our framework to test a C program with an effectively infinite state space, a module in JPL's next Mars rover mission. More generally, we show how the ability of the SPIN model checker to call C code can be used to extend SPIN's features, and hope to inspire others to use the same methods to implement dynamic analyses that can make use of efficient state storage, matching, and backtracking.

In-beam γ -ray spectroscopy of Mn 63

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baugher, T.; Gade, A.; Janssens, R. V. F.

2016-01-01

Background: Neutron-rich, even-mass chromium and iron isotopes approaching neutron number N = 40 have been important benchmarks in the development of shell-model effective interactions incorporating the effects of shell evolution in the exotic regime. Odd-mass manganese nuclei have received less attention, but provide important and complementary sensitivity to these interactions. Purpose: We report the observation of two new γ -ray transitions in 63 Mn , which establish the ( 9 / 2 - ) and ( 11 / 2 - ) levels on top of the previously known ( 7 / 2 - ) first-excited state. The lifetime for themore » ( 7 / 2 - ) and ( 9 / 2 - ) excited states were determined for the first time, while an upper limit could be established for the ( 11 / 2 - ) level. Method: Excited states in 63 Mn have been populated in inelastic scattering from a 9 Be target and in the fragmentation of 65 Fe . γ γ coincidence relationships were used to establish the decay level scheme. A Doppler line-shape analysis for the Doppler-broadened ( 7 / 2 - ) → 5 / 2 - , ( 9 / 2 - ) → ( 7 / 2 - ) , and ( 11 / 2 - ) → ( 9 / 2 - ) transitions was used to determine (limits for) the corresponding excited-state lifetimes. Results: The low-lying level scheme and the excited-state lifetimes were compared with large-scale shell-model calculations using different model spaces and effective interactions in order to isolate important aspects of shell evolution in this region of structural change. Conclusions: While the theoretical ( 7 / 2 - ) and ( 9 / 2 - ) excitation energies show little dependence on the model space, the calculated lifetime of the ( 7 / 2 - ) level and calculated energy of the ( 11 / 2 - ) level reveal the importance of including the neutron g 9 / 2 and d 5 / 2 orbitals in the model space. The LNPS effective shell-model interaction provides the best overall agreement with the new data.« less

Numerical Estimation of Balanced and Falling States for Constrained Legged Systems

NASA Astrophysics Data System (ADS)

Mummolo, Carlotta; Mangialardi, Luigi; Kim, Joo H.

2017-08-01

Instability and risk of fall during standing and walking are common challenges for biped robots. While existing criteria from state-space dynamical systems approach or ground reference points are useful in some applications, complete system models and constraints have not been taken into account for prediction and indication of fall for general legged robots. In this study, a general numerical framework that estimates the balanced and falling states of legged systems is introduced. The overall approach is based on the integration of joint-space and Cartesian-space dynamics of a legged system model. The full-body constrained joint-space dynamics includes the contact forces and moments term due to current foot (or feet) support and another term due to altered contact configuration. According to the refined notions of balanced, falling, and fallen, the system parameters, physical constraints, and initial/final/boundary conditions for balancing are incorporated into constrained nonlinear optimization problems to solve for the velocity extrema (representing the maximum perturbation allowed to maintain balance without changing contacts) in the Cartesian space at each center-of-mass (COM) position within its workspace. The iterative algorithm constructs the stability boundary as a COM state-space partition between balanced and falling states. Inclusion in the resulting six-dimensional manifold is a necessary condition for a state of the given system to be balanced under the given contact configuration, while exclusion is a sufficient condition for falling. The framework is used to analyze the balance stability of example systems with various degrees of complexities. The manifold for a 1-degree-of-freedom (DOF) legged system is consistent with the experimental and simulation results in the existing studies for specific controller designs. The results for a 2-DOF system demonstrate the dependency of the COM state-space partition upon joint-space configuration (elbow-up vs. elbow-down). For both 1- and 2-DOF systems, the results are validated in simulation environments. Finally, the manifold for a biped walking robot is constructed and illustrated against its single-support walking trajectories. The manifold identified by the proposed framework for any given legged system can be evaluated beforehand as a system property and serves as a map for either a specified state or a specific controller's performance.

Deriving Tools from Real-Time Runs: A New CCMC Support for SEC and AFWA

NASA Technical Reports Server (NTRS)

Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

2007-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. In particular, the CCMC provides to the research community the execution of "runs-on-request" for specific events of interest to space science researchers. Through this activity and the concurrent development of advanced visualization tools, CCMC provides, to the general science community, unprecedented access to a large number of state-of-the-art research models. CCMC houses models that cover the entire domain from the Sun to the Earth. In this presentation, we will provide an overview of CCMC modeling services that are available to support activities at the Space Environment Center, or at the Air Force Weather Agency.

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ron

2017-01-01

The Natural Environments Branch of the Engineering Directorate at Marshall Space Flight Center (MSFC) provides solar cycle forecasts for NASA space flight programs and the aerospace community. These forecasts provide future statistical estimates of sunspot number, solar radio 10.7 cm flux (F10.7), and the geomagnetic planetary index, Ap, for input to various space environment models. For example, many thermosphere density computer models used in spacecraft operations, orbital lifetime analysis, and the planning of future spacecraft missions require as inputs the F10.7 and Ap. The solar forecast is updated each month by executing MSAFE using historical and the latest month's observed solar indices to provide estimates for the balance of the current solar cycle. The forecasted solar indices represent the 13-month smoothed values consisting of a best estimate value stated as a 50 percentile value along with approximate +/- 2 sigma values stated as 95 and 5 percentile statistical values. This presentation will give an overview of the MSAFE model and the forecast for the current solar cycle.

Scalable approximate policies for Markov decision process models of hospital elective admissions.

PubMed

Zhu, George; Lizotte, Dan; Hoey, Jesse

2014-05-01

To demonstrate the feasibility of using stochastic simulation methods for the solution of a large-scale Markov decision process model of on-line patient admissions scheduling. The problem of admissions scheduling is modeled as a Markov decision process in which the states represent numbers of patients using each of a number of resources. We investigate current state-of-the-art real time planning methods to compute solutions to this Markov decision process. Due to the complexity of the model, traditional model-based planners are limited in scalability since they require an explicit enumeration of the model dynamics. To overcome this challenge, we apply sample-based planners along with efficient simulation techniques that given an initial start state, generate an action on-demand while avoiding portions of the model that are irrelevant to the start state. We also propose a novel variant of a popular sample-based planner that is particularly well suited to the elective admissions problem. Results show that the stochastic simulation methods allow for the problem size to be scaled by a factor of almost 10 in the action space, and exponentially in the state space. We have demonstrated our approach on a problem with 81 actions, four specialities and four treatment patterns, and shown that we can generate solutions that are near-optimal in about 100s. Sample-based planners are a viable alternative to state-based planners for large Markov decision process models of elective admissions scheduling. Copyright © 2014 Elsevier B.V. All rights reserved.

A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process

NASA Technical Reports Server (NTRS)

Wang, Yi; Tamai, Tetsuo

2009-01-01

Since the complexity of software systems continues to grow, most engineers face two serious problems: the state space explosion problem and the problem of how to debug systems. In this paper, we propose a game-theoretic approach to full branching time model checking on three-valued semantics. The three-valued models and logics provide successful abstraction that overcomes the state space explosion problem. The game style model checking that generates counter-examples can guide refinement or identify validated formulas, which solves the system debugging problem. Furthermore, output of our game style method will give significant information to engineers in detecting where errors have occurred and what the causes of the errors are.

Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law

NASA Astrophysics Data System (ADS)

Falugi, P.; Olaru, S.; Dumur, D.

2010-08-01

This article proposes an explicit robust predictive control solution based on linear matrix inequalities (LMIs). The considered predictive control strategy uses different local descriptions of the system dynamics and uncertainties and thus allows the handling of less conservative input constraints. The computed control law guarantees constraint satisfaction and asymptotic stability. The technique is effective for a class of nonlinear systems embedded into polytopic models. A detailed discussion of the procedures which adapt the partition of the state space is presented. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms.

Systematics of first 2+ state g factors around mass 80

NASA Astrophysics Data System (ADS)

Mertzimekis, T. J.; Stuchbery, A. E.; Benczer-Koller, N.; Taylor, M. J.

2003-11-01

The systematics of the first 2+ state g factors in the mass 80 region are investigated in terms of an IBM-II analysis, a pairing-corrected geometrical model, and a shell-model approach. Subshell closure effects at N=38 and overall trends were examined using IBM-II. A large-space shell-model calculation was successful in describing the behavior for N=48 and N=50 nuclei, where single-particle features are prominent. A schematic truncated-space calculation was applied to the lighter isotopes. The variations of the effective boson g factors are discussed in connection with the role of F -spin breaking, and comparisons are made between the mass 80 and mass 180 regions.

Implications of tristability in pattern-forming ecosystems

NASA Astrophysics Data System (ADS)

Zelnik, Yuval R.; Gandhi, Punit; Knobloch, Edgar; Meron, Ehud

2018-03-01

Many ecosystems show both self-organized spatial patterns and multistability of possible states. The combination of these two phenomena in different forms has a significant impact on the behavior of ecosystems in changing environments. One notable case is connected to tristability of two distinct uniform states together with patterned states, which has recently been found in model studies of dryland ecosystems. Using a simple model, we determine the extent of tristability in parameter space, explore its effects on the system dynamics, and consider its implications for state transitions or regime shifts. We analyze the bifurcation structure of model solutions that describe uniform states, periodic patterns, and hybrid states between the former two. We map out the parameter space where these states exist, and note how the different states interact with each other. We further focus on two special implications with ecological significance, breakdown of the snaking range and complex fronts. We find that the organization of the hybrid states within a homoclinic snaking structure breaks down as it meets a Maxwell point where simple fronts are stationary. We also discover a new series of complex fronts between the uniform states, each with its own velocity. We conclude with a brief discussion of the significance of these findings for the dynamics of regime shifts and their potential control.

Nonequilibrium life-cycles in Ocean Heat Content

NASA Astrophysics Data System (ADS)

Weiss, Jeffrey B.; Fox-Kemper, Baylor; Mandal, Dibyendu; Zia, Royce K. P.

2014-03-01

Natural climate variability can be considered as fluctuations in a nonequilibrium steady state. A fundamental property of nonequilibrium steady states is the phase space current which provides a preferred direction for fluctuations, and is manifested as preferred life-cycles for climate fluctuations. We propose a new quantity, the phase space angular momentum, to quantify the phase space rotation. In analogy with traditional angular momentum, which quantifies the rotation of mass in physical space, the phase space angular momentum quantifies the rotation of probability in phase space. It has the additional advantage that it is straightforward to calculate from a time series. We investigate the phase space angular momentum for fluctuations in ocean heat content in both observations and ocean general circulation models. We gratefully acknowledge financial support from the National Science Foundation (USA) under grant OCE 1245944.

Model Adaptation in Parametric Space for POD-Galerkin Models

NASA Astrophysics Data System (ADS)

Gao, Haotian; Wei, Mingjun

2017-11-01

The development of low-order POD-Galerkin models is largely motivated by the expectation to use the model developed with a set of parameters at their native values to predict the dynamic behaviors of the same system under different parametric values, in other words, a successful model adaptation in parametric space. However, most of time, even small deviation of parameters from their original value may lead to large deviation or unstable results. It has been shown that adding more information (e.g. a steady state, mean value of a different unsteady state, or an entire different set of POD modes) may improve the prediction of flow with other parametric states. For a simple case of the flow passing a fixed cylinder, an orthogonal mean mode at a different Reynolds number may stabilize the POD-Galerkin model when Reynolds number is changed. For a more complicated case of the flow passing an oscillatory cylinder, a global POD-Galerkin model is first applied to handle the moving boundaries, then more information (e.g. more POD modes) is required to predicate the flow under different oscillatory frequencies. Supported by ARL.

Dynamical initial-state model for relativistic heavy-ion collisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Chun; Schenke, Bjorn

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a uctuating time depending on sampled final rapidities. Energy is deposited in space-time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directlymore » from the initial state model, including net-baryon rapidity distributions, 2-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. Here, we also present the implementation of the model with 3+1 dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial state model at proper times greater than the initial time for the hydrodynamic simulation.« less

Dynamical initial-state model for relativistic heavy-ion collisions

NASA Astrophysics Data System (ADS)

Shen, Chun; Schenke, Björn

2018-02-01

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy-ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a fluctuating time depending on sampled final rapidities. Energy is deposited in space time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directly from the initial-state model, including net-baryon rapidity distributions, two-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. We also present the implementation of the model with 3+1-dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial-state model at proper times greater than the initial time for the hydrodynamic simulation.

Dynamical initial-state model for relativistic heavy-ion collisions

DOE PAGES

Shen, Chun; Schenke, Bjorn

2018-02-15

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a uctuating time depending on sampled final rapidities. Energy is deposited in space-time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directlymore » from the initial state model, including net-baryon rapidity distributions, 2-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. Here, we also present the implementation of the model with 3+1 dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial state model at proper times greater than the initial time for the hydrodynamic simulation.« less

Official portrait of astronaut Brewster H. Shaw, Jr

NASA Technical Reports Server (NTRS)

1989-01-01

Official portrait of Brewster H. Shaw, Jr, United States Air Force (USAF) Colonel, member of Astronaut Class 8 (1978), and space shuttle commander. Shaw wears blue pressure suit with space shuttle model displayed on table on his left.

Use of density functional theory orbitals in the GVVPT2 variant of second-order multistate multireference perturbation theory.

PubMed

Hoffmann, Mark R; Helgaker, Trygve

2015-03-05

A new variation of the second-order generalized van Vleck perturbation theory (GVVPT2) for molecular electronic structure is suggested. In contrast to the established procedure, in which CASSCF or MCSCF orbitals are first obtained and subsequently used to define a many-electron model (or reference) space, the use of an orbital space obtained from the local density approximation (LDA) variant of density functional theory is considered. Through a final, noniterative diagonalization of an average Fock matrix within orbital subspaces, quasicanonical orbitals that are otherwise indistinguishable from quasicanonical orbitals obtained from a CASSCF or MCSCF calculation are obtained. Consequently, all advantages of the GVVPT2 method are retained, including use of macroconfigurations to define incomplete active spaces and rigorous avoidance of intruder states. The suggested variant is vetted on three well-known model problems: the symmetric stretching of the O-H bonds in water, the dissociation of N2, and the stretching of ground and excited states C2 to more than twice the equilibrium bond length of the ground state. It is observed that the LDA-based GVVPT2 calculations yield good results, of comparable quality to conventional CASSCF-based calculations. This is true even for the C2 model problem, in which the orbital space for each state was defined by the LDA orbitals. These results suggest that GVVPT2 can be applied to much larger problems than previously accessible.

Simulation of the Effect of Realistic Space Vehicle Environments on Binary Metal Alloys

NASA Technical Reports Server (NTRS)

Westra, Douglas G.; Poirier, D. R.; Heinrich, J. C.; Sung, P. K.; Felicelli, S. D.; Phelps, Lisa (Technical Monitor)

2001-01-01

Simulations that assess the effect of space vehicle acceleration environments on the solidification of Pb-Sb alloys are reported. Space microgravity missions are designed to provide a near zero-g acceleration environment for various types of scientific experiments. Realistically. these space missions cannot provide a perfect environment. Vibrations caused by crew activity, on-board experiments, support systems stems (pumps, fans, etc.), periodic orbital maneuvers, and water dumps can all cause perturbations to the microgravity environment. In addition, the drag on the space vehicle is a source of acceleration. Therefore, it is necessary to predict the impact of these vibration-perturbations and the steady-state drag acceleration on the experiments. These predictions can be used to design mission timelines. so that the experiment is run during times that the impact of the acceleration environment is acceptable for the experiment of interest. The simulations reported herein were conducted using a finite element model that includes mass, species, momentum, and energy conservation. This model predicts the existence of "channels" within the processing mushy zone and subsequently "freckles" within the fully processed solid, which are the effects of thermosolutal convection. It is necessary to mitigate thermosolutal convection during space experiments of metal alloys, in order to study and characterize diffusion-controlled transport phenomena (microsegregation) that are normally coupled with macrosegregation. The model allows simulation of steady-state and transient acceleration values ranging from no acceleration (0 g). to microgravity conditions (10(exp -6) to 10(exp -3) g), to terrestrial gravity conditions (1 g). The transient acceleration environments simulated were from the STS-89 SpaceHAB mission and from the STS-94 SpaceLAB mission. with on-orbit accelerometer data during different mission periods used as inputs for the simulation model. Periods of crew exercise, quiet (no crew activity), and nominal conditions from STS-89 were used as simulation inputs as were periods of nominal. overboard water-dump, and free-drift (no orbit maneuvering operations) from STS-94. Steady-state acceleration environments of 0.0 and 10(exp -6) to 10(exp -1) g were also simulated, to serve as a comparison to the transient data and to assess an acceptable magnitude for the steady-state vehicle drag

Steady-state kinetic modeling constrains cellular resting states and dynamic behavior.

PubMed

Purvis, Jeremy E; Radhakrishnan, Ravi; Diamond, Scott L

2009-03-01

A defining characteristic of living cells is the ability to respond dynamically to external stimuli while maintaining homeostasis under resting conditions. Capturing both of these features in a single kinetic model is difficult because the model must be able to reproduce both behaviors using the same set of molecular components. Here, we show how combining small, well-defined steady-state networks provides an efficient means of constructing large-scale kinetic models that exhibit realistic resting and dynamic behaviors. By requiring each kinetic module to be homeostatic (at steady state under resting conditions), the method proceeds by (i) computing steady-state solutions to a system of ordinary differential equations for each module, (ii) applying principal component analysis to each set of solutions to capture the steady-state solution space of each module network, and (iii) combining optimal search directions from all modules to form a global steady-state space that is searched for accurate simulation of the time-dependent behavior of the whole system upon perturbation. Importantly, this stepwise approach retains the nonlinear rate expressions that govern each reaction in the system and enforces constraints on the range of allowable concentration states for the full-scale model. These constraints not only reduce the computational cost of fitting experimental time-series data but can also provide insight into limitations on system concentrations and architecture. To demonstrate application of the method, we show how small kinetic perturbations in a modular model of platelet P2Y(1) signaling can cause widespread compensatory effects on cellular resting states.

Spectral Factorization and Homogenization Methods for Modeling and Control of Flexible Structures.

DTIC Science & Technology

1986-12-15

to the computation of hybrid, state-space modeling of an integrated space platform . Throughout this effort we have focused on the potential for...models can provide an effective tool for analysis of dynamics of vibrations and their effect on small angle motions for complex space platforms . In this... WIX 1 v .41(Ac 0 0o4 1 2.. 9 2% - L .0U V)V14IC Ma a * 9L 0 a soe - a a.. x m c 4. i.! 0~~~I W ** PMiscellaneous Routines• Power Series Expansion

An optimum organizational structure for a large earth-orbiting multidisciplinary space base. Ph.D. Thesis - Fla. State Univ., 1973

NASA Technical Reports Server (NTRS)

Ragusa, J. M.

1975-01-01

An optimum hypothetical organizational structure was studied for a large earth-orbiting, multidisciplinary research and applications space base manned by a crew of technologists. Because such a facility does not presently exist, in situ empirical testing was not possible. Study activity was, therefore, concerned with the identification of a desired organizational structural model rather than with the empirical testing of the model. The essential finding of this research was that a four-level project type total matrix model will optimize the efficiency and effectiveness of space base technologists.

Extending existing structural identifiability analysis methods to mixed-effects models.

PubMed

Janzén, David L I; Jirstrand, Mats; Chappell, Michael J; Evans, Neil D

2018-01-01

The concept of structural identifiability for state-space models is expanded to cover mixed-effects state-space models. Two methods applicable for the analytical study of the structural identifiability of mixed-effects models are presented. The two methods are based on previously established techniques for non-mixed-effects models; namely the Taylor series expansion and the input-output form approach. By generating an exhaustive summary, and by assuming an infinite number of subjects, functions of random variables can be derived which in turn determine the distribution of the system's observation function(s). By considering the uniqueness of the analytical statistical moments of the derived functions of the random variables, the structural identifiability of the corresponding mixed-effects model can be determined. The two methods are applied to a set of examples of mixed-effects models to illustrate how they work in practice. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessment of current state of the art in modeling techniques and analysis methods for large space structures

NASA Technical Reports Server (NTRS)

Noor, A. K.

1983-01-01

Advances in continuum modeling, progress in reduction methods, and analysis and modeling needs for large space structures are covered with specific attention given to repetitive lattice trusses. As far as continuum modeling is concerned, an effective and verified analysis capability exists for linear thermoelastic stress, birfurcation buckling, and free vibration problems of repetitive lattices. However, application of continuum modeling to nonlinear analysis needs more development. Reduction methods are very effective for bifurcation buckling and static (steady-state) nonlinear analysis. However, more work is needed to realize their full potential for nonlinear dynamic and time-dependent problems. As far as analysis and modeling needs are concerned, three areas are identified: loads determination, modeling and nonclassical behavior characteristics, and computational algorithms. The impact of new advances in computer hardware, software, integrated analysis, CAD/CAM stems, and materials technology is also discussed.

MIST - MINIMUM-STATE METHOD FOR RATIONAL APPROXIMATION OF UNSTEADY AERODYNAMIC FORCE COEFFICIENT MATRICES

NASA Technical Reports Server (NTRS)

Karpel, M.

1994-01-01

Various control analysis, design, and simulation techniques of aeroservoelastic systems require the equations of motion to be cast in a linear, time-invariant state-space form. In order to account for unsteady aerodynamics, rational function approximations must be obtained to represent them in the first order equations of the state-space formulation. A computer program, MIST, has been developed which determines minimum-state approximations of the coefficient matrices of the unsteady aerodynamic forces. The Minimum-State Method facilitates the design of lower-order control systems, analysis of control system performance, and near real-time simulation of aeroservoelastic phenomena such as the outboard-wing acceleration response to gust velocity. Engineers using this program will be able to calculate minimum-state rational approximations of the generalized unsteady aerodynamic forces. Using the Minimum-State formulation of the state-space equations, they will be able to obtain state-space models with good open-loop characteristics while reducing the number of aerodynamic equations by an order of magnitude more than traditional approaches. These low-order state-space mathematical models are good for design and simulation of aeroservoelastic systems. The computer program, MIST, accepts tabular values of the generalized aerodynamic forces over a set of reduced frequencies. It then determines approximations to these tabular data in the LaPlace domain using rational functions. MIST provides the capability to select the denominator coefficients in the rational approximations, to selectably constrain the approximations without increasing the problem size, and to determine and emphasize critical frequency ranges in determining the approximations. MIST has been written to allow two types data weighting options. The first weighting is a traditional normalization of the aerodynamic data to the maximum unit value of each aerodynamic coefficient. The second allows weighting the importance of different tabular values in determining the approximations based upon physical characteristics of the system. Specifically, the physical weighting capability is such that each tabulated aerodynamic coefficient, at each reduced frequency value, is weighted according to the effect of an incremental error of this coefficient on aeroelastic characteristics of the system. In both cases, the resulting approximations yield a relatively low number of aerodynamic lag states in the subsequent state-space model. MIST is written in ANSI FORTRAN 77 for DEC VAX series computers running VMS. It requires approximately 1Mb of RAM for execution. The standard distribution medium for this package is a 9-track 1600 BPI magnetic tape in DEC VAX FILES-11 format. It is also available on a TK50 tape cartridge in DEC VAX BACKUP format. MIST was developed in 1991. DEC VAX and VMS are trademarks of Digital Equipment Corporation. FORTRAN 77 is a registered trademark of Lahey Computer Systems, Inc.

Controlling state explosion during automatic verification of delay-insensitive and delay-constrained VLSI systems using the POM verifier

NASA Technical Reports Server (NTRS)

Probst, D.; Jensen, L.

1991-01-01

Delay-insensitive VLSI systems have a certain appeal on the ground due to difficulties with clocks; they are even more attractive in space. We answer the question, is it possible to control state explosion arising from various sources during automatic verification (model checking) of delay-insensitive systems? State explosion due to concurrency is handled by introducing a partial-order representation for systems, and defining system correctness as a simple relation between two partial orders on the same set of system events (a graph problem). State explosion due to nondeterminism (chiefly arbitration) is handled when the system to be verified has a clean, finite recurrence structure. Backwards branching is a further optimization. The heart of this approach is the ability, during model checking, to discover a compact finite presentation of the verified system without prior composition of system components. The fully-implemented POM verification system has polynomial space and time performance on traditional asynchronous-circuit benchmarks that are exponential in space and time for other verification systems. We also sketch the generalization of this approach to handle delay-constrained VLSI systems.

New limits on coupled dark energy model after Planck 2015

NASA Astrophysics Data System (ADS)

Li, Hang; Yang, Weiqiang; Wu, Yabo; Jiang, Ying

2018-06-01

We used the Planck 2015 cosmic microwave background anisotropy, baryon acoustic oscillation, type-Ia supernovae, redshift-space distortions, and weak gravitational lensing to test the model parameter space of coupled dark energy. We assumed the constant and time-varying equation of state parameter for dark energy, and treated dark matter and dark energy as the fluids whose energy transfer was proportional to the combined term of the energy densities and equation of state, such as Q = 3 Hξ(1 +wx) ρx and Q = 3 Hξ [ 1 +w0 +w1(1 - a) ] ρx, the full space of equation of state could be measured when we considered the term (1 +wx) in the energy exchange. According to the joint observational constraint, the results showed that wx = - 1.006-0.027+0.047 and ξ = 0.098-0.098>+0.026 for coupled dark energy with a constant equation of state, w0 = -1.076-0.076+0.085, w1 = - 0.069-0.319+0.361, and ξ = 0.210-0.210+0.048 for a variable equation of state. We did not get any clear evidence for the coupling in the dark fluids at 1 σ region.

Steady state micro-g environment on Space Station

NASA Technical Reports Server (NTRS)

Waters, L.; Heck, M.; Deryder, L.

1988-01-01

In circular earth orbit, the Space Station (SS) will sense acceleration from external environmental forces due to the gravitational gradient, rotational accelerations, and atmospheric drag. This paper discusses these forces and how they will affect the SS micro-g environment. The effect of SS attitude on the micro-g profile is addressed. Sources for nonsteady state acceleration levels for which disturbance models are currently being developed are briefly considered.

SATware: A Semantic Approach for Building Sentient Spaces

NASA Astrophysics Data System (ADS)

Massaguer, Daniel; Mehrotra, Sharad; Vaisenberg, Ronen; Venkatasubramanian, Nalini

This chapter describes the architecture of a semantic-based middleware environment for building sensor-driven sentient spaces. The proposed middleware explicitly models sentient space semantics (i.e., entities, spaces, activities) and supports mechanisms to map sensor observations to the state of the sentient space. We argue how such a semantic approach provides a powerful programming environment for building sensor spaces. In addition, the approach provides natural ways to exploit semantics for variety of purposes including scheduling under resource constraints and sensor recalibration.

Space Monitoring Data Center at Moscow State University

NASA Astrophysics Data System (ADS)

Kalegaev, Vladimir; Bobrovnikov, Sergey; Barinova, Vera; Myagkova, Irina; Shugay, Yulia; Barinov, Oleg; Dolenko, Sergey; Mukhametdinova, Ludmila; Shiroky, Vladimir

Space monitoring data center of Moscow State University provides operational information on radiation state of the near-Earth space. Internet portal http://swx.sinp.msu.ru/ gives access to the actual data characterizing the level of solar activity, geomagnetic and radiation conditions in the magnetosphere and heliosphere in the real time mode. Operational data coming from space missions (ACE, GOES, ELECTRO-L1, Meteor-M1) at L1, LEO and GEO and from the Earth’s surface are used to represent geomagnetic and radiation state of near-Earth environment. On-line database of measurements is also maintained to allow quick comparison between current conditions and conditions experienced in the past. The models of space environment working in autonomous mode are used to generalize the information obtained from observations on the whole magnetosphere. Interactive applications and operational forecasting services are created on the base of these models. They automatically generate alerts on particle fluxes enhancements above the threshold values, both for SEP and relativistic electrons using data from LEO orbits. Special forecasting services give short-term forecast of SEP penetration to the Earth magnetosphere at low altitudes, as well as relativistic electron fluxes at GEO. Velocities of recurrent high speed solar wind streams on the Earth orbit are predicted with advance time of 3-4 days on the basis of automatic estimation of the coronal hole areas detected on the images of the Sun received from the SDO satellite. By means of neural network approach, Dst and Kp indices online forecasting 0.5-1.5 hours ahead, depending on solar wind and the interplanetary magnetic field, measured by ACE satellite, is carried out. Visualization system allows representing experimental and modeling data in 2D and 3D.

Exploring the Tomlin-Varadarajan quantum constraints in U (1 )3 loop quantum gravity: Solutions and the Minkowski theorem

NASA Astrophysics Data System (ADS)

Lewandowski, Jerzy; Lin, Chun-Yen

2017-03-01

We explicitly solved the anomaly-free quantum constraints proposed by Tomlin and Varadarajan for the weak Euclidean model of canonical loop quantum gravity, in a large subspace of the model's kinematic Hilbert space, which is the space of the charge network states. In doing so, we first identified the subspace on which each of the constraints acts convergingly, and then by explicitly evaluating such actions we found the complete set of the solutions in the identified subspace. We showed that the space of solutions consists of two classes of states, with the first class having a property that involves the condition known from the Minkowski theorem on polyhedra, and the second class satisfying a weaker form of the spatial diffeomorphism invariance.

Viewing hybrid systems as products of control systems and automata

NASA Technical Reports Server (NTRS)

Grossman, R. L.; Larson, R. G.

1992-01-01

The purpose of this note is to show how hybrid systems may be modeled as products of nonlinear control systems and finite state automata. By a hybrid system, we mean a network of consisting of continuous, nonlinear control system connected to discrete, finite state automata. Our point of view is that the automata switches between the control systems, and that this switching is a function of the discrete input symbols or letters that it receives. We show how a nonlinear control system may be viewed as a pair consisting of a bialgebra of operators coding the dynamics, and an algebra of observations coding the state space. We also show that a finite automata has a similar representation. A hybrid system is then modeled by taking suitable products of the bialgebras coding the dynamics and the observation algebras coding the state spaces.

Space Weather Modeling at the Community Coordinated Modeling Center

NASA Astrophysics Data System (ADS)

Hesse, M.; Falasca, A.; Johnson, J.; Keller, K.; Kuznetsova, M.; Rastaetter, L.

2003-04-01

The Community Coordinated Modeling Center (CCMC) is a multi-agency partnership aimed at the creation of next generation space weather models. The goal of the CCMC is to support the research and developmental work necessary to substantially increase the present-day modeling capability for space weather purposes, and to provide models for transition to the rapid prototyping centers at the space weather forecast centers. This goal requires close collaborations with and substantial involvement of the research community. The physical regions to be addressed by CCMC-related activities range from the solar atmosphere to the Earth's upper atmosphere. The CCMC is an integral part of NASA's Living With a Star (LWS) initiative, of the National Space Weather Program Implementation Plan, and of the Department of Defense Space Weather Transition Plan. CCMC includes a facility at NASA Goddard Space Flight Center, as well as distributed computing facilities provided by the US Air Force. CCMC also provides, to the research community, access to state-of-the-art space research models. In this paper we will provide updates on CCMC status, on current plans, research and development accomplishments and goals, and on the model testing and validation process undertaken as part of the CCMC mandate. We will demonstrate the capabilities of models resident at CCMC via the analysis of a geomagnetic storm, driven by a shock in the solar wind.

Cross-shell excitations in Si 31

DOE PAGES

Tai, P. -L.; Tabor, S. L.; Lubna, R. S.; ...

2017-07-28

The Si-31 nucleus was produced through the O-18(18O, an) fusion-evaporation reaction at E-lab = 24 MeV. Evaporated a particles from the reaction were detected and identified in the Microball detector array for channel selection. Multiple gamma-ray coincidence events were detected in Gammasphere. The energy and angle information for the alpha particles was used to determine the Si-31 recoil kinematics on an event-by-event basis for a more accurate Doppler correction. A total of 22 new states and 52 new gamma transitions were observed, including 14 from states above the neutron separation energy. The positive-parity states predicted by the shell-model calculations inmore » the sd model space agree well with experiment. The negative-parity states were compared with shell-model calculations in the psdpf model space with some variations in the N = 20 shell gap. The best agreement was found with a shell gap intermediate between that originally used for A approximate to 20 nuclei and that previously adapted for P-32,P-34. This variation suggests the need for a more universal cross-shell interaction.« less

Group field theories for all loop quantum gravity

NASA Astrophysics Data System (ADS)

Oriti, Daniele; Ryan, James P.; Thürigen, Johannes

2015-02-01

Group field theories represent a second quantized reformulation of the loop quantum gravity state space and a completion of the spin foam formalism. States of the canonical theory, in the traditional continuum setting, have support on graphs of arbitrary valence. On the other hand, group field theories have usually been defined in a simplicial context, thus dealing with a restricted set of graphs. In this paper, we generalize the combinatorics of group field theories to cover all the loop quantum gravity state space. As an explicit example, we describe the group field theory formulation of the KKL spin foam model, as well as a particular modified version. We show that the use of tensor model tools allows for the most effective construction. In order to clarify the mathematical basis of our construction and of the formalisms with which we deal, we also give an exhaustive description of the combinatorial structures entering spin foam models and group field theories, both at the level of the boundary states and of the quantum amplitudes.

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

Lee-Carter state space modeling: Application to the Malaysia mortality data

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-06-01

This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.

Qualitative models for space system engineering

NASA Technical Reports Server (NTRS)

Forbus, Kenneth D.

1990-01-01

The objectives of this project were: (1) to investigate the implications of qualitative modeling techniques for problems arising in the monitoring, diagnosis, and design of Space Station subsystems and procedures; (2) to identify the issues involved in using qualitative models to enhance and automate engineering functions. These issues include representing operational criteria, fault models, alternate ontologies, and modeling continuous signals at a functional level of description; and (3) to develop a prototype collection of qualitative models for fluid and thermal systems commonly found in Space Station subsystems. Potential applications of qualitative modeling to space-systems engineering, including the notion of intelligent computer-aided engineering are summarized. Emphasis is given to determining which systems of the proposed Space Station provide the most leverage for study, given the current state of the art. Progress on using qualitative models, including development of the molecular collection ontology for reasoning about fluids, the interaction of qualitative and quantitative knowledge in analyzing thermodynamic cycles, and an experiment on building a natural language interface to qualitative reasoning is reported. Finally, some recommendations are made for future research.

Stability and bistability in a one-dimensional model of coastal foredune height

NASA Astrophysics Data System (ADS)

Goldstein, Evan B.; Moore, Laura J.

2016-05-01

On sandy coastlines, foredunes provide protection from coastal storms, potentially sheltering low areas—including human habitat—from elevated water level and wave erosion. In this contribution we develop and explore a one-dimensional model for coastal dune height based on an impulsive differential equation. In the model, coastal foredunes continuously grow in a logistic manner as the result of a biophysical feedback and they are destroyed by recurrent storm events that are discrete in time. Modeled dunes can be in one of two states: a high "resistant-dune" state or a low "overwash-flat" state. The number of stable states (equilibrium dune heights) depends on the value of two parameters, the nondimensional storm frequency (the ratio of storm frequency to the intrinsic growth rate of dunes) and nondimensional storm magnitude (the ratio of total water level during storms to the maximum theoretical dune height). Three regions of phase space exist (1) when nondimensional storm frequency is small, a single high resistant-dune attracting state exists; (2) when both the nondimensional storm frequency and magnitude are large, there is a single overwash-flat attracting state; (3) within a defined region of phase space model dunes exhibit bistable behavior—both the resistant-dune and the low overwash-flat states are stable. Comparisons to observational studies suggest that there is evidence for each state to exist independently, the coexistence of both states (i.e., segments of barrier islands consisting of overwash-flats and segments of islands having large dunes that resist erosion by storms), as well as transitions between states.

Transition probability spaces in loop quantum gravity

NASA Astrophysics Data System (ADS)

Guo, Xiao-Kan

2018-03-01

We study the (generalized) transition probability spaces, in the sense of Mielnik and Cantoni, for spacetime quantum states in loop quantum gravity. First, we show that loop quantum gravity admits the structures of transition probability spaces. This is exemplified by first checking such structures in covariant quantum mechanics and then identifying the transition probability spaces in spin foam models via a simplified version of general boundary formulation. The transition probability space thus defined gives a simple way to reconstruct the discrete analog of the Hilbert space of the canonical theory and the relevant quantum logical structures. Second, we show that the transition probability space and in particular the spin foam model are 2-categories. Then we discuss how to realize in spin foam models two proposals by Crane about the mathematical structures of quantum gravity, namely, the quantum topos and causal sites. We conclude that transition probability spaces provide us with an alternative framework to understand various foundational questions of loop quantum gravity.

Periodic synchronization in a system of coupled phase oscillators with attractive and repulsive interactions

NASA Astrophysics Data System (ADS)

Yuan, Di; Tian, Jun-Long; Lin, Fang; Ma, Dong-Wei; Zhang, Jing; Cui, Hai-Tao; Xiao, Yi

2018-06-01

In this study we investigate the collective behavior of the generalized Kuramoto model with an external pinning force in which oscillators with positive and negative coupling strengths are conformists and contrarians, respectively. We focus on a situation in which the natural frequencies of the oscillators follow a uniform probability density. By numerically simulating the model, it is shown that the model supports multistable synchronized states such as a traveling wave state, π state and periodic synchronous state: an oscillating π state. The oscillating π state may be characterized by the phase distribution oscillating in a confined region and the phase difference between conformists and contrarians oscillating around π periodically. In addition, we present the parameter space of the oscillating π state and traveling wave state of the model.

System Identification of a Vortex Lattice Aerodynamic Model

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Kholodar, Denis; Dowell, Earl H.

2001-01-01

The state-space presentation of an aerodynamic vortex model is considered from a classical and system identification perspective. Using an aerodynamic vortex model as a numerical simulator of a wing tunnel experiment, both full state and limited state data or measurements are considered. Two possible approaches for system identification are presented and modal controllability and observability are also considered. The theory then is applied to the system identification of a flow over an aerodynamic delta wing and typical results are presented.

Hybrid Reduced Order Modeling Algorithms for Reactor Physics Calculations

NASA Astrophysics Data System (ADS)

Bang, Youngsuk

Reduced order modeling (ROM) has been recognized as an indispensable approach when the engineering analysis requires many executions of high fidelity simulation codes. Examples of such engineering analyses in nuclear reactor core calculations, representing the focus of this dissertation, include the functionalization of the homogenized few-group cross-sections in terms of the various core conditions, e.g. burn-up, fuel enrichment, temperature, etc. This is done via assembly calculations which are executed many times to generate the required functionalization for use in the downstream core calculations. Other examples are sensitivity analysis used to determine important core attribute variations due to input parameter variations, and uncertainty quantification employed to estimate core attribute uncertainties originating from input parameter uncertainties. ROM constructs a surrogate model with quantifiable accuracy which can replace the original code for subsequent engineering analysis calculations. This is achieved by reducing the effective dimensionality of the input parameter, the state variable, or the output response spaces, by projection onto the so-called active subspaces. Confining the variations to the active subspace allows one to construct an ROM model of reduced complexity which can be solved more efficiently. This dissertation introduces a new algorithm to render reduction with the reduction errors bounded based on a user-defined error tolerance which represents the main challenge of existing ROM techniques. Bounding the error is the key to ensuring that the constructed ROM models are robust for all possible applications. Providing such error bounds represents one of the algorithmic contributions of this dissertation to the ROM state-of-the-art. Recognizing that ROM techniques have been developed to render reduction at different levels, e.g. the input parameter space, the state space, and the response space, this dissertation offers a set of novel hybrid ROM algorithms which can be readily integrated into existing methods and offer higher computational efficiency and defendable accuracy of the reduced models. For example, the snapshots ROM algorithm is hybridized with the range finding algorithm to render reduction in the state space, e.g. the flux in reactor calculations. In another implementation, the perturbation theory used to calculate first order derivatives of responses with respect to parameters is hybridized with a forward sensitivity analysis approach to render reduction in the parameter space. Reduction at the state and parameter spaces can be combined to render further reduction at the interface between different physics codes in a multi-physics model with the accuracy quantified in a similar manner to the single physics case. Although the proposed algorithms are generic in nature, we focus here on radiation transport models used in support of the design and analysis of nuclear reactor cores. In particular, we focus on replacing the traditional assembly calculations by ROM models to facilitate the generation of homogenized cross-sections for downstream core calculations. The implication is that assembly calculations could be done instantaneously therefore precluding the need for the expensive evaluation of the few-group cross-sections for all possible core conditions. Given the generic natures of the algorithms, we make an effort to introduce the material in a general form to allow non-nuclear engineers to benefit from this work.

Development of a restricted state space stochastic differential equation model for bacterial growth in rich media.

PubMed

Møller, Jan Kloppenborg; Bergmann, Kirsten Riber; Christiansen, Lasse Engbo; Madsen, Henrik

2012-07-21

In the present study, bacterial growth in a rich media is analysed in a Stochastic Differential Equation (SDE) framework. It is demonstrated that the SDE formulation and smoothened state estimates provide a systematic framework for data driven model improvements, using random walk hidden states. Bacterial growth is limited by the available substrate and the inclusion of diffusion must obey this natural restriction. By inclusion of a modified logistic diffusion term it is possible to introduce a diffusion term flexible enough to capture both the growth phase and the stationary phase, while concentration is restricted to the natural state space (substrate and bacteria non-negative). The case considered is the growth of Salmonella and Enterococcus in a rich media. It is found that a hidden state is necessary to capture the lag phase of growth, and that a flexible logistic diffusion term is needed to capture the random behaviour of the growth model. Further, it is concluded that the Monod effect is not needed to capture the dynamics of bacterial growth in the data presented. Copyright © 2012 Elsevier Ltd. All rights reserved.

Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data.

PubMed

Nagasaki, Masao; Yamaguchi, Rui; Yoshida, Ryo; Imoto, Seiya; Doi, Atsushi; Tamada, Yoshinori; Matsuno, Hiroshi; Miyano, Satoru; Higuchi, Tomoyuki

2006-01-01

We propose an automatic construction method of the hybrid functional Petri net as a simulation model of biological pathways. The problems we consider are how we choose the values of parameters and how we set the network structure. Usually, we tune these unknown factors empirically so that the simulation results are consistent with biological knowledge. Obviously, this approach has the limitation in the size of network of interest. To extend the capability of the simulation model, we propose the use of data assimilation approach that was originally established in the field of geophysical simulation science. We provide genomic data assimilation framework that establishes a link between our simulation model and observed data like microarray gene expression data by using a nonlinear state space model. A key idea of our genomic data assimilation is that the unknown parameters in simulation model are converted as the parameter of the state space model and the estimates are obtained as the maximum a posteriori estimators. In the parameter estimation process, the simulation model is used to generate the system model in the state space model. Such a formulation enables us to handle both the model construction and the parameter tuning within a framework of the Bayesian statistical inferences. In particular, the Bayesian approach provides us a way of controlling overfitting during the parameter estimations that is essential for constructing a reliable biological pathway. We demonstrate the effectiveness of our approach using synthetic data. As a result, parameter estimation using genomic data assimilation works very well and the network structure is suitably selected.

State and local governments

NASA Technical Reports Server (NTRS)

Barnes, Dennis

1990-01-01

The Virginia Space Grant Consortium approach to a close working relation to state and local governments is presented as a model for consideration. State government relations are especially important in that this is a primary resource in securing matching funds. Avenues for establishing these relationships are listed and discussed.

Mathematical analysis of an age-structured population model with space-limited recruitment.

PubMed

Kamioka, Katumi

2005-11-01

In this paper, we investigate structured population model of marine invertebrate whose life stage is composed of sessile adults and pelagic larvae, such as barnacles contained in a local habitat. First we formulate the basic model as an Cauchy problem on a Banach space to discuss the existence and uniqueness of non-negative solution. Next we define the basic reproduction number R0 to formulate the invasion condition under which the larvae can successfully settle down in the completely vacant habitat. Subsequently we examine existence and stability of steady states. We show that the trivial steady state is globally asymptotically stable if R0 < or = 1, whereas it is unstable if R0 > 1. Furthermore, we show that a positive (non-trivial) steady state uniquely exists if R0 > 1 and it is locally asymptotically stable as far as absolute value of R0 - 1 is small enough.

Dissipative stability analysis and control of two-dimensional Fornasini-Marchesini local state-space model

NASA Astrophysics Data System (ADS)

Wang, Lanning; Chen, Weimin; Li, Lizhen

2017-06-01

This paper is concerned with the problems of dissipative stability analysis and control of the two-dimensional (2-D) Fornasini-Marchesini local state-space (FM LSS) model. Based on the characteristics of the system model, a novel definition of 2-D FM LSS (Q, S, R)-α-dissipativity is given first, and then a sufficient condition in terms of linear matrix inequality (LMI) is proposed to guarantee the asymptotical stability and 2-D (Q, S, R)-α-dissipativity of the systems. As its special cases, 2-D passivity performance and 2-D H∞ performance are also discussed. Furthermore, by use of this dissipative stability condition and projection lemma technique, 2-D (Q, S, R)-α-dissipative state-feedback control problem is solved as well. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Analysis of helicopter flight dynamics through modeling and simulation of primary flight control actuation system

NASA Astrophysics Data System (ADS)

Nelson, Hunter Barton

A simplified second-order transfer function actuator model used in most flight dynamics applications cannot easily capture the effects of different actuator parameters. The present work integrates a nonlinear actuator model into a nonlinear state space rotorcraft model to determine the effect of actuator parameters on key flight dynamics. The completed actuator model was integrated with a swashplate kinematics where step responses were generated over a range of key hydraulic parameters. The actuator-swashplate system was then introduced into a nonlinear state space rotorcraft simulation where flight dynamics quantities such as bandwidth and phase delay analyzed. Frequency sweeps were simulated for unique actuator configurations using the coupled nonlinear actuator-rotorcraft system. The software package CIFER was used for system identification and compared directly to the linearized models. As the actuator became rate saturated, the effects on bandwidth and phase delay were apparent on the predicted handling qualities specifications.

The development rainfall forecasting using kalman filter

NASA Astrophysics Data System (ADS)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

Three-body problem in d-dimensional space: Ground state, (quasi)-exact-solvability

NASA Astrophysics Data System (ADS)

Turbiner, Alexander V.; Miller, Willard; Escobar-Ruiz, M. A.

2018-02-01

As a straightforward generalization and extension of our previous paper [A. V. Turbiner et al., "Three-body problem in 3D space: Ground state, (quasi)-exact-solvability," J. Phys. A: Math. Theor. 50, 215201 (2017)], we study the aspects of the quantum and classical dynamics of a 3-body system with equal masses, each body with d degrees of freedom, with interaction depending only on mutual (relative) distances. The study is restricted to solutions in the space of relative motion which are functions of mutual (relative) distances only. It is shown that the ground state (and some other states) in the quantum case and the planar trajectories (which are in the interaction plane) in the classical case are of this type. The quantum (and classical) Hamiltonian for which these states are eigenfunctions is derived. It corresponds to a three-dimensional quantum particle moving in a curved space with special d-dimension-independent metric in a certain d-dependent singular potential, while at d = 1, it elegantly degenerates to a two-dimensional particle moving in flat space. It admits a description in terms of pure geometrical characteristics of the interaction triangle which is defined by the three relative distances. The kinetic energy of the system is d-independent; it has a hidden sl(4, R) Lie (Poisson) algebra structure, alternatively, the hidden algebra h(3) typical for the H3 Calogero model as in the d = 3 case. We find an exactly solvable three-body S3-permutationally invariant, generalized harmonic oscillator-type potential as well as a quasi-exactly solvable three-body sextic polynomial type potential with singular terms. For both models, an extra first order integral exists. For d = 1, the whole family of 3-body (two-dimensional) Calogero-Moser-Sutherland systems as well as the Tremblay-Turbiner-Winternitz model is reproduced. It is shown that a straightforward generalization of the 3-body (rational) Calogero model to d > 1 leads to two primitive quasi-exactly solvable problems. The extension to the case of non-equal masses is straightforward and is briefly discussed.

Modelling and Characterisation of Detection Models in WAMI for Handling Negative Information

DTIC Science & Technology

2014-02-01

behaviour of the multi-stage detectors used in LoFT. This model is then used in a Probabilistic Hypothesis Density Filter (PHD). Unlike most multitarget...Therefore, we decided to use machine learning techniques which could model — and pre- dict — the behaviour of the detectors in LoFT. Because we are using...on feature detectors [8], motion models [13] and descriptor and template adaptation [9]. 2.3.2 State Model The state space of LoFT is defined in 2D

Reduced-order modeling for hyperthermia control.

PubMed

Potocki, J K; Tharp, H S

1992-12-01

This paper analyzes the feasibility of using reduced-order modeling techniques in the design of multiple-input, multiple-output (MIMO) hyperthermia temperature controllers. State space thermal models are created based upon a finite difference expansion of the bioheat transfer equation model of a scanned focused ultrasound system (SFUS). These thermal state space models are reduced using the balanced realization technique, and an order reduction criterion is tabulated. Results show that a drastic reduction in model dimension can be achieved using the balanced realization. The reduced-order model is then used to design a reduced-order optimal servomechanism controller for a two-scan input, two thermocouple output tissue model. In addition, a full-order optimal servomechanism controller is designed for comparison and validation purposes. These two controllers are applied to a variety of perturbed tissue thermal models to test the robust nature of the reduced-order controller. A comparison of the two controllers validates the use of open-loop balanced reduced-order models in the design of MIMO hyperthermia controllers.

An approach to solving large reliability models

NASA Technical Reports Server (NTRS)

Boyd, Mark A.; Veeraraghavan, Malathi; Dugan, Joanne Bechta; Trivedi, Kishor S.

1988-01-01

This paper describes a unified approach to the problem of solving large realistic reliability models. The methodology integrates behavioral decomposition, state trunction, and efficient sparse matrix-based numerical methods. The use of fault trees, together with ancillary information regarding dependencies to automatically generate the underlying Markov model state space is proposed. The effectiveness of this approach is illustrated by modeling a state-of-the-art flight control system and a multiprocessor system. Nonexponential distributions for times to failure of components are assumed in the latter example. The modeling tool used for most of this analysis is HARP (the Hybrid Automated Reliability Predictor).

Proceedings of the Second NASA Formal Methods Symposium

NASA Technical Reports Server (NTRS)

Munoz, Cesar (Editor)

2010-01-01

This publication contains the proceedings of the Second NASA Formal Methods Symposium sponsored by the National Aeronautics and Space Administration and held in Washington D.C. April 13-15, 2010. Topics covered include: Decision Engines for Software Analysis using Satisfiability Modulo Theories Solvers; Verification and Validation of Flight-Critical Systems; Formal Methods at Intel -- An Overview; Automatic Review of Abstract State Machines by Meta Property Verification; Hardware-independent Proofs of Numerical Programs; Slice-based Formal Specification Measures -- Mapping Coupling and Cohesion Measures to Formal Z; How Formal Methods Impels Discovery: A Short History of an Air Traffic Management Project; A Machine-Checked Proof of A State-Space Construction Algorithm; Automated Assume-Guarantee Reasoning for Omega-Regular Systems and Specifications; Modeling Regular Replacement for String Constraint Solving; Using Integer Clocks to Verify the Timing-Sync Sensor Network Protocol; Can Regulatory Bodies Expect Efficient Help from Formal Methods?; Synthesis of Greedy Algorithms Using Dominance Relations; A New Method for Incremental Testing of Finite State Machines; Verification of Faulty Message Passing Systems with Continuous State Space in PVS; Phase Two Feasibility Study for Software Safety Requirements Analysis Using Model Checking; A Prototype Embedding of Bluespec System Verilog in the PVS Theorem Prover; SimCheck: An Expressive Type System for Simulink; Coverage Metrics for Requirements-Based Testing: Evaluation of Effectiveness; Software Model Checking of ARINC-653 Flight Code with MCP; Evaluation of a Guideline by Formal Modelling of Cruise Control System in Event-B; Formal Verification of Large Software Systems; Symbolic Computation of Strongly Connected Components Using Saturation; Towards the Formal Verification of a Distributed Real-Time Automotive System; Slicing AADL Specifications for Model Checking; Model Checking with Edge-valued Decision Diagrams; and Data-flow based Model Analysis.

Approximate symmetries in atomic nuclei from a large-scale shell-model perspective

NASA Astrophysics Data System (ADS)

Launey, K. D.; Draayer, J. P.; Dytrych, T.; Sun, G.-H.; Dong, S.-H.

2015-05-01

In this paper, we review recent developments that aim to achieve further understanding of the structure of atomic nuclei, by capitalizing on exact symmetries as well as approximate symmetries found to dominate low-lying nuclear states. The findings confirm the essential role played by the Sp(3, ℝ) symplectic symmetry to inform the interaction and the relevant model spaces in nuclear modeling. The significance of the Sp(3, ℝ) symmetry for a description of a quantum system of strongly interacting particles naturally emerges from the physical relevance of its generators, which directly relate to particle momentum and position coordinates, and represent important observables, such as, the many-particle kinetic energy, the monopole operator, the quadrupole moment and the angular momentum. We show that it is imperative that shell-model spaces be expanded well beyond the current limits to accommodate particle excitations that appear critical to enhanced collectivity in heavier systems and to highly-deformed spatial structures, exemplified by the second 0+ state in 12C (the challenging Hoyle state) and 8Be. While such states are presently inaccessible by large-scale no-core shell models, symmetry-based considerations are found to be essential.

ACOSS FIVE (Active Control of Space Structures). Phase 1A

DTIC Science & Technology

1982-03-01

The control design MKUCTUKAL MOOC L PtRFOHMANCl MÜDtL DISTURBANCE MODEL I ’ II Q|S£) XM=) STATE SPACE MODEL KEDUCED MODELS (HAC... library ) whose detailed numerical procedures, structural reduction, eigen-computations, etc., are implemented dif- ferently than in NASTRAN. SPAR was...i-i. rCappesser ..ctn. ..ir. A. .^llliars i /ui N. t-t. i.yer orlva ..rlin^ton, ^\\ 22209 o j i c e 7 11 \\ttn. iULO Library

Model and on-orbit study of the International space station contamination processes by jets of its orientation thrusters

NASA Astrophysics Data System (ADS)

Yarygin, V. N.; Gerasimov, Yu I.; Krylov, A. N.; Prikhodko, V. G.; Skorovarov, A. Yu; Yarygin, I. V.

2017-11-01

The main objective of this paper is to describe the current state of research for the problem of the International Space Station contamination by plumes of its orientation thrusters. Results of experiments carried out at the Institute of Thermophysics SB RAS modeling space vehicles orientation thruster’s plumes are presented and experimental setup is discussed. A novel approach to reduction of contamination by thrusters with the help of special gas-dynamic protective devices mounted at the exit part of the nozzle is suggested. The description and results of on-orbit experiment at the International Space Station are given. Results show good agreement for model and on-orbit experiments validating our approach.

Modeling AWSoM CMEs with EEGGL: A New Approach for Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Jin, M.; Manchester, W.; van der Holst, B.; Sokolov, I.; Toth, G.; Vourlidas, A.; de Koning, C. A.; Gombosi, T. I.

2015-12-01

The major source of destructive space weather is coronal mass ejections (CMEs). However, our understanding of CMEs and their propagation in the heliosphere is limited by the insufficient observations. Therefore, the development of first-principals numerical models plays a vital role in both theoretical investigation and providing space weather forecasts. Here, we present results of the simulation of CME propagation from the Sun to 1AU by combining the analytical Gibson & Low (GL) flux rope model with the state-of-art solar wind model AWSoM. We also provide an approach for transferring this research model to a space weather forecasting tool by demonstrating how the free parameters of the GL flux rope can be prescribed based on remote observations via the new Eruptive Event Generator by Gibson-Low (EEGGL) toolkit. This capability allows us to predict the long-term evolution of the CME in interplanetary space. We perform proof-of-concept case studies to show the capability of the model to capture physical processes that determine CME evolution while also reproducing many observed features both in the corona and at 1 AU. We discuss the potential and limitations of this model as a future space weather forecasting tool.

Practical Application of Model-based Programming and State-based Architecture to Space Missions

NASA Technical Reports Server (NTRS)

Horvath, Gregory A.; Ingham, Michel D.; Chung, Seung; Martin, Oliver; Williams, Brian

2006-01-01

Innovative systems and software engineering solutions are required to meet the increasingly challenging demands of deep-space robotic missions. While recent advances in the development of an integrated systems and software engineering approach have begun to address some of these issues, they are still at the core highly manual and, therefore, error-prone. This paper describes a task aimed at infusing MIT's model-based executive, Titan, into JPL's Mission Data System (MDS), a unified state-based architecture, systems engineering process, and supporting software framework. Results of the task are presented, including a discussion of the benefits and challenges associated with integrating mature model-based programming techniques and technologies into a rigorously-defined domain specific architecture.

Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Conyers, Howard J.; Mavris, Dimitri N.

2014-01-01

This paper introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio and number of control surfaces. A doublet lattice approach is taken to compute generalized forces. A rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. Although, all parameters can be easily modified if desired.The focus of this paper is on tool presentation, verification and validation. This process is carried out in stages throughout the paper. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool. Therefore the flutter speed and frequency for a clamped plate are computed using V-g and V-f analysis. The computational results are compared to a previously published computational analysis and wind tunnel results for the same structure. Finally a case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to V-g and V-f analysis. This also includes the analysis of the model in response to a 1-cos gust.

Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Conyers, Howard J.; Mavris, Dimitri N.

2015-01-01

This paper introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing-edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio, and number of control surfaces. Using this information, the generalized forces are computed using the doublet-lattice method. Using Roger's approximation, a rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. All parameters can be easily modified if desired. The focus of this paper is on tool presentation, verification, and validation. These processes are carried out in stages throughout the paper. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool, therefore, the flutter speed and frequency for a clamped plate are computed using damping-versus-velocity and frequency-versus-velocity analysis. The computational results are compared to a previously published computational analysis and wind-tunnel results for the same structure. A case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to damping-versus-velocity and frequency-versus-velocity analysis, including the analysis of the model in response to a 1-cos gust.

Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Conyers, Howard Jason; Mavris, Dimitri N.

2015-01-01

This report introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing-edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio, and number of control surfaces. Using this information, the generalized forces are computed using the doublet-lattice method. Using Roger's approximation, a rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. All parameters can be easily modified if desired. The focus of this report is on tool presentation, verification, and validation. These processes are carried out in stages throughout the report. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool, therefore, the flutter speed and frequency for a clamped plate are computed using damping-versus-velocity and frequency-versus-velocity analysis. The computational results are compared to a previously published computational analysis and wind-tunnel results for the same structure. A case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to damping-versus-velocity and frequency-versus-velocity analysis, including the analysis of the model in response to a 1-cos gust.

Space Radiation Monitoring Center at SINP MSU

NASA Astrophysics Data System (ADS)

Kalegaev, Vladimir; Barinova, Wera; Barinov, Oleg; Bobrovnikov, Sergey; Dolenko, Sergey; Mukhametdinova, Ludmila; Myagkova, Irina; Nguen, Minh; Panasyuk, Mikhail; Shiroky, Vladimir; Shugay, Julia

2015-04-01

Data on energetic particle fluxes from Russian satellites have been collected in Space monitoring data center at Moscow State University in the near real-time mode. Web-portal http://smdc.sinp.msu.ru/ provides operational information on radiation state of the near-Earth space. Operational data are coming from space missions ELECTRO-L1, Meteor-M2. High-resolution data on energetic electron fluxes from MSU's satellite VERNOV with RELEC instrumentation on board are also available. Specific tools allow the visual representation of the satellite orbit in 3D space simultaneously with particle fluxes variations. Concurrent operational data coming from other spacecraft (ACE, GOES, SDO) and from the Earth's surface (geomagnetic indices) are used to represent geomagnetic and radiation state of near-Earth environment. Internet portal http://swx.sinp.msu.ru provides access to the actual data characterizing the level of solar activity, geomagnetic and radiation conditions in heliosphere and the Earth's magnetosphere in the real-time mode. Operational forecasting services automatically generate alerts on particle fluxes enhancements above the threshold values, both for SEP and relativistic electrons, using data from LEO and GEO orbits. The models of space environment working in autonomous mode are used to generalize the information obtained from different missions for the whole magnetosphere. On-line applications created on the base of these models provide short-term forecasting for SEP particles and relativistic electron fluxes at GEO and LEO, Dst and Kp indices online forecasting up to 1.5 hours ahead. Velocities of high-speed streams in solar wind on the Earth orbit are estimated with advance time of 3-4 days. Visualization system provides representation of experimental and modeling data in 2D and 3D.

Energy content of stormtime ring current from phase space mapping simulations

NASA Technical Reports Server (NTRS)

Chen, Margaret W.; Schulz, Michael; Lyons, Larry R.

1993-01-01

We perform a phase space mapping study to estimate the enhancement in energy content that results from stormtime particle transport in the equatorial magnetosphere. Our pre-storm phase space distribution is based on a steady-state transport model. Using results from guiding-center simulations of ion transport during model storms having main phases of 3 hr, 6 hr, and 12 hr, we map phase space distributions of ring current protons from the pre-storm distribution in accordance with Liouville's theorem. We find that transport can account for the entire ten to twenty-fold increase in magnetospheric particle energy content typical of a major storm if a realistic stormtime enhancement of the phase space density f is imposed at the nightside tail plasma sheet (represented by an enhancement of f at the neutral line in our model).

Transfer matrix method for dynamics modeling and independent modal space vibration control design of linear hybrid multibody system

NASA Astrophysics Data System (ADS)

Rong, Bao; Rui, Xiaoting; Lu, Kun; Tao, Ling; Wang, Guoping; Ni, Xiaojun

2018-05-01

In this paper, an efficient method of dynamics modeling and vibration control design of a linear hybrid multibody system (MS) is studied based on the transfer matrix method. The natural vibration characteristics of a linear hybrid MS are solved by using low-order transfer equations. Then, by constructing the brand-new body dynamics equation, augmented operator and augmented eigenvector, the orthogonality of augmented eigenvector of a linear hybrid MS is satisfied, and its state space model expressed in each independent model space is obtained easily. According to this dynamics model, a robust independent modal space-fuzzy controller is designed for vibration control of a general MS, and the genetic optimization of some critical control parameters of fuzzy tuners is also presented. Two illustrative examples are performed, which results show that this method is computationally efficient and with perfect control performance.

Linear canonical transformations of coherent and squeezed states in the Wigner phase space. III - Two-mode states

NASA Technical Reports Server (NTRS)

Han, D.; Kim, Y. S.; Noz, Marilyn E.

1990-01-01

It is shown that the basic symmetry of two-mode squeezed states is governed by the group SP(4) in the Wigner phase space which is locally isomorphic to the (3 + 2)-dimensional Lorentz group. This symmetry, in the Schroedinger picture, appears as Dirac's two-oscillator representation of O(3,2). It is shown that the SU(2) and SU(1,1) interferometers exhibit the symmetry of this higher-dimensional Lorentz group. The mathematics of two-mode squeezed states is shown to be applicable to other branches of physics including thermally excited states in statistical mechanics and relativistic extended hadrons in the quark model.

The analysis of 3-phase squirrel-cage induction motors including space harmonics and mutual slotting in transient and steady state

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paap, G.C.

1991-03-01

From general equations which describe the transient electromechanical behavior of the asynchronous squirrel-cage motor, and which include the influence of space harmonics and mutual slotting, simplified models are derived and compared. The models derived are demonstrated in examples where special attention is paid to the influence of the place of the harmonics in the mutual inductance matrix and the influence of mutual slotting. Further, the steady-state equations are derived and the back-transformation for the stator and rotor currents is given. One example is compared with the result of measurements.

An introduction to chaotic and random time series analysis

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.

1989-01-01

The origin of chaotic behavior and the relation of chaos to randomness are explained. Two mathematical results are described: (1) a representation theorem guarantees the existence of a specific time-domain model for chaos and addresses the relation between chaotic, random, and strictly deterministic processes; (2) a theorem assures that information on the behavior of a physical system in its complete state space can be extracted from time-series data on a single observable. Focus is placed on an important connection between the dynamical state space and an observable time series. These two results lead to a practical deconvolution technique combining standard random process modeling methods with new embedded techniques.

Derivative-Free Estimation of the Score Vector and Observed Information Matrix with Application to State-Space Models

DTIC Science & Technology

2015-07-14

2008). Sequential Monte Carlo smoothing with applica- tion to parameter estimation in non-linear state space models. Bernoulli , 14, 155-179. [22] Parikh...1BcΣ(θ?,δ)(Θ) ] = o ( τk ) for all k ∈ N. (45) The other integral is over the ball BΣ(θ?, δ), i.e. close to θ?; hence we perform a Taylor expansion of...1] R3 (θ, θ?) = ∑ |α|=4 ∂αϕ (θ? + cθ (θ − θ?)) (θ − θ?)α α! . 26 We now use the symmetry of the normal distribution N ( θ?, τ2Σ ) on the ball BΣ(θ

Development of Reduced-Order Models for Aeroelastic and Flutter Prediction Using the CFL3Dv6.0 Code

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Bartels, Robert E.

2002-01-01

A reduced-order model (ROM) is developed for aeroelastic analysis using the CFL3D version 6.0 computational fluid dynamics (CFD) code, recently developed at the NASA Langley Research Center. This latest version of the flow solver includes a deforming mesh capability, a modal structural definition for nonlinear aeroelastic analyses, and a parallelization capability that provides a significant increase in computational efficiency. Flutter results for the AGARD 445.6 Wing computed using CFL3D v6.0 are presented, including discussion of associated computational costs. Modal impulse responses of the unsteady aerodynamic system are then computed using the CFL3Dv6 code and transformed into state-space form. Important numerical issues associated with the computation of the impulse responses are presented. The unsteady aerodynamic state-space ROM is then combined with a state-space model of the structure to create an aeroelastic simulation using the MATLAB/SIMULINK environment. The MATLAB/SIMULINK ROM is used to rapidly compute aeroelastic transients including flutter. The ROM shows excellent agreement with the aeroelastic analyses computed using the CFL3Dv6.0 code directly.

Cognitive engineering models in space systems

NASA Technical Reports Server (NTRS)

Mitchell, Christine M.

1992-01-01

NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. The human operator monitors and fine-tunes computer-based control systems and is responsible for ensuring safe and efficient system operation. In such systems, the potential consequences of human mistakes and errors may be very large, and low probability of such events is likely. Thus, models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision support aids. The operator function model represents normative operator behavior-expected operator activities given current system state. The extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications is discussed.

Attention control learning in the decision space using state estimation

NASA Astrophysics Data System (ADS)

Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

2016-05-01

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

Quantum correlations and dynamics from classical random fields valued in complex Hilbert spaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khrennikov, Andrei

2010-08-15

One of the crucial differences between mathematical models of classical and quantum mechanics (QM) is the use of the tensor product of the state spaces of subsystems as the state space of the corresponding composite system. (To describe an ensemble of classical composite systems, one uses random variables taking values in the Cartesian product of the state spaces of subsystems.) We show that, nevertheless, it is possible to establish a natural correspondence between the classical and the quantum probabilistic descriptions of composite systems. Quantum averages for composite systems (including entangled) can be represented as averages with respect to classical randommore » fields. It is essentially what Albert Einstein dreamed of. QM is represented as classical statistical mechanics with infinite-dimensional phase space. While the mathematical construction is completely rigorous, its physical interpretation is a complicated problem. We present the basic physical interpretation of prequantum classical statistical field theory in Sec. II. However, this is only the first step toward real physical theory.« less

Generalized thermodynamic relations for a system experiencing heat and mass diffusion in the far-from-equilibrium realm based on steepest entropy ascent.

PubMed

Li, Guanchen; von Spakovsky, Michael R

2016-09-01

This paper presents a nonequilibrium thermodynamic model for the relaxation of a local, isolated system in nonequilibrium using the principle of steepest entropy ascent (SEA), which can be expressed as a variational principle in thermodynamic state space. The model is able to arrive at the Onsager relations for such a system. Since no assumption of local equilibrium is made, the conjugate fluxes and forces are intrinsic to the subspaces of the system's state space and are defined using the concepts of hypoequilibrium state and nonequilibrium intensive properties, which describe the nonmutual equilibrium status between subspaces of the thermodynamic state space. The Onsager relations are shown to be a thermodynamic kinematic feature of the system independent of the specific details of the micromechanical dynamics. Two kinds of relaxation processes are studied with different constraints (i.e., conservation laws) corresponding to heat and mass diffusion. Linear behavior in the near-equilibrium region as well as nonlinear behavior in the far-from-equilibrium region are discussed. Thermodynamic relations in the equilibrium and near-equilibrium realm, including the Gibbs relation, the Clausius inequality, and the Onsager relations, are generalized to the far-from-equilibrium realm. The variational principle in the space spanned by the intrinsic conjugate fluxes and forces is expressed via the quadratic dissipation potential. As an application, the model is applied to the heat and mass diffusion of a system represented by a single-particle ensemble, which can also be applied to a simple system of many particles. Phenomenological transport coefficients are also derived in the near-equilibrium realm.

Exploring Ackermann and LQR stability control of stochastic state-space model of hexacopter equipped with robotic arm

NASA Astrophysics Data System (ADS)

Ibrahim, I. N.; Akkad, M. A. Al; Abramov, I. V.

2018-05-01

This paper discusses the control of Unmanned Aerial Vehicles (UAVs) for active interaction and manipulation of objects. The manipulator motion with an unknown payload was analysed concerning force and moment disturbances, which influence the mass distribution, and the centre of gravity (CG). Therefore, a general dynamics mathematical model of a hexacopter was formulated where a stochastic state-space model was extracted in order to build anti-disturbance controllers. Based on the compound pendulum method, the disturbances model that simulates the robotic arm with a payload was inserted into the stochastic model. This study investigates two types of controllers in order to study the stability of a hexacopter. A controller based on Ackermann’s method and the other - on the linear quadratic regulator (LQR) approach - were presented. The latter constitutes a challenge for UAV control performance especially with the presence of uncertainties and disturbances.

US EPA 2012 Air Quality Fused Surface for the Conterminous U.S. Map Service

EPA Pesticide Factsheets

This web service contains a polygon layer that depicts fused air quality predictions for 2012 for census tracts in the conterminous United States. Fused air quality predictions (for ozone and PM2.5) are modeled using a Bayesian space-time downscaling fusion model approach described in a series of three published journal papers: 1) (Berrocal, V., Gelfand, A. E. and Holland, D. M. (2012). Space-time fusion under error in computer model output: an application to modeling air quality. Biometrics 68, 837-848; 2) Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975; and 3) Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. J. of Agricultural, Biological,and Environmental Statistics 15, 176-197) is used to provide daily, predictive PM2.5 (daily average) and O3 (daily 8-hr maximum) surfaces for 2012. Summer (O3) and annual (PM2.5) means calculated and published. The downscaling fusion model uses both air quality monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Models-3/Community Multiscale Air Quality (CMAQ). Currently, predictions at the US census tract centroid locations within the 12 km CMAQ domain are archived. Predictions at the CMAQ grid cell centroids, or any desired set of locations co

Communications satellite systems operations with the space station, volume 2

NASA Technical Reports Server (NTRS)

Price, K.; Dixon, J.; Weyandt, C.

1987-01-01

A financial model was developed which described quantitatively the economics of the space segment of communication satellite systems. The model describes the economics of the space system throughout the lifetime of the satellite. The expected state-of-the-art status of communications satellite systems and operations beginning service in 1995 were assessed and described. New or enhanced space-based activities and associated satellite system designs that have the potential to achieve future communications satellite operations in geostationary orbit with improved economic performance were postulated and defined. Three scenarios using combinations of space-based activities were analyzed: a spin stabilized satellite, a three axis satellite, and assembly at the Space Station and GEO servicing. Functional and technical requirements placed on the Space Station by the scenarios were detailed. Requirements on the satellite were also listed.

Angular motion estimation using dynamic models in a gyro-free inertial measurement unit.

PubMed

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters.

Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement Unit

PubMed Central

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters. PMID:22778586

Electrochemical carbon dioxide concentrator subsystem math model. [for manned space station

NASA Technical Reports Server (NTRS)

Marshall, R. D.; Carlson, J. N.; Schubert, F. H.

1974-01-01

A steady state computer simulation model has been developed to describe the performance of a total six man, self-contained electrochemical carbon dioxide concentrator subsystem built for the space station prototype. The math model combines expressions describing the performance of the electrochemical depolarized carbon dioxide concentrator cells and modules previously developed with expressions describing the performance of the other major CS-6 components. The model is capable of accurately predicting CS-6 performance over EDC operating ranges and the computer simulation results agree with experimental data obtained over the prediction range.

Identification of Aerodynamic Coefficients Using Computational Neural Networks

DTIC Science & Technology

1992-01-09

the Am-. icar , Institete ur Aeronautics and mation model, excellent matches of aerodynamic coef- Astronautics, Inc. All rights reserved. ficient...UL NSN 7540-01-2EO-SSO0 Standard Form 296 (Rev. 2-89) ft"""~e by Ar t4ed. Z39-1 SAIA A_ AIAA 92-0172 Identification of Aerodynamic Coefficients Using...state and control space. While the partitions span the space, these global models are, in general, not contin- Precise, smooth aerodynamic models are

Review of Quantum Electromagnetic States

DTIC Science & Technology

1999-10-01

product space. 227 The wave functions used in the Jaynes - Cummings ’ model reside in a direct product space Va ɠ> Vf where Va and Vf are the Hubert...Dependent Probability 215 5.1.3 The Relaxation Term 217 5.2 Jaynes - Cummings ’ Model for Matter-Field Interactions 218 5.2.1 The Atomic Hamiltonian 218...5.3 The Interaction Representation for the Jaynes - Cummings ’ Model 224 5.3.1 Atomic Creation and Annihilation Operators 224 5.3.2 The Boson Creation

Contribution towards a draft revision of recommendations 681: Propagation data required for the design of Earth-space land mobile telecommunications systems

NASA Technical Reports Server (NTRS)

Davarian, Faramaz; Bishop, Dennis

1993-01-01

Propagation models that can be used for the design of earth-space land mobile-satellite telecommunications systems are presented. These models include: empirical roadside shadowing, attenuation frequency scaling, fade and non-fade duration distribution, multipath in a mountain environment, and multipath in a roadside tree environment. Propagation data from helicopter-mobile and satellite-mobile measurements in Australia and the United States were used to develop the models.

Contribution Towards a Draft Revision of Recommendation 681 Propogation Data Required for the Design of Earth-Space Land Mobile Telecommunications Systems

NASA Technical Reports Server (NTRS)

Davarian, F.; Bishop, D.

1993-01-01

Propogation models that can be used for the design of Earth-space land mobile-satellite telecommunications systems are presented. These models include: empirical roadside shadowing, attenuation frequency scaling, fade and non-fade duration distribution, multipath in a mountain environment, and multipath in a roadside tree environment. Propogation data from helicopter-mobile and satellite-mobile measurements in Australia and the United States were used to develop the models.

Generalized Ehrenfest Relations, Deformation Quantization, and the Geometry of Inter-model Reduction

NASA Astrophysics Data System (ADS)

Rosaler, Joshua

2018-03-01

This study attempts to spell out more explicitly than has been done previously the connection between two types of formal correspondence that arise in the study of quantum-classical relations: one the one hand, deformation quantization and the associated continuity between quantum and classical algebras of observables in the limit \\hbar → 0, and, on the other, a certain generalization of Ehrenfest's Theorem and the result that expectation values of position and momentum evolve approximately classically for narrow wave packet states. While deformation quantization establishes a direct continuity between the abstract algebras of quantum and classical observables, the latter result makes in-eliminable reference to the quantum and classical state spaces on which these structures act—specifically, via restriction to narrow wave packet states. Here, we describe a certain geometrical re-formulation and extension of the result that expectation values evolve approximately classically for narrow wave packet states, which relies essentially on the postulates of deformation quantization, but describes a relationship between the actions of quantum and classical algebras and groups over their respective state spaces that is non-trivially distinct from deformation quantization. The goals of the discussion are partly pedagogical in that it aims to provide a clear, explicit synthesis of known results; however, the particular synthesis offered aspires to some novelty in its emphasis on a certain general type of mathematical and physical relationship between the state spaces of different models that represent the same physical system, and in the explicitness with which it details the above-mentioned connection between quantum and classical models.

Validation of International Space Station Electrical Performance Model via On-orbit Telemetry

NASA Technical Reports Server (NTRS)

Jannette, Anthony G.; Hojnicki, Jeffrey S.; McKissock, David B.; Fincannon, James; Kerslake, Thomas W.; Rodriguez, Carlos D.

2002-01-01

The first U.S. power module on International Space Station (ISS) was activated in December 2000. Comprised of solar arrays, nickel-hydrogen (NiH2) batteries, and a direct current power management and distribution (PMAD) system, the electric power system (EPS) supplies power to housekeeping and user electrical loads. Modeling EPS performance is needed for several reasons, but primarily to assess near-term planned and off-nominal operations and because the EPS configuration changes over the life of the ISS. The System Power Analysis for Capability Evaluation (SPACE) computer code is used to assess the ISS EPS performance. This paper describes the process of validating the SPACE EPS model via ISS on-orbit telemetry. To accomplish this goal, telemetry was first used to correct assumptions and component models in SPACE. Then on-orbit data was directly input to SPACE to facilitate comparing model predictions to telemetry. It will be shown that SPACE accurately predicts on-orbit component and system performance. For example, battery state-of-charge was predicted to within 0.6 percentage points over a 0 to 100 percent scale and solar array current was predicted to within a root mean square (RMS) error of 5.1 Amps out of a typical maximum of 220 Amps. First, SPACE model predictions are compared to telemetry for the ISS EPS components: solar arrays, NiH2 batteries, and the PMAD system. Second, SPACE predictions for the overall performance of the ISS EPS are compared to telemetry and again demonstrate model accuracy.

Space power technology into the 21st century

NASA Technical Reports Server (NTRS)

Faymon, K. A.; Fordyce, J. S.

1984-01-01

This paper discusses the space power systems of the early 21st century. The focus is on those capabilities which are anticipated to evolve from today's state-of-the-art and the technology development programs presently in place or planned for the remainder of the century. The power system technologies considered include solar thermal, nuclear, radioisotope, photovoltaic, thermionic, thermoelectric, and dynamic conversion systems such as the Brayton and Stirling cycles. Energy storage technologies considered include nickel hydrogen biopolar batteries, advanced high energy rechargeable batteries, regenerative fuel cells, and advanced primary batteries. The present state-of-the-art of these space power and energy technologies is discussed along with their projections, trends and goals. A speculative future mission model is postulated which includes manned orbiting space stations, manned lunar bases, unmanned earth orbital and interplanetary spacecraft, manned interplanetary missions, military applications, and earth to space and space to space transportation systems. The various space power/energy system technologies anticipated to be operational by the early 21st century are matched to these missions.

Space power technology into the 21st Century

NASA Technical Reports Server (NTRS)

Faymon, K. A.; Fordyce, J. S.

1983-01-01

The space power systems of the early 21st century are discussed. The capabilities which are anticipated to evolve from today's state of the art and the technology development programs presently in place or planned for the remainder of the century are emphasized. The power system technologies considered include: solar thermal, nuclear, radioisotope, photovoltaic, thermionic, thermoelectric, and dynamic conversion systems such as the Brayton and Stirling cycles. Energy storage technologies considered include: nickel hydrogen biopolar batteries, advanced high energy rechargeable batteries, regenerative fuel cells, and advanced primary batteries. The present state of the art of these space power and energy technologies is discussed along with their projections, trends and goals. A speculative future mission model is postulated which includes manned orbiting space stations, manned lunar bases, unmanned Earth orbital and interplanetary spacecraft, manned interplanetary missions, military applications, and Earth to space and space to space transportation systems. The various space power/energy system technologies which are anticipated to be operational by the early 21st century are matched to these missions.

Official portrait of astronaut Charles J. Precourt

NASA Technical Reports Server (NTRS)

1991-01-01

Official portrait of astronaut Charles J. Precourt. Precourt, a member of Astronaut Class 13 and United States Air Force (USAF), wears blue flight suit and poses with space shuttle orbiter model with a United States flag creating the backdrop.

Density dependence and climate effects in Rocky Mountain elk: an application of regression with instrumental variables for population time series with sampling error.

PubMed

Creel, Scott; Creel, Michael

2009-11-01

1. Sampling error in annual estimates of population size creates two widely recognized problems for the analysis of population growth. First, if sampling error is mistakenly treated as process error, one obtains inflated estimates of the variation in true population trajectories (Staples, Taper & Dennis 2004). Second, treating sampling error as process error is thought to overestimate the importance of density dependence in population growth (Viljugrein et al. 2005; Dennis et al. 2006). 2. In ecology, state-space models are used to account for sampling error when estimating the effects of density and other variables on population growth (Staples et al. 2004; Dennis et al. 2006). In econometrics, regression with instrumental variables is a well-established method that addresses the problem of correlation between regressors and the error term, but requires fewer assumptions than state-space models (Davidson & MacKinnon 1993; Cameron & Trivedi 2005). 3. We used instrumental variables to account for sampling error and fit a generalized linear model to 472 annual observations of population size for 35 Elk Management Units in Montana, from 1928 to 2004. We compared this model with state-space models fit with the likelihood function of Dennis et al. (2006). We discuss the general advantages and disadvantages of each method. Briefly, regression with instrumental variables is valid with fewer distributional assumptions, but state-space models are more efficient when their distributional assumptions are met. 4. Both methods found that population growth was negatively related to population density and winter snow accumulation. Summer rainfall and wolf (Canis lupus) presence had much weaker effects on elk (Cervus elaphus) dynamics [though limitation by wolves is strong in some elk populations with well-established wolf populations (Creel et al. 2007; Creel & Christianson 2008)]. 5. Coupled with predictions for Montana from global and regional climate models, our results predict a substantial reduction in the limiting effect of snow accumulation on Montana elk populations in the coming decades. If other limiting factors do not operate with greater force, population growth rates would increase substantially.

An Indirect Mixed-Sensitivity Approach to Microgravity Vibration Isolation: The Exploitation of Kinematic Coupling In Frequency-Weighting Design-Filter Selections

NASA Technical Reports Server (NTRS)

Hampton, R. David; Whorton, Mark S.

2000-01-01

Many space science experiments need an active isolation system to provide them with the requisite microgravity environment. The isolation systems planned for use with the International Space Station have been appropriately modeled using relative position, relative velocity, and acceleration states. In theory, frequency design filters can be applied to these state-space models, in order to develop optimal H, or mixed-norm controllers with desired stability- and performance characteristics. In practice. however, the kinematic coupling among the various states can lead, through the associated frequency-weighting-filters, to conflicting demands on the Riccati design "machinery." The results can be numerically ill-conditioned regulator and estimator Riccati equations and/or reduced intuition in the design process. In addition, kinematic coupling can result in a redundancy in the demands imposed by the frequency weights. Failure properly to account for this type of coupling can lead to an unnecessary increase in controller dimensionality and, in turn, controller complexity. This paper suggests a rational approach to the assignment of frequency weighting design filters, in the presence of the kinematic coupling among states that exists in the microgravity vibration isolation problem.

An Indirect Mixed-Sensitivity Approach to Microgravity Vibration Isolation: The Exploitation of Kinematic Coupling In Frequency-weighting Design-Filter Selections

NASA Technical Reports Server (NTRS)

Hampton, R. David; Whorton, Mark S.

2000-01-01

Many space-science experiments need an active isolation system to provide them with the requisite microgravity environment. The isolation systems planned for use with the International Space Station have been appropriately modeled using relative position relative velocity, and acceleration states. In theory, frequency-weighting design filters can be applied to these state-space models, in order to develop optimal H2 or mixed-norm controllers with desired stability and performance characteristics. In practice, however, the kinematic coupling among the various states can lead, through the associated frequency-weighting-filters, to conflicting demands on the Riccati design "machinery." The results can be numerically ill-conditioned regulator and estimator Riccati equations and/or reduced intuition in the design process. In addition, kinematic coupling can result in a redundancy in the demands imposed by the frequency weights. Failure properly to account for this type of coupling can lead to an unnecessary increase in controller dimensionality and, in turn, controller complexity. This paper suggests a rational approach to the assignment of frequency-weighting design filters, in the presence of the kinematic coupling among states that exists in the microgravity vibration isolation problem.

Operational Space Weather Activities in the US

NASA Astrophysics Data System (ADS)

Berger, Thomas; Singer, Howard; Onsager, Terrance; Viereck, Rodney; Murtagh, William; Rutledge, Robert

2016-07-01

We review the current activities in the civil operational space weather forecasting enterprise of the United States. The NOAA/Space Weather Prediction Center is the nation's official source of space weather watches, warnings, and alerts, working with partners in the Air Force as well as international operational forecast services to provide predictions, data, and products on a large variety of space weather phenomena and impacts. In October 2015, the White House Office of Science and Technology Policy released the National Space Weather Strategy (NSWS) and associated Space Weather Action Plan (SWAP) that define how the nation will better forecast, mitigate, and respond to an extreme space weather event. The SWAP defines actions involving multiple federal agencies and mandates coordination and collaboration with academia, the private sector, and international bodies to, among other things, develop and sustain an operational space weather observing system; develop and deploy new models of space weather impacts to critical infrastructure systems; define new mechanisms for the transition of research models to operations and to ensure that the research community is supported for, and has access to, operational model upgrade paths; and to enhance fundamental understanding of space weather through support of research models and observations. The SWAP will guide significant aspects of space weather operational and research activities for the next decade, with opportunities to revisit the strategy in the coming years through the auspices of the National Science and Technology Council.

Effective field theory in the harmonic oscillator basis

DOE PAGES

Binder, S.; Ekström, Jan A.; Hagen, Gaute; ...

2016-04-25

In this paper, we develop interactions from chiral effective field theory (EFT) that are tailored to the harmonic oscillator basis. As a consequence, ultraviolet convergence with respect to the model space is implemented by construction and infrared convergence can be achieved by enlarging the model space for the kinetic energy. In oscillator EFT, matrix elements of EFTs formulated for continuous momenta are evaluated at the discrete momenta that stem from the diagonalization of the kinetic energy in the finite oscillator space. By fitting to realistic phase shifts and deuteron data we construct an effective interaction from chiral EFT at next-to-leadingmore » order. Finally, many-body coupled-cluster calculations of nuclei up to 132Sn converge fast for the ground-state energies and radii in feasible model spaces.« less

Numerical Models for Sound Propagation in Long Spaces

NASA Astrophysics Data System (ADS)

Lai, Chenly Yuen Cheung

Both reverberation time and steady-state sound field are the key elements for assessing the acoustic condition in an enclosed space. They affect the noise propagation, speech intelligibility, clarity index, and definition. Since the sound field in a long space is non diffuse, classical room acoustics theory does not apply in this situation. The ray tracing technique and the image source methods are two common models to fathom both reverberation time and steady-state sound field in long enclosures nowadays. Although both models can give an accurate estimate of reverberation times and steady-state sound field directly or indirectly, they often involve time-consuming calculations. In order to simplify the acoustic consideration, a theoretical formulation has been developed for predicting both steady-state sound fields and reverberation times in street canyons. The prediction model is further developed to predict the steady-state sound field in a long enclosure. Apart from the straight long enclosure, there are other variations such as a cross junction, a long enclosure with a T-intersection, an U-turn long enclosure. In the present study, an theoretical and experimental investigations were conducted to develop formulae for predicting reverberation times and steady-state sound fields in a junction of a street canyon and in a long enclosure with T-intersection. The theoretical models are validated by comparing the numerical predictions with published experimental results. The theoretical results are also compared with precise indoor measurements and large-scale outdoor experimental results. In all of previous acoustical studies related to long enclosure, most of the studies are focused on the monopole sound source. Besides non-directional noise source, many noise sources in long enclosure are dipole like, such as train noise and fan noise. In order to study the characteristics of directional noise sources, a review of available dipole source was conducted. A dipole was constructed which was subsequent used for experimental studies. In additional, a theoretical model was developed for predicting dipole sound fields. The theoretical model can be used to study the effect of a dipole source on the speech intelligibility in long enclosures.

Representation of natural numbers in quantum mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benioff, Paul

2001-03-01

This paper represents one approach to making explicit some of the assumptions and conditions implied in the widespread representation of numbers by composite quantum systems. Any nonempty set and associated operations is a set of natural numbers or a model of arithmetic if the set and operations satisfy the axioms of number theory or arithmetic. This paper is limited to k-ary representations of length L and to the axioms for arithmetic modulo k{sup L}. A model of the axioms is described based on an abstract L-fold tensor product Hilbert space H{sup arith}. Unitary maps of this space onto a physicalmore » parameter based product space H{sup phy} are then described. Each of these maps makes states in H{sup phy}, and the induced operators, a model of the axioms. Consequences of the existence of many of these maps are discussed along with the dependence of Grover's and Shor's algorithms on these maps. The importance of the main physical requirement, that the basic arithmetic operations are efficiently implementable, is discussed. This condition states that there exist physically realizable Hamiltonians that can implement the basic arithmetic operations and that the space-time and thermodynamic resources required are polynomial in L.« less

Imitate or innovate: Competition of strategy updating attitudes in spatial social dilemma games

NASA Astrophysics Data System (ADS)

Danku, Zsuzsa; Wang, Zhen; Szolnoki, Attila

2018-01-01

Evolution is based on the assumption that competing players update their strategies to increase their individual payoffs. However, while the applied updating method can be different, most of previous works proposed uniform models where players use identical way to revise their strategies. In this work we explore how imitation-based or learning attitude and innovation-based or myopic best-response attitude compete for space in a complex model where both attitudes are available. In the absence of additional cost the best response trait practically dominates the whole snow-drift game parameter space which is in agreement with the average payoff difference of basic models. When additional cost is involved then the imitation attitude can gradually invade the whole parameter space but this transition happens in a highly nontrivial way. However, the role of competing attitudes is reversed in the stag-hunt parameter space where imitation is more successful in general. Interestingly, a four-state solution can be observed for the latter game which is a consequence of an emerging cyclic dominance between possible states. These phenomena can be understood by analyzing the microscopic invasion processes, which reveals the unequal propagation velocities of strategies and attitudes.

Recent Geoeffective Space Weather Events and Technological System Impacts

NASA Astrophysics Data System (ADS)

Redmon, R. J.; Denig, W. F.; Loto'aniu, P. T. M.; Singer, H. J.; Wilkinson, D. C.; Knipp, D. J.; Kilcommons, L. M.

2015-12-01

We review the state of the space environment for three recent intense geoeffective storms using NOAA observations and model predictions. On February 27, 2014, the US Wide Area Augmentation System (WAAS) navigation service over eastern Alaska and northeastern continental US was degraded due to a strong ionospheric storm. Similarly, on March 17, the St. Patrick's Day geomagnetic storm commenced, resulting in the most intense storm of the solar cycle to date with mid-latitude auroral sightings, intense ionospheric irregularities and WAAS degradation. On June 22, a strong (G4) geomagnetic storm commenced following the impact of 3 coronal mass ejections (CMEs). Late on June 22, solar protons entered the polar regions along open magnetic field lines producing intense radio absorption. We summarize, compare and contrast the space environmental state for each of these events from the perspective of NOAA observations and model predictions. We do so by leveraging GOES and POES/MetOp observations of the space radiation environment, DMSP observations of precipitating particles and bulk plasma parameters, OVATION Prime predictions of the auroral energy input and the US Total Electron Content (USTEC) and D-Region Absorption Prediction (DRAP) modeled response of the ionosphere. We discuss impacts to technological systems as available.

A low-order model of the equatorial ocean-atmosphere system

NASA Astrophysics Data System (ADS)

Legnani, Roberto

A low order model of the equatorial ocean-atmosphere coupled system is presented. The model atmosphere includes a hydrological cycle with cloud-radiation interaction. The model ocean is based on mixed layer dynamics with a parameterization of entrainment processes. The coupling takes place via transfer to momentum, sensible heat, latent heat and short wave and long wave radiation through the ocean surface. The dynamical formulation is that of the primitive equations of an equatorial beta-plane, with zonally periodic and meridionally infinite geometry. The system is expanded into the set of normal modes pertinent to the linear problem and severly truncated to a few modes; 54 degrees of freedom are retained. Some nonlinear terms of the equations are evaluated in physical space and then projected onto the functional space; other terms are evaluated directly in the functional space. Sensitivity tests to variations of the parameters are performed, and some results from 10-year initial value simulations are presented. The model is capable of supporting oscillations of different time scales, ranging from a few days to a few years; it prefers a particular zonally asymmetric state, but temporarily switches to a different (opposite) zonally asymmetric state in an event-like fashion.

a Low-Order Model of the Equatorial Ocean-Atmosphere System.

NASA Astrophysics Data System (ADS)

Legnani, Roberto

A low order model of the equatorial ocean-atmosphere coupled system is presented. The model atmosphere includes a hydrological cycle with cloud-radiation interaction. The model ocean is based on mixed layer dynamics with a parameterization of entrainment processes. The coupling takes place via transfer to momentum, sensible heat, latent heat and short -wave and long-wave radiation through the ocean surface. The dynamical formulation is that of the primitive equations of an equatorial beta-plane, with zonally periodic and meridionally infinite geometry. The system is expanded into the set of normal modes pertinent to the linear problem and severely truncated to a few modes; 54 degrees of freedom are retained. Some nonlinear terms of the equations are evaluated in physical space and then projected onto the functional space; other terms are evaluated directly in the functional space. Sensitivity tests to variations of the parameters are performed, and some results from 10-year initial value simulations are presented. The model is capable of supporting oscillations of different time scales, ranging from a few days to a few years; it prefers a particular zonally asymmetric state, but temporarily switches to a different (opposite) zonally asymmetric state in an event-like fashion.

Space Transportation Operations: Assessment of Methodologies and Models

NASA Technical Reports Server (NTRS)

Joglekar, Prafulla

2001-01-01

The systems design process for future space transportation involves understanding multiple variables and their effect on lifecycle metrics. Variables such as technology readiness or potential environmental impact are qualitative, while variables such as reliability, operations costs or flight rates are quantitative. In deciding what new design concepts to fund, NASA needs a methodology that would assess the sum total of all relevant qualitative and quantitative lifecycle metrics resulting from each proposed concept. The objective of this research was to review the state of operations assessment methodologies and models used to evaluate proposed space transportation systems and to develop recommendations for improving them. It was found that, compared to the models available from other sources, the operations assessment methodology recently developed at Kennedy Space Center has the potential to produce a decision support tool that will serve as the industry standard. Towards that goal, a number of areas of improvement in the Kennedy Space Center's methodology are identified.

Space Transportation Operations: Assessment of Methodologies and Models

NASA Technical Reports Server (NTRS)

Joglekar, Prafulla

2002-01-01

The systems design process for future space transportation involves understanding multiple variables and their effect on lifecycle metrics. Variables such as technology readiness or potential environmental impact are qualitative, while variables such as reliability, operations costs or flight rates are quantitative. In deciding what new design concepts to fund, NASA needs a methodology that would assess the sum total of all relevant qualitative and quantitative lifecycle metrics resulting from each proposed concept. The objective of this research was to review the state of operations assessment methodologies and models used to evaluate proposed space transportation systems and to develop recommendations for improving them. It was found that, compared to the models available from other sources, the operations assessment methodology recently developed at Kennedy Space Center has the potential to produce a decision support tool that will serve as the industry standard. Towards that goal, a number of areas of improvement in the Kennedy Space Center's methodology are identified.

Space Particle Hazard Measurement and Modeling

DTIC Science & Technology

2016-09-01

understand the interactions of the physical processes driving, then specify and ultimately predict the state of the energetic particle populations...Hudson, and B. T. Kress (2013), Direct observation of the CRAND proton radiation belt source, J. Geophys. Res. Space Physics , 118, doi:10.1002...anticritical temperature for spacecraft charging, J. Geophys Res.: Space Physics , 113, 2156-2202, doi: 10.1029/2008JA013161 2010 – Tested basic

Alpha-canonical form representation of the open loop dynamics of the Space Shuttle main engine

NASA Technical Reports Server (NTRS)

Duyar, Almet; Eldem, Vasfi; Merrill, Walter C.; Guo, Ten-Huei

1991-01-01

A parameter and structure estimation technique for multivariable systems is used to obtain a state space representation of open loop dynamics of the space shuttle main engine in alpha-canonical form. The parameterization being used is both minimal and unique. The simplified linear model may be used for fault detection studies and control system design and development.

The Space Debris Environment for the ISS Orbit

NASA Technical Reports Server (NTRS)

Theall, Jeff; Liou, Jer-Chyi; Matney, Mark; Kessler, Don

2001-01-01

With thirty-five planned missions over the next five years, the International Space Station (ISS) will be the focus for manned space activity. At least 6 different vehicles will transport crew and supplies to and from the nominally 400 km, 51.6 degree orbit. When completed, the ISS will be the largest space structure ever assembled and hence the largest target for space debris. Recent work at the Johnson Space Center has focused on updating the existing space debris models. The Orbital Debris Engineering Model, has been restructured to take advantage of state of the art desktop computing capability and revised with recent measurements from Haystack and Goldstone radars, additional analysis of LDEF and STS impacts, and the most recent SSN catalog. The new model also contains the capability to extrapolate the current environment in time to the year 2030. A revised meteoroid model based on the work of Divine has also been developed, and is called the JSC Meteoroid Model. The new model defines flux on the target per unit angle per unit speed, and for Earth orbit, includes the meteor showers. This paper quantifies the space debris environment for the ISS orbit from natural and anthropogenic sources. Particle flux and velocity distributions as functions of size and angle are be given for particles 10 microns and larger for altitudes from 350 to 450 km. The environment is projected forward in time until 2030.

Constructing 1/omegaalpha noise from reversible Markov chains.

PubMed

Erland, Sveinung; Greenwood, Priscilla E

2007-09-01

This paper gives sufficient conditions for the output of 1/omegaalpha noise from reversible Markov chains on finite state spaces. We construct several examples exhibiting this behavior in a specified range of frequencies. We apply simple representations of the covariance function and the spectral density in terms of the eigendecomposition of the probability transition matrix. The results extend to hidden Markov chains. We generalize the results for aggregations of AR1-processes of C. W. J. Granger [J. Econometrics 14, 227 (1980)]. Given the eigenvalue function, there is a variety of ways to assign values to the states such that the 1/omegaalpha condition is satisfied. We show that a random walk on a certain state space is complementary to the point process model of 1/omega noise of B. Kaulakys and T. Meskauskas [Phys. Rev. E 58, 7013 (1998)]. Passing to a continuous state space, we construct 1/omegaalpha noise which also has a long memory.

Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

NASA Astrophysics Data System (ADS)

Swinburne, Thomas D.; Perez, Danny

2018-05-01

A massively parallel method to build large transition rate matrices from temperature-accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space, providing crucial uncertainty quantification for higher-scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The estimators are additionally used to optimize where exploration is performed and the degree of temperature acceleration on the fly, giving an autonomous, optimal procedure to explore the state space of complex systems. The method is tested against exactly solvable models and used to explore the dynamics of C15 interstitial defects in iron. Our uncertainty quantification scheme allows for accurate modeling of the evolution of these defects over timescales of several seconds.

A numerical identifiability test for state-space models--application to optimal experimental design.

PubMed

Hidalgo, M E; Ayesa, E

2001-01-01

This paper describes a mathematical tool for identifiability analysis, easily applicable to high order non-linear systems modelled in state-space and implementable in simulators with a time-discrete approach. This procedure also permits a rigorous analysis of the expected estimation errors (average and maximum) in calibration experiments. The methodology is based on the recursive numerical evaluation of the information matrix during the simulation of a calibration experiment and in the setting-up of a group of information parameters based on geometric interpretations of this matrix. As an example of the utility of the proposed test, the paper presents its application to an optimal experimental design of ASM Model No. 1 calibration, in order to estimate the maximum specific growth rate microH and the concentration of heterotrophic biomass XBH.

A Parallel Saturation Algorithm on Shared Memory Architectures

NASA Technical Reports Server (NTRS)

Ezekiel, Jonathan; Siminiceanu

2007-01-01

Symbolic state-space generators are notoriously hard to parallelize. However, the Saturation algorithm implemented in the SMART verification tool differs from other sequential symbolic state-space generators in that it exploits the locality of ring events in asynchronous system models. This paper explores whether event locality can be utilized to efficiently parallelize Saturation on shared-memory architectures. Conceptually, we propose to parallelize the ring of events within a decision diagram node, which is technically realized via a thread pool. We discuss the challenges involved in our parallel design and conduct experimental studies on its prototypical implementation. On a dual-processor dual core PC, our studies show speed-ups for several example models, e.g., of up to 50% for a Kanban model, when compared to running our algorithm only on a single core.

Observing spatio-temporal dynamics of excitable media using reservoir computing

NASA Astrophysics Data System (ADS)

Zimmermann, Roland S.; Parlitz, Ulrich

2018-04-01

We present a dynamical observer for two dimensional partial differential equation models describing excitable media, where the required cross prediction from observed time series to not measured state variables is provided by Echo State Networks receiving input from local regions in space, only. The efficacy of this approach is demonstrated for (noisy) data from a (cubic) Barkley model and the Bueno-Orovio-Cherry-Fenton model describing chaotic electrical wave propagation in cardiac tissue.

A general U-block model-based design procedure for nonlinear polynomial control systems

NASA Astrophysics Data System (ADS)

Zhu, Q. M.; Zhao, D. Y.; Zhang, Jianhua

2016-10-01

The proposition of U-model concept (in terms of 'providing concise and applicable solutions for complex problems') and a corresponding basic U-control design algorithm was originated in the first author's PhD thesis. The term of U-model appeared (not rigorously defined) for the first time in the first author's other journal paper, which established a framework for using linear polynomial control system design approaches to design nonlinear polynomial control systems (in brief, linear polynomial approaches → nonlinear polynomial plants). This paper represents the next milestone work - using linear state-space approaches to design nonlinear polynomial control systems (in brief, linear state-space approaches → nonlinear polynomial plants). The overall aim of the study is to establish a framework, defined as the U-block model, which provides a generic prototype for using linear state-space-based approaches to design the control systems with smooth nonlinear plants/processes described by polynomial models. For analysing the feasibility and effectiveness, sliding mode control design approach is selected as an exemplary case study. Numerical simulation studies provide a user-friendly step-by-step procedure for the readers/users with interest in their ad hoc applications. In formality, this is the first paper to present the U-model-oriented control system design in a formal way and to study the associated properties and theorems. The previous publications, in the main, have been algorithm-based studies and simulation demonstrations. In some sense, this paper can be treated as a landmark for the U-model-based research from intuitive/heuristic stage to rigour/formal/comprehensive studies.

Cascading Failures as Continuous Phase-Space Transitions

DOE PAGES

Yang, Yang; Motter, Adilson E.

2017-12-14

In network systems, a local perturbation can amplify as it propagates, potentially leading to a large-scale cascading failure. We derive a continuous model to advance our understanding of cascading failures in power-grid networks. The model accounts for both the failure of transmission lines and the desynchronization of power generators and incorporates the transient dynamics between successive steps of the cascade. In this framework, we show that a cascade event is a phase-space transition from an equilibrium state with high energy to an equilibrium state with lower energy, which can be suitably described in a closed form using a global Hamiltonian-likemore » function. From this function, we show that a perturbed system cannot always reach the equilibrium state predicted by quasi-steady-state cascade models, which would correspond to a reduced number of failures, and may instead undergo a larger cascade. We also show that, in the presence of two or more perturbations, the outcome depends strongly on the order and timing of the individual perturbations. These results offer new insights into the current understanding of cascading dynamics, with potential implications for control interventions.« less

Cascading Failures as Continuous Phase-Space Transitions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Yang; Motter, Adilson E.

In network systems, a local perturbation can amplify as it propagates, potentially leading to a large-scale cascading failure. We derive a continuous model to advance our understanding of cascading failures in power-grid networks. The model accounts for both the failure of transmission lines and the desynchronization of power generators and incorporates the transient dynamics between successive steps of the cascade. In this framework, we show that a cascade event is a phase-space transition from an equilibrium state with high energy to an equilibrium state with lower energy, which can be suitably described in a closed form using a global Hamiltonian-likemore » function. From this function, we show that a perturbed system cannot always reach the equilibrium state predicted by quasi-steady-state cascade models, which would correspond to a reduced number of failures, and may instead undergo a larger cascade. We also show that, in the presence of two or more perturbations, the outcome depends strongly on the order and timing of the individual perturbations. These results offer new insights into the current understanding of cascading dynamics, with potential implications for control interventions.« less

Scientific Benefits of Space Science Models Archiving at Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Kuznetsova, Maria M.; Berrios, David; Chulaki, Anna; Hesse, Michael; MacNeice, Peter J.; Maddox, Marlo M.; Pulkkinen, Antti; Rastaetter, Lutz; Taktakishvili, Aleksandre

2009-01-01

The Community Coordinated Modeling Center (CCMC) hosts a set of state-of-the-art space science models ranging from the solar atmosphere to the Earth's upper atmosphere. CCMC provides a web-based Run-on-Request system, by which the interested scientist can request simulations for a broad range of space science problems. To allow the models to be driven by data relevant to particular events CCMC developed a tool that automatically downloads data from data archives and transform them to required formats. CCMC also provides a tailored web-based visualization interface for the model output, as well as the capability to download the simulation output in portable format. CCMC offers a variety of visualization and output analysis tools to aid scientists in interpretation of simulation results. During eight years since the Run-on-request system became available the CCMC archived the results of almost 3000 runs that are covering significant space weather events and time intervals of interest identified by the community. The simulation results archived at CCMC also include a library of general purpose runs with modeled conditions that are used for education and research. Archiving results of simulations performed in support of several Modeling Challenges helps to evaluate the progress in space weather modeling over time. We will highlight the scientific benefits of CCMC space science model archive and discuss plans for further development of advanced methods to interact with simulation results.

Understanding Epileptiform After-Discharges as Rhythmic Oscillatory Transients.

PubMed

Baier, Gerold; Taylor, Peter N; Wang, Yujiang

2017-01-01

Electro-cortical activity in patients with epilepsy may show abnormal rhythmic transients in response to stimulation. Even when using the same stimulation parameters in the same patient, wide variability in the duration of transient response has been reported. These transients have long been considered important for the mapping of the excitability levels in the epileptic brain but their dynamic mechanism is still not well understood. To investigate the occurrence of abnormal transients dynamically, we use a thalamo-cortical neural population model of epileptic spike-wave activity and study the interaction between slow and fast subsystems. In a reduced version of the thalamo-cortical model, slow wave oscillations arise from a fold of cycles (FoC) bifurcation. This marks the onset of a region of bistability between a high amplitude oscillatory rhythm and the background state. In vicinity of the bistability in parameter space, the model has excitable dynamics, showing prolonged rhythmic transients in response to suprathreshold pulse stimulation. We analyse the state space geometry of the bistable and excitable states, and find that the rhythmic transient arises when the impending FoC bifurcation deforms the state space and creates an area of locally reduced attraction to the fixed point. This area essentially allows trajectories to dwell there before escaping to the stable steady state, thus creating rhythmic transients. In the full thalamo-cortical model, we find a similar FoC bifurcation structure. Based on the analysis, we propose an explanation of why stimulation induced epileptiform activity may vary between trials, and predict how the variability could be related to ongoing oscillatory background activity. We compare our dynamic mechanism with other mechanisms (such as a slow parameter change) to generate excitable transients, and we discuss the proposed excitability mechanism in the context of stimulation responses in the epileptic cortex.

Characterizing segregation in the Schelling-Voter model

NASA Astrophysics Data System (ADS)

Caridi, I.; Pinasco, J. P.; Saintier, N.; Schiaffino, P.

2017-12-01

In this work we analyze several aspects related with segregation patterns appearing in the Schelling-Voter model in which an unhappy agent can change her location or her state in order to live in a neighborhood where she is happy. Briefly, agents may be in two possible states, each one represents an individually-chosen feature, such as the language she speaks or the opinion she supports; and an individual is happy in a neighborhood if she has, at least, some proportion of agents of her own type, defined in terms of a fixed parameter T. We study the model in a regular two dimensional lattice. The parameters of the model are ρ, the density of empty sites, and p, the probability of changing locations. The stationary states reached in a system of N agents as a function of the model parameters entail the extinction of one of the states, the coexistence of both, segregated patterns with conglomerated clusters of agents of the same state, and a diluted region. Using indicators as the energy and perimeter of the populations of agents in the same state, the inner radius of their locations (i.e., the side of the maximum square which could fit with empty spaces or agents of only one type), and the Shannon Information of the empty sites, we measure the segregation phenomena. We have found that there is a region within the coexistence phase where both populations take advantage of space in an equitable way, which is sustained by the role of the empty sites.

Assessment of type II diabetes mellitus using irregularly sampled measurements with missing data.

PubMed

Barazandegan, Melissa; Ekram, Fatemeh; Kwok, Ezra; Gopaluni, Bhushan; Tulsyan, Aditya

2015-04-01

Diabetes mellitus is one of the leading diseases in the developed world. In order to better regulate blood glucose in a diabetic patient, improved modelling of insulin-glucose dynamics is a key factor in the treatment of diabetes mellitus. In the current work, the insulin-glucose dynamics in type II diabetes mellitus can be modelled by using a stochastic nonlinear state-space model. Estimating the parameters of such a model is difficult as only a few blood glucose and insulin measurements per day are available in a non-clinical setting. Therefore, developing a predictive model of the blood glucose of a person with type II diabetes mellitus is important when the glucose and insulin concentrations are only available at irregular intervals. To overcome these difficulties, we resort to online sequential Monte Carlo (SMC) estimation of states and parameters of the state-space model for type II diabetic patients under various levels of randomly missing clinical data. Our results show that this method is efficient in monitoring and estimating the dynamics of the peripheral glucose, insulin and incretins concentration when 10, 25 and 50% of the simulated clinical data were randomly removed.

Chaos control in delayed phase space constructed by the Takens embedding theory

NASA Astrophysics Data System (ADS)

Hajiloo, R.; Salarieh, H.; Alasty, A.

2018-01-01

In this paper, the problem of chaos control in discrete-time chaotic systems with unknown governing equations and limited measurable states is investigated. Using the time-series of only one measurable state, an algorithm is proposed to stabilize unstable fixed points. The approach consists of three steps: first, using Takens embedding theory, a delayed phase space preserving the topological characteristics of the unknown system is reconstructed. Second, a dynamic model is identified by recursive least squares method to estimate the time-series data in the delayed phase space. Finally, based on the reconstructed model, an appropriate linear delayed feedback controller is obtained for stabilizing unstable fixed points of the system. Controller gains are computed using a systematic approach. The effectiveness of the proposed algorithm is examined by applying it to the generalized hyperchaotic Henon system, prey-predator population map, and the discrete-time Lorenz system.

Entanglement Holographic Mapping of Many-Body Localized System by Spectrum Bifurcation Renormalization Group

NASA Astrophysics Data System (ADS)

You, Yi-Zhuang; Qi, Xiao-Liang; Xu, Cenke

We introduce the spectrum bifurcation renormalization group (SBRG) as a generalization of the real-space renormalization group for the many-body localized (MBL) system without truncating the Hilbert space. Starting from a disordered many-body Hamiltonian in the full MBL phase, the SBRG flows to the MBL fixed-point Hamiltonian, and generates the local conserved quantities and the matrix product state representations for all eigenstates. The method is applicable to both spin and fermion models with arbitrary interaction strength on any lattice in all dimensions, as long as the models are in the MBL phase. In particular, we focus on the 1 d interacting Majorana chain with strong disorder, and map out its phase diagram using the entanglement entropy. The SBRG flow also generates an entanglement holographic mapping, which duals the MBL state to a fragmented holographic space decorated with small blackholes.

Model Breaking Points Conceptualized

ERIC Educational Resources Information Center

Vig, Rozy; Murray, Eileen; Star, Jon R.

2014-01-01

Current curriculum initiatives (e.g., National Governors Association Center for Best Practices and Council of Chief State School Officers 2010) advocate that models be used in the mathematics classroom. However, despite their apparent promise, there comes a point when models break, a point in the mathematical problem space where the model cannot,…

Regime Switching State-Space Models Applied to Psychological Processes: Handling Missing Data and Making Inferences

ERIC Educational Resources Information Center

Hamaker, E. L.; Grasman, R. P. P. P.

2012-01-01

Many psychological processes are characterized by recurrent shifts between distinct regimes or states. Examples that are considered in this paper are the switches between different states associated with premenstrual syndrome, hourly fluctuations in affect during a major depressive episode, and shifts between a "hot hand" and a…

A novel multisensor traffic state assessment system based on incomplete data.

PubMed

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Jiang, Yaoliang

2014-01-01

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.

A Novel Multisensor Traffic State Assessment System Based on Incomplete Data

PubMed Central

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Jiang, Yaoliang

2014-01-01

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system. PMID:25162055

DUAL STATE-PARAMETER UPDATING SCHEME ON A CONCEPTUAL HYDROLOGIC MODEL USING SEQUENTIAL MONTE CARLO FILTERS

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin

Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.

FAST TRACK COMMUNICATION: \\ {P}\\ {T}-symmetry, Cartan decompositions, Lie triple systems and Krein space-related Clifford algebras

NASA Astrophysics Data System (ADS)

Günther, Uwe; Kuzhel, Sergii

2010-10-01

Gauged \\ {P}\\ {T} quantum mechanics (PTQM) and corresponding Krein space setups are studied. For models with constant non-Abelian gauge potentials and extended parity inversions compact and noncompact Lie group components are analyzed via Cartan decompositions. A Lie-triple structure is found and an interpretation as \\ {P}\\ {T}-symmetrically generalized Jaynes-Cummings model is possible with close relation to recently studied cavity QED setups with transmon states in multilevel artificial atoms. For models with Abelian gauge potentials a hidden Clifford algebra structure is found and used to obtain the fundamental symmetry of Krein space-related J-self-adjoint extensions for PTQM setups with ultra-localized potentials.

A computational approach to extinction events in chemical reaction networks with discrete state spaces.

PubMed

Johnston, Matthew D

2017-12-01

Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results. Copyright © 2017 Elsevier Inc. All rights reserved.

Modeling and Analysis of Large Amplitude Flight Maneuvers

NASA Technical Reports Server (NTRS)

Anderson, Mark R.

2004-01-01

Analytical methods for stability analysis of large amplitude aircraft motion have been slow to develop because many nonlinear system stability assessment methods are restricted to a state-space dimension of less than three. The proffered approach is to create regional cell-to-cell maps for strategically located two-dimensional subspaces within the higher-dimensional model statespace. These regional solutions capture nonlinear behavior better than linearized point solutions. They also avoid the computational difficulties that emerge when attempting to create a cell map for the entire state-space. Example stability results are presented for a general aviation aircraft and a micro-aerial vehicle configuration. The analytical results are consistent with characteristics that were discovered during previous flight-testing.

Comparisons of Four Methods for Estimating a Dynamic Factor Model

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R.

2008-01-01

Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a…

Observation of a new high-spin isomer in {sup 94}Pd

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brock, T. S.; Nara Singh, B. S.; Wadsworth, R.

2010-12-15

A second {gamma}-decaying high-spin isomeric state, with a half-life of 197(22)ns, has been identified in the N=Z+2 nuclide {sup 94}Pd as part of a stopped-beam Rare Isotope Spectroscopic INvestigation at GSI (RISING) experiment. Weisskopf estimates were used to establish a tentative spin/parity of 19{sup -}, corresponding to the maximum possible spin of a negative parity state in the restricted (p{sub 1/2}, g{sub 9/2}) model space of empirical shell model calculations. The reproduction of the E3 decay properties of the isomer required an extension of the model space to include the f{sub 5/2} and p{sub 3/2} orbitals using the CD-Bonn potential.more » This is the first time that such an extension has been required for a high-spin isomer in the vicinity of {sup 100}Sn and reveals the importance of such orbits for understanding the decay properties of high-spin isomers in this region. However, despite the need for the extended model space for the E3 decay, the dominant configuration for the 19{sup -} state remains ({pi}p{sub 1/2}{sup -1}g{sub 9/2}{sup -3}){sub 11} x ({nu}g{sub 9/2}{sup -2}){sub 8}. The half-life of the known, 14{sup +}, isomer was remeasured and yielded a value of 499(13) ns.« less

Transition paths of Met-enkephalin from Markov state modeling of a molecular dynamics trajectory.

PubMed

Banerjee, Rahul; Cukier, Robert I

2014-03-20

Conformational states and their interconversion pathways of the zwitterionic form of the pentapeptide Met-enkephalin (MetEnk) are identified. An explicit solvent molecular dynamics (MD) trajectory is used to construct a Markov state model (MSM) based on dihedral space clustering of the trajectory, and transition path theory (TPT) is applied to identify pathways between open and closed conformers. In the MD trajectory, only four of the eight backbone dihedrals exhibit bistable behavior. Defining a conformer as the string XXXX with X = "+" or "-" denoting, respectively, positive or negative values of a given dihedral angle and obtaining the populations of these conformers shows that only four conformers are highly populated, implying a strong correlation among these dihedrals. Clustering in dihedral space to construct the MSM finds the same four bistable dihedral angles. These state populations are very similar to those found directly from the MD trajectory. TPT is used to obtain pathways, parametrized by committor values, in dihedral state space that are followed in transitioning from closed to open states. Pathway costs are estimated by introducing a kinetics-based procedure that orders pathways from least (shortest) to greater cost paths. The least costly pathways in dihedral space are found to only involve the same XXXX set of dihedral angles, and the conformers accessed in the closed to open transition pathways are identified. For these major pathways, a correlation between reaction path progress (committors) and the end-to-end distance is identified. A dihedral space principal component analysis of the MD trajectory shows that the first three modes capture most of the overall fluctuation, and pick out the same four dihedrals having essentially all the weight in those modes. A MSM based on root-mean-square backbone clustering was also carried out, with good agreement found with dihedral clustering for the static information, but with results that differ significantly for the pathway analysis.

Organization and management of space grant programs

NASA Technical Reports Server (NTRS)

Sheppard, Sallie; Nichols, Steve

1990-01-01

The 21 Space Grant Programs represent a broad range of organizational structures which operate programs ranging in size from single university organizations to organizations including up to 41 members involving a composite of industrial organizations such as state agencies, and universities. Some of the space grant awards were made to organizations already in existence with on-going programs while other awards were made to consortia newly formed for the purpose of applying to the Space Grant Program. The workshop on organization and management of Space Grant Programs provided an opportunity for directors and program representatives to discuss and compare the relative advantages and disadvantages of the various models being used. This paper offers examples of the diversity of organizations, summarizes the common concerns to be met by each organizational model, and provides a case study of the Texas Space Grant Consortium organization.

The Modeling and Control of Acoustic/Structure Interaction Problems via Piezoceramic Actuators: 2-D Numerical Examples

DTIC Science & Technology

1992-04-01

the voltage applied to the it" patch, K ’ is a parameter which depends on the geometry and piezoceramic...in the state space II L 2(fQ) x L2 (F0 ). Here L2(Q) is the quotient space of L2 over the constant functions. The use of the quotient space results...form of the problem, we also define the Hilbert space V = fti(Q) x H(F 0 ) where h!(Q) is the quotient space of Il’ over the constant functions

AFFECTS - Advanced Forecast For Ensuring Communications Through Space

NASA Astrophysics Data System (ADS)

Bothmer, Volker

2013-04-01

Through the AFFECTS project funded by the European Union's 7th Framework Programme, European and US scientists develop an advanced proto-type space weather warning system to safeguard the operation of telecommunication and navigation systems on Earth to the threat of solar storms. The project is led by the University of Göttingen's Institute for Astrophysics and comprises worldwide leading research and academic institutions and industrial enterprises from Germany, Belgium, Ukraine, Norway and the United States. The key objectives of the AFFECTS project are: State-of-the-art analysis and modelling of the Sun-Earth chain of effects on the Earth's ionosphere and their subsequent impacts on communication systems based on multipoint space observations and complementary ground-based data. Development of a prototype space weather early warning system and reliable space weather forecasts, with specific emphasis on ionospheric applications. Dissemination of new space weather products and services to end users, the scientific community and general public. The presentation summarizes the project highlights, with special emphasis on the developed space weather forecast tools.

Mathematical Model of the Public Understanding of Space Science

NASA Astrophysics Data System (ADS)

Prisniakov, V.; Prisniakova, L.

The success in deployment of the space programs now in many respects depends on comprehension by the citizens of necessity of programs, from "space" erudition of country. Purposefulness and efficiency of the "space" teaching and educational activity depend on knowledge of relationships between separate variables of such process. The empirical methods of ``space'' well-information of the taxpayers should be supplemented by theoretical models permitting to demonstrate a ways of control by these processes. Authors on the basis of their experience of educational activity during 50- years of among the students of space-rocket profession obtain an equation of ``space" state of the society determining a degree of its knowledge about Space, about achievements in its development, about indispensable lines of investigations, rates of informatization of the population. It is supposed, that the change of the space information consists of two parts: (1) - from going of the information about practical achievements, about development special knowledge requiring of independent financing, and (2) from intensity of dissemination of the ``free" information of a general educational line going to the population through mass-media, book, in family, in educational institutions, as a part of obligatory knowledge of any man, etc. In proposed model the level space well-information of the population depends on intensity of dissemination in the society of the space information, and also from a volume of financing of space-rocket technology, from a part of population of the employment in the space-rocket programs, from a factor of education of the population in adherence to space problems, from welfare and mentality of the people, from a rate of unemployment and material inequality. Obtained in the report on these principles the equation of a space state of the society corresponds to catastrophe such as cusp, the analysis has shown which one ways of control of the public understanding of space science. The boundary sectioning area of effective and unefficient modes of training and education of the population of country in space spirit is determined. The mathematical model of quality of process of education concern to an outer space exploration is reviewed separately. The coefficient of quality of education in an estimation of space event is submitted as relation Δ I' to mismatch of the universal standard of behavior with the information, which is going to the external spectator, about the applicable reacting of the considered individual Δ I''. The obtained outcomes allow to control a learning process and education of the society spirit of adherence to space ideals of mankind.

Reduced order modeling of head related transfer functions for virtual acoustic displays

NASA Astrophysics Data System (ADS)

Willhite, Joel A.; Frampton, Kenneth D.; Grantham, D. Wesley

2003-04-01

The purpose of this work is to improve the computational efficiency in acoustic virtual applications by creating and testing reduced order models of the head related transfer functions used in localizing sound sources. State space models of varying order were generated from zero-elevation Head Related Impulse Responses (HRIRs) using Kungs Single Value Decomposition (SVD) technique. The inputs to the models are the desired azimuths of the virtual sound sources (from minus 90 deg to plus 90 deg, in 10 deg increments) and the outputs are the left and right ear impulse responses. Trials were conducted in an anechoic chamber in which subjects were exposed to real sounds that were emitted by individual speakers across a numbered speaker array, phantom sources generated from the original HRIRs, and phantom sound sources generated with the different reduced order state space models. The error in the perceived direction of the phantom sources generated from the reduced order models was compared to errors in localization using the original HRIRs.

Multivariable control of the Space Shuttle Remote Manipulator System using linearization by state feedback

NASA Technical Reports Server (NTRS)

Gettman, Chang-Ching L.; Adams, Neil; Bedrossian, Nazareth; Valavani, Lena

1993-01-01

This paper demonstrates an approach to nonlinear control system design that uses linearization by state feedback to allow faster maneuvering of payloads by the Shuttle Remote Manipulator System (SRMS). A nonlinear feedback law is defined to cancel the nonlinear plant dynamics so that a linear controller can be designed for the SRMS. First a nonlinear design model was generated via SIMULINK. This design model included nonlinear arm dynamics derived from the Lagrangian approach, linearized servo model, and linearized gearbox model. The current SRMS position hold controller was implemented on this system. Next, a trajectory was defined using a rigid body kinematics SRMS tool, KRMS. The maneuver was simulated. Finally, higher bandwidth controllers were developed. Results of the new controllers were compared with the existing SRMS automatic control modes for the Space Station Freedom Mission Build 4 Payload extended on the SRMS.

Exactly solvable quantum cosmologies from two killing field reductions of general relativity

NASA Astrophysics Data System (ADS)

Husain, Viqar; Smolin, Lee

1989-11-01

An exact and, possibly, general solution to the quantum constraints is given for the sector of general relativity containing cosmological solutions with two space-like, commuting, Killing fields. The dynamics of these model space-times, which are known as Gowdy space-times, is formulated in terms of Ashtekar's new variables. The quantization is done by using the recently introduced self-dual and loop representations. On the classical phase space we find four explicit physical observables, or constants of motion, which generate a GL(2) symmetry group on the space of solutions. In the loop representations we find that a complete description of the physical state space, consisting of the simultaneous solutions to all of the constraints, is given in terms of the equivalence classes, under Diff(S1), of a pair of densities on the circle. These play the same role that the link classes play in the loop representation solution to the full 3+1 theory. An infinite dimensional algebra of physical observables is found on the physical state space, which is a GL(2) loop algebra. In addition, by freezing the local degrees of freedom of the model, we find a finite dimensional quantum system which describes a set of degenerate quantum cosmologies on T3 in which the length of one of the S1's has gone to zero, while the area of the remaining S1×S1 is quantized in units of the Planck area. The quantum kinematics of this sector of the model is identical to that of a one-plaquette SU(2) lattice gauge theory.

Minimum complexity echo state network.

PubMed

Rodan, Ali; Tino, Peter

2011-01-01

Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly with many successful applications. However, RC has been criticized for not being principled enough. Reservoir construction is largely driven by a series of randomized model-building stages, with both researchers and practitioners having to rely on a series of trials and errors. To initialize a systematic study of the field, we concentrate on one of the most popular classes of RC methods, namely echo state network, and ask: What is the minimal complexity of reservoir construction for obtaining competitive models and what is the memory capacity (MC) of such simplified reservoirs? On a number of widely used time series benchmarks of different origin and characteristics, as well as by conducting a theoretical analysis we show that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology. The (short-term) MC of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.

Phase-space networks of geometrically frustrated systems.

PubMed

Han, Yilong

2009-11-01

We illustrate a network approach to the phase-space study by using two geometrical frustration models: antiferromagnet on triangular lattice and square ice. Their highly degenerated ground states are mapped as discrete networks such that the quantitative network analysis can be applied to phase-space studies. The resulting phase spaces share some comon features and establish a class of complex networks with unique Gaussian spectral densities. Although phase-space networks are heterogeneously connected, the systems are still ergodic due to the random Poisson processes. This network approach can be generalized to phase spaces of some other complex systems.

Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

PubMed

Aprasoff, Jonathan; Donchin, Opher

2012-04-01

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

Predictability of Circulation Transitions (Observed and Modeled): Non-diffusive Dynamics, Markov Chains and Error Growth.

NASA Astrophysics Data System (ADS)

Straus, D. M.

2006-12-01

The transitions between portions of the state space of the large-scale flow is studied from daily wintertime data over the Pacific North America region using the NCEP reanalysis data set (54 winters) and very large suites of hindcasts made with the COLA atmospheric GCM with observed SST (55 members for each of 18 winters). The partition of the large-scale state space is guided by cluster analysis, whose statistical significance and relationship to SST is reviewed (Straus and Molteni, 2004; Straus, Corti and Molteni, 2006). The determination of the global nature of the flow through state space is studied using Markov Chains (Crommelin, 2004). In particular the non-diffusive part of the flow is contrasted in nature (small data sample) and the AGCM (large data sample). The intrinsic error growth associated with different portions of the state space is studied through sets of identical twin AGCM simulations. The goal is to obtain realistic estimates of predictability times for large-scale transitions that should be useful in long-range forecasting.

The environmental zero-point problem in evolutionary reaction norm modeling.

PubMed

Ergon, Rolf

2018-04-01

There is a potential problem in present quantitative genetics evolutionary modeling based on reaction norms. Such models are state-space models, where the multivariate breeder's equation in some form is used as the state equation that propagates the population state forward in time. These models use the implicit assumption of a constant reference environment, in many cases set to zero. This zero-point is often the environment a population is adapted to, that is, where the expected geometric mean fitness is maximized. Such environmental reference values follow from the state of the population system, and they are thus population properties. The environment the population is adapted to, is, in other words, an internal population property, independent of the external environment. It is only when the external environment coincides with the internal reference environment, or vice versa, that the population is adapted to the current environment. This is formally a result of state-space modeling theory, which is an important theoretical basis for evolutionary modeling. The potential zero-point problem is present in all types of reaction norm models, parametrized as well as function-valued, and the problem does not disappear when the reference environment is set to zero. As the environmental reference values are population characteristics, they ought to be modeled as such. Whether such characteristics are evolvable is an open question, but considering the complexity of evolutionary processes, such evolvability cannot be excluded without good arguments. As a straightforward solution, I propose to model the reference values as evolvable mean traits in their own right, in addition to other reaction norm traits. However, solutions based on an evolvable G matrix are also possible.

An Empirical State Error Covariance Matrix Orbit Determination Example

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2015-01-01

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. First, consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. Then it follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix of the estimate will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully include all of the errors in the state estimate. The empirical error covariance matrix is determined from a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm. It is a formally correct, empirical state error covariance matrix obtained through use of the average form of the weighted measurement residual variance performance index rather than the usual total weighted residual form. Based on its formulation, this matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty and whether the source is anticipated or not. It is expected that the empirical error covariance matrix will give a better, statistical representation of the state error in poorly modeled systems or when sensor performance is suspect. In its most straight forward form, the technique only requires supplemental calculations to be added to existing batch estimation algorithms. In the current problem being studied a truth model making use of gravity with spherical, J2 and J4 terms plus a standard exponential type atmosphere with simple diurnal and random walk components is used. The ability of the empirical state error covariance matrix to account for errors is investigated under four scenarios during orbit estimation. These scenarios are: exact modeling under known measurement errors, exact modeling under corrupted measurement errors, inexact modeling under known measurement errors, and inexact modeling under corrupted measurement errors. For this problem a simple analog of a distributed space surveillance network is used. The sensors in this network make only range measurements and with simple normally distributed measurement errors. The sensors are assumed to have full horizon to horizon viewing at any azimuth. For definiteness, an orbit at the approximate altitude and inclination of the International Space Station is used for the study. The comparison analyses of the data involve only total vectors. No investigation of specific orbital elements is undertaken. The total vector analyses will look at the chisquare values of the error in the difference between the estimated state and the true modeled state using both the empirical and theoretical error covariance matrices for each of scenario.

Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon.

PubMed

Elghafghuf, Adel; Vanderstichel, Raphael; St-Hilaire, Sophie; Stryhn, Henrik

2018-04-11

Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses. In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Modelling non-linear effects of dark energy

NASA Astrophysics Data System (ADS)

Bose, Benjamin; Baldi, Marco; Pourtsidou, Alkistis

2018-04-01

We investigate the capabilities of perturbation theory in capturing non-linear effects of dark energy. We test constant and evolving w models, as well as models involving momentum exchange between dark energy and dark matter. Specifically, we compare perturbative predictions at 1-loop level against N-body results for four non-standard equations of state as well as varying degrees of momentum exchange between dark energy and dark matter. The interaction is modelled phenomenologically using a time dependent drag term in the Euler equation. We make comparisons at the level of the matter power spectrum and the redshift space monopole and quadrupole. The multipoles are modelled using the Taruya, Nishimichi and Saito (TNS) redshift space spectrum. We find perturbation theory does very well in capturing non-linear effects coming from dark sector interaction. We isolate and quantify the 1-loop contribution coming from the interaction and from the non-standard equation of state. We find the interaction parameter ξ amplifies scale dependent signatures in the range of scales considered. Non-standard equations of state also give scale dependent signatures within this same regime. In redshift space the match with N-body is improved at smaller scales by the addition of the TNS free parameter σv. To quantify the importance of modelling the interaction, we create mock data sets for varying values of ξ using perturbation theory. This data is given errors typical of Stage IV surveys. We then perform a likelihood analysis using the first two multipoles on these sets and a ξ=0 modelling, ignoring the interaction. We find the fiducial growth parameter f is generally recovered even for very large values of ξ both at z=0.5 and z=1. The ξ=0 modelling is most biased in its estimation of f for the phantom w=‑1.1 case.

Accelerating Cogent Confabulation: An Exploration in the Architecture Design Space

DTIC Science & Technology

2008-06-01

DATES COVERED (From - To) 1-8 June 2008 4. TITLE AND SUBTITLE ACCELERATING COGENT CONFABULATION: AN EXPLORATION IN THE ARCHITECTURE DESIGN SPACE 5a...spiking neural networks is proposed in reference [8]. Reference [9] investigates the architecture design of a Brain-state-in-a-box model. The...Richard Linderman2, Thomas Renz2, Qing Wu1 Accelerating Cogent Confabulation: an Exploration in the Architecture Design Space POSTPRINT complexity

Evaluation of Primary Dendrite Arm Spacings from Aluminum-7wt% Silicon alloys Directionally Solidified aboard the International Space Station - Comparison with Theory

NASA Technical Reports Server (NTRS)

Angart, Samuel; Lauer, Mark; Poirier, David; Tewari, Surendra; Rajamure, Ravi; Grugel, Richard

2015-01-01

Aluminum – 7wt% silicon alloys were directionally solidified in the microgravity environment aboard the International Space Station as part of the “MIcrostructure Formation in CASTing of Technical Alloys under Diffusive and Magnetically Controlled Convective Conditions” (MICAST) European led program. Cross-sections of the sample during periods of steady-state growth were metallographically prepared from which the primary dendrite arm spacing (lambda 1) was measured. These spacings were found to be in reasonable agreement with the Hunt-Lu model which assumes a diffusion-controlled, convectionless, environment during controlled solidification. Deviation from the model was found and is attributed to gravity-independent thermocapillary convection where, over short distances, the liquid appears to have separated from the crucible wall.

From model rockets to spacewalks: an astronaut physician's journey and the science of the United States' space program.

PubMed

Parazynski, Scott E

2006-01-01

From simple childhood dreams to their fulfillment, this presentation chronicles the author's life journey from young model rocketteer through his medical training and eventual career as a NASA astronaut. Over the course of four Space Shuttle flights and a cumulative 6 weeks in space, including 20 hours of Extravehicular Activity (EVA, or spacewalking), this article describes a wide range of activities and scientific payloads that are representative of the unique and valuable science that can be accomplished in the microgravity of space. NASA's efforts to develop inspection and repair capabilities in the aftermath of the Columbia tragedy are also covered, as are the nation's plans for returning to the Moon and continuing on to Mars as part of the Vision for Space Exploration (VSE).

Modelling the performance of the tapered artery heat pipe design for use in the radiator of the solar dynamic power system of the NASA Space Station

NASA Technical Reports Server (NTRS)

Evans, Austin Lewis

1988-01-01

The paper presents a computer program developed to model the steady-state performance of the tapered artery heat pipe for use in the radiator of the solar dynamic power system of the NASA Space Station. The program solves six governing equations to ascertain which one is limiting the maximum heat transfer rate of the heat pipe. The present model appeared to be slightly better than the LTV model in matching the 1-g data for the standard 15-ft test heat pipe.

CCMC: Serving research and space weather communities with unique space weather services, innovative tools and resources

NASA Astrophysics Data System (ADS)

Zheng, Yihua; Kuznetsova, Maria M.; Pulkkinen, Antti; Maddox, Marlo

2015-04-01

With the addition of Space Weather Research Center (a sub-team within CCMC) in 2010 to address NASA’s own space weather needs, CCMC has become a unique entity that not only facilitates research through providing access to the state-of-the-art space science and space weather models, but also plays a critical role in providing unique space weather services to NASA robotic missions, developing innovative tools and transitioning research to operations via user feedback. With scientists, forecasters and software developers working together within one team, through close and direct connection with space weather customers and trusted relationship with model developers, CCMC is flexible, nimble and effective to meet customer needs. In this presentation, we highlight a few unique aspects of CCMC/SWRC’s space weather services, such as addressing space weather throughout the solar system, pushing the frontier of space weather forecasting via the ensemble approach, providing direct personnel and tool support for spacecraft anomaly resolution, prompting development of multi-purpose tools and knowledge bases, and educating and engaging the next generation of space weather scientists.

Model reduction and frequency residuals for a robust estimation of nonlinearities in subspace identification

NASA Astrophysics Data System (ADS)

De Filippis, G.; Noël, J. P.; Kerschen, G.; Soria, L.; Stephan, C.

2017-09-01

The introduction of the frequency-domain nonlinear subspace identification (FNSI) method in 2013 constitutes one in a series of recent attempts toward developing a realistic, first-generation framework applicable to complex structures. If this method showed promising capabilities when applied to academic structures, it is still confronted with a number of limitations which needs to be addressed. In particular, the removal of nonphysical poles in the identified nonlinear models is a distinct challenge. In the present paper, it is proposed as a first contribution to operate directly on the identified state-space matrices to carry out spurious pole removal. A modal-space decomposition of the state and output matrices is examined to discriminate genuine from numerical poles, prior to estimating the extended input and feedthrough matrices. The final state-space model thus contains physical information only and naturally leads to nonlinear coefficients free of spurious variations. Besides spurious variations due to nonphysical poles, vibration modes lying outside the frequency band of interest may also produce drifts of the nonlinear coefficients. The second contribution of the paper is to include residual terms, accounting for the existence of these modes. The proposed improved FNSI methodology is validated numerically and experimentally using a full-scale structure, the Morane-Saulnier Paris aircraft.

Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2017-10-01

We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space. Copyright © 2017 Elsevier B.V. All rights reserved.

Symbolic Heuristic Search for Factored Markov Decision Processes

NASA Technical Reports Server (NTRS)

Morris, Robert (Technical Monitor); Feng, Zheng-Zhu; Hansen, Eric A.

2003-01-01

We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states. We combine these two approaches in a novel way that exploits symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.

Model description document for a computer program for the emulation/simulation of a space station environmental control and life support system (ESCM)

NASA Technical Reports Server (NTRS)

Yanosy, James L.

1988-01-01

Emulation/Simulation Computer Model (ESCM) computes the transient performance of a Space Station air revitalization subsystem with carbon dioxide removal provided by a solid amine water desorbed subsystem called SAWD. This manual describes the mathematical modeling and equations used in the ESCM. For the system as a whole and for each individual component, the fundamental physical and chemical laws which govern their operations are presented. Assumptions are stated, and when necessary, data is presented to support empirically developed relationships.

A new adaptive estimation method of spacecraft thermal mathematical model with an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Akita, T.; Takaki, R.; Shima, E.

2012-04-01

An adaptive estimation method of spacecraft thermal mathematical model is presented. The method is based on the ensemble Kalman filter, which can effectively handle the nonlinearities contained in the thermal model. The state space equations of the thermal mathematical model is derived, where both temperature and uncertain thermal characteristic parameters are considered as the state variables. In the method, the thermal characteristic parameters are automatically estimated as the outputs of the filtered state variables, whereas, in the usual thermal model correlation, they are manually identified by experienced engineers using trial-and-error approach. A numerical experiment of a simple small satellite is provided to verify the effectiveness of the presented method.

Community Coordinated Modeling Center: A Powerful Resource in Space Science and Space Weather Education

NASA Astrophysics Data System (ADS)

Chulaki, A.; Kuznetsova, M. M.; Rastaetter, L.; MacNeice, P. J.; Shim, J. S.; Pulkkinen, A. A.; Taktakishvili, A.; Mays, M. L.; Mendoza, A. M. M.; Zheng, Y.; Mullinix, R.; Collado-Vega, Y. M.; Maddox, M. M.; Pembroke, A. D.; Wiegand, C.

2015-12-01

Community Coordinated Modeling Center (CCMC) is a NASA affiliated interagency partnership with the primary goal of aiding the transition of modern space science models into space weather forecasting while supporting space science research. Additionally, over the past ten years it has established itself as a global space science education resource supporting undergraduate and graduate education and research, and spreading space weather awareness worldwide. A unique combination of assets, capabilities and close ties to the scientific and educational communities enable this small group to serve as a hub for raising generations of young space scientists and engineers. CCMC resources are publicly available online, providing unprecedented global access to the largest collection of modern space science models (developed by the international research community). CCMC has revolutionized the way simulations are utilized in classrooms settings, student projects, and scientific labs and serves hundreds of educators, students and researchers every year. Another major CCMC asset is an expert space weather prototyping team primarily serving NASA's interplanetary space weather needs. Capitalizing on its unrivaled capabilities and experiences, the team provides in-depth space weather training to students and professionals worldwide, and offers an amazing opportunity for undergraduates to engage in real-time space weather monitoring, analysis, forecasting and research. In-house development of state-of-the-art space weather tools and applications provides exciting opportunities to students majoring in computer science and computer engineering fields to intern with the software engineers at the CCMC while also learning about the space weather from the NASA scientists.

Estimating the actual subject-specific genetic correlations in behavior genetics.

PubMed

Molenaar, Peter C M

2012-10-01

Generalization of the standard behavior longitudinal genetic factor model for the analysis of interindividual phenotypic variation to a genetic state space model for the analysis of intraindividual variation enables the possibility to estimate subject-specific heritabilities.

NASA Acting Administrator Robert Lightfoot presents the 2018 "St

NASA Image and Video Library

2018-02-12

Marshall Space Flight Center Director Todd May introduces NASA Acting Adminstrator Robert Lightfoot prior to his delivery of the "State of NASA", February 12, 2018, at the Marshall Space Flight Center in Huntsville, Alabama. In his address, Lightfoot discussed what the President's Fiscal Year 2019 budget request means for America's space agency. According to Lightfoot, it "reflects the administration's confidence that America will lead the way back to the Moon and take the next giant leap". Lightfoot delivered the "State of NASA" address in Marshall's Center for Advanced Manufacturing where engineers are pushing boundaries in the fields of additive manufacturing, 3D printing, and more. Hardware for NASA's Space Launch System and a model of the agency's Orion spacecraft served as a backdrop for the annual event. SLS, which is managed by Marshall, will enable a new era of exploration beyond Earth's orbit by launching astronauts on missions to deep-space destinations including the Moon and Mars.

THE SPACE PUBLIC OUTREACH TEAM (SPOT)

NASA Astrophysics Data System (ADS)

Williamson, Kathryn; National Radio Astronomy Observatory; Montana Space Grant Consortium; West Virginia Space Grant Consortium; NASA Independent Verification and Validation Center

2014-01-01

The Space Public Outreach Team (SPOT) has shown over 17 years of success in bringing astronomy and space science-themed presentations to approximately 10,000 students per year in Montana, and the program is now being piloted in West Virginia through a joint partnership between the National Radio Astronomy Observatory (NRAO), the West Virginia Space Grant Consortium, and NASA Independent Verification and Validation Center. SPOT recruits and trains undergraduate presenters from all over the state to learn interactive slide shows that highlight the state’s on-going and world-class space science research. Presenters then travel to K-12 schools to deliver these presentations and provide teachers additional supplemental information for when the SPOT team leaves. As a large-scale, low-cost, and sustainable program being implemented in both Montana and West Virginia, SPOT has the potential to become a nation-wide effort that institutions in other states can model to increase their education and public outreach presence.

Strongly contracted canonical transformation theory

NASA Astrophysics Data System (ADS)

Neuscamman, Eric; Yanai, Takeshi; Chan, Garnet Kin-Lic

2010-01-01

Canonical transformation (CT) theory describes dynamic correlation in multireference systems with large active spaces. Here we discuss CT theory's intruder state problem and why our previous approach of overlap matrix truncation becomes infeasible for sufficiently large active spaces. We propose the use of strongly and weakly contracted excitation operators as alternatives for dealing with intruder states in CT theory. The performance of these operators is evaluated for the H2O, N2, and NiO molecules, with comparisons made to complete active space second order perturbation theory and Davidson-corrected multireference configuration interaction theory. Finally, using a combination of strongly contracted CT theory and orbital-optimized density matrix renormalization group theory, we evaluate the singlet-triplet gap of free base porphin using an active space containing all 24 out-of-plane 2p orbitals. Modeling dynamic correlation with an active space of this size is currently only possible using CT theory.

Assessing the value of variational assimilation of streamflow data into distributed hydrologic models for improved streamflow monitoring and prediction at ungauged and gauged locations in the catchment

NASA Astrophysics Data System (ADS)

Lee, Hak Su; Seo, Dong-Jun; Liu, Yuqiong; McKee, Paul; Corby, Robert

2010-05-01

State updating of distributed hydrologic models via assimilation of streamflow data is subject to "overfitting" because large dimensionality of the state space of the model may render the assimilation problem seriously underdetermined. To examine the issue in the context of operational hydrology, we carried out a set of real-world experiments in which we assimilate streamflow data at interior and/or outlet locations into gridded SAC and kinematic-wave routing models of the U.S. National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM). We used for the experiments nine basins in the southern plains of the U.S. The experiments consist of selectively assimilating streamflow at different gauge locations, outlet and/or interior, and carrying out both dependent and independent validation. To assess the sensitivity of the quality of assimilation-aided streamflow simulation to the reduced dimensionality of the state space, we carried out data assimilation at spatially semi-distributed or lumped scale and by adjusting biases in precipitation and potential evaporation at a 6-hourly or larger scale. In this talk, we present the results and findings.

State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems

NASA Astrophysics Data System (ADS)

Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M.; Derocher, Andrew E.; Lewis, Mark A.; Jonsen, Ian D.; Mills Flemming, Joanna

2016-05-01

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

The Importance of Accurate Secondary Electron Yields in Modeling Spacecraft Charging

DTIC Science & Technology

1986-05-01

Release; Distribution Unlimited AIR FORCE GEOPHYSICS LABORATORY AIR FORCE SYSTEMS COMMAND •IDTIC UNITED STATES AIR FORCE FLECTE HANSCOM AIR FORCE BASE...properties are taken to be those of solor cell rover slip model developed for NASCAP (MandeU et at, (1984)) since most of the exterior surface of the...Research 85, 1155, 1980. Garrett, H. B., "Spacecraft Charging: A Review", in Space Systems and Their Interactions with the Earth’. Space Environment, H

Quarter-BPS states in orbifold sigma models with ADE singularities

NASA Astrophysics Data System (ADS)

Wong, Kenny

2017-06-01

We study the elliptic genera of two-dimensional orbifold CFTs, where the orbifolding procedure introduces du Val surface singularities on the target space. The N=4 characterdecompositionsoftheellipticgenuscontributionsfromthetwistedsectors at the singularities obey a consistent scaling property, and contain information about the arrangement of exceptional rational curves in the resolution. We also discuss how these twisted sector elliptic genera are related to twining genera and Hodge elliptic genera for sigma models with K3 target space.

Parameter reduction in nonlinear state-space identification of hysteresis

NASA Astrophysics Data System (ADS)

Fakhrizadeh Esfahani, Alireza; Dreesen, Philippe; Tiels, Koen; Noël, Jean-Philippe; Schoukens, Johan

2018-05-01

Recent work on black-box polynomial nonlinear state-space modeling for hysteresis identification has provided promising results, but struggles with a large number of parameters due to the use of multivariate polynomials. This drawback is tackled in the current paper by applying a decoupling approach that results in a more parsimonious representation involving univariate polynomials. This work is carried out numerically on input-output data generated by a Bouc-Wen hysteretic model and follows up on earlier work of the authors. The current article discusses the polynomial decoupling approach and explores the selection of the number of univariate polynomials with the polynomial degree. We have found that the presented decoupling approach is able to reduce the number of parameters of the full nonlinear model up to about 50%, while maintaining a comparable output error level.

Dynamic Vision for Control

DTIC Science & Technology

2009-02-05

the best of our knowledge, the first approach to design a proper filter (observer) in the infinite - dimensional space of shapes (closed Jordan...curves). This is based on endowing the space with a Riemaimian (Sobolev) metric , then shooting geodesies from the current best estimate of the state...handing nuisance transformations and endowing the models with a

Recovering a Probabilistic Knowledge Structure by Constraining Its Parameter Space

ERIC Educational Resources Information Center

Stefanutti, Luca; Robusto, Egidio

2009-01-01

In the Basic Local Independence Model (BLIM) of Doignon and Falmagne ("Knowledge Spaces," Springer, Berlin, 1999), the probabilistic relationship between the latent knowledge states and the observable response patterns is established by the introduction of a pair of parameters for each of the problems: a lucky guess probability and a careless…

Atmospheric Turbulence Relative to Aviation, Missile, and Space Programs

NASA Technical Reports Server (NTRS)

Camp, Dennis W. (Editor); Frost, Walter (Editor)

1987-01-01

The purpose of the workshop was to bring together various disciplines of the aviation, missile, and space programs involved in predicting, measuring, modeling, and understanding the processes of atmospheric turbulence. Working committees re-examined the current state of knowledge, identified present and future needs, and documented and prioritized integrated and cooperative research programs.

A Test Methodology for Determining Space-Readiness of Xilinx SRAM-Based FPGA Designs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quinn, Heather M; Graham, Paul S; Morgan, Keith S

2008-01-01

Using reconfigurable, static random-access memory (SRAM) based field-programmable gate arrays (FPGAs) for space-based computation has been an exciting area of research for the past decade. Since both the circuit and the circuit's state is stored in radiation-tolerant memory, both could be alterd by the harsh space radiation environment. Both the circuit and the circuit's state can be prote cted by triple-moduler redundancy (TMR), but applying TMR to FPGA user designs is often an error-prone process. Faulty application of TMR could cause the FPGA user circuit to output incorrect data. This paper will describe a three-tiered methodology for testing FPGA usermore » designs for space-readiness. We will describe the standard approach to testing FPGA user designs using a particle accelerator, as well as two methods using fault injection and a modeling tool. While accelerator testing is the current 'gold standard' for pre-launch testing, we believe the use of fault injection and modeling tools allows for easy, cheap and uniform access for discovering errors early in the design process.« less

Computing the Entropy of Kerr-Newman Black Hole Without Brick Walls Method

NASA Astrophysics Data System (ADS)

Zhang, Li-Chun; Wu, Yue-Qin; Li, Huai-Fan; Ren, Zhao

By using the entanglement entropy method, the statistical entropy of the Bose and Fermi fields in a thin film is calculated and the Bekenstein-Hawking entropy of Kerr-Newman black hole is obtained. Here, the Bose and Fermi fields are entangled with the quantum states in Kerr-Newman black hole and are outside of the horizon. The divergence of brick-wall model is avoided without any cutoff by the new equation of state density obtained with the generalized uncertainty principle. The calculation implies that the high density quantum states near the event horizon are strongly correlated with the quantum states in black hole. The black hole entropy is a quantum effect. It is an intrinsic characteristic of space-time. The ultraviolet cutoff in the brick-wall model is unreasonable. The generalized uncertainty principle should be considered in the high energy quantum field near the event horizon. From the calculation, the constant λ introduced in the generalized uncertainty principle is related to polar angle θ in an axisymmetric space-time.

Predicted torque equilibrium attitude utilization for Space Station attitude control

NASA Technical Reports Server (NTRS)

Kumar, Renjith R.; Heck, Michael L.; Robertson, Brent P.

1990-01-01

An approximate knowledge of the torque equilibrium attitude (TEA) is shown to improve the performance of a control moment gyroscope (CMG) momentum management/attitude control law for Space Station Freedom. The linearized equations of motion are used in conjunction with a state transformation to obtain a control law which uses full state feedback and the predicted TEA to minimize both attitude excursions and CMG peak and secular momentum. The TEA can be computationally determined either by observing the steady state attitude of a 'controlled' spacecraft using arbitrary initial attitude, or by simulating a fixed attitude spacecraft flying in desired orbit subject to realistic environmental disturbance models.

NeuroPlace: Categorizing urban places according to mental states

PubMed Central

2017-01-01

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture. PMID:28898244

Tethered Satellites as an Enabling Platform for Operational Space Weather Monitoring Systems

NASA Technical Reports Server (NTRS)

Gilchrist, Brian E.; Krause, Linda Habash; Gallagher, Dennis Lee; Bilen, Sven Gunnar; Fuhrhop, Keith; Hoegy, Walt R.; Inderesan, Rohini; Johnson, Charles; Owens, Jerry Keith; Powers, Joseph;

Distributions in the error space: goal-directed movements described in time and state-space representations.

PubMed

Fisher, Moria E; Huang, Felix C; Wright, Zachary A; Patton, James L

2014-01-01

Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.

A New Critical State Model for Geomechanical Behavior of Methane Hydrate-Bearing Sands

NASA Astrophysics Data System (ADS)

Lin, J. S.; Xing, P.; Rutqvist, J.; Seol, Y.; Choi, J. H.

2014-12-01

Methane hydrate bearing sands behave like sands once the hydrate has dissociated, but could exhibit a substantial increase in the shear strength, stiffness and dilatancy as the degree of hydrate saturation increases. A new critical state model was developed that incorporates the spatially mobilized plane (SMP) concept, which has been proven effective in modeling mechanical behavior of sands. While this new model was built on the basic constructs of the critical state model, important enhancements were introduced. The model adopted the t-stress concept, which defined the normal and shear stress on the SMP, in describing the plastic behavior of the soil. In this connection the versatile Matsuoka-Nakai yield criterion was also employed, which defined the general three dimensional yield behavior. The resulting constitutive law was associated in the t-stress space, but became non-associated in the conventional p-q stress space as it should be for sands. The model also introduced a generalized degree of hydrate saturation concept that was modified from the pioneering work of the Cambridge group. The model gives stress change when the sands are subjected to straining, and/or to hydrate saturation changes. The performance of the model has been found satisfactory using data from laboratory triaxial tests on reconstituted samples and core samples taken from Nankai Trough, Japan. The model has been implemented into FLAC3D. A coupling example with the multiphase flow code, TOUGH+, is presented which simulates the mechanical behavior of a sample when the surrounding temperature has been raised, and the hydrate undergoes state change and no longer resides in the stability zone.

Quality-by-Design (QbD): An integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and process design space development.

PubMed

Wu, Huiquan; White, Maury; Khan, Mansoor A

2011-02-28

The aim of this work was to develop an integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and design space development. A dynamic co-precipitation process by gradually introducing water to the ternary system of naproxen-Eudragit L100-alcohol was monitored at real-time in situ via Lasentec FBRM and PVM. 3D map of count-time-chord length revealed three distinguishable process stages: incubation, transition, and steady-state. The effects of high risk process variables (slurry temperature, stirring rate, and water addition rate) on both derived co-precipitation process rates and final chord-length-distribution were evaluated systematically using a 3(3) full factorial design. Critical process variables were identified via ANOVA for both transition and steady state. General linear models (GLM) were then used for parameter estimation for each critical variable. Clear trends about effects of each critical variable during transition and steady state were found by GLM and were interpreted using fundamental process principles and Nyvlt's transfer model. Neural network models were able to link process variables with response variables at transition and steady state with R(2) of 0.88-0.98. PVM images evidenced nucleation and crystal growth. Contour plots illustrated design space via critical process variables' ranges. It demonstrated the utility of integrated PAT approach for QbD development. Published by Elsevier B.V.

State-space modeling of population sizes and trends in Nihoa Finch and Millerbird

USGS Publications Warehouse

Gorresen, P. Marcos; Brinck, Kevin W.; Camp, Richard J.; Farmer, Chris; Plentovich, Sheldon M.; Banko, Paul C.

2016-01-01

Both of the 2 passerines endemic to Nihoa Island, Hawai‘i, USA—the Nihoa Millerbird (Acrocephalus familiaris kingi) and Nihoa Finch (Telespiza ultima)—are listed as endangered by federal and state agencies. Their abundances have been estimated by irregularly implemented fixed-width strip-transect sampling from 1967 to 2012, from which area-based extrapolation of the raw counts produced highly variable abundance estimates for both species. To evaluate an alternative survey method and improve abundance estimates, we conducted variable-distance point-transect sampling between 2010 and 2014. We compared our results to those obtained from strip-transect samples. In addition, we applied state-space models to derive improved estimates of population size and trends from the legacy time series of strip-transect counts. Both species were fairly evenly distributed across Nihoa and occurred in all or nearly all available habitat. Population trends for Nihoa Millerbird were inconclusive because of high within-year variance. Trends for Nihoa Finch were positive, particularly since the early 1990s. Distance-based analysis of point-transect counts produced mean estimates of abundance similar to those from strip-transects but was generally more precise. However, both survey methods produced biologically unrealistic variability between years. State-space modeling of the long-term time series of abundances obtained from strip-transect counts effectively reduced uncertainty in both within- and between-year estimates of population size, and allowed short-term changes in abundance trajectories to be smoothed into a long-term trend.

Grey-box state-space identification of nonlinear mechanical vibrations

NASA Astrophysics Data System (ADS)

Noël, J. P.; Schoukens, J.

2018-05-01

The present paper deals with the identification of nonlinear mechanical vibrations. A grey-box, or semi-physical, nonlinear state-space representation is introduced, expressing the nonlinear basis functions using a limited number of measured output variables. This representation assumes that the observed nonlinearities are localised in physical space, which is a generic case in mechanics. A two-step identification procedure is derived for the grey-box model parameters, integrating nonlinear subspace initialisation and weighted least-squares optimisation. The complete procedure is applied to an electrical circuit mimicking the behaviour of a single-input, single-output (SISO) nonlinear mechanical system and to a single-input, multiple-output (SIMO) geometrically nonlinear beam structure.

IHY Modeling Support at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Chulaki, A.; Hesse, Michael; Kuznetsova, Masha; MacNeice, P.; Rastaetter, L.

2005-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. In particular, the CCMC provides to the research community the execution of "runs-onrequest" for specific events of interest to space science researchers. Through this activity and the concurrent development of advanced visualization tools, CCMC provides, to the general science community, unprecedented access to a large number of state-of-the-art research models. CCMC houses models that cover the entire domain from the Sun to the Earth. In this presentation, we will provide an overview of CCMC modeling services that are available to support activities during the International Heliospheric Year. In order to tailor CCMC activities to IHY needs, we will also invite community input into our IHY planning activities.

Quasi-degenerate perturbation theory using matrix product states

NASA Astrophysics Data System (ADS)

Sharma, Sandeep; Jeanmairet, Guillaume; Alavi, Ali

2016-01-01

In this work, we generalize the recently proposed matrix product state perturbation theory (MPSPT) for calculating energies of excited states using quasi-degenerate (QD) perturbation theory. Our formulation uses the Kirtman-Certain-Hirschfelder canonical Van Vleck perturbation theory, which gives Hermitian effective Hamiltonians at each order, and also allows one to make use of Wigner's 2n + 1 rule. Further, our formulation satisfies Granovsky's requirement of model space invariance which is important for obtaining smooth potential energy curves. Thus, when we use MPSPT with the Dyall Hamiltonian, we obtain a model space invariant version of quasi-degenerate n-electron valence state perturbation theory (NEVPT), a property that the usual formulation of QD-NEVPT2 based on a multipartitioning technique lacked. We use our method on the benchmark problems of bond breaking of LiF which shows ionic to covalent curve crossing and the twist around the double bond of ethylene where significant valence-Rydberg mixing occurs in the excited states. In accordance with our previous work, we find that multi-reference linearized coupled cluster theory is more accurate than other multi-reference theories of similar cost.

Dichromatic State Sum Models for Four-Manifolds from Pivotal Functors

NASA Astrophysics Data System (ADS)

Bärenz, Manuel; Barrett, John

2017-11-01

A family of invariants of smooth, oriented four-dimensional manifolds is defined via handle decompositions and the Kirby calculus of framed link diagrams. The invariants are parametrised by a pivotal functor from a spherical fusion category into a ribbon fusion category. A state sum formula for the invariant is constructed via the chain-mail procedure, so a large class of topological state sum models can be expressed as link invariants. Most prominently, the Crane-Yetter state sum over an arbitrary ribbon fusion category is recovered, including the nonmodular case. It is shown that the Crane-Yetter invariant for nonmodular categories is stronger than signature and Euler invariant. A special case is the four-dimensional untwisted Dijkgraaf-Witten model. Derivations of state space dimensions of TQFTs arising from the state sum model agree with recent calculations of ground state degeneracies in Walker-Wang models. Relations to different approaches to quantum gravity such as Cartan geometry and teleparallel gravity are also discussed.

Dichromatic State Sum Models for Four-Manifolds from Pivotal Functors

NASA Astrophysics Data System (ADS)

Bärenz, Manuel; Barrett, John

2018-06-01

A family of invariants of smooth, oriented four-dimensional manifolds is defined via handle decompositions and the Kirby calculus of framed link diagrams. The invariants are parametrised by a pivotal functor from a spherical fusion category into a ribbon fusion category. A state sum formula for the invariant is constructed via the chain-mail procedure, so a large class of topological state sum models can be expressed as link invariants. Most prominently, the Crane-Yetter state sum over an arbitrary ribbon fusion category is recovered, including the nonmodular case. It is shown that the Crane-Yetter invariant for nonmodular categories is stronger than signature and Euler invariant. A special case is the four-dimensional untwisted Dijkgraaf-Witten model. Derivations of state space dimensions of TQFTs arising from the state sum model agree with recent calculations of ground state degeneracies in Walker-Wang models. Relations to different approaches to quantum gravity such as Cartan geometry and teleparallel gravity are also discussed.

Modeling of transient heat pipe operation

NASA Technical Reports Server (NTRS)

Colwell, Gene T.

1989-01-01

Mathematical models and an associated computer program for heat pipe startup from the frozen state have been developed. Finite element formulations of the governing equations are written for each heat pipe region for each operating condition during startup from the frozen state. The various models were checked against analytical and experimental data available in the literature for three specific types of operation. Computations using the methods developed were made for a space shuttle reentry mission where a heat pipe cooled leading edge was used on the wing.

FAST TRACK COMMUNICATION: The nonlinear fragmentation equation

NASA Astrophysics Data System (ADS)

Ernst, Matthieu H.; Pagonabarraga, Ignacio

2007-04-01

We study the kinetics of nonlinear irreversible fragmentation. Here, fragmentation is induced by interactions/collisions between pairs of particles and modelled by general classes of interaction kernels, for several types of breakage models. We construct initial value and scaling solutions of the fragmentation equations, and apply the 'non-vanishing mass flux' criterion for the occurrence of shattering transitions. These properties enable us to determine the phase diagram for the occurrence of shattering states and of scaling states in the phase space of model parameters.

An Integrated Modeling Suite for Simulating the Core Induction and Kinetic Effects in Mercury's Magnetosphere

NASA Astrophysics Data System (ADS)

Jia, X.; Slavin, J.; Chen, Y.; Poh, G.; Toth, G.; Gombosi, T.

2018-05-01

We present results from state-of-the-art global models of Mercury's space environment capable of self-consistently simulating the induction effect at the core and resolving kinetic physics important for magnetic reconnection.

Model Checking a Byzantine-Fault-Tolerant Self-Stabilizing Protocol for Distributed Clock Synchronization Systems

NASA Technical Reports Server (NTRS)

Malekpour, Mahyar R.

2007-01-01

This report presents the mechanical verification of a simplified model of a rapid Byzantine-fault-tolerant self-stabilizing protocol for distributed clock synchronization systems. This protocol does not rely on any assumptions about the initial state of the system. This protocol tolerates bursts of transient failures, and deterministically converges within a time bound that is a linear function of the self-stabilization period. A simplified model of the protocol is verified using the Symbolic Model Verifier (SMV) [SMV]. The system under study consists of 4 nodes, where at most one of the nodes is assumed to be Byzantine faulty. The model checking effort is focused on verifying correctness of the simplified model of the protocol in the presence of a permanent Byzantine fault as well as confirmation of claims of determinism and linear convergence with respect to the self-stabilization period. Although model checking results of the simplified model of the protocol confirm the theoretical predictions, these results do not necessarily confirm that the protocol solves the general case of this problem. Modeling challenges of the protocol and the system are addressed. A number of abstractions are utilized in order to reduce the state space. Also, additional innovative state space reduction techniques are introduced that can be used in future verification efforts applied to this and other protocols.

Spin squeezing as an indicator of quantum chaos in the Dicke model.

PubMed

Song, Lijun; Yan, Dong; Ma, Jian; Wang, Xiaoguang

2009-04-01

We study spin squeezing, an intrinsic quantum property, in the Dicke model without the rotating-wave approximation. We show that the spin squeezing can reveal the underlying chaotic and regular structures in phase space given by a Poincaré section, namely, it acts as an indicator of quantum chaos. Spin squeezing vanishes after a very short time for an initial coherent state centered in a chaotic region, whereas it persists over a longer time for the coherent state centered in a regular region of the phase space. We also study the distribution of the mean spin directions when quantum dynamics takes place. Finally, we discuss relations among spin squeezing, bosonic quadrature squeezing, and two-qubit entanglement in the dynamical processes.

Real time evolution at finite temperatures with operator space matrix product states

NASA Astrophysics Data System (ADS)

Pižorn, Iztok; Eisler, Viktor; Andergassen, Sabine; Troyer, Matthias

2014-07-01

We propose a method to simulate the real time evolution of one-dimensional quantum many-body systems at finite temperature by expressing both the density matrices and the observables as matrix product states. This allows the calculation of expectation values and correlation functions as scalar products in operator space. The simulations of density matrices in inverse temperature and the local operators in the Heisenberg picture are independent and result in a grid of expectation values for all intermediate temperatures and times. Simulations can be performed using real arithmetics with only polynomial growth of computational resources in inverse temperature and time for integrable systems. The method is illustrated for the XXZ model and the single impurity Anderson model.

Equilibrium points of the tilted perfect fluid Bianchi VIh state space

NASA Astrophysics Data System (ADS)

Apostolopoulos, Pantelis S.

2005-05-01

We present the full set of evolution equations for the spatially homogeneous cosmologies of type VIh filled with a tilted perfect fluid and we provide the corresponding equilibrium points of the resulting dynamical state space. It is found that only when the group parameter satisfies h > -1 a self-similar solution exists. In particular we show that for h > -{1/9} there exists a self-similar equilibrium point provided that γ ∈ ({2(3+sqrt{-h})/5+3sqrt{-h}},{3/2}) whereas for h < -{frac 19} the state parameter belongs to the interval γ ∈(1,{2(3+sqrt{-h})/5+3sqrt{-h}}). This family of new exact self-similar solutions belongs to the subclass nαα = 0 having non-zero vorticity. In both cases the equilibrium points have a six-dimensional stable manifold and may act as future attractors at least for the models satisfying nαα = 0. Also we give the exact form of the self-similar metrics in terms of the state and group parameter. As an illustrative example we provide the explicit form of the corresponding self-similar radiation model (γ = {frac 43}), parametrised by the group parameter h. Finally we show that there are no tilted self-similar models of type III and irrotational models of type VIh.

Automation and Robotics for Space-Based Systems, 1991

NASA Technical Reports Server (NTRS)

Williams, Robert L., II (Editor)

1992-01-01

The purpose of this in-house workshop was to assess the state-of-the-art of automation and robotics for space operations from an LaRC perspective and to identify areas of opportunity for future research. Over half of the presentations came from the Automation Technology Branch, covering telerobotic control, extravehicular activity (EVA) and intra-vehicular activity (IVA) robotics, hand controllers for teleoperation, sensors, neural networks, and automated structural assembly, all applied to space missions. Other talks covered the Remote Manipulator System (RMS) active damping augmentation, space crane work, modeling, simulation, and control of large, flexible space manipulators, and virtual passive controller designs for space robots.

Generalized probabilistic scale space for image restoration.

PubMed

Wong, Alexander; Mishra, Akshaya K

2010-10-01

A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.

Mouse infection models for space flight immunology

NASA Technical Reports Server (NTRS)

Chapes, Stephen Keith; Ganta, Roman Reddy; Chapers, S. K. (Principal Investigator)

2005-01-01

Several immunological processes can be affected by space flight. However, there is little evidence to suggest that flight-induced immunological deficits lead to illness. Therefore, one of our goals has been to define models to examine host resistance during space flight. Our working hypothesis is that space flight crews will come from a heterogeneous population; the immune response gene make-up will be quite varied. It is unknown how much the immune response gene variation contributes to the potential threat from infectious organisms, allergic responses or other long term health problems (e.g. cancer). This article details recent efforts of the Kansas State University gravitational immunology group to assess how population heterogeneity impacts host health, either in laboratory experimental situations and/or using the skeletal unloading model of space-flight stress. This paper details our use of several mouse strains with several different genotypes. In particular, mice with varying MHCII allotypes and mice on the C57BL background with different genetic defects have been particularly useful tools with which to study infections by Staphylococcus aureus, Salmonella typhimurium, Pasteurella pneumotropica and Ehrlichia chaffeensis. We propose that some of these experimental challenge models will be useful to assess the effects of space flight on host resistance to infection.

Semigroup theory and numerical approximation for equations in linear viscoelasticity

NASA Technical Reports Server (NTRS)

Fabiano, R. H.; Ito, K.

1990-01-01

A class of abstract integrodifferential equations used to model linear viscoelastic beams is investigated analytically, applying a Hilbert-space approach. The basic equation is rewritten as a Cauchy problem, and its well-posedness is demonstrated. Finite-dimensional subspaces of the state space and an estimate of the state operator are obtained; approximation schemes for the equations are constructed; and the convergence is proved using the Trotter-Kato theorem of linear semigroup theory. The actual convergence behavior of different approximations is demonstrated in numerical computations, and the results are presented in tables.

From Glass Formation to Icosahedral Ordering by Curving Three-Dimensional Space.

PubMed

Turci, Francesco; Tarjus, Gilles; Royall, C Patrick

2017-05-26

Geometric frustration describes the inability of a local molecular arrangement, such as icosahedra found in metallic glasses and in model atomic glass formers, to tile space. Local icosahedral order, however, is strongly frustrated in Euclidean space, which obscures any causal relationship with the observed dynamical slowdown. Here we relieve frustration in a model glass-forming liquid by curving three-dimensional space onto the surface of a 4-dimensional hypersphere. For sufficient curvature, frustration vanishes and the liquid "freezes" in a fully icosahedral structure via a sharp "transition." Frustration increases upon reducing the curvature, and the transition to the icosahedral state smoothens while glassy dynamics emerge. Decreasing the curvature leads to decoupling between dynamical and structural length scales and the decrease of kinetic fragility. This sheds light on the observed glass-forming behavior in Euclidean space.

A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons

PubMed Central

Vavoulis, Dimitrios V.; Straub, Volko A.; Aston, John A. D.; Feng, Jianfeng

2012-01-01

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models. PMID:22396632

A Bayesian state-space approach for damage detection and classification

NASA Astrophysics Data System (ADS)

Dzunic, Zoran; Chen, Justin G.; Mobahi, Hossein; Büyüköztürk, Oral; Fisher, John W.

2017-11-01

The problem of automatic damage detection in civil structures is complex and requires a system that can interpret collected sensor data into meaningful information. We apply our recently developed switching Bayesian model for dependency analysis to the problems of damage detection and classification. The model relies on a state-space approach that accounts for noisy measurement processes and missing data, which also infers the statistical temporal dependency between measurement locations signifying the potential flow of information within the structure. A Gibbs sampling algorithm is used to simultaneously infer the latent states, parameters of the state dynamics, the dependence graph, and any changes in behavior. By employing a fully Bayesian approach, we are able to characterize uncertainty in these variables via their posterior distribution and provide probabilistic estimates of the occurrence of damage or a specific damage scenario. We also implement a single class classification method which is more realistic for most real world situations where training data for a damaged structure is not available. We demonstrate the methodology with experimental test data from a laboratory model structure and accelerometer data from a real world structure during different environmental and excitation conditions.

Structural and practical identifiability analysis of S-system.

PubMed

Zhan, Choujun; Li, Benjamin Yee Shing; Yeung, Lam Fat

2015-12-01

In the field of systems biology, biological reaction networks are usually modelled by ordinary differential equations. A sub-class, the S-systems representation, is a widely used form of modelling. Existing S-systems identification techniques assume that the system itself is always structurally identifiable. However, due to practical limitations, biological reaction networks are often only partially measured. In addition, the captured data only covers a limited trajectory, therefore data can only be considered as a local snapshot of the system responses with respect to the complete set of state trajectories over the entire state space. Hence the estimated model can only reflect partial system dynamics and may not be unique. To improve the identification quality, the structural and practical identifiablility of S-system are studied. The S-system is shown to be identifiable under a set of assumptions. Then, an application on yeast fermentation pathway was conducted. Two case studies were chosen; where the first case is based on a larger state trajectories and the second case is based on a smaller one. By expanding the dataset which span a relatively larger state space, the uncertainty of the estimated system can be reduced. The results indicated that initial concentration is related to the practical identifiablity.

Dynamics of large-scale brain activity in normal arousal states and epileptic seizures

NASA Astrophysics Data System (ADS)

Robinson, P. A.; Rennie, C. J.; Rowe, D. L.

2002-04-01

Links between electroencephalograms (EEGs) and underlying aspects of neurophysiology and anatomy are poorly understood. Here a nonlinear continuum model of large-scale brain electrical activity is used to analyze arousal states and their stability and nonlinear dynamics for physiologically realistic parameters. A simple ordered arousal sequence in a reduced parameter space is inferred and found to be consistent with experimentally determined parameters of waking states. Instabilities arise at spectral peaks of the major clinically observed EEG rhythms-mainly slow wave, delta, theta, alpha, and sleep spindle-with each instability zone lying near its most common experimental precursor arousal states in the reduced space. Theta, alpha, and spindle instabilities evolve toward low-dimensional nonlinear limit cycles that correspond closely to EEGs of petit mal seizures for theta instability, and grand mal seizures for the other types. Nonlinear stimulus-induced entrainment and seizures are also seen, EEG spectra and potentials evoked by stimuli are reproduced, and numerous other points of experimental agreement are found. Inverse modeling enables physiological parameters underlying observed EEGs to be determined by a new, noninvasive route. This model thus provides a single, powerful framework for quantitative understanding of a wide variety of brain phenomena.

Independent-Cluster Parametrizations of Wave Functions in Model Field Theories III. The Coupled-Cluster Phase Spaces and Their Geometrical Structure

NASA Astrophysics Data System (ADS)

Arponen, J. S.; Bishop, R. F.

1993-11-01

In this third paper of a series we study the structure of the phase spaces of the independent-cluster methods. These phase spaces are classical symplectic manifolds which provide faithful descriptions of the quantum mechanical pure states of an arbitrary system. They are "superspaces" in the sense that the full physical many-body or field-theoretic system is described by a point of the space, in contrast to "ordinary" spaces for which the state of the physical system is described rather by the whole space itself. We focus attention on the normal and extended coupled-cluster methods (NCCM and ECCM). Both methods provide parametrizations of the Hilbert space which take into account in increasing degrees of completeness the connectivity properties of the associated perturbative diagram structure. This corresponds to an increasing incorporation of locality into the description of the quantum system. As a result the degree of nonlinearity increases in the dynamical equations that govern the temporal evolution and determine the equilibrium state. Because of the nonlinearity, the structure of the manifold becomes geometrically complicated. We analyse the neighbourhood of the ground state of the one-mode anharmonic bosonic field theory and derive the nonlinear expansion beyond the linear response regime. The expansion is given in terms of normal-mode amplitudes, which provide the best local coordinate system close to the ground state. We generalize the treatment to other nonequilibrium states by considering the similarly defined normal coordinates around the corresponding phase space point. It is pointed out that the coupled-cluster method (CCM) maps display such features as (an)holonomy, or geometric phase. For example, a physical state may be represented by a number of different points on the CCM manifold. For this reason the whole phase spaces in the NCCM or ECCM cannot be covered by a single chart. To account for this non-Euclidean nature we introduce a suitable pseudo-Riemannian metric structure which is compatible with an important subset of all canonical transformations. It is then shown that the phase space of the configuration-interaction method is flat, namely the complex Euclidean space; that the NCCM manifold has zero curvature even though its Reimann tensor does not vanish; and that the ECCM manifold is intrinsically curved. It is pointed out that with the present metrization many of the dimensions of the ECCM phase space are effectively compactified and that the overall topological structure of the space is related to the distribution of the zeros of the Bargmann wave function.

Space charge effects for multipactor in coaxial lines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sorolla, E., E-mail: eden.sorolla@xlim.fr; Sounas, A.; Mattes, M.

2015-03-15

Multipactor is a hazardous vacuum discharge produced by secondary electron emission within microwave devices of particle accelerators and telecommunication satellites. This work analyzes the dynamics of the multipactor discharge within a coaxial line for the mono-energetic electron emission model taking into account the space charge effects. The steady-state is predicted by the proposed model and an analytical expression for the maximum number of electrons released by the discharge presented. This could help to link simulations to experiments and define a multipactor onset criterion.

Quantum anharmonic oscillator plus delta-function potential: a molecular view of pairing formation and breaking in the coordinate space

NASA Astrophysics Data System (ADS)

Sumaryada, Tony; Maha Putra, Bima; Pramudito, Sidikrubadi

2017-05-01

We propose an alternative way to describe the pairing formation and breaking via a quantum anharmonic oscillator with a delta-function potential model. Unlike BCS theory, which describes the pairing formation in the momentum space, this model works in the coordinate space and is able to give a molecular view of pairing formation and breaking in the coordinate space. By exploring the dynamical interplay between the intrinsic factor (dissociation energy) and external factor (pairing strength) of this system additional information was gained, including the critical pairing strength and critical scattering length, which might relate to the BCS-BEC crossover phenomena and halo state formation. Although only the energetic aspect of pairing is described by this model, its simplicity and pedagogical steps might help undergraduate students to understand the pairing problem in a simple way.

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

NASA Astrophysics Data System (ADS)

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

2018-05-01

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

A Subspace Approach to the Structural Decomposition and Identification of Ankle Joint Dynamic Stiffness.

PubMed

Jalaleddini, Kian; Tehrani, Ehsan Sobhani; Kearney, Robert E

2017-06-01

The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness. First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway. Extensive simulation studies demonstrate that SDSS has significant performance advantages over some other methods. Thus, SDSS was more robust under high noise conditions, converging where others failed; it was more accurate, giving estimates with lower bias and random errors. The method also worked well in practice and yielded high-quality estimates of intrinsic and reflex stiffnesses when applied to experimental data at three muscle activation levels. The simulation and experimental results demonstrate that SDSS accurately decomposes the intrinsic and reflex torques and provides accurate estimates of physiologically meaningful parameters. SDSS will be a valuable tool for studying joint stiffness under functionally important conditions. It has important clinical implications for the diagnosis, assessment, objective quantification, and monitoring of neuromuscular diseases that change the muscle tone.

Einstein's steady-state theory: an abandoned model of the cosmos

NASA Astrophysics Data System (ADS)

O'Raifeartaigh, Cormac; McCann, Brendan; Nahm, Werner; Mitton, Simon

2014-09-01

We present a translation and analysis of an unpublished manuscript by Albert Einstein in which he attempted to construct a `steady-state' model of the universe. The manuscript, which appears to have been written in early 1931, demonstrates that Einstein once explored a cosmic model in which the mean density of matter in an expanding universe is maintained constant by the continuous formation of matter from empty space. This model is very different to previously known Einsteinian models of the cosmos (both static and dynamic) but anticipates the later steady-state cosmology of Hoyle, Bondi and Gold in some ways. We find that Einstein's steady-state model contains a fundamental flaw and suggest that it was abandoned for this reason. We also suggest that he declined to explore a more sophisticated version because he found such theories rather contrived. The manuscript is of historical interest because it reveals that Einstein debated between steady-state and evolving models of the cosmos decades before a similar debate took place in the cosmological community.

Measurement-based quantum teleportation on finite AKLT chains

NASA Astrophysics Data System (ADS)

Fujii, Akihiko; Feder, David

In the measurement-based model of quantum computation, universal quantum operations are effected by making repeated local measurements on resource states which contain suitable entanglement. Resource states include two-dimensional cluster states and the ground state of the Affleck-Kennedy-Lieb-Tasaki (AKLT) state on the honeycomb lattice. Recent studies suggest that measurements on one-dimensional systems in the Haldane phase teleport perfect single-qubit gates in the correlation space, protected by the underlying symmetry. As laboratory realizations of symmetry-protected states will necessarily be finite, we investigate the potential for quantum gate teleportation in finite chains of a bilinear-biquadratic Hamiltonian which is a generalization of the AKLT model representing the full Haldane phase.

Constructing 1/ωα noise from reversible Markov chains

NASA Astrophysics Data System (ADS)

Erland, Sveinung; Greenwood, Priscilla E.

2007-09-01

This paper gives sufficient conditions for the output of 1/ωα noise from reversible Markov chains on finite state spaces. We construct several examples exhibiting this behavior in a specified range of frequencies. We apply simple representations of the covariance function and the spectral density in terms of the eigendecomposition of the probability transition matrix. The results extend to hidden Markov chains. We generalize the results for aggregations of AR1-processes of C. W. J. Granger [J. Econometrics 14, 227 (1980)]. Given the eigenvalue function, there is a variety of ways to assign values to the states such that the 1/ωα condition is satisfied. We show that a random walk on a certain state space is complementary to the point process model of 1/ω noise of B. Kaulakys and T. Meskauskas [Phys. Rev. E 58, 7013 (1998)]. Passing to a continuous state space, we construct 1/ωα noise which also has a long memory.

Identification of Computational and Experimental Reduced-Order Models

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Hong, Moeljo S.; Bartels, Robert E.; Piatak, David J.; Scott, Robert C.

2003-01-01

The identification of computational and experimental reduced-order models (ROMs) for the analysis of unsteady aerodynamic responses and for efficient aeroelastic analyses is presented. For the identification of a computational aeroelastic ROM, the CFL3Dv6.0 computational fluid dynamics (CFD) code is used. Flutter results for the AGARD 445.6 Wing and for a Rigid Semispan Model (RSM) computed using CFL3Dv6.0 are presented, including discussion of associated computational costs. Modal impulse responses of the unsteady aerodynamic system are computed using the CFL3Dv6.0 code and transformed into state-space form. The unsteady aerodynamic state-space ROM is then combined with a state-space model of the structure to create an aeroelastic simulation using the MATLAB/SIMULINK environment. The MATLAB/SIMULINK ROM is then used to rapidly compute aeroelastic transients, including flutter. The ROM shows excellent agreement with the aeroelastic analyses computed using the CFL3Dv6.0 code directly. For the identification of experimental unsteady pressure ROMs, results are presented for two configurations: the RSM and a Benchmark Supercritical Wing (BSCW). Both models were used to acquire unsteady pressure data due to pitching oscillations on the Oscillating Turntable (OTT) system at the Transonic Dynamics Tunnel (TDT). A deconvolution scheme involving a step input in pitch and the resultant step response in pressure, for several pressure transducers, is used to identify the unsteady pressure impulse responses. The identified impulse responses are then used to predict the pressure responses due to pitching oscillations at several frequencies. Comparisons with the experimental data are then presented.

Simulator of Space Communication Networks

NASA Technical Reports Server (NTRS)

Clare, Loren; Jennings, Esther; Gao, Jay; Segui, John; Kwong, Winston

2005-01-01

Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.

Eigenspace techniques for active flutter suppression

NASA Technical Reports Server (NTRS)

Garrard, W. L.

1982-01-01

Mathematical models to be used in the control system design were developed. A computer program, which takes aerodynamic and structural data for the ARW-2 aircraft and converts these data into state space models suitable for use in modern control synthesis procedures, was developed. Reduced order models of inboard and outboard control surface actuator dynamics and a second order vertical wind gust model were developed. An analysis of the rigid body motion of the ARW-2 was conducted. The deletion of the aerodynamic lag states in the rigid body modes resulted in more accurate values for the eigenvalues associated with the plunge and pitch modes than were obtainable if the lag states were retained.

Mathematical Models of IABG Thermal-Vacuum Facilities

NASA Astrophysics Data System (ADS)

Doring, Daniel; Ulfers, Hendrik

2014-06-01

IABG in Ottobrunn, Germany, operates thermal-vacuum facilities of different sizes and complexities as a service for space-testing of satellites and components. One aspect of these tests is the qualification of the thermal control system that keeps all onboard components within their save operating temperature band. As not all possible operation / mission states can be simulated within a sensible test time, usually a subset of important and extreme states is tested at TV facilities to validate the thermal model of the satellite, which is then used to model all other possible mission states. With advances in the precision of customer thermal models, simple assumptions of the test environment (e.g. everything black & cold, one solar constant of light from this side) are no longer sufficient, as real space simulation chambers do deviate from this ideal. For example the mechanical adapters which support the spacecraft are usually not actively cooled. To enable IABG to provide a model that is sufficiently detailed and realistic for current system tests, Munich engineering company CASE developed ESATAN models for the two larger chambers. CASE has many years of experience in thermal analysis for space-flight systems and ESATAN. The two models represent the rather simple (and therefore very homogeneous) 3m-TVA and the extremely complex space simulation test facility and its solar simulator. The cooperation of IABG and CASE built up extensive knowledge of the facilities thermal behaviour. This is the key to optimally support customers with their test campaigns in the future. The ESARAD part of the models contains all relevant information with regard to geometry (CAD data), surface properties (optical measurements) and solar irradiation for the sun simulator. The temperature of the actively cooled thermal shrouds is measured and mapped to the thermal mesh to create the temperature field in the ESATAN part as boundary conditions. Both models comprise switches to easily establish multiple possible set-ups (e.g. exclude components like the motion system or enable / disable the solar simulator). Both models were validated by comparing calculated results (thermal balance temperatures for simple passive test articles) with measured temperatures generated in actual tests in these facilities. This paper presents information about the chambers, the modelling approach, properties of the models and their performance in the validation tests.

Tensor discriminant color space for face recognition.

PubMed

Wang, Su-Jing; Yang, Jian; Zhang, Na; Zhou, Chun-Guang

2011-09-01

Recent research efforts reveal that color may provide useful information for face recognition. For different visual tasks, the choice of a color space is generally different. How can a color space be sought for the specific face recognition problem? To address this problem, this paper represents a color image as a third-order tensor and presents the tensor discriminant color space (TDCS) model. The model can keep the underlying spatial structure of color images. With the definition of n-mode between-class scatter matrices and within-class scatter matrices, TDCS constructs an iterative procedure to obtain one color space transformation matrix and two discriminant projection matrices by maximizing the ratio of these two scatter matrices. The experiments are conducted on two color face databases, AR and Georgia Tech face databases, and the results show that both the performance and the efficiency of the proposed method are better than those of the state-of-the-art color image discriminant model, which involve one color space transformation matrix and one discriminant projection matrix, specifically in a complicated face database with various pose variations.

Model reduction in a subset of the original states

NASA Technical Reports Server (NTRS)

Yae, K. H.; Inman, D. J.

1992-01-01

A model reduction method is investigated to provide a smaller structural dynamic model for subsequent structural control design. A structural dynamic model is assumed to be derived from finite element analysis. It is first converted into the state space form, and is further reduced by the internal balancing method. Through the co-ordinate transformation derived from the states that are deleted during reduction, the reduced model is finally expressed with the states that are members of the original states. Therefore, the states in the final reduced model represent the degrees of freedom of the nodes that are selected by the designer. The procedure provides a more practical implementation of model reduction for applications in which specific nodes, such as sensor and/or actuator attachment points, are to be retained in the reduced model. Thus, it ensures that the reduced model is under the same input and output condition as the original physical model. The procedure is applied to two simple examples and comparisons are made between the full and reduced order models. The method can be applied to a linear, continuous and time-invariant model of structural dynamics with nonproportional viscous damping.

Space Shuttle propulsion performance reconstruction from flight data

NASA Technical Reports Server (NTRS)

Rogers, Robert M.

1989-01-01

The aplication of extended Kalman filtering to estimating Space Shuttle Solid Rocket Booster (SRB) performance, specific impulse, from flight data in a post-flight processing computer program. The flight data used includes inertial platform acceleration, SRB head pressure, and ground based radar tracking data. The key feature in this application is the model used for the SRBs, which represents a reference quasi-static internal ballistics model normalized to the propellant burn depth. Dynamic states of mass overboard and propellant burn depth are included in the filter model to account for real-time deviations from the reference model used. Aerodynamic, plume, wind and main engine uncertainties are included.

Langley's CSI evolutionary model: Phase O

NASA Technical Reports Server (NTRS)

Belvin, W. Keith; Elliott, Kenny B.; Horta, Lucas G.; Bailey, Jim P.; Bruner, Anne M.; Sulla, Jeffrey L.; Won, John; Ugoletti, Roberto M.

1991-01-01

A testbed for the development of Controls Structures Interaction (CSI) technology to improve space science platform pointing is described. The evolutionary nature of the testbed will permit the study of global line-of-sight pointing in phases 0 and 1, whereas, multipayload pointing systems will be studied beginning with phase 2. The design, capabilities, and typical dynamic behavior of the phase 0 version of the CSI evolutionary model (CEM) is documented for investigator both internal and external to NASA. The model description includes line-of-sight pointing measurement, testbed structure, actuators, sensors, and real time computers, as well as finite element and state space models of major components.

Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data.

PubMed

Lawson, Andrew B; Choi, Jungsoon; Cai, Bo; Hossain, Monir; Kirby, Russell S; Liu, Jihong

2012-09-01

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008.

Three-Space Interaction in Doubly Sinusoidal Periodic Media

NASA Astrophysics Data System (ADS)

Tian-Lin, Dong; Ping, Chen

2006-06-01

Three-space-harmonic (3SH) interaction in doubly sinusoidal periodic (DSP) medium is investigated. Associated physical effects such as additional gap, defect state, and indirect gaps, are theoretically and numerically revealed. This simple DSP model can facilitate the understanding and utilizing of a series of effects in rather complicated periodic structures with additional defect or modulation.

Using Parameter Constraints to Choose State Structures in Cost-Effectiveness Modelling.

PubMed

Thom, Howard; Jackson, Chris; Welton, Nicky; Sharples, Linda

2017-09-01

This article addresses the choice of state structure in a cost-effectiveness multi-state model. Key model outputs, such as treatment recommendations and prioritisation of future research, may be sensitive to state structure choice. For example, it may be uncertain whether to consider similar disease severities or similar clinical events as the same state or as separate states. Standard statistical methods for comparing models require a common reference dataset but merging states in a model aggregates the data, rendering these methods invalid. We propose a method that involves re-expressing a model with merged states as a model on the larger state space in which particular transition probabilities, costs and utilities are constrained to be equal between states. This produces a model that gives identical estimates of cost effectiveness to the model with merged states, while leaving the data unchanged. The comparison of state structures can be achieved by comparing maximised likelihoods or information criteria between constrained and unconstrained models. We can thus test whether the costs and/or health consequences for a patient in two states are the same, and hence if the states can be merged. We note that different structures can be used for rates, costs and utilities, as appropriate. We illustrate our method with applications to two recent models evaluating the cost effectiveness of prescribing anti-depressant medications by depression severity and the cost effectiveness of diagnostic tests for coronary artery disease. State structures in cost-effectiveness models can be compared using standard methods to compare constrained and unconstrained models.

Innovation Zones: Creating Policy Flexibility for Personalized Learning. Issue Brief

ERIC Educational Resources Information Center

Patrick, Susan; Gentz, Susan

2016-01-01

There is a new state education policy concept termed either innovation zones or districts of innovation. State education agencies interested in shifting their role from enforcing compliance to one of supporting innovation and building capacity in districts are working to spur new innovative instructional models and create space for…

Enhanced sampling of molecular dynamics simulation of peptides and proteins by double coupling to thermal bath.

PubMed

Chen, Changjun; Huang, Yanzhao; Xiao, Yi

2013-01-01

Low sampling efficiency in conformational space is the well-known problem for conventional molecular dynamics. It greatly increases the difficulty for molecules to find the transition path to native state, and costs amount of CPU time. To accelerate the sampling, in this paper, we re-couple the critical degrees of freedom in the molecule to environment temperature, like dihedrals in generalized coordinates or nonhydrogen atoms in Cartesian coordinate. After applying to ALA dipeptide model, we find that this modified molecular dynamics greatly enhances the sampling behavior in the conformational space and provides more information about the state-to-state transition, while conventional molecular dynamics fails to do so. Moreover, from the results of 16 independent 100 ns simulations by the new method, it shows that trpzip2 has one-half chances to reach the naive state in all the trajectories, which is greatly higher than conventional molecular dynamics. Such an improvement would provide a potential way for searching the conformational space or predicting the most stable states of peptides and proteins.

The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution

NASA Astrophysics Data System (ADS)

Shobe, Charles M.; Tucker, Gregory E.; Barnhart, Katherine R.

2017-12-01

Models of landscape evolution by river erosion are often either transport-limited (sediment is always available but may or may not be transportable) or detachment-limited (sediment must be detached from the bed but is then always transportable). While several models incorporate elements of, or transition between, transport-limited and detachment-limited behavior, most require that either sediment or bedrock, but not both, are eroded at any given time. Modeling landscape evolution over large spatial and temporal scales requires a model that can (1) transition freely between transport-limited and detachment-limited behavior, (2) simultaneously treat sediment transport and bedrock erosion, and (3) run in 2-D over large grids and be coupled with other surface process models. We present SPACE (stream power with alluvium conservation and entrainment) 1.0, a new model for simultaneous evolution of an alluvium layer and a bedrock bed based on conservation of sediment mass both on the bed and in the water column. The model treats sediment transport and bedrock erosion simultaneously, embracing the reality that many rivers (even those commonly defined as bedrock rivers) flow over a partially alluviated bed. SPACE improves on previous models of bedrock-alluvial rivers by explicitly calculating sediment erosion and deposition rather than relying on a flux-divergence (Exner) approach. The SPACE model is a component of the Landlab modeling toolkit, a Python-language library used to create models of Earth surface processes. Landlab allows efficient coupling between the SPACE model and components simulating basin hydrology, hillslope evolution, weathering, lithospheric flexure, and other surface processes. Here, we first derive the governing equations of the SPACE model from existing sediment transport and bedrock erosion formulations and explore the behavior of local analytical solutions for sediment flux and alluvium thickness. We derive steady-state analytical solutions for channel slope, alluvium thickness, and sediment flux, and show that SPACE matches predicted behavior in detachment-limited, transport-limited, and mixed conditions. We provide an example of landscape evolution modeling in which SPACE is coupled with hillslope diffusion, and demonstrate that SPACE provides an effective framework for simultaneously modeling 2-D sediment transport and bedrock erosion.

Flight Guidance System Validation Using SPIN

NASA Technical Reports Server (NTRS)

Naydich, Dimitri; Nowakowski, John

1998-01-01

To verify the requirements for the mode control logic of a Flight Guidance System (FGS) we applied SPIN, a widely used software package that supports the formal verification of distributed systems. These requirements, collectively called the FGS specification, were developed at Rockwell Avionics & Communications and expressed in terms of the Consortium Requirements Engineering (CoRE) method. The properties to be verified are the invariants formulated in the FGS specification, along with the standard properties of consistency and completeness. The project had two stages. First, the FGS specification and the properties to be verified were reformulated in PROMELA, the input language of SPIN. This involved a semantics issue, as some constructs of the FGS specification do not have well-defined semantics in CoRE. Then we attempted to verify the requirements' properties using the automatic model checking facilities of SPIN. Due to the large size of the state space of the FGS specification an exhaustive state space analysis with SPIN turned out to be impossible. So we used the supertrace model checking procedure of SPIN that provides for a partial analysis of the state space. During this process, we found some subtle errors in the FGS specification.

State-Space Estimation of Soil Organic Carbon Stock

NASA Astrophysics Data System (ADS)

Ogunwole, Joshua O.; Timm, Luis C.; Obidike-Ugwu, Evelyn O.; Gabriels, Donald M.

2014-04-01

Understanding soil spatial variability and identifying soil parameters most determinant to soil organic carbon stock is pivotal to precision in ecological modelling, prediction, estimation and management of soil within a landscape. This study investigates and describes field soil variability and its structural pattern for agricultural management decisions. The main aim was to relate variation in soil organic carbon stock to soil properties and to estimate soil organic carbon stock from the soil properties. A transect sampling of 100 points at 3 m intervals was carried out. Soils were sampled and analyzed for soil organic carbon and other selected soil properties along with determination of dry aggregate and water-stable aggregate fractions. Principal component analysis, geostatistics, and state-space analysis were conducted on the analyzed soil properties. The first three principal components explained 53.2% of the total variation; Principal Component 1 was dominated by soil exchange complex and dry sieved macroaggregates clusters. Exponential semivariogram model described the structure of soil organic carbon stock with a strong dependence indicating that soil organic carbon values were correlated up to 10.8m.Neighbouring values of soil organic carbon stock, all waterstable aggregate fractions, and dithionite and pyrophosphate iron gave reliable estimate of soil organic carbon stock by state-space.

An introduction to Space Weather Integrated Modeling

NASA Astrophysics Data System (ADS)

Zhong, D.; Feng, X.

2012-12-01

The need for a software toolkit that integrates space weather models and data is one of many challenges we are facing with when applying the models to space weather forecasting. To meet this challenge, we have developed Space Weather Integrated Modeling (SWIM) that is capable of analysis and visualizations of the results from a diverse set of space weather models. SWIM has a modular design and is written in Python, by using NumPy, matplotlib, and the Visualization ToolKit (VTK). SWIM provides data management module to read a variety of spacecraft data products and a specific data format of Solar-Interplanetary Conservation Element/Solution Element MHD model (SIP-CESE MHD model) for the study of solar-terrestrial phenomena. Data analysis, visualization and graphic user interface modules are also presented in a user-friendly way to run the integrated models and visualize the 2-D and 3-D data sets interactively. With these tools we can locally or remotely analysis the model result rapidly, such as extraction of data on specific location in time-sequence data sets, plotting interplanetary magnetic field lines, multi-slicing of solar wind speed, volume rendering of solar wind density, animation of time-sequence data sets, comparing between model result and observational data. To speed-up the analysis, an in-situ visualization interface is used to support visualizing the data 'on-the-fly'. We also modified some critical time-consuming analysis and visualization methods with the aid of GPU and multi-core CPU. We have used this tool to visualize the data of SIP-CESE MHD model in real time, and integrated the Database Model of shock arrival, Shock Propagation Model, Dst forecasting model and SIP-CESE MHD model developed by SIGMA Weather Group at State Key Laboratory of Space Weather/CAS.

Abnormal sleep/wake dynamics in orexin knockout mice.

PubMed

Diniz Behn, Cecilia G; Klerman, Elizabeth B; Mochizuki, Takatoshi; Lin, Shih-Chieh; Scammell, Thomas E

2010-03-01

Narcolepsy with cataplexy is caused by a loss of orexin (hypocretin) signaling, but the physiologic mechanisms that result in poor maintenance of wakefulness and fragmented sleep remain unknown. Conventional scoring of sleep cannot reveal much about the process of transitioning between states or the variations within states. We developed an EEG spectral analysis technique to determine whether the state instability in a mouse model of narcolepsy reflects abnormal sleep or wake states, faster movements between states, or abnormal transitions between states. We analyzed sleep recordings in orexin knockout (OXKO) mice and wild type (WT) littermates using a state space analysis technique. This non-categorical approach allows quantitative and unbiased examination of sleep/wake states and state transitions. OXKO mice spent less time in deep, delta-rich NREM sleep and in active, theta-rich wake and instead spent more time near the transition zones between states. In addition, while in the midst of what should be stable wake, OXKO mice initiated rapid changes into NREM sleep with high velocities normally seen only in transition regions. Consequently, state transitions were much more frequent and rapid even though the EEG progressions during state transitions were normal. State space analysis enables visualization of the boundaries between sleep and wake and shows that narcoleptic mice have less distinct and more labile states of sleep and wakefulness. These observations provide new perspectives on the abnormal state dynamics resulting from disrupted orexin signaling and highlight the usefulness of state space analysis in understanding narcolepsy and other sleep disorders.

Composite load spectra for select space propulsion structural components

NASA Technical Reports Server (NTRS)

Newell, J. F.; Ho, H. W.; Kurth, R. E.

1991-01-01

The work performed to develop composite load spectra (CLS) for the Space Shuttle Main Engine (SSME) using probabilistic methods. The three methods were implemented to be the engine system influence model. RASCAL was chosen to be the principal method as most component load models were implemented with the method. Validation of RASCAL was performed. High accuracy comparable to the Monte Carlo method can be obtained if a large enough bin size is used. Generic probabilistic models were developed and implemented for load calculations using the probabilistic methods discussed above. Each engine mission, either a real fighter or a test, has three mission phases: the engine start transient phase, the steady state phase, and the engine cut off transient phase. Power level and engine operating inlet conditions change during a mission. The load calculation module provides the steady-state and quasi-steady state calculation procedures with duty-cycle-data option. The quasi-steady state procedure is for engine transient phase calculations. In addition, a few generic probabilistic load models were also developed for specific conditions. These include the fixed transient spike model, the poison arrival transient spike model, and the rare event model. These generic probabilistic load models provide sufficient latitude for simulating loads with specific conditions. For SSME components, turbine blades, transfer ducts, LOX post, and the high pressure oxidizer turbopump (HPOTP) discharge duct were selected for application of the CLS program. They include static pressure loads and dynamic pressure loads for all four components, centrifugal force for the turbine blade, temperatures of thermal loads for all four components, and structural vibration loads for the ducts and LOX posts.

Fuzzy Edge Connectivity of Graphical Fuzzy State Space Model in Multi-connected System

NASA Astrophysics Data System (ADS)

Harish, Noor Ainy; Ismail, Razidah; Ahmad, Tahir

2010-11-01

Structured networks of interacting components illustrate complex structure in a direct or intuitive way. Graph theory provides a mathematical modeling for studying interconnection among elements in natural and man-made systems. On the other hand, directed graph is useful to define and interpret the interconnection structure underlying the dynamics of the interacting subsystem. Fuzzy theory provides important tools in dealing various aspects of complexity, imprecision and fuzziness of the network structure of a multi-connected system. Initial development for systems of Fuzzy State Space Model (FSSM) and a fuzzy algorithm approach were introduced with the purpose of solving the inverse problems in multivariable system. In this paper, fuzzy algorithm is adapted in order to determine the fuzzy edge connectivity between subsystems, in particular interconnected system of Graphical Representation of FSSM. This new approach will simplify the schematic diagram of interconnection of subsystems in a multi-connected system.

Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment.

PubMed

da Fonseca Neto, João Viana; Abreu, Ivanildo Silva; da Silva, Fábio Nogueira

2010-04-01

Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance. The genetic search convergence evaluation is performed in terms of the fitness function statistics and the RNN convergence, which is evaluated by landscapes of the energy and norm, as a function of the parameter deviations. The control problem solution is evaluated in the time and frequency domains by the impulse response, singular values, and modal analysis.

Self-organized perturbations enhance class IV behavior and 1/f power spectrum in elementary cellular automata.

PubMed

Nakajima, Kohei; Haruna, Taichi

2011-09-01

In this paper, we propose a new class of cellular automata based on the modification of its state space. It is introduced to model a computation which is exposed to an environment. We formalized the computation as extension and projection processes of its state space and resulting misidentifications of the state. This is motivated to embed the role of an environment into the system itself, which naturally induces self-organized internal perturbations rather than the usual external perturbations. Implementing this structure into the elementary cellular automata, we characterized its effect by means of input entropy and power spectral analysis. As a result, the cellular automata with this structure showed robust class IV behavior and a 1/f power spectrum in a wide range of rule space comparative to the notion of the edge of chaos. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

The NASA Marshall Space Flight Center Earth Global Reference Atmospheric Model-2010 Version

NASA Technical Reports Server (NTRS)

Leslie, F. W.; Justus, C. G.

2011-01-01

Reference or standard atmospheric models have long been used for design and mission planning of various aerospace systems. The NASA Marshall Space Flight Center Global Reference Atmospheric Model was developed in response to the need for a design reference atmosphere that provides complete global geographical variability and complete altitude coverage (surface to orbital altitudes), as well as complete seasonal and monthly variability of the thermodynamic variables and wind components. In addition to providing the geographical, height, and monthly variation of the mean atmospheric state, it includes the ability to simulate spatial and temporal perturbations.

Flatness-based control and Kalman filtering for a continuous-time macroeconomic model

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Ghosh, T.; Busawon, K.; Binns, R.

2017-11-01

The article proposes flatness-based control for a nonlinear macro-economic model of the UK economy. The differential flatness properties of the model are proven. This enables to introduce a transformation (diffeomorphism) of the system's state variables and to express the state-space description of the model in the linear canonical (Brunowsky) form in which both the feedback control and the state estimation problem can be solved. For the linearized equivalent model of the macroeconomic system, stabilizing feedback control can be achieved using pole placement methods. Moreover, to implement stabilizing feedback control of the system by measuring only a subset of its state vector elements the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied on the linearized equivalent model of the financial system and of an inverse transformation that is based again on differential flatness theory. The asymptotic stability properties of the control scheme are confirmed.

Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents.

PubMed

Ohayon, Elan L; Kalitzin, Stiliyan; Suffczynski, Piotr; Jin, Frank Y; Tsang, Paul W; Borrett, Donald S; Burnham, W McIntyre; Kwan, Hon C

2004-01-01

The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 *10(26) possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. [1] First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. [2] Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. [3] The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach [1]. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space.

Analysis of DIRAC's behavior using model checking with process algebra

NASA Astrophysics Data System (ADS)

Remenska, Daniela; Templon, Jeff; Willemse, Tim; Bal, Henri; Verstoep, Kees; Fokkink, Wan; Charpentier, Philippe; Graciani Diaz, Ricardo; Lanciotti, Elisa; Roiser, Stefan; Ciba, Krzysztof

2012-12-01

DIRAC is the grid solution developed to support LHCb production activities as well as user data analysis. It consists of distributed services and agents delivering the workload to the grid resources. Services maintain database back-ends to store dynamic state information of entities such as jobs, queues, staging requests, etc. Agents use polling to check and possibly react to changes in the system state. Each agent's logic is relatively simple; the main complexity lies in their cooperation. Agents run concurrently, and collaborate using the databases as shared memory. The databases can be accessed directly by the agents if running locally or through a DIRAC service interface if necessary. This shared-memory model causes entities to occasionally get into inconsistent states. Tracing and fixing such problems becomes formidable due to the inherent parallelism present. We propose more rigorous methods to cope with this. Model checking is one such technique for analysis of an abstract model of a system. Unlike conventional testing, it allows full control over the parallel processes execution, and supports exhaustive state-space exploration. We used the mCRL2 language and toolset to model the behavior of two related DIRAC subsystems: the workload and storage management system. Based on process algebra, mCRL2 allows defining custom data types as well as functions over these. This makes it suitable for modeling the data manipulations made by DIRAC's agents. By visualizing the state space and replaying scenarios with the toolkit's simulator, we have detected race-conditions and deadlocks in these systems, which, in several cases, were confirmed to occur in the reality. Several properties of interest were formulated and verified with the tool. Our future direction is automating the translation from DIRAC to a formal model.

40. Photocopy of building model photograph, ca., 1974, photographer unknown. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

40. Photocopy of building model photograph, ca., 1974, photographer unknown. Original photograph property of United States Air Force, 21" Space Command. CAPE COD AIR STATION PAVE PAWS FACILITY MODEL - ELEVATION SHOWING FLOOR AND EQUIPMENT LAYOUT. - Cape Cod Air Station, Technical Facility-Scanner Building & Power Plant, Massachusetts Military Reservation, Sandwich, Barnstable County, MA

39. Photocopy of building model photograph, ca. 1974, photographer unknown. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

39. Photocopy of building model photograph, ca. 1974, photographer unknown. Original photograph property of United States Air Force, 21" Space Command. CAPE COD AIR STATION PAVE PAWS FACILITY MODEL - SHOWING "A" AND "B" FACES. - Cape Cod Air Station, Technical Facility-Scanner Building & Power Plant, Massachusetts Military Reservation, Sandwich, Barnstable County, MA

A survey of numerical models for wind prediction

NASA Technical Reports Server (NTRS)

Schonfeld, D.

1980-01-01

A literature review is presented of the work done in the numerical modeling of wind flows. Pertinent computational techniques are described, as well as the necessary assumptions used to simplify the governing equations. A steady state model is outlined, based on the data obtained at the Deep Space Communications complex at Goldstone, California.

Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art

ERIC Educational Resources Information Center

Rupp, Andre A.; Templin, Jonathan L.

2008-01-01

"Diagnostic classification models" (DCM) are frequently promoted by psychometricians as important modelling alternatives for analyzing response data in situations where multivariate classifications of respondents are made on the basis of multiple postulated latent skills. In this review paper, a definitional boundary of the space of DCM…

3D glasma initial state for relativistic heavy ion collisions

DOE PAGES

Schenke, Björn; Schlichting, Sören

2016-10-13

We extend the impact-parameter-dependent Glasma model to three dimensions using explicit small-x evolution of the two incoming nuclear gluon distributions. We compute rapidity distributions of produced gluons and the early-time energy momentum tensor as a function of space-time rapidity and transverse coordinates. Finally, we study rapidity correlations and fluctuations of the initial geometry and multiplicity distributions and make comparisons to existing models for the three-dimensional initial state.

Particle Tracing Modeling with SHIELDS

NASA Astrophysics Data System (ADS)

Woodroffe, J. R.; Brito, T. V.; Jordanova, V. K.

2017-12-01

The near-Earth inner magnetosphere, where most of the nation's civilian and military space assets operate, is an extremely hazardous region of the space environment which poses major risks to our space infrastructure. Failure of satellite subsystems or even total failure of a spacecraft can arise for a variety of reasons, some of which are related to the space environment: space weather events like single-event-upsets and deep dielectric charging caused by high energy particles, or surface charging caused by low to medium energy particles; other space hazards are collisions with natural or man-made space debris, or intentional hostile acts. A recently funded project through the Los Alamos National Laboratory (LANL) Directed Research and Development (LDRD) program aims at developing a new capability to understand, model, and predict Space Hazards Induced near Earth by Large Dynamic Storms, the SHIELDS framework. The project goals are to understand the dynamics of the surface charging environment (SCE), the hot (keV) electrons on both macro- and microscale. These challenging problems are addressed using a team of world-class experts and state-of-the-art physics-based models and computational facilities. We present first results of a coupled BATS-R-US/RAM-SCB/Particle Tracing Model to evaluate particle fluxes in the inner magnetosphere. We demonstrate that this setup is capable of capturing the earthward particle acceleration process resulting from dipolarization events in the tail region of the magnetosphere.

Search for supersymmetry in hadronic final states using M T2 in pp collisions at $$ \\sqrt{s}=7 $$ TeV

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.

A search for supersymmetry or other new physics resulting in similar final states is presented using a data sample of 4.73 inverse femtobarns of pp collisions collected atmore » $$ \\sqrt{s}=7 $$ TeV with the CMS detector at the LHC. Fully hadronic final states are selected based on the variable MT2, an extension of the transverse mass in events with two invisible particles. Two complementary studies are performed. The first targets the region of parameter space with medium to high squark and gluino masses, in which the signal can be separated from the standard model backgrounds by a tight requirement on MT2. The second is optimized to be sensitive to events with a light gluino and heavy squarks. In this case, the MT2 requirement is relaxed, but a higher jet multiplicity and at least one b-tagged jet are required. No significant excess of events over the standard model expectations is observed. Exclusion limits are derived for the parameter space of the constrained minimal supersymmetric extension of the standard model, as well as on a variety of simplified model spectra.« less

Addressing Dynamic Issues of Program Model Checking

NASA Technical Reports Server (NTRS)

Lerda, Flavio; Visser, Willem

2001-01-01

Model checking real programs has recently become an active research area. Programs however exhibit two characteristics that make model checking difficult: the complexity of their state and the dynamic nature of many programs. Here we address both these issues within the context of the Java PathFinder (JPF) model checker. Firstly, we will show how the state of a Java program can be encoded efficiently and how this encoding can be exploited to improve model checking. Next we show how to use symmetry reductions to alleviate some of the problems introduced by the dynamic nature of Java programs. Lastly, we show how distributed model checking of a dynamic program can be achieved, and furthermore, how dynamic partitions of the state space can improve model checking. We support all our findings with results from applying these techniques within the JPF model checker.

Nucleon Axial and Electromagnetic Form Factors

NASA Astrophysics Data System (ADS)

Jang, Yong-Chull; Bhattacharya, Tanmoy; Gupta, Rajan; Lin, Huey-Wen; Yoon, Boram

2018-03-01

We present results for the isovector axial, induced pseudoscalar, electric, and magnetic form factors of the nucleon. The calculations were done using 2 + 1 + 1-flavor HISQ ensembles generated by the MILC collaboration with lattice spacings a ≈ 0.12, 0.09, 0.06 fm and pion masses Mπ ≈ 310, 220, 130 MeV. Excited-states contamination is controlled by using four-state fits to two-point correlators and by comparing two-versus three-states in three-point correlators. The Q2 behavior is analyzed using the model independent z-expansion and the dipole ansatz. Final results for the charge radii and magnetic moment are obtained using a simultaneous fit in Mπ, lattice spacing a and finite volume.

Three dimensional modelling of earthquake rupture cycles on frictional faults

NASA Astrophysics Data System (ADS)

Simpson, Guy; May, Dave

2017-04-01

We are developing an efficient MPI-parallel numerical method to simulate earthquake sequences on preexisting faults embedding within a three dimensional viscoelastic half-space. We solve the velocity form of the elasto(visco)dynamic equations using a continuous Galerkin Finite Element Method on an unstructured pentahedral mesh, which thus permits local spatial refinement in the vicinity of the fault. Friction sliding is coupled to the viscoelastic solid via rate- and state-dependent friction laws using the split-node technique. Our coupled formulation employs a picard-type non-linear solver with a fully implicit, first order accurate time integrator that utilises an adaptive time step that efficiently evolves the system through multiple seismic cycles. The implementation leverages advanced parallel solvers, preconditioners and linear algebra from the Portable Extensible Toolkit for Scientific Computing (PETSc) library. The model can treat heterogeneous frictional properties and stress states on the fault and surrounding solid as well as non-planar fault geometries. Preliminary tests show that the model successfully reproduces dynamic rupture on a vertical strike-slip fault in a half-space governed by rate-state friction with the ageing law.

Information-theoretic measures of hydrogen-like ions in weakly coupled Debye plasmas

NASA Astrophysics Data System (ADS)

Zan, Li Rong; Jiao, Li Guang; Ma, Jia; Ho, Yew Kam

2017-12-01

Recent development of information theory provides researchers an alternative and useful tool to quantitatively investigate the variation of the electronic structure when atoms interact with the external environment. In this work, we make systematic studies on the information-theoretic measures for hydrogen-like ions immersed in weakly coupled plasmas modeled by Debye-Hückel potential. Shannon entropy, Fisher information, and Fisher-Shannon complexity in both position and momentum spaces are quantified in high accuracy for the hydrogen atom in a large number of stationary states. The plasma screening effect on embedded atoms can significantly affect the electronic density distributions, in both conjugate spaces, and it is quantified by the variation of information quantities. It is shown that the composite quantities (the Shannon entropy sum and the Fisher information product in combined spaces and Fisher-Shannon complexity in individual space) give a more comprehensive description of the atomic structure information than single ones. The nodes of wave functions play a significant role in the changes of composite information quantities caused by plasmas. With the continuously increasing screening strength, all composite quantities in circular states increase monotonously, while in higher-lying excited states where nodal structures exist, they first decrease to a minimum and then increase rapidly before the bound state approaches the continuum limit. The minimum represents the most reduction of uncertainty properties of the atom in plasmas. The lower bounds for the uncertainty product of the system based on composite information quantities are discussed. Our research presents a comprehensive survey in the investigation of information-theoretic measures for simple atoms embedded in Debye model plasmas.

Dynamics from a mathematical model of a two-state gas laser

NASA Astrophysics Data System (ADS)

Kleanthous, Antigoni; Hua, Tianshu; Manai, Alexandre; Yawar, Kamran; Van Gorder, Robert A.

2018-05-01

Motivated by recent work in the area, we consider the behavior of solutions to a nonlinear PDE model of a two-state gas laser. We first review the derivation of the two-state gas laser model, before deriving a non-dimensional model given in terms of coupled nonlinear partial differential equations. We then classify the steady states of this system, in order to determine the possible long-time asymptotic solutions to this model, as well as corresponding stability results, showing that the only uniform steady state (the zero motion state) is unstable, while a linear profile in space is stable. We then provide numerical simulations for the full unsteady model. We show for a wide variety of initial conditions that the solutions tend toward the stable linear steady state profiles. We also consider traveling wave solutions, and determine the unique wave speed (in terms of the other model parameters) which allows wave-like solutions to exist. Despite some similarities between the model and the inviscid Burger's equation, the solutions we obtain are much more regular than the solutions to the inviscid Burger's equation, with no evidence of shock formation or loss of regularity.

Dynamics of aquatic ecosystems and models under toxicant stress: State space analysis, covariance structure, and ecological risk

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, A.R.; Bartell, S.M.

1988-06-01

The state of an ecosystem at any time t may be characterized by a multidimensional state vector x(t). Changes in state are represented by the trajectory traced out by x(t) over time. The effects of toxicant stress are summarized by the displacement of a perturbed state vector, x/sub p/(t), relative to an appropriate control, x/sub c/(t). Within a multivariate statistical framework, the response of an ecosystem to perturbation is conveniently quantified by the distance separating x/sub p/(t) from x/sub c/(t) as measured by a Mahalanobis metric. Use of the Mahalanobis metric requires that the covariance matrix associated with the controlmore » state vector be estimated. State space displacement analysis was applied to data on the response of aquatic microcosms and outdoor ponds to alkylphenols. Dose-response relationships were derived using calculated state space separations as integrated measures of the ecological effects of toxicant exposure. Inspection of the data also revealed that the covariance structure varied both with time and with toxicant exposure, suggesting that analysis of such changes might be a useful tool for probing control mechanisms underlying ecosystem dynamics. 90 refs., 53 figs., 9 tabs.« less

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

OBJECTIVE REDUCTION OF THE SPACE-TIME DOMAIN DIMENSIONALITY FOR EVALUATING MODEL PERFORMANCE

EPA Science Inventory

In the United States, photochemical air quality models are the principal tools used by governmental agencies to develop emission reduction strategies aimed at achieving National Ambient Air Quality Standards (NAAQS). Before they can be applied with confidence in a regulatory sett...

Mayer control problem with probabilistic uncertainty on initial positions

NASA Astrophysics Data System (ADS)

Marigonda, Antonio; Quincampoix, Marc

2018-03-01

In this paper we introduce and study an optimal control problem in the Mayer's form in the space of probability measures on Rn endowed with the Wasserstein distance. Our aim is to study optimality conditions when the knowledge of the initial state and velocity is subject to some uncertainty, which are modeled by a probability measure on Rd and by a vector-valued measure on Rd, respectively. We provide a characterization of the value function of such a problem as unique solution of an Hamilton-Jacobi-Bellman equation in the space of measures in a suitable viscosity sense. Some applications to a pursuit-evasion game with uncertainty in the state space is also discussed, proving the existence of a value for the game.

A joint modelling approach for multistate processes subject to resolution and under intermittent observations.

PubMed

Yiu, Sean; Tom, Brian

2017-02-10

Multistate processes provide a convenient framework when interest lies in characterising the transition intensities between a set of defined states. If, however, there is an unobserved event of interest (not known if and when the event occurs), which when it occurs stops future transitions in the multistate process from occurring, then drawing inference from the joint multistate and event process can be problematic. In health studies, a particular example of this could be resolution, where a resolved patient can no longer experience any further symptoms, and this is explored here for illustration. A multistate model that includes the state space of the original multistate process but partitions the state representing absent symptoms into a latent absorbing resolved state and a temporary transient state of absent symptoms is proposed. The expanded state space explicitly distinguishes between resolved and temporary spells of absent symptoms through disjoint states and allows the uncertainty of not knowing if resolution has occurred to be easily captured when constructing the likelihood; observations of absent symptoms can be considered to be temporary or having resulted from resolution. The proposed methodology is illustrated on a psoriatic arthritis data set where the outcome of interest is a set of intermittently observed disability scores. Estimated probabilities of resolving are also obtained from the model. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Deconvolution of mixing time series on a graph

PubMed Central

Blocker, Alexander W.; Airoldi, Edoardo M.

2013-01-01

In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt = Axt, where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series. PMID:25309135

Raetrad model extensions for radon entry into multi-level buildings with basements or crawl spaces.

PubMed

Nielson, K K; Rogers, V C; Rogers, V; Holt, R B

1997-10-01

The RAETRAD model was generalized to characterize radon generation and movement from soils and building materials into multi-level buildings with basements or crawl spaces. With the generalization, the model retains its original simplicity and ease of use. The model calculates radon entry rates that are consistent with measurements published for basement test structures at Colorado State University, confirming approximately equal contributions from diffusion and pressure-driven air flow at indoor-outdoor air pressure differences of deltaP(i-o) = -3.5 Pa. About one-fourth of the diffusive radon entry comes from concrete slabs and three-fourths comes from the surrounding soils. Calculated radon entry rates with and without a barrier over floor-wall shrinkage cracks generally agree with Colorado State University measurements when a sustained pressure of deltaP(i-o) = -2 Pa is used to represent calm wind (<1 m s(-1)) conditions. Calculated radon distributions in a 2-level house also are consistent with published measurements and equations.

Joint Model and Parameter Dimension Reduction for Bayesian Inversion Applied to an Ice Sheet Flow Problem

NASA Astrophysics Data System (ADS)

Ghattas, O.; Petra, N.; Cui, T.; Marzouk, Y.; Benjamin, P.; Willcox, K.

2016-12-01

Model-based projections of the dynamics of the polar ice sheets play a central role in anticipating future sea level rise. However, a number of mathematical and computational challenges place significant barriers on improving predictability of these models. One such challenge is caused by the unknown model parameters (e.g., in the basal boundary conditions) that must be inferred from heterogeneous observational data, leading to an ill-posed inverse problem and the need to quantify uncertainties in its solution. In this talk we discuss the problem of estimating the uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem--i.e., the posterior probability density--is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal sliding coefficient field). To overcome these twin computational challenges, it is essential to exploit problem structure (e.g., sensitivity of the data to parameters, the smoothing property of the forward model, and correlations in the prior). To this end, we present a data-informed approach that identifies low-dimensional structure in both parameter space and the forward model state space. This approach exploits the fact that the observations inform only a low-dimensional parameter space and allows us to construct a parameter-reduced posterior. Sampling this parameter-reduced posterior still requires multiple evaluations of the forward problem, therefore we also aim to identify a low dimensional state space to reduce the computational cost. To this end, we apply a proper orthogonal decomposition (POD) approach to approximate the state using a low-dimensional manifold constructed using ``snapshots'' from the parameter reduced posterior, and the discrete empirical interpolation method (DEIM) to approximate the nonlinearity in the forward problem. We show that using only a limited number of forward solves, the resulting subspaces lead to an efficient method to explore the high-dimensional posterior.

Sensitivity of Earth Wheat Markets to Space Weather: Comparative Analysis based on data from Medieval European Markets

NASA Astrophysics Data System (ADS)

Pustil'Nik, Lev

We consider a problem of the possible influence of unfavorable states of the space weather on agriculture markets through the chain of connections: "space weather"-"earth weather"- "agriculture crops"-"price reaction". We show that new manifestations of "space weather"- "earth weather" relations discovered in the recent time allow revising a wide range of the expected solar-terrestrial connections. In the previous works we proposed possible mechanisms of wheat market reaction on the specific unfavorable states of space weather in the form of price bursts and price asymmetry. We point out that implementation of considered "price reaction scenarios" is possible only for the case of simultaneous realization of several necessary conditions: high sensitivity of local earth weather in the selected region to space weather; the state of "high risk agriculture" in the selected agriculture zone; high sensitivity of agricultural market to a possible deficit of yield. Results of our previous works (I, II), including application of this approach to the Medieval England wheat market (1250-1700) and to the modern USA durum market (1910-1992), showed that connection between wheat price bursts and space weather state in these cases was absolutely real. The aim of the present work is to answer the question why wheat markets in one selected region may be sensitive to a space weather factor, while in other regions wheat markets demonstrate absolutely indifferent reaction on the space weather. For this aim, we consider dependence of sensitivity of wheat markets to space weather as a function of their location in different climatic zones of Europe. We analyze a database of 95 European wheat markets from 14 countries for the 600-year period (1260-1912). We show that the observed sensitivity of wheat markets to space weather effects is controlled, first of all, by a type of predominant climate in different zones of agricultural production. Wheat markets in the Northern and, partly, in Central Europe (England, Holland, Belgium) show high sensitivity to space weather in minimum states of solar activity, when excess of the high energy cosmic ray stimulate additional cloudiness and precipitation. In the same time, wheat markets in the Southern Europe (Spain, Italy) show high sensitivity to space weather state in the opposite (maximum) phase of solar activity when a deficit of cosmic ray entering into the earth atmosphere leads to decrease of cloudiness and to increase of probability of drought weather periods. We demonstrate that the large part of markets in the Central Europe zone show absence of any effects of sensitivity to space weather state and show that this North-South asymmetry is in good accordance with the suggested model of expected wheat market reaction. We discuss possible increasing of sensitivity of wheat markets to space weather effects under conditions of fast and drastic change of modern climate with a shift of numerous agriculture regions to the state of "high risk agriculture zone".

Predictability of the geospace variations and measuring the capability to model the state of the system

NASA Astrophysics Data System (ADS)

Pulkkinen, A.

2012-12-01

Empirical modeling has been the workhorse of the past decades in predicting the state of the geospace. For example, numerous empirical studies have shown that global geoeffectiveness indices such as Kp and Dst are generally well predictable from the solar wind input. These successes have been facilitated partly by the strongly externally driven nature of the system. Although characterizing the general state of the system is valuable and empirical modeling will continue playing an important role, refined physics-based quantification of the state of the system has been the obvious next step in moving toward more mature science. Importantly, more refined and localized products are needed also for space weather purposes. Predictions of local physical quantities are necessary to make physics-based links to the impacts on specific systems. As we have introduced more localized predictions of the geospace state one central question is how predictable these local quantities are? This complex question can be addressed by rigorously measuring the model performance against the observed data. Space sciences community has made great advanced on this topic over the past few years and there are ongoing efforts in SHINE, CEDAR and GEM to carry out community-wide evaluations of the state-of-the-art solar and heliospheric, ionosphere-thermosphere and geospace models, respectively. These efforts will help establish benchmarks and thus provide means to measure the progress in the field analogous to monitoring of the improvement in lower atmospheric weather predictions carried out rigorously since 1980s. In this paper we will discuss some of the latest advancements in predicting the local geospace parameters and give an overview of some of the community efforts to rigorously measure the model performances. We will also briefly discuss some of the future opportunities for advancing the geospace modeling capability. These will include further development in data assimilation and ensemble modeling (e.g. taking into account uncertainty in the inflow boundary conditions).

Algorithms for Performance, Dependability, and Performability Evaluation using Stochastic Activity Networks

NASA Technical Reports Server (NTRS)

Deavours, Daniel D.; Qureshi, M. Akber; Sanders, William H.

1997-01-01

Modeling tools and technologies are important for aerospace development. At the University of Illinois, we have worked on advancing the state of the art in modeling by Markov reward models in two important areas: reducing the memory necessary to numerically solve systems represented as stochastic activity networks and other stochastic Petri net extensions while still obtaining solutions in a reasonable amount of time, and finding numerically stable and memory-efficient methods to solve for the reward accumulated during a finite mission time. A long standing problem when modeling with high level formalisms such as stochastic activity networks is the so-called state space explosion, where the number of states increases exponentially with size of the high level model. Thus, the corresponding Markov model becomes prohibitively large and solution is constrained by the the size of primary memory. To reduce the memory necessary to numerically solve complex systems, we propose new methods that can tolerate such large state spaces that do not require any special structure in the model (as many other techniques do). First, we develop methods that generate row and columns of the state transition-rate-matrix on-the-fly, eliminating the need to explicitly store the matrix at all. Next, we introduce a new iterative solution method, called modified adaptive Gauss-Seidel, that exhibits locality in its use of data from the state transition-rate-matrix, permitting us to cache portions of the matrix and hence reduce the solution time. Finally, we develop a new memory and computationally efficient technique for Gauss-Seidel based solvers that avoids the need for generating rows of A in order to solve Ax = b. This is a significant performance improvement for on-the-fly methods as well as other recent solution techniques based on Kronecker operators. Taken together, these new results show that one can solve very large models without any special structure.

Dynamic State Estimation of Terrestrial and Solar Plasmas

NASA Astrophysics Data System (ADS)

Kamalabadi, Farzad

A pervasive problem in virtually all branches of space science is the estimation of multi-dimensional state parameters of a dynamical system from a collection of indirect, often incomplete, and imprecise measurements. Subsequent scientific inference is predicated on rigorous analysis, interpretation, and understanding of physical observations and on the reliability of the associated quantitative statistical bounds and performance characteristics of the algorithms used. In this work, we focus on these dynamic state estimation problems and illustrate their importance in the context of two timely activities in space remote sensing. First, we discuss the estimation of multi-dimensional ionospheric state parameters from UV spectral imaging measurements anticipated to be acquired the recently selected NASA Heliophysics mission, Ionospheric Connection Explorer (ICON). Next, we illustrate that similar state-space formulations provide the means for the estimation of 3D, time-dependent densities and temperatures in the solar corona from a series of white-light and EUV measurements. We demonstrate that, while a general framework for the stochastic formulation of the state estimation problem is suited for systematic inference of the parameters of a hidden Markov process, several challenges must be addressed in the assimilation of an increasing volume and diversity of space observations. These challenges are: (1) the computational tractability when faced with voluminous and multimodal data, (2) the inherent limitations of the underlying models which assume, often incorrectly, linear dynamics and Gaussian noise, and (3) the unavailability or inaccuracy of transition probabilities and noise statistics. We argue that pursuing answers to these questions necessitates cross-disciplinary research that enables progress toward systematically reconciling observational and theoretical understanding of the space environment.

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE PAGES

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

2018-03-01

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

State-of-charge estimation in lithium-ion batteries: A particle filter approach

NASA Astrophysics Data System (ADS)

Tulsyan, Aditya; Tsai, Yiting; Gopaluni, R. Bhushan; Braatz, Richard D.

2016-11-01

The dynamics of lithium-ion batteries are complex and are often approximated by models consisting of partial differential equations (PDEs) relating the internal ionic concentrations and potentials. The Pseudo two-dimensional model (P2D) is one model that performs sufficiently accurately under various operating conditions and battery chemistries. Despite its widespread use for prediction, this model is too complex for standard estimation and control applications. This article presents an original algorithm for state-of-charge estimation using the P2D model. Partial differential equations are discretized using implicit stable algorithms and reformulated into a nonlinear state-space model. This discrete, high-dimensional model (consisting of tens to hundreds of states) contains implicit, nonlinear algebraic equations. The uncertainty in the model is characterized by additive Gaussian noise. By exploiting the special structure of the pseudo two-dimensional model, a novel particle filter algorithm that sweeps in time and spatial coordinates independently is developed. This algorithm circumvents the degeneracy problems associated with high-dimensional state estimation and avoids the repetitive solution of implicit equations by defining a 'tether' particle. The approach is illustrated through extensive simulations.

A State Space Model for Spatial Updating of Remembered Visual Targets during Eye Movements

PubMed Central

Mohsenzadeh, Yalda; Dash, Suryadeep; Crawford, J. Douglas

2016-01-01

In the oculomotor system, spatial updating is the ability to aim a saccade toward a remembered visual target position despite intervening eye movements. Although this has been the subject of extensive experimental investigation, there is still no unifying theoretical framework to explain the neural mechanism for this phenomenon, and how it influences visual signals in the brain. Here, we propose a unified state-space model (SSM) to account for the dynamics of spatial updating during two types of eye movement; saccades and smooth pursuit. Our proposed model is a non-linear SSM and implemented through a recurrent radial-basis-function neural network in a dual Extended Kalman filter (EKF) structure. The model parameters and internal states (remembered target position) are estimated sequentially using the EKF method. The proposed model replicates two fundamental experimental observations: continuous gaze-centered updating of visual memory-related activity during smooth pursuit, and predictive remapping of visual memory activity before and during saccades. Moreover, our model makes the new prediction that, when uncertainty of input signals is incorporated in the model, neural population activity and receptive fields expand just before and during saccades. These results suggest that visual remapping and motor updating are part of a common visuomotor mechanism, and that subjective perceptual constancy arises in part from training the visual system on motor tasks. PMID:27242452

Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

DOE Office of Scientific and Technical Information (OSTI.GOV)

Melin, Alexander M.; Olama, Mohammed M.; Dong, Jin

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed tomore » estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.« less

The National Space Weather Strategy: Policy on Observations

NASA Astrophysics Data System (ADS)

Murtagh, W. J.

2016-12-01

Ensuring that the United States is prepared to respond to and recover from severe space weather storms is a priority to the President and to this Administration. We cannot ignore the potential impact space weather may have on key infrastructures and technologies including aviation and satellite operations, the electric power grid, and GPS applications. These technologies form the very backbone of the critical technology infrastructure we rely on for so much of what we do today. In October 2015, OSTP Director John Holdren announced the release of the National Space Weather Strategy and the National Space Weather Action Plan. The Strategy identifies goals and establishes the principles that will guide efforts to develop national space-weather preparedness in both the near and long term, while the Action Plan identifies specific activities, outcomes, and timelines that the Federal government must pursue to be prepared for and resilient to future space-weather events. The Strategy recognizes that observations are the backbone of forecast and warning capabilities. The Strategy also recognized that to achieve a robust operational program for space-weather observations, the United States must: (1) establish and sustain a foundational set of observations; (2) when feasible and cost effective, use data from multiple sources, including international, Federal, State, and local governments, as well as from the academic and industry sectors; (3) ensure the continuity of critical data sources; (4) continue to support sensors for solar and space physics research; (5) ensure data-assimilation techniques are in place; and (6) maintain archives for ground- and space-based data, which are essential for model development and benchmarking. In this talk we explore elements in the Space Weather Action Plan that will ensure our Nation has the information we need to enhance resilience to the risk of space weather.

Traveling waves in a spatially-distributed Wilson-Cowan model of cortex: From fronts to pulses

NASA Astrophysics Data System (ADS)

Harris, Jeremy D.; Ermentrout, Bard

2018-04-01

Wave propagation in excitable media has been studied in various biological, chemical, and physical systems. Waves are among the most common evoked and spontaneous organized activity seen in cortical networks. In this paper, we study traveling fronts and pulses in a spatially-extended version of the Wilson-Cowan equations, a neural firing rate model of sensory cortex having two population types: Excitatory and inhibitory. We are primarily interested in the case when the local or space-clamped dynamics has three fixed points: (1) a stable down state; (2) a saddle point with stable manifold that acts as a threshold for firing; (3) an up state having stability that depends on the time scale of the inhibition. In the case when the up state is stable, we look for wave fronts, which transition the media from a down to up state, and when the up state is unstable, we are interested in pulses, a transient increase in firing that returns to the down state. We explore the behavior of these waves as the time and space scales of the inhibitory population vary. Some interesting findings include bistability between a traveling front and pulse, fronts that join the down state to an oscillation or spatiotemporal pattern, and pulses which go through an oscillatory instability.

Improved Orbit Determination and Forecasts with an Assimilative Tool for Atmospheric Density and Satellite Drag Specification

NASA Astrophysics Data System (ADS)

Crowley, G.; Pilinski, M.; Sutton, E. K.; Codrescu, M.; Fuller-Rowell, T. J.; Matsuo, T.; Fedrizzi, M.; Solomon, S. C.; Qian, L.; Thayer, J. P.

2016-12-01

Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. We describe ongoing work to build a comprehensive nowcast and forecast system for specifying the neutral atmospheric state related to orbital drag conditions. The system outputs include neutral density, winds, temperature, composition, and the satellite drag derived from these parameters. This modeling tool is based on several state-of-the-art coupled models of the thermosphere-ionosphere as well as several empirical models running in real-time and uses assimilative techniques to produce a thermospheric nowcast. This software will also produce 72 hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition and using near real-time and predicted space weather data and indices as the inputs. Features of this technique include: • Satellite drag specifications with errors lower than current models • Altitude coverage up to 1000km • Background state representation using both first principles and empirical models • Assimilation of satellite drag and other datatypes • Real time capability • Ability to produce 72-hour forecasts of the atmospheric state In this paper, we will summarize the model design and assimilative architecture, and present preliminary validation results. Validation results will be presented in the context of satellite orbit errors and compared with several leading atmospheric models including the High Accuracy Satellite Drag Model, which is currently used operationally by the Air Force to specify neutral densities. As part of the analysis, we compare the drag observed by a variety of satellites which were not used as part of the assimilation-dataset and whose perigee altitudes span a range from 200km to 700 km.

Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Hao; Mey, Antonia S. J. S.; Noé, Frank

2014-12-07

We propose a discrete transition-based reweighting analysis method (dTRAM) for analyzing configuration-space-discretized simulation trajectories produced at different thermodynamic states (temperatures, Hamiltonians, etc.) dTRAM provides maximum-likelihood estimates of stationary quantities (probabilities, free energies, expectation values) at any thermodynamic state. In contrast to the weighted histogram analysis method (WHAM), dTRAM does not require data to be sampled from global equilibrium, and can thus produce superior estimates for enhanced sampling data such as parallel/simulated tempering, replica exchange, umbrella sampling, or metadynamics. In addition, dTRAM provides optimal estimates of Markov state models (MSMs) from the discretized state-space trajectories at all thermodynamic states. Under suitablemore » conditions, these MSMs can be used to calculate kinetic quantities (e.g., rates, timescales). In the limit of a single thermodynamic state, dTRAM estimates a maximum likelihood reversible MSM, while in the limit of uncorrelated sampling data, dTRAM is identical to WHAM. dTRAM is thus a generalization to both estimators.« less

Autonomous Space Object Catalogue Construction and Upkeep Using Sensor Control Theory

NASA Astrophysics Data System (ADS)

Moretti, N.; Rutten, M.; Bessell, T.; Morreale, B.

The capability to track objects in space is critical to safeguard domestic and international space assets. Infrequent measurement opportunities, complex dynamics and partial observability of orbital state makes the tracking of resident space objects nontrivial. It is not uncommon for human operators to intervene with space tracking systems, particularly in scheduling sensors. This paper details the development of a system that maintains a catalogue of geostationary objects through dynamically tasking sensors in real time by managing the uncertainty of object states. As the number of objects in space grows the potential for collision grows exponentially. Being able to provide accurate assessment to operators regarding costly collision avoidance manoeuvres is paramount; the accuracy of which is highly dependent on how object states are estimated. The system represents object state and uncertainty using particles and utilises a particle filter for state estimation. Particle filters capture the model and measurement uncertainty accurately, allowing for a more comprehensive representation of the state’s probability density function. Additionally, the number of objects in space is growing disproportionally to the number of sensors used to track them. Maintaining precise positions for all objects places large loads on sensors, limiting the time available to search for new objects or track high priority objects. Rather than precisely track all objects our system manages the uncertainty in orbital state for each object independently. The uncertainty is allowed to grow and sensor data is only requested when the uncertainty must be reduced. For example when object uncertainties overlap leading to data association issues or if the uncertainty grows to beyond a field of view. These control laws are formulated into a cost function, which is optimised in real time to task sensors. By controlling an optical telescope the system has been able to construct and maintain a catalogue of approximately 100 geostationary objects.

Structural Continuum Modeling of Space Shuttle External Tank Foam Insulation

NASA Technical Reports Server (NTRS)

Steeve, Brian; Ayala, Sam; Purlee, T. Eric; Shaw, Phillip

2006-01-01

The Space Shuttle External Tank is covered with rigid polymeric closed-cell foam insulation to prevent ice formation, protect the metallic tank from aerodynamic heating, and control the breakup of the tank during re-entry. The cryogenic state of the tank, as well as the ascent into a vacuum environment, places this foam under significant stress. Because the loss of the foam during ascent poses a critical risk to the shuttle orbiter, there is much interest in understanding the stress state in the foam insulation and how it may contribute to fracture and debris loss. Several foam applications on the external tank have been analyzed using finite element methods. This presentation describes the approach used to model the foam material behavior and compares analytical results to experiments.

Test of the Chevallier-Polarski-Linder parametrization for rapid dark energy equation of state transitions

NASA Astrophysics Data System (ADS)

Linden, Sebastian; Virey, Jean-Marc

2008-07-01

We test the robustness and flexibility of the Chevallier-Polarski-Linder (CPL) parametrization of the dark energy equation of state w(z)=w0+wa(z)/(1+z) in recovering a four-parameter steplike fiducial model. We constrain the parameter space region of the underlying fiducial model where the CPL parametrization offers a reliable reconstruction. It turns out that non-negligible biases leak into the results for recent (z<2.5) rapid transitions, but that CPL yields a good reconstruction in all other cases. The presented analysis is performed with supernova Ia data as forecasted for a space mission like SNAP/JDEM, combined with future expectations for the cosmic microwave background shift parameter R and the baryonic acoustic oscillation parameter A.

Structure of N-(5-ethyl-[1,3,4]-thiadiazole-2-yl)toluenesulfonamide by combined X-ray powder diffraction, 13C solid-state NMR and molecular modelling.

PubMed

Hangan, Adriana; Borodi, Gheorghe; Filip, Xenia; Tripon, Carmen; Morari, Cristian; Oprean, Luminita; Filip, Claudiu

2010-12-01

The crystal structure solution of the title compound is determined from microcrystalline powder using a multi-technique approach that combines X-ray powder diffraction (XRPD) data analysis based on direct-space methods with information from (13)C solid-state NMR (SSNMR), and molecular modelling using the GIPAW (gauge including projector augmented-wave) method. The space group is Pbca with one molecule in the asymmetric unit. The proposed methodology proves very useful for unambiguously characterizing the supramolecular arrangement adopted by the N-(5-ethyl-[1,3,4]-thiadiazole-2-yl)toluenesulfonamide molecules in the crystal, which consists of extended double strands held together by C-H···π non-covalent interactions.

GPC-Based Stable Reconfigurable Control

NASA Technical Reports Server (NTRS)

Soloway, Don; Shi, Jian-Jun; Kelkar, Atul

2004-01-01

This paper presents development of multi-input multi-output (MIMO) Generalized Pre-dictive Control (GPC) law and its application to reconfigurable control design in the event of actuator saturation. A Controlled Auto-Regressive Integrating Moving Average (CARIMA) model is used to describe the plant dynamics. The control law is derived using input-output description of the system and is also related to the state-space form of the model. The stability of the GPC control law without reconfiguration is first established using Riccati-based approach and state-space formulation. A novel reconfiguration strategy is developed for the systems which have actuator redundancy and are faced with actuator saturation type failure. An elegant reconfigurable control design is presented with stability proof. Several numerical examples are presented to demonstrate the application of various results.

Creating State-based Alliances to Support Earth and Space Science Education Reform

NASA Astrophysics Data System (ADS)

Geary, E. E.; Manduca, C. A.; Barstow, D.

2002-05-01

Seven years after the publication of the National Science Education Standards and adoption of new state science education standards, Earth and space science remains outside the mainstream K-12 curriculum. Currently, less than ten percent of high school students in the United States of America take an Earth or space science course before graduation. This state of affairs is simply unacceptable. "All of us who live on this planet have the right and the obligation to understand Earth's unique history, its dynamic processes, its abundant resources, and its intriguing mysteries. As citizens of Earth, with the power to modify our climate and ecosystems, we also have a personal and collective responsibility to understand Earth so that we can make wise decisions about its and our future". As one step toward addressing this situation, we support the establishment of state-based alliances to promote Earth and space science education reform. "In many ways, states are the most vital locus of change in our nation's schools. State departments of education define curriculum frameworks, establish testing policies, support professional development and, in some cases, approve textbooks and materials for adoption". State alliance partners should include a broad spectrum of K-16 educators, scientists, policy makers, parents, and community leaders from academic institutions, businesses, museums, technology centers, and not-for profit organizations. The focus of these alliances should be on systemic and sustainable reform of K-16 Earth and space science education. Each state-based alliance should focus on specific educational needs within their state, but work together to share ideas, resources, and models for success. As we build these alliances we need to take a truly collaborative approach working with the other sciences, geography, and mathematics so that collectively we can improve the caliber and scope of science and mathematics education for all students.

A Simplified Baseband Prefilter Model with Adaptive Kalman Filter for Ultra-Tight COMPASS/INS Integration

PubMed Central

Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing

2012-01-01

COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load. PMID:23012564

Space Weather Influence on the Earth wheat markets: past, present, and future.

NASA Astrophysics Data System (ADS)

Pustil'Nik, Lev

We consider problem of a possible influence of unfavorable states of the space weather on agriculture market through chain of connections: "space weather"-"earth weather"-"agriculture crops"-"price reaction". We show that new manifestations of "space weather"-"earth weather" relations discovered in the last time allow to revise wide field of expected solar-terrestrial connections. In the previous works we proposed possible mechanisms of wheat market reaction in the form of price bursts on the specific unfavorable states of space weather. We show that implementation of considered "price reaction scenarios" is possible only for condition of simultaneous realization of several necessary conditions: high sensitivity of local earth weather in selected region to space weather; state of "high risk agriculture" in selected agriculture zone; high sensitivity of agricultural market to possible deficit of supply. Results of previous works (I, II) included application of this approach to wheat market in Medieval England and to modern USA durum market showed that real connection between wheat price bursts and space weather state is observed with high confidence level. The aim of present work is answer on the question, why wheat markets in one region are sensitive to space weather factor, while another regional wheat markets demonstrate absolute indifferent reaction on this factor. For this aim we consider distribution of sensitivity of wheat markets in Europe to space weather as function of localization in different climatic zones. We analyze giant database of 95 European wheat markets from 14 countries during about 600-year period (1260-1912). We show that observed sensitivity of wheat market to space weather effects controlled, first of all, by type of predominant climate in different zones of agriculture. Wheat markets in the North and part of Central Europe (England, Iceland, Holland) shows reliable sensitivity to space weather in minimum states of solar activity with low solar wind, high cosmic ray flux and North Atlantic cloudiness, caused by CR excess, with negative sequences for wheat agriculture in this humid zone. In the same time wheat markets in the South Europe (Spain, Italy) show reliable sensitivity to space weather state in the opposite (maximum) phase of solar activity with strong solar wind, low cosmic ray flux and deficit of CR input in cloudiness in North Atlantic with next deficit of precipitations in the arid zones of the South Europe. In the same time the large part of markets in the Central Europe zone, functioned far from "high risk agriculture state" show the absence of any effects-responses on space weather. This asymmetry is in accordance with model expectation in the frame of proposed approach. For extremely case of the Iceland agriculture we show that drop of agriculture production in unfavorable states of space weather leads to mass mortality from famines correlated with phase of solar activity with high confi- dence level. We discuss possible increasing of sensitivity of wheat markets to space weather effects in condition of drastic and fast change of modern climate, caused by global warming of the Earth atmosphere with fast and unexpected shift of numerous agriculture regions in the world to state of "high risk agriculture zone". Publications on the theme of review: I. "INFLUENCE OF SOLAR ACTIVITY ON THE STATE OF THE WHEAT MARKET IN MEDIEVAL ENGLAND", Solar Physics 223: 335-356, 2004. c 2004 Kluwer Academic Publishers II. "SPACE CLIMATE MANIFESTATION IN EARTH PRICES - FROM MEDIEVAL ENGLAND UP TO MODERN U.S.A.", LEV PUSTIL'NIK and GREGORY YOM DIN, Solar Physics, 224: 473-481 c Springer 2005

Improved Orbit Determination and Forecasts with an Assimilative Tool for Satellite Drag Specification

NASA Astrophysics Data System (ADS)

Pilinski, M.; Crowley, G.; Sutton, E.; Codrescu, M.

2016-09-01

Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. As the population of satellites in Earth orbit grows, higher space-weather prediction accuracy is required for critical missions, such as accurate catalog maintenance, collision avoidance for manned and unmanned space flight, reentry prediction, satellite lifetime prediction, defining on-board fuel requirements, and satellite attitude dynamics. We describe ongoing work to build a comprehensive nowcast and forecast system for specifying the neutral atmospheric state related to orbital drag conditions. The system outputs include neutral density, winds, temperature, composition, and the satellite drag derived from these parameters. This modeling tool is based on several state-of-the-art coupled models of the thermosphere-ionosphere as well as several empirical models running in real-time and uses assimilative techniques to produce a thermospheric nowcast. This software will also produce 72 hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition and using near real-time and predicted space weather data and indices as the inputs. In this paper, we will review the driving requirements for our model, summarize the model design and assimilative architecture, and present preliminary validation results. Validation results will be presented in the context of satellite orbit errors and compared with several leading atmospheric models. As part of the analysis, we compare the drag observed by a variety of satellites which were not used as part of the assimilation-dataset and whose perigee altitudes span a range from 200 km to 700 km.

Physiome-model-based state-space framework for cardiac deformation recovery.

PubMed

Wong, Ken C L; Zhang, Heye; Liu, Huafeng; Shi, Pengcheng

2007-11-01

To more reliably recover cardiac information from noise-corrupted, patient-specific measurements, it is essential to employ meaningful constraining models and adopt appropriate optimization criteria to couple the models with the measurements. Although biomechanical models have been extensively used for myocardial motion recovery with encouraging results, the passive nature of such constraints limits their ability to fully count for the deformation caused by active forces of the myocytes. To overcome such limitations, we propose to adopt a cardiac physiome model as the prior constraint for cardiac motion analysis. The cardiac physiome model comprises an electric wave propagation model, an electromechanical coupling model, and a biomechanical model, which are connected through a cardiac system dynamics for a more complete description of the macroscopic cardiac physiology. Embedded within a multiframe state-space framework, the uncertainties of the model and the patient's measurements are systematically dealt with to arrive at optimal cardiac kinematic estimates and possibly beyond. Experiments have been conducted to compare our proposed cardiac-physiome-model-based framework with the solely biomechanical model-based framework. The results show that our proposed framework recovers more accurate cardiac deformation from synthetic data and obtains more sensible estimates from real magnetic resonance image sequences. With the active components introduced by the cardiac physiome model, cardiac deformations recovered from patient's medical images are more physiologically plausible.

A 4-cylinder Stirling engine computer program with dynamic energy equations

NASA Technical Reports Server (NTRS)

Daniele, C. J.; Lorenzo, C. F.

1983-01-01

A computer program for simulating the steady state and transient performance of a four cylinder Stirling engine is presented. The thermodynamic model includes both continuity and energy equations and linear momentum terms (flow resistance). Each working space between the pistons is broken into seven control volumes. Drive dynamics and vehicle load effects are included. The model contains 70 state variables. Also included in the model are piston rod seal leakage effects. The computer program includes a model of a hydrogen supply system, from which hydrogen may be added to the system to accelerate the engine. Flow charts are provided.