The Loeb Space of Denumerable Infinite Dimensional Probability Product Measure Spaces
Institute of Scientific and Technical Information of China (English)
陈东立
2003-01-01
Let {(Xi, Si, μi) : i ∈N} be a sequence of probability measure spaces and (↑*Xi, L(↑*Si), L(↑*μi)) be the Loeb measure space with respect to (Xi, Si, μi)for i∈N.lET x=↑∞×i=1 Xi,S=↑∞×i=1 Si,μ=↑∞×i=1 μi,We prove that ↑∞×i=1 L(↑*Si)（∪→）L1(↑*L)and L(↑*μ）|↑∞×i=L(↑*Si)=L(↑*μi) in embedding meaning.
DEFF Research Database (Denmark)
Mailund, Thomas
The thesis describes the sweep-line method, a newly developed reduction method for alleviating the state explosion problem inherent in explicit-state state space exploration. The basic idea underlying the sweep-line method is, when calculating the state space, to recognise and delete states...... that are not reachable from the currently unprocessed states. Intuitively we drag a sweep-line through the state space with the invariant that all states behind the sweep-line have been processed and are unreachable from the states in front of the sweep-line. When calculating the state space of a system we iteratively...
DEFF Research Database (Denmark)
Mailund, Thomas
The thesis describes the sweep-line method, a newly developed reduction method for alleviating the state explosion problem inherent in explicit-state state space exploration. The basic idea underlying the sweep-line method is, when calculating the state space, to recognise and delete states...... that are not reachable from the currently unprocessed states. Intuitively we drag a sweep-line through the state space with the invariant that all states behind the sweep-line have been processed and are unreachable from the states in front of the sweep-line. When calculating the state space of a system we iteratively...
R. Dekker (Rommert); A. Hordijk (Arie)
1988-01-01
textabstractIn this paper we consider a (discrete-time) Markov decision chain with a denumerabloe state space and compact action sets and we assume that for all states the rewards and transition probabilities depend continuously on the actions. The first objective of this paper is to develop an anal
Directory of Open Access Journals (Sweden)
Thomas Doan
2011-05-01
Full Text Available This paper uses several examples to show how the econometrics program RATS can be used to analyze state space models. It demonstrates Kalman filtering and smoothing, estimation of hyperparameters, unconditional and conditional simulation. It also provides a more complicated example where a dynamic simultaneous equations model is transformed into a proper state space representation and its unknown parameters are estimated.
My Life with State Space Models
DEFF Research Database (Denmark)
Lundbye-Christensen, Søren
2007-01-01
. The conceptual idea behind the state space model is that the evolution over time in the object we are observing and the measurement process itself are modelled separately. My very first serious analysis of a data set was done using a state space model, and since then I seem to have been "haunted" by state space...
State Space Methods for Timed Petri Nets
DEFF Research Database (Denmark)
Christensen, Søren; Jensen, Kurt; Mailund, Thomas
2001-01-01
We present two recently developed state space methods for timed Petri nets. The two methods reconciles state space methods and time concepts based on the introduction of a global clock and associating time stamps to tokens. The first method is based on an equivalence relation on states which makes...... it possible to condense the usually infinite state space of a timed Petri net into a finite condensed state space without loosing analysis power. The second method supports on-the-fly verification of certain safety properties of timed systems. We discuss the application of the two methods in a number...
Mean Shift Detection for State Space Models
Kuhn, J.; Mandjes, M.; Taimre, T.; Weber, T.; McPhee, M.J.; Anderssen, R.S.
2015-01-01
In this paper we develop and validate a procedure for testing against a shift in mean in the observations and hidden state sequence of state space models with Gaussian noise. State space models are popular for modelling stochastic networks as they allow to take into account that observations of the
State space consistency and differentiability
Serakos, Demetrios
2014-01-01
By investigating the properties of the natural state, this book presents an analysis of input-output systems with regard to the mathematical concept of state. The state of a system condenses the effects of past inputs to the system in a useful manner. This monograph emphasizes two main properties of the natural state; the first has to do with the possibility of determining the input-output system from its natural state set and the second deals with differentiability properties involving the natural state inherited from the input-output system, including differentiability of the natural state and natural state trajectories. The results presented in this title aid in modeling physical systems since system identification from a state set holds in most models. Researchers and engineers working in electrical, aerospace, mechanical, and chemical fields along with applied mathematicians working in systems or differential equations will find this title useful due to its rigorous mathematics.
Coherent states in the fermionic Fock space
Oeckl, Robert
2015-01-01
We construct the coherent states in the sense of Gilmore and Perelomov for the fermionic Fock space. Our treatment is from the outset adapted to the infinite-dimensional case. The fermionic Fock space becomes in this way a reproducing kernel Hilbert space of continuous holomorphic functions.
State-Space Formulation for Circuit Analysis
Martinez-Marin, T.
2010-01-01
This paper presents a new state-space approach for temporal analysis of electrical circuits. The method systematically obtains the state-space formulation of nondegenerate linear networks without using concepts of topology. It employs nodal/mesh systematic analysis to reduce the number of undesired variables. This approach helps students to…
Continuous expected utility for arbitrary state spaces
Wakker, P.P.
1985-01-01
Subjective expected utility maximization with continuous utility is characterized, extending the result of Wakker (1984, Journal of Mathematical Psychology) to infinite state spaces. In Savage (1954, The Foundations of Statistics) the main restriction, P6, requires structure for the state space, e.g
Pruning state spaces with extended beam search
Dashti, M.T.; Wijs, A.J.
2007-01-01
This paper focuses on using beam search, a heuristic search algorithm, for pruning state spaces while generating. The original beam search is adapted to the state space generation setting and two new search variants are devised. The resulting framework encompasses some known algorithms, such as $A^*
Projective loop quantum gravity. I. State space
Lanéry, Suzanne; Thiemann, Thomas
2016-12-01
Instead of formulating the state space of a quantum field theory over one big Hilbert space, it has been proposed by Kijowski to describe quantum states as projective families of density matrices over a collection of smaller, simpler Hilbert spaces. Beside the physical motivations for this approach, it could help designing a quantum state space holding the states we need. In a latter work by Okolów, the description of a theory of Abelian connections within this framework was developed, an important insight being to use building blocks labeled by combinations of edges and surfaces. The present work generalizes this construction to an arbitrary gauge group G (in particular, G is neither assumed to be Abelian nor compact). This involves refining the definition of the label set, as well as deriving explicit formulas to relate the Hilbert spaces attached to different labels. If the gauge group happens to be compact, we also have at our disposal the well-established Ashtekar-Lewandowski Hilbert space, which is defined as an inductive limit using building blocks labeled by edges only. We then show that the quantum state space presented here can be thought as a natural extension of the space of density matrices over this Hilbert space. In addition, it is manifest from the classical counterparts of both formalisms that the projective approach allows for a more balanced treatment of the holonomy and flux variables, so it might pave the way for the development of more satisfactory coherent states.
Graph Subsumption in Abstract State Space Exploration
Zambon, Eduardo; Rensink, Arend; Wijs, A.; Bosnacki, D.; Edelkamp, S.
In this paper we present the extension of an existing method for abstract graph-based state space exploration, called neighbourhood abstraction, with a reduction technique based on subsumption. Basically, one abstract state subsumes another when it covers more concrete states; in such a case, the
Approximate Methods for State-Space Models
Koyama, Shinsuke; Shalizi, Cosma Rohilla; Kass, Robert E; 10.1198/jasa.2009.tm08326
2010-01-01
State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models, which uses Laplace's method, an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution. This {\\em Laplace-Gaussian filter} (LGF) gives fast, recursive, deterministic state estimates, with an error which is set by the stochastic characteristics of the model and is, we show, stable over time. We illustrate the estimation ability of the LGF by applying it to the problem of neural decoding and compare it to sequential Monte Carlo both in simulat...
Projective Loop Quantum Gravity I. State Space
Lanéry, Suzanne
2014-01-01
Instead of formulating the state space of a quantum field theory over one big Hilbert space, it has been proposed by Kijowski to describe quantum states as projective families of density matrices over a collection of smaller, simpler Hilbert spaces. Beside the physical motivations for this approach, it could help designing a quantum state space holding the states we need. In [Oko{\\l}\\'ow 2013, arXiv:1304.6330] the description of a theory of Abelian connections within this framework was developed, an important insight being to use building blocks labeled by combinations of edges and surfaces. The present work generalizes this construction to an arbitrary gauge group G (in particular, G is neither assumed to be Abelian nor compact). This involves refining the definition of the label set, as well as deriving explicit formulas to relate the Hilbert spaces attached to different labels. If the gauge group happens to be compact, we also have at our disposal the well-established Ashtekar-Lewandowski Hilbert space, wh...
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Parameter and state estimator for state space models.
Ding, Ruifeng; Zhuang, Linfan
2014-01-01
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Macro and micro view on steady states in state space
Sobota, Branislav
2010-01-01
This paper describes visualization of chaotic attractor and elements of the singularities in 3D space. 3D view of these effects enables to create a demonstrative projection about relations of chaos generated by physical circuit, the Chua's circuit. Via macro views on chaotic attractor is obtained not only visual space illustration of representative point motion in state space, but also its relation to planes of singularity elements. Our created program enables view on chaotic attractor both in 2D and 3D space together with plane objects visualization -- elements of singularities.
The STAMP Software for State Space Models
Directory of Open Access Journals (Sweden)
Roy Mendelssohn
2011-05-01
Full Text Available This paper reviews the use of STAMP (Structural Time Series Analyser, Modeler and Predictor for modeling time series data using state-space methods with unobserved components. STAMP is a commercial, GUI-based program that runs on Windows, Linux and Macintosh computers as part of the larger OxMetrics System. STAMP can estimate a wide-variety of both univariate and multivariate state-space models, provides a wide array of diagnostics, and has a batch mode capability. The use of STAMP is illustrated for the Nile river data which is analyzed throughout this issue, as well as by modeling a variety of oceanographic and climate related data sets. The analyses of the oceanographic and climate data illustrate the breadth of models available in STAMP, and that state-space methods produce results that provide new insights into important scientific problems.
Space groups for solid state scientists
Glazer, Michael; Glazer, Alexander N
2014-01-01
This Second Edition provides solid state scientists, who are not necessarily experts in crystallography, with an understandable and comprehensive guide to the new International Tables for Crystallography. The basic ideas of symmetry, lattices, point groups, and space groups are explained in a clear and detailed manner. Notation is introduced in a step-by-step way so that the reader is supplied with the tools necessary to derive and apply space group information. Of particular interest in this second edition are the discussions of space groups application to such timely topics as high-te
Coherent states in projected Hilbert spaces
Drummond, P. D.; Reid, M. D.
2016-12-01
Coherent states in a projected Hilbert space have many useful properties. When there are conserved quantities, a representation of the entire Hilbert space is not necessary. The same issue arises when conditional observations are made with postselected measurement results. In these cases, only a part of the Hilbert space needs to be represented, and one can define this restriction by way of a projection operator. Here coherent state bases and normally ordered phase-space representations are introduced for treating such projected Hilbert spaces, including existence theorems and dynamical equations. These techniques are very useful in studying novel optical or microwave integrated photonic quantum technologies, such as boson sampling or Josephson quantum computers. In these cases, states become strongly restricted due to inputs, nonlinearities, or conditional measurements. This paper focuses on coherent phase states, which have especially simple properties. Practical applications are reported on calculating recurrences in anharmonic oscillators, the effects of arbitrary phase noise on Schrödinger cat fringe visibility, and on boson sampling interferometry for large numbers of modes.
State-space Correlations and Stabilities
Bellucci, Stefano
2010-01-01
The state-space pair correlation functions and notion of stability of extremal and non-extremal black holes in string theory and M-theory are considered from the viewpoints of thermodynamic Ruppeiner geometry. From the perspective of intrinsic Riemannian geometry, the stability properties of these black branes are divulged from the positivity of principle minors of the space-state metric tensor. We have explicitly analyzed the state-space configurations for (i) the two and three charge extremal black holes, (ii) the four and six charge non-extremal black branes, which both arise from the string theory solutions. An extension is considered for the $D_6$-$D_4$-$D_2$-$D_0$ multi-centered black branes, fractional small black branes and two charge rotating fuzzy rings in the setup of Mathur's fuzzball configurations. The state-space pair correlations and nature of stabilities have been investigated for three charged bubbling black brane foams, and thereby the M-theory solutions are brought into the present conside...
Multimedia Mapping using Continuous State Space Models
DEFF Research Database (Denmark)
Lehn-Schiøler, Tue
2004-01-01
In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in 'face space'. Simulations...... are performed on recordings of 3-5 sec. video sequences with sentences from the Timit database. The model is able to construct an image sequence from an unknown noisy speech sequence fairly well even though the number of training examples are limited....
Approximate Methods for State-Space Models.
Koyama, Shinsuke; Pérez-Bolde, Lucia Castellanos; Shalizi, Cosma Rohilla; Kass, Robert E
2010-03-01
State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models, which uses Laplace's method, an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution. This Laplace-Gaussian filter (LGF) gives fast, recursive, deterministic state estimates, with an error which is set by the stochastic characteristics of the model and is, we show, stable over time. We illustrate the estimation ability of the LGF by applying it to the problem of neural decoding and compare it to sequential Monte Carlo both in simulations and with real data. We find that the LGF can deliver superior results in a small fraction of the computing time.
The State Space Models Toolbox for MATLAB
Directory of Open Access Journals (Sweden)
Jyh-Ying Peng
2011-05-01
Full Text Available State Space Models (SSM is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy- namic models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman fil- tering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with com- mon analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.
Condensed State Spaces for Symmetrical Coloured Petri Nets
DEFF Research Database (Denmark)
Jensen, Kurt
1996-01-01
This paper deals with state spaces. A state space is a directed graph with a node for each reachable state and an arc for each possible state change. We describe how symmetries of the modelled system can be exploited to obtain much more succinct state space analysis. The symmetries induce equival...
Fractional State Space Analysis of Economic Systems
Directory of Open Access Journals (Sweden)
J. A. Tenreiro Machado
2015-07-01
Full Text Available This paper examines modern economic growth according to the multidimensional scaling (MDS method and state space portrait (SSP analysis. Electing GDP per capita as the main indicator for economic growth and prosperity, the long-run perspective from 1870 to 2010 identifies the main similarities among 34 world partners’ modern economic growth and exemplifies the historical waving mechanics of the largest world economy, the USA. MDS reveals two main clusters among the European countries and their old offshore territories, and SSP identifies the Great Depression as a mild challenge to the American global performance, when compared to the Second World War and the 2008 crisis.
Multivariable Wind Modeling in State Space
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Pedersen, B. J.
2011-01-01
Turbulence of the incoming wind field is of paramount importance to the dynamic response of wind turbines. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper an empirical...... cross-spectral density function for the along-wind turbulence component over the rotor plane is taken as the starting point. The spectrum is spatially discretized in terms of a Hermitian cross-spectral density matrix for the turbulence state vector which turns out not to be positive definite. Since...... the succeeding state space and ARMA modeling of the turbulence rely on the positive definiteness of the cross-spectral density matrix, the problem with the non-positive definiteness of such matrices is at first addressed and suitable treatments regarding it are proposed. From the adjusted positive definite cross...
A Sweep-Line Method for State Space Exploration
DEFF Research Database (Denmark)
Christensen, Søren; Kristensen, Lars Michael; Mailund, Thomas
2001-01-01
We present a state space exploration method for on-the-fly verification. The method is aimed at systems for which it is possible to define a measure of progress based on the states of the system. The measure of progress makes it possible to delete certain states on-the-fly during state space...... of the method on a number of Coloured Petri Net models, and give a first evaluation of its practicality by means of an implementation based on the Design/CPN state space tool. Our experiments show significant reductions in both space and time used during state space exploration. The method is not specific...
A Sweep-Line Method for State Space Exploration
DEFF Research Database (Denmark)
Christensen, Søren; Kristensen, Lars Michael; Mailund, Thomas
2001-01-01
We present a state space exploration method for on-the-fly verification. The method is aimed at systems for which it is possible to define a measure of progress based on the states of the system. The measure of progress makes it possible to delete certain states on-the-fly during state space...... of the method on a number of Coloured Petri Net models, and give a first evaluation of its practicality by means of an implementation based on the Design/CPN state space tool. Our experiments show significant reductions in both space and time used during state space exploration. The method is not specific...... generation, since these states can never be reached again. This in turn reduces the memory used for state space storage during the task of verification. Examples of progress measures are sequence numbers in communication protocols and time in certain models with time. We illustrate the application...
Granger causality for state-space models.
Barnett, Lionel; Seth, Anil K
2015-04-01
Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations-commonplace in application domains as diverse as climate science, econometrics, and the neurosciences-induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.
Topological properties of flat electroencephalography's state space
Ken, Tan Lit; Ahmad, Tahir bin; Mohd, Mohd Sham bin; Ngien, Su Kong; Suwa, Tohru; Meng, Ong Sie
2016-02-01
Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic cell which is highly challenging. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, Flat EEG is equivalent to epileptic seizure hence, making it a great platform to study epileptic seizure. Throughout the years, various mathematical tools have been applied on Flat EEG to extract hidden information that is hardly noticeable by traditional visual inspection. While these tools have given worthy results, the journey towards understanding seizure process completely is yet to be succeeded. Since the underlying structure of Flat EEG is dynamic and is deemed to contain wealthy information regarding brainstorm, it would certainly be appealing to explore in depth its structures. To better understand the complex seizure process, this paper studies the event of epileptic seizure via Flat EEG in a more general framework by means of topology, particularly, on the state space where the event of Flat EEG lies.
A Compositional Sweep-Line State Space Exploration Method
DEFF Research Database (Denmark)
Kristensen, Lars Michael; Mailund, Thomas
2002-01-01
State space exploration is a main approach to verification of finite-state systems. The sweep-line method exploits a certain kind of progress present in many systems to reduce peak memory usage during state space exploration. We present a new sweep-line algorithm for a compositional setting where...
On Path Dependent State Space for the Proca Field
Gaitan, R
1999-01-01
A gauge formulation for the Proca model quantum theory in an open path functional space representation is revisited. The path dependent vacuum state is obtained. Starting from this one, other excited states can be obtained too. Additionally, the functional integration measure needed to define an internal product in the state space is constructed.
An introduction to state space time series analysis.
Commandeur, J.J.F. & Koopman, S.J.
2007-01-01
Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is...
ASAP: An Extensible Platform for State Space Analysis
DEFF Research Database (Denmark)
Westergaard, Michael; Evangelista, Sami; Kristensen, Lars Michael
2009-01-01
The ASCoVeCo State space Analysis Platform (ASAP) is a tool for performing explicit state space analysis of coloured Petri nets (CPNs) and other formalisms. ASAP supports a wide range of state space reduction techniques and is intended to be easy to extend and to use, making it a suitable tool...... for students, researchers, and industrial users that would like to analyze protocols and/or experiment with different algorithms. This paper presents ASAP from these two perspectives....
State space Newton's method for topology optimization
DEFF Research Database (Denmark)
Evgrafov, Anton
2014-01-01
We introduce a new algorithm for solving certain classes of topology optimization problems, which enjoys fast local convergence normally achieved by the full space methods while working in a smaller reduced space. The computational complexity of Newton’s direction finding subproblem in the algori......We introduce a new algorithm for solving certain classes of topology optimization problems, which enjoys fast local convergence normally achieved by the full space methods while working in a smaller reduced space. The computational complexity of Newton’s direction finding subproblem...
The State-of-the-art in Space Robotics
da Fonseca, Ijar M.; Pontuschka, Maurício N.
2015-10-01
This paper deals with the space robotics and associate space applications. An overview of the space era and the robotic space probes is presented to contextualize the space robotics in the space exploration scenario. Concepts, classification and key-questions associated with robotics for space applications are presented and discussed. Safety-critical aspects of the space robotics are discussed as well the human limitation to operate in the hostile space environment and long time duration missions. The paper also focuses on the state-of-the- art of robotics for the International Space Station EVA operations, for the planetary exploration such as the ongoing Mars exploration, Hayabusa rendezvous and landing in asteroids and the robotic probe Rosetta landed in a comet recently. The paper also includes a discussion of the applications of new concepts like the robonauts, the space tugs applications and robots for future planetary exploration.
Active Affordance Learning in Continuous State and Action Spaces
Wang, C.; Hindriks, K.V.; Babuska, R.
2014-01-01
Learning object affordances and manipulation skills is essential for developing cognitive service robots. We propose an active affordance learning approach in continuous state and action spaces without manual discretization of states or exploratory motor primitives. During exploration in the action
A General Theory of Additive State Space Abstractions
Yang, Fan; Holte, Robert; Zahavi, Uzi; Felner, Ariel; 10.1613/jair.2486
2011-01-01
Informally, a set of abstractions of a state space S is additive if the distance between any two states in S is always greater than or equal to the sum of the corresponding distances in the abstract spaces. The first known additive abstractions, called disjoint pattern databases, were experimentally demonstrated to produce state of the art performance on certain state spaces. However, previous applications were restricted to state spaces with special properties, which precludes disjoint pattern databases from being defined for several commonly used testbeds, such as Rubiks Cube, TopSpin and the Pancake puzzle. In this paper we give a general definition of additive abstractions that can be applied to any state space and prove that heuristics based on additive abstractions are consistent as well as admissible. We use this new definition to create additive abstractions for these testbeds and show experimentally that well chosen additive abstractions can reduce search time substantially for the (18,4)-TopSpin puz...
Determining state-space models from sequential output data
Lin, Jiguan Gene
1988-01-01
This talk focuses on the determination of state-space models for large space systems using only the output data. The output data could be generated by the unknown or deliberate initial conditions of the space structure in question. We shall review some relevant fundamental work on the state-space modeling of sequential output data that is potentially applicable to large space structures. If formulated in terms of some generalized Markov parameters, this approach is in some sense similar to, but much simpler than, the Juang-Pappa Eigensystem Realization Algorithm (ERA) and the Ho-Kalman construction procedure.
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
Embedding a State Space Model Into a Markov Decision Process
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Jørgensen, Erik; Højsgaard, Søren
2011-01-01
estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for modeling repeated measurements over time where the model parameters can evolve dynamically. In this paper we consider methods for embedding an SSM into an MDP with finite state and action space. Different...
Complexity in Simplicity: Flexible Agent-based State Space Exploration
DEFF Research Database (Denmark)
Rasmussen, Jacob Illum; Larsen, Kim Guldstrand
2007-01-01
In this paper, we describe a new flexible framework for state space exploration based on cooperating agents. The idea is to let various agents with different search patterns explore the state space individually and communicate information about fruitful subpaths of the search tree to each other...
An introduction to state space modeling (in Russian)
Alexander Tsyplakov
2011-01-01
Many time series models, primarily various models with unobservable components, can be represented in a so called state space form. A state space model is a powerful tool that allows one to apply to the original model a wide range of standard procedures including estimation and forecasting. This essay provides a survey of this universal class of models and related procedures.
Adaptive importance sampling of random walks on continuous state spaces
Energy Technology Data Exchange (ETDEWEB)
Baggerly, K.; Cox, D.; Picard, R.
1998-11-01
The authors consider adaptive importance sampling for a random walk with scoring in a general state space. Conditions under which exponential convergence occurs to the zero-variance solution are reviewed. These results generalize previous work for finite, discrete state spaces in Kollman (1993) and in Kollman, Baggerly, Cox, and Picard (1996). This paper is intended for nonstatisticians and includes considerable explanatory material.
An introduction to state space time series analysis.
Commandeur, J.J.F. & Koopman, S.J.
2007-01-01
Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor wi
Complexity in Simplicity: Flexible Agent-based State Space Exploration
DEFF Research Database (Denmark)
Rasmussen, Jacob Illum; Larsen, Kim Guldstrand
2007-01-01
In this paper, we describe a new flexible framework for state space exploration based on cooperating agents. The idea is to let various agents with different search patterns explore the state space individually and communicate information about fruitful subpaths of the search tree to each other...
The sweep-line state space exploration method
DEFF Research Database (Denmark)
Jensen, Kurt; Kristensen, Lars M.; Mailund, Thomas
2012-01-01
The sweep-line method exploits intrinsic progress in concurrent systems to alleviate the state explosion problem in explicit state model checking. The concept of progress makes it possible to delete states from the memory during state space exploration and thereby reduce peak memory usage...
The HIVE Tool for Informed Swarm State Space Exploration
Wijs, Anton
2011-01-01
Swarm verification and parallel randomised depth-first search are very effective parallel techniques to hunt bugs in large state spaces. In case bugs are absent, however, scalability of the parallelisation is completely lost. In recent work, we proposed a mechanism to inform the workers which parts of the state space to explore. This mechanism is compatible with any action-based formalism, where a state space can be represented by a labelled transition system. With this extension, each worker can be strictly bounded to explore only a small fraction of the state space at a time. In this paper, we present the HIVE tool together with two search algorithms which were added to the LTSmin tool suite to both perform a preprocessing step, and execute a bounded worker search. The new tool is used to coordinate informed swarm explorations, and the two new LTSmin algorithms are employed for preprocessing a model and performing the individual searches.
Black Strings, Black Rings and State-space Manifold
Bellucci, Stefano
2011-01-01
State-space geometry is considered, for diverse three and four parameter non-spherical horizon rotating black brane configurations, in string theory and $M$-theory. We have explicitly examined the case of unit Kaluza-Klein momentum $D_1D_5P$ black strings, circular strings, small black rings and black supertubes. An investigation of the state-space pair correlation functions shows that there exist two classes of brane statistical configurations, {\\it viz.}, the first category divulges a degenerate intrinsic equilibrium basis, while the second yields a non-degenerate, curved, intrinsic Riemannian geometry. Specifically, the solutions with finitely many branes expose that the two charged rotating $D_1D_5$ black strings and three charged rotating small black rings consort real degenerate state-space manifolds. Interestingly, arbitrary valued $M_5$-dipole charged rotating circular strings and Maldacena Strominger Witten black rings exhibit non-degenerate, positively curved, comprehensively regular state-space con...
RESULTS OF INTERBANK EXCHANGE RATES FORECASTING USING STATE SPACE MODEL
Directory of Open Access Journals (Sweden)
Muhammad Kashif
2008-07-01
Full Text Available This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the best performance among all the models.
Induced measures in the space of mixed quantum states
Energy Technology Data Exchange (ETDEWEB)
Zyczkowski, Karol [Centrum Fizyki Teoretycznej, Polska Akademia Nauk, Warsaw, Poland and Instytut Fizyki, Uniwersytet Jagiellonski, Crakow (Poland)). E-mail: karol@cft.edu.pl; Sommers, Hans-Juergen [Fachbereich Physik, Universitaet-Gesamthochschule Essen, Essen (Germany)). E-mail: sommers@next30.theo-phys.uni-essen.de
2001-09-07
We analyse several product measures in the space of mixed quantum states. In particular, we study measures induced by the operation of partial tracing. The natural, rotationally invariant measure on the set of all pure states of a NxK composite system, induces a unique measure in the space of NxN mixed states (or in the space of KxK mixed states, if the reduction takes place with respect to the first subsystem). For K=N the induced measure is equal to the Hilbert-Schmidt measure, which is shown to coincide with the measure induced by singular values of non-Hermitian random Gaussian matrices pertaining to the Ginibre ensemble. We compute several averages with respect to this measure and show that the mean entanglement of NxN pure states behaves as lnN-1/2. (author)
Induced measures in the space of mixed quantum states
Zyczkowski, K; Zyczkowski, Karol; Sommers, Hans-Juergen
2001-01-01
We analyze several product measures in the space of mixed quantum states. In particular we study measures induced by the operation of partial tracing. The natural, rotationally invariant measure on the set of all pure states of a N x K composite system, induces a unique measure in the space of N x N mixed states (or in the space of K x K mixed states, if the reduction takes place with respect to the first subsystem). For K=N the induced measure is equal to the Hilbert-Schmidt measure, which is shown to coincide with the measure induced by singular values of non-Hermitian random Gaussian matrices pertaining to the Ginibre ensemble. We compute several averages with respect to this measure and show that the mean entanglement of $N \\times N$ pure states behaves as lnN-1/2.
Space research scientific and educational project of Moscow State University
Krasotkin, S. A.; Mjagkova, I. N.; Panasyuk, M. I.; Radchenko, V. V.; Ryazantseva, M. O.
The scientific and educational project of space research was initiated in Lomonosov Moscow State University in order to incorporate modern space research in the university and high education, to popularize basics of space physics, and to enhance public interest in space exploration. On 20 January, 2005 the First Russian University Satellite UNIVERSITETSKIY was launched into circular polar orbit (inclination 83 deg., altitude 940-980 km). The onboard scientific complex TATYANA as well as the mission control and information receiving center, was designed and developed in Moscow State University. The scientific program of the mission include measurements of space radiation in different energy channels, and Earth UV luminosity and lightening. A multimedia lectures "Life of the Earth in the Solar Atmosphere" containing the basic information and demonstrations of the heliophysics (including Sun structure and solar activity, heliosphere and geophysics, solar-terrestrial connections and solar influence on the Earth's life) was created for upper high-school and junior university students. For the upper-university students there was created a dozen of special computerized lab exercises based on the experimental quasi-realtime data obtained from our satellites. Students specialized in space physics from a few Russian universities are involved in scientific work based. Educational program of the project (both the multimedia lectures and lab exercises) is concentrated to upper high school, middle university and special level for space physics students. The space research scientific and educational activity of Moscow State University is a non-profit project and is open for all interested parties.
A Learning State-Space Model for Image Retrieval
Directory of Open Access Journals (Sweden)
Lee Greg C
2007-01-01
Full Text Available This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.
On the Squeezed Number States and their Phase Space Representations
Albano, L; Stephany, J
2002-01-01
We compute the photon number distribution, the Q distribution function and the wave functions in the momentum and position representation for a single mode squeezed number state. We discuss the oscillations which appear in the photon number distribution of squeezed number states for high values of the squeezing parameter. We compare our results with the formalism based on the interference in phase space.
Dynamic State Space Partitioning for External Memory Model Checking
DEFF Research Database (Denmark)
Evangelista, Sami; Kristensen, Lars Michael
2009-01-01
We describe a dynamic partitioning scheme usable by model checking techniques that divide the state space into partitions, such as most external memory and distributed model checking algorithms. The goal of the scheme is to reduce the number of transitions that link states belonging to different...
Balanced state-space representations : a polynomial algebraic approach
Rapisarda, P.; Trentelman, H.L.
2009-01-01
We show how to compute a minimal Riccati-balanced state map and a minimal Riccati-balanced state space representation starting from an image representation of a strictly dissipative system. The result is based on an iterative procedure to solve a generalization of the Nevanlinna interpolation proble
A Compositional Sweep-Line State Space Exploration Method
DEFF Research Database (Denmark)
Kristensen, Lars Michael; Mailund, Thomas
2002-01-01
State space exploration is a main approach to verification of finite-state systems. The sweep-line method exploits a certain kind of progress present in many systems to reduce peak memory usage during state space exploration. We present a new sweep-line algorithm for a compositional setting where...... systems are composed of subsystems. The compositional setting makes it possible to divide subsystem progress measures into monotone and non-monotone progress measures to further reduce peak memory usage. We show that in a compositional setting, it is possible to automatically obtain a progress measure...
Quantum-dot Semiconductor Optical Amplifiers in State Space Model
Institute of Scientific and Technical Information of China (English)
Hussein Taleb; Kambiz Abedi; Saeed Golmohammadi
2013-01-01
A state space model (SSM) is derived for quantum-dot semiconductor optical amplifiers (QD-SOAs).Rate equations of QD-SOA are formulated in the form of state update equations,where average occupation probabilities along QD-SOA cavity are considered as state variables of the system.Simulations show that SSM calculates QD-SOA's static and dynamic characteristics with high accuracy.
Real-space renormalization yields finitely correlated states
Barthel, Thomas; Eisert, Jens
2010-01-01
Real-space renormalization approaches for quantum lattice systems generate certain hierarchical classes of states that are subsumed by the multi-scale entanglement renormalization ansatz (MERA). It is shown that, with the exception of one dimension, MERA states can be efficiently mapped to finitely-correlated states, also known as projected entangled pair states (PEPS), with a bond dimension independent of the system size. Hence, MERA states form an efficiently contractible class of PEPS and obey an area law for the entanglement entropy. It is shown further that there exist other efficiently contractible schemes violating the area law.
State-Vector Space and Canonical Coherent States in Noncommutative Plane
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The structure of the state-vector space of identical bosons in noncommutative spaces is investigated. To maintain Bose-Einstein statistics the commutation relations of phase space variables should simultaneously include coordinate-coordinate non-commutativity and momentum-momentum non-commutativity, which leads to a kind of de-formed Heisenberg-Weyl algebra. Although there is no ordinary number representation in this state-vector space, several set of orthogonal and complete state-vectors can be derived which are common eigenvectors of corresponding pairs of commuting Hermitian operators. As a simple application of this state-vector space, an explicit form of two-dimensional canonical coherent state is constructed and its properties are discussed.
Bethe-Salpeter bound-state structure in Minkowski space
Gutierrez, C; Frederico, T; Salmè, G; Viviani, M; Tomio, Lauro
2016-01-01
The quantitative investigation of the scalar Bethe-Salpeter equation in Minkowski space, within the ladder-approximation framework, is extended to include the excited states. This study has been carried out for an interacting system composed by two massive bosons exchanging a massive scalar, by adopting (i) the Nakanishi integral representation of the Bethe-Salpeter amplitude, and (ii) the formally exact projection onto the null plane. Our analysis, on one hand, confirms the reliability of the method already applied to the ground state and, on the other one, extends the investigation from the valence distribution in momentum space to the corresponding quantity in the impact-parameter space, pointing out some relevant features, like (i) the equivalence between Minkowski and Euclidean transverse-momentum amplitudes, and (ii) the leading exponential fall-off of the valence wave function in the impact-parameter space.
Ongoing Space Nuclear Systems Development in the United States
Energy Technology Data Exchange (ETDEWEB)
S. Bragg-Sitton; J. Werner; S. Johnson; Michael G. Houts; Donald T. Palac; Lee S. Mason; David I. Poston; A. Lou Qualls
2011-10-01
Reliable, long-life power systems are required for ambitious space exploration missions. Nuclear power and propulsion options can enable a bold, new set of missions and introduce propulsion capabilities to achieve access to science destinations that are not possible with more conventional systems. Space nuclear power options can be divided into three main categories: radioisotope power for heating or low power applications; fission power systems for non-terrestrial surface application or for spacecraft power; and fission power systems for electric propulsion or direct thermal propulsion. Each of these areas has been investigated in the United States since the 1950s, achieving various stages of development. While some nuclear systems have achieved flight deployment, others continue to be researched today. This paper will provide a brief overview of historical space nuclear programs in the U.S. and will provide a summary of the ongoing space nuclear systems research, development, and deployment in the United States.
Bethe–Salpeter bound-state structure in Minkowski space
Energy Technology Data Exchange (ETDEWEB)
Gutierrez, C. [Instituto de Física Teórica, Universidade Estadual Paulista, 01156-970 São Paulo, SP (Brazil); Gigante, V.; Frederico, T. [Instituto Tecnológico de Aeronáutica, DCTA, 12.228-900 São José dos Campos, SP (Brazil); Salmè, G. [Istituto Nazionale di Fisica Nucleare, Sezione di Roma, P.le A. Moro 2, 00185 Roma (Italy); Viviani, M. [Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Pontecorvo 3, 56100 Pisa (Italy); Tomio, Lauro, E-mail: tomio@ift.unesp.br [Instituto de Física Teórica, Universidade Estadual Paulista, 01156-970 São Paulo, SP (Brazil); Instituto Tecnológico de Aeronáutica, DCTA, 12.228-900 São José dos Campos, SP (Brazil)
2016-08-10
The quantitative investigation of the scalar Bethe–Salpeter equation in Minkowski space, within the ladder-approximation framework, is extended to include the excited states. This study has been carried out for an interacting system composed by two massive bosons exchanging a massive scalar, by adopting (i) the Nakanishi integral representation of the Bethe–Salpeter amplitude, and (ii) the formally exact projection onto the null plane. Our analysis, on one hand, confirms the reliability of the method already applied to the ground state and, on the other one, extends the investigation from the valence distribution in momentum space to the corresponding quantity in the impact-parameter space, pointing out some relevant features, like (i) the equivalence between Minkowski and Euclidean transverse-momentum amplitudes, and (ii) the leading exponential fall-off of the valence wave function in the impact-parameter space.
The structures of state space concerning Quantum Dynamical Semigroups
Baumgartner, Bernhard
2011-01-01
Each semigroup describing the time evolution of an open quantum system on a finite dimensional Hilbert space is related to a special structure of this space. It is shown how the space can be decomposed into subspaces: One is related to decay, orthogonal subspaces support the stationary states. Specialities where the complete positivity of evolutions is actually needed for analysis, mainly for evolution of coherence, are highlighted. Decompositions are done the same way for evolutions in discrete as in continuous time, but evolutions may show differences, only for discrete semigroups there may appear cases of sudden decay and of perpetual oscillation. Concluding the analysis we identify the relation of the state space structure to the processes of Decay, Decoherence, Dissipation and Dephasing.
Space Sciences Education and Outreach Project of Moscow State University
Krasotkin, S.
2006-11-01
sergekras@mail.ru The space sciences education and outreach project was initiated at Moscow State University in order to incorporate modern space research into the curriculum popularize the basics of space physics, and enhance public interest in space exploration. On 20 January 2005 the first Russian University Satellite “Universitetskiy-Tatyana” was launched into circular polar orbit (inclination 83 deg., altitude 940-980 km). The onboard scientific complex “Tatyana“, as well as the mission control and information receiving centre, was designed and developed at Moscow State University. The scientific programme of the mission includes measurements of space radiation in different energy channels and Earth UV luminosity and lightning. The current education programme consists of basic multimedia lectures “Life of the Earth in the Solar Atmosphere” and computerized practice exercises “Space Practice” (based on the quasi-real-time data obtained from “Universitetskiy-Tatyana” satellite and other Internet resources). A multimedia lectures LIFE OF EARTH IN THE SOLAR ATMOSPHERE containing the basic information and demonstrations of heliophysics (including Sun structure and solar activity, heliosphere and geophysics, solar-terrestrial connections and solar influence on the Earth’s life) was created for upper high-school and junior university students. For the upper-university students there a dozen special computerized hands-on exercises were created based on the experimental quasi-real-time data obtained from our satellites. Students specializing in space physics from a few Russian universities are involved in scientific work. Educational materials focus on upper high school, middle university and special level for space physics students. Moscow State University is now extending its space science education programme by creating multimedia lectures on remote sensing, space factors and materials study, satellite design and development, etc. The space
Optical Characterization of Deep-Space Object Rotation States
2014-09-01
Optical Characterization of Deep-Space Object Rotation States Doyle Hall 1 and Paul Kervin 2 1 Boeing LTS, Kihei, Maui, HI and Colorado Springs, CO...0646, OPS-14-6494) Cleared for Public Release (Release # 377ABW-2014-0646, OPS-14-6494) 3. Wallach, B., Somers, P. and Scott , R., “Determination of...Wallace, B., Somers, P., and Scott , R. L., “Determination of Spin Axis Orientation of Geosynchronous Objects Using Space-Based Sensors: An Initial
Discrete state space modeling and control of nonlinear unknown systems.
Savran, Aydogan
2013-11-01
A novel procedure for integrating neural networks (NNs) with conventional techniques is proposed to design industrial modeling and control systems for nonlinear unknown systems. In the proposed approach, a new recurrent NN with a special architecture is constructed to obtain discrete-time state-space representations of nonlinear dynamical systems. It is referred as the discrete state-space neural network (DSSNN). In the DSSNN, the outputs of the hidden layer neurons of the DSSNN represent the system's (pseudo) state. The inputs are fed to output neurons and the delayed outputs of the hidden layer neurons are fed to their inputs via adjustable weights. The discrete state space model of the actual system is directly obtained by training the DSSNN with the input-output data. A training procedure based on the back-propagation through time (BPTT) algorithm is developed. The Levenberg-Marquardt (LM) method with a trust region approach is used to update the DSSNN weights. Linear state space models enable to use well developed conventional analysis and design techniques. Thus, building a linear model of a system has primary importance in industrial applications. Thus, a suitable linearization procedure is proposed to derive the linear state space model from the nonlinear DSSNN representation. The controllability, observability and stability properties are examined. The state feedback controllers are designed with both the linear quadratic regulator (LQR) and the pole placement techniques. The regulator and servo control problems are both addressed. A full order observer is also designed to estimate the state variables. The performance of the proposed procedure is demonstrated by applying for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
State Space Exploration of RT Systems in the Cloud
Bellettini, Carlo; Capra, Lorenzo; Monga, Mattia
2012-01-01
The growing availability of distributed and cloud computing frameworks make it possible to face complex computational problems in a more effective and convenient way. A notable example is state-space exploration of discrete-event systems specified in a formal way. The exponential complexity of this task is a major limitation to the usage of consolidated analysis techniques and tools. We present and compare two different approaches to state-space explosion, relying on distributed and cloud frameworks, respectively. These approaches were designed and implemented following the same computational schema, a sort of map & fold. They are applied on symbolic state-space exploration of real-time systems specified by (a timed extension of) Petri Nets, by readapting a sequential algorithm implemented as a command-line Java tool. The outcome of several tests performed on a benchmarking specification are presented, thus showing the convenience of cloud approaches.
Neuromorphic Continuous-Time State Space Pole Placement Adaptive Control
Institute of Scientific and Technical Information of China (English)
卢钊; 孙明伟
2003-01-01
A neuromorphic continuous-time state space pole assignment adaptive controller is proposed, which is particularly appropriate for controlling a large-scale time-variant state-space model due to the parallely distributed nature of neurocomputing. In our approach, Hopfield neural network is exploited to identify the parameters of a continuous-time state-space model, and a dedicated recurrent neural network is designed to compute pole placement feedback control law in real time. Thus the identification and the control computation are incorporated in the closed-loop, adaptive, real-time control system. The merit of this approach is that the neural networks converge to their solutions very quickly and simultaneously.
Multivariate time series with linear state space structure
Gómez, Víctor
2016-01-01
This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students wor...
State-Space Modelling of Loudspeakers using Fractional Derivatives
DEFF Research Database (Denmark)
King, Alexander Weider; Agerkvist, Finn T.
2015-01-01
This work investigates the use of fractional order derivatives in modeling moving-coil loudspeakers. A fractional order state-space solution is developed, leading the way towards incorporating nonlinearities into a fractional order system. The method is used to calculate the response....... It is shown that the identified parameters can be used in a linear fractional order state-space model to simulate the loudspeakers’ time domain response...... of a fractional harmonic oscillator, representing the mechanical part of a loudspeaker, showing the effect of the fractional derivative and its relationship to viscoelasticity. Finally, a loudspeaker model with a fractional order viscoelastic suspension and fractional order voice coil is fit to measurement data...
Set point control in the state space setting
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad
. The focus is in this report related to the problem of handling a set point or a constant reference in a state space setting. In principle just about any (state space control) design methodology may be applied. Here the presentation is based on LQ design, but other types such as poleplacement can be applied......This report is intented as a supplement or an extension to the material used in connection to or after the courses Stochastic Adaptive Control (02421) and Static and Dynamic Optimization (02711) given at the Department of Informatics and Mathematical Modelling, The Technical University of Denmark...
State space modeling of Memristor-based Wien oscillator
Talukdar, Abdul Hafiz Ibne
2011-12-01
State space modeling of Memristor based Wien \\'A\\' oscillator has been demonstrated for the first time considering nonlinear ion drift in Memristor. Time dependant oscillating resistance of Memristor is reported in both state space solution and SPICE simulation which plausibly provide the basis of realizing parametric oscillation by Memristor based Wien oscillator. In addition to this part Memristor is shown to stabilize the final oscillation amplitude by means of its nonlinear dynamic resistance which hints for eliminating diode in the feedback network of conventional Wien oscillator. © 2011 IEEE.
Transformation of state space for two-parameter Markov processes
Institute of Scientific and Technical Information of China (English)
周健伟
1996-01-01
Let X=(X) be a two-parameter *-Markov process with a transition function (p1, p2, p), where X, takes values in the state space (Er,), T=[0,)2. For each r T, let f, be a measurable transformation of (E,) into the state space (E’r, ). Set Y,=f,(X,), r T. A sufficient condition is given for the process Y=(Yr) still to be a two-parameter *-Markov process with a transition function in terms of transition function (p1, p2, p) and fr. For *-Markov families of two-parameter processes with a transition function, a similar problem is also discussed.
A Right Coprime Factorization of Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
2007-01-01
In recent years, various methods for identification of nonlinear systems in closed loop using open-loop approaches have received considerable attention. However, these methods rely on differentially coprime factorizations of the nonlinear plants, which can be difficult to compute in practice....... To address this issue, this paper presents various technical results leading up to explicit formulae for right coprime factorizations of neural state space models, i.e., nonlinear system models represented in state space using neural networks, which satisfy a Bezout identity. ...
Interaction Network, State Space and Control in Social Dynamics
Aydogdu, Aylin; McQuade, Sean; Piccoli, Benedetto; Duteil, Nastassia Pouradier; Rossi, Francesco; Trélat, Emmanuel
2016-01-01
In the present chapter we study the emergence of global patterns in large groups in first and second-order multi-agent systems, focusing on two ingredients that influence the dynamics: the interaction network and the state space. The state space determines the types of equilibrium that can be reached by the system. Meanwhile, convergence to specific equilibria depends on the connectivity of the interaction network and on the interaction potential. When the system does not satisfy the necessary conditions for convergence to the desired equilibrium, control can be exerted, both on finite-dimensional systems and on their mean-field limit.
Optimal State-Space Reduction for Pedigree Hidden Markov Models
Kirkpatrick, Bonnie
2012-01-01
To analyze whole-genome genetic data inherited in families, the likelihood is typically obtained from a Hidden Markov Model (HMM) having a state space of 2^n hidden states where n is the number of meioses or edges in the pedigree. There have been several attempts to speed up this calculation by reducing the state-space of the HMM. One of these methods has been automated in a calculation that is more efficient than the naive HMM calculation; however, that method treats a special case and the efficiency gain is available for only those rare pedigrees containing long chains of single-child lineages. The other existing state-space reduction method treats the general case, but the existing algorithm has super-exponential running time. We present three formulations of the state-space reduction problem, two dealing with groups and one with partitions. One of these problems, the maximum isometry group problem was discussed in detail by Browning and Browning. We show that for pedigrees, all three of these problems hav...
Estimation methods for nonlinear state-space models in ecology
DEFF Research Database (Denmark)
Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro
2011-01-01
The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...
Semiclassical Approximations in Phase Space with Coherent States
Baranger, Michel; de Aguiar, Marcus A. M.; Keck, Frank; Korsch, Hans-Jürgen; Schellhaaß, Bernd
2001-01-01
We present a complete derivation of the semiclassical limit of the coherent state propagator in one dimension, starting from path integrals in phase space. We show that the arbitrariness in the path integral representation, which follows from the overcompleteness of the coherent states, results in many different semiclassical limits. We explicitly derive two possible semiclassical formulae for the propagator, we suggest a third one, and we discuss their relationships. We also derive an initia...
A state-space algorithm for the spectral factorization
Kraffer, F.; Kwakernaak, H.
1997-01-01
This paper presents an algorithm for the spectral factorization of a para-Hermitian polynomial matrix. The algorithm is based on polynomial matrix to state space and vice versa conversions, and avoids elementary polynomial operations in computations; It relies on well-proven methods of numerical lin
Hybrid state-space time integration of rotating beams
DEFF Research Database (Denmark)
Krenk, Steen; Nielsen, Martin Bjerre
2012-01-01
An efficient time integration algorithm for the dynamic equations of flexible beams in a rotating frame of reference is presented. The equations of motion are formulated in a hybrid state-space format in terms of local displacements and local components of the absolute velocity. With inspiration ...
Fast Filtering and Smoothing for Multivariate State Space Models
Koopman, S.J.M.; Durbin, J.
1998-01-01
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually. This strategy leads to computationally efficient methods f
Parameter redundancy in discrete state-space and integrated models.
Cole, Diana J; McCrea, Rachel S
2016-09-01
Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Projective Limits of State Spaces IV. Fractal Label Sets
Lanéry, Suzanne
2015-01-01
Instead of formulating the state space of a quantum field theory over one big Hilbert space, it has been proposed by Kijowski [Kijowski 1977] to represent quantum states as projective families of density matrices over a collection of smaller, simpler Hilbert spaces. One can thus bypass the need to select a vacuum state for the theory, and still be provided with an explicit and constructive description of the quantum state space, at least as long as the label set indexing the projective structure is countable. Because uncountable label sets are much less practical in this context, we develop in the present article a general procedure to trim an originally uncountable label set down to countable cardinality. In particular, we investigate how to perform this tightening of the label set in a way that preserves both the physical content of the algebra of observables and its symmetries. This work is notably motivated by applications to the holonomy-flux algebra underlying Loop Quantum Gravity. Building on earlier w...
State Space Reduction of Linear Processes Using Control Flow Reconstruction
Pol, van de Jaco; Timmer, Mark; Liu, Z.; Ravn, A.P.
2009-01-01
We present a new method for fighting the state space explosion of process algebraic specifications, by performing static analysis on an intermediate format: linear process equations (LPEs). Our method consists of two steps: (1) we reconstruct the LPE's control flow, detecting control flow parameters
State Space Reduction of Linear Processes using Control Flow Reconstruction
Pol, van de Jaco; Timmer, Mark
2009-01-01
We present a new method for fighting the state space explosion of process algebraic specifications, by performing static analysis on an intermediate format: linear process equations (LPEs). Our method consists of two steps: (1) we reconstruct the LPE's control flow, detecting control flow parameters
An Embeddable Virtual Machine for State Space Generation
Weber, M.; Bosnacki, D.; Edelkamp, S.
2007-01-01
The semantics of modelling languages are not always specified in a precise and formal way, and their rather complex underlying models make it a non-trivial exercise to reuse them in newly developed tools. We report on experiments with a virtual machine-based approach for state space generation. The
Projective limits of state spaces II. Quantum formalism
Lanéry, Suzanne; Thiemann, Thomas
2017-06-01
In this series of papers, we investigate the projective framework initiated by Kijowski (1977) and Okołów (2009, 2014, 2013), which describes the states of a quantum theory as projective families of density matrices. A short reading guide to the series can be found in Lanéry (2016). After discussing the formalism at the classical level in a first paper (Lanéry, 2017), the present second paper is devoted to the quantum theory. In particular, we inspect in detail how such quantum projective state spaces relate to inductive limit Hilbert spaces and to infinite tensor product constructions (Lanéry, 2016, subsection 3.1) [1]. Regarding the quantization of classical projective structures into quantum ones, we extend the results by Okołów (2013), that were set up in the context of linear configuration spaces, to configuration spaces given by simply-connected Lie groups, and to holomorphic quantization of complex phase spaces (Lanéry, 2016, subsection 2.2) [1].
State Space Path Integrals for Electronically Nonadiabatic Reaction Rates
Duke, Jessica Ryan
2016-01-01
We present a state-space-based path integral method to calculate the rate of electron transfer (ET) in multi-state, multi-electron condensed-phase processes. We employ an exact path integral in discrete electronic states and continuous Cartesian nuclear variables to obtain a transition state theory (TST) estimate to the rate. A dynamic recrossing correction to the TST rate is then obtained from real-time dynamics simulations using mean field ring polymer molecular dynamics. We employ two different reaction coordinates in our simulations and show that, despite the use of mean field dynamics, the use of an accurate dividing surface to compute TST rates allows us to achieve remarkable agreement with Fermi's golden rule rates for nonadiabatic ET in the normal regime of Marcus theory. Further, we show that using a reaction coordinate based on electronic state populations allows us to capture the turnover in rates for ET in the Marcus inverted regime.
Replacement Capability Options for the United States Space Shuttle
2013-09-01
first designed for reuse ” (NASA, 2000). 1. United States Space Shuttle Program (1981–2011) The first operational Space Shuttle was Columbia (OV-102...Week article on China’s future plans for their Long March Launch vehicles, “China is developing three basic rocket modules, with diameters of 2.25... wastewater , which will burn up with the spacecraft when it re-enters the Earth’s atmosphere. The Cargo Module can hold 1,000 to 1,700 kilograms (2,205
Tomsk State University: Space-planning development concept
Directory of Open Access Journals (Sweden)
Elena Grigoryeva
2015-05-01
Full Text Available The article features the space-planning development concept for National Research Tomsk State University and the subsequent sketch design. Together with extension of educational and laboratory area, the system of open exterior and interior public spaces is created for interpersonal communication, independent work, leisure, self-presentations, team building events, etc. One of the leading principles is preservation of the University historical heritage together with appliance of advanced architectural and spatial methods and integration of facilities built at different times into one complex.
Bauman Moscow State Technical University Youth Space Centre: Student's Way in Space Technologies
Mayorova, Victoria; Zelentsov, Victor
2002-01-01
The Youth Space Center (YSC) was established in Bauman Moscow State Technical University (BMSTU) in 1989 to provide primary aerospace education for young people, stimulate youth creative research thinking, promote space science and technology achievements and develop cooperation with other youth organizations in the international aerospace community. The center is staffed by the Dr. Victoria Mayorova, BMSTU Associate Professor, the YSC director, Dr. Boris Kovalev, BMSTU Associate Professor, the YSC scientific director, 5 student consultants and many volunteers. Informally YSC is a community of space enthusiasts, an open club for BMSTU students interested in space science and technology and faculty teaching in this field. YSC educational activities are based on the concept of uninterrupted aerospace education, developed and implemented by the center. The concept includes working with young space interested people both in school and university and then assisting them in getting interesting job in Russian Space Industry. The school level educational activities of the center has got different forms, such as lecturing, summer scientific camps and even Classes from Space given by Mir space station flight crew in Mission Control Center - Moscow and done in cooperation with All- Russian Aerospace Society Soyuz (VAKO Soyuz). This helps to stimulate the young people interest to the fundamental sciences ( physics, mathematics, computer science, etc.) exploiting and developing their interest to space and thus increase the overall educational level in the country. YSC hosts annual Cosmonautics conference for high school students that provides the University with capability to select well-prepared and motivated students for its' rocket and space related departments. For the conference participants it's a good opportunity to be enrolled to the University without entrance examinations. BMSTU students can participate in such YSC activities as annual international workshop for space
Pure state consciousness and its local reduction to neuronal space
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.
Some Modal Relations and Generalized Velocity Method in State Space
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Real mode theory in configuration space has shown that the mode acceleration method converges faster than the mode displacement method. This paper demonstrates a similar conclusion in the state space. Some new expressions on modal parameter matrices were set up first. A generalized velocity method (GVM) is then demonstrated in a systematic way. This method is the so-called complex mode velocity method, but the expressions and schemes are given in terms of parametric matrices in configuration space. Theoretical comparison of this GVM with the traditional complex mode method shows some interesting conclusions. The latter approach is actually a generalized displacement method (GDM). Without mode reduction, the displacement responses of the concerned system resulting from both approaches are identical. On the other hand, both approaches have to adopt mode reduction to become practical. Under this situation, GVM has advantages because it compensates for the contribution of the omitted high-order modes to the displacement responses.
Validation of ecological state space models using the Laplace approximation
DEFF Research Database (Denmark)
Thygesen, Uffe Høgsbro; Albertsen, Christoffer Moesgaard; Berg, Casper Willestofte
2017-01-01
Many statistical models in ecology follow the state space paradigm. For such models, the important step of model validation rarely receives as much attention as estimation or hypothesis testing, perhaps due to lack of available algorithms and software. Model validation is often based on a naive...... for estimation in general mixed effects models. Implementing one-step predictions in the R package Template Model Builder, we demonstrate that it is possible to perform model validation with little effort, even if the ecological model is multivariate, has non-linear dynamics, and whether observations...... are continuous or discrete. With both simulated data, and a real data set related to geolocation of seals, we demonstrate both the potential and the limitations of the techniques. Our results fill a need for convenient methods for validating a state space model, or alternatively, rejecting it while indicating...
Latent state-space models for neural decoding.
Aghagolzadeh, Mehdi; Truccolo, Wilson
2014-01-01
Ensembles of single-neurons in motor cortex can show strong low-dimensional collective dynamics. In this study, we explore an approach where neural decoding is applied to estimated low-dimensional dynamics instead of to the full recorded neuronal population. A latent state-space model (SSM) approach is used to estimate the low-dimensional neural dynamics from the measured spiking activity in population of neurons. A second state-space model representation is then used to decode kinematics, via a Kalman filter, from the estimated low-dimensional dynamics. The latent SSM-based decoding approach is illustrated on neuronal activity recorded from primary motor cortex in a monkey performing naturalistic 3-D reach and grasp movements. Our analysis show that 3-D reach decoding performance based on estimated low-dimensional dynamics is comparable to the decoding performance based on the full recorded neuronal population.
State-space Manifold and Rotating Black Holes
Bellucci, Stefano
2010-01-01
We study a class of fluctuating higher dimensional black hole configurations obtained in string theory/ $M$-theory compactifications. We explore the intrinsic Riemannian geometric nature of Gaussian fluctuations arising from the Hessian of the coarse graining entropy, defined over an ensemble of brane microstates. It has been shown that the state-space geometry spanned by the set of invariant parameters is non-degenerate, regular and has a negative scalar curvature for the rotating Myers-Perry black holes, Kaluza-Klein black holes, supersymmetric $AdS_5$ black holes, $D_1$-$D_5$ configurations and the associated BMPV black holes. Interestingly, these solutions demonstrate that the principal components of the state-space metric tensor admit a positive definite form, while the off diagonal components do not. Furthermore, the ratio of diagonal components weakens relatively faster than the off diagonal components, and thus they swiftly come into an equilibrium statistical configuration. Novel aspects of the scali...
Semiclassical approximations in phase space with coherent states
Energy Technology Data Exchange (ETDEWEB)
Baranger, M. [Center for Theoretical Physics, Laboratory for Nuclear Science and Department of Physics, Massachusetts Institute of Technology, Cambridge, MA (United States); De Aguiar, M.A.M. [Center for Theoretical Physics, Laboratory for Nuclear Science and Department of Physics, Massachusetts Institute of Technology, Cambridge, MA (United States); Instituto de Fisica ' Gleb Wataghin' , Universidade Estadual de Campinas, Campinas (Brazil); Keck, F.; Korsch, H.J.; Schellhaass, B. [FB Physik, Universitaet Kaiserslautern, Kaiserslautern (Germany)
2001-09-14
We present a complete derivation of the semiclassical limit of the coherent-state propagator in one dimension, starting from path integrals in phase space. We show that the arbitrariness in the path integral representation, which follows from the overcompleteness of the coherent states, results in many different semiclassical limits. We explicitly derive two possible semiclassical formulae for the propagator, we suggest a third one, and we discuss their relationships. We also derive an initial-value representation for the semiclassical propagator, based on an initial Gaussian wavepacket. It turns out to be related to, but different from, Heller's thawed Gaussian approximation. It is very different from the Herman-Kluk formula, which is not a correct semiclassical limit. We point out errors in two derivations of the latter. Finally we show how the semiclassical coherent-state propagators lead to WKB-type quantization rules and to approximations for the Husimi distributions of stationary states. (author)
Semiclassical approximations in phase space with coherent states
Baranger, M.; de Aguiar, M. A. M.; Keck, F.; Korsch, H. J.; Schellhaaß, B.
2001-09-01
We present a complete derivation of the semiclassical limit of the coherent-state propagator in one dimension, starting from path integrals in phase space. We show that the arbitrariness in the path integral representation, which follows from the overcompleteness of the coherent states, results in many different semiclassical limits. We explicitly derive two possible semiclassical formulae for the propagator, we suggest a third one, and we discuss their relationships. We also derive an initial-value representation for the semiclassical propagator, based on an initial Gaussian wavepacket. It turns out to be related to, but different from, Heller's thawed Gaussian approximation. It is very different from the Herman-Kluk formula, which is not a correct semiclassical limit. We point out errors in two derivations of the latter. Finally we show how the semiclassical coherent-state propagators lead to WKB-type quantization rules and to approximations for the Husimi distributions of stationary states.
Entangled Bloch Spheres: Bloch Matrix And Two Qubit State Space
Gamel, Omar
2016-01-01
We represent a two qubit density matrix in the basis of Pauli matrix tensor products, with the coefficients constituting a Bloch matrix, analogous to the single qubit Bloch vector. We find the quantum state positivity requirements on the Bloch matrix components, leading to three important inequalities, allowing us to parameterize and visualize the two qubit state space. Applying the singular value decomposition naturally separates the degrees of freedom to local and nonlocal, and simplifies the positivity inequalities. It also allows us to geometrically represent a state as two entangled Bloch spheres with superimposed correlation axes. It is shown that unitary transformations, local or nonlocal, have simple interpretations as axis rotations or mixing of certain degrees of freedom. The nonlocal unitary invariants of the state are then derived in terms of local unitary invariants. The positive partial transpose criterion for entanglement is generalized, and interpreted as a reflection, or a change of a single ...
Attention control learning in the decision space using state estimation
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.
Prediction and interpolation of time series by state space models
Helske, Jouni
2015-01-01
A large amount of data collected today is in the form of a time series. In order to make realistic inferences based on time series forecasts, in addition to point predictions, prediction intervals or other measures of uncertainty should be presented. Multiple sources of uncertainty are often ignored due to the complexities involved in accounting them correctly. In this dissertation, some of these problems are reviewed and some new solutions are presented. A state space approach...
State Space identification of Civil Engineering Structures from Output Measurements
1996-01-01
This paper presents the results from a state space system identification simulation study of a 5-degrees-of freedom system driven by white noise. The aim of the study is to compare the durability of the fairly new Stochastic Subspace Technique (SST) with more well-known techniques for identification of civil engineering structures. The SST is compared with the stochastic realization estimator Matrix Block Hankel (MBH) and a prediction error method (PEM). The results show that the investigated...
State-Space Methods for µ-Analysis
Helmersson, Anders
1994-01-01
This paper discusses state-space methods for analyzing stability of continuous time linear systems subject to structured uncertainties. Four types of uncertainties are discussed: linear parametric and dynamic uncertainties (real and complex µ) and nonlinear parametric and dynamic uncertainties. The method employs LMIs equipped with a scaling matrix adapted to the type of uncertainty. For parametric uncertainties conservativeness is reduced by branch and bound schemes. Different types of uncer...
Automatic Design of a Maglev Controller in State Space
1991-12-01
conventional trains with steel wheels on steel rails. Several experimen- tal maglev systems in Germany and Japan have demonstrated that this mode of...Design of a Maglev Controller in State Space Feng Zhao Richard Thornton Abstract We describe the automatic synthesis of a global nonlinear controller for...the global switching points of the controller is presented. The synthesized control system can stabilize the maglev vehicle with large initial displace
Solving Bethe-Salpeter scattering state equation in Minkowski space
Carbonell, J
2014-01-01
We present a method to directly solving the Bethe-Salpeter equation in Minkowski space, both for bound and scattering states. It is based on a proper treatment of the singularities which appear in the kernel, propagators and Bethe-Salpeter amplitude itself. The off-mass shell scattering amplitude for spinless particles interacting by a one boson exchange is computed for the first time.
State Space identification of Civil Engineering Structures from Output Measurements
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.
1997-01-01
This paper presents the results from a state space system identification simulation study of a 5-degrees-of freedom system driven by white noise. The aim of the study is to compare the durability of the fairly new Stochastic Subspace Technique (SST) with more well-known techniques......, it is found that the new SST technique gives quickly good results compared with the PEM which takes more time with only a limited improvement of the fit on data....
State-Space Methods for µ-Analysis
Helmersson, Anders
1994-01-01
This paper discusses state-space methods for analyzing stability of continuous time linear systems subject to structured uncertainties. Four types of uncertainties are discussed: linear parametric and dynamic uncertainties (real and complex µ) and nonlinear parametric and dynamic uncertainties. The method employs LMIs equipped with a scaling matrix adapted to the type of uncertainty. For parametric uncertainties conservativeness is reduced by branch and bound schemes. Different types of uncer...
Advanced Solid State Lighting for AES Deep Space Hab Project
Holbert, Eirik
2015-01-01
The advanced Solid State Lighting (SSL) assemblies augmented 2nd generation modules under development for the Advanced Exploration Systems Deep Space Habitat in using color therapy to synchronize crew circadian rhythms. Current RGB LED technology does not produce sufficient brightness to adequately address general lighting in addition to color therapy. The intent is to address both through a mix of white and RGB LEDs designing for fully addressable alertness/relaxation levels as well as more dramatic circadian shifts.
Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.
Liu, Ziyue; Cappola, Anne R; Crofford, Leslie J; Guo, Wensheng
2014-01-01
The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.
14 CFR 1217.106 - Articles brought into the United States by NASA from space.
2010-01-01
... NASA from space. 1217.106 Section 1217.106 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION DUTY-FREE ENTRY OF SPACE ARTICLES § 1217.106 Articles brought into the United States by NASA from... territory of the United States by NASA from space shall not be considered an importation, and...
Complex network analysis of state spaces for random Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Shreim, Amer [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Berdahl, Andrew [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Sood, Vishal [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Grassberger, Peter [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Paczuski, Maya [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada)
2008-01-15
We apply complex network analysis to the state spaces of random Boolean networks (RBNs). An RBN contains N Boolean elements each with K inputs. A directed state space network (SSN) is constructed by linking each dynamical state, represented as a node, to its temporal successor. We study the heterogeneity of these SSNs at both local and global scales, as well as sample to-sample fluctuations within an ensemble of SSNs. We use in-degrees of nodes as a local topological measure, and the path diversity (Shreim A et al 2007 Phys. Rev. Lett. 98 198701) of an SSN as a global topological measure. RBNs with 2 {<=} K {<=} 5 exhibit non-trivial fluctuations at both local and global scales, while K = 2 exhibits the largest sample-to-sample (possibly non-self-averaging) fluctuations. We interpret the observed 'multi scale' fluctuations in the SSNs as indicative of the criticality and complexity of K = 2 RBNs. 'Garden of Eden' (GoE) states are nodes on an SSN that have in-degree zero. While in-degrees of non-GoE nodes for K > 1 SSNs can assume any integer value between 0 and 2{sup N}, for K = 1 all the non-GoE nodes in a given SSN have the same in-degree which is always a power of two.
Space Monitoring Data Center at Moscow State University
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.
Tensorial dynamics on the space of quantum states
Cariñena, J. F.; Clemente-Gallardo, J.; Jover-Galtier, J. A.; Marmo, G.
2017-09-01
A geometric description of the space of states of a finite-dimensional quantum system and of the Markovian evolution associated with the Kossakowski-Lindblad operator is presented. This geometric setting is based on two composition laws on the space of observables defined by a pair of contravariant tensor fields. The first one is a Poisson tensor field that encodes the commutator product and allows us to develop a Hamiltonian mechanics. The other tensor field is symmetric, encodes the Jordan product and provides the variances and covariances of measures associated with the observables. This tensorial formulation of quantum systems is able to describe, in a natural way, the Markovian dynamical evolution as a vector field on the space of states. Therefore, it is possible to consider dynamical effects on non-linear physical quantities, such as entropies, purity and concurrence. In particular, in this work the tensorial formulation is used to consider the dynamical evolution of the symmetric and skew-symmetric tensors and to read off the corresponding limits as giving rise to a contraction of the initial Jordan and Lie products.
Mapping from Speech to Images Using Continuous State Space Models
DEFF Research Database (Denmark)
Lehn-Schiøler, Tue; Hansen, Lars Kai; Larsen, Jan
2005-01-01
In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in 'face space......'. The performance of the system is critically dependent on the number of hidden variables, with too few variables the model cannot represent data, and with too many overfitting is noticed. Simulations are performed on recordings of 3-5 sec.\\$\\backslash\\$ video sequences with sentences from the Timit database. From...... a subjective point of view the model is able to construct an image sequence from an unknown noisy speech sequence even though the number of training examples are limited....
On Volterra quadratic stochastic operators with continual state space
Energy Technology Data Exchange (ETDEWEB)
Ganikhodjaev, Nasir; Hamzah, Nur Zatul Akmar [Department of Computational and Theoretical Sciences, Faculty of Science, International Islamic University, Jalan Sultan Ahmad Shah, Bandar Indera Mahkota, 25200 Kuantan, Pahang (Malaysia)
2015-05-15
Let (X,F) be a measurable space, and S(X,F) be the set of all probability measures on (X,F) where X is a state space and F is σ - algebraon X. We consider a nonlinear transformation (quadratic stochastic operator) defined by (Vλ)(A) = ∫{sub X}∫{sub X}P(x,y,A)dλ(x)dλ(y), where P(x, y, A) is regarded as a function of two variables x and y with fixed A ∈ F . A quadratic stochastic operator V is called a regular, if for any initial measure the strong limit lim{sub n→∞} V{sup n }(λ) is exists. In this paper, we construct a family of quadratic stochastic operators defined on the segment X = [0,1] with Borel σ - algebra F on X , prove their regularity and show that the limit measure is a Dirac measure.
A hierarchical state space approach to affective dynamics
Lodewyckx, Tom; Tuerlinckx, Francis; Kuppens, Peter; Allen, Nicholas; Sheeber, Lisa
2010-01-01
Linear dynamical system theory is a broad theoretical framework that has been applied in various research areas such as engineering, econometrics and recently in psychology. It quantifies the relations between observed inputs and outputs that are connected through a set of latent state variables. State space models are used to investigate the dynamical properties of these latent quantities. These models are especially of interest in the study of emotion dynamics, with the system representing the evolving emotion components of an individual. However, for simultaneous modeling of individual and population differences, a hierarchical extension of the basic state space model is necessary. Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters. Further, we apply our model to data that were collected using the Oregon adolescent interaction task: 66 normal and 67 depressed adolescents engaged in a conflict interaction with their parents and second-to-second physiological and behavioral measures were obtained. System parameters in normal and depressed adolescents were compared, which led to interesting discussions in the light of findings in recent literature on the links between cardiovascular processes, emotion dynamics and depression. We illustrate that our approach is flexible and general: The model can be applied to any time series for multiple systems (where a system can represent any entity) and moreover, one is free to focus on whatever component of the versatile model. PMID:21516216
Nonlinear state space model identification of synchronous generators
Energy Technology Data Exchange (ETDEWEB)
Dehghani, M.; Nikravesh, S.K.Y. [Electrical Engineering Department, Amirkabir University of Technology, Tehran (Iran)
2008-05-15
A method for identification of a synchronous generator is suggested in this paper. The method uses the theoretical relations of machine parameters and the Prony method to find the state space model of the system. Such models are useful for controller design and stability tests. The proposed identification method is applied to a third order model of a synchronous generator. In this study, the field voltage is considered as the input and the active output power and the rotor angle are considered as the outputs of the synchronous generator. Simulation results show good accuracy of the identified model. (author)
Averaging in Parametrically Excited Systems – A State Space Formulation
Directory of Open Access Journals (Sweden)
Pfau Bastian
2016-01-01
Full Text Available Parametric excitation can lead to instabilities as well as to an improved stability behavior, depending on whether a parametric resonance or anti-resonance is induced. In order to calculate the stability domains and boundaries, the method of averaging is applied. The problem is reformulated in state space representation, which allows a general handling of the averaging method especially for systems with non-symmetric system matrices. It is highlighted that this approach can enhance the first order approximation significantly. Two example systems are investigated: a generic mechanical system and a flexible rotor in journal bearings with adjustable geometry.
H_2-Optimal Decentralized Control over Posets: A State-Space Solution for State-Feedback
Shah, Parikshit
2011-01-01
We develop a complete state-space solution to H_2-optimal decentralized control of poset-causal systems with state-feedback. Our solution is based on the exploitation of a key separability property of the problem, that enables an efficient computation of the optimal controller by solving a small number of uncoupled standard Riccati equations. Our approach gives important insight into the structure of optimal controllers, such as controller degree bounds that depend on the structure of the poset. A novel element in our state-space characterization of the controller is a remarkable pair of transfer functions, that belong to the incidence algebra of the poset, are inverses of each other, and are intimately related to prediction of the state along the different paths on the poset. The results are illustrated by a numerical example.
A nonlinear state-space approach to hysteresis identification
Noël, J. P.; Esfahani, A. F.; Kerschen, G.; Schoukens, J.
2017-02-01
Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinear state-space models to identify hysteresis dynamics. This approach is shown to provide a general framework to hysteresis identification, featuring flexibility and parsimony of representation. Nonlinear model terms are constructed as a multivariate polynomial in the state variables, and parameter estimation is performed by minimising weighted least-squares cost functions. Technical issues, including the selection of the model order and the polynomial degree, are discussed, and model validation is achieved in both broadband and sine conditions. The study is carried out numerically by exploiting synthetic data generated via the Bouc-Wen equations.
Scherpen, Jacquelien M.A.; Gray, W. Steven
2000-01-01
In this paper a set of sufficient conditions is developed in terms of controllability and observability functions under which a given state-space realization of a formal power series is minimal. Specifically, it is shown that positivity of these functions, in addition to a stability requirement and
State space approach to single molecule localization in fluorescence microscopy.
Vahid, Milad R; Chao, Jerry; Kim, Dongyoung; Ward, E Sally; Ober, Raimund J
2017-03-01
Single molecule super-resolution microscopy enables imaging at sub-diffraction-limit resolution by producing images of subsets of stochastically photoactivated fluorophores over a sequence of frames. In each frame of the sequence, the fluorophores are accurately localized, and the estimated locations are used to construct a high-resolution image of the cellular structures labeled by the fluorophores. Many methods have been developed for localizing fluorophores from the images. The majority of these methods comprise two separate steps: detection and estimation. In the detection step, fluorophores are identified. In the estimation step, the locations of the identified fluorophores are estimated through an iterative approach. Here, we propose a non-iterative state space-based localization method which combines the detection and estimation steps. We demonstrate that the estimated locations obtained from the proposed method can be used as initial conditions in an estimation routine to potentially obtain improved location estimates. The proposed method models the given image as the frequency response of a multi-order system obtained with a balanced state space realization algorithm based on the singular value decomposition of a Hankel matrix. The locations of the poles of the resulting system determine the peak locations in the frequency domain, and the locations of the most significant peaks correspond to the single molecule locations in the original image. The performance of the method is validated using both simulated and experimental data.
Nonlinear regime-switching state-space (RSSS) models.
Chow, Sy-Miin; Zhang, Guangjian
2013-10-01
Nonlinear dynamic factor analysis models extend standard linear dynamic factor analysis models by allowing time series processes to be nonlinear at the latent level (e.g., involving interaction between two latent processes). In practice, it is often of interest to identify the phases--namely, latent "regimes" or classes--during which a system is characterized by distinctly different dynamics. We propose a new class of models, termed nonlinear regime-switching state-space (RSSS) models, which subsumes regime-switching nonlinear dynamic factor analysis models as a special case. In nonlinear RSSS models, the change processes within regimes, represented using a state-space model, are allowed to be nonlinear. An estimation procedure obtained by combining the extended Kalman filter and the Kim filter is proposed as a way to estimate nonlinear RSSS models. We illustrate the utility of nonlinear RSSS models by fitting a nonlinear dynamic factor analysis model with regime-specific cross-regression parameters to a set of experience sampling affect data. The parallels between nonlinear RSSS models and other well-known discrete change models in the literature are discussed briefly.
Iterative feedback tuning of uncertain state space systems
Directory of Open Access Journals (Sweden)
J. K. Huusom
2010-09-01
Full Text Available Iterative Feedback Tuning is a purely data driven tuning algorithm for optimizing control parameters based on closed loop data. The algorithm is designed to produce an unbiased estimate of the performance cost function gradient for iteratively improving the control parameters to achieve optimal loop performance. This tuning method has been developed for systems based on a transfer function representation. This paper presents a state feedback control system with a state observer and its transfer function equivalent in terms of input output dynamics. It is shown how the parameters in the closed loop state space system can be tuned by Iterative Feedback Tuning utilizing this equivalent representation. A simulation example illustrates that the tuning converges to the known analytical solution for the feedback control gain and to the Kalman gain in the state observer. In case of parametric uncertainty, different choices of tuning parameters are investigated. It is shown that the data driven tuning method produces optimal performance for convex problems when it is the model parameter estimates in the observer that are tuned.
A Markovian state-space framework for integrating flexibility into space system design decisions
Lafleur, Jarret M.
The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of
Extended knots and the space of states of quantum gravity
Griego, J R
1996-01-01
In the loop representation the quantum constraints of gravity can be solved. This fact allowed significant progress in the understanding of the space of states of the theory. The analysis of the constraints over loop dependent wavefunctions has been traditionally based upon geometric (in contrast to analytic) properties of the loops. The reason for this preferred way is twofold: for one hand the inherent difficulties associated with the analytic loop calculus, and on the other our limited knowledge about the analytic properties of knots invariants. Extended loops provide a way to overcome the difficulties at both levels. For one hand, a systematic method to construct analytic expressions of diffeomorphism invariants (the extended knots) in terms of the Chern-Simons propagators can be developed. Extended knots are simply related to ordinary knots (at least formally). The analytic expressions of knot invariants could be produced then in a generic way. On the other hand, the evaluation of the Hamiltonian over ex...
Practical Application of Neural Networks in State Space Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... applied to a realistic process. The thesis therefore strives to provide a thorough treatment of two classes of neural network-based controllers, and to make a rigorous comparison between them and a classical linear controller. Thus, the thesis starts out with a short review of some relevant system...... theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train...
Noise in oscillators: a review of state space decomposition approaches
Traversa, Fabio L; Corinto, Fernando; Bonani, Fabrizio
2014-01-01
We review the state space decomposition techniques for the assessment of the noise properties of autonomous oscillators, a topic of great practical and theoretical importance for many applications in many different fields, from electronics, to optics, to biology. After presenting a rigorous definition of phase, given in terms of the autonomous system isochrons, we provide a generalized projection technique that allows to decompose the oscillator fluctuations in terms of phase and amplitude noise, pointing out that the very definition of phase (and orbital) deviations depends of the base chosen to define the aforementioned projection. After reviewing the most advanced theories for phase noise, based on the use of the Floquet basis and of the reduction of the projected model by neglecting the orbital fluctuations, we discuss the intricacies of the phase reduction process pointing out the presence of possible variations of the noisy oscillator frequency due to amplitude-related effects.
Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies
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.
Non-Parametric Bayesian State Space Estimator for Negative Information
Directory of Open Access Journals (Sweden)
Guillaume de Chambrier
2017-09-01
Full Text Available Simultaneous Localization and Mapping (SLAM is concerned with the development of filters to accurately and efficiently infer the state parameters (position, orientation, etc. of an agent and aspects of its environment, commonly referred to as the map. A mapping system is necessary for the agent to achieve situatedness, which is a precondition for planning and reasoning. In this work, we consider an agent who is given the task of finding a set of objects. The agent has limited perception and can only sense the presence of objects if a direct contact is made, as a result most of the sensing is negative information. In the absence of recurrent sightings or direct measurements of objects, there are no correlations from the measurement errors that can be exploited. This renders SLAM estimators, for which this fact is their backbone such as EKF-SLAM, ineffective. In addition for our setting, no assumptions are taken with respect to the marginals (beliefs of both the agent and objects (map. From the loose assumptions we stipulate regarding the marginals and measurements, we adopt a histogram parametrization. We introduce a Bayesian State Space Estimator (BSSE, which we name Measurement Likelihood Memory Filter (MLMF, in which the values of the joint distribution are not parametrized but instead we directly apply changes from the measurement integration step to the marginals. This is achieved by keeping track of the history of likelihood functions’ parameters. We demonstrate that the MLMF gives the same filtered marginals as a histogram filter and show two implementations: MLMF and scalable-MLMF that both have a linear space complexity. The original MLMF retains an exponential time complexity (although an order of magnitude smaller than the histogram filter while the scalable-MLMF introduced independence assumption such to have a linear time complexity. We further quantitatively demonstrate the scalability of our algorithm with 25 beliefs having up to
United States Changing Demographics - English/Spanish Space Education
Leon, R.
2002-01-01
Accordingly the United States Census Bureau, the ethnic group adding the largest number of people to the national population is the Hispanic exceeding 12 percent of the population and growing by almost 60 percent between 1990 and 2000. The status of the nation's educational system with respect to Hispanic students is perhaps one of the most influential issues facing the largest economy of the world. The low income, lack of language skills, highest drop-out rate in the nation, are some of the reasons why Hispanics are less likely to receive a university degree than any other ethical group. In short, the government requires to implement compensatory programs and bilingual education to ensure global leadership. Because of ongoing immigration, Spanish persists longer among Hispanics than it did among other immigrant groups. Spanish is the fourth most spoken language in the world after Mandarin, Hindustani and English. Although not all U.S. Hispanics speak Spanish, almost all U.S. Spanish speakers are Hispanics. This paper is intended to outline the challenging implementation of a bilingual education project affiliated to NASA Johnson Space Center encouraging greater academic success of Hispanics in engineering, math and science. The prospective project covers the overall role of space activities in the development of science and technology, socioeconomic issues and international cooperation. An existent JSC project is the starting stage to keep on developing an interactive video teleconference and web-media technology and produce stimulating learning products in English and Spanish for students and teachers across the nation and around the world.
Crack spacing of unsaturated soils in the critical state
Institute of Scientific and Technical Information of China (English)
SUN JiChao; WANG GuangQian; SUN QiCheng
2009-01-01
The cracking mechanism of unsaturated soils due to evaporation is poorly understood, and the magnitude of crack spacing is usually hard to estimate. In this work, cracks were postulated to occur suc-cedently rather than simultaneously, that is, secondary cracks appear after primary cracks as evaporation continues. Formulae of the secondary crack spacing and secondary trend crack spacing were then derived after stress analysis. The calculated spacing values were consistent with the published experimental data. Meanwhile, the effect of the Poisson ratio on the crack spacing was analyzed, which showed that the magnitude of crack spacing was proportional to the Poisson ratio in the range of [0.30,0.35].
The Fermionic Signature Operator and Quantum States in Rindler Space-Time
Finster, Felix; Röken, Christian
2016-01-01
The fermionic signature operator is constructed in Rindler space-time. It is shown to be an unbounded self-adjoint operator on the Hilbert space of solutions of the massive Dirac equation. In two-dimensional Rindler space-time, we prove that the resulting fermionic projector state coincides with the Fulling-Rindler vacuum. Moreover, the fermionic signature operator gives a covariant construction of general thermal states, in particular of the Unruh state. The fermionic signature operator is shown to be well-defined in asymptotically Rindler space-times. In four-dimensional Rindler space-time, our construction gives rise to new quantum states.
A Bayesian state-space formulation of dynamic occupancy models.
Royle, J Andrew; Kéry, Marc
2007-07-01
Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site
A Bayesian state-space formulation of dynamic occupancy models
Royle, J. Andrew; Kery, M.
2007-01-01
Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by nondetection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and Win BUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site
Parallel symbolic state-space exploration is difficult, but what is the alternative?
Ciardo, Gianfranco; Jin, Xiaoqing; 10.4204/EPTCS.14.1
2009-01-01
State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is...
Quantum-Dot Semiconductor Optical Amplifiers: State Space Model versus Rate Equation Model
Directory of Open Access Journals (Sweden)
Hussein Taleb
2013-01-01
Full Text Available A simple and accurate dynamic model for QD-SOAs is proposed. The proposed model is based on the state space theory, where by eliminating the distance dependence of the rate equation model of the QD-SOA; we derive a state space model for the device. A comparison is made between the rate equation model and the state space model under both steady state and transient regimes. Simulation results demonstrate that the derived state space model not only is much simpler and faster than the rate equation model, but also it is as accurate as the rate equation model.
Holography and quantum states in elliptic de Sitter space
Halpern, Illan F
2015-01-01
We outline a program for interpreting the higher-spin dS/CFT model in terms of physics in the causal patch of a dS observer. The proposal is formulated in "elliptic" de Sitter space dS_4/Z_2, obtained by identifying antipodal points in dS_4. We discuss recent evidence that the higher-spin model is especially well-suited for this, since the antipodal symmetry of bulk solutions has a simple encoding on the boundary. For context, we test some other (free and interacting) theories for the same property. Next, we analyze the notion of quantum field states in the non-time-orientable dS_4/Z_2. We compare the physics seen by different observers, with the outcome depending on whether they share an arrow of time. Finally, we implement the marriage between higher-spin holography and observers in dS_4/Z_2, in the limit of free bulk fields. We succeed in deriving an observer's operator algebra and Hamiltonian from the CFT, but not her S-matrix. We speculate on the extension of this to interacting higher-spin theory.
Interactive state-space analysis of concurrent systems
Energy Technology Data Exchange (ETDEWEB)
Morgan, E.T.; Razouk, R.R.
1987-10-01
The introduction of concurrency into programs has added to the complexity of the software design process. This is most evident in the design of communications protocols where concurrency is inherent to the behavior of the system. The complexity exhibited by such software systems makes more evident the need for computer-aided tools for automatically analyzing behavior. The Distributed Systems project at UCI has been developing techniques and tools, based on Petri nets, which support the design and evaluation of concurrent software systems. Techniques based on constructing reachability graphs that represent projections and selections of complete state-spaces have been developed. This paper focuses attention on the computer-aided analysis of these graphs for the purpose of proving correctness of the modeled system. The application of the analysis technique to evaluating simulation results for correctness is discussed. The tool which supports this analysis (the reachability graph analyzer, RGA) is also described. This tool provides mechanisms for proving general system properties (e.g., deadlock-freeness) as well as system-specific properties. The tool is sufficiently general to allow a user to apply complex user-defined analysis algorithms to reachability graphs. The alternating-bit protocol, with a bounded channel, is used to demonstrate the power of the tool and to point to future extensions.
Modeling diurnal hormone profiles by hierarchical state space models.
Liu, Ziyue; Guo, Wensheng
2015-10-30
Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing (1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls and (2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls. Copyright © 2015 John Wiley & Sons, Ltd.
A Knowledge Discovery from POS Data using State Space Models
Sato, Tadahiko; Higuchi, Tomoyuki
The number of competing-brands changes by new product's entry. The new product introduction is endemic among consumer packaged goods firm and is an integral component of their marketing strategy. As a new product's entry affects markets, there is a pressing need to develop market response model that can adapt to such changes. In this paper, we develop a dynamic model that capture the underlying evolution of the buying behavior associated with the new product. This extends an application of a dynamic linear model, which is used by a number of time series analyses, by allowing the observed dimension to change at some point in time. Our model copes with a problem that dynamic environments entail: changes in parameter over time and changes in the observed dimension. We formulate the model with framework of a state space model. We realize an estimation of the model using modified Kalman filter/fixed interval smoother. We find that new product's entry (1) decreases brand differentiation for existing brands, as indicated by decreasing difference between cross-price elasticities; (2) decreases commodity power for existing brands, as indicated by decreasing trend; and (3) decreases the effect of discount for existing brands, as indicated by a decrease in the magnitude of own-brand price elasticities. The proposed framework is directly applicable to other fields in which the observed dimension might be change, such as economic, bioinformatics, and so forth.
Nonlinear State Space Modeling and System Identification for Electrohydraulic Control
Directory of Open Access Journals (Sweden)
Jun Yan
2013-01-01
Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.
Analysis of Life Histories: A State Space Approach
Directory of Open Access Journals (Sweden)
Rajulton, Fernando
2001-01-01
Full Text Available EnglishThe computer package LIFEHIST written by the author, is meant for analyzinglife histories through a state-space approach. Basic ideas on which the various programs have beenbuilt are described in this paper in a non-mathematical language. Users can use various programs formultistate analyses based on Markov and semi-Markov frameworks and sequences of transitions implied inlife histories. The package is under constant revision and programs for using a few specific modelsthe author thinks will be useful for analyzing longitudinal data will be incorporated in the nearfuture.FrenchLe système d'ordinateur LIFEHIST écrit par l'auteur est établi pour analyser desévénements au cours de la vie par une approche qui tient compte des états aucours du temps. Les idées fondamentales à la base des divers programmes dumodule sont décrites dans un langage non-mathématique. Le systèmeLIFEHIST peut être utilisé pour des analyses Markov et semi-Markov desséquences d’événements au cours de la vie. Le module est sous révisionconstante, et des programmes que l’auteur compte ajouter pour l'usage dedonnées longitudinales sont décrit.
State Space Reduction in the Maude-NRL Protocol Analyzer
Escobar, Santiago; Meseguer, Jose
2011-01-01
The Maude-NRL Protocol Analyzer (Maude-NPA) is a tool and inference system for reasoning about the security of cryptographic protocols in which the cryptosystems satisfy different equational properties. It both extends and provides a formal framework for the original NRL Protocol Analyzer, which supported equational reasoning in a more limited way. Maude-NPA supports a wide variety of algebraic properties that includes many crypto-systems of interest such as, for example, one-time pads and Diffie-Hellman. Maude-NPA, like the original NPA, looks for attacks by searching backwards from an insecure attack state, and assumes an unbounded number of sessions. Because of the unbounded number of sessions and the support for different equational theories, it is necessary to develop ways of reducing the search space and avoiding infinite search paths. In order for the techniques to prove useful, they need not only to speed up the search, but should not violate completeness, so that failure to find attacks still guarant...
Geometry of state space in plane Couette flow
Cvitanović, P.; Gibson, J. F.
A large conceptual gap separates the theory of low-dimensional chaotic dynamics from the infinite-dimensional nonlinear dynamics of turbulence. Recent advances in experimental imaging, computational methods, and dynamical systems theory suggest a way to bridge this gap in our understanding of turbulence. Recent discoveries show that recurrent coherent structures observed in wall-bounded shear flows (such as pipes and plane Couette flow) result from close passes to weakly unstable invariant solutions of the Navier-Stokes equations. These 3D, fully nonlinear solutions (equilibria, traveling waves, and periodic orbits) structure the state space of turbulent flows and provide a skeleton for analyzing their dynamics. We calculate a hierarchy of invariant solutions for plane Couette, a canonical wall-bounded shear flow. These solutions reveal organization in the flow's turbulent dynamics and can be used to predict directly from the fundamental equations physical quantities such as bulk flow rate and mean wall drag. All results and the code that generates them are disseminated through through our group's open-source CFD software and solution database Channelflow.org and the collaborative e-book ChaosBook.org.
Forecasting seasonal influenza with a state-space SIR model.
Osthus, Dave; Hickmann, Kyle S; Caragea, Petruţa C; Higdon, Dave; Del Valle, Sara Y
2017-03-01
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.
Rank functions and state spaces of K0
Institute of Scientific and Technical Information of China (English)
Feng; Lianggui
2001-01-01
［1］Cohn, P.M., Rank functions on rings, J. Alg., 1990, 133: 373.［2］Goodearl, K.R., Von Neumann Regular Rings, London: Pitman, 1979.［3］Bass, H., Algebraic K-theory, New York: Benjamin, 1968.［4］Silvester, J. R., Introduction to Algebraic K-Theory, London: Chapman and Hall, 1981.［5］Zhu, X. S., Tong, W. T., The category of power stably free modules and its K0 group, Science in China (in Chinese), Ser. A, 1997, 27(9): 812.［6］Cohn, P.M., Schofield, A. H., On the law of nullity, Math. Proc. Cambridge Philos. Soc., 1982, 91: 357.［7］Cohn, P. M., An invariant characterization of pseudo-valuations on a field, Proc. Cambridge Philos Soc., 1954, 50: 159.［8］Goodearl, K. R., State space of K0 of noetherian rings, J. Alg., 1981, 71: 322.［9］Goodearl, K. R., K-theoretically simple Von Neumann regular rings, J. Alg., 1995, 174: 659.［10］Kaplansky, I., Projective modules, Math. Ann., 1958, 68: 372.［11］Tong, W. T., PF-rings and the Grothendieck groups of groups rings, J. of Math. Res. and Exposition(in Chinese), 1990, 10(2): 157.
Hierarchical state-space estimation of leatherback turtle navigation ability.
Mills Flemming, Joanna; Jonsen, Ian D; Myers, Ransom A; Field, Christopher A
2010-12-28
Remotely sensed tracking technology has revealed remarkable migration patterns that were previously unknown; however, models to optimally use such data have developed more slowly. Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels. We formulate models that allow the navigation ability of animals to be estimated and demonstrate how information can be combined over many animals to allow improved estimation. We also show how formal hypothesis testing regarding navigation ability can easily be accomplished in this framework. Using Argos satellite tracking data from 14 leatherback turtles, 7 males and 7 females, during their southward migration from Nova Scotia, Canada, we find that the circle of confusion (the radius around an animal's location within which it is unable to determine its location precisely) is approximately 96 km. This estimate suggests that the turtles' navigation does not need to be highly accurate, especially if they are able to use more reliable cues as they near their destination. Moreover, for the 14 turtles examined, there is little evidence to suggest that male and female navigation abilities differ. Because of the minimal assumptions made about the movement process, our approach can be used to estimate and compare navigation ability for many migratory species that are able to carry electronic tracking devices.
Coherent States of the Deformed Heisenberg-Weyl Algebra in Noncommutative Space
Yin, Q; Yin, Qi-jun; Zhang, Jian-Zu
2005-01-01
In two-dimensional space a subtle point that for the case of both space-space and momentum-momentum noncommuting, different from the case of only space-space noncommuting, the deformed Heisenberg-Weyl algebra in noncommutative space is not completely equivalent to the undeformed Heisenberg-Weyl algebra in commutative space is clarified. It follows that there is no well defined procedure to construct the deformed position-position coherent state or the deformed momentum-momentum coherent state from the undeformed position-momentum coherent state. Identifications of the deformed position-position and deformed momentum-momentum coherent states with the lowest energy states of a cold Rydberg atom in special conditions and a free particle, respectively, are demonstrated.
Transformation of Neural State Space Models into LFT Models for Robust Control Design
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non...
State-space reduction and equivalence class sampling for a molecular self-assembly model.
Packwood, Daniel M; Han, Patrick; Hitosugi, Taro
2016-07-01
Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.
An Approach to Distributed State Space Exploration for Coloured Petri Nets
DEFF Research Database (Denmark)
Kristensen, Lars Michael; Petrucci, Laure
2004-01-01
We present an approach and associated computer tool support for conducting distributed state space exploration for Coloured Petri Nets (CPNs). The distributed state space exploration is based on the introduction of a coordinating process and a number of worker processes. The worker processes...... Tools. This makes the distributed state space exploration and analysis largely transparent to the analyst. We illustrate the use of the developed tool on an example....
Harmonic oscillator Floquet states in the Bargmann-Segal space
Energy Technology Data Exchange (ETDEWEB)
Palma, A. [Instituto Nacional de Astrofisica, Optica y Electronica (INAOE), Puebla (Mexico)]. E-mail: palma@sirio.ifuap.buap.mx; Leon, V. [Instituto de Fisica, BUAP, Puebla (Mexico); Lefebvre, R. [Laboratoire de Photophysique Moleculaire du CNRS, Universite Paris-Sud, Orsay (France); UFR de Physique Fondamentale et Appliquee, Universite Pierre et Marie Curie, Paris (France)
2002-01-18
The Floquet quasi-energies and eigenfunctions for the harmonic oscillator interacting with a monochromatic electric field are obtained by using the so-called Bargmann-Segal space. The Schroedinger second-order differential equation in configuration space is transformed into a linear first-order equation in such a space, which is easily solved by means of an auxiliary system (called the Lagrange system) of ordinary differential equations. This method compares favourably with others previously used. (author)
United States Civil Space Policy: Summary of a Workshop
2008-01-01
What are the principal purposes, goals, and priorities of the U.S. civil space program? This question was the focus of the workshop on civil space policy held November 29-30, 2007, by the Space Studies Board (SSB) and the Aeronautics and Space Engineering Board (ASEB) of the National Research Council (NRC). In addressing this question, invited speakers and panelists and the general discussion from this public workshop explored a series of topics, including the following: (1) Key changes and developments in the U.S. civil space program since the new national Vision for Space Exploration2 (the Vision) was articulated by the executive branch in 2004; (2) The fit of space exploration within a broader national and international context; (3) Affordability, public interest, and political will to sustain the civil space program; (4) Definitions, metrics, and decision criteria for the mix and balance of activities within the program portfolio; (5) Roles of government in Earth observations from space; and (6) Gaps in capabilities and infrastructure to support the program.
State Machine Modeling of the Space Launch System Solid Rocket Boosters
Harris, Joshua A.; Patterson-Hine, Ann
2013-01-01
The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.
Formulating state space models in R with focus on longitudinal regression models
DEFF Research Database (Denmark)
Dethlefsen, Claus; Lundbye-Christensen, Søren
We provide a language for formulating a range of state space models. The described methodology is implemented in the R -package sspir available from cran.r-project.org . A state space model is specified similarly to a generalized linear model in R , by marking the time-varying terms in the form...... We provide a language for formulating a range of state space models. The described methodology is implemented in the R -package sspir available from cran.r-project.org . A state space model is specified similarly to a generalized linear model in R , by marking the time-varying terms...
Robotics technology developments in the United States space telerobotics program
Lavery, David
1994-01-01
In the same way that the launch of Yuri Gagarin in April 1961 announced the beginning of human space flight, last year's flight of the German ROTEX robot flight experiment is heralding the start of a new era of space robotics. After a gap of twelve years since the introduction of a new capability in space remote manipulation, ROTEX is the first of at least ten new robotic systems and experiments which will fly before the year 2000. As a result of redefining the development approach for space robotic systems, and capitalizing on opportunities associated with the assembly and maintenance of the space station, the space robotics community is preparing a whole new generation of operational robotic capabilities. Expanding on the capabilities of earlier manipulation systems such as the Viking and Surveyor soil scoops, the Russian Lunakhods, and the Shuttle Remote Manipulator System (RMS), these new space robots will augment astronaut on-orbit capabilities and extend virtual human presence to lunar and planetary surfaces.
A circular polymer chain in a gel - the reduction of the state space
Krawczyk, Malgorzata J
2012-01-01
The state space of a polymer molecule is analysed. We show how the size of the state space can be reduced on the basis of symmetry. In the reduced state space, the probability of a new state (termed below as class) is equal to the number of old states represented by the new state multiplied by the probability of each old state. As an application, the electrophoretic motion of the molecule in gel is considered. We discuss the influence of the gel medium and of external field on the molecule states, with absorbing states of hooked molecules playing a major role. We show that in the case of strong fields both the velocity and the diffusion coefficient decrease with field. Finally, we evaluate the time of relaxation to and from the absorbing states. This is done with a continuous version of the exact enumeration method for weighted networks.
The United States Space Force: Not If, But When
2016-06-01
this is the merger of the terms “air and space” and “aerospace.” He indicates that this was done to irreversibly marry the two domains together as...that precipitated the Commission’s report. In other words, the issues of leadership, doctrine, personnel, funding, acquisition , and the support to...who is symmetrical with respect to its space capabilities. It is unquestionable that US reliance on space is great and the asymmetric advantages
State Space Formulas for a Solution of the Suboptimal Nehari Problem on the Unit Disc
Curtain, Ruth F.; Opmeer, Mark R.
2009-01-01
We give state space formulas for a ("central") solution of the suboptimal Nehari problem for functions defined on the unit disc and taking values in the space of bounded operators in separable Hilbert spaces. Instead of assuming exponential stability, we assume a weaker stability concept (the combin
Gu, Fei; Preacher, Kristopher J; Wu, Wei; Yung, Yiu-Fai
2014-01-01
Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.
A fast, reliable algorithm for computing frequency responses of state space models
Wette, Matt
1991-01-01
Computation of frequency responses for large order systems described by time invariant state space systems often provides a bottleneck in control system analysis. It is shown that banding the A-matrix in the state space model can effectively reduce the computation time for such systems while maintaining reliability in the results produced.
State-Space Modeling, System Identification and Control of a 4th Order Rotational Mechanical System
2009-12-01
state-space form. Identification of the state-space parameters was accomplished using the parameter estimation function in Matlab’s System ... Identification Toolbox utilizing experimental input/output data. The identified model was then constructed in Simulink and the accuracy of the identified model
Exponential estimation of generalized state-space time-delay systems
Energy Technology Data Exchange (ETDEWEB)
Lien, C-H; Yu, K-W [Department of Marine Engineering, National Kaohsiung Marine University, Taiwan 811 (China); Lin, J-S; Hung, M-L [Department of Electrical Engineering, Far East University, Tainan, Taiwan 744 (China)], E-mail: chlien@mail.nkmu.edu.tw
2008-02-15
In this paper, global exponential stability for a class of generalized state-space time-delay systems is considered. Delay-dependent criteria are proposed to guarantee the exponential stability and estimate the convergence rate for the generalized state-space systems with two cases of uncertainties. Finally, some numerical examples are illustrated to show the usefulness of the theory.
Formulating state space models in R with focus on longitudinal regression models
DEFF Research Database (Denmark)
Dethlefsen, Claus; Lundbye-Christensen, Søren
2006-01-01
We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear model in R, and then the time-varying terms...
State-space size considerations for disease-progression models.
Regnier, Eva D; Shechter, Steven M
2013-09-30
Markov models of disease progression are widely used to model transitions in patients' health state over time. Usually, patients' health status may be classified according to a set of ordered health states. Modelers lump together similar health states into a finite and usually small, number of health states that form the basis of a Markov chain disease-progression model. This increases the number of observations used to estimate each parameter in the transition probability matrix. However, lumping together observably distinct health states also obscures distinctions among them and may reduce the predictive power of the model. Moreover, as we demonstrate, precision in estimating the model parameters does not necessarily improve as the number of states in the model declines. This paper explores the tradeoff between lumping error introduced by grouping distinct health states and sampling error that arises when there are insufficient patient data to precisely estimate the transition probability matrix. Copyright © 2013 John Wiley & Sons, Ltd.
China’s Military Space Program: A Threat to the United States or a Peaceful Endeavor
2012-02-15
seizure of Taiwan.”29 Japan has historically advocated for the peaceful use of space in large part due to its pacifist constitution. So it was no...space objectives, and the judgments of space experts from the United States, Russia, Japan , and India. The paper concludes that the Chinese are...boom has propelled the Peoples Republic of China (PRC) ahead of Japan as the second largest economy behind the United States (U.S.) and has
Configuration space Faddeev calculations. I. Triton ground state properties
Payne, G. L.; Friar, J. L.; Gibson, B. F.; Afnan, I. R.
1980-08-01
The formulation of Faddeev-type equations in configuration space is discussed. Numerical solutions are obtained using splines and the method of orthogonal collocation. Triton observables and wave-function probabilities are calculated for s-wave NN interaction models of Malfliet and Tjon and the tensor force model of Reid. Comparison with previously published triton results is made; our full five-channel results for the Reid soft-core potential are in excellent agreement with those obtained by Afnan and Birrell using separable expansion methods. NUCLEAR STRUCTURE 3H, Faddeev calculations configuration space.
On the squeezed number states and their phase space representations
Energy Technology Data Exchange (ETDEWEB)
Albano, L [Universidad Simon Bolivar, Departamento de Fisica, Apartado Postal 89000, Caracas 1080-A (Venezuela); Mundarain, D F [Universidad Simon Bolivar, Departamento de Fisica, Apartado Postal 89000, Caracas 1080-A (Venezuela); Stephany, J [Universidad Simon Bolivar, Departamento de Fisica, Apartado Postal 89000, Caracas 1080-A (Venezuela)
2002-10-01
We compute the photon-number distribution, the Q({alpha}) distribution function and the wavefunctions in the momentum and position representation for a single mode squeezed number state using generating functions which allow one to obtain any matrix element in the squeezed number state representation from the matrix elements in the squeezed coherent state representation. For highly squeezed number states we discuss the previously unnoted oscillations which appear in the Q({alpha}) function. We also note that these oscillations can be related to the photon-number distribution oscillations and to the momentum representation of the wavefunction.
Transformation of Neural State Space Models into LFT Models for Robust Control Design
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non-conservative ......This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non......-conservative way is proposed, and it is demonstrated how a standard robust control law can be designed for a system described by means of a multi layer perceptron....
State-space models for bio-loggers: A methodological road map
DEFF Research Database (Denmark)
Jonsen, I.D.; Basson, M.; Bestley, S.
2012-01-01
development of state-space modelling approaches for animal movement data provides statistical rigor for inferring hidden behavioural states, relating these states to bio-physical data, and ultimately for predicting the potential impacts of climate change. Despite the widespread utility, and current popularity......-physical datasets to understand physiological and ecological influences on habitat selection. In most cases, however, the behavioural context is not directly observable and therefore, must be inferred. Animal movement data are complex in structure, entailing a need for stochastic analysis methods. The recent......, of state-space models for analysis of animal tracking data, these tools are not simple and require considerable care in their use. Here we develop a methodological “road map” for ecologists by reviewing currently available state-space implementations. We discuss appropriate use of state-space methods...
The State of the US Army and Space Operations
2007-11-02
March 1996 Strategic Crisis Exercise 1998, approved for public release. R. C. Webb, Lew Cohn and Joan Perre , "The Cost Differential to Harden DoD...Shield and Desert Storm Assessment (S/NF). Peterson AFB, CO: U.S. Space Command, 31 January 1992. Webb, R. C, Lew Cohn and Joan Perre . "The Cost
On a state space approach to nonlinear H∞ control
Schaft, van der A.J.
1991-01-01
We study the standard H∞ optimal control problem using state feedback for smooth nonlinear control systems. The main theorem obtained roughly states that the L2-induced norm (from disturbances to inputs and outputs) can be made smaller than a constant γ > 0 if the corresponding H∞ norm for the syste
THE STATE OF GREEN SPACES IN KUMASI CITY (GHANA: LESSONS FOR OTHER AFRICAN CITIES
Directory of Open Access Journals (Sweden)
Collins ADJEI MENSAH
2016-12-01
Full Text Available Integrating green spaces such as parks and gardens into the physical landscape of cities has been identified to enhance the health and wellbeing of urban dwellers. This paper assesses the state of green spaces in Kumasi city (Ghana, once known as the garden city of West Africa. Using a case study approach, a mixture of qualitative research techniques were employed whilst a set of eight themes were put together to guide the assessment. In all, green spaces were found to be in poor state. With the exception of conservation and heritage theme, the remaining seven themes that were used for the assessment all found the green spaces to be in poor state. It is therefore recommended that there should be an attitudinal change towards the maintenance of green spaces, the application of a collaborative governance approach, and priority giving to green spaces in all development agendas by city authorities.
World Space Observatory - Ultraviolet mission: state of art 2016
Sachkov, Mikhail; Gomez De Castro, Ana; Shustov, Boris M.
2016-07-01
The WSO-UV (World Space Observatory - Ultraviolet) project is intended to built and operate an international space observatory designed for observations in the UV (115 - 300 nm) range, where some of the most important astrophysical processes can be efficiently studied. The observatory includes a 170 cm aperture telescope capable of high-resolution spectroscopy and long slit low-resolution spectroscopy with the WUVS instrument; moreover UV imaging will be available with cameras. WSO-UV is a Russian led mission that will be operating in high Earth orbit (geosynchronous with inclination 51.^o6) for five+five years grating access to the UV range to the world-wide astronomical community in the post-Hubble era. Spain is a major partner to the project. Updated information of the WSO-UV project is provided periodically in the COSPAR meetings. Henceforth, this review provides a summary on the project, its status and the major outcomes since the last COSPAR Assembly.
State Space Modelling and Data Analysis Exercises in LISA Pathfinder
Nofrarias, M.; Antonucci, F.; Armano, M.; Audley, H.; Auger, G.; Benedetti, M.; Binetruy, P.; Bogenstahl, J.; Bortoluzzi, D.; Brandt, N.; Caleno, M.; Cavalleri, A.; Congedo, G.; Cruise, M.; Danzmann, K.; De Marchi, F.; Diaz-Aguilo, M.; Diepholz, I.; Dixon, G.; Dolesi, R.; Dunbar, N.; Fauste, J.; Ferraioli, L.; Ferroni, V.; Fichter, W.; Fitzsimons, E.; Freschi, M.; García Marirrodriga, C.; Gerndt, R.; Gesa, L.; Gibert, F.; Giardini, D.; Grimani, C.; Grynagier, A.; Guzmán, F.; Harrison, I.; Heinzel, G.; Hewitson, M.; Hollington, D.; Hoyland, D.; Hueller, M.; Huesler, J.; Jennrich, O.; Jetzer, P.; Johlander, B.; Karnesis, N.; Korsakova, N.; Killow, C.; Llamas, X.; Lloro, I.; Lobo, A.; Maarschalkerweerd, R.; Madden, S.; Mance, D.; Martin, V.; Mateos, I.; McNamara, P.; Mendes, J.; Mitchell, E.; Nicolodi, D.; Perreur-Lloyd, M.; Plagnol, E.; Prat, P.; Ramos-Castro, J.; Reiche, J.; Romera Perez, J. A.; Robertson, D.; Rozemeijer, H.; Russano, G.; Schleicher, A.; Shaul, D.; Sopuerta, C. F.; Sumner, T. J.; Taylor, A.; Texier, D.; Trenkel, C.; Tu, H. B.; Vitale, S.; Wanner, G.; Ward, H.; Waschke, S.; Wass, P.; Wealthy, D.; Wen, S.; Weber, W.; Ziegler, T.; Zweifel, P.
2013-01-01
LISA Pathfinder is a mission planned by the European Space Agency (ESA) to test the key technologies that will allow the detection of gravitational waves in space. The instrument on-board, the LISA Technology package, will undergo an exhaustive campaign of calibrations and noise characterisation campaigns in order to fully describe the noise model. Data analysis plays an important role in the mission and for that reason the data analysis team has been developing a toolbox which contains all the functionality required during operations. In this contribution we give an overview of recent activities, focusing on the improvements in the modelling of the instrument and in the data analysis campaigns performed both with real and simulated data.
Atmosphere-Space Interactions Monitor (ASIM: State of the Art
Directory of Open Access Journals (Sweden)
Pere Blay
2014-12-01
Full Text Available Atmosphere-Space Interactions Monitor (ASIM mission is an ESA pay load which will be installed in the Columbus module of the International Space Station (ISS. ASIM is optimized to the observation and monitoring of luminescent phenomena in the upper atmosphere, the so called Transient Luminous Event (TLEs and Terrestrial Gamma Ray Flashes(TGFs. Both TLEs and TGFs have been discovered recently (past two decades and opened a new field of research in high energetic phenomena in the atmosphere. We will review the capabilities of ASIM and how it will help researchers to gain deeper knowledge of TGFs, TLEs, their inter-relationship and how they are linked to severe thunderstorms and the phenomena of lightning.
State space modelling and data analysis exercises in LISA Pathfinder
Nofrarias, M; Armano, M; Audley, H; Auger, G; Benedetti, M; Binetruy, P; Bogenstahl, J; Bortoluzzi, D; Bosetti, P; Brandt, N; Caleno, M; Cañizares, P; Cavalleri, A; Cesa, M; Chmeissani, M; Conchillo, A; Congedo, G; Cristofolin, I; Cruise, M; Danzmann, K; De Marchi, F; Diaz-Aguilo, M; Diepholz, I; Dixon, G; Dolesi, R; Dunbar, N; Fauste, J; Ferraioli, L; Fichter, V Ferroni W; Fitzsimons, E; Freschi, M; Marin, A García; Marirrodriga, C García; Gesa, R Gerndt L; Gibert, F; Giardini, D; Grimani, C; Grynagier, A; Guillaume, B; Guzmán, F; Harrison, I; Heinzel, G; Hernández, V; Hewitson, M; Hollington, D; Hough, J; Hoyland, D; Hueller, M; Huesler, J; Jennrich, O; Jetzer, P; Johlander, B; Killow, C; Llamas, X; Lloro, I; Lobo, A; Maarschalkerweerd, R; Madden, S; Mance, D; Mateos, I; McNamara, P W; Mendes, J; Mitchell, E; Monsky, A; Nicolini, D; Nicolodi, D; Pedersen, F; Perreur-Lloyd, M; Plagnol, E; Prat, P; Racca, G D; Ramos-Castro, J; Reiche, J; Perez, J A Romera; Robertson, D; Rozemeijer, H; Sanjuan, J; Schleicher, A; Schulte, M; Shaul, D; Stagnaro, L; Strandmoe, S; Steier, F; Sumner, T J; Taylor, A; Texier, D; Trenkel, C; Vitale, H-B Tu S; Wanner, G; Ward, H; Waschke, S; Wass, P; Weber, W J; Ziegler, T; Zweifel, P
2013-01-01
LISA Pathfinder is a mission planned by the European Space Agency to test the key technologies that will allow the detection of gravitational waves in space. The instrument on-board, the LISA Technology package, will undergo an exhaustive campaign of calibrations and noise characterisation campaigns in order to fully describe the noise model. Data analysis plays an important role in the mission and for that reason the data analysis team has been developing a toolbox which contains all the functionalities required during operations. In this contribution we give an overview of recent activities, focusing on the improvements in the modelling of the instrument and in the data analysis campaigns performed both with real and simulated data.
Preparation of entangled states through Hilbert space engineering
Lin, Y; Reiter, F; Tan, T R; Bowler, R; Wan, Y; Keith, A; Knill, E; Glancy, S; Coakley, K; Sørensen, A S; Leibfried, D; Wineland, D J
2016-01-01
Entangled states are a crucial resource for quantum-based technologies such as quantum computers and quantum communication systems (1,2). Exploring new methods for entanglement generation is important for diversifying and eventually improving current approaches. Here, we create entanglement in atomic ions by applying laser fields to constrain the evolution to a restricted number of states, in an approach that has become known as "quantum Zeno dynamics" (3-5). With two trapped $^9\\rm{Be}^+$ ions, we obtain Bell state fidelities up to $0.990^{+2}_{-5}$, with three ions, a W-state (6) fidelity of $0.910^{+4}_{-7}$ is obtained. Compared to other methods of producing entanglement in trapped ions, this procedure is relatively insensitive to certain imperfections such as fluctuations in laser intensity, laser frequency, and ion-motion frequencies.
Evapotranspiration from Urban Green Spaces in the Northeast United States
DiGiovanni, K. A.; Montalto, F. A.; Gaffin, S.
2012-12-01
The measurement and estimation of urban evapotranspiration (ET) has historically received limited consideration from researchers in the hydrologic and climatologic communities yet are arguably vital to both. In the studies presented, ET rates from four different urban green spaces have been measured using weighing lysimeter setups for periods ranging from one to three years. The experimental sites predominantly include in-situ engineered urban green spaces or green infrastructure installations throughout the boroughs of New York City, specifically a green roof, irrigated bioretention area, un-irrigated bioretention area, and a wooded area in one of the last remaining sections of old growth urban forest in NYC. Comparison of ET rates between these urban green spaces at a daily time-step show statistically significant differences between the rates at each site at the 0.05 significance level. Examination of the factors impacting ET rates across sites (including net radiation, wind speed, relative humidity, air temperature and media volumetric water content) was also performed for a total of eight (8) sites including the four at which ET was directly measured using weighing lysimeters. Findings suggest that statistically significant differences in micro-climate conditions do exist across the city and that these are partially responsible for differences in rates of ET. Soil moisture (irrigated vs. un-irrigated bioretention areas) conditions and vegetation types (green roof vs. bioretention area) also play a role.
State-space control of prosthetic hand shape.
Velliste, M; McMorland, A J C; Diril, E; Clanton, S T; Schwartz, A B
2012-01-01
In the field of neuroprosthetic control, there is an emerging need for simplified control of high-dimensional devices. Advances in robotic technology have led to the development of prosthetic arms that now approach the look and number of degrees of freedom (DoF) of a natural arm. These arms, and especially hands, now have more controllable DoFs than the number of control DoFs available in many applications. In natural movements, high correlations exist between multiple joints, such as finger flexions. Therefore, discrepancy between the number of control and effector DoFs can be overcome by a control scheme that maps low-DoF control space to high-DoF joint space. Imperfect effectors, sensor noise and interactions with external objects require the use of feedback controllers. The incorporation of feedback in a system where the command is in a different space, however, is challenging, requiring a potentially difficult inverse high-DoF to low-DoF transformation. Here we present a solution to this problem based on the Extended Kalman Filter.
Directory of Open Access Journals (Sweden)
Tatjewski Piotr
2014-06-01
Full Text Available Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors. The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2×2 example process problem.
Canonical Form and Separability of PPT States on Multiple Quantum Spaces
Wang, X H; Wang, Xiao-Hong; Fei, Shao-Ming
2005-01-01
By using the "subtracting projectors" method in proving the separability of PPT states on multiple quantum spaces, we derive a canonical form of PPT states in ${\\Cb}^{K_1} \\otimes {\\Cb}^{K_2} \\otimes ... \\otimes {\\Cb}^{K_m} \\otimes {\\Cb}^N$ composite quantum systems with rank $N$, from which a sufficient separability condition for these states is presented.
Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Stoustrup, Jakob
2003-01-01
This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training...... samples from the system, after which linearized state space models are extracted from the neural network in a number of operating points according to a simple and computationally cheap scheme. Robust observer-based controllers can then be designed in each of these operating points,and gain scheduling...
A new adaptive state space construction method for the mobile robot navigation
Institute of Scientific and Technical Information of China (English)
Huang Bingqiang; Cao Guangyi; Fei Yanqiong; Li Jianhua
2008-01-01
In order to solve the combinative explosion problems in a continuous and high dimensional state space, a function approximation approach is usually used to represent the state space. The normalized radial basis function (NRBF) was adopted as the local function approximator and a kind of adaptive state space construction strategy based on the NRBF (ASC-NRBF) was proposed, which enables the system to allocate appropriate number and size of the basis functions automatically. Combined with the reinforcement learning method, the proposed ASC-NRBF method was applied to the robot navigation problem. Simulation results illustrate the performance of the proposed method.
Configuration space Faddeev calculations. I. Triton ground-state properties
Energy Technology Data Exchange (ETDEWEB)
Payne, G.L.; Friar, J.L.; Gibson, B.F.; Afnan, I.R.
1980-08-01
The formulation of Faddeev-type equations in configuration space is discussed. Numerical solutions are obtained using splines and the method of orthogonal collocation. Triton observables and wave-function probabilities are calculated for s-wave NN interaction models of Malfliet and Tjon and the tensor force model of Reid. Comparison with previously published triton results is made; our full five-channel results for the Reid soft-core potential are in excellent agreement with those obtained by Afnan and Birrell using separable expansion methods.
Solid State Welding Development at Marshall Space Flight Center
Ding, Robert J.; Walker, Bryant
2012-01-01
What is TSW and USW? TSW is a solid state weld process consisting of an induction coil heating source, a stir rod, and non-rotating containment plates Independent heating, stirring and forging controls Decouples the heating, stirring and forging process elements of FSW. USW is a solid state weld process consisting of an induction coil heating source, a stir rod, and a non-rotating containment plate; Ultrasonic energy integrated into non-rotating containment plate and stir rod; Independent heating, stirring and forging controls; Decouples the heating, stirring and forging process elements of FSW.
Directory of Open Access Journals (Sweden)
V. Comnac
2009-12-01
Full Text Available The paper presents sensorless state-space control of two-inertia drive system with resilient coupling. The control structure contains an I+PI controller for load speed regulation and a state feedback controller for effective vibration suppression of the elastic coupling. Mechanical state variable of two-inertia drive are obtained by using a linear minimum-order (Gopinath state observer. The design of the combined (I+PI and state feedback controller is achieved with the extended version of the modulus criterion [5]. The dynamic behavior of presented control structure has been examined, for different conditions, using MATLAB/SIMULINK simulation.
Semiclassical States Associated with Isotropic Submanifolds of Phase Space
Guillemin, V.; Uribe, A.; Wang, Z.
2016-05-01
We define classes of quantum states associated with isotropic submanifolds of cotangent bundles. The classes are stable under the action of semiclassical pseudo-differential operators and covariant under the action of semiclassical Fourier integral operators. We develop a symbol calculus for them; the symbols are symplectic spinors. We outline various applications.
Robust Performance of Systems with Structured Uncertainties in State Space
DEFF Research Database (Denmark)
Zhou, Kemin; Khargonekar, Pramod P.; Stoustrup, Jakob
1995-01-01
This paper considers robust performance analysis and state feedback design for systems with time-varying parameter uncertainties. The notion of a strongly robust % performance criterion is introduced, and its applications in robust performance analysis and synthesis for nominally linear systems...
Hyperstate matrix models : extending demographic state spaces to higher dimensions
Roth, G.; Caswell, H.
2016-01-01
1. Demographic models describe population dynamics in terms of the movement of individuals among states (e.g. size, age, developmental stage, parity, frailty, physiological condition). Matrix population models originally classified individuals by a single characteristic. This was enlarged to two cha
Projective Limits of State Spaces: Quantum Field Theory without a Vacuum
Lanéry, Suzanne
2016-01-01
Instead of formulating the states of a Quantum Field Theory (QFT) as density matrices over a single large Hilbert space, it has been proposed by Kijowski [Kijowski, 1977] to construct them as consistent families of partial density matrices, the latter being defined over small 'building block' Hilbert spaces. In this picture, each small Hilbert space can be physically interpreted as extracting from the full theory specific degrees of freedom. This allows to reduce the quantization of a classical field theory to the quantization of finite-dimensional sub-systems, thus sidestepping some of the common ambiguities (specifically, the issues revolving around the choice of a 'vacuum state'), while obtaining robust and well-controlled quantum states spaces. The present letter provides a self-contained introduction to this formalism, detailing its motivations as well as its relations to other approaches to QFT (such as conventional Fock-like Hilbert spaces, path-integral quantization, and the algebraic formulation). At...
A theory of state space reconstruction in the presence of noise
Energy Technology Data Exchange (ETDEWEB)
Casdagli, M.; Eubank, S.; Farmer, J.D.; Gibson, J. (Los Alamos National Lab., NM (USA) Santa Fe Inst., NM (USA))
1990-01-01
Takens' theorem demonstrates that in the absence of noise a multidimensional state space can be reconstructed from a single time series. This theorem does not treat the effect of noise, however, and so gives no guidance about practical considerations for reconstructing a good state space. We study the problem of reconstructing a state space with observational noise, examining the likelihood for a particular state given a series of noisy observations. We define a quantity called distortion, which is proportional to the covariance of the likelihood function in a reconstructed state space. This is related to the noise amplification, which corresponds to the root-mean-square errors for time series prediction with an ideal model. We prove that in the low noise limit minimizing the distortion is equivalent to minimizing the noise amplification. We derive several asymptotic scaling laws for distortion and noise amplification. They depend on properties of the state space reconstruction, such as the sampling time and the reconstruction dimension, and properties of the dynamical system, such as the dimension and Lyapunov exponents. When the dimension and Lyapunov exponents are sufficiently large these scaling laws show that, no matter how the state space is reconstructed, there is an explosion in the noise amplification -- from a practical point of view all determinism is lost, even for short times, so that the time series is effectively a random process. In the low noise, large data limit we show that the technique of local principal value decomposition (PVD) is an optimal method of state space reconstruction, in the sense that it achieves the minimum distortion in a state space of the lowest possible dimension. 20 refs., 12 figs.
An Extra Phase for Two-Mode Coherent States Displaced in Noncommutative Phase Space
Institute of Scientific and Technical Information of China (English)
YAN Long; FENG Xun-Li; ZHANG Zhi-Ming; LIU Song-Hao
2012-01-01
Using deformed boson algebra,we study the property of two-mode coherent states in noncommutative phase space.When a two-mode field evolves in the noncommutative phase space,it can acquire an extra θ-dependent phase compared to the case of commutative space.This phase is detectable and may be used to test noncommutativity.%Using deformed boson algebra, we study the property of two-mode coherent states in noncommutative phase space. When a two-mode field evolves in the noncommutative phase space, it can acquire an extra 9-dependent phase compared to the case of commutative space. This phase is detectable and may be used to test noncommutativity.
Directory of Open Access Journals (Sweden)
Nataliya Chukhrova
2017-05-01
Full Text Available This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving. Most of these state space representations are matrix-based, which complicates their applications. Therefore, to facilitate the implementation of state space models in practice, we present a scalar state space model for cumulative payments, which is an extension of the well-known chain ladder (CL method. The presented model is distribution-free, forms a basis for determining the entire unobservable lower and upper run-off triangles and can easily be applied in practice using the Kalman-filter for prediction, filtering and smoothing of cumulative payments. In addition, the model provides an easy way to find outliers in the data and to determine outlier effects. Finally, an empirical comparison of the scalar state space model, promising prior state space models and some popular stochastic claims reserving methods is performed.
Solid state radioisotopic energy converter for space nuclear power
Energy Technology Data Exchange (ETDEWEB)
Brown, P.M. (IsoGen Radioisotopic Research Laboratory, 315 S. McLoughlin Blvd., Oregon City, Oregon 97045 (United States))
1993-01-10
Recent developments in materials technology now make it possible to fabricate nonthermal thin-film radioisotopic energy converters (REC) with a specific power of 24 W/kg and a 10 year working life at 5 to 10 watts. This creates applications never before possible, such as placing the power supply directly on integrated circuit chips. The efficiency of the REC is about 25% which is two to three times greater than the 6 to 8% capabilities of current thermoelectric systems. Radioisotopic energy converters have the potential to meet many future space power requirements for a wide variety of applications with less mass, better efficiency, and less total area than other power conversion options. These benefits result in significant dollar savings over the projected mission lifetime.
Gu, Fei; Wu, Hao
2016-09-01
The specifications of state space model for some principal component-related models are described, including the independent-group common principal component (CPC) model, the dependent-group CPC model, and principal component-based multivariate analysis of variance. Some derivations are provided to show the equivalence of the state space approach and the existing Wishart-likelihood approach. For each model, a numeric example is used to illustrate the state space approach. In addition, a simulation study is conducted to evaluate the standard error estimates under the normality and nonnormality conditions. In order to cope with the nonnormality conditions, the robust standard errors are also computed. Finally, other possible applications of the state space approach are discussed at the end.
Abellán-Nebot, J. V.; Liu, J.; Romero, F.
2009-11-01
The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.
Monthly version of HadISST sea surface temperature state-space components
National Oceanic and Atmospheric Administration, Department of Commerce — State-Space Decomposition of Monthly version of HadISST sea surface temperature component (1-degree). See Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C....
State-Space Realization of the Wave-Radiation Force within FAST: Preprint
Energy Technology Data Exchange (ETDEWEB)
Duarte, T.; Sarmento, A.; Alves, M.; Jonkman, J.
2013-06-01
Several methods have been proposed in the literature to find a state-space model for the wave-radiation forces. In this paper, four methods were compared, two in the frequency domain and two in the time domain. The frequency-response function and the impulse response of the resulting state-space models were compared against the ones derived by the numerical code WAMIT. The implementation of the state-space module within the FAST offshore wind turbine computer-aided engineering (CAE) tool was verified, comparing the results against the previously implemented numerical convolution method. The results agreed between the two methods, with a significant reduction in required computational time when using the state-space module.
Fuzzy de Sitter space-times via coherent states quantization
Gazeau, J P; Queva, J; Gazeau, Jean-Pierre; Mourad, Jihad; Queva, Julien
2006-01-01
A construction of the 2d and 4d fuzzy de Sitter hyperboloids is carried out by using a (vector) coherent state quantization. We get a natural discretization of the dS "time" axis based on the spectrum of Casimir operators of the respective maximal compact subgroups SO(2) and SO(4) of the de Sitter groups SO\\_0(1,2) and SO\\_0(1,4). The continuous limit at infinite spins is examined.
Approximate controllability of infinite dimensional linear systems in nonreflexive state spaces
Institute of Scientific and Technical Information of China (English)
Xin YU; Chao XU
2005-01-01
This paper deals with the problem of approximate controllability of infinite dimensional linear systems in nonreflexive state spaces.A necessary and sufficient condition for approximate controllability via Lp([0,T],U),1≤p<∞ is obtained,where Lp([0,T],U) is the control function space.
Weaponizing the Final Frontier: The United States and the New Space Race
2017-06-09
allowing foreign nations to rapidly expand their space portfolio . The United States has the opportunity to take advantage and leverage its...affecting military operations. The sub-variables of this variable are the demographic mix, social volatility , education level, ethnic diversity...capability or social volatility that could a factor in shaping a policy that could impact U.S. interest in space
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
For the first time we construct the eigenstate |(Τ)> of noncommutative coordinate. It turns out that |(Τ)> is an entangled state in the ordinary space. Remarkably, its Schmidt decomposition has definite expression in the coordinate representation and the momentum representation. The |(Τ)> representation can simph'fy some calculations for obtaining energy level of two-dimensional oscillator in noncommutative space.
Directory of Open Access Journals (Sweden)
Maziar Nekovee
2010-01-01
Full Text Available Cognitive radio is being intensively researched as the enabling technology for license-exempt access to the so-called TV White Spaces (TVWS, large portions of spectrum in the UHF/VHF bands which become available on a geographical basis after digital switchover. Both in the US, and more recently, in the UK the regulators have given conditional endorsement to this new mode of access. This paper reviews the state-of-the-art in technology, regulation, and standardisation of cognitive access to TVWS. It examines the spectrum opportunity and commercial use cases associated with this form of secondary access.
A review of Bayesian state-space modelling of capture-recapture-recovery data.
King, Ruth
2012-04-06
Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture-recapture-recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture-recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.
A review of Bayesian state-space modelling of capture–recapture–recovery data
King, Ruth
2012-01-01
Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture–recapture–recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture–recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS. PMID:23565333
Making Faces - State-Space Models Applied to Multi-Modal Signal Processing
DEFF Research Database (Denmark)
Lehn-Schiøler, Tue
2005-01-01
The two main focus areas of this thesis are State-Space Models and multi modal signal processing. The general State-Space Model is investigated and an addition to the class of sequential sampling methods is proposed. This new algorithm is denoted as the Parzen Particle Filter. Furthermore, the Ma...... application an information theoretic vector quantizer is also proposed. Based on interactions between particles, it is shown how a quantizing scheme based on an analytic cost function can be derived....
Minimal state space realisation of continuous-time linear time-variant input-output models
Goos, J.; Pintelon, R.
2016-04-01
In the linear time-invariant (LTI) framework, the transformation from an input-output equation into state space representation is well understood. Several canonical forms exist that realise the same dynamic behaviour. If the coefficients become time-varying however, the LTI transformation no longer holds. We prove by induction that there exists a closed-form expression for the observability canonical state space model, using binomial coefficients.
Global exponential stability conditions for generalized state-space systems with time-varying delays
Energy Technology Data Exchange (ETDEWEB)
Yu, K.-W. [Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan (China)], E-mail: kwyu@mail.nkmu.edu.tw; Lien, C.-H. [Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan (China)], E-mail: chlien.ee@msa.hinet.net
2008-05-15
A unified approach is proposed to deal with the exponential stability for generalized state-space systems with time-varying delays. Many systems models can be regarded as special cases of the considered systems; such as neutral time-delay systems and delayed cellular neural networks. Delay-dependent stability criteria are proposed to guarantee the global exponential stability for generalized state-space systems with two cases of uncertainties. Two numerical examples are given to show the effectiveness of our method.
Mixed-Effects State Space Models for Analysis of Longitudinal Dynamic Systems
Liu, Dacheng; Lu, Tao; Niu, Xu-Feng; Wu, Hulin
2010-01-01
The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject-specific biomedical systems can be described by a set of differential or difference equations which are similar to engineering dynamic systems. In this paper, motivated by HIV dynamic studies, we propose a class of mixed-effects state space models based on the longitudinal feature of dynamic systems. State space models with mixed-ef...
THE DECOMPOSITION OF STATE SPACE FOR MARKOV CHAIN IN RANDOM ENVIRONMENT
Institute of Scientific and Technical Information of China (English)
Hu Dihe
2005-01-01
This paper is a continuation of [8] and [9]. The author obtains the decomposition of state space χof an Markov chain in random environment by making use of the results in [8] and [9], gives three examples, random walk in random environment, renewal process in random environment and queue process in random environment, and obtains the decompositions of the state spaces of these three special examples.
Walrus Bayesian State-space Model Output from the Bering Sea and Chukchi Sea, 2008-2012
U.S. Geological Survey, Department of the Interior — State-space models offer researchers an objective approach to modeling complex animal location datasets, and state-space model behavior classifications are often...
Exploiting Stabilizers and Parallelism in State Space Generation with the Symmetry Method
DEFF Research Database (Denmark)
Lorentsen, Louise; Kristensen, Lars Michael
2001-01-01
The symmetry method is a main reduction paradigm for alleviating the state explosion problem. For large symmetry groups deciding whether two states are symmetric becomes time expensive due to the apparent high time complexity of the orbit problem. The contribution of this paper is to alleviate th...... the negative impact of the orbit problem by the specification of canonical representatives for equivalence classes of states in Coloured Petri Nets, and by giving algorithms exploiting stabilizers and parallelism for computing the condensed state space....
Parallel symbolic state-space exploration is difficult, but what is the alternative?
Directory of Open Access Journals (Sweden)
Gianfranco Ciardo
2009-12-01
Full Text Available State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1 parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2 symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal.
Robustness and state-space structure of Boolean gene regulatory models.
Willadsen, Kai; Wiles, Janet
2007-12-21
Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing models of the Drosophila melanogaster segment polarity network and the Saccharomyces cerevisiae cell-cycle network, we show that the structure of attractor basins can yield insight into the underlying decision making required of the system, and also the way in which the system maximises its robustness. In particular, gene networks implementing decisions based on a few genes have simple state-space structures, and their attractors are robust by virtue of their simplicity. Gene networks with decisions that involve many interacting genes have correspondingly more complicated state-space structures, and robustness cannot be achieved through the structure of the attractor basins, but is achieved by larger attractor basins that dominate the state space. These different types of robustness are demonstrated by the two models: the D. melanogaster segment polarity network is robust due to simple attractor basins that implement decisions based on spatial signals; the S. cerevisiae cell-cycle network has a complicated state-space structure, and is robust only due to a giant attractor basin that dominates the state space.
Modeling and Control of a Photovoltaic Energy System Using the State-Space Averaging Technique
Directory of Open Access Journals (Sweden)
Mohd S. Jamri
2010-01-01
Full Text Available Problem statement: This study presented the modeling and control of a stand-alone Photovoltaic (PV system using the state-space averaging technique. Approach: The PV module was modeled based on the parameters obtained from a commercial PV data sheet while state-space method is used to model the power converter. A DC-DC boost converter was chosen to step up the input DC voltage of the PV module while the DC-AC single-phase full-bridge square-wave inverter was chosen to convert the input DC comes from boost converter into AC element. The integrated state-space model was simulated under a constant and a variable change of solar irradiance and temperature. In addition to that, maximum power point tracking method was also included in the model to ensure that optimum use of PV module is made. A circuitry simulation was performed under the similar test conditions in order to validate the state-space model. Results: Results showed that the state-space averaging model yields the similar performance as produced by the circuitry simulation in terms of the voltage, current and power generated. Conclusion/Recommendations: The state-space averaging technique is simple to be implemented in modeling and control of either simple or complex system, which yields the similar performance as the results from circuitry method.
Projective limits of state spaces I. Classical formalism
Lanéry, Suzanne; Thiemann, Thomas
2017-01-01
In this series of papers, we investigate the projective framework initiated by Jerzy Kijowski (1977) and Andrzej Okołów (2009, 2013, 2014), which describes the states of a quantum (field) theory as projective families of density matrices. A short reading guide to the series can be found in [27]. The present first paper aims at clarifying the classical structures that underlies this formalism, namely projective limits of symplectic manifolds [27, subsection 2.1]. In particular, this allows us to discuss accurately the issues hindering an easy implementation of the dynamics in this context, and to formulate a strategy for overcoming them [27, subsection 4.1].
Tracking with particle filter for high-dimensional observation and state spaces
Dubuisson, Séverine
2015-01-01
This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions.
Global properties of linear constraints in state space and motion planning
Institute of Scientific and Technical Information of China (English)
陈滨; 朱海平
1997-01-01
Study of nonholonomic motion planning needs further research into the global properties of linear constraints in state space.The global properties of constraints,which contain the holonomicity and the nonholonomici-ty by regions,the existence of the isolated integral manifolds and the singular points and so on,have essential influence on motion planning.By analysis of the point sets in total space,the complete sketch of the global properties of linear constraints in state space is obtained,which can directly be applied to motion planning.
Spin flip of multiqubit states in discrete phase space
Srinivasan, K.; Raghavan, G.
2017-02-01
Time reversal and spin flip are discrete symmetry operations of substantial importance to quantum information and quantum computation. Spin flip arises in the context of separability, quantification of entanglement and the construction of universal NOT gates. The present work investigates the relationship between the quantum state of a multiqubit system represented by the discrete Wigner function (DWFs) and its spin-flipped counterpart. The two are shown to be related through a Hadamard matrix that is independent of the choice of the quantum net used for the tomographic reconstruction of the DWF. These results are of interest to cases involving the direct tomographic reconstruction of the DWF from experimental data, and in the analysis of entanglement related properties purely in terms of the DWF.
Discrete coherent states and probability distributions in finite-dimensional spaces
Energy Technology Data Exchange (ETDEWEB)
Galetti, D.; Marchiolli, M.A.
1995-06-01
Operator bases are discussed in connection with the construction of phase space representatives of operators in finite-dimensional spaces and their properties are presented. It is also shown how these operator bases allow for the construction of a finite harmonic oscillator-like coherent state. Creation and annihilation operators for the Fock finite-dimensional space are discussed and their expressions in terms of the operator bases are explicitly written. The relevant finite-dimensional probability distributions are obtained and their limiting behavior for an infinite-dimensional space are calculated which agree with the well know results. (author). 20 refs, 2 figs.
DEFF Research Database (Denmark)
Mohd. Azam, Sazuan Nazrah
2017-01-01
In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing...... dynamics of the system of stochastic differential equations is linearized to produce the deterministic-stochastic linear transfer function. Then the linear transfer function is discretized to produce a linear discrete-time state space model that has a deterministic and a stochastic component. The filtered...... part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations....
Azam, Sazuan N. M.
2017-01-01
In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing dynamics of the system of stochastic differential equations is linearized to produce the deterministic-stochastic linear transfer function. Then the linear transfer function is discretized to produce a linear discrete-time state space model that has a deterministic and a stochastic component. The filtered part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations.
Quantum scattering theory of a single-photon Fock state in three-dimensional spaces.
Liu, Jingfeng; Zhou, Ming; Yu, Zongfu
2016-09-15
A quantum scattering theory is developed for Fock states scattered by two-level systems in three-dimensional free space. It is built upon the one-dimensional scattering theory developed in waveguide quantum electrodynamics. The theory fully quantizes the incident light as Fock states and uses a non-perturbative method to calculate the scattering matrix.
A weakly universal cellular automaton in the hyperbolic 3D space with three states
Maurice, Margenstern
2010-01-01
In this paper, we significantly improve a previous result by the same author showing the existence of a weakly universal cellular automaton with five states living in the hyperbolic 3D-space. Here, we get such a cellular automaton with three states only.
Coherent States for generalized oscillator with finite-dimensional Hilbert space
Borzov, Vadim V.; Damaskinsky, Eugene V.
2006-01-01
The construction of oscillator-like systems connected with the given set of orthogonal polynomials and coherent states for such systems developed by authors is extended to the case of the systems with finite-dimensional state space. As example we consider the generalized oscillator connected with Krawtchouk polynomials.
State space structure and entanglement of rotationally invariant spin systems
Breuer, H P
2005-01-01
We investigate the structure of SO(3)-invariant quantum systems which are composed of two particles with spins j_1 and j_2. The states of the composite spin system are represented by means of two complete sets of rotationally invariant operators, namely by the projections P_J onto the eigenspaces of the total angular momentum J, and by certain invariant operators Q_K which are built out of spherical tensor operators of rank K. It is shown that these representations are connected by an orthogonal matrix whose elements are expressible in terms of Wigner's 6-j symbols. The operation of the partial time reversal of the combined spin system is demonstrated to be diagonal in the Q_K-representation. These results are employed to obtain a complete characterization of spin systems with j_1 = 1 and arbitrary j_2 > 1. We prove that the Peres-Horodecki criterion of positive partial transposition (PPT) is necessary and sufficient for separability if j_2 is an integer, while for half-integer spins j_2 there always exist en...
Identification of a class of nonlinear state-space models using RPE techniques
DEFF Research Database (Denmark)
Zhou, W. W.; Blanke, Mogens
1986-01-01
The recursive prediction error methods in state-space form have been efficiently used as parameter identifiers for linear systems, and especially Ljung's innovations filter using a Newton search direction has proved to be quite ideal. In this paper, the RPE method in state-space form is developed...... to the nonlinear case and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows...... a quite convincing performance of the filter as combined parameter and state estimator....
A simplified state-space model of biventricular assist device-cardiovascular system interaction.
Koh, Vivian C A; Einly Lim; Boon Chiang Ng; Yong Kuen Ho; Lovell, Nigel H
2016-08-01
A simplified state-space model of biventricular assist device (BiVAD)-cardiovascular system (CVS) interaction is presented. The state-space equations includes a six-compartments CVS model incorporating the ventricles, the pulmonary and systemic circulations as well as the non-linear behavior of the valve flow, together with a left ventricular assist device (LVAD) and a right ventricular assist device (RVAD) component. The left and right pump speeds serve as the input variables for the state-space model. The model is simulated with three operational modes, i.e. (i) RVAD speed state hemodynamics is also studied with and without an outflow banding restriction. Our simulated results are validated with experimental data obtained from clinical, in vivo and in vitro studies provided in the literatures. We observed that despite its simplicity, the model is able to reproduce the observed trends in the reported studies, thus making it feasible for the development of robust yet practical control algorithms.
Establishing formal state space models via quantization for quantum control systems
Institute of Scientific and Technical Information of China (English)
Dong Daoyi; Chen Zonghai
2005-01-01
Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrodinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems,such as momentum, energy and Hamiltonian, so Schrodinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrodinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.
Exploring the phase space of multiple states in highly turbulent Taylor-Couette flow
van der Veen, Roeland C. A.; Huisman, Sander G.; Dung, On-Yu; Tang, Ho L.; Sun, Chao; Lohse, Detlef
2016-06-01
We investigate the existence of multiple turbulent states in highly turbulent Taylor-Couette flow in the range of Ta =1011 to 9 ×1012 by measuring the global torques and the local velocities while probing the phase space spanned by the rotation rates of the inner and outer cylinders. The multiple states are found to be very robust and are expected to persist beyond Ta =1013 . The rotation ratio is the parameter that most strongly controls the transitions between the flow states; the transitional values only weakly depend on the Taylor number. However, complex paths in the phase space are necessary to unlock the full region of multiple states. By mapping the flow structures for various rotation ratios in a Taylor-Couette setup with an equal radius ratio but a larger aspect ratio than before, multiple states are again observed. Here they are characterized by even richer roll structure phenomena, including an antisymmetrical roll state.
Unification and extension of monolithic state space and iterative cochlear models.
Rapson, Michael J; Tapson, Jonathan C; Karpul, David
2012-05-01
Time domain cochlear models have primarily followed a method introduced by Allen and Sondhi [J. Acoust. Soc. Am. 66, 123-132 (1979)]. Recently the "state space formalism" proposed by Elliott et al. [J. Acoust. Soc. Am. 122, 2759-2771 (2007)] has been used to simulate a wide range of nonlinear cochlear models. It used a one-dimensional approach that is extended to two dimensions in this paper, using the finite element method. The recently developed "state space formalism" in fact shares a close relationship to the earlier approach. Working from Diependaal et al. [J. Acoust. Soc. Am. 82, 1655-1666 (1987)] the two approaches are compared and the relationship formalized. Understanding this relationship allows models to be converted from one to the other in order to utilize each of their strengths. A second method to derive the state space matrices required for the "state space formalism" is also presented. This method offers improved numerical properties because it uses the information available about the model more effectively. Numerical results support the claims regarding fluid dimension and the underlying similarity of the two approaches. Finally, the recent advances in the state space formalism [Bertaccini and Sisto, J. Comp. Phys. 230, 2575-2587 (2011)] are discussed in terms of this relationship.
Gu, Yuanyuan; Norman, Richard; Viney, Rosalie
2014-09-01
Using discrete choice experiments (DCEs) to estimate health state utility values has become an important alternative to the conventional methods of Time Trade-Off and Standard Gamble. Studies using DCEs have typically used the conditional logit to estimate the underlying utility function. The conditional logit is known for several limitations. In this paper, we propose two types of models based on the mixed logit: one using preference space and the other using quality-adjusted life year (QALY) space, a concept adapted from the willingness-to-pay literature. These methods are applied to a dataset collected using the EQ-5D. The results showcase the advantages of using QALY space and demonstrate that the preferred QALY space model provides lower estimates of the utility values than the conditional logit, with the divergence increasing with worsening health states. Copyright © 2014 John Wiley & Sons, Ltd.
Cao, Youfang; Terebus, Anna; Liang, Jie
2016-04-01
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 (MEGs), 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 our theories. Overall, the novel state space
State vector solutions for nonaxisymmetric problem of multilayered half space piezoelectric medium
Institute of Scientific and Technical Information of China (English)
王建国
1999-01-01
A state space formulation is established for the nonaxisymmetric space problem of transversely isotropic piezoelectric media in a system of cylindrical coordinate by introducing the state vector. Using the Hankel transform and the Fourier series, the state vector equations are transformed into ordinary differential equations. By the use of the matrix methods, the analytical solutions of a single piezoelectric layer are presented in the form of the product of initial state variables and transfer matrix. The applications of state vector solutions are discussed. An analytical solution for a semiinfinite piezoelectric medium subjected to the vertical point force P_z, horizontal point force P_x along x-direction and point electric charge Q at the origin of the surface is presented. According to the continuity conditions at the interfaces, the general solution formulation for N-layered transversely isotropic piezoelectric media is given.
Thermodynamical state space measure and typical entanglement of pure Gaussian states
Serafini, A; Plenio, M B; Dahlsten, Oscar C.O.; Plenio, Martin B.; Serafini, Alessio
2006-01-01
We introduce a 'microcanonical' measure (complying with the "general canonical principle") over the second moments of pure Gaussian states under an energy constraint. We apply the defined measure to investigate the statistical properties of the bipartite entanglement of pure Gaussian states. Under the proposed measure, the distribution of the entanglement concentrates around a finite value at the thermodynamical limit and, in general, the typical entanglement of Gaussian states with maximal energy E is not close to the maximum allowed by E.
Modeling State Space Search Technique for a Real World Adversarial Problem Solving
Directory of Open Access Journals (Sweden)
Kester O. Omoregie
2015-02-01
Full Text Available In problem solving, there is a search for the appropriate solution. A state space is a problem domain consisting of the start state, the goal state and the operations that will necessitate the various moves from the start state to the goal state. Each move operation takes one away from the start state and closer to the goal state. In this work we have attempted implementing this concept in adversarial problem solving, which is a more complex problem space. We noted that real world adversarial problems vary in their types and complexities, and therefore solving an adversarial problem would depend on the nature of the adversarial problem itself. Specifically, we examined a real world case, "the prisoner's dilemma" which is a critical, mutually independent, decision making adversarial problem. We combined the idea of the Thagard's Theory of Explanatory Coherence (TEC with Bayes' theorem of conditional probability to construct the model of an opponent that includes the opponent's model of the agent. A further conversion of the model into a series of state space structures led us into the use of breadth-first search strategy to arrive at our decision goal.
Multiple Transition States and Roaming in Ion-Molecule Reactions: a Phase Space Perspective
Mauguiere, Frederic A L; Ezra, Gregory S; Farantos, Stavros C; Wiggins, Stephen
2013-01-01
We provide a dynamical interpretation of the recently identified `roaming' mechanism for molecular dissociation reactions in terms of geometrical structures in phase space. These are NHIMs (Normally Hyperbolic Invariant Manifolds) and their stable/unstable manifolds that define transition states for ion-molecule association or dissociation reactions. The associated dividing surfaces rigorously define a roaming region of phase space, in which both reactive and nonreactive trajectories can be trapped for arbitrarily long times.
An application of gain-scheduled control using state-space interpolation to hydroactive gas bearings
DEFF Research Database (Denmark)
Theisen, Lukas Roy Svane; Camino, Juan F.; Niemann, Hans Henrik
2016-01-01
, it is possible to design a gain-scheduled controller using multiple controllers optimised for a single frequency. Gain-scheduling strategies using the Youla parametrisation can guarantee stability at the cost of increased controller order and performance loss in the interpolation region. This paper contributes...... with a gain-scheduling strategy using state-space interpolation, which avoids both the performance loss and the increase of controller order associated to the Youla parametrisation. The proposed state-space interpolation for gain-scheduling is applied for mass imbalance rejection for a controllable gas...... bearing scheduled in two parameters. Comparisons against the Youla-based scheduling demonstrate the superiority of the state-space interpolation....
State-Space Geometry, Statistical Fluctuations, and Black Holes in String Theory
Directory of Open Access Journals (Sweden)
Stefano Bellucci
2014-01-01
Full Text Available We study the state-space geometry of various extremal and nonextremal black holes in string theory. From the notion of the intrinsic geometry, we offer a state-space perspective to the black hole vacuum fluctuations. For a given black hole entropy, we explicate the intrinsic geometric meaning of the statistical fluctuations, local and global stability conditions, and long range statistical correlations. We provide a set of physical motivations pertaining to the extremal and nonextremal black holes, namely, the meaning of the chemical geometry and physics of correlation. We illustrate the state-space configurations for general charge extremal black holes. In sequel, we extend our analysis for various possible charge and anticharge nonextremal black holes. From the perspective of statistical fluctuation theory, we offer general remarks, future directions, and open issues towards the intrinsic geometric understanding of the vacuum fluctuations and black holes in string theory.
State-space Geometry, Statistical Fluctuations and Black Holes in String Theory
Bellucci, Stefano
2011-01-01
We study the state-space geometry of various extremal and nonextremal black holes in string theory. From the notion of the intrinsic geometry, we offer a new perspective of black hole vacuum fluctuations. For a given black hole entropy, we explicate the intrinsic state-space geometric meaning of the statistical fluctuations, local and global stability conditions and long range statistical correlations. We provide a set of physical motivations pertaining to the extremal and nonextremal black holes, \\textit{viz.}, the meaning of the chemical geometry and physics of correlation. We illustrate the state-space configurations for general charge extremal black holes. In sequel, we extend our analysis for various possible charge and anticharge nonextremal black holes. From the perspective of statistical fluctuation theory, we offer general remarks, future directions and open issues towards the intrinsic geometric understanding of the vacuum fluctuations and black holes in string theory. Keywords: Intrinsic Geometry; ...
A simpler and elegant algorithm for computing fractal dimension in higher dimensional state space
Indian Academy of Sciences (India)
S Ghorui; A K Das; N Venkatramani
2000-02-01
Chaotic systems are now frequently encountered in almost all branches of sciences. Dimension of such systems provides an important measure for easy characterization of dynamics of the systems. Conventional algorithms for computing dimension of such systems in higher dimensional state space face an unavoidable problem of enormous storage requirement. Here we present an algorithm, which uses a simple but very powerful technique and faces no problem in computing dimension in higher dimensional state space. The unique indexing technique of hypercubes, used in this algorithm, provides a clever means to drastically reduce the requirement of storage. It is shown that theoretically this algorithm faces no problem in computing capacity dimension in any dimension of the embedding state space as far as the actual dimension of the attractor is ﬁnite. Unlike the existing algorithms, memory requirement offered by this algorithm depends only on the actual dimension of the attractor and has no explicit dependence on the number of data points considered.
Center for Space Telemetering and Telecommunications Systems, New Mexico State University
Horan, Stephen; DeLeon, Phillip; Borah, Deva; Lyman, Ray
2002-01-01
This viewgraph presentation gives an overview of the Center for Space Telemetering and Telecommunications Systems activities at New Mexico State University. Presentations cover the following topics: (1) small satellite communications, including nanosatellite radio and virtual satellite development; (2) modulation and detection studies, including details on smooth phase interpolated keying (SPIK) spectra and highlights of an adaptive turbo multiuser detector; (3) decoupled approaches to nonlinear ISI compensation; (4) space internet testing; (4) optical communication; (5) Linux-based receiver for lightweight optical communications without a laser in space, including software design, performance analysis, and the receiver algorithm; (6) carrier tracking hardware; and (7) subband transforms for adaptive direct sequence spread spectrum receivers.
An overview of United States manned space flight from Mercury to the Shuttle
Faget, M. A.
1981-01-01
Technical considerations in the design, development and operation of United States manned spacecraft from Project Mercury to the Space Shuttle are reviewed. The design and mission philosophies, launch vehicle and spacecraft characteristics, mode of operation, flight results and influence on later programs are discussed for Project Mercury, and Gemini Apollo and Skylab programs, the Apollo-Soyuz Test Project and the Space Shuttle program. The Space Shuttle is shown to represent a major departure from the trend established in previous programs, requiring major advancements in the fields of flight control, thermal protection, and liquid-propellant rocket technology.
Robust Quasi-LPV Control Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2002-01-01
In this paper we derive a synthesis result for robust LPV output feedback controllers for nonlinear systems modelled by neural state space models. This result is achieved by writing the neural state space model on a linear fractional transformation form in a non-conservative way, separating...... that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of LMIs with added constraints, some implementation issues are addressed and a simulation example is presented....
State-space models - from the EM algorithm to a gradient approach
DEFF Research Database (Denmark)
Olsson, Rasmus Kongsgaard; Petersen, Kaare Brandt; Lehn-Schiøler, Tue
2007-01-01
Slow convergence is observed in the EM algorithm for linear state-space models. We propose to circumvent the problem by applying any off-the-shelf quasi-Newton-type optimizer, which operates on the gradient of the log-likelihood function. Such an algorithm is a practical alternative due to the fact...... that the exact gradient of the log-likelihood function can be computed by recycling components of the expectation-maximization (EM) algorithm. We demonstrate the efficiency of the proposed method in three relevant instances of the linear state-space model. In high signal-to-noise ratios, where EM is particularly...
Robust Quasi-LPV Control Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
In this paper we derive a synthesis result for robust LPV output feedback controllers for nonlinear systems modelled by neural state space models. This result is achieved by writing the neural state space model on a linear fractional transformation form in a non-conservative way, separating...... the system description into a linear part and a nonlinear part. Linear parameter-varying control synthesis methods are then applied to design a nonlinear control law for this system. Since the model is assumed to have been identified from input-output measurement data only, it must be expected...
On completeness of coherent states in noncommutative spaces with generalised uncertainty principle
Dey, Sanjib
2016-01-01
Coherent states are required to form a complete set of vectors in the Hilbert space by providing the resolution of identity. We study the completeness of coherent states for two different models in a noncommutative space associated with the generalised uncertainty relation by finding the resolution of unity with a positive definite weight function. The weight function, which is sometimes known as the Borel measure, is obtained through explicit analytic solutions of the Stieltjes and Hausdorff moment problem with the help of the standard techniques of inverse Mellin transform.
State-space geometry, non-extremal black holes and Kaluza-Klein monopoles
Bellucci, Stefano
2012-01-01
We examine the statistical nature of the charged anticharged non-extremal black holes in string theory. From the perspective of the intrinsic Riemannian Geometry, the first principle of the statistical mechanics shows that the stability properties of general nonextremal nonlarge charged black brane solutions are divulged from the positivity of the corresponding principle minors of the space-state metric tensor. Under the addition of the Kaluza-Klein monopoles, a novel aspect of the Gaussian fluctuations demonstrates that the canonical fluctuations can be ascertained without any approximation. We offer the state-space geometric implication for the most general non-extremal black brane configurations in string theory.
Reinforcement Learning in Large State Spaces Simulated Robotic Soccer as a Testbed
Tuyls, Karl; Maes, Sam; Manderick, Bernard
2003-01-01
Large state spaces and incomplete information are two problems that stand out in learning in multi-agent systems. In this paper we tackle them both by using a combination of decision trees and Bayesian networks (BNs) to model the environment and the Q-function. Simulated robotic soccer is used as a testbed, since there agents are faced with both large state spaces and incomplete information. The long-term goal of this research is to define generic techniques that allow agents to learn in larg...
A State Space Method for Modal Identification of Mechanical Systems from Time Domain Responses
Directory of Open Access Journals (Sweden)
Xiaobo Liu
2005-01-01
Full Text Available A new state space method is presented for modal identification of a mechanical system from its time domain impulse or initial condition responses. A key step in this method is the identification of the characteristic polynomial coefficients of an adjoint system. Once these coefficients are determined, a canonical state space realization of the adjoint system and the system's modal parameters are formulated straightforwardly. This method is conceptually and mathematically simple and is easy to be implemented. Detailed mathematical treatments are demonstrated and numerical examples are provided to illustrate the use and effectiveness of the method.
Creating State-based Alliances to Support Earth and Space Science Education Reform
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.
Ramakrishnan, Rajasekhar; Ramakrishnan, Janak D
2010-11-01
Tracer studies are analyzed almost universally by multicompartmental models where the state variables are tracer amounts or activities in the different pools. The model parameters are rate constants, defined naturally by expressing fluxes as fractions of the source pools. We consider an alternative state space with tracer enrichments or specific activities as the state variables, with the rate constants redefined by expressing fluxes as fractions of the destination pools. Although the redefinition may seem unphysiological, the commonly computed fractional synthetic rate actually expresses synthetic flux as a fraction of the product mass (destination pool). We show that, for a variety of structures, provided the structure is linear and stationary, the model in the enrichment state space has fewer parameters than that in the activities state space, and is hence better both to study identifiability and to estimate parameters. The superiority of enrichment modeling is shown for structures where activity model unidentifiability is caused by multiple exit pathways; on the other hand, with a single exit pathway but with multiple untraced entry pathways, activity modeling is shown to be superior. With the present-day emphasis on mass isotopes, the tracer in human studies is often of a precursor, labeling most or all entry pathways. It is shown that for these tracer studies, models in the activities state space are always unidentifiable when there are multiple exit pathways, even if the enrichment in every pool is observed; on the other hand, the corresponding models in the enrichment state space have fewer parameters and are more often identifiable. Our results suggest that studies with labeled precursors are modeled best with enrichments.
Discrimination and synthesis of recursive quantum states in high-dimensional Hilbert spaces
Simon, David S.; Fitzpatrick, Casey A.; Sergienko, Alexander V.
2015-04-01
We propose an interferometric method for statistically discriminating between nonorthogonal states in high-dimensional Hilbert spaces for use in quantum information processing. The method is illustrated for the case of photon orbital angular momentum (OAM) states. These states belong to pairs of bases that are mutually unbiased on a sequence of two-dimensional subspaces of the full Hilbert space, but the vectors within the same basis are not necessarily orthogonal to each other. Over multiple trials, this method allows distinguishing OAM eigenstates from superpositions of multiple such eigenstates. Variations of the same method are then shown to be capable of preparing and detecting arbitrary linear combinations of states in Hilbert space. One further variation allows the construction of chains of states obeying recurrence relations on the Hilbert space itself, opening a new range of possibilities for more abstract information-coding algorithms to be carried out experimentally in a simple manner. Among other applications, we show that this approach provides a simplified means of switching between pairs of high-dimensional mutually unbiased OAM bases.
State Space Composition Technique for Intelligent Wheel Chair Adapting to Environment.
Hamagami, Tomoki; Hirata, Hironori
This paper describes a state space composition technique for the adaptation to environment in the autonomous behavior of intelligent wheel chair (IWC).In the product like IWC with actual sensors, composing state space is difficult problem since environmental information can not be observed sufficiently from restricted sensor inputs.A lot of states observed from same environment position raise the fail of the learning and adaptation with active learning approach.In order to compensate for the effects of the sensor configuration, that is sensor position, angle and precision, a normalization processing of position detector is introduced.In sensor normalization process, IWC scans present environment via range sensors with executing spot-turn, and prepare scan-patterns of each sensor.Then the normalization process adjusts the phase and dynamic range of each pattern to the reference sensor scan-pattern, analyzing phase differences and scale factors of each pattern against reference pattern.Using phase difference and scale factors, automated state space composition is possible.From the simulation experiment with both artificial and real-worlddraft, the automated state space construction is confirmed as a practical approach for pre-processing for environment learning and adaptation.
Directory of Open Access Journals (Sweden)
Nacer Tabib
2016-01-01
Full Text Available This paper proposes a new framework based on Binary Decision Diagrams (BDD for the graph distribution problem in the context of explicit model checking. The BDD are yet used to represent the state space for a symbolic verification model checking. Thus, we took advantage of high compression ratio of BDD to encode not only the state space, but also the place where each state will be put. So, a fitness function that allows a good balance load of states over the nodes of an homogeneous network is used. Furthermore, a detailed explanation of how to calculate the inter-site edges between different nodes based on the adapted data structure is presented.
Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods
Vaswani, Namrata
2008-01-01
We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the state space dimension is large or both. When the OL is multimodal, but the state transition pdf (STP) is narrow enough, the optimal importance density is usually unimodal. Under this assumption, many techniques have been proposed. But when the STP is broad, this assumption does not hold. We study how existing techniques can be generalized to situations where the optimal importance density is multimodal, but is unimodal conditioned on a part of the state vector. Sufficient conditions to test for the unimodality of this conditional posterior are derived. The number of particles, N, to accurately track using a PF increases with state space dimension, thus making any regular PF impractical for large dimensional tracking problems. We propose a solution that partially addresses this problem. An important class of large dimensional problems...
Identification of a Class of Non-linear State Space Models using RPE Techniques
DEFF Research Database (Denmark)
Zhou, Wei-Wu; Blanke, Mogens
1989-01-01
The RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions...... of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator...
MCMC for non-linear state space models using ensembles of latent sequences
2013-01-01
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no straightforward solution. We take a Bayesian approach to the inference of unknown parameters of a non-linear state model; this, in turn, requires the availability of efficient Markov Chain Monte Carlo (MCMC) sampling methods for the latent (hidden) variables and model parameters. Using the ensemble ...
2000-01-01
We propose in this paper two methods to compute Markovian bounds for monotone functions of a discrete time homogeneous Markov chain evolving in a totally ordered state space. The main interest of such methods is to propose algorithms to simplify analysis of transient characteristics such as the output process of a queue, or sojourn time in a subset of states. Construction of bounds are based on two kinds of results: well-known results on stochastic comparison between Markov cha...
状态空间与Birkhoff力学%The State Space and Birkhoffian Mechanics
Institute of Scientific and Technical Information of China (English)
丁光涛
2012-01-01
In this paper, we expound in three ways that Birkhoffian mechanics is the analytical mechanics in the state space. (1) In the reduction of Newton' s equations to the first-order form to obtain the Birkhoffian representation of the equations, it is declared that Birkhoffian variables are the general state variables which are transformed form the coordinate-velocity state variables. (2) Birkhoffian equations are the analytical equations of motion according as the structure of Lagrange's equations in the state space and of Birkhoff's equations are the same, and the Lagrangian in the state space can consist of Birkhoffian function and functions. (3) Since by noncanonical transformation the phase space as a particular state space can be transformed into the general state space, and Hamiltonian equation can be transformed into Birkhoffian equation, Birkhoffian mechanics can be regarded as the analytical dynamics in the state space.%本文从三方面论述Birkhoff力学是状态空间中的分析动力学:(1)从Newton运动微分方程一次化而引入Birkhoff表示的过程中,说明Birkhoff变量是从坐标-速度状态变量变换而来的,即Birkhoff变量本质上是系统的广义状态变量.(2)论证系统状态空间中Lagrange方程与Birkhoff方程具有相同的结构,状态空间中系统的Lagrange函数可以由Birkhoff函数和函数组构成,就是说Birkhoff方程是状态空间中系统的分析力学运动方程.(3)相空间是一种特殊的状态空间,经非正则变换成为一般的状态空间,而Hamilton方程经非正则变换成为Birkhoff方程,再次说明Birkhoff力学是状态空间中分析动力学.
Bayesian state space models for dynamic genetic network construction across multiple tissues.
Liang, Yulan; Kelemen, Arpad
2016-08-01
Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.
Sun, Xiaodian; Jin, Li; Xiong, Momiao
2008-01-01
It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.
State-space modeling indicates rapid invasion of an alien shrub in coastal dunes
DEFF Research Database (Denmark)
Damgaard, Christian Frølund; Nygaard, Bettina; Ejrnæs, Rasmus
2011-01-01
Invasion by alien plants has negative effects on coastal dunes. Monitoring local spread of invasive species depends on long-term data with sufficient spatial resolution. Bayesian state-space models are a new method for monitoring invasive plants based on unbalanced permanent-plot data. The method...
Evolved finite state controller for hybrid system in reduced search space
DEFF Research Database (Denmark)
Dupuis, Jean-Francois; Fan, Zhun
2009-01-01
This paper presents an evolutionary methodology to automatically generate finite state automata (FSA) controllers to control hybrid systems. The proposed approach reduces the search space using an invariant analysis of the system. FSA controllers for a case study of two-tank system have been...
Strict System Equivalence of 2D Linear Discrete State Space Models
Directory of Open Access Journals (Sweden)
Mohamed S. Boudellioua
2012-01-01
Full Text Available The connection between the polynomial matrix descriptions (PMDs of the well-known regular and singular 2D linear discrete state space models is considered. It is shown that the transformation of strict system equivalence in the sense of Fuhrmann provides the basis for this connection. The exact form of the transformation is established for both the regular and singular cases.
Analysis of Convergence Rates of Some Gibbs Samplers on Continuous State Spaces
Smith, Aaron
2011-01-01
We use a non-Markovian coupling and small modi?cations of techniques from the theory of ?nite Markov chains to analyze some Markov chains on continuous state spaces. The ?rst is a Gibbs sampler on narrow contingency tables, the second a gen- eralization of a sampler introduced by Randall and Winkler.
Statistical Algorithms for Models in State Space Using SsfPack 2.2
Koopman, S.J.M.; Shephard, N.; Doornik, J.A.
1998-01-01
This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing envi
Equivalence and Differences between Structural Equation Modeling and State-Space Modeling Techniques
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…
Mixed-effects state-space models for analysis of longitudinal dynamic systems.
Liu, Dacheng; Lu, Tao; Niu, Xu-Feng; Wu, Hulin
2011-06-01
The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject-specific biomedical systems can be described by a set of differential or difference equations that are similar to engineering dynamic systems. In this article, motivated by HIV dynamic studies, we propose a class of mixed-effects state-space models based on the longitudinal feature of dynamic systems. State-space models with mixed-effects components are very flexible in modeling the serial correlation of within-subject observations and between-subject variations. The Bayesian approach and the maximum likelihood method for standard mixed-effects models and state-space models are modified and investigated for estimating unknown parameters in the proposed models. In the Bayesian approach, full conditional distributions are derived and the Gibbs sampler is constructed to explore the posterior distributions. For the maximum likelihood method, we develop a Monte Carlo EM algorithm with a Gibbs sampler step to approximate the conditional expectations in the E-step. Simulation studies are conducted to compare the two proposed methods. We apply the mixed-effects state-space model to a data set from an AIDS clinical trial to illustrate the proposed methodologies. The proposed models and methods may also have potential applications in other biomedical system analyses such as tumor dynamics in cancer research and genetic regulatory network modeling. © 2010, The International Biometric Society.
Comment on "Network analysis of the state space of discrete dynamical systems"
Li, Chengqing; Shu, Shi
2016-01-01
This paper comments the letter entitled "Network analysis of the state space of discrete dynamical systems" by A. Shreim et al. [Physical Review Letters, 98, 198701 (2007)]. We found that some theoretical analyses are wrong and the proposed indicators based on parameters of phase network can not discriminate dynamical complexity of the discrete dynamical systems composed by 1-D Cellular Automata.
Identification of Nonlinear Nonautonomous State Space Systems from Input-Output Measurements
Verdult, Vincent; Verhaegen, Michel; Scherpen, Jacquelien
2000-01-01
This paper presents a method to determine a nonlinear state space model from a finite number of measurements of the inputs and outputs. The method is based on embedding theory for nonlinear systems, and can be viewed as an extension of the subspace identification method for linear systems. The paper
State space investigation of the bullwhip problem with ARMA(1,1) demand processes
Gaalman, Gerard; Disney, Stephen M.
2006-01-01
Using state space techniques we study a "myopic" order-up-to policy. The policy is myopic because it is optimal at minimising local inventory holding and shortage costs. In particular we study the bullwhip effect produced by the replenishment policy reacting to a stochastic ARMA(l,l) demand processe
Choosing the observational likelihood in state-space stock assessment models
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro
By implementing different observational likelihoods in a state-space age-based stock assessment model, we are able to compare the goodness-of-fit and effects on estimated fishing mortallity for different model choices. Model fit is improved by estimating suitable correlations between agegroups. We...
Wigner's dynamical transition state theory in phase space : classical and quantum
Waalkens, Holger; Schubert, Roman; Wiggins, Stephen
2008-01-01
We develop Wigner's approach to a dynamical transition state theory in phase space in both the classical and quantum mechanical settings. The key to our development is the construction of a normal form for describing the dynamics in the neighbourhood of a specific type of saddle point that governs t
A discounted model for a repairable system with continuous state space
Bruns, P.B.
2000-01-01
We examine repairable systems with a continous state space and partial repair options, carried out at fixed times $n=1,2,...$. Every time interval $[n,n+1)$ there is a manufacturing cost and a repair cost. These cost functions are not restricted to the class of bounded functions in this study. Condi
Statistical Algorithms for Models in State Space Using SsfPack 2.2
Koopman, S.J.M.; Shephard, N.; Doornik, J.A.
1998-01-01
This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing envi
Ground states for a modified capillary surface equation in weighted Orlicz-Sobolev space
Directory of Open Access Journals (Sweden)
Guoqing Zhang
2015-03-01
Full Text Available In this article, we prove a compact embedding theorem for the weighted Orlicz-Sobolev space of radially symmetric functions. Using the embedding theorem and critical points theory, we prove the existence of multiple radial solutions and radial ground states for the following modified capillary surface equation $$\\displaylines{ -\\operatorname{div}\\Big(\\frac{|\
Equivalence and differences between structural equation modeling and state-space modeling techniques
Chow, Sy-Miin; Ho, Moon-ho R.; Hamaker, E.L.; Dolan, C.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
Linear State-Space Identification of Interconnected Systems: A structured approach
Torres Tapia, P.I.
2014-01-01
In this thesis, three novel state-space identification algorithms for linear interconnected systems are proposed. The computational complexity and the topology reconstruction of the interconnected system are addressed. Possible applications of this theory can be found in Biology, Economics, Transpor
State-space solutions to the h_inf/ltr design problem
DEFF Research Database (Denmark)
Niemann, Hans Henrik
1993-01-01
phase case, though, the order of the controllers can be reduced to n in all cases. The control problems corresponding to the various controller types are given as four different singular state-space problems, and the solutions are given in terms of the relevant equations and inequalities...
Equivalence and Differences between Structural Equation Modeling and State-Space Modeling Techniques
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…
A state-space model for estimating detailed movements and home range from acoustic receiver data
DEFF Research Database (Denmark)
Pedersen, Martin Wæver; Weng, Kevin
2013-01-01
We present a state-space model for acoustic receiver data to estimate detailed movement and home range of individual fish while accounting for spatial bias. An integral part of the approach is the detection function, which models the probability of logging tag transmissions as a function...... is used to estimate home range and movement of a reef fish in the Pacific Ocean....
State-space-based harmonic stability analysis for paralleled grid-connected inverters
DEFF Research Database (Denmark)
Wang, Yanbo; Wang, Xiongfei; Chen, Zhe
2016-01-01
This paper addresses a state-space-based harmonic stability analysis of paralleled grid-connected inverters system. A small signal model of individual inverter is developed, where LCL filter, the equivalent delay of control system, and current controller are modeled. Then, the overall small signa...
Saraceno, Marcos; Ermann, Leonardo; Cormick, Cecilia
2017-03-01
The problem of finding symmetric informationally complete positive-operator-valued-measures (SIC-POVMs) has been solved numerically for all dimensions d up to 67 [A. J. Scott and M. Grassl, J. Math. Phys. 51, 042203 (2010), 10.1063/1.3374022], but a general proof of existence is still lacking. For each dimension, it was shown that it is possible to find a SIC-POVM that is generated from a fiducial state upon application of the operators of the Heisenberg-Weyl group. We draw on the numerically determined fiducial states to study their phase-space features, as displayed by the characteristic function and the Wigner, Bargmann, and Husimi representations, adapted to a Hilbert space of finite dimension. We analyze the phase-space localization of fiducial states, and observe that the SIC-POVM condition is equivalent to a maximal delocalization property. Finally, we explore the consequences in phase space of the conjectured Zauner symmetry. In particular, we construct a Hermitian operator commuting with this symmetry that leads to a representation of fiducial states in terms of eigenfunctions with definite semiclassical features.
System Identification of Civil Engineering Structures using State Space and ARMAV Models
DEFF Research Database (Denmark)
Andersen, P.; Kirkegaard, Poul Henning; Brincker, Rune
In this paper the relations between an ambient excited structural system, represented by an innovation state space system, and the Auto-Regressive Moving Average Vector (ARMAV) model are considered. It is shown how to obtain a multivariate estimate of the ARMAV model from output measurements, usi...
Quantum-optical states in finite-dimensional Hilbert space; 1, General formalism
Miranowicz, A; Imoto, N; Miranowicz, Adam; Leonski, Wieslaw; Imoto, Nobuyuki
2001-01-01
The interest in quantum-optical states confined in finite-dimensional Hilbert spaces has recently been stimulated by the progress in quantum computing, quantum-optical state preparation and measurement techniques, in particular, by the development of the discrete quantum-state tomography. In the first part of our review we present two essentially different approaches to define harmonic oscillator states in the finite-dimensional Hilbert spaces. One of them is related to the truncation scheme of Pegg, Phillips and Barnett [Phys. Rev. Lett. 81, 1604 (1998)] -- the so-called quantum scissors device. The second method corresponds to the truncation scheme of Leo\\'nski and Tana\\'s [Phys. Rev. A 49, R20 (1994)]. We propose some new definitions of the states related to these truncation schemes and find their explicit forms in the Fock representation. We discuss finite-dimensional generalizations of coherent states, phase coherent states, displaced number states, Schr\\"odinger cats, and squeezed vacuum. We show some i...
Longitudinal phase space of multiparticle final states in high energy hadron-hadron collisions
Institute of Scientific and Technical Information of China (English)
吴元芳; 刘连寿
1995-01-01
The highly anisotropic phase space (known as longitudinal phase space) of multipartide final states in high energy hh collisions is studied in detail. It is pointed out that the anisotropy of phase space should manifest itself not only in the dramatic difference in magnitude between the average transverse and longitudinal momenta, but also in the anisotropy of dynamical fluctuations in the two directions. It means that the particle distribution in phase space has the property of selfaffine fractal. A method for experimentally testing the selfaffine fractality and measuring its cbaracteristic parameterHurst exponent is given. In addition, the correlation between the degree of longitudinal fractality and the magnitude of average transverse momentum is discussed. A new characteristic quantity--average transverse momentum per event--for de scribing the dynamical property of an event (hard, soft or ultrasoft) is proposed. A comparison of the results with experimental data is given.
State-of-the-art Space Weather Forecast with AFFECTS and HELCATS
Bothmer, Volker; Affects Team; Helcats Team
2016-04-01
The space weather projects fostered through the European Union FP7 and Horizon 2020 programs have opened up new horizons in the field of space weather research and have facilitated state-of-the-art-forecasts. Here we present an overview on the services and space weather forecasts the EU FP7 project AFFECTS (Advanced Forecast For Ensuring Communications Through Space) is providing and how the precision of the forecast is qualitatively greatly enhanced by new results derived from the EU FP7 project HELCATS (Heliospheric Cataloguing, Analysis, and Techniques Services). The forecast techniques base on near-real time multipoint analysis of coronal mass ejections observed by SOHO and STEREO and simulations of their Sun to Earth evolution.
Ruess, Jakob
2015-12-28
Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
Ruess, Jakob
2015-12-01
Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
H2-optimal control with generalized state-space models for use in control-structure optimization
Wette, Matt
1991-01-01
Several advances are provided solving combined control-structure optimization problems. The author has extended solutions from H2 optimal control theory to the use of generalized state space models. The generalized state space models preserve the sparsity inherent in finite element models and hence provide some promise for handling very large problems. Also, expressions for the gradient of the optimal control cost are derived which use the generalized state space models.
Multipartite State Representations in Multi-mode Fock Space and Their Squeezing Transformations
Institute of Scientific and Technical Information of China (English)
YUAN Hong-Chun; LI Heng-Mei; QI Kai-Guo
2007-01-01
We present the continuous state vector of the total coordinate of multi-particle and the state vector of their total momentum, respectively, which possess completeness relation in multi-mode Fock space by virtue of the integration within an order product (IWOP) technique. We also calculate the transition from classical transformation of variables in the states to quantum unitary operator, deduce a new multi-mode squeezing operator, and discuss its squeezing effect. In progress, it indicates that the IWOP technique provides a convenient way to construct new representation in quantum mechanics.
Examples of bosonic de Finetti states over finite dimensional Hilbert spaces
Gottlieb, A D
2005-01-01
According to the Quantum de Finetti Theorem, locally normal infinite particle states with Bose-Einstein symmetry can be represented as mixtures of infinite tensor powers of vector states. This note presents examples of infinite-particle states with Bose-Einstein symmetry that arise as limits of Gibbs ensembles on finite dimensional spaces, and displays their de Finetti representations. We consider Gibbs ensembles for systems of bosons in a finite dimensional setting and discover limits as the number of particles tends to infinity, provided the temperature is scaled in proportion to particle number.
Representations of coherent and squeezed states in an extended two-parameter Fock space
Institute of Scientific and Technical Information of China (English)
M. K. Tavassoly; M. H. Lake
2012-01-01
Recently an f-deformed Fock space which is spanned by ｜n〉λ was introduced.These bases are the eigenstates of a deformed non-Hermitian Hamiltonian.In this contribution,we will use rather new nonorthogonal basis vectors for the construction of coherent and squeezed states,which in special case lead to the earlier known states.For this purpose,we first generalize the previously introduced Fock space spanned by ｜n〉λ bases,to a new one,spanned by extended two-parameters bases ｜n〉λ1,λ2.These bases are now the eigenstates of a non-Hermitian Hamiltonian Hλ1,λ2 =a(+)1,λ2a +1/2,where a(+)λ1,λ2 =a(+) + λ1a + λ2 and a are,respectively,the deformed creation and ordinary bosonic annihilation operators.The bases ｜n〉λ1,λ2 are nonorthogonal (squeezed states),but normalizable.Then,we deduce the new representations of coherent and squeezed states in our two-parameter Fock space.Finally,we discuss the quantum statistical properties,as well as the non-classical properties of the obtained states numerically.
A one-step-ahead pseudo-DIC for comparison of Bayesian state-space models.
Millar, R B; McKechnie, S
2014-12-01
In the context of state-space modeling, conventional usage of the deviance information criterion (DIC) evaluates the ability of the model to predict an observation at time t given the underlying state at time t. Motivated by the failure of conventional DIC to clearly choose between competing multivariate nonlinear Bayesian state-space models for coho salmon population dynamics, and the computational challenge of alternatives, this work proposes a one-step-ahead DIC, DICp, where prediction is conditional on the state at the previous time point. Simulations revealed that DICp worked well for choosing between state-space models with different process or observation equations. In contrast, conventional DIC could be grossly misleading, with a strong preference for the wrong model. This can be explained by its failure to account for inflated estimates of process error arising from the model mis-specification. DICp is not based on a true conditional likelihood, but is shown to have interpretation as a pseudo-DIC in which the compensatory behavior of the inflated process errors is eliminated. It can be easily calculated using the DIC monitors within popular BUGS software when the process and observation equations are conjugate. The improved performance of DICp is demonstrated by application to the multi-stage modeling of coho salmon abundance in Lobster Creek, Oregon. © 2014, The International Biometric Society.
Integrated system identification and modal state estimation for control of flexible space structures
Chen, Chung-Wen; Huang, Jen-Kuang; Phan, Minh; Juang, Jer-Nan
1990-01-01
A novel approach of integrated system identification and modal state estimation is proposed for control of linear dynamical systems including flexible space structures. There are four steps involved in this approach. First, the relation between a stochastic state space model of a dynamical system and the coefficients of its autoregressive model with exogenous input is derived. Second, an adaptive least-squares transversal predictor is used to estimate the coefficients of the model. Third, a state space model and a steady state Kalman filter gain of the dynamical system are then identified from the coefficients of the model by using the eigensystem realization algorithm. Fourth, a modal state estimator is constructed using the modal parameters of the identified model. On-line implementation of this algorithm can continually improve the modal parameters and the filter gain. It can also gradually update the system model when the system characteristics are slowly changing. A numerical example is used to illustrate the feasibility of the new approach.
A Stochastic and State Space Model for Tumour Growth and Applications
Directory of Open Access Journals (Sweden)
Wai-Yuan Tan
2009-01-01
Full Text Available We develop a state space model documenting Gompertz behaviour of tumour growth. The state space model consists of two sub-models: a stochastic system model that is an extension of the deterministic model proposed by Gyllenberg and Webb (1991, and an observation model that is a statistical model based on data for the total number of tumour cells over time. In the stochastic system model we derive through stochastic equations the probability distributions of the numbers of different types of tumour cells. Combining with the statistic model, we use these distribution results to develop a generalized Bayesian method and a Gibbs sampling procedure to estimate the unknown parameters and to predict the state variables (number of tumour cells. We apply these models and methods to real data and to computer simulated data to illustrate the usefulness of the models, the methods, and the procedures.
Analytic State Space Model for an Unsteady Finite-Span Wing
Izraelevitz, Jacob; Zhu, Qiang; Triantafyllou, Michael
2015-11-01
Real-time control of unsteady flows, such as force control in flapping wings, requires simple wake models that easily translate into robust control designs. We analytically derive a state-space model for the unsteady trailing vortex system behind a finite aspect-ratio flapping wing. Contrary to prior models, the downwash and lift distributions over the span can be arbitrary, including tip effects. The wake vorticity is assumed to be a fully unsteady distribution, with the exception of quasi-steady (no rollup) geometry. Each discretization along the span has one to four states to represent the local unsteady wake-induced downwash, lift, and circulation. The model supports independently time-varying velocity, heave, and twist along the span. We validate this state-space model through comparison with existing analytic solutions for elliptic wings and an unsteady inviscid panel method.
Chen, Jinsong; Hubbard, Susan S.; Williams, Kenneth H.; Pride, Steve; Li, Li; Steefel, Carl; Slater, Lee
2009-08-01
We develop a state-space Bayesian framework to combine time-lapse geophysical data with other types of information for quantitative estimation of biogeochemical parameters during bioremediation. We consider characteristics of end products of biogeochemical transformations as state vectors, which evolve under constraints of local environments through evolution equations, and consider time-lapse geophysical data as available observations, which could be linked to the state vectors through petrophysical models. We estimate the state vectors and their associated unknown parameters over time using Markov chain Monte Carlo sampling methods. To demonstrate the use of the state-space approach, we apply it to complex resistivity data collected during laboratory column biostimulation experiments that were poised to precipitate iron and zinc sulfides during sulfate reduction. We develop a petrophysical model based on sphere-shaped cells to link the sulfide precipitate properties to the time-lapse geophysical attributes and estimate volume fraction of the sulfide precipitates, fraction of the dispersed, sulfide-encrusted cells, mean radius of the aggregated clusters, and permeability over the course of the experiments. Results of the case study suggest that the developed state-space approach permits the use of geophysical data sets for providing quantitative estimates of end-product characteristics and hydrological feedbacks associated with biogeochemical transformations. Although tested here on laboratory column experiment data sets, the developed framework provides the foundation needed for quantitative field-scale estimation of biogeochemical parameters over space and time using direct, but often sparse wellbore data with indirect, but more spatially extensive geophysical data sets.
Bayesian state space models for inferring and predicting temporal gene expression profiles.
Liang, Yulan; Kelemen, Arpad
2007-12-01
Prediction of gene dynamic behavior is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. Unfortunately, most existing techniques used for the inclusion of the temporal correlations treat the time course as evenly distributed time intervals and use stationary models with time-invariant settings. This is an assumption that is often violated in microarray time course data since the time course expression data are at unequal time points, where the difference in sampling times varies from minutes to days. Furthermore, the unevenly spaced short time courses with sudden changes make the prediction of genetic dynamics difficult. In this paper, we develop two types of Bayesian state space models to tackle this challenge for inferring and predicting the gene expression profiles associated with diseases. In the univariate time-varying Bayesian state space models we treat both the stochastic transition matrix and the observation matrix time-variant with linear setting and point out that this can easily be extended to nonlinear setting. In the multivariate Bayesian state space model we include temporal correlation structures in the covariance matrix estimations. In both models, the unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters and hidden variables. We apply our models to multiple tissue polygenetic affymetrix data sets. Results show that the predictions of the genomic dynamic behavior can be well captured by the proposed models. (c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
You Pretty Little Flocker: Exploring the Aesthetic State Space of Creative Ecosystems.
Eldridge, Alice
2015-01-01
Artificial life models constitute a rich compendium of tools for the generative arts; complex, self-organizing, emergent behaviors have great interactive and generative potential. But how can we go beyond simply visualizing scientific simulations and manipulate these models for use in design and creative art contexts? You Pretty Little Flocker is a proof-of-concept study in expanding and exploring the aesthetic state space of a model for generative design. A modified version of Reynolds' flocking algorithm (1987) is described in which the space of possible images is extended and navigable in a way that at once provides user control and maintains generative autonomy.
Solid state nuclear track detectors in hadrontherapy and radiation protection in space
Energy Technology Data Exchange (ETDEWEB)
Scampoli, Paola, E-mail: paola.scampoli@na.infn.i [Department of Radiation Oncology, Inselspital Bern, Bern (Switzerland); Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, Napoli (Italy)
2009-10-15
The recent widespread of carbon-therapy for cancer treatment and the long duration manned exploration planned by NASA require the knowledge of nuclear data both for assessing the correct dose distribution in the target volume and surrounding healthy tissue (radiation therapy), and for a better knowledge of the mixed radiation field to which astronauts will be exposed (radiation protection in space). Nuclear fragmentation taking place in traversed material, even human body itself, is indeed responsible for a beam quality change whose biological effects have to be evaluated. Solid state nuclear track detectors (SSNTD) provide accurate measurements of fluence and fragmentation of heavy ions needed for hadrontherapy and space radiation-protection purposes.
Thyroid Function Changes Related to Use of Iodinated Water in United States Space Program
McMonigal, Kathleen A.; Braverman, Lewis E.; Dunn, John T.; Stanbury, John B.; Wear, Mary L.; Hamm, Peggy B.; Sauer, Richard L.; Billica, Roger D.; Pool, Sam L.
1999-01-01
The National Aeronautics and Space Administration (NASA) has used iodination as a method of microbial disinfection of potable water systems in United States spacecraft and long-duration habitability modules. A review of the effects on the thyroid following consumption o iodinated water by NASA astronauts was conducted. Pharmacological doses of iodine consumed by astronauts transiently decreased thyroid function, as reflected in serum TSH values. Although the adverse effects of excess iodine consumption in susceptible individuals are well documented, exposure to high doses of iodine during space flight did not result in a statistically significant increase in long-term thyroid disease in the astronaut population.
On phase-space representations of quantum mechanics using Glauber coherent states
Indian Academy of Sciences (India)
DIÓGENES CAMPOS
2016-08-01
A phase-space formulation of quantum mechanics is proposed by constructing two representations (identified as $pq$ and $qp$) in terms of the Glauber coherent states, in which phase-space wave functions (probability amplitudes) play the central role, and position $q$ and momentum $p$ are treated on equal footing. After finding some basic properties of the $pq$ and $qp$ wave functions, the quantum operators in phase-space are represented by differential operators, and the Schrödinger equation is formulated in both pictures. Afterwards, the method is generalized to work with the density operator by converting the quantum Liouville equation into $pq$ and $qp$ equations of motion for two-point functions in phase-space. A coordinate transformation between those points allows one to construct a cell in phase-space, whose central point can be treated as a parameter. In this way, one gets equations of motion describing the evolution of one-point functions in phase-space. Finally, it is shown that some quantities obtained in this paper are related in a natural way with cross-Wigner functions, which are constructed with either the position or the momentum wave functions.
Inference and Decoding of Motor Cortex Low-Dimensional Dynamics via Latent State-Space Models.
Aghagolzadeh, Mehdi; Truccolo, Wilson
2016-02-01
Motor cortex neuronal ensemble spiking activity exhibits strong low-dimensional collective dynamics (i.e., coordinated modes of activity) during behavior. Here, we demonstrate that these low-dimensional dynamics, revealed by unsupervised latent state-space models, can provide as accurate or better reconstruction of movement kinematics as direct decoding from the entire recorded ensemble. Ensembles of single neurons were recorded with triple microelectrode arrays (MEAs) implanted in ventral and dorsal premotor (PMv, PMd) and primary motor (M1) cortices while nonhuman primates performed 3-D reach-to-grasp actions. Low-dimensional dynamics were estimated via various types of latent state-space models including, for example, Poisson linear dynamic system (PLDS) models. Decoding from low-dimensional dynamics was implemented via point process and Kalman filters coupled in series. We also examined decoding based on a predictive subsampling of the recorded population. In this case, a supervised greedy procedure selected neuronal subsets that optimized decoding performance. When comparing decoding based on predictive subsampling and latent state-space models, the size of the neuronal subset was set to the same number of latent state dimensions. Overall, our findings suggest that information about naturalistic reach kinematics present in the recorded population is preserved in the inferred low-dimensional motor cortex dynamics. Furthermore, decoding based on unsupervised PLDS models may also outperform previous approaches based on direct decoding from the recorded population or on predictive subsampling.
Solid-State, High Energy 2-Micron Laser Development for Space-Based Remote Sensing
Singh, Upendra N.
2010-01-01
report in 2000 strongly advocated that NASA maintain in-house laser and lidar capability, and that NASA should work to lower the technology risk for all future lidar missions. A multi-Center NASA team formulated an integrated NASA strategy to provide the technology and maturity of systems necessary to make Lidar/Laser systems viable for space-based study and monitoring of the Earth's atmosphere. In 2002 the NASA Earth Science Enterprise (ESE) and Office of Aerospace Technology (OAT) created the Laser Risk Reduction Program (LRRP) and directed NASA Langley Research Center (LaRC) and Goddard Space Flight Center to carry out synergistic and complementary research towards solid-state lasers/lidars developments for space-based remote sensing applications.
State-space model with deep learning for functional dynamics estimation in resting-state fMRI.
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.
Development of Unsteady Aerodynamic State-Space Models from CFD-Based Pulse Responses
Silva, Walter A.; Raveh, Daniella E.
2001-01-01
A method for computing discrete-time state-space models of linearized unsteady aerodynamic behavior directly from aeroelastic CFD codes is presented. The method involves the treatment of CFD-based pulse responses as Markov parameters for use in a system identification /realization algorithm. Results are presented for the AGARD 445.6 Aeroelastic Wing with four aeroelastic modes at a Mach number of 0.96 using the EZNSS Euler/Navier-Stokes flow solver with aeroelastic capability. The System/Observer/Controller Identification Toolbox (SOCIT) algorithm, based on the Ho-Kalman realization algorithm, is used to generate 15th- and 32nd-order discrete-time state-space models of the unsteady aerodynamic response of the wing over the entire frequency range of interest.
Cointegration between trends and their estimators in state space models and CVAR models
DEFF Research Database (Denmark)
Johansen, Søren; Tabor, Morten Nyboe
2017-01-01
In a linear state space model Y(t)=BT(t) e(t), we investigate if the unobserved trend, T(t), cointegrates with the predicted trend, E(t), and with the estimated predicted trend, in the sense that the spreads are stationary. We find that this result holds for the spread B......(T(t)-E(t)) and the estimated spread. For the spread between the trend and the estimated trend, T(t)-E(t), however, cointegration depends on the identification of B. The same results are found, if the observations Y(t), from the state space model are analysed using a cointegrated vector autoregressive model, where the trend...... is defined as the common trend. Finally, we investigate cointegration between the spread between trends and their estimators based on the two models, and find the same results. We illustrate with two examples and confirm the results by a small simulation study....
Algorithms for a parallel implementation of Hidden Markov Models with a small state space
DEFF Research Database (Denmark)
Nielsen, Jesper; Sand, Andreas
2011-01-01
Two of the most important algorithms for Hidden Markov Models are the forward and the Viterbi algorithms. We show how formulating these using linear algebra naturally lends itself to parallelization. Although the obtained algorithms are slow for Hidden Markov Models with large state spaces......, they require very little communication between processors, and are fast in practice on models with a small state space. We have tested our implementation against two other imple- mentations on artificial data and observe a speed-up of roughly a factor of 5 for the forward algorithm and more than 6...... for the Viterbi algorithm. We also tested our algorithm in the Coalescent Hidden Markov Model framework, where it gave a significant speed-up....
The state space of a model for the Bray-Liebhafsky oscillating reaction
Schmitz, G.; Kolar-Anić, Lj.
2007-09-01
It has been known for a long time that the decomposition of hydrogen peroxide catalyzed by hydrogen and iodate ions, the Bray-Liebhafsky reaction, can generate oscillations in a batch reactor. Recently, mixed-mode oscillations and chaos have also been observed in a CSTR. The model we had previously proposed to explain the kinetics in a batch reactor can also simulate these new complex behaviors. Time series give only a limited view of the features of the calculated behaviors and more information is obtained studying the properties of the state space. We use projections of the trajectories, calculation of the correlation dimension of the attractor, Poincaré sections, and return maps. As the state space of the model is six-dimensional, we try to answer the questions of whether the projections into a 3D subspace give correct pictures of the real trajectories and whether we have reasons to prefer a special subspace.
Robust control of uncertain dynamic systems a linear state space approach
Yedavalli, Rama K
2014-01-01
This textbook aims to provide a clear understanding of the various tools of analysis and design for robust stability and performance of uncertain dynamic systems. In model-based control design and analysis, mathematical models can never completely represent the “real world” system that is being modeled, and thus it is imperative to incorporate and accommodate a level of uncertainty into the models. This book directly addresses these issues from a deterministic uncertainty viewpoint and focuses on the interval parameter characterization of uncertain systems. Various tools of analysis and design are presented in a consolidated manner. This volume fills a current gap in published works by explicitly addressing the subject of control of dynamic systems from linear state space framework, namely using a time-domain, matrix-theory based approach. This book also: Presents and formulates the robustness problem in a linear state space model framework Illustrates various systems level methodologies with examples and...
Modeling individual effects in the Cormack-Jolly-Seber Model: A state-space formulation
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 state space approach for the eigenvalue problem of marine risers
Alfosail, Feras
2017-10-05
A numerical state-space approach is proposed to examine the natural frequencies and critical buckling limits of marine risers. A large axial tension in the riser model causes numerical limitations. These limitations are overcome by using the modified Gram–Schmidt orthonormalization process as an intermediate step during the numerical integration process with the fourth-order Runge–Kutta scheme. The obtained results are validated against those obtained with other numerical methods, such as the finite-element, Galerkin, and power-series methods, and are found to be in good agreement. The state-space approach is shown to be computationally more efficient than the other methods. Also, we investigate the effect of a high applied tension, a high apparent weight, and higher-order modes on the accuracy of the numerical scheme. We demonstrate that, by applying the orthonormalization process, the stability and convergence of the approach are significantly improved.
DEFF Research Database (Denmark)
Poulsen, Tjalfe; Møldrup, Per; Nielsen, Don
2003-01-01
field were used. Multiple regression and ARIMA models yielded similar prediction accuracy, whereas state-space models generally gave significantly higher accuracy. State-space modeling suggested K-S at a given location could be predicted using nearby values of K-S, k(a100) and air-filled porosity......Estimates of soil hydraulic conductivity (K) and air permeability (k(a)) at given soil-water potentials are often used as reference points in constitutive models for K and k(a) as functions of moisture content and are, therefore, a prerequisite for predicting migration of water, air, and dissolved...... and gaseous chemicals in the vadose zone. In this study, three modeling approaches were used to identify the dependence of saturated hydraulic conductivity (K-S) and air permeability at -100 cm H2O soil-water potential (k(a100)) on soil physical properties in undisturbed soil: (i) Multiple regression, (ii...
STATE SPACE MODELING AND SIMULATION OF SENSORLESS PERMANENT MAGNET BLDC MOTOR
Directory of Open Access Journals (Sweden)
N. MURUGANANTHAM
2010-10-01
Full Text Available Brushless DC (BLDC motor simulation can be simply implemented with the required control scheme using specialized simulink built-in tools and block sets such as simpower systems toolbox. But it requires powerful processor requirements, large random access memory and long simulation time. To overcome these drawbacks this paper presents a state space modeling, simulation and control of permanent magnet brushless DC motor. By reading the instantaneous position of the rotor as an output, different variables of the motor can be controlled without the need of any external sensors or position detection techniques. Simulink is utilized with the assistance of MATLAB to give a very flexible and reliable simulation. With state space model representation, the motor performance can be analyzed for variation of motor parameters.
Modeling and Simulation of DC Power Electronics Systems Using Harmonic State Space (HSS) Method
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Bak, Claus Leth
2015-01-01
For the efficiency and simplicity of electric systems, the dc based power electronics systems are widely used in variety applications such as electric vehicles, ships, aircrafts and also in homes. In these systems, there could be a number of dynamic interactions between loads and other dc...... based on the state-space averaging and generalized averaging, these also have limitations to show the same results as with the non-linear time domain simulations. This paper presents a modeling and simulation method for a large dc power electronic system by using Harmonic State Space (HSS) modeling....... Through this method, the required computation time and CPU memory for large dc power electronics systems can be reduced. Besides, the achieved results show the same results as with the non-linear time domain simulation, but with the faster simulation time which is beneficial in a large network....
State-space-based harmonic stability assessment of paralleled grid-connected inverters system
DEFF Research Database (Denmark)
Wang, Yanbo; Wang, Xiongfei; Chen, Zhe;
2016-01-01
model of paralleled grid-connected inverters is built. Finally, the state space-based stability analysis approach is developed to explain the harmonic resonance phenomenon. The eigenvalue traces associated with time delay and coupled grid impedance are obtained, which accounts for how the unstable......This paper addresses a state-space-based harmonic stability analysis of paralleled grid-connected inverters system. A small signal model of individual inverter is developed, where LCL filter, the equivalent delay of control system, and current controller are modeled. Then, the overall small signal...... inverter produces the harmonic resonance and leads to the instability of whole paralleled system. The proposed approach reveals the contributions of the grid impedance as well as the coupled effect on other grid-connected inverters under different grid conditions. Simulation and experimental results...
Addressing challenges in single species assessments via a simple state-space assessment model
DEFF Research Database (Denmark)
Nielsen, Anders
Single-species and age-structured fish stock assessments still remains the main tool for managing fish stocks. A simple state-space assessment model is presented as an alternative to (semi) deterministic procedures and the full parametric statistical catch at age models. It offers a solution...... of state-space assessment models is that they tend to be more conservative (react slower to changes) than the alternatives. A solution to this criticism is offered by introducing a mixture distribution for the transitions steps. The model presented is used for several commercially important stocks...... to some of the key challenges of these models. Compared to the deterministic procedures it solves a list of problems originating from falsely assuming that age classified catches are known without errors and allows quantification of uncertainties of estimated quantities of interest. Compared to full...
State-space-split method for some generalized Fokker-Planck-Kolmogorov equations in high dimensions.
Er, Guo-Kang; Iu, Vai Pan
2012-06-01
The state-space-split method for solving the Fokker-Planck-Kolmogorov equations in high dimensions is extended to solving the generalized Fokker-Planck-Kolmogorov equations in high dimensions for stochastic dynamical systems with a polynomial type of nonlinearity and excited by Poissonian white noise. The probabilistic solution of the motion of the stretched Euler-Bernoulli beam with cubic nonlinearity and excited by uniformly distributed Poissonian white noise is analyzed with the presented solution procedure. The numerical analysis shows that the results obtained with the state-space-split method together with the exponential polynomial closure method are close to those obtained with the Monte Carlo simulation when the relative value of the basic system relaxation time and the mean arrival time of the Poissonian impulse is in some limited range.
Active absorption of acoustic wave using state-space control approach
Wu, Zhen; Varadan, Vijay K.; Varadan, Vasundara V.; Lee, Kwang Y.
1994-05-01
This paper presents a computer modeling and simulation of an active sound absorbing system with an optimal state-feedback controller. First, a state-space model is developed to describe one-dimensional sound reflection and transmission in the time domain. In the model derivation, the difficulty of discretizing the wave equation in an unbounded region is overcome by combining the finite-difference and analytical solutions. Numerical simulation of the open- loop model response is performed, which shows a good agreement with the well known frequency domain solutions. Second, a state-feedback controller including a linear quadratic regulator and a Kalman filter type state-estimator is designed using the optimal control theory. Numerical simulation of the closed-loop model response of an active sound control system containing two sensors and one actuator is presented. It is shown that a broadband attenuation of more than 30 dB over 2 octaves has been reached.
Beatty, William; Jay, Chadwick V.; Fischbach, Anthony S.
2016-01-01
State-space models offer researchers an objective approach to modeling complex animal location data sets, and state-space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two-state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state-space models, and reconcile these parameters with the study species and its expected behaviors.
DEFF Research Database (Denmark)
Wang, Yanbo; Wang, Xiongfei; Blaabjerg, Frede
2017-01-01
This paper presents a harmonic instability analysis method using state-space modeling and participation analysis in the inverter-fed ac power systems. A full-order state-space model for the droop-controlled Distributed Generation (DG) inverter is built first, including the time delay of the digit...
Adaptive control for space debris removal with uncertain kinematics, dynamics and states
Huang, Panfeng; Zhang, Fan; Meng, Zhongjie; Liu, Zhengxiong
2016-11-01
As the Tethered Space Robot is considered to be a promising solution for the Active Debris Removal, a lot of problems arise in the approaching, capturing and removing phases. Particularly, kinematics and dynamics parameters of the debris are unknown, and parts of the states are unmeasurable according to the specifics of tether, which is a tough problem for the target retrieval/de-orbiting. This work proposes a full adaptive control strategy for the space debris removal via a Tethered Space Robot with unknown kinematics, dynamics and part of the states. First we derive a dynamics model for the retrieval by treating the base satellite (chaser) and the unknown space debris (target) as rigid bodies in the presence of offsets, and involving the flexibility and elasticity of tether. Then, a full adaptive controller is presented including a control law, a dynamic adaption law, and a kinematic adaption law. A modified controller is also presented according to the peculiarities of this system. Finally, simulation results are presented to illustrate the performance of two proposed controllers.
Deep-inelastic final states in a space-time description of shower development and hadronization
Ellis, John R.; Kowalski, H.; Ellis, John; Geiger, Klaus; Kowalski, Henryk
1996-01-01
We extend a quantum kinetic approach to the description of hadronic showers in space, time and momentum space to deep-inelastic ep collisions, with particular reference to experiments at HERA. We follow the history of hard scattering events back to the initial hadronic state and forward to the formation of colour-singlet pre-hadronic clusters and their decays into hadrons. The time evolution of the space-like initial-state shower and the time-like secondary partons are treated similarly, and cluster formation is treated using a spatial criterion motivated by confinement and a non-perturbative model for hadronization. We calculate the time evolution of particle distributions in rapidity, transverse and longitudinal space. We also compare the transverse hadronic energy flow and the distribution of observed hadronic masses with experimental data from HERA, and find encouraging results. The techniques developed in this paper may be applied in the future to more complicated processes such as eA, pp, pA and AA coll...
Recursive prediction error methods for online estimation in nonlinear state-space models
Directory of Open Access Journals (Sweden)
Dag Ljungquist
1994-04-01
Full Text Available Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included, as well as a new method based on a line-search strategy. A comparison of the algorithms illustrates that they are very similar although the differences can be important for the online tracking capabilities and robustness. Simulation experiments on a simple nonlinear process show that the performance under certain conditions can be improved by including a line-search strategy.
A preferred ground state for the scalar field in de Sitter space
Aslanbeigi, Siavash; Buck, Michel
2013-01-01
We investigate a recent proposal for a distinguished vacuum state of a free scalar quantum field in an arbitrarily curved spacetime, known as the Sorkin-Johnston (SJ) vacuum, by applying it to de Sitter space. We derive the associated two-point functions on both the global and Poincar\\'e (cosmological) patches in general d+1 dimensions. In all cases where it is defined, the SJ vacuum belongs to the family of de Sitter invariant alpha-vacua. We obtain different states depending on the spacetim...
Input Harmonic Analysis on the Slim DC-Link Drive Using Harmonic State Space Model
DEFF Research Database (Denmark)
Yang, Feng; Kwon, Jun Bum; Wang, Xiongfei
2017-01-01
the shortcomings of the present harmonic analysis methods, such as the time-domain simulation, or the Fourier analysis, this paper proposes a Harmonic State Space model to study the harmonics performance for this type of drive. In this study, this model is utilized to describe the behavior of the harmonic...... variation according to the switching instant, the harmonics at the steady-state condition, as well as the coupling between the multiple harmonic impedances. By using this model, the impaction on the harmonics performance by the film capacitor and the grid inductance is derived. Simulation and experimental...
Design and performance evaluation of a state-space based AQM
Ariba, Yassine; Gouaisbaut, Frédéric; 10.1109/CTRQ.2008.15
2009-01-01
Recent research has shown the link between congestion control in communication networks and feedback control system. In this paper, the design of an active queue management (AQM) which can be viewed as a controller, is considered. Based on a state space representation of a linearized fluid flow model of TCP, the AQM design is converted to a state feedback synthesis problem for time delay systems. Finally, an example extracted from the literature and simulations via a network simulator NS (under cross traffic conditions) support our study.
Exploring the phase space of multiple states in highly turbulent Taylor-Couette flow
van der Veen, Roeland C A; Dung, On-Yu; Tang, Ho L; Sun, Chao; Lohse, Detlef
2016-01-01
We investigate the existence of multiple turbulent states in highly turbulent Taylor-Couette flow in the range of $\\mathrm{Ta}=10^{11}$ to $9\\cdot10^{12}$, by measuring the global torques and the local velocities while probing the phase space spanned by the rotation rates of the inner and outer cylinder. The multiple states are found to be very robust and are expected to persist beyond $\\mathrm{Ta}=10^{13}$. The rotation ratio is the parameter that most strongly controls the transitions between the flow states; the transitional values only weakly depend on the Taylor number. However, complex paths in the phase space are necessary to unlock the full region of multiple states. Lastly, by mapping the flow structures for various rotation ratios in a Taylor-Couette setup with an equal radius ratio but a larger aspect ratio than before, multiple states were again observed. Here, they are characterized by even richer roll structure phenomena, including, for the first time observed in highly turbulent TC flow, an ant...
Reliability Analysis of a 3-Machine Power Station Using State Space Approach
Directory of Open Access Journals (Sweden)
WasiuAkande Ahmed
2014-07-01
Full Text Available With the advent of high-integrity fault-tolerant systems, the ability to account for repairs of partially failed (but still operational systems become increasingly important. This paper presents a systemic method of determining the reliability of a 3-machine electric power station, taking into consideration the failure rates and repair rates of the individual component (machine that make up the system. A state-space transition process for a 3-machine with 23 states was developed and consequently, steady state equations were generated based on Markov mathematical modeling of the power station. Important reliability components were deduced from this analysis. This research simulation was achieved with codes written in Excel® -VBA programming environment. System reliability using state space approach proofs to be a viable and efficient technique of reliability prediction as it is able to predict the state of the system under consideration. For the purpose of neatness and easy entry of data, Graphic User Interface (GUI was designed.
Representations of Coherent and Squeezed States in an Extended Two-parameters Fock Space
Tavassoly, M K
2012-01-01
Recently a $f$-deformed Fock space which is spanned by $|n>_{\\lambda}$ has been introduced. These bases are indeed the eigen-states of a deformed non-Hermitian Hamiltonian. In this contribution, we will use a rather new non-orthogonal basis vectors for the construction of coherent and squeezed states, which in special case lead to the earlier known states. For this purpose, we first generalize the previously introduced Fock space spanned by $|n>_{\\lambda}$ bases, to a new one, spanned by an extended two-parameters bases $|n>_{\\lambda_{1},\\lambda_{2}}$. These bases are now the eigen-states of a non-Hermitian Hamiltonian $H_{\\lambda_{1},\\lambda_{2}}=a^{\\dagger}_{\\lambda_{1},\\lambda_{2}}a+1/2$, where $a^{\\dagger}_{\\lambda_{1},\\lambda_{2}}=a^{\\dagger}+\\lambda_{1}a + \\lambda_{2}$ and $a$ are respectively, the deformed creation and ordinary bosonic annihilation operators. The bases $|n>_{\\lambda_{1},\\lambda_{2}}$ are non-orthogonal (squeezed states), but normalizable. Then, we deduce the new representations of cohe...
Operator analogue of the Krein-Milman theorem in the generalized state spaces
Institute of Scientific and Technical Information of China (English)
吴畏
2001-01-01
We discuss the Krein-Milman-type problems in the C-convexity theory for the generalized state space SCn(A) of C-algebra A. The main results are that every BW-compact, C-convex subset of SCn(A) possesses a C-extreme point and every BW-compact, C-convex subset of SCn(A) is the C -convex hull of its C -extreme points.
The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications
Andreasen, Martin Møller; Fernández-Villaverde, Jesús; Juan F Rubio-Ramírez
2013-01-01
This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for first and second unconditional moments and impulse response functions. Thus, our analysis introduces GMM estimation for DSGE models approximated up to third-order and provides the foundation for indirect inference and SMM when simulation is requir...
Hierarchical approximate policy iteration with binary-tree state space decomposition.
Xu, Xin; Liu, Chunming; Yang, Simon X; Hu, Dewen
2011-12-01
In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI.
State-space prediction of spring discharge in a karst catchment in southwest China
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.
The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer
Davis, Anthony B.
2012-01-01
Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.
The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer
Davis, Anthony B.
2012-01-01
Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.
Modal contribution and state space order selection in operational modal analysis
Cara, F. Javier; Juan, Jesús; Alarcón, Enrique; Reynders, Edwin; De Roeck, Guido
2013-07-01
The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.
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.
Density-dependent state-space model for population-abundance data with unequal time intervals.
Dennis, Brian; Ponciano, José Miguel
2014-08-01
The Gompertz state-space (GSS) model is a stochastic model for analyzing time-series observations of population abundances. The GSS model combines density dependence, environmental process noise, and observation error toward estimating quantities of interest in biological monitoring and population viability analysis. However, existing methods for estimating the model parameters apply only to population data with equal time intervals between observations. In the present paper, we extend the GSS model to data with unequal time intervals, by embedding it within a state-space version of the Ornstein-Uhlenbeck process, a continuous-time model of an equilibrating stochastic system. Maximum likelihood and restricted maximum likelihood calculations for the Ornstein-Uhlenbeck state-space model involve only numerical maximization of an explicit multivariate normal likelihood, and so the extension allows for easy bootstrapping, yielding confidence intervals for model parameters, statistical hypothesis testing of density dependence, and selection among sub-models using information criteria. Ecologists and managers previously drawn to models lacking density dependence or observation error because such models accommodated unequal time intervals (for example, due to missing data) now have an alternative analysis framework incorporating density dependence, process noise, and observation error.
State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices.
Hong, Keum-Shik; Nguyen, Hoang-Dung
2014-06-01
THE PAPER PRESENTS STATE SPACE MODELS OF THE HEMODYNAMIC RESPONSE (HR) OF FNIRS TO AN IMPULSE STIMULUS IN THREE BRAIN REGIONS: motor cortex (MC), somatosensory cortex (SC), and visual cortex (VC). Nineteen healthy subjects were examined. For each cortex, three impulse HRs experimentally obtained were averaged. The averaged signal was converted to a state space equation by using the subspace method. The activation peak and the undershoot peak of the oxy-hemoglobin (HbO) in MC are noticeably higher than those in SC and VC. The time-to-peaks of the HbO in three brain regions are almost the same (about 6.76 76 ± 0.2 s). The time to undershoot peak in VC is the largest among three. The HbO decreases in the early stage (~0.46 s) in MC and VC, but it is not so in SC. These findings were well described with the developed state space equations. Another advantage of the proposed method is its easy applicability in generating the expected HR to arbitrary stimuli in an online (or real-time) imaging. Experimental results are demonstrated.
State-space models of head-related transfer functions for virtual auditory scene synthesis.
Adams, Norman H; Wakefield, Gregory H
2009-06-01
This study investigates the use of reduced-order state-space models of collections of head-related transfer functions (HRTFs). Recent head-phone applications have motivated interest in binaural displays that can render multiple simultaneous virtual sound sources, acoustic reflections, and source and listener motion. In the present study, a multi-direction framework is considered that can render such phenomena by filtering source signals with a collection of HRTFs rather than individual HRTFs. The collection of HRTFs is implemented in the state-space, and approximation techniques are applied to construct low-order approximants that are indiscriminable from full-order HRTFs. Two experiments are described in which five observers are asked to discriminate between state-space and full-order renderings. Depending on the stimulus conditions and discrimination task, order thresholds of 7
A new look at state-space models for neural data.
Paninski, Liam; Ahmadian, Yashar; Ferreira, Daniel Gil; Koyama, Shinsuke; Rahnama Rad, Kamiar; Vidne, Michael; Vogelstein, Joshua; Wu, Wei
2010-08-01
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.
Density dependent state space model for population abundance data with unequal time intervals
Dennis, Brian; Ponciano, José Miguel
2014-01-01
The Gompertz state-space (GSS) model is a stochastic model for analyzing time series observations of population abundances. The GSS model combines density dependence, environmental process noise, and observation error toward estimating quantities of interest in biological monitoring and population viability analysis. However, existing methods for estimating the model parameters apply only to population data with equal time intervals between observations. In the present paper, we extend the GSS model to data with unequal time intervals, by embedding it within a state-space version of the Ornstein-Uhlenbeck process, a continuous-time model of an equilibrating stochastic system. Maximum likelihood and restricted maximum likelihood calculations for the Ornstein-Uhlenbeck state-space model involve only numerical maximization of an explicit multivariate normal likelihood, and so the extension allows for easy bootstrapping, yielding confidence intervals for model parameters, statistical hypothesis testing of density dependence, and selection among sub-models using information criteria. Ecologists and managers previously drawn to models lacking density dependence or observation error because such models accommodated unequal time intervals (for example, due to missing data) now have an alternative analysis framework incorporating density dependence, process noise and observation error. PMID:25230459
Three-body problem in 3D space: ground state, (quasi)-exact-solvability
Turbiner, Alexander V; Escobar-Ruiz, Adrian M
2016-01-01
We study aspects of the quantum and classical dynamics of a $3$-body system in 3D space with interaction depending only on mutual distances. The study is restricted to solutions in the space of relative motion which are functions of mutual distances only. It is shown that the ground state (and some other states) in the quantum case and the planar trajectories in the classical case are of this type. The quantum (and classical) system for which these states are eigenstates is found and its Hamiltonian is constructed. It corresponds to a three-dimensional quantum particle moving in a curved space with special metric. The kinetic energy of the system has a hidden $sl(4,R)$ Lie (Poisson) algebra structure, alternatively, the hidden algebra $h^{(3)}$ typical for the $H_3$ Calogero model. We find an exactly solvable three-body generalized harmonic oscillator-type potential as well as a quasi-exactly-solvable three-body sextic polynomial type potential.
Institute of Scientific and Technical Information of China (English)
田兆青; 来新民; 林忠钦
2004-01-01
Dimensional quality is one of the most critical challenges in industries, which uses the multistage manufacturing process (MMP) such as assembly and machining for automotive and aerospace industries. According to investigations, fixture faults accounted for 72% of all the dimensional faults. Previous studies focused on only one fault or multiple faults occurred in one station or one fault in multiple stations, but these cases rarely appear in the real manufacturing. This paper presents a method for diagnosis of multiple fixture faults in the multi-station manufacturing process. The proposed method is based on the state space model of the MMP processes, which carries the information of the fixture layout geometry and sensor position. To identify the root cause, three continuous steps were used: a) development of the state space model and the construction of the statistics variables on offline mode, b) measurement of the coordinate measuring machines data on online mode and calculation of the statistics variables, and c) diagnostic algorithm for identifying the root cause. The presented paper integrates the state space model of the manufacturing processes and hypothesis test considering the impact of the measure noises. A case study verifies the proposed method.
On the State Space Geometry of the Kuramoto--Sivashinsky Flow in a Periodic Domain
Cvitanovic, Predrag; Davidchack, Ruslan L.; Siminos, Evangelos
2010-01-01
The continuous and discrete symmetries of the Kuramoto-Sivashinsky system restricted to a spatially periodic domain play a prominent role in shaping the invariant sets of its chaotic dynamics. The continuous spatial translation symmetry leads to relative equilibrium (traveling wave) and relative periodic orbit (modulated traveling wave) solutions. The discrete symmetries lead to existence of equilibrium and periodic orbit solutions, induce decomposition of state space into invariant subspaces, and enforce certain structurally stable heteroclinic connections between equilibria. We show, for the example of a particular small-cell Kuramoto-Sivashinsky system, how the geometry of its dynamical state space is organized by a rigid cage built by heteroclinic connections between equilibria, and demonstrate the preponderance of unstable relative periodic orbits and their likely role as the skeleton underpinning spatiotemporal turbulence in systems with continuous symmetries. We also offer novel visualizations of the high-dimensional Kuramoto-Sivashinsky state space flow through projections onto low-dimensional, PDE representation-independent, dynamically invariant intrinsic coordinate frames, as well as in terms of the physical, symmetry invariant energy transfer rates.
Fast and Stable Signal Deconvolution via Compressible State-Space Models.
Kazemipour, Abbas; Liu, Ji; Solarana, Krystyna; Nagode, Daniel; Kanold, Patrick; Wu, Min; Babadi, Behtash
2017-04-13
Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred and undersampled data, where the signals are in the form of discrete events distributed in time and space. We introduce compressible state-space models as a framework to model and estimate such discrete events. These state-space models admit abrupt changes in the states and have a convergent transition matrix, and are coupled with compressive linear measurements. We consider a dynamic compressive sensing optimization problem and develop a fast solution, using two nested Expectation Maximization algorithms, to jointly estimate the states as well as their transition matrices. Under suitable sparsity assumptions on the dynamics, we prove optimal stability guarantees for the recovery of the states and present a method for the identification of the underlying discrete events with precise confidence bounds. We present simulation studies as well as application to calcium deconvolution and sleep spindle detection, which verify our theoretical results and show significant improvement over existing techniques. Our results show that by explicitly modeling the dynamics of the underlying signals, it is possible to construct signal deconvolution solutions that are scalable, statistically robust, and achieve high temporal resolution. Our proposed methodology provides a framework for modeling and deconvolution of noisy, blurred, and undersampled measurements in a fast and stable fashion, with potential application to a wide range of biological data.
The phase response and state space of slow wave contractions in the small intestine.
Parsons, Sean P; Huizinga, Jan D
2017-09-01
What is the central question of this study? What are the dynamical rules governing interstitial cell of Cajal (ICC)-generated slow wave contractions in the small intestine, as reflected in their phase response curve and state space? What is the main finding and its importance? The phase response curve has a region of phase advance surrounding a phase delay peak. This pattern is important in generating a stable synchrony within the ICC network and is related to the state space of the ICC; in particular, the phase delay peak corresponds to the unstable equilibrium point that threads the ICC's limit cycle. Interstitial cells of Cajal (ICCs) generate electrical oscillations in the gut. Synchronization of the ICC population is required for generation of coherent electrical waves ('slow waves') that cause muscular contraction and thereby move gut content. The phase response curve (PRC) is an experimental measure of the dynamical rules governing a population of oscillators that determine their synchrony and gives an experimental window onto the state space of the oscillator, its dynamical landscape. We measured the PRC of slow wave contractions in the mouse small intestine by the novel combination of diameter mapping and single pulse electrical field stimulation. Phase change (τ) was measured as a function of old phase (ϕ) and distance from the stimulation electrode (d). Plots of τ(ϕ, d) showed an arrowhead-shaped region of phase advance enclosing at its base a phase delay peak. The phase change mirrored the perturbed pattern of contraction waves in response to a pulse. The (ϕ, d) plane is the surface of a displacement tube extending from the limit cycle through state space. To visualize the state space vector field on this tube, latent phase (ϕlat ) was calculated from τ. At the transition from advance to delay, isochrons made boomerang turns before tightening and winding around the phase delay peak corresponding to the unstable equilibrium point that threads the
Space-time measures for subluminal and superluminal motions
Calvo-Mozo, Benjam\\'\\in
2014-01-01
In present work we examine the implications on both, space-time measures and causal structure, of a generalization of the local causality postulate by asserting its validity to all motion regimes, the subluminal and superluminal ones. The new principle implies the existence of a denumerable set of metrical null cone speeds, \\{$c_k\\}$, where $c_1$ is the speed of light in vacuum, and $c_k/c \\simeq \\epsilon^{-k+1}$ for $k\\geq2$, where $\\epsilon^2$ is a tiny dimensionless constant which we introduce to prevent the divergence of the $x, t$ measures in Lorentz transformations, such that their generalization keeps $c_k$ invariant and as the top speed for every regime of motion. The non divergent factor $\\gamma_k$ equals $k\\epsilon^{-1}$ at speed $c_k$. We speak then of $k-$timelike and $k-$null intervals and of k-timelike and k-null paths on space-time, and construct a causal structure for each regime. We discuss also the possible transition of a material particle from the subluminal to the first superluminal regim...
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 preferred ground state for the scalar field in de Sitter space
Aslanbeigi, Siavash
2013-01-01
We investigate a recent proposal for a distinguished vacuum state of a free scalar quantum field in an arbitrarily curved spacetime, known as the Sorkin-Johnston (SJ) vacuum, by applying it to de Sitter space. We derive the associated two-point functions on both the global and Poincar\\'e (cosmological) patches in general d+1 dimensions. In all cases where it is defined, the SJ vacuum belongs to the family of de Sitter invariant alpha-vacua. We obtain different states depending on the spacetime dimension, mass of the scalar field, and whether the state is evaluated on the global or Poincar\\'e patch. We find that the SJ vacuum agrees with the Euclidean/Bunch-Davies state for heavy ("principal series") fields on the global patch in even spacetime dimensions. We also compute the SJ vacuum on a causal set corresponding to a causal diamond in 1+1 dimensional de Sitter space. Our simulations show that the mean of the SJ two-point function on the causal set agrees well with its expected continuum counterpart.
A preferred ground state for the scalar field in de Sitter space
Aslanbeigi, S.; Buck, M.
2013-08-01
We investigate a recent proposal for a distinguished vacuum state of a free scalar quantum field in an arbitrarily curved spacetime, known as the Sorkin-Johnston (SJ) vacuum, by applying it to de Sitter space. We derive the associated two-point functions on both the global and Poincaré (cosmological) patches in general d + 1 dimensions. In all cases where it is defined, the SJ vacuum belongs to the family of de Sitter invariant α-vacua. We obtain different states depending on the spacetime dimension, mass of the scalar field, and whether the state is evaluated on the global or Poincaré patch. We find that the SJ vacuum agrees with the Euclidean/Bunch-Davies state for heavy ("principal series") fields on the global patch in even spacetime dimensions. We also compute the SJ vacuum on a causal set corresponding to a causal diamond in 1 + 1 dimensional de Sitter space. Our simulations show that the mean of the SJ two-point function on the causal set agrees well with its expected continuum counterpart.
Hybrid state-space self-tuning control of uncertain linear systems
Shieh, L. S.; Wang, Y. J.; Sunkel, J. W.
1993-01-01
The paper presents a hybrid state-space self-tuner using a new dual-rate sampling scheme for digital adaptive control of continuous-time uncertain linear systems. A state-space-based recursive least-squares algorithm, together with a variable forgetting factor, is used for direct estimations of both the equivalent discrete-time uncertain linear system parameters and the associated discrete-time state of a continuous-time uncertain linear system from the sampled input and output data. An analogue optimal regional pole-placement design method is used for designing an optimal observer-based analogue controller. A suboptimal observer-based digital controller is then designed from the designed analogue controller using digital redesign technique. To enhance the robustness of parameter identification and state estimation algorithms, a dynamic bound for a class of uncertain bilinear parameters and a fast-rate digital controller are developed at each fast-sampling period. Also, to accommodate computation loads and computation delay for developing the advanced hybrid self-tuner, the designed analogue controller and observer gains are both updated at each slow-sampling period. This control technique has been successfully applied to benchmark control problems.
Ensemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems
Luo, Xiaodong
2014-12-01
This study considers the data assimilation problem in coupled systems, which consists of two components (subsystems) interacting with each other through certain coupling terms. A straightforward way to tackle the assimilation problem in such systems is to concatenate the states of the subsystems into one augmented state vector, so that a standard ensemble Kalman filter (EnKF) can be directly applied. This work presents a divided state-space estimation strategy, in which data assimilation is carried out with respect to each individual subsystem, involving quantities from the subsystem itself and correlated quantities from other coupled subsystems. On top of the divided state-space estimation strategy, the authors also consider the possibility of running the subsystems separately. Combining these two ideas, a few variants of the EnKF are derived. The introduction of these variants is mainly inspired by the current status and challenges in coupled data assimilation problems and thus might be of interest from a practical point of view. Numerical experiments with a multiscale Lorenz 96 model are conducted to evaluate the performance of these variants against that of the conventional EnKF. In addition, specific for coupled data assimilation problems, two prototypes of extensions of the presented methods are also developed in order to achieve a trade-offbetween efficiency and accuracy.
Ullmann, R Thomas; Ullmann, G Matthias
2011-01-27
We present a generalized free energy perturbation theory that is inspired by Monte Carlo techniques and based on a microstate description of a transformation between two states of a physical system. It is shown that the present free energy perturbation theory stated by the Zwanzig equation follows as a special case of our theory. Our method uses a stochastic mapping of the end states that associates a given microstate from one ensemble with a microstate from the adjacent ensemble according to a probability distribution. In contrast, previous free energy perturbation methods use a static, deterministic mapping that associates fixed pairs of microstates from the two ensembles. The advantages of our approach are that end states of differing configuration space volume can be treated easily also in the case of discrete configuration spaces and that the method does not require the potentially cumbersome search for an optimal deterministic mapping. The application of our theory is illustrated by some example problems. We discuss practical applications for which our findings could be relevant and point out perspectives for further development of the free energy perturbation theory.
A Machine-Checked Proof of A State-Space Construction Algorithm
Catano, Nestor; Siminiceanu, Radu I.
2010-01-01
This paper presents the correctness proof of Saturation, an algorithm for generating state spaces of concurrent systems, implemented in the SMART tool. Unlike the Breadth First Search exploration algorithm, which is easy to understand and formalise, Saturation is a complex algorithm, employing a mutually-recursive pair of procedures that compute a series of non-trivial, nested local fixed points, corresponding to a chaotic fixed point strategy. A pencil-and-paper proof of Saturation exists, but a machine checked proof had never been attempted. The key element of the proof is the characterisation theorem of saturated nodes in decision diagrams, stating that a saturated node represents a set of states encoding a local fixed-point with respect to firing all events affecting only the node s level and levels below. For our purpose, we have employed the Prototype Verification System (PVS) for formalising the Saturation algorithm, its data structures, and for conducting the proofs.
Approximation of a class of Markov-modulated Poisson processes with a large state space
Energy Technology Data Exchange (ETDEWEB)
Sitaraman, H.
1989-01-01
Many queueing systems have an arrival process that can be modeled by a Markov-modulated Poisson process. The Markov-modulated Poisson process (MMPP) is a doubly stochastic Poisson process in which the arrival rate varies according to a finite state irreducible Markov process. In many applications of MMPPs, the point process is constructed by superpositions or similar constructions, which lead to modulating Markov processes with a large state space. Since this limits the feasibility of numerical computations, a useful problem is to approximate an MMPP represented by a large Markov process by one with fewer states. The author focuses his attention in particular, to approximating a simple but useful special case of the MMPP, namely the Birth and Death Modulated Poisson process. In the validation stage, the quality of the approximation is examined in relation to the MMPP/G/1 queue.
Zhang, Yu-Lin
This paper states the application of state-space method to the analysis of the dynamic characteristics of a variable thrust liquid propellant rocket engine and presents a set of state equations for describing the dynamic process of the engine. An efficient numerical method for solving these system equations is developed. The theoretical solutions agree well with the experimental data. The analysis leads to the following conclusion: the set coefficient of the pulse width, the working frequency of the solenoid valves and the deviation of the critical working points of these valves are important parameters for determining the dynamic response time and the control precision of this engine. The methods developed in this paper may be used effectively in the analysis of dynamic characteristics of variable thrust liquid propellant rocket engines.
An Investigation of State-Space Model Fidelity for SSME Data
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.
Kalegaev, Vladimir; Shugay, Yulia; Bobrovnikov, Sergey; Kuznetsov, Nikolay; Barinova, Vera; Myagkova, Irina; Panasyuk, Mikhail
2016-07-01
Space Monitoring Data Center (SMDC) of Moscow State University provides mission support for Russian satellites and give operational analysis of radiation conditions in space. SMDC Web-sites (http://smdc.sinp.msu.ru/ and http://swx.sinp.msu.ru/) give access to current data on the level of solar activity, geomagnetic and radiation state of Earth's magnetosphere and heliosphere in near-real time. For data analysis the models of space environment factors working online have been implemented. Interactive services allow one to retrieve and analyze data at a given time moment. Forecasting applications including solar wind parameters, geomagnetic and radiation condition forecasts have been developed. Radiation dose and SEE rate control are of particular importance in practical satellite operation. Satellites are always under the influence of high-energy particle fluxes during their orbital flight. The three main sources of particle fluxes: the Earth's radiation belts, the galactic cosmic rays, and the solar energetic particles (SEP), are taken into account by SMDC operational services to estimate the radiation dose caused by high-energy particles to a satellite at LEO orbits. ISO 15039 and AP8/AE8 physical models are used to estimate effects of galactic cosmic rays and radiation belt particle fluxes. Data of geosynchronous satellites (GOES or Electro-L1) allow to reconstruct the SEP fluxes spectra at a given low Earth orbit taking into account the geomagnetic cut-off depending on geomagnetic activity level.
Adaptive internal state space construction method for reinforcement learning of a real-world agent.
Samejima, K; Omori, T
1999-10-01
One of the difficulties encountered in the application of the reinforcement learning to real-world problems is the construction of a discrete state space from a continuous sensory input signal. In the absence of a priori knowledge about the task, a straightforward approach to this problem is to discretize the input space into a grid, and to use a lookup table. However, this method suffers from the curse of dimensionality. Some studies use continuous function approximators such as neural networks instead of lookup tables. However, when global basis functions such as sigmoid functions are used, convergence cannot be guaranteed. To overcome this problem, we propose a method in which local basis functions are incrementally assigned depending on the task requirement. Initially, only one basis function is allocated over the entire space. The basis function is divided according to the statistical property of locally weighted temporal difference error (TD error) of the value function. We applied this method to an autonomous robot collision avoidance problem, and evaluated the validity of the algorithm in simulation. The proposed algorithm, which we call adaptive basis division (ABD) algorithm, achieved the task using a smaller number of basis functions than the conventional methods. Moreover, we applied the method to a goal-directed navigation problem of a real mobile robot. The action strategy was learned using a database of sensor data, and it was then used for navigation of a real machine. The robot reached the goal using a smaller number of internal states than with the conventional methods.
Entangled Bloch spheres: Bloch matrix and two-qubit state space
Gamel, Omar
2016-06-01
We represent a two-qubit density matrix in the basis of Pauli matrix tensor products, with the coefficients constituting a Bloch matrix, analogous to the single qubit Bloch vector. We find the quantum state positivity requirements on the Bloch matrix components, leading to three important inequalities, allowing us to parametrize and visualize the two-qubit state space. Applying the singular value decomposition naturally separates the degrees of freedom to local and nonlocal, and simplifies the positivity inequalities. It also allows us to geometrically represent a state as two entangled Bloch spheres with superimposed correlation axes. It is shown that unitary transformations, local or nonlocal, have simple interpretations as axis rotations or mixing of certain degrees of freedom. The nonlocal unitary invariants of the state are then derived in terms of local unitary invariants. The positive partial transpose criterion for entanglement is generalized, and interpreted as a reflection, or a change of a single sign. The formalism is used to characterize maximally entangled states, and generalize two qubit isotropic and Werner states.
On observation distributions for state space models of population survey data.
Knape, Jonas; Jonzén, Niclas; Sköld, Martin
2011-11-01
1. State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2. To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal distributions may all be adequate for modelling observation errors for the data. We fit each of these to the data and compare them using AIC. 3. The state space models were fitted with maximum likelihood methods using a recent importance sampling technique that relies on the Kalman filter. The method relaxes the assumption of Gaussian observation errors required by the basic Kalman filter. Matlab code for fitting linear state space models with Poisson observations is provided. 4. The ability of AIC to identify the correct observation model was investigated in a small simulation study. For the parameter values used in the study, without replicated observations, the correct observation distribution could sometimes be identified but model selection was prone to misclassification. On the other hand, when observations were replicated, the correct distribution could typically be identified. 5. Our results illustrate that inferences may differ markedly depending on the observation distributions used, suggesting that choosing an adequate observation model can be critical. Model selection and simulations show that for the models and parameter values in this study, a suitable observation model can typically be identified if observations are
Two Temperature Magneto-Thermoelasticity with Initial Stress: State Space Formulation
Directory of Open Access Journals (Sweden)
Sunita Deswal
2013-01-01
Full Text Available Magneto-thermoelastic interactions in an initially stressed isotropic homogeneous elastic half-space with two temperatures are studied using mathematical methods under the purview of the L-S model of linear theory of generalized thermoelasticity. The formalism deals with the state space approach with the purpose of counteracting the difficulties of handling the displacement potential functions. Of specific concern here is the propagation of waves owing to ramp type increase in temperature and load. The medium is considered to be permeated by a uniform magnetic field. The expressions for different field parameters such as displacement, temperature, strain, and stress in the physical domain are obtained by applying a numerical inversion technique. Results of some earlier workers have been deduced from the present formulation. Numerical work is also performed for a suitable material with the aim of illustrating the results.
Institute of Scientific and Technical Information of China (English)
MENG Guang-hui; LIN Xin; HUANG Wei-dong
2008-01-01
The average lamellar spacing and interface undercooling in steady-state irregular eutectic growth were estimated based on the Jackson and Hunt's analysis by relaxing the isothermal interface assumption. At low growth rates, the average lamellar spacing and average interface undercooling are dependent only on the characteristic thermo-physical properties of a binary eutectic system. For a general Al-Si eutectic, it is found that the eutectic characteristic length based on the present non-isothermal analysis is consistent with that obtained from isothermal analysis; however, the average interface undercooling is remarkably different between them, and such discrepancy in average interface undercooling increases with increasing of growth rate. The measured interface undercooling obtained from literature is reasonably interpreted by present non-isothermal analysis.
Phase space interference and the WKB approximation for squeezed number states
Mundarain, D F
2003-01-01
Squeezed number states for a single mode Hamiltonian are investigated from two complementary points of view. Firstly the more relevant features of their photon distribution are discussed using the WKB wave functions. In particular the oscillations of the distribution and the parity behavior are derived and compared with the exact results. The accuracy is verified and it is shown that for high photon number it fails to reproduce the true distribution. This is contrasted with the fact that for moderate squeezing the WKB approximation gives the analytical justification to the interpretation of the oscillations as the result of the interference of areas with definite phases in phase space. It is shown with a computation at high squeezing using a modified prescription for the phase space representation which is based on Wigner-Cohen distributions that the failure of the WKB approximation does not invalidate the area overlap picture.
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi
Directory of Open Access Journals (Sweden)
Lawrence M. Murray
2015-10-01
Full Text Available LibBi is a software package for state space modelling and Bayesian inference on modern computer hardware, including multi-core central processing units, many-core graphics processing units, and distributed-memory clusters of such devices. The software parses a domain-specific language for model specification, then optimizes, generates, compiles and runs code for the given model, inference method and hardware platform. In presenting the software, this work serves as an introduction to state space models and the specialized methods developed for Bayesian inference with them. The focus is on sequential Monte Carlo (SMC methods such as the particle filter for state estimation, and the particle Markov chain Monte Carlo and SMC2 methods for parameter estimation. All are well-suited to current computer hardware. Two examples are given and developed throughout, one a linear three-element windkessel model of the human arterial system, the other a nonlinear Lorenz '96 model. These are specified in the prescribed modelling language, and LibBi demonstrated by performing inference with them. Empirical results are presented, including a performance comparison of the software with different hardware configurations.
Rate control system algorithm developed in state space for models with parameter uncertainties
Directory of Open Access Journals (Sweden)
Adilson Jesus Teixeira
2011-09-01
Full Text Available Researching in weightlessness above the atmosphere needs a payload to carry the experiments. To achieve the weightlessness, the payload uses a rate control system (RCS in order to reduce the centripetal acceleration within the payload. The rate control system normally has actuators that supply a constant force when they are turned on. The development of an algorithm control for this rate control system will be based on the minimum-time problem method in the state space to overcome the payload and actuators dynamics uncertainties of the parameters. This control algorithm uses the initial conditions of optimal trajectories to create intermediate points or to adjust existing points of a switching function. It associated with inequality constraint will form a decision function to turn on or off the actuators. This decision function, for linear time-invariant systems in state space, needs only to test the payload state variables instead of spent effort in solving differential equations and it will be tuned in real time to the payload dynamic. It will be shown, through simulations, the results obtained for some cases of parameters uncertainties that the rate control system algorithm reduced the payload centripetal acceleration below μg level and keep this way with no limit cycle.
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi
Directory of Open Access Journals (Sweden)
Lawrence M. Murray
2015-10-01
Full Text Available LibBi is a software package for state space modelling and Bayesian inference on modern computer hardware, including multi-core central processing units, many-core graphics processing units, and distributed-memory clusters of such devices. The software parses a domain-specific language for model specification, then optimizes, generates, compiles and runs code for the given model, inference method and hardware platform. In presenting the software, this work serves as an introduction to state space models and the specialized methods developed for Bayesian inference with them. The focus is on sequential Monte Carlo (SMC methods such as the particle filter for state estimation, and the particle Markov chain Monte Carlo and SMC2 methods for parameter estimation. All are well-suited to current computer hardware. Two examples are given and developed throughout, one a linear three-element windkessel model of the human arterial system, the other a nonlinear Lorenz '96 model. These are specified in the prescribed modelling language, and LibBi demonstrated by performing inference with them. Empirical results are presented, including a performance comparison of the software with different hardware configurations.
State space orderings for Gauss-Seidel in Markov chains revisited
Energy Technology Data Exchange (ETDEWEB)
Dayar, T. [Bilkent Univ., Ankara (Turkey)
1996-12-31
Symmetric state space orderings of a Markov chain may be used to reduce the magnitude of the subdominant eigenvalue of the (Gauss-Seidel) iteration matrix. Orderings that maximize the elemental mass or the number of nonzero elements in the dominant term of the Gauss-Seidel splitting (that is, the term approximating the coefficient matrix) do not necessarily converge faster. An ordering of a Markov chain that satisfies Property-R is semi-convergent. On the other hand, there are semi-convergent symmetric state space orderings that do not satisfy Property-R. For a given ordering, a simple approach for checking Property-R is shown. An algorithm that orders the states of a Markov chain so as to increase the likelihood of satisfying Property-R is presented. The computational complexity of the ordering algorithm is less than that of a single Gauss-Seidel iteration (for sparse matrices). In doing all this, the aim is to gain an insight for faster converging orderings. Results from a variety of applications improve the confidence in the algorithm.
Jonsen, Ian D; Myers, Ransom A; James, Michael C
2006-09-01
1. Biological and statistical complexity are features common to most ecological data that hinder our ability to extract meaningful patterns using conventional tools. Recent work on implementing modern statistical methods for analysis of such ecological data has focused primarily on population dynamics but other types of data, such as animal movement pathways obtained from satellite telemetry, can also benefit from the application of modern statistical tools. 2. We develop a robust hierarchical state-space approach for analysis of multiple satellite telemetry pathways obtained via the Argos system. State-space models are time-series methods that allow unobserved states and biological parameters to be estimated from data observed with error. We show that the approach can reveal important patterns in complex, noisy data where conventional methods cannot. 3. Using the largest Atlantic satellite telemetry data set for critically endangered leatherback turtles, we show that the diel pattern in travel rates of these turtles changes over different phases of their migratory cycle. While foraging in northern waters the turtles show similar travel rates during day and night, but on their southward migration to tropical waters travel rates are markedly faster during the day. These patterns are generally consistent with diving data, and may be related to changes in foraging behaviour. Interestingly, individuals that migrate southward to breed generally show higher daytime travel rates than individuals that migrate southward in a non-breeding year. 4. Our approach is extremely flexible and can be applied to many ecological analyses that use complex, sequential data.
United States Human Access to Space, Exploration of the Moon and Preparation for Mars Exploration
Rhatigan, Jennifer L.
2009-01-01
In the past, men like Leonardo da Vinci and Jules Verne imagined the future and envisioned fantastic inventions such as winged flying machines, submarines, and parachutes, and posited human adventures like transoceanic flight and journeys to the Moon. Today, many of their ideas are reality and form the basis for our modern world. While individual visionaries like da Vinci and Verne are remembered for the accuracy of their predictions, today entire nations are involved in the process of envisioning and defining the future development of mankind, both on and beyond the Earth itself. Recently, Russian, European, and Chinese teams have all announced plans for developing their own next generation human space vehicles. The Chinese have announced their intention to conduct human lunar exploration, and have flown three crewed space missions since 2003, including a flight with three crew members to test their extravehicular (spacewalking) capabilities in September 2008. Very soon, the prestige, economic development, scientific discovery, and strategic security advantage historically associated with leadership in space exploration and exploitation may no longer be the undisputed province of the United States. Much like the sponsors of the seafaring explorers of da Vinci's age, we are motivated by the opportunity to obtain new knowledge and new resources for the growth and development of our own civilization. NASA's new Constellation Program, established in 2005, is tasked with maintaining the United States leadership in space, exploring the Moon, creating a sustained human lunar presence, and eventually extending human operations to Mars and beyond. Through 2008, the Constellation Program developed a full set of detailed program requirements and is now completing the preliminary design phase for the new Orion Crew Exploration Vehicle (CEV), the Ares I Crew Launch Vehicle, and the associated infrastructure necessary for humans to explore the Moon. Component testing is well
Nadeem, Khurram; Moore, Jeffrey E; Zhang, Ying; Chipman, Hugh
2016-07-01
Stochastic versions of Gompertz, Ricker, and various other dynamics models play a fundamental role in quantifying strength of density dependence and studying long-term dynamics of wildlife populations. These models are frequently estimated using time series of abundance estimates that are inevitably subject to observation error and missing data. This issue can be addressed with a state-space modeling framework that jointly estimates the observed data model and the underlying stochastic population dynamics (SPD) model. In cases where abundance data are from multiple locations with a smaller spatial resolution (e.g., from mark-recapture and distance sampling studies), models are conventionally fitted to spatially pooled estimates of yearly abundances. Here, we demonstrate that a spatial version of SPD models can be directly estimated from short time series of spatially referenced distance sampling data in a unified hierarchical state-space modeling framework that also allows for spatial variance (covariance) in population growth. We also show that a full range of likelihood based inference, including estimability diagnostics and model selection, is feasible in this class of models using a data cloning algorithm. We further show through simulation experiments that the hierarchical state-space framework introduced herein efficiently captures the underlying dynamical parameters and spatial abundance distribution. We apply our methodology by analyzing a time series of line-transect distance sampling data for fin whales (Balaenoptera physalus) off the U.S. west coast. Although there were only seven surveys conducted during the study time frame, 1991-2014, our analysis detected presence of strong density regulation and provided reliable estimates of fin whale densities. In summary, we show that the integrative framework developed herein allows ecologists to better infer key population characteristics such as presence of density regulation and spatial variability in a
Full-potential multiple scattering theory with space-filling cells for bound and continuum states.
Hatada, Keisuke; Hayakawa, Kuniko; Benfatto, Maurizio; Natoli, Calogero R
2010-05-12
We present a rigorous derivation of a real-space full-potential multiple scattering theory (FP-MST) that is free from the drawbacks that up to now have impaired its development (in particular the need to expand cell shape functions in spherical harmonics and rectangular matrices), valid both for continuum and bound states, under conditions for space partitioning that are not excessively restrictive and easily implemented. In this connection we give a new scheme to generate local basis functions for the truncated potential cells that is simple, fast, efficient, valid for any shape of the cell and reduces to the minimum the number of spherical harmonics in the expansion of the scattering wavefunction. The method also avoids the need for saturating 'internal sums' due to the re-expansion of the spherical Hankel functions around another point in space (usually another cell center). Thus this approach provides a straightforward extension of MST in the muffin-tin (MT) approximation, with only one truncation parameter given by the classical relation l(max) = kR(b), where k is the electron wavevector (either in the excited or ground state of the system under consideration) and R(b) is the radius of the bounding sphere of the scattering cell. Moreover, the scattering path operator of the theory can be found in terms of an absolutely convergent procedure in the l(max) --> ∞ limit. Consequently, this feature provides a firm ground for the use of FP-MST as a viable method for electronic structure calculations and makes possible the computation of x-ray spectroscopies, notably photo-electron diffraction, absorption and anomalous scattering among others, with the ease and versatility of the corresponding MT theory. Some numerical applications of the theory are presented, both for continuum and bound states.
The consciousness state space (CSS – a unifying model for consciousness and self
Directory of Open Access Journals (Sweden)
Aviva eBerkovich-Ohana
2014-04-01
Full Text Available 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 includes 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 outlaying several testable predictions raised by the CSS model.
The consciousness state space (CSS)-a unifying model for consciousness and self.
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.
Test-retest reliability of resting-state magnetoencephalography power in sensor and source space.
Martín-Buro, María Carmen; Garcés, Pilar; Maestú, Fernando
2016-01-01
Several studies have reported changes in spontaneous brain rhythms that could be used as clinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), high beta (20-30 Hz), and gamma (30-45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted in high within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials.
Kamarchik, Eugene; Jasper, Ahren W.
2013-05-01
An algorithm is presented for calculating fully anharmonic vibrational state counts, state densities, and partition functions for molecules using Monte Carlo integration of classical phase space. The algorithm includes numerical evaluations of the elements of the Jacobian and is general enough to allow for sampling in arbitrary curvilinear or rectilinear coordinate systems. Invariance to the choice of coordinate system is demonstrated for vibrational state densities of methane, where we find comparable sampling efficiency when using curvilinear z-matrix and rectilinear Cartesian normal mode coordinates. In agreement with past work, we find that anharmonicity increases the vibrational state density of methane by a factor of ˜2 at its dissociation threshold. For the vinyl radical, we find a significant (˜10×) improvement in sampling efficiency when using curvilinear z-matrix coordinates relative to Cartesian normal mode coordinates. We attribute this improved efficiency, in part, to a more natural curvilinear coordinate description of the double well associated with the H2C-C-H wagging motion. The anharmonicity correction for the vinyl radical state density is ˜1.4 at its dissociation threshold. Finally, we demonstrate that with trivial parallelizations of the Monte Carlo step, tractable calculations can be made for the vinyl radical using direct ab initio potential energy surface evaluations and a composite QCISD(T)/MP2 method.
Equilibrium points of the tilted perfect fluid Bianchi VIh state space
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 VIh.
Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach
DEFF Research Database (Denmark)
Monache, Davide Delle; Grassi, Stefano; Santucci de Magistris, Paolo
Short memory models contaminated by level shifts have similar long-memory features as fractionally integrated processes. This makes hard to verify whether the true data generating process is a pure fractionally integrated process when employing standard estimation methods based...... on the autocorrelation function or the periodogram. In this paper, we propose a robust testing procedure, based on an encompassing parametric specification that allows to disentangle the level shifts from the fractionally integrated component. The estimation is carried out on the basis of a state-space methodology...
Markov chain Monte Carlo methods for state-space models with point process observations.
Yuan, Ke; Girolami, Mark; Niranjan, Mahesan
2012-06-01
This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.
Directory of Open Access Journals (Sweden)
Mohammad Shahzad
2016-05-01
Full Text Available This study deals with the control of chaotic dynamics of tumor cells, healthy host cells, and effector immune cells in a chaotic Three Dimensional Cancer Model (TDCM by State Space Exact Linearization (SSEL technique based on Lie algebra. A non-linear feedback control law is designed which induces a coordinate transformation thereby changing the original chaotic TDCM system into a controlled one linear system. Numerical simulation has been carried using Mathematica that witness the robustness of the technique implemented on the chosen chaotic system.
Research of united model of knowledge discovery state space and its application
Institute of Scientific and Technical Information of China (English)
You Fucheng; Song Wei; Yang Bingru
2005-01-01
There are both associations and differences between structured and unstructured data mining. How to unite them together to be a united theoretical framework and to guide the research of knowledge discovery and data mining has become an urgent problem to be solved. On the base of analysis and study of existing research results, the united model of knowledge discovery state space (UMKDSS) is presented, and the structured data mining and the complex type data mining are associated together. UMKDSS can provide theoretical guidance for complex type data mining. An application example of UMKDSS is given at last.
Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach
DEFF Research Database (Denmark)
Boldrini, Lorenzo
In this paper we propose a continuous-time, Gaussian, linear, state-space system to model the relation between global mean sea level (GMSL) and the global mean temperature (GMT), with the aim of making long-term projections for the GMSL. We provide a justification for the model specification based......) and the temperature reconstruction from Hansen et al. (2010). We compare the forecasting performance of the proposed specification to the procedures developed in Rahmstorf (2007b) and Vermeer and Rahmstorf (2009). Finally, we compute projections for the sea-level rise conditional on the 21st century SRES temperature...
State-space approach to vibration of gold nano-beam induced by ramp type heating
Institute of Scientific and Technical Information of China (English)
Hamdy M Youssef; Khaled A Elsibai
2010-01-01
In the nanoscale beam, two effects become domineering. One is the non-Fourier effect in heat conduction and the other is the coupling effect between temperature and strain rate. In the present study, a generalized solution for the generalized thermoelastic vibration of gold nano-beam resonator induced by ramp type heating is developed. The solution takes into account the above two effects. State-space and Laplace transform methods are used to determine the lateral vibration, the temperature, the displacement, the stress and the strain energy of the beam. The effects of the relaxation time and the ramping time parameters have been studied.
A Beddoes-Leishman type dynamic stall model in state-space and indicial formulations
DEFF Research Database (Denmark)
Hansen, M.H.; Gaunaa, Mac; Aagaard Madsen, Helge
2004-01-01
This report contains a description of a Beddoes-Leishman type dynamic stall model in both a state-space and an indicial function formulation. The model predicts the unsteady aerodynamic forces and moment on an airfoil section undergoing arbitrary motionin heave, lead-lag, and pitch. The model...... features, such as overshoot of the lift, in the stall region. The linearized model is shown to give identicalresults to the full model for small amplitude oscillations. Furthermore, it is shown that the response of finite thichkness airfoils can be reproduced to a high accuracy by the use of specific...
A SAS/IML program using the Kalman filter for estimating state space models.
Gu, Fei; Yung, Yiu-Fai
2013-03-01
To help disseminate the knowledge and software implementation of a state space model (SSM), this article provides a SAS/IML (SAS Institute, 2010) program for estimating the parameters of general linear Gaussian SSMs using the Kalman filter algorithm. In order to use this program, the user should have SAS installed on a computer and have a valid license for SAS/IML. Since the code is completely open, it is expected that this program can be used not only by applied researchers, but also by quantitative methodologists who are interested in improving their methods and promoting SSM as a research instrument.
Jamell, Christopher Ray
In this thesis, we focus on two broad categories of problems, exciton condensation and bound states, and two complimentary approaches, real and momentum space, to solve these problems. In chapter 2 we begin by developing the self-consistent mean field equations, in momentum space, used to calculate exciton condensation in semiconductor heterostructures/double quantum wells and graphene. In the double quantum well case, where we have one layer containing electrons and the other layer with holes separated by a distance d, we extend the analytical solution to the two dimensional hydrogen atom in order to provide a semi-quantitative measure of when a system of excitons can be considered dilute. Next we focus on the problem of electron-electron screening, using the random phase approximation, in double layer graphene. The literature contains calculations showing that when screening is not taken into account the temperature at which excitons in double layer graphene condense is approximately room temperature. Also in the literature is a calculation showing that under certain assumptions the transition temperature is approximately mK. The essential result is that the condensate is exponentially suppressed by the number of electron species in the system. Our mean field calculations show that the condensate, is in fact, not exponentially suppressed. Next, in chapter 3, we show the use of momentum space to solve the Schrodinger equation for a class of potentials that are not usually a part of a quantum mechanics courses. Our approach avoids the typical pitfalls that exist when one tries to discretize the real space Schrodinger equation. This technique widens the number of problems that can presented in an introductory quantum mechanics course while at the same time, because of the ease of its implementation, provides a simple introduction to numerical techniques and programming in general to students. We have furthered this idea by creating a modular program that allows
Maximum efficiency of state-space models of nanoscale energy conversion devices.
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.
Online variational inference for state-space models with point-process observations.
Mangion, Andrew Zammit; Yuan, Ke; Kadirkamanathan, Visakan; Niranjan, Mahesan; Sanguinetti, Guido
2011-08-01
We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time.
Maximum efficiency of state-space models of nanoscale energy conversion devices
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.
Institute of Scientific and Technical Information of China (English)
袁保红; 孙秀冬; 姜永远; 周忠祥; 姚凤凤; 李焱
2002-01-01
We have proven theoretically that there are sublinear, linear and superlinear relations between the response ratesand total incident intensity for different cases of traps in photorefractive polymer materials. These relations wereobserved in inorganic photorefractive crystals many years ago. Also, the steady-state space-charge field is a functionof the total incident intensity, which has also been found in inorganic photorefractive crystals. We have measured therelations of the steady-state diffraction efficiency and the response rate with respect to the total incident intensity in thephotorefractive composite consisting of the polymer (N-vinylcarbazole) (PVK) doped with 4,4'-n-pentylcyanobiphenyl(5CB) and C60. The results obtained show that the composite belongs to the case of low trap density.
PERFORMANCE OPTIMIZATION OF THE DIODE-PUMPED SOLID-STATE LASER FOR SPACE APPLICATIONS
Directory of Open Access Journals (Sweden)
D. A. Arkhipov
2015-11-01
Full Text Available Subject of Research. Thermophysical and optical techniques of parameter regulation for diode pumped solid-state laser are studied as applied to space laser communication and laser ranging lines. Methods. The investigations are carried out on the base of the original design of diode pumped solid-state laser module that includes the following: Nd:YAG slab element, diode pumped by 400W QCW produced by NORTHROP GRUMMAN; two-pass unstable resonator with rotation of the laser beam aperture about its axis through 1800; the output mirror of the resonator with a variable reflection coefficient; hyperthermal conductive plates for thermal stabilization of the laser diode generation modes. The presence of thermal conductive plates excludes conventional running water systems applied as cooling systems for solid-state laser components. The diodes temperature stabilization is achieved by applying the algorithm of pulse-width modulation of power of auxiliary electric heaters. To compensate for non-stationary thermal distortions of the slab refractive index, the laser resonator scheme comprises a prism reflector with an apex angle of 1200. Narrow sides of the prism are covered with reflective coating, and its wide side is sprayed with antireflection coating. The beam aperture is turned around its axis through 1800 because of triple reflection of the beam inside the prism. The turning procedure leads to compensating for the output beam phase distortions in view of symmetric character of the aberrations of slab refractive index. To suppress parasitic oscillations inside the slab, dielectric coatings of wide sides of the slab are used. Main Results. We have demonstrated theoretically and experimentally that the usage of hyperthermal conductive plates together with the algorithm of pulse-width modulation provides stabilizing of the diode substrate temperature accurate within ± 0.1 °С and smoothing the temperature distribution along the plate surface accurate
U(1) Gauge Field in 6D Space-Time With Compact Noncommutative Dimensions: A Coherent State Approach
Nasseri, M; Souri, M
2012-01-01
We consider the U(1) gauge field defined over a six dimensional space-time with extra dimensions compactified on a noncommutative toroidal orbifold, within the context of coherent state approach to the noncommutative spaces. We demonstrate that the fuzzines of extra dimensions can lead to the canceling of the part of electrostatic interaction mediated by the massive KK modes.
Henke, D.; Schubert, A.; Small, D.; Meier, E.; Lüthi, M. P.; Vieli, A.
2014-12-01
A new method for glacier surface velocity (GSV) estimates is proposed here which combines ground- and space-based measurements with hidden state space modeling (HSSM). Examples of such a fusion of physical models with remote sensing (RS) observations were described in (Henke & Meier, Hidden State Space Models for Improved Remote Sensing Applications, ITISE 2014, p. 1242-1255) and are currently adapted for GSV estimation. GSV can be estimated using in situ measurements, RS methods or numerical simulations based on ice-flow models. In situ measurements ensure high accuracy but limited coverage and time consuming field work, while RS methods offer regular observations with high spatial coverage generally not possible with in situ methods. In particular, spaceborne Synthetic Aperture Radar (SAR) can obtain useful images independent of daytime and cloud cover. A ground portable radar interferometer (GPRI) is useful for investigating a particular area in more detail than is possible from space, but provides local coverage only. Several processing methods for deriving GSV from radar sensors have been established, including interferometry and offset tracking (Schubert et al, Glacier surface velocity estimation using repeat TerraSAR-X images. ISPRS Journal of P&RS, p. 49-62, 2013). On the other hand, it is also possible to derive glacier parameters from numerical ice-flow modeling alone. Given a well-parameterized model, GSV can in theory be derived and propagated continuously in time. However, uncertainties in the glacier flow dynamics and model errors increase with excessive propagation. All of these methods have been studied independently, but attempts to combine them have only rarely been made. The HSSM we propose recursively estimates the GSV based on 1) a process model making use of temporal and spatial interdependencies between adjacent states, and 2) observations (RS and optional in situ). The in situ and GPRI images currently being processed were acquired in the
A state space representation of VAR models with sparse learning for dynamic gene networks.
Kojima, Kaname; Yamaguchi, Rui; Imoto, Seiya; Yamauchi, Mai; Nagasaki, Masao; Yoshida, Ryo; Shimamura, Teppei; Ueno, Kazuko; Higuchi, Tomoyuki; Gotoh, Noriko; Miyano, Satoru
2010-01-01
We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.
Population dynamics of an Arctiid caterpillar-tachinid parasitoid system using state-space models.
Karban, Richard; de Valpine, Perry
2010-05-01
1. Population dynamics of insect host-parasitoid systems are important in many natural and managed ecosystems and have inspired much ecological theory. However, ecologists have a limited knowledge about the relative strengths of species interactions, abiotic effects and density dependence in natural host-parasitoid dynamics. Statistical time-series analyses would be more informative by incorporating multiple factors, measurement error and noisy dynamics. 2. We use a novel maximum likelihood and model-selection analysis of a state-space model for host-parasitoid dynamics to examine 21 years of annual census data for woolly bear caterpillars (Platyprepia virginalis) and their locally host-specific tachinid parasitoids (Thelaira americana). 3. Caterpillar densities varied by three orders of magnitude and were driven by density dependence and precipitation from the previous March but not detectably by parasitoids, despite variable and sometimes high (>50%) parasitism. 4. Fly fluctuations, as estimated from per cent parasitism, were affected by density dependence and precipitation from the previous July. There was marginal evidence that host abundance drives fly fluctuations as a generic linear effect but no evidence for classical Nicholson-Bailey coupling. 5. The state-space model analysis includes new methods for likelihood calculation and allows a balanced consideration of effect magnitude and statistical significance in a nonlinear model with multiple alternative explanatory variables.
Cointegration between trends and their estimators in state space models and CVAR models
DEFF Research Database (Denmark)
Johansen, Søren; Tabor, Morten Nyboe
In a linear state space model, y_{t+1}=BT_{t}+eps_{t+1}, we investigate if the unobserved trend, T_{t}, cointegrates with the extracted trend E_{t}T_{t}, and with the estimated trend E^_{t}T_{t}, in the sense that the spreads T_{t}-E_{t}T_{t} and E_{t}T_{t}-E^_{t}T_{t} are stationary. We find...... that this result holds for BT_{t}-BE_{t}T_{t} and BE_{t}T_{t}-B^E^_{t}T_{t}. For the trends T_{t} and E^_{t}T_{t}, however, this type cointegration depends on the identification of B and T_{t}. The same results are found, if the observations, y_{t}, from the state space model are analysed using a cointegrated...... vector autoregressive model, where the trend is defined as the common trend. Finally we investigate cointegration between trends and their estimators based on the two models, and find the same results. We illustrate with two examples and confirm the results by a small simulation study....
Liu, Peipei; Sohn, Hoon; Park, Byeongjin
2015-06-01
Damage often causes a structural system to exhibit severe nonlinear behaviors, and the resulting nonlinear features are often much more sensitive to the damage than their linear counterparts. This study develops a laser nonlinear wave modulation spectroscopy (LNWMS) so that certain types of damage can be detected without any sensor placement. The proposed LNWMS utilizes a pulse laser to generate ultrasonic waves and a laser vibrometer for ultrasonic measurement. Under the broadband excitation of the pulse laser, a nonlinear source generates modulations at various frequency values due to interactions among various input frequency components. State space attractors are reconstructed from the ultrasonic responses measured by LNWMS, and a damage feature called Bhattacharyya distance (BD) is computed from the state space attractors to quantify the degree of damage-induced nonlinearity. By computing the BD values over the entire target surface using laser scanning, damage can be localized and visualized without relying on the baseline data obtained from the pristine condition of a target structure. The proposed technique has been successfully used for visualizing fatigue crack in an aluminum plate and delamination and debonding in a glass fiber reinforced polymer wind turbine blade.
Directory of Open Access Journals (Sweden)
Esfandiar, H.
2013-05-01
Full Text Available In this paper, based on the VoigtKelvin constitutive model, nonlinear dynamic modelling and state space representation of a viscoelastic beam acting as a flexible robotic manipulator is investigated. Complete nonlinear dynamic modelling of a viscoelastic beam without premature linearisation of dynamic equations is developed. The adopted method is capable of reproducing nonlinear dynamic effects, such as beam stiffening due to centrifugal and Coriolis forces induced by rotation of the joints. Structural damping effects on the models dynamic behaviour are also shown. A reliable model for a viscoelastic beam is subsequently presented. The governing equations of motion are derived using Hamiltons principle, and using the finite difference method, nonlinear partial differential equations are reduced to ordinary differential equations. For the purpose of flexible manipulator control, the standard form of state space equations for the viscoelastic link and the actuator is obtained. Simulation results indicate substantial improvements in dynamic behaviour, and a parameter sensitivity study is carried out to investigate the effect of structural damping on the vibration amplitude.
State-space models of mental processes from fMRI.
Janoos, Firdaus; Singh, Shantanu; Machiraju, Raghu; Wells, William M; Mórocz, Istvan A
2011-01-01
In addition to functional localization and integration, the problem of determining whether the data encode some information about the mental state of the subject, and if so, how this information is represented has become an important research agenda in functional neuroimaging. Multivariate classifiers, commonly used for brain state decoding, are restricted to simple experimental paradigms with a fixed number of alternatives and are limited in their representation of the temporal dimension of the task. Moreover, they learn a mapping from the data to experimental conditions and therefore do not explain the intrinsic patterns in the data. In this paper, we present a data-driven approach to building a spatio-temporal representation of mental processes using a state-space formalism, without reference to experimental conditions. Efficient Monte Carlo algorithms for estimating the parameters of the model along with a method for model-size selection are developed. The advantages of such a model in determining the mental-state of the subject over pattern classifiers are demonstrated using an fMRI study of mental arithmetic.
Niemi, Jarad; West, Mike
2010-06-01
We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.
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.
Real-space Mapping of Surface Trap States in CIGSe Nanocrystals using 4D Electron Microscopy
Bose, Riya
2016-05-26
Surface trap states in semiconductor copper indium gallium selenide nanocrystals (NCs) which serve as undesirable channels for non-radiative carrier recombination, remain a great challenge impeding the development of solar and optoelectronics devices based on these NCs. In order to design efficient passivation techniques to minimize these trap states, a precise knowledge about the charge carrier dynamics on the NCs surface is essential. However, selective mapping of surface traps requires capabilities beyond the reach of conventional laser spectroscopy and static electron microscopy; it can only be accessed by using a one-of-a-kind, second-generation four-dimensional scanning ultrafast electron microscope (4D S-UEM) with sub-picosecond temporal and nanometer spatial resolutions. Here, we precisely map the surface charge carrier dynamics of copper indium gallium selenide NCs before and after surface passivation in real space and time using S-UEM. The time-resolved snapshots clearly demonstrate that the density of the trap states is significantly reduced after zinc sulfide (ZnS) shelling. Furthermore, removal of trap states and elongation of carrier lifetime are confirmed by the increased photocurrent of the self-biased photodetector fabricated using the shelled NCs.
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.
State-space modeling of population sizes and trends in Nihoa Finch and Millerbird
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.
State-space based analysis and forecasting of macroscopic road safety trends in Greece.
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.
Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models
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
Dynamic Baysesian state-space model with a neural network for an online river flow prediction
Ham, Jonghwa; Hong, Yoon-Seok
2013-04-01
The usefulness of artificial neural networks in complex hydrological modeling has been demonstrated by successful applications. Several different types of neural network have been used for the hydrological modeling task but the multi-layer perceptron (MLP) neural network (also known as the feed-forward neural network) has enjoyed a predominant position because of its simplicity and its ability to provide good approximations. In many hydrological applications of MLP neural networks, the gradient descent-based batch learning algorithm such as back-propagation, quasi-Newton, Levenburg-Marquardt, and conjugate gradient algorithms has been used to optimize the cost function (usually by minimizing the error function in the prediction) by updating the parameters and structure in a neural network defined using a set of input-output training examples. Hydrological systems are highly with time-varying inputs and outputs, and are characterized by data that arrive sequentially. The gradient descent-based batch learning approaches that are implemented in MLP neural networks have significant disadvantages for online dynamic hydrological modeling because they could not update the model structure and parameter when a new set of hydrological measurement data becomes available. In addition, a large amount of training data is always required off-line with a long model training time. In this work, a dynamic nonlinear Bayesian state-space model with a multi-layer perceptron (MLP) neural network via a sequential Monte Carlo (SMC) learning algorithm is proposed for an online dynamic hydrological modeling. This proposed new method of modeling is herein known as MLP-SMC. The sequential Monte Carlo learning algorithm in the MLP-SMC is designed to evolve and adapt the weight of a MLP neural network sequentially in time on the arrival of each new item of hydrological data. The weight of a MLP neural network is treated as the unknown dynamic state variable in the dynamic Bayesian state-space
Inferring gene regulatory networks via nonlinear state-space models and exploiting sparsity.
Noor, Amina; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem N
2012-01-01
This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.
Ma, Chihua; Luciani, Timothy; Terebus, Anna; Liang, Jie; Marai, G Elisabeta
2017-02-15
Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.
Project for the Space Science in Moscow State University of Geodesy and Cartography (MIIGAiK)
Semenov, M.; Oberst, J.; Malinnikov, V.; Shingareva, K.; Grechishchev, A.; Karachevtseva, I.; Konopikhin, A.
2012-04-01
Introduction: Based on the proposal call of the Government of Russian Federation 40 of international scientists came to Russia for developing and support-ing research capabilities of national educational institutions. Moscow State University of Geodesy and Cartography (MIIGAiK) and invited scientist Prof. Dr. Jurgen Oberst were awarded a grant to establish a capable research facility concerned with Planetary Geodesy, Cartography and Space Exploration. Objectives: The goals of the project are to build laboratory infrastructure, and suitable capability for MIIGAiK to participate in the planning, execution and analyses of data from future Russian planetary mis-sions and also to integrate into the international science community. Other important tasks are to develop an attractive work place and job opportunities for planetary geodesy and cartography students. For this purposes new MIIGAiK Extraterrestrial Laboratory (MExLab) was organized. We involved professors, researchers, PhD students in to the projects of Moon and planets exploration at the new level of Russian Space Science development. Main results: MExLab team prepare data for upcom-ing Russian space missions, such as LUNA-GLOB and LUNA-RESOURSE. We established cooperation with Russian and international partners (IKI, ESA, DLR, and foreign Universities) and actively participated in international conferences and workshops. Future works: For the future science development we investigated the old Soviet Archives and received the access to the telemetry data of the Moon rovers Lunokhod-1 and Lunokhod-2. That data will be used in education purposes and could be the perfect base for the analysis, development and support in new Russian and international missions and especially Moon exploration projects. MExLab is open to cooperate and make the consortiums for science projects for the Moon and planets exploration. Acknowledgement: Works are funded by the Rus-sian Government (Project name: "Geodesy, cartography and the
DEFF Research Database (Denmark)
Bach, Christian; Christensen, Bent Jesper
We include simultaneously both realized volatility measures based on high-frequency asset returns and implied volatilities backed out of individual traded at the money option prices in a state space approach to the analysis of true underlying volatility. We model integrated volatility as a latent...... fi…rst order Markov process and show that our model is closely related to the CEV and Barndorff-Nielsen & Shephard (2001) models for local volatility. We show that if measurement noise in the observable volatility proxies is not accounted for, then the estimated autoregressive parameter in the latent...... process is downward biased. Implied volatility performs better than any of the alternative realized measures when forecasting future integrated volatility. The results are largely similar across the stock market (S&P 500), bond market (30-year U.S. T-bond), and foreign currency exchange market ($/£ )....
Chen, Cheng; Guo, Xiangxin
2016-01-01
The space charge layer (SCL) effects were initially developed to explain the anomalous conductivity enhancement in composite ionic conductors. They were further extended to qualitatively as well as quantitatively understand the interfacial phenomena in many other ionic-conducting systems. Especially in nanometre-scale systems, the SCL effects could be used to manipulate the conductivity and construct artificial conductors. Recently, existence of such effects either at the electrolyte/cathode interface or at the interfaces inside the composite electrode in all solid state lithium batteries (ASSLB) has attracted attention. Therefore, in this article, the principle of SCL on basis of defect chemistry is first presented. The SCL effects on the carrier transport and storage in typical conducting systems are reviewed. For ASSLB, the relevant effects reported so far are also reviewed. Finally, the perspective of interface engineer related to SCL in ASSLB is addressed.
Modeling fuzzy state space of reheater system for simulation and analysis
Munirah, W. M. Wan; Ahmad, T.; Ashaari, A.; Abdullah, M. Adib
2014-07-01
Reheater is one of the important heat exchange components in a high capacity power plant of a boiler system. The aim of this study is to improve heat transfer of a reheater system. The method is to maximize steam production and at the same time, keeping variables within constraints. Fuzzy arithmetic is a powerful tool used to solve engineering problems with uncertain parameters. Therefore, in order to determine heat transfer efficiency, the state space of reheater is simulated using fuzzy arithmetic by taking into account the uncertainties in the reheater system. The uncertain model parameters and the model inputs are represented by fuzzy numbers with their shape derived from quasi-Gaussian function. Finally, this paper discusses how the mathematical model can be manipulated in order to produce maximum heat transfer with least loss of energy. Furthermore, the improvement of the reheater efficiency and the quantification of the heat supplied parameters are presented in this paper.
An optical flow-based state-space model of the vocal folds
DEFF Research Database (Denmark)
Granados, Alba; Brunskog, Jonas
2017-01-01
. 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......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...... to capture different deformation patterns between the computed optical flow and the finite element deformation, controlled by the choice of the model tissue parameters....
Representing time-varying cyclic dynamics using multiple-subject state-space models.
Chow, Sy-Miin; Hamaker, Ellen L; Fujita, Frank; Boker, Steven M
2009-11-01
Over the last few decades, researchers have become increasingly aware of the need to consider intraindividual variability in the form of cyclic processes. In this paper, we review two contemporary cyclic state-space models: Young and colleagues' dynamic harmonic regression model and Harvey and colleagues' stochastic cycle model. We further derive the analytic equivalence between the two models, discuss their unique strengths and propose multiple-subject extensions. Using data from a study on human postural dynamics and a daily affect study, we demonstrate the use of these models to represent within-person non-stationarities in cyclic dynamics and interindividual differences therein. The use of diagnostic tools for evaluating model fit is also illustrated.
State space modeling of reactor core in a pressurized water reactor
Ashaari, A.; Ahmad, T.; Shamsuddin, Mustaffa; M, Wan Munirah W.; Abdullah, M. Adib
2014-07-01
The power control system of a nuclear reactor is the key system that ensures a safe operation for a nuclear power plant. However, a mathematical model of a nuclear power plant is in the form of nonlinear process and time dependent that give very hard to be described. One of the important components of a Pressurized Water Reactor is the Reactor core. The aim of this study is to analyze the performance of power produced from a reactor core using temperature of the moderator as an input. Mathematical representation of the state space model of the reactor core control system is presented and analyzed in this paper. The data and parameters are taken from a real time VVER-type Pressurized Water Reactor and will be verified using Matlab and Simulink. Based on the simulation conducted, the results show that the temperature of the moderator plays an important role in determining the power of reactor core.
Directory of Open Access Journals (Sweden)
Emran Tohidi
2013-01-01
Full Text Available The idea of approximation by monomials together with the collocation technique over a uniform mesh for solving state-space analysis and optimal control problems (OCPs has been proposed in this paper. After imposing the Pontryagins maximum principle to the main OCPs, the problems reduce to a linear or nonlinear boundary value problem. In the linear case we propose a monomial collocation matrix approach, while in the nonlinear case, the general collocation method has been applied. We also show the efficiency of the operational matrices of differentiation with respect to the operational matrices of integration in our numerical examples. These matrices of integration are related to the Bessel, Walsh, Triangular, Laguerre, and Hermite functions.
Torus breakdown in the symmetry-reduced state space of the Kuramoto-Sivashinsky system
Budanur, Nazmi Burak
2015-01-01
Systems such as fluid flows in channels and pipes or the complex Ginzburg-Landau system, defined over periodic domains, exhibit both continuous symmetries, translational and rotational, as well as discrete symmetries under spatial reflections or complex conjugation. The simplest, and very common symmetry of this type is the equivariance of the defining equations under the orthogonal group O(2). We formulate a novel symmetry-reduction scheme for such systems by combining the method of slices with invariant polynomial methods, and show how it works by applying it to the Kuramoto-Sivashinsky system in one spatial dimension. As an example, we track a relative periodic orbit through a sequence of bifurcation to the onset of chaos. Within the symmetry-reduced state space we are able to compute and visualize the unstable manifolds of relative periodic orbits, their torus bifurcations, a transition to chaos via torus breakdown, and heteroclinic connections between various relative periodic orbits. It would be very ha...
Harmonic Interaction Analysis in Grid Connected Converter using Harmonic State Space (HSS) Modeling
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Bak, Claus Leth
2015-01-01
An increasing number of power electronics based Distributed Generation (DG) systems and loads generate coupled harmonic as well as non-characteristic harmonic with each other. Several methods like impedance based analysis, which is derived from conventional small signal- and average-model, are in...... behavior interaction and dynamic transfer procedure. Frequency domain as well as time domain simulation results are represented by means of HSS modeling to verify the theoretical analysis. Experimental results are also included to validate the method....... during the modeling process. This paper investigates grid connected converter by means of Harmonic State Space (HSS) small signal model, which is modeled from Linear Time varying Periodically (LTP) system. Further, a grid connected converter harmonic matrix is investigated to analyze the harmonic...
State space modeling of reactor core in a pressurized water reactor
Energy Technology Data Exchange (ETDEWEB)
Ashaari, A.; Ahmad, T.; M, Wan Munirah W. [Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor (Malaysia); Shamsuddin, Mustaffa [Institute of Ibnu Sina, Universiti Teknologi Malaysia, 81310 Skudai, Johor (Malaysia); Abdullah, M. Adib [Swinburne University of Technology, Faculty of Engineering, Computing and Science, Jalan Simpang Tiga, 93350 Kuching, Sarawak (Malaysia)
2014-07-10
The power control system of a nuclear reactor is the key system that ensures a safe operation for a nuclear power plant. However, a mathematical model of a nuclear power plant is in the form of nonlinear process and time dependent that give very hard to be described. One of the important components of a Pressurized Water Reactor is the Reactor core. The aim of this study is to analyze the performance of power produced from a reactor core using temperature of the moderator as an input. Mathematical representation of the state space model of the reactor core control system is presented and analyzed in this paper. The data and parameters are taken from a real time VVER-type Pressurized Water Reactor and will be verified using Matlab and Simulink. Based on the simulation conducted, the results show that the temperature of the moderator plays an important role in determining the power of reactor core.
Discrete Simulation of Flexible Plate Structure Using State-Space Formulation
Institute of Scientific and Technical Information of China (English)
S. Md. Salleh; M. O. Tokhi
2008-01-01
This paper presents the development of dynamic simulation of a flexible plate structure with various boundary conditions. A flexible square plate is considered. A finite-difference method is used to discretise the governing partial differential equation formulation describing its dynamic behaviour. The model thus developed has been validated against characteristic parameters of the plate. The model thus developed is further formulated using discrete state-space representation, to allow easy and fast implementation for simulating the dynamic behaviour of the plate with various boundary conditions. The simulation algorithm thus developed is validated, and accurate results with representation of the first five modes of vibration of the plate have been achieved. The algorithm thus developed is used in subsequent research work as a platform for development and verification of suitable control strategies for vibration suppression of flexible plate structures.
State-space model identification and feedback control of unsteady aerodynamic forces
Brunton, Steven L; Rowley, Clarence W
2014-01-01
Unsteady aerodynamic models are necessary to accurately simulate forces and develop feedback controllers for wings in agile motion; however, these models are often high dimensional or incompatible with modern control techniques. Recently, reduced-order unsteady aerodynamic models have been developed for a pitching and plunging airfoil by linearizing the discretized Navier-Stokes equation with lift-force output. In this work, we extend these reduced-order models to include multiple inputs (pitch, plunge, and surge) and explicit parameterization by the pitch-axis location, inspired by Theodorsen's model. Next, we investigate the na\\"{\\i}ve application of system identification techniques to input--output data and the resulting pitfalls, such as unstable or inaccurate models. Finally, robust feedback controllers are constructed based on these low-dimensional state-space models for simulations of a rigid flat plate at Reynolds number 100. Various controllers are implemented for models linearized at base angles of ...
PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models
Directory of Open Access Journals (Sweden)
Christopher Strickland
2014-04-01
Full Text Available PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models. PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries NumPy and SciPy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimized and parallelized Fortran routines. These Fortran routines heavily utilize basic linear algebra and linear algebra Package functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.
Erickson, Robert J.; Howe, John, Jr.; Kulp, Galen W.; VanKeuren, Steven P.
2008-01-01
The International Space Station (ISS) United States Orbital Segment (USOS) Oxygen Generation System (OGS) was originally intended to be installed in ISS Node 3. The OGS rack delivery was accelerated, and it was launched to ISS in July of 2006 and installed in the US Laboratory Module. Various modification kits were installed to provide its interfaces, and the OGS was first activated in July of 2007 for 15 hours, In October of 2007 it was again activated for 76 hours with varied production rates and day/night cycling. Operational time in each instance was limited by the quantity of feedwater in a Payload Water Reservoir (PWR) bag. Feedwater will be provided by PWR bag until the USOS Water Recovery System (WRS) is delivered to SS in fall of 2008. This paper will discuss operating experience and characteristics of the OGS, as well as operational issues and their resolution.
Choosing the observational likelihood in state-space stock assessment models
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro
2016-01-01
Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations...... a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We...... propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood...
Institute of Scientific and Technical Information of China (English)
ZHOU Jie; TANG Aiping; FENG Hailin
2016-01-01
The statistical inference for generalized mixed-effects state space models (MESSM) are investigated when the random effects are unknown.Two filtering algorithms are designed both of which are based on mixture Kalman filter.These algorithms are particularly useful when the longitudinal measurements are sparse.The authors also propose a globally convergent algorithm for parameter estimation of MESSM which can be used to locate the initial value of parameters for local while more efficient algorithms.Simulation examples are carried out which validate the efficacy of the proposed approaches.A data set from the clinical trial is investigated and a smaller mean square error is achieved compared to the existing results in literatures.
State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis
Directory of Open Access Journals (Sweden)
MRUGALSKI, M.
2012-08-01
Full Text Available Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach.
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.
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Blaabjerg, Frede
2016-01-01
For the efficiency and simplicity of electric systems, the dc power electronic systems are widely used in a variety of applications such as electric vehicles, ships, aircraft and also in homes. In these systems, there could be a number of dynamic interactions and frequency coupling between network...... with different switching frequency or harmonics from ac-dc converters makes that harmonics and frequency coupling are both problems of ac system and challenges of dc system. This paper presents a modeling and simulation method for a large dc power electronic system by using Harmonic State Space (HSS) modeling...... and loads and other converters. Hence, time-domain simulations are usually required to consider such a complex system behavior. However, simulations in the time-domain may increase the calculation time and the utilization of computer memory. Furthermore, frequency coupling driven by multiple converters...
Institute of Scientific and Technical Information of China (English)
Liu Huafeng; You Hongshun; Shi Pengcheng
2007-01-01
Quantitative estimation of radioactivity map has important clinical implications for better diagnosis and understanding of cancers. Although attenuation map and activity map are usually treated sequentially, they can obviously benefit a great deal when the transmission data is missing. In this paper, we propose a novel scheme of simultaneously solving for attenuation map and activity distribution from emission sinograms. Our strategy combines the measurement model of PET, and the attenuation parameters are treated as random variables with known prior statistics. After the conversion to state space representation, the extended Kalman filtering procedures are adopted to linearize the equations and to provide the joint estimates in an approximate optimal sense. Experiments have been performed on both synthetic data to illustrate its abilities and benefits.
Choosing the observational likelihood in state-space stock assessment models
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro
2017-01-01
propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood......Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations...... a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We...
DEFF Research Database (Denmark)
Poulsen, T.G.; Christophersen, Mette; Moldrup, P.
2003-01-01
were applied: (I) State-space analysis was used to identify relations between gas flux and short-term (hourly) variations in atmospheric pressure. (II) A numerical gas transport model was fitted to the data and used to quantify short-term impacts of variations in atmospheric pressure, volumetric soil......-water content, soil gas permeability, soil gas diffusion coefficients, and biological CH4 degradation rate upon landfill gas concentration and fluxes in the soil. Fluxes and concentrations were found to be most sensitive to variations in volumetric soil water content, atmospheric pressure variations and gas...... permeability whereas variations in CH4 oxidation rate and molecular coefficients had less influence. Fluxes appeared to be most sensitive to atmospheric pressure at intermediate distances from the landfill edge. Also overall CH4 fluxes out of the soil over longer periods (years) were largest during periods...
Testing for causality in reconstructed state spaces by an optimized mixed prediction method
Krakovská, Anna; Hanzely, Filip
2016-11-01
In this study, a method of causality detection was designed to reveal coupling between dynamical systems represented by time series. The method is based on the predictions in reconstructed state spaces. The results of the proposed method were compared with outcomes of two other methods, the Granger VAR test of causality and the convergent cross-mapping. We used two types of test data. The first test example is a unidirectional connection of chaotic systems of Rössler and Lorenz type. The second one, the fishery model, is an example of two correlated observables without a causal relationship. The results showed that the proposed method of optimized mixed prediction was able to reveal the presence and the direction of coupling and distinguish causality from mere correlation as well.
Stochastic State Space Modelling of Nonlinear systems - With application to Marine Ecosystems
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg
to conflict with the concept of mass balances. One of the central conclusions of the thesis is that the stochastic formulations should be an integral part of the model formulation. As discrete-time stochastic processes are simpler to handle numerically than continuous-time stochastic processes, I start......This thesis deals with stochastic dynamical systems in discrete and continuous time. Traditionally dynamical systems in continuous time are modelled using Ordinary Differential Equations (ODEs). Even the most complex system of ODEs will not be able to capture every detail of a complex system like...... a natural ecosystem, and hence residual variation between the model and observations will always remain. In stochastic state-space models the residual variation is separated into observation and system noise and a main theme of the thesis is a proper description of the system noise. Additive Gaussian noise...
Long, Christopher J; Temereanca, Simona; Desai, Neil U; Hämäläinen, Matti S; Brown, Emery N; 10.1214/11-AOAS483
2011-01-01
Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an $L^2$ regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candida...
Contaminant ingress into multizone buildings: An analytical state-space approach
Parker, Simon
2013-08-13
The ingress of exterior contaminants into buildings is often assessed by treating the building interior as a single well-mixed space. Multizone modelling provides an alternative way of representing buildings that can estimate concentration time series in different internal locations. A state-space approach is adopted to represent the concentration dynamics within multizone buildings. Analysis based on this approach is used to demonstrate that the exposure in every interior location is limited to the exterior exposure in the absence of removal mechanisms. Estimates are also developed for the short term maximum concentration and exposure in a multizone building in response to a step-change in concentration. These have considerable potential for practical use. The analytical development is demonstrated using a simple two-zone building with an inner zone and a range of existing multizone models of residential buildings. Quantitative measures are provided of the standard deviation of concentration and exposure within a range of residential multizone buildings. Ratios of the maximum short term concentrations and exposures to single zone building estimates are also provided for the same buildings. © 2013 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.
Summary results of the first United States manned orbital space flight
Glenn, J. H. Jr
1963-01-01
This paper describes the principal findings of the first United States manned orbital space flight in light of the flight mission. Consideration is given to the coordinated tracking network, recovery forces and to the spacecraft and its several functional systems. These include mechanisms for heat protection, escape maneuvers, spacecraft control, power supply, communications, life support and landing. A few difficulties encountered in the flight and deviations from the planned sequence are described. Craft preparation, aeromedical studies, flight plan and particularly flight observations--including the color, light, horizon visibility by day and by night, cloud formations and sunrise and sunset effects are given in some detail. The general conclusion from the MA-6 flight is that man can adapt well to new conditions encountered in space flight and that man can contribute importantly to mission reliability and toward mission achievement through his capacities to control the spacecraft and its multiple systems contribute to decision making and adaptation of programming as well as to direct exploratory and experimental observations.
Optimal Scheme for Search State Space and Scheduling on Multiprocessor Systems
Youness, Hassan A.; Sakanushi, Keishi; Takeuchi, Yoshinori; Salem, Ashraf; Wahdan, Abdel-Moneim; Imai, Masaharu
A scheduling algorithm aims to minimize the overall execution time of the program by properly allocating and arranging the execution order of the tasks on the core processors such that the precedence constraints among the tasks are preserved. In this paper, we present a new scheduling algorithm by using geometry analysis of the Task Precedence Graph (TPG) based on A* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity and pruning techniques to produce an optimal solution for the allocation/scheduling problem of a parallel application to parallel and multiprocessor architecture. The main goal of this work is to significantly reduce the search space and achieve the optimality or near optimal solution. We implemented the algorithm on general task graph problems that are processed on most of related search work and obtain the optimal scheduling with a small number of states. The proposed algorithm reduced the exhaustive search by at least 50% of search space. The viability and potential of the proposed algorithm is demonstrated by an illustrative example.
Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.
Directory of Open Access Journals (Sweden)
Mahdi Jalili
Full Text Available BACKGROUND: The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals. METHODS/RESULTS: To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or
Tkachova, P.; Krot, A.; Minervina, H.
It is well known that there is chaos in convective process in atmosphere and ocean. In particular,dynamic model of Lorenz [1] describes the Rayleigh-Benard convection phenomenon. Phase trajectories of Lorenz equation system are characterized by strange alternative properties: on the one hand, they diverge (because of positive Lyapunov exponents), on the second hand, they attract to the limited domain of phase space called an attractor [1]. The Lorenz attractor has specific geometrical structure and can be characterized by means of fractal dimension. In this connection the aim of this work is development of analysis of Lorenz attractor based on the proposed nonlinear decomposition into matrix series [2]. This analysis permits to estimate the values of characteristic parameters (including control one) of Lorenz attractors and predict their evolution in time. Using results of matrix decomposition [2], it is not difficult to see that the change of vector function (describing the Lorenz attractor) can be approximated by only linear and quadratic terms [3]. Because values of the first and second order derivatives can be calculated by means of numerical methods we can estimate the change of the vector function from computational experiment. In result, the values of parameters of the Lorenz's attractor can be estimated. This permits us to solve the identification task of the current dynamical state of a convective aerodynamic flows. Moreover, using the results of matrix decomposition we can estimate the minimal embedding dimension [4] for the Lorenz attractor based on experimental data. References: [1] P.Berge,Y.Pomeau and C.Vidal. L'ordre dans le chaos: Vers une approche deterministe de la turbulence. Hermann:Paris,1988. [2] A.M.Krot, "Matrix decompositions of vector functions and shift operators on the trajectories of a nonlinear dynamical system", Nonlinear Phenomena in Complex Systems,vol.4, N2, pp.106- 115, 2001. [3] A.M.Krot and P
Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M; Derocher, Andrew E; Lewis, Mark A; Jonsen, Ian D; Mills Flemming, Joanna
2016-05-25
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.
Estimation of cortical connectivity from EEG using state-space models.
Cheung, Bing Leung Patrick; Riedner, Brady Alexander; Tononi, Giulio; Van Veen, Barry D
2010-09-01
A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG. A state equation represents the MVAR model of cortical dynamics, while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume that the cortical signals originate from known regions of cortex, but the spatial distribution of activity within each region is unknown. An expectation-maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next, an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa
2015-08-13
This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.
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.
Visceral leishmaniasis in the state of Sao Paulo, Brazil: spatial and space-time analysis.
Cardim, Marisa Furtado Mozini; Guirado, Marluci Monteiro; Dibo, Margareth Regina; Chiaravalloti, Francisco
2016-08-11
To perform both space and space-time evaluations of visceral leishmaniasis in humans in the state of Sao Paulo, Brazil. The population considered in the study comprised autochthonous cases of visceral leishmaniasis and deaths resulting from it in Sao Paulo, between 1999 and 2013. The analysis considered the western region of the state as its studied area. Thematic maps were created to show visceral leishmaniasis dissemination in humans in the municipality. Spatial analysis tools Kernel and Kernel ratio were used to respectively obtain the distribution of cases and deaths and the distribution of incidence and mortality. Scan statistics were used in order to identify spatial and space-time clusters of cases and deaths. The visceral leishmaniasis cases in humans, during the studied period, were observed to occur in the western portion of Sao Paulo, and their territorial extension mainly followed the eastbound course of the Marechal Rondon highway. The incidences were characterized as two sequences of concentric ellipses of decreasing intensities. The first and more intense one was found to have its epicenter in the municipality of Castilho (where the Marechal Rondon highway crosses the border of the state of Mato Grosso do Sul) and the second one in Bauru. Mortality was found to have a similar behavior to incidence. The spatial and space-time clusters of cases were observed to coincide with the two areas of highest incidence. Both the space-time clusters identified, even without coinciding in time, were started three years after the human cases were detected and had the same duration, that is, six years. The expansion of visceral leishmaniasis in Sao Paulo has been taking place in an eastbound direction, focusing on the role of highways, especially Marechal Rondon, in this process. The space-time analysis detected the disease occurred in cycles, in different spaces and time periods. These meetings, if considered, may contribute to the adoption of actions that aim to
Directory of Open Access Journals (Sweden)
José Eduardo Spinelli
2006-03-01
Full Text Available Microstructures are the strategic link between materials processing and materials behavior. A dendritic structure is the most frequently observed pattern of solidified alloys. The microstructural scales of dendrites, such as primary and secondary arm spacings, control the segregation profiles and the formation of secondary phases within interdendritic regions, determine the properties of cast structures. In this work, the influence of thermosolutal convection on dendrite arm spacings is experimentally examined in the downward vertical unsteady-state directional solidification of Sn-Pb hypoeutectic alloys. The experimental observations are compared not only with the main predictive theoretical models for dendritic spacings but also with experimental results obtained for Sn-Pb alloys solidified vertically upwards. Primary dendritic arm spacings have been affected by the direction of growth, decreasing in conditions of downward vertical solidification when compared with those grown vertically upwards. Further, the unsteady-state lambda1 predictive models did not generate the experimental observations.
Directory of Open Access Journals (Sweden)
Brdyś Mietek A.
2016-03-01
Full Text Available The paper considers the forecasting of the euro/Polish złoty (EUR/PLN spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.
Pan, Shuokai; Elliott, Stephen J; Teal, Paul D; Lineton, Ben
2015-06-01
Nonlinear models of the cochlea are best implemented in the time domain, but their computational demands usually limit the duration of the simulations that can reasonably be performed. This letter presents a modified state space method and its application to an example nonlinear one-dimensional transmission-line cochlear model. The sparsity pattern of the individual matrices for this alternative formulation allows the use of significantly faster numerical algorithms. Combined with a more efficient implementation of the saturating nonlinearity, the computational speed of this modified state space method is more than 40 times faster than that of the original formulation.
State-space approach for the analysis of soil water content and temperature in a sugarcane crop
Directory of Open Access Journals (Sweden)
Dourado-Neto Durval
1999-01-01
Full Text Available The state-space approach is used to describe surface soil water content and temperature behaviour, in a field experiment in which sugarcane is submitted to different management practices. The treatments consisted of harvest trash mulching, bare soil, and burned trash, all three in a ratoon crop, after first cane harvest. One transect of 84 points was sampled, meter by meter, covering all treatments and borders. The state-space approach is described in detail and the results show that soil water contents measured along the transect could successfully be estimated from water content and temperature observations made at the first neighbour.
Computational state space models for activity and intention recognition. A feasibility study.
Krüger, Frank; Nyolt, Martin; Yordanova, Kristina; Hein, Albert; Kirste, Thomas
2014-01-01
Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. The symbolic domain model was found to have more than 10(8) states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also
Particle MCMC algorithms and architectures for accelerating inference in state-space models.
Mingas, Grigorios; Bottolo, Leonardo; Bouganis, Christos-Savvas
2017-04-01
Particle Markov Chain Monte Carlo (pMCMC) is a stochastic algorithm designed to generate samples from a probability distribution, when the density of the distribution does not admit a closed form expression. pMCMC is most commonly used to sample from the Bayesian posterior distribution in State-Space Models (SSMs), a class of probabilistic models used in numerous scientific applications. Nevertheless, this task is prohibitive when dealing with complex SSMs with massive data, due to the high computational cost of pMCMC and its poor performance when the posterior exhibits multi-modality. This paper aims to address both issues by: 1) Proposing a novel pMCMC algorithm (denoted ppMCMC), which uses multiple Markov chains (instead of the one used by pMCMC) to improve sampling efficiency for multi-modal posteriors, 2) Introducing custom, parallel hardware architectures, which are tailored for pMCMC and ppMCMC. The architectures are implemented on Field Programmable Gate Arrays (FPGAs), a type of hardware accelerator with massive parallelization capabilities. The new algorithm and the two FPGA architectures are evaluated using a large-scale case study from genetics. Results indicate that ppMCMC achieves 1.96x higher sampling efficiency than pMCMC when using sequential CPU implementations. The FPGA architecture of pMCMC is 12.1x and 10.1x faster than state-of-the-art, parallel CPU and GPU implementations of pMCMC and up to 53x more energy efficient; the FPGA architecture of ppMCMC increases these speedups to 34.9x and 41.8x respectively and is 173x more power efficient, bringing previously intractable SSM-based data analyses within reach.
Computational state space models for activity and intention recognition. A feasibility study.
Directory of Open Access Journals (Sweden)
Frank Krüger
Full Text Available BACKGROUND: Computational state space models (CSSMs enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. METHODS: A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. RESULTS: The symbolic domain model was found to have more than 10(8 states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters were found to perform substantially inferior in comparison to a marginal filtering procedure. CONCLUSIONS: Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data are
On coherent-state representations of quantum mechanics: Wave mechanics in phase space
DEFF Research Database (Denmark)
Møller, Klaus Braagaard; Jørgensen, Thomas Godsk; Torres-Vega, Gabino
1997-01-01
one wants to solve the stationary Schrodinger equation in phase space and we devise two schemes for the removal of these ambiguities. The physical interpretation of the phase-space wave functions is discussed and a procedure for computing expectation values as integrals over phase space is presented...
The Merits of Cold Gas Micropropulsion in State-of-the-Art Space Missions
Nguyen, H.; Köhler, J.; Stenmark, L.
2002-01-01
Cold gas micropropulsion is a sound choice for space missions that require extreme stabilisation, pointing precision or contamination-free operation. The use of forces in the micronewton range for spacecraft operations have been identified as a mission-critical item in several demanding space systems currently under development. The required micropropulsion systems are emerging, using various principles, e.g. field emission, colloid acceleration, solid combustion, and cold gas expulsion. Cold gas micropropulsion systems share merits with traditional cold gas systems in being of simple design, clean, safe, and robust. They do not generate net charge to the spacecraft, and typically operate on low-power. The necessary extreme miniaturisation of system parts furthermore works well to increasing other merits of these systems, making them truly competitive for state-of-the-art spacecraft: e.g. DARWIN, LISA, or high-performance nanosatellites. Silicon microsystems technology can be used for the cold gas micropropulsion system manufacture. Here, the decrease of dimensions is not restricted to fit standard components or tools. This allows for an astonishing mass reduction, e.g. 80 g for a unit comprising four independent nozzles, proportional valves, particle filters, control electronics, and housing. The minute size is also suitable for inclusion on nanosatellites. The dynamic range of a cold gas micropropulsion system can be quite wide (e.g. 1 μN - 10 mN) by using differently sized nozzles in parallel systems. Again, the microsystems technology makes this scheme possible without compromising the mass budget. The micropropulsion system benefits greatly from using a continuously proportional control on the thrust. In this system, the impulse is obtained as the difference of two opposite thrusters in the same unit. Here, the minimum impulse bit is reduced to virtually zero, while simultaneously avoiding any troubles emerging from extremely low flows at near-zero thrust
Application of space technologies for the purpose of education at the Belarusian state university
Liashkevich, Siarhey
Application of space technologies for the purpose of education at the Aerospace Educational Center of Belarusian state university is discussed. The aim of the work is to prepare launch of small satellite. Students are expected to participate in the design of control station, systems of communication, earth observation, navigation, and positioning. Benefit of such project-based learning from economical perspective is discussed. At present our training system at the base of EyasSat classroom satellite is used for management of satellite orientation and stabilization system. Principles of video processing, communication technologies and informational security for small spacecraft are developed at the base of Wi9M-2443 developer kit. More recent equipment allows obtaining the skills in digital signal processing at the base of FPGA. Development of ground station includes setup of 2.6 meter diameter dish for L-band, and spiral rotational antennas for UHF and VHF bands. Receiver equipment from National Instruments is used for digital signal processing and signal management.
Evaluating a fish monitoring protocol using state-space hierarchical models
Russell, Robin E.; Schmetterling, David A.; Guy, Chris S.; Shepard, Bradley B.; McFarland, Robert; Skaar, Donald
2012-01-01
Using data collected from three river reaches in Montana, we evaluated our ability to detect population trends and predict fish future fish abundance. Data were collected as part of a long-term monitoring program conducted by Montana Fish, Wildlife and Parks to primarily estimate rainbow (Oncorhynchus mykiss) and brown trout (Salmo trutta) abundance in numerous rivers across Montana. We used a hierarchical Bayesian mark-recapture model to estimate fish abundance over time in each of the three river reaches. We then fit a state-space Gompertz model to estimate current trends and future fish populations. Density dependent effects were detected in 1 of the 6 fish populations. Predictions of future fish populations displayed wide credible intervals. Our simulations indicated that given the observed variation in the abundance estimates, the probability of detecting a 30% decline in fish populations over a five-year period was less than 50%. We recommend a monitoring program that is closely tied to management objectives and reflects the precision necessary to make informed management decisions.
Hooker, Giles; Ellner, Stephen P; Roditi, Laura De Vargas; Earn, David J D
2011-07-06
Parameter estimation for infectious disease models is important for basic understanding (e.g. to identify major transmission pathways), for forecasting emerging epidemics, and for designing control measures. Differential equation models are often used, but statistical inference for differential equations suffers from numerical challenges and poor agreement between observational data and deterministic models. Accounting for these departures via stochastic model terms requires full specification of the probabilistic dynamics, and computationally demanding estimation methods. Here, we demonstrate the utility of an alternative approach, generalized profiling, which provides robustness to violations of a deterministic model without needing to specify a complete probabilistic model. We introduce novel means for estimating the robustness parameters and for statistical inference in this framework. The methods are applied to a model for pre-vaccination measles incidence in Ontario, and we demonstrate the statistical validity of our inference through extensive simulation. The results confirm that school term versus summer drives seasonality of transmission, but we find no effects of short school breaks and the estimated basic reproductive ratio (0) greatly exceeds previous estimates. The approach applies naturally to any system for which candidate differential equations are available, and avoids many challenges that have limited Monte Carlo inference for state-space models.
Advancing brain-machine interfaces: moving beyond linear state space models.
Rouse, Adam G; Schieber, Marc H
2015-01-01
Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider (i) the dynamic range and precision of natural movements, (ii) differences between cortical activity and actual body movement, (iii) kinematic and muscular synergies, and (iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.
Frailty in state-space models: application to actuarial senescence in the Dipper.
Marzolin, Gilbert; Charmantier, Anne; Gimenez, Olivier
2011-03-01
Senescence, a decrease in life history traits with age, is a within-individual process. The lack of suitable methods to deal with individual heterogeneity has long impeded progress in exploring senescence in wild populations. Analyses of survival senescence are additionally complicated by the often neglected issue of imperfect detectability. To deal with both these issues, we developed state-space models to analyze capture-mark-recapture data while accounting for individual heterogeneity by incorporating random effects. We illustrated our approach by applying it to 29 years of data on breeding females in a Dipper (Cinclus cinclus) population. We highlighted patterns of age-related variation in annual survival by statistical comparisons of piecewise linear, quadratic, Gompertz, and Weibull survival models. The Gompertz model was ranked first in our set. It provided strong evidence for actuarial senescence with an onset of senescence estimated at about 2.3 years. The probability for this model to involve a frailty was 0.15, and the probability to involve an individual latent effect in detection was about 0.4. The estimated mean age at first reproduction was 1.2 years. The general case model described here in detail should encourage the reanalysis of actuarial senescence in cases where imperfect detection or individual heterogeneity is suspected.
A state space based approach to localizing single molecules from multi-emitter images.
Vahid, Milad R; Chao, Jerry; Ward, E Sally; Ober, Raimund J
2017-01-28
Single molecule super-resolution microscopy is a powerful tool that enables imaging at sub-diffraction-limit resolution. In this technique, subsets of stochastically photoactivated fluorophores are imaged over a sequence of frames and accurately localized, and the estimated locations are used to construct a high-resolution image of the cellular structures labeled by the fluorophores. Available localization methods typically first determine the regions of the image that contain emitting fluorophores through a process referred to as detection. Then, the locations of the fluorophores are estimated accurately in an estimation step. We propose a novel localization method which combines the detection and estimation steps. The method models the given image as the frequency response of a multi-order system obtained with a balanced state space realization algorithm based on the singular value decomposition of a Hankel matrix, and determines the locations of intensity peaks in the image as the pole locations of the resulting system. The locations of the most significant peaks correspond to the locations of single molecules in the original image. Although the accuracy of the location estimates is reasonably good, we demonstrate that, by using the estimates as the initial conditions for a maximum likelihood estimator, refined estimates can be obtained that have a standard deviation close to the Cramér-Rao lower bound-based limit of accuracy. We validate our method using both simulated and experimental multi-emitter images.
SiGN-SSM: open source parallel software for estimating gene networks with state space models.
Tamada, Yoshinori; Yamaguchi, Rui; Imoto, Seiya; Hirose, Osamu; Yoshida, Ryo; Nagasaki, Masao; Miyano, Satoru
2011-04-15
SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. tamada@ims.u-tokyo.ac.jp.
An Analysis of North Pacific Subsurface Temperatures Using State-Space Techniques
Bessey, Cindy
2012-01-01
North Pacific subsurface temperature data from the Simple Ocean Data Assimilation model at 10m, 50m, 75m, 100m and 150m depths, are analyzed using a combination of state-space decomposition and subspace identification techniques to examine the spatial structure of thermal variability within the upper water column. We identify four common trends from our analysis that display the major broad-scale patterns in the North Pacific over a 47 year period (1958-2004): (1) a basin-wide near-surface warming trend that identifies the mid 1980's as a change point from a cooling to a warming trend; (2) a contrasting cooling in the central basin and warming along the coast of North America that began in the early 1970's; (3) a cooling along the transition zone and the west coast of North America that becomes dominant around 1998; (4) and contrasting differences in the subarctic and subtropical gyres displaying differences in processes at each depth. We also provide a detailed analysis of the temperature variability at four...
A receptor state space model of the insulin signalling system in glucose transport.
Gray, Catheryn W; Coster, Adelle C F
2015-12-01
Insulin is a potent peptide hormone that regulates glucose levels in the blood. Insulin-sensitive cells respond to insulin stimulation with the translocation of glucose transporter 4 (GLUT4) to the plasma membrane (PM), enabling the clearance of glucose from the blood. Defects in this process can give rise to insulin resistance and ultimately diabetes. One widely cited model of insulin signalling leading to glucose transport is that of Sedaghat et al. (2002) Am. J. Physiol. Endocrinol. Metab. 283, E1084-E1101. Consisting of 20 deterministic ordinary differential equations (ODEs), it is the most comprehensive model of insulin signalling to date. However, the model possesses some major limitations, including the non-conservation of key components. In the current work, we detail mathematical and sensitivity analyses of the Sedaghat model. Based on the results of these analyses, we propose a reduced state space model of the insulin receptor subsystem. This reduced model maintains the input-output relation of the original model but is computationally more efficient, analytically tractable and resolves some of the limitations of the Sedaghat model.
Using State Space Methods to Reveal Dynamical Associations Between Cortisol and Depression.
Toonen, Roelof B; Wardenaar, Klaas J; van Ockenburg, Sonja L; Bos, Elisabeth H; de Jonge, Peter
2016-01-01
Despite extensive research, the link between etiological factors and depression remains poorly understood. This may in part be due to a focus on strictly linear definitions of causality, derived at the group level. However, etiological relations in depression are likely to be dynamical, nonlinear and potentially unquantifiable with traditional statistics. Therefore the aim of this study was to evaluate the use of the convergent cross-mapping (CCM) method in investigating possible nonlinear relationships between supposed etiological factors and depressive symptomatology. Time series data from six healthy individuals were used to model the relationship between 24-h urinary free cortisol and negative affect using CCM and dewdrop embeddings. CCM is a nonlinear measure of causality, based on state space reconstruction with lagged coordinate embeddings. The results showed that nonlinear dynamical relationships between cortisol and negative affect may be present within participants, as demonstrated by a positive cross-map convergence from negative affect to cortisol. However, analyses also showed that noise and influential points had considerable impact on the results. Convergent crossmapping can be used to reveal possible nonlinear dynamical relationships between etiological factors and psychopathology that may remain undetected with traditional linear causality measures.
Energy Technology Data Exchange (ETDEWEB)
Sahmani, S.; Ansari, R. [University of Guilan, Rasht (Iran, Islamic Republic of)
2011-09-15
Buckling analysis of nanobeams is investigated using nonlocal continuum beam models of the different classical beam theories namely as Euler-Bernoulli beam theory (EBT), Timoshenko beam theory (TBT), and Levinson beam theory (LBT). To this end, Eringen's equations of nonlocal elasticity are incorporated into the classical beam theories for buckling of nanobeams with rectangular cross-section. In contrast to the classical theories, the nonlocal elastic beam models developed here have the capability to predict critical buckling loads that allowing for the inclusion of size effects. The values of critical buckling loads corresponding to four commonly used boundary conditions are obtained using state-space method. The results are presented for different geometric parameters, boundary conditions, and values of nonlocal parameter to show the effects of each of them in detail. Then the results are fitted with those of molecular dynamics simulations through a nonlinear least square fitting procedure to find the appropriate values of nonlocal parameter for the buckling analysis of nanobeams relevant to each type of nonlocal beam model and boundary conditions analysis.
Lee-Carter state space modeling: Application to the Malaysia mortality data
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.
Tomicic, Alemka; Martínez, Claudio; Pérez, J Carola; Hollenstein, Tom; Angulo, Salvador; Gerstmann, Adam; Barroux, Isabelle; Krause, Mariane
2015-01-01
This study seeks to provide evidence of the dynamics associated with the configurations of discourse-voice regulatory strategies in patient-therapist interactions in relevant episodes within psychotherapeutic sessions. Its central assumption is that discourses manifest themselves differently in terms of their prosodic characteristics according to their regulatory functions in a system of interactions. The association between discourse and vocal quality in patients and therapists was analyzed in a sample of 153 relevant episodes taken from 164 sessions of five psychotherapies using the state space grid (SSG) method, a graphical tool based on the dynamic systems theory (DST). The results showed eight recurrent and stable discourse-voice regulatory strategies of the patients and three of the therapists. Also, four specific groups of these discourse-voice strategies were identified. The latter were interpreted as regulatory configurations, that is to say, as emergent self-organized groups of discourse-voice regulatory strategies constituting specific interactional systems. Both regulatory strategies and their configurations differed between two types of relevant episodes: Change Episodes and Rupture Episodes. As a whole, these results support the assumption that speaking and listening, as dimensions of the interaction that takes place during therapeutic conversation, occur at different levels. The study not only shows that these dimensions are dependent on each other, but also that they function as a complex and dynamic whole in therapeutic dialog, generating relational offers which allow the patient and the therapist to regulate each other and shape the psychotherapeutic process that characterizes each type of relevant episode.
Robust maximum likelihood estimation for stochastic state space model with observation outliers
AlMutawa, J.
2016-08-01
The objective of this paper is to develop a robust maximum likelihood estimation (MLE) for the stochastic state space model via the expectation maximisation algorithm to cope with observation outliers. Two types of outliers and their influence are studied in this paper: namely,the additive outlier (AO) and innovative outlier (IO). Due to the sensitivity of the MLE to AO and IO, we propose two techniques for robustifying the MLE: the weighted maximum likelihood estimation (WMLE) and the trimmed maximum likelihood estimation (TMLE). The WMLE is easy to implement with weights estimated from the data; however, it is still sensitive to IO and a patch of AO outliers. On the other hand, the TMLE is reduced to a combinatorial optimisation problem and hard to implement but it is efficient to both types of outliers presented here. To overcome the difficulty, we apply the parallel randomised algorithm that has a low computational cost. A Monte Carlo simulation result shows the efficiency of the proposed algorithms. An earlier version of this paper was presented at the 8th Asian Control Conference, Kaohsiung, Taiwan, 2011.
Chávez, Ricardo
2014-01-01
We propose to use HII galaxies (HIIG) to trace the redshift-distance relation, by means of their $L(\\mathrm{H}\\beta) - \\sigma$ correlation, in an attempt to constrain the dark energy equation of state parameter solution space, as an alternative to the cosmological use of type Ia supernovae. For a sample of 128 local compact HIIG with high equivalent widths of their Balmer emission lines we obtained ionised gas velocity dispersion from high S/N, high-dispersion spectroscopy (Subaru-HDS and ESO VLT-UVES) and integrated H$\\beta$ fluxes from low dispersion wide aperture spectrophotometry. We find that the $L(\\mathrm{H}\\beta) - \\sigma$ relation is strong and stable against restrictions in the sample. The size of the starforming region is an important second parameter, while adding the emission line equivalent width or the continuum colour and metallicity, produces the solution with the smallest rms scatter. We have used the $L(\\mathrm{H}\\beta) - \\sigma$ relation from a local sample of HIIG and a local calibration ...
Identification of the parameters of a DC motor state space model
Directory of Open Access Journals (Sweden)
Momir Ranislav Stanković
2013-06-01
Full Text Available A method for the identification of the DC state space model parameters based on the minimization of the error function using the least squares method is described in this paper. The algorithm is practically applied in the laboratory environment on an industrial DC motor. The verification of the results was performed by comparing the characteristic signals of real and modeled systems. The results show that the quality of the identification is satisfactory. Introduction The identification of system parameters is the first step in the analysis and synthesis of control systems. Identification Quality strongly impacts on the results of all other computations. In the theory of automatic control, many methods of identification are developed. Which method will be applied depends on the characteristics of the system. In this paper, we described an identification algorithm based on the least squares method. A practical test of this algorithm of estimation is done on a DC motor. parameter estimation with the least squares method A DC motor is a second-order system described with two differential equations: one which describes electrical and one which describes mechanical parts of the motor. The idea is to analyse the motor as two first-order systems. The main signals are responses of two first order sub-systems on appropriate inputs. Using a discrete state-space model of the motor and applying the least square method on the recorded signals, we get straightforward equations for the computation of all the necessary parameters: Rr, Lr , Je , Fe , Kme and Kem (Eykhoff, Wilsoons, 1974. Experimental results The practical application was realized in the laboratory where a DC middle-power motor was used as a control object. It is coupled with a DC generator which serves as a load. Generation of the input signals and measure of the responses were performed with the acquisition system based on the appropriate acquisition card and the MATLAB
Determinants of road traffic safety: New evidence from Australia using state-space analysis.
Nghiem, Son; Commandeur, Jacques J F; Connelly, Luke B
2016-09-01
This paper examines the determinants of road traffic crash fatalities in Queensland for the period 1958-2007 using a state-space time-series model. In particular, we investigate the effects of policies that aimed to reduce drink-driving on traffic fatalities, as well as indicators of the economic environment that may affect exposure to traffic, and hence affect the number of accidents and fatalities. The results show that the introduction of a random breath testing program in 1988 was associated with a 11.3% reduction in traffic fatalities; its expansion in 1998 was associated with a 26.2% reduction in traffic fatalities; and the effect of the "Safe4life" program, which was introduced in 2004, was a 14.3% reduction in traffic fatalities. Reductions in economic activity are also associated with reductions in road fatalities: we estimate that a one percent increase in the unemployment rate is associated with a 0.2% reduction in traffic fatalities.
Analyzing the Low State of EF Eridani with Hubble Space Telescope Ultraviolet Spectra
Szkody, Paula; Gaensicke, Boris T; Campbell, Ryan K; Harrison, Thomas E; Howell, Steve B; Holtzman, Jon; Walter, Frederick M; Henden, Arne; Dillon, William; Boberg, Owen; Dealaman, Shannon; Perone, Christian S
2010-01-01
Time-resolved spectra throughout the orbit of EF Eri during its low accretion state were obtained with the Solar Blind Channel on the Advanced Camera for Surveys onboard the Hubble Space Telescope. The overall spectral distribution exhibits peaks at 1500 and 1700A, while the UV light curves display a quasi-sinusoidal modulation over the binary orbit. Models of white dwarfs with a hot spot and cyclotron emission were attempted to fit the spectral variations throughout the orbit. A non-magnetic white dwarf with a temperature of ~10,000K and a hot spot with central temperature of 15,000K generally matches the broad absorptions at 1400 and 1600A with those expected for the quasimolecular H features H2 and H+2 . However, the flux in the core of the Lyalpha absorption does not go to zero, implying an additional component, and the flux variations throughout the orbit are not well matched at long wavelengths. Alternatively, a 9500K white dwarf with a 100 MG cyclotron component can fit the lowest (phase 0.0) fluxes, b...
Forecasting seasonal influenza with a state-space SIR model1
Osthus, Dave; Hickmann, Kyle S.; Caragea, Petruţa C.; Higdon, Dave; Del Valle, Sara Y.
2017-01-01
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention’s influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics. PMID:28979611
Phase Space Diagnostics of Trapped Atoms By Magnetic Ground-State Manipulation
Cahn, S. B.; Kumarakrishnan, A.; Shim, U.; Sleator, T.
1997-04-01
The in-situ measurement of the phase space distribution of atoms in a trap is important in the study of both ordinary and Bose-condensed matter. The current techniques for measuring the density distribution involve imaging the light emitted by atoms in the trap, time-of-flight measurement of the atoms as they fall through a sheet of light(C.D. Wallace, et al, JOSA B,11),703 (1994), resonant absorption imaging of the cloud(J.R. Ensher, et al, PRL 77), 4984 (1996), or off-resonant dispersive imaging. The first two techniques are in general use for imaging magneto-optical traps (MOTs) and the second two for Bose condensates. Velocity information is obtained indirectly by recording the expansion of the trap at different times following shut-off. By exploiting the magnetic field dependence of ground-state magnetic sublevel coherences, we have employed two techniques, MGE and MGFID(B. Dubetsky and P.R. Berman, Appl. Phys. B, 59), 147 (1994), to obtain atomic spatial information. This variant of atomic beam magnetic imaging(J.E. Thomas and L.J. Wang, Physics Reports 262), 311-366 (1995) also yields correlated position-velocity information by appropriate orientation of the applied magnetic field, as the detuning of the atom depends on both its position and velocity. Initial studies have given the velocity distribution and size of the MOT, and future experiments to measure correlations are proposed.
Kramer, Leonard
2014-01-01
A plasma diagnostic package is deployed on the International Space Station (ISS). The system - a Floating Potential Measurement Unit (FPMU) - is used by NASA to monitor the electrical floating potential of the vehicle to assure astronaut safety during extravehicular activity. However, data from the unit also reflects the ionosphere state and seems to represent an unutilized scientific resource in the form of an archive of scientific plasma state data. The unit comprises a Floating Potential probe and two Langmuir probes. There is also an unused but active plasma impedance probe. The data, at one second cadence, are collected, typically for a two week period surrounding extravehicular activity events. Data is also collected any time a visiting vehicle docks with ISS and also when any large solar events occur. The telemetry system is unusual because the package is mounted on a television camera stanchion and its data is impressed on a video signal that is transmitted to the ground and streamed by internet to two off center laboratory locations. The data quality has in the past been challenged by weaknesses in the integrated ground station and distribution systems. These issues, since mid-2010, have been largely resolved and the ground stations have been upgraded. Downstream data reduction has been developed using physics based modeling of the electron and ion collecting character in the plasma. Recursive algorithms determine plasma density and temperature from the raw Langmuir probe current voltage sweeps and this is made available in real time for situational awareness. The purpose of this paper is to describe and record the algorithm for data reduction and to show that the Floating probe and Langmuir probes are capable of providing long term plasma state measurement in the ionosphere. Geophysical features such as the Appleton anomaly and high latitude modulation at the edge of the Auroral zones are regularly observed in the nearly circular, 51 deg inclined, 400 km
Energy Technology Data Exchange (ETDEWEB)
Zhu Jiuyun (Department of Physics, Hunan Normal University, Hunan 410006 (China)); Kuang Leman (Theoretical Physics Division, Nankai Institute of Mathematics, Tianjin 300071 (China) Department of Physics and Institute of Physics, Hunan Normal University, Hunan 410081 (China))
1994-10-03
The even and odd coherent states (CSs) of a finite-dimensional Hilbert space harmonic oscillator (FDHSHO) are constructed and some properties of these states are studied. Their quadrature squeezing and amplitude-squared squeezing are investigated in detail. It is shown that, while the squeezing behaviour of the even and odd CSs of the FDHSHO approaches that of the even and odd CSs of the usual harmonic oscillator as the dimension of the Hilbert space tends to infinity, this behaviour is nontrivally different if the dimension of the Hilbert space is finite. In the latter case, it is found that the even and odd CSs exhibit both amplitude-squared squeezing and quadrature squeezing. ((orig.))
Varying likelihood of Megafire across space and time in the western contiguous United States
Stavros, E.; Abatzoglou, J. T.; Larkin, N. K.; McKenzie, D.; Steel, E.
2013-12-01
Studies project that a warming climate will likely increase wildfire activity. These analyses, however, are of aggregate statistics of annual area burned and to anticipate future events, especially those of particular concern like megafires, we need more fire specific projections. Megafires account for a disproportionate amount of damage and are defined quantitatively here as fires that burn >20,234 ha ~50,000 ac. Megafires account for the top two percent of all fires and represent 33% of all area burned in the western contiguous United States from 1984 to 2010. Multiple megafires often occur in one region during a single fire season, suggesting that regional climate is a driver. Therefore, we used composite records of climate and fire to investigate the spatial and temporal variability of the megafire climate space. We then developed logistic regression models to predict the probability that a megafire will occur in a given week. Accuracy was good (AUC > 0.80) for all models. These analyses provide a coarse-scale assessment for operationally defined regions of megafire risk, which can be projected to determine how the likelihood of megafire varies across space and time using the Intergovernmental Panel on Climate Change representative concentration pathways (RCPs) 4.5 and 8.5. In general, with the exception of Northern California (NCAL), Southern California, and the Western Great Basin, there is increasing proportional change over time in the probability of a megafire. There was a significant (p≤0.05) difference between the historical modeled ensemble mean probability of a megafire occurrence from 1979 to 2010 and both RCP 4.5 and 8.5 means during 2031 to 2060. Generally, with the exception of the Southwest and NCAL, there are higher probabilities of megafire occurrence more frequently and for longer periods both throughout the fire season and from year to year, with more pronounced patterns under RCP 8.5 than RCP 4.5. Our results provide a quantitative
Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Whoriskey, Kim; Yurkowski, David
2015-01-01
State-space models (SSM) are often used for analyzing complex ecological processes that are not observed directly, such as marine animal movement. When outliers are present in the measurements, special care is needed in the analysis to obtain reliable location and process estimates. Here we...
Using Innovative Outliers to Detect Discrete Shifts in Dynamics in Group-Based State-Space Models
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…
Ayupov, Sh A
2011-01-01
In the present article we prove a fixed point theorem for reflections of compact convex sets and give a new characterization of state space of JB-algebras among compact convex sets. Namely they are exactly those compact convex sets which are strongly spectral and symmetric.
Yeh, Leehwa
1993-01-01
The phase-space-picture approach to quantum non-equilibrium statistical mechanics via the characteristic function of infinite-mode squeezed coherent states is introduced. We use quantum Brownian motion as an example to show how this approach provides an interesting geometrical interpretation of quantum non-equilibrium phenomena.
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…
Bergboer, N.H; Verdult, V.; Verhaegen, M.H.G.
2002-01-01
We present a numerically efficient implementation of the nonlinear least squares and maximum likelihood identification of multivariable linear time-invariant (LTI) state-space models. This implementation is based on a local parameterization of the system and a gradient search in the resulting parame
State of erythrocyte membrane in man and monkeys after space flight
Yarlikova, Yu. V.; Ivanova, S. M.
The lipid and phospholipid composition of the erythrocyte membrane was investigated in man after long space flight and monkey after short space. The result obtained confirm structural changes in EM under the influence of SF factors and show that an increase of Ch and ChE fractions and in the Ch&ChE/PL ratio combined with a decrease of PL fractions. It was noticed that the magnitude of these changes is depend on duration of space flight.
State-space modeling to support management of brucellosis in the Yellowstone bison population
Hobbs, N. Thompson; Geremia, Chris; Treanor, John; Wallen, Rick; White, P.J.; Hooten, Mevin B.; Rhyan, Jack C.
2015-01-01
The bison (Bison bison) of the Yellowstone ecosystem, USA, exemplify the difficulty of conserving large mammals that migrate across the boundaries of conservation areas. Bison are infected with brucellosis (Brucella abortus) and their seasonal movements can expose livestock to infection. Yellowstone National Park has embarked on a program of adaptive management of bison, which requires a model that assimilates data to support management decisions. We constructed a Bayesian state-space model to reveal the influence of brucellosis on the Yellowstone bison population. A frequency-dependent model of brucellosis transmission was superior to a density-dependent model in predicting out-of-sample observations of horizontal transmission probability. A mixture model including both transmission mechanisms converged on frequency dependence. Conditional on the frequency-dependent model, brucellosis median transmission rate was 1.87 yr−1. The median of the posterior distribution of the basic reproductive ratio (R0) was 1.75. Seroprevalence of adult females varied around 60% over two decades, but only 9.6 of 100 adult females were infectious. Brucellosis depressed recruitment; estimated population growth rate λ averaged 1.07 for an infected population and 1.11 for a healthy population. We used five-year forecasting to evaluate the ability of different actions to meet management goals relative to no action. Annually removing 200 seropositive female bison increased by 30-fold the probability of reducing seroprevalence below 40% and increased by a factor of 120 the probability of achieving a 50% reduction in transmission probability relative to no action. Annually vaccinating 200 seronegative animals increased the likelihood of a 50% reduction in transmission probability by fivefold over no action. However, including uncertainty in the ability to implement management by representing stochastic variation in the number of accessible bison dramatically reduced the probability of
Brainard, George C.; Coyle, William; Ayers, Melissa; Kemp, John; Warfield, Benjamin; Maida, James; Bowen, Charles; Bernecker, Craig; Lockley, Steven W.; Hanifin, John P.
2013-11-01
The International Space Station (ISS) uses General Luminaire Assemblies (GLAs) that house fluorescent lamps for illuminating the astronauts' working and living environments. Solid-state light emitting diodes (LEDs) are attractive candidates for replacing the GLAs on the ISS. The advantages of LEDs over conventional fluorescent light sources include lower up-mass, power consumption and heat generation, as well as fewer toxic materials, greater resistance to damage and long lamp life. A prototype Solid-State Lighting Assembly (SSLA) was developed and successfully installed on the ISS. The broad aim of the ongoing work is to test light emitted by prototype SSLAs for supporting astronaut vision and assessing neuroendocrine, circadian, neurobehavioral and sleep effects. Three completed ground-based studies are presented here including experiments on visual performance, color discrimination, and acute plasma melatonin suppression in cohorts of healthy, human subjects under different SSLA light exposure conditions within a high-fidelity replica of the ISS Crew Quarters (CQ). All visual tests were done under indirect daylight at 201 lx, fluorescent room light at 531 lx and 4870 K SSLA light in the CQ at 1266 lx. Visual performance was assessed with numerical verification tests (NVT). NVT data show that there are no significant differences in score (F=0.73, p=0.48) or time (F=0.14, p=0.87) for subjects performing five contrast tests (10%-100%). Color discrimination was assessed with Farnsworth-Munsell 100 Hue tests (FM-100). The FM-100 data showed no significant differences (F=0.01, p=0.99) in color discrimination for indirect daylight, fluorescent room light and 4870 K SSLA light in the CQ. Plasma melatonin suppression data show that there are significant differences (F=29.61, pmelatonin for five corneal irradiances, ranging from 0 to 405 μW/cm2 of 4870 K SSLA light in the CQ (0-1270 lx). Risk factors for the health and safety of astronauts include disturbed circadian
Modulation depth estimation and variable selection in state-space models for neural interfaces.
Malik, Wasim Q; Hochberg, Leigh R; Donoghue, John P; Brown, Emery N
2015-02-01
Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems.
Directory of Open Access Journals (Sweden)
Jin Hwan Do
2015-10-01
Full Text Available This study compared a parkinsonian neurotoxin 1-methyl-4-phenylpyridinium (MPP+ response in two distinct phenotypes of human neuroblastoma cell lines: neuronal N-type SH-SY5Y cells and flat substrate-adherent S-type SH-EP cells. SH-SY5Y and SH-EP cells shared only 14% of their own MPP+ response genes, and their gene ontology (GO analysis revealed significant endoplasmic reticulum (ER stress by misfolded proteins. Gene modules, which are groups of transcriptionally co-expressed genes with similar biological functions, were identified for SH-SY5Y and SH-EP cells by using time-series microarray data with the state space model (SSM. All modules of SH-SY5Y and SH-EP cells showed strong positive auto-regulation that was often mediated via signal molecules and may cause bi-stability. Interactions in gene levels were calculated by using SSM parameters obtained in the process of module identification. Gene networks that were constructed from the gene interaction matrix showed different hub genes with high node degrees between SH-SY5Y and SH-EP cells. That is, key hub genes of SH-SY5Y cells were DCN, HIST1H2BK, and C5orf40, whereas those of SH-EP cells were MSH6, RBCK1, MTHFD2, ZNF26, CTH, and CARS. These results suggest that inhibition of the mitochondrial complex I by MPP+ might induce different downstream processes that are cell type dependent.