Continuous measurement of an atomic current

NASA Astrophysics Data System (ADS)

Laflamme, C.; Yang, D.; Zoller, P.

2017-04-01

We are interested in dynamics of quantum many-body systems under continuous observation, and its physical realizations involving cold atoms in lattices. In the present work we focus on continuous measurement of atomic currents in lattice models, including the Hubbard model. We describe a Cavity QED setup, where measurement of a homodyne current provides a faithful representation of the atomic current as a function of time. We employ the quantum optical description in terms of a diffusive stochastic Schrödinger equation to follow the time evolution of the atomic system conditional to observing a given homodyne current trajectory, thus accounting for the competition between the Hamiltonian evolution and measurement back action. As an illustration, we discuss minimal models of atomic dynamics and continuous current measurement on rings with synthetic gauge fields, involving both real space and synthetic dimension lattices (represented by internal atomic states). Finally, by "not reading" the current measurements the time evolution of the atomic system is governed by a master equation, where—depending on the microscopic details of our CQED setups—we effectively engineer a current coupling of our system to a quantum reservoir. This provides interesting scenarios of dissipative dynamics generating "dark" pure quantum many-body states.

Time, space and equilibrium means of continuous vector functions on the phase space of a dynamical system

NASA Astrophysics Data System (ADS)

Gurevich, Boris M.; Tempel'man, Arcady A.

2010-05-01

For a dynamical system \\tau with 'time' \\mathbb Z^d and compact phase space X, we introduce three subsets of the space \\mathbb R^m related to a continuous function f\\colon X\\to\\mathbb R^m: the set of time means of f and two sets of space means of f, namely those corresponding to all \\tau-invariant probability measures and those corresponding to some equilibrium measures on X. The main results concern topological properties of these sets of means and their mutual position. Bibliography: 18 titles.

Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.

PubMed

Fu, Yue; Fu, Jun; Chai, Tianyou

2015-12-01

In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

Optimal control of nonlinear continuous-time systems in strict-feedback form.

PubMed

Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani

2015-10-01

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Simulating transient dynamics of the time-dependent time fractional Fokker-Planck systems

NASA Astrophysics Data System (ADS)

Kang, Yan-Mei

2016-09-01

For a physically realistic type of time-dependent time fractional Fokker-Planck (FP) equation, derived as the continuous limit of the continuous time random walk with time-modulated Boltzmann jumping weight, a semi-analytic iteration scheme based on the truncated (generalized) Fourier series is presented to simulate the resultant transient dynamics when the external time modulation is a piece-wise constant signal. At first, the iteration scheme is demonstrated with a simple time-dependent time fractional FP equation on finite interval with two absorbing boundaries, and then it is generalized to the more general time-dependent Smoluchowski-type time fractional Fokker-Planck equation. The numerical examples verify the efficiency and accuracy of the iteration method, and some novel dynamical phenomena including polarized motion orientations and periodic response death are discussed.

Dynamics of a linear system coupled to a chain of light nonlinear oscillators analyzed through a continuous approximation

NASA Astrophysics Data System (ADS)

Charlemagne, S.; Ture Savadkoohi, A.; Lamarque, C.-H.

2018-07-01

The continuous approximation is used in this work to describe the dynamics of a nonlinear chain of light oscillators coupled to a linear main system. A general methodology is applied to an example where the chain has local nonlinear restoring forces. The slow invariant manifold is detected at fast time scale. At slow time scale, equilibrium and singular points are sought around this manifold in order to predict periodic regimes and strongly modulated responses of the system. Analytical predictions are in good accordance with numerical results and represent a potent tool for designing nonlinear chains for passive control purposes.

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

NASA Astrophysics Data System (ADS)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

Frontiers in Applied and Computational Mathematics 05’

DTIC Science & Technology

2005-03-01

dynamics, forcing subsets to have the same oscillation numbers and interleaving spiking times . Our analysis follows the theory of coupled systems of...continuum is described by a continuous- time stochastic process, as are their internal dynamics. Soluble factors, such as cytokines, are represent- ed...scale of a partide pas- sage time through the reaction zone. Both are realistic for many systems of physical interest. A higher order theory includes

Non-Markovian continuous-time quantum walks on lattices with dynamical noise

NASA Astrophysics Data System (ADS)

Benedetti, Claudia; Buscemi, Fabrizio; Bordone, Paolo; Paris, Matteo G. A.

2016-04-01

We address the dynamics of continuous-time quantum walks on one-dimensional disordered lattices inducing dynamical noise in the system. Noise is described as time-dependent fluctuations of the tunneling amplitudes between adjacent sites, and attention is focused on non-Gaussian telegraph noise, going beyond the usual assumption of fast Gaussian noise. We observe the emergence of two different dynamical behaviors for the walker, corresponding to two opposite noise regimes: slow noise (i.e., strong coupling with the environment) confines the walker into few lattice nodes, while fast noise (weak coupling) induces a transition between quantum and classical diffusion over the lattice. A phase transition between the two dynamical regimes may be observed by tuning the ratio between the autocorrelation time of the noise and the coupling between the walker and the external environment generating the noise. We also address the non-Markovianity of the quantum map by assessing its memory effects, as well as evaluating the information backflow to the system. Our results suggest that the non-Markovian character of the evolution is linked to the dynamical behavior in the slow noise regime, and that fast noise induces a Markovian dynamics for the walker.

Discretization chaos - Feedback control and transition to chaos

NASA Technical Reports Server (NTRS)

Grantham, Walter J.; Athalye, Amit M.

1990-01-01

Problems in the design of feedback controllers for chaotic dynamical systems are considered theoretically, focusing on two cases where chaos arises only when a nonchaotic continuous-time system is discretized into a simpler discrete-time systems (exponential discretization and pseudo-Euler integration applied to Lotka-Volterra competition and prey-predator systems). Numerical simulation results are presented in extensive graphs and discussed in detail. It is concluded that care must be taken in applying standard dynamical-systems methods to control systems that may be discontinuous or nondifferentiable.

Continuous variable quantum optical simulation for time evolution of quantum harmonic oscillators

PubMed Central

Deng, Xiaowei; Hao, Shuhong; Guo, Hong; Xie, Changde; Su, Xiaolong

2016-01-01

Quantum simulation enables one to mimic the evolution of other quantum systems using a controllable quantum system. Quantum harmonic oscillator (QHO) is one of the most important model systems in quantum physics. To observe the transient dynamics of a QHO with high oscillation frequency directly is difficult. We experimentally simulate the transient behaviors of QHO in an open system during time evolution with an optical mode and a logical operation system of continuous variable quantum computation. The time evolution of an atomic ensemble in the collective spontaneous emission is analytically simulated by mapping the atomic ensemble onto a QHO. The measured fidelity, which is used for quantifying the quality of the simulation, is higher than its classical limit. The presented simulation scheme provides a new tool for studying the dynamic behaviors of QHO. PMID:26961962

Cavity master equation for the continuous time dynamics of discrete-spin models.

PubMed

Aurell, E; Del Ferraro, G; Domínguez, E; Mulet, R

2017-05-01

We present an alternate method to close the master equation representing the continuous time dynamics of interacting Ising spins. The method makes use of the theory of random point processes to derive a master equation for local conditional probabilities. We analytically test our solution studying two known cases, the dynamics of the mean-field ferromagnet and the dynamics of the one-dimensional Ising system. We present numerical results comparing our predictions with Monte Carlo simulations in three different models on random graphs with finite connectivity: the Ising ferromagnet, the random field Ising model, and the Viana-Bray spin-glass model.

Cavity master equation for the continuous time dynamics of discrete-spin models

NASA Astrophysics Data System (ADS)

Aurell, E.; Del Ferraro, G.; Domínguez, E.; Mulet, R.

2017-05-01

We present an alternate method to close the master equation representing the continuous time dynamics of interacting Ising spins. The method makes use of the theory of random point processes to derive a master equation for local conditional probabilities. We analytically test our solution studying two known cases, the dynamics of the mean-field ferromagnet and the dynamics of the one-dimensional Ising system. We present numerical results comparing our predictions with Monte Carlo simulations in three different models on random graphs with finite connectivity: the Ising ferromagnet, the random field Ising model, and the Viana-Bray spin-glass model.

Fractional dynamics of globally slow transcription and its impact on deterministic genetic oscillation.

PubMed

Wei, Kun; Gao, Shilong; Zhong, Suchuan; Ma, Hong

2012-01-01

In dynamical systems theory, a system which can be described by differential equations is called a continuous dynamical system. In studies on genetic oscillation, most deterministic models at early stage are usually built on ordinary differential equations (ODE). Therefore, gene transcription which is a vital part in genetic oscillation is presupposed to be a continuous dynamical system by default. However, recent studies argued that discontinuous transcription might be more common than continuous transcription. In this paper, by appending the inserted silent interval lying between two neighboring transcriptional events to the end of the preceding event, we established that the running time for an intact transcriptional event increases and gene transcription thus shows slow dynamics. By globally replacing the original time increment for each state increment by a larger one, we introduced fractional differential equations (FDE) to describe such globally slow transcription. The impact of fractionization on genetic oscillation was then studied in two early stage models--the Goodwin oscillator and the Rössler oscillator. By constructing a "dual memory" oscillator--the fractional delay Goodwin oscillator, we suggested that four general requirements for generating genetic oscillation should be revised to be negative feedback, sufficient nonlinearity, sufficient memory and proper balancing of timescale. The numerical study of the fractional Rössler oscillator implied that the globally slow transcription tends to lower the chance of a coupled or more complex nonlinear genetic oscillatory system behaving chaotically.

A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity.

PubMed

Quan, Quan; Cai, Kai-Yuan

2016-02-01

In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-time, equality-constrained optimization is developed. First, the equality constraint is transformed into a continuous-time dynamical system with solutions that always satisfy the equality constraint. Second, a new projection matrix without singularity is proposed to realize the transformation. An update (or say a controller) is subsequently designed to decrease the objective function along the solutions of the transformed continuous-time dynamical system. The invariance principle is then applied to analyze the behavior of the solution. Furthermore, the proposed method is modified to address cases in which solutions do not satisfy the equality constraint. Finally, the proposed optimization approach is applied to three examples to demonstrate its effectiveness.

Lindeberg theorem for Gibbs-Markov dynamics

NASA Astrophysics Data System (ADS)

Denker, Manfred; Senti, Samuel; Zhang, Xuan

2017-12-01

A dynamical array consists of a family of functions \\{ fn, i: 1≤slant i≤slant k_n, n≥slant 1\\} and a family of initial times \\{τn, i: 1≤slant i≤slant k_n, n≥slant 1\\} . For a dynamical system (X, T) we identify distributional limits for sums of the form for suitable (non-random) constants s_n>0 and an, i\\in { R} . We derive a Lindeberg-type central limit theorem for dynamical arrays. Applications include new central limit theorems for functions which are not locally Lipschitz continuous and central limit theorems for statistical functions of time series obtained from Gibbs-Markov systems. Our results, which hold for more general dynamics, are stated in the context of Gibbs-Markov dynamical systems for convenience.

Real-time high-resolution heterodyne-based measurements of spectral dynamics in fibre lasers

PubMed Central

Sugavanam, Srikanth; Fabbri, Simon; Le, Son Thai; Lobach, Ivan; Kablukov, Sergey; Khorev, Serge; Churkin, Dmitry

2016-01-01

Conventional tools for measurement of laser spectra (e.g. optical spectrum analysers) capture data averaged over a considerable time period. However, the generation spectrum of many laser types may involve spectral dynamics whose relatively fast time scale is determined by their cavity round trip period, calling for instrumentation featuring both high temporal and spectral resolution. Such real-time spectral characterisation becomes particularly challenging if the laser pulses are long, or they have continuous or quasi-continuous wave radiation components. Here we combine optical heterodyning with a technique of spatio-temporal intensity measurements that allows the characterisation of such complex sources. Fast, round-trip-resolved spectral dynamics of cavity-based systems in real-time are obtained, with temporal resolution of one cavity round trip and frequency resolution defined by its inverse (85 ns and 24 MHz respectively are demonstrated). We also show how under certain conditions for quasi-continuous wave sources, the spectral resolution could be further increased by a factor of 100 by direct extraction of phase information from the heterodyned dynamics or by using double time scales within the spectrogram approach. PMID:26984634

Noise-constrained switching times for heteroclinic computing

NASA Astrophysics Data System (ADS)

Neves, Fabio Schittler; Voit, Maximilian; Timme, Marc

2017-03-01

Heteroclinic computing offers a novel paradigm for universal computation by collective system dynamics. In such a paradigm, input signals are encoded as complex periodic orbits approaching specific sequences of saddle states. Without inputs, the relevant states together with the heteroclinic connections between them form a network of states—the heteroclinic network. Systems of pulse-coupled oscillators or spiking neurons naturally exhibit such heteroclinic networks of saddles, thereby providing a substrate for general analog computations. Several challenges need to be resolved before it becomes possible to effectively realize heteroclinic computing in hardware. The time scales on which computations are performed crucially depend on the switching times between saddles, which in turn are jointly controlled by the system's intrinsic dynamics and the level of external and measurement noise. The nonlinear dynamics of pulse-coupled systems often strongly deviate from that of time-continuously coupled (e.g., phase-coupled) systems. The factors impacting switching times in pulse-coupled systems are still not well understood. Here we systematically investigate switching times in dependence of the levels of noise and intrinsic dissipation in the system. We specifically reveal how local responses to pulses coact with external noise. Our findings confirm that, like in time-continuous phase-coupled systems, piecewise-continuous pulse-coupled systems exhibit switching times that transiently increase exponentially with the number of switches up to some order of magnitude set by the noise level. Complementarily, we show that switching times may constitute a good predictor for the computation reliability, indicating how often an input signal must be reiterated. By characterizing switching times between two saddles in conjunction with the reliability of a computation, our results provide a first step beyond the coding of input signal identities toward a complementary coding for the intensity of those signals. The results offer insights on how future heteroclinic computing systems may operate under natural, and thus noisy, conditions.

Dynamics of dissipative self-assembly of particles interacting through oscillatory forces

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

Tagliazucchi, M.; Szleifer, I.

Dissipative self-assembly is the formation of ordered structures far from equilibrium, which continuously uptake energy and dissipate it into the environment. Due to its dynamical nature, dissipative self-assembly can lead to new phenomena and possibilities of self-organization that are unavailable to equilibrium systems. Understanding the dynamics of dissipative self-assembly is required in order to direct the assembly to structures of interest. In the present work, Brownian dynamics simulations and analytical theory were used to study the dynamics of self-assembly of a mixture of particles coated with weak acids and bases under continuous oscillations of the pH. The pH of themore » system modulates the charge of the particles and, therefore, the interparticle forces oscillate in time. This system produces a variety of self-assembled structures, including colloidal molecules, fibers and different types of crystalline lattices. The most important conclusions of our study are: (i) in the limit of fast oscillations, the whole dynamics (and not only those at the non-equilibrium steady state) of a system of particles interacting through time-oscillating interparticle forces can be described by an effective potential that is the time average of the time-dependent potential over one oscillation period; (ii) the oscillation period is critical to determine the order of the system. In some cases the order is favored by very fast oscillations while in others small oscillation frequencies increase the order. In the latter case, it is shown that slow oscillations remove kinetic traps and, thus, allow the system to evolve towards the most stable non-equilibrium steady state.« less

Robust ADP Design for Continuous-Time Nonlinear Systems With Output Constraints.

PubMed

Fan, Bo; Yang, Qinmin; Tang, Xiaoyu; Sun, Youxian

2018-06-01

In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

Detection of coupling delay: A problem not yet solved

NASA Astrophysics Data System (ADS)

Coufal, David; Jakubík, Jozef; Jajcay, Nikola; Hlinka, Jaroslav; Krakovská, Anna; Paluš, Milan

2017-08-01

Nonparametric detection of coupling delay in unidirectionally and bidirectionally coupled nonlinear dynamical systems is examined. Both continuous and discrete-time systems are considered. Two methods of detection are assessed—the method based on conditional mutual information—the CMI method (also known as the transfer entropy method) and the method of convergent cross mapping—the CCM method. Computer simulations show that neither method is generally reliable in the detection of coupling delays. For continuous-time chaotic systems, the CMI method appears to be more sensitive and applicable in a broader range of coupling parameters than the CCM method. In the case of tested discrete-time dynamical systems, the CCM method has been found to be more sensitive, while the CMI method required much stronger coupling strength in order to bring correct results. However, when studied systems contain a strong oscillatory component in their dynamics, results of both methods become ambiguous. The presented study suggests that results of the tested algorithms should be interpreted with utmost care and the nonparametric detection of coupling delay, in general, is a problem not yet solved.

Online Solution of Two-Player Zero-Sum Games for Continuous-Time Nonlinear Systems With Completely Unknown Dynamics.

PubMed

Fu, Yue; Chai, Tianyou

2016-12-01

Regarding two-player zero-sum games of continuous-time nonlinear systems with completely unknown dynamics, this paper presents an online adaptive algorithm for learning the Nash equilibrium solution, i.e., the optimal policy pair. First, for known systems, the simultaneous policy updating algorithm (SPUA) is reviewed. A new analytical method to prove the convergence is presented. Then, based on the SPUA, without using a priori knowledge of any system dynamics, an online algorithm is proposed to simultaneously learn in real time either the minimal nonnegative solution of the Hamilton-Jacobi-Isaacs (HJI) equation or the generalized algebraic Riccati equation for linear systems as a special case, along with the optimal policy pair. The approximate solution to the HJI equation and the admissible policy pair is reexpressed by the approximation theorem. The unknown constants or weights of each are identified simultaneously by resorting to the recursive least square method. The convergence of the online algorithm to the optimal solutions is provided. A practical online algorithm is also developed. Simulation results illustrate the effectiveness of the proposed method.

Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai

2017-03-01

This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.

Control of nonlinear systems with applications to constrained robots and spacecraft attitude stabilization

NASA Technical Reports Server (NTRS)

Krishnan, Hariharan

1993-01-01

This thesis is organized in two parts. In Part 1, control systems described by a class of nonlinear differential and algebraic equations are introduced. A procedure for local stabilization based on a local state realization is developed. An alternative approach to local stabilization is developed based on a classical linearization of the nonlinear differential-algebraic equations. A theoretical framework is established for solving a tracking problem associated with the differential-algebraic system. First, a simple procedure is developed for the design of a feedback control law which ensures, at least locally, that the tracking error in the closed loop system lies within any given bound if the reference inputs are sufficiently slowly varying. Next, by imposing additional assumptions, a procedure is developed for the design of a feedback control law which ensures that the tracking error in the closed loop system approaches zero exponentially for reference inputs which are not necessarily slowly varying. The control design methodologies are used for simultaneous force and position control in constrained robot systems. The differential-algebraic equations are shown to characterize the slow dynamics of a certain nonlinear control system in nonstandard singularly perturbed form. In Part 2, the attitude stabilization (reorientation) of a rigid spacecraft using only two control torques is considered. First, the case of momentum wheel actuators is considered. The complete spacecraft dynamics are not controllable. However, the spacecraft dynamics are small time locally controllable in a reduced sense. The reduced spacecraft dynamics cannot be asymptotically stabilized using continuous feedback, but a discontinuous feedback control strategy is constructed. Next, the case of gas jet actuators is considered. If the uncontrolled principal axis is not an axis of symmetry, the complete spacecraft dynamics are small time locally controllable. However, the spacecraft attitude cannot be asymptotically stabilized using continuous feedback, but a discontinuous stabilizing feedback control strategy is constructed. If the uncontrolled principal axis is an axis of symmetry, the complete spacecraft dynamics cannot be stabilized. However, the spacecraft dynamics are small time locally controllable in a reduced sense. The reduced spacecraft dynamics cannot be asymptotically stabilized using continuous feedback, but again a discontinuous feedback control strategy is constructed.

Continuous-time system identification of a smoking cessation intervention

NASA Astrophysics Data System (ADS)

Timms, Kevin P.; Rivera, Daniel E.; Collins, Linda M.; Piper, Megan E.

2014-07-01

Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.

Continuous analog of multiplicative algebraic reconstruction technique for computed tomography

NASA Astrophysics Data System (ADS)

Tateishi, Kiyoko; Yamaguchi, Yusaku; Abou Al-Ola, Omar M.; Kojima, Takeshi; Yoshinaga, Tetsuya

2016-03-01

We propose a hybrid dynamical system as a continuous analog to the block-iterative multiplicative algebraic reconstruction technique (BI-MART), which is a well-known iterative image reconstruction algorithm for computed tomography. The hybrid system is described by a switched nonlinear system with a piecewise smooth vector field or differential equation and, for consistent inverse problems, the convergence of non-negatively constrained solutions to a globally stable equilibrium is guaranteed by the Lyapunov theorem. Namely, we can prove theoretically that a weighted Kullback-Leibler divergence measure can be a common Lyapunov function for the switched system. We show that discretizing the differential equation by using the first-order approximation (Euler's method) based on the geometric multiplicative calculus leads to the same iterative formula of the BI-MART with the scaling parameter as a time-step of numerical discretization. The present paper is the first to reveal that a kind of iterative image reconstruction algorithm is constructed by the discretization of a continuous-time dynamical system for solving tomographic inverse problems. Iterative algorithms with not only the Euler method but also the Runge-Kutta methods of lower-orders applied for discretizing the continuous-time system can be used for image reconstruction. A numerical example showing the characteristics of the discretized iterative methods is presented.

Waiting time distribution for continuous stochastic systems

NASA Astrophysics Data System (ADS)

Gernert, Robert; Emary, Clive; Klapp, Sabine H. L.

2014-12-01

The waiting time distribution (WTD) is a common tool for analyzing discrete stochastic processes in classical and quantum systems. However, there are many physical examples where the dynamics is continuous and only approximately discrete, or where it is favourable to discuss the dynamics on a discretized and a continuous level in parallel. An example is the hindered motion of particles through potential landscapes with barriers. In the present paper we propose a consistent generalization of the WTD from the discrete case to situations where the particles perform continuous barrier crossing characterized by a finite duration. To this end, we introduce a recipe to calculate the WTD from the Fokker-Planck (Smoluchowski) equation. In contrast to the closely related first passage time distribution (FPTD), which is frequently used to describe continuous processes, the WTD contains information about the direction of motion. As an application, we consider the paradigmatic example of an overdamped particle diffusing through a washboard potential. To verify the approach and to elucidate its numerical implications, we compare the WTD defined via the Smoluchowski equation with data from direct simulation of the underlying Langevin equation and find full consistency provided that the jumps in the Langevin approach are defined properly. Moreover, for sufficiently large energy barriers, the WTD defined via the Smoluchowski equation becomes consistent with that resulting from the analytical solution of a (two-state) master equation model for the short-time dynamics developed previously by us [Phys. Rev. E 86, 061135 (2012), 10.1103/PhysRevE.86.061135]. Thus, our approach "interpolates" between these two types of stochastic motion. We illustrate our approach for both symmetric systems and systems under constant force.

A hybrid-system model of the coagulation cascade: simulation, sensitivity, and validation.

PubMed

Makin, Joseph G; Narayanan, Srini

2013-10-01

The process of human blood clotting involves a complex interaction of continuous-time/continuous-state processes and discrete-event/discrete-state phenomena, where the former comprise the various chemical rate equations and the latter comprise both threshold-limited behaviors and binary states (presence/absence of a chemical). Whereas previous blood-clotting models used only continuous dynamics and perforce addressed only portions of the coagulation cascade, we capture both continuous and discrete aspects by modeling it as a hybrid dynamical system. The model was implemented as a hybrid Petri net, a graphical modeling language that extends ordinary Petri nets to cover continuous quantities and continuous-time flows. The primary focus is simulation: (1) fidelity to the clinical data in terms of clotting-factor concentrations and elapsed time; (2) reproduction of known clotting pathologies; and (3) fine-grained predictions which may be used to refine clinical understanding of blood clotting. Next we examine sensitivity to rate-constant perturbation. Finally, we propose a method for titrating between reliance on the model and on prior clinical knowledge. For simplicity, we confine these last two analyses to a critical purely-continuous subsystem of the model.

Approximate N-Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System.

PubMed

Johnson, Marcus; Kamalapurkar, Rushikesh; Bhasin, Shubhendu; Dixon, Warren E

2015-08-01

An approximate online equilibrium solution is developed for an N -player nonzero-sum game subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic cost. A novel actor-critic-identifier structure is used, wherein a robust dynamic neural network is used to asymptotically identify the uncertain system with additive disturbances, and a set of critic and actor NNs are used to approximate the value functions and equilibrium policies, respectively. The weight update laws for the actor neural networks (NNs) are generated using a gradient-descent method, and the critic NNs are generated by least square regression, which are both based on the modified Bellman error that is independent of the system dynamics. A Lyapunov-based stability analysis shows that uniformly ultimately bounded tracking is achieved, and a convergence analysis demonstrates that the approximate control policies converge to a neighborhood of the optimal solutions. The actor, critic, and identifier structures are implemented in real time continuously and simultaneously. Simulations on two and three player games illustrate the performance of the developed method.

The stochastic system approach for estimating dynamic treatments effect.

PubMed

Commenges, Daniel; Gégout-Petit, Anne

2015-10-01

The problem of assessing the effect of a treatment on a marker in observational studies raises the difficulty that attribution of the treatment may depend on the observed marker values. As an example, we focus on the analysis of the effect of a HAART on CD4 counts, where attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual/potential response formalism. Another approach to causality is based on dynamical models, and causal influence has been formalized in the framework of the Doob-Meyer decomposition of stochastic processes. Causal inference however needs assumptions that we detail in this paper and we call this approach to causality the "stochastic system" approach. First we treat this problem in discrete time, then in continuous time. This approach allows incorporating biological knowledge naturally. When working in continuous time, the mechanistic approach involves distinguishing the model for the system and the model for the observations. Indeed, biological systems live in continuous time, and mechanisms can be expressed in the form of a system of differential equations, while observations are taken at discrete times. Inference in mechanistic models is challenging, particularly from a numerical point of view, but these models can yield much richer and reliable results.

Quantum cooling and squeezing of a levitating nanosphere via time-continuous measurements

NASA Astrophysics Data System (ADS)

Genoni, Marco G.; Zhang, Jinglei; Millen, James; Barker, Peter F.; Serafini, Alessio

2015-07-01

With the purpose of controlling the steady state of a dielectric nanosphere levitated within an optical cavity, we study its conditional dynamics under simultaneous sideband cooling and additional time-continuous measurement of either the output cavity mode or the nanosphere’s position. We find that the average phonon number, purity and quantum squeezing of the steady-states can all be made more non-classical through the addition of time-continuous measurement. We predict that the continuous monitoring of the system, together with Markovian feedback, allows one to stabilize the dynamics for any value of the laser frequency driving the cavity. By considering state of the art values of the experimental parameters, we prove that one can in principle obtain a non-classical (squeezed) steady-state with an average phonon number {n}{ph}≈ 0.5.

Local and global dynamics of Ramsey model: From continuous to discrete time.

PubMed

Guzowska, Malgorzata; Michetti, Elisabetta

2018-05-01

The choice of time as a discrete or continuous variable may radically affect equilibrium stability in an endogenous growth model with durable consumption. In the continuous-time Ramsey model [F. P. Ramsey, Econ. J. 38(152), 543-559 (1928)], the steady state is locally saddle-path stable with monotonic convergence. However, in the discrete-time version, the steady state may be unstable or saddle-path stable with monotonic or oscillatory convergence or periodic solutions [see R.-A. Dana et al., Handbook on Optimal Growth 1 (Springer, 2006) and G. Sorger, Working Paper No. 1505 (2015)]. When this occurs, the discrete-time counterpart of the continuous-time model is not consistent with the initial framework. In order to obtain a discrete-time Ramsey model preserving the main properties of the continuous-time counterpart, we use a general backward and forward discretisation as initially proposed by Bosi and Ragot [Theor. Econ. Lett. 2(1), 10-15 (2012)]. The main result of the study here presented is that, with this hybrid discretisation method, fixed points and local dynamics do not change. For what it concerns global dynamics, i.e., long-run behavior for initial conditions taken on the state space, we mainly perform numerical analysis with the main scope of comparing both qualitative and quantitative evolution of the two systems, also varying some parameters of interest.

Local and global dynamics of Ramsey model: From continuous to discrete time

NASA Astrophysics Data System (ADS)

Guzowska, Malgorzata; Michetti, Elisabetta

2018-05-01

The choice of time as a discrete or continuous variable may radically affect equilibrium stability in an endogenous growth model with durable consumption. In the continuous-time Ramsey model [F. P. Ramsey, Econ. J. 38(152), 543-559 (1928)], the steady state is locally saddle-path stable with monotonic convergence. However, in the discrete-time version, the steady state may be unstable or saddle-path stable with monotonic or oscillatory convergence or periodic solutions [see R.-A. Dana et al., Handbook on Optimal Growth 1 (Springer, 2006) and G. Sorger, Working Paper No. 1505 (2015)]. When this occurs, the discrete-time counterpart of the continuous-time model is not consistent with the initial framework. In order to obtain a discrete-time Ramsey model preserving the main properties of the continuous-time counterpart, we use a general backward and forward discretisation as initially proposed by Bosi and Ragot [Theor. Econ. Lett. 2(1), 10-15 (2012)]. The main result of the study here presented is that, with this hybrid discretisation method, fixed points and local dynamics do not change. For what it concerns global dynamics, i.e., long-run behavior for initial conditions taken on the state space, we mainly perform numerical analysis with the main scope of comparing both qualitative and quantitative evolution of the two systems, also varying some parameters of interest.

A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

NASA Astrophysics Data System (ADS)

Song, Rui-Zhuo; Xiao, Wen-Dong; Wei, Qing-Lai

2014-05-01

We develop an online adaptive dynamic programming (ADP) based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.

Diffusion in randomly perturbed dissipative dynamics

NASA Astrophysics Data System (ADS)

Rodrigues, Christian S.; Chechkin, Aleksei V.; de Moura, Alessandro P. S.; Grebogi, Celso; Klages, Rainer

2014-11-01

Dynamical systems having many coexisting attractors present interesting properties from both fundamental theoretical and modelling points of view. When such dynamics is under bounded random perturbations, the basins of attraction are no longer invariant and there is the possibility of transport among them. Here we introduce a basic theoretical setting which enables us to study this hopping process from the perspective of anomalous transport using the concept of a random dynamical system with holes. We apply it to a simple model by investigating the role of hyperbolicity for the transport among basins. We show numerically that our system exhibits non-Gaussian position distributions, power-law escape times, and subdiffusion. Our simulation results are reproduced consistently from stochastic continuous time random walk theory.

DynamO: a free O(N) general event-driven molecular dynamics simulator.

PubMed

Bannerman, M N; Sargant, R; Lue, L

2011-11-30

Molecular dynamics algorithms for systems of particles interacting through discrete or "hard" potentials are fundamentally different to the methods for continuous or "soft" potential systems. Although many software packages have been developed for continuous potential systems, software for discrete potential systems based on event-driven algorithms are relatively scarce and specialized. We present DynamO, a general event-driven simulation package, which displays the optimal O(N) asymptotic scaling of the computational cost with the number of particles N, rather than the O(N) scaling found in most standard algorithms. DynamO provides reference implementations of the best available event-driven algorithms. These techniques allow the rapid simulation of both complex and large (>10(6) particles) systems for long times. The performance of the program is benchmarked for elastic hard sphere systems, homogeneous cooling and sheared inelastic hard spheres, and equilibrium Lennard-Jones fluids. This software and its documentation are distributed under the GNU General Public license and can be freely downloaded from http://marcusbannerman.co.uk/dynamo. Copyright © 2011 Wiley Periodicals, Inc.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Numerically Exact Long Time Magnetization Dynamics Near the Nonequilibrium Kondo Regime

NASA Astrophysics Data System (ADS)

Cohen, Guy; Gull, Emanuel; Reichman, David; Millis, Andrew; Rabani, Eran

2013-03-01

The dynamical and steady-state spin response of the nonequilibrium Anderson impurity model to magnetic fields, bias voltages, and temperature is investigated by a numerically exact method which allows access to unprecedentedly long times. The method is based on using real, continuous time bold Monte Carlo techniques--quantum Monte Carlo sampling of diagrammatic corrections to a partial re-summation--in order to compute the kernel of a memory function, which is then used to determine the reduced density matrix. The method owes its effectiveness to the fact that the memory kernel is dominated by relatively short-time properties even when the system's dynamics are long-ranged. We make predictions regarding the non-monotonic temperature dependence of the system at high bias voltage and the oscillatory quench dynamics at high magnetic fields. We also discuss extensions of the method to the computation of transport properties and correlation functions, and its suitability as an impurity solver free from the need for analytical continuation in the context of dynamical mean field theory. This work is supported by the US Department of Energy under grant DE-SC0006613, by NSF-DMR-1006282 and by the US-Israel Binational Science Foundation. GC is grateful to the Yad Hanadiv-Rothschild Foundation for the award of a Rothschild Fellowship.

Systemic Case Formulation, Individualized Process Monitoring, and State Dynamics in a Case of Dissociative Identity Disorder.

PubMed

Schiepek, Günter K; Stöger-Schmidinger, Barbara; Aichhorn, Wolfgang; Schöller, Helmut; Aas, Benjamin

2016-01-01

Objective: The aim of this case report is to demonstrate the feasibility of a systemic procedure (synergetic process management) including modeling of the idiographic psychological system and continuous high-frequency monitoring of change dynamics in a case of dissociative identity disorder. The psychotherapy was realized in a day treatment center with a female client diagnosed with borderline personality disorder (BPD) and dissociative identity disorder. Methods: A three hour long co-creative session at the beginning of the treatment period allowed for modeling the systemic network of the client's dynamics of cognitions, emotions, and behavior. The components (variables) of this idiographic system model (ISM) were used to create items for an individualized process questionnaire for the client. The questionnaire was administered daily through an internet-based monitoring tool (Synergetic Navigation System, SNS), to capture the client's individual change process continuously throughout the therapy and after-care period. The resulting time series were reflected by therapist and client in therapeutic feedback sessions. Results: For the client it was important to see how the personality states dominating her daily life were represented by her idiographic system model and how the transitions between each state could be explained and understood by the activating and inhibiting relations between the cognitive-emotional components of that system. Continuous monitoring of her cognitions, emotions, and behavior via SNS allowed for identification of important triggers, dynamic patterns, and psychological mechanisms behind seemingly erratic state fluctuations. These insights enabled a change in management of the dynamics and an intensified trauma-focused therapy. Conclusion: By making use of the systemic case formulation technique and subsequent daily online monitoring, client and therapist continuously refer to detailed visualizations of the mental and behavioral network and its dynamics (e.g., order transitions). Effects on self-related information processing, on identity development, and toward a more pronounced autonomy in life (instead of feeling helpless against the chaoticity of state dynamics) were evident in the presented case and documented by the monitoring system.

Finite-time and fixed-time leader-following consensus for multi-agent systems with discontinuous inherent dynamics

NASA Astrophysics Data System (ADS)

Ning, Boda; Jin, Jiong; Zheng, Jinchuan; Man, Zhihong

2018-06-01

This paper is concerned with finite-time and fixed-time consensus of multi-agent systems in a leader-following framework. Different from conventional leader-following tracking approaches where inherent dynamics satisfying the Lipschitz continuous condition is required, a more generalised case is investigated: discontinuous inherent dynamics. By nonsmooth techniques, a nonlinear protocol is first proposed to achieve the finite-time leader-following consensus. Then, based on fixed-time stability strategies, the fixed-time leader-following consensus problem is solved. An upper bound of settling time is obtained by using a new protocol, and such a bound is independent of initial states, thereby providing additional options for designers in practical scenarios where initial conditions are unavailable. Finally, numerical simulations are provided to demonstrate the effectiveness of the theoretical results.

Optimal control on hybrid ode systems with application to a tick disease model.

PubMed

Ding, Wandi

2007-10-01

We are considering an optimal control problem for a type of hybrid system involving ordinary differential equations and a discrete time feature. One state variable has dynamics in only one season of the year and has a jump condition to obtain the initial condition for that corresponding season in the next year. The other state variable has continuous dynamics. Given a general objective functional, existence, necessary conditions and uniqueness for an optimal control are established. We apply our approach to a tick-transmitted disease model with age structure in which the tick dynamics changes seasonally while hosts have continuous dynamics. The goal is to maximize disease-free ticks and minimize infected ticks through an optimal control strategy of treatment with acaricide. Numerical examples are given to illustrate the results.

Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems

NASA Technical Reports Server (NTRS)

Fields, Chris

1989-01-01

Continuous dynamical systems intuitively seem capable of more complex behavior than discrete systems. If analyzed in the framework of the traditional theory of computation, a continuous dynamical system with countably many quasistable states has at least the computational power of a universal Turing machine. Such an analysis assumes, however, the classical notion of measurement. If measurement is viewed nonclassically, a continuous dynamical system cannot, even in principle, exhibit behavior that cannot be simulated by a universal Turing machine.

Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems

NASA Technical Reports Server (NTRS)

Fields, Chris

1989-01-01

Continuous dynamical systems intuitively seem capable of more complex behavior than discrete systems. If analyzed in the framework of the traditional theory of computation, a continuous dynamical system with countablely many quasistable states has at least the computational power of a universal Turing machine. Such an analyses assumes, however, the classical notion of measurement. If measurement is viewed nonclassically, a continuous dynamical system cannot, even in principle, exhibit behavior that cannot be simulated by a universal Turing machine.

Ecological dynamics of continuous and categorical decision-making: the regatta start in sailing.

PubMed

Araújo, Duarte; Davids, Keith; Diniz, Ana; Rocha, Luis; Santos, João Coelho; Dias, Gonçalo; Fernandes, Orlando

2015-01-01

Ecological dynamics of decision-making in the sport of sailing exemplifies emergent, conditionally coupled, co-adaptive behaviours. In this study, observation of the coupling dynamics of paired boats during competitive sailing showed that decision-making can be modelled as a self-sustained, co-adapting system of informationally coupled oscillators (boats). Bytracing the spatial-temporal displacements of the boats, time series analyses (autocorrelations, periodograms and running correlations) revealed that trajectories of match racing boats are coupled more than 88% of the time during a pre-start race, via continuous, competing co-adaptions between boats. Results showed that both the continuously selected trajectories of the sailors (12 years of age) and their categorical starting point locations were examples of emergent decisions. In this dynamical conception of decision-making behaviours, strategic positioning (categorical) and continuous displacement of a boat over the course in match-race sailing emerged as a function of interacting task, personal and environmental constraints. Results suggest how key interacting constraints could be manipulated in practice to enhance sailors' perceptual attunement to them in competition.

Fault-tolerant continuous flow systems modelling

NASA Astrophysics Data System (ADS)

Tolbi, B.; Tebbikh, H.; Alla, H.

2017-01-01

This paper presents a structural modelling of faults with hybrid Petri nets (HPNs) for the analysis of a particular class of hybrid dynamic systems, continuous flow systems. HPNs are first used for the behavioural description of continuous flow systems without faults. Then, faults' modelling is considered using a structural method without having to rebuild the model to new. A translation method is given in hierarchical way, it gives a hybrid automata (HA) from an elementary HPN. This translation preserves the behavioural semantics (timed bisimilarity), and reflects the temporal behaviour by giving semantics for each model in terms of timed transition systems. Thus, advantages of the power modelling of HPNs and the analysis ability of HA are taken. A simple example is used to illustrate the ideas.

Robust uniform persistence in discrete and continuous dynamical systems using Lyapunov exponents.

PubMed

Salceanu, Paul L

2011-07-01

This paper extends the work of Salceanu and Smith [12, 13] where Lyapunov exponents were used to obtain conditions for uniform persistence ina class of dissipative discrete-time dynamical systems on the positive orthant of R(m), generated by maps. Here a united approach is taken, for both discrete and continuous time, and the dissipativity assumption is relaxed. Sufficient conditions are given for compact subsets of an invariant part of the boundary of R(m+) to be robust uniform weak repellers. These conditions require Lyapunov exponents be positive on such sets. It is shown how this leads to robust uniform persistence. The results apply to the investigation of robust uniform persistence of the disease in host populations, as shown in an application.

Supercritical nonlinear parametric dynamics of Timoshenko microbeams

NASA Astrophysics Data System (ADS)

Farokhi, Hamed; Ghayesh, Mergen H.

2018-06-01

The nonlinear supercritical parametric dynamics of a Timoshenko microbeam subject to an axial harmonic excitation force is examined theoretically, by means of different numerical techniques, and employing a high-dimensional analysis. The time-variant axial load is assumed to consist of a mean value along with harmonic fluctuations. In terms of modelling, a continuous expression for the elastic potential energy of the system is developed based on the modified couple stress theory, taking into account small-size effects; the kinetic energy of the system is also modelled as a continuous function of the displacement field. Hamilton's principle is employed to balance the energies and to obtain the continuous model of the system. Employing the Galerkin scheme along with an assumed-mode technique, the energy terms are reduced, yielding a second-order reduced-order model with finite number of degrees of freedom. A transformation is carried out to convert the second-order reduced-order model into a double-dimensional first order one. A bifurcation analysis is performed for the system in the absence of the axial load fluctuations. Moreover, a mean value for the axial load is selected in the supercritical range, and the principal parametric resonant response, due to the time-variant component of the axial load, is obtained - as opposed to transversely excited systems, for parametrically excited system (such as our problem here), the nonlinear resonance occurs in the vicinity of twice any natural frequency of the linear system; this is accomplished via use of the pseudo-arclength continuation technique, a direct time integration, an eigenvalue analysis, and the Floquet theory for stability. The natural frequencies of the system prior to and beyond buckling are also determined. Moreover, the effect of different system parameters on the nonlinear supercritical parametric dynamics of the system is analysed, with special consideration to the effect of the length-scale parameter.

State-space self-tuner for on-line adaptive control

NASA Technical Reports Server (NTRS)

Shieh, L. S.

1994-01-01

Dynamic systems, such as flight vehicles, satellites and space stations, operating in real environments, constantly face parameter and/or structural variations owing to nonlinear behavior of actuators, failure of sensors, changes in operating conditions, disturbances acting on the system, etc. In the past three decades, adaptive control has been shown to be effective in dealing with dynamic systems in the presence of parameter uncertainties, structural perturbations, random disturbances and environmental variations. Among the existing adaptive control methodologies, the state-space self-tuning control methods, initially proposed by us, are shown to be effective in designing advanced adaptive controllers for multivariable systems. In our approaches, we have embedded the standard Kalman state-estimation algorithm into an online parameter estimation algorithm. Thus, the advanced state-feedback controllers can be easily established for digital adaptive control of continuous-time stochastic multivariable systems. A state-space self-tuner for a general multivariable stochastic system has been developed and successfully applied to the space station for on-line adaptive control. Also, a technique for multistage design of an optimal momentum management controller for the space station has been developed and reported in. Moreover, we have successfully developed various digital redesign techniques which can convert a continuous-time controller to an equivalent digital controller. As a result, the expensive and unreliable continuous-time controller can be implemented using low-cost and high performance microprocessors. Recently, we have developed a new hybrid state-space self tuner using a new dual-rate sampling scheme for on-line adaptive control of continuous-time uncertain systems.

Discrete integration of continuous Kalman filtering equations for time invariant second-order structural systems

NASA Technical Reports Server (NTRS)

Park, K. C.; Belvin, W. Keith

1990-01-01

A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete Kalman filtering equations involving only symmetric sparse N x N solution matrices.

Chaotic behaviour of the Rossler model and its analysis by using bifurcations of limit cycles and chaotic attractors

NASA Astrophysics Data System (ADS)

Ibrahim, K. M.; Jamal, R. K.; Ali, F. H.

2018-05-01

The behaviour of certain dynamical nonlinear systems are described in term as chaos, i.e., systems’ variables change with the time, displaying very sensitivity to initial conditions of chaotic dynamics. In this paper, we study archetype systems of ordinary differential equations in two-dimensional phase spaces of the Rössler model. A system displays continuous time chaos and is explained by three coupled nonlinear differential equations. We study its characteristics and determine the control parameters that lead to different behavior of the system output, periodic, quasi-periodic and chaos. The time series, attractor, Fast Fourier Transformation and bifurcation diagram for different values have been described.

Processor tradeoffs in distributed real-time systems

NASA Technical Reports Server (NTRS)

Krishna, C. M.; Shin, Kang G.; Bhandari, Inderpal S.

1987-01-01

The problem of the optimization of the design of real-time distributed systems is examined with reference to a class of computer architectures similar to the continuously reconfigurable multiprocessor flight control system structure, CM2FCS. Particular attention is given to the impact of processor replacement and the burn-in time on the probability of dynamic failure and mean cost. The solution is obtained numerically and interpreted in the context of real-time applications.

Performance Characterization, Development, and Application of Artificial Potential Function Guidance Methods

DTIC Science & Technology

2014-03-01

to determine if a system is stabilizable with feedback. 12 that asymptotic stability is guaranteed by Lyapunov theory. The advantage of this method are...discretized dynamics are a sufficient representation of the continuous system . Given these assumptions, the optimal control problem for minimum transit time is...tion (APF) guidance performance when applied to systems with limited control au- thority in a dynamic environment and then to use the findings to

Time step rescaling recovers continuous-time dynamical properties for discrete-time Langevin integration of nonequilibrium systems.

PubMed

Sivak, David A; Chodera, John D; Crooks, Gavin E

2014-06-19

When simulating molecular systems using deterministic equations of motion (e.g., Newtonian dynamics), such equations are generally numerically integrated according to a well-developed set of algorithms that share commonly agreed-upon desirable properties. However, for stochastic equations of motion (e.g., Langevin dynamics), there is still broad disagreement over which integration algorithms are most appropriate. While multiple desiderata have been proposed throughout the literature, consensus on which criteria are important is absent, and no published integration scheme satisfies all desiderata simultaneously. Additional nontrivial complications stem from simulating systems driven out of equilibrium using existing stochastic integration schemes in conjunction with recently developed nonequilibrium fluctuation theorems. Here, we examine a family of discrete time integration schemes for Langevin dynamics, assessing how each member satisfies a variety of desiderata that have been enumerated in prior efforts to construct suitable Langevin integrators. We show that the incorporation of a novel time step rescaling in the deterministic updates of position and velocity can correct a number of dynamical defects in these integrators. Finally, we identify a particular splitting (related to the velocity Verlet discretization) that has essentially universally appropriate properties for the simulation of Langevin dynamics for molecular systems in equilibrium, nonequilibrium, and path sampling contexts.

Qubit models of weak continuous measurements: markovian conditional and open-system dynamics

NASA Astrophysics Data System (ADS)

Gross, Jonathan A.; Caves, Carlton M.; Milburn, Gerard J.; Combes, Joshua

2018-04-01

In this paper we approach the theory of continuous measurements and the associated unconditional and conditional (stochastic) master equations from the perspective of quantum information and quantum computing. We do so by showing how the continuous-time evolution of these master equations arises from discretizing in time the interaction between a system and a probe field and by formulating quantum-circuit diagrams for the discretized evolution. We then reformulate this interaction by replacing the probe field with a bath of qubits, one for each discretized time segment, reproducing all of the standard quantum-optical master equations. This provides an economical formulation of the theory, highlighting its fundamental underlying assumptions.

Potential of turbidity monitoring for real time control of pollutant discharge in sewers during rainfall events.

PubMed

Lacour, C; Joannis, C; Gromaire, M-C; Chebbo, G

2009-01-01

Turbidity sensors can be used to continuously monitor the evolution of pollutant mass discharge. For two sites within the Paris combined sewer system, continuous turbidity, conductivity and flow data were recorded at one-minute time intervals over a one-year period. This paper is intended to highlight the variability in turbidity dynamics during wet weather. For each storm event, turbidity response aspects were analysed through different classifications. The correlation between classification and common parameters, such as the antecedent dry weather period, total event volume per impervious hectare and both the mean and maximum hydraulic flow for each event, was also studied. Moreover, the dynamics of flow and turbidity signals were compared at the event scale. No simple relation between turbidity responses, hydraulic flow dynamics and the chosen parameters was derived from this effort. Knowledge of turbidity dynamics could therefore potentially improve wet weather management, especially when using pollution-based real-time control (P-RTC) since turbidity contains information not included in hydraulic flow dynamics and not readily predictable from such dynamics.

Adaptive hybrid simulations for multiscale stochastic reaction networks

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

Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa

2015-01-21

The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such amore » partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.« less

Adaptive hybrid simulations for multiscale stochastic reaction networks.

PubMed

Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa

2015-01-21

The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.

Dynamic model of time-dependent complex networks.

PubMed

Hill, Scott A; Braha, Dan

2010-10-01

The characterization of the "most connected" nodes in static or slowly evolving complex networks has helped in understanding and predicting the behavior of social, biological, and technological networked systems, including their robustness against failures, vulnerability to deliberate attacks, and diffusion properties. However, recent empirical research of large dynamic networks (characterized by irregular connections that evolve rapidly) has demonstrated that there is little continuity in degree centrality of nodes over time, even when their degree distributions follow a power law. This unexpected dynamic centrality suggests that the connections in these systems are not driven by preferential attachment or other known mechanisms. We present an approach to explain real-world dynamic networks and qualitatively reproduce these dynamic centrality phenomena. This approach is based on a dynamic preferential attachment mechanism, which exhibits a sharp transition from a base pure random walk scheme.

Extensions to the Dynamic Aerospace Vehicle Exchange Markup Language

NASA Technical Reports Server (NTRS)

Brian, Geoffrey J.; Jackson, E. Bruce

2011-01-01

The Dynamic Aerospace Vehicle Exchange Markup Language (DAVE-ML) is a syntactical language for exchanging flight vehicle dynamic model data. It provides a framework for encoding entire flight vehicle dynamic model data packages for exchange and/or long-term archiving. Version 2.0.1 of DAVE-ML provides much of the functionality envisioned for exchanging aerospace vehicle data; however, it is limited in only supporting scalar time-independent data. Additional functionality is required to support vector and matrix data, abstracting sub-system models, detailing dynamics system models (both discrete and continuous), and defining a dynamic data format (such as time sequenced data) for validation of dynamics system models and vehicle simulation packages. Extensions to DAVE-ML have been proposed to manage data as vectors and n-dimensional matrices, and record dynamic data in a compatible form. These capabilities will improve the clarity of data being exchanged, simplify the naming of parameters, and permit static and dynamic data to be stored using a common syntax within a single file; thereby enhancing the framework provided by DAVE-ML for exchanging entire flight vehicle dynamic simulation models.

Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

USGS Publications Warehouse

Nichols, J.M.; Moniz, L.; Nichols, J.D.; Pecora, L.M.; Cooch, E.

2005-01-01

A number of important questions in ecology involve the possibility of interactions or ?coupling? among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator?prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.

Equation-free analysis of agent-based models and systematic parameter determination

NASA Astrophysics Data System (ADS)

Thomas, Spencer A.; Lloyd, David J. B.; Skeldon, Anne C.

2016-12-01

Agent based models (ABM)s are increasingly used in social science, economics, mathematics, biology and computer science to describe time dependent systems in circumstances where a description in terms of equations is difficult. Yet few tools are currently available for the systematic analysis of ABM behaviour. Numerical continuation and bifurcation analysis is a well-established tool for the study of deterministic systems. Recently, equation-free (EF) methods have been developed to extend numerical continuation techniques to systems where the dynamics are described at a microscopic scale and continuation of a macroscopic property of the system is considered. To date, the practical use of EF methods has been limited by; (1) the over-head of application-specific implementation; (2) the laborious configuration of problem-specific parameters; and (3) large ensemble sizes (potentially) leading to computationally restrictive run-times. In this paper we address these issues with our tool for the EF continuation of stochastic systems, which includes algorithms to systematically configuration problem specific parameters and enhance robustness to noise. Our tool is generic and can be applied to any 'black-box' simulator and determines the essential EF parameters prior to EF analysis. Robustness is significantly improved using our convergence-constraint with a corrector-repeat (C3R) method. This algorithm automatically detects outliers based on the dynamics of the underlying system enabling both an order of magnitude reduction in ensemble size and continuation of systems at much higher levels of noise than classical approaches. We demonstrate our method with application to several ABM models, revealing parameter dependence, bifurcation and stability analysis of these complex systems giving a deep understanding of the dynamical behaviour of the models in a way that is not otherwise easily obtainable. In each case we demonstrate our systematic parameter determination stage for configuring the system specific EF parameters.

Landsat Data Continuity Mission (LDCM) Flight Dynamics System (FDS)

NASA Technical Reports Server (NTRS)

Good, Susan M.; Nicholson, Ann M.

2012-01-01

The Landsat Data Continuity Mission (LDCM) will be launched in January 2013 to continue the legacy of Landsat land imagery collection that has been on-going for the past 40 years. While the overall mission and science goals are designed to produce the SAME data over the years, the ground systems designed to support the mission objectives have evolved immensely. The LDCM Flight Dynamics System (FDS) currently being tested and deployed for operations is highly automated and well integrated with the other ground system elements. The FDS encompasses the full suite of flight dynamics functional areas, including orbit and attitude determination and prediction, orbit and attitude maneuver planning and execution, and planning product generation. The integration of the orbit, attitude, maneuver, and products functions allows a very smooth flow for daily operations support with minimal input needed from the operator. The system also provides a valuable real-time component that monitors the on-board orbit and attitude during every ground contact and will autonomously alert the Flight Operations Team (FOT) personnel when any violations are found. This paper provides an overview of the LDCM Flight Dynamics System and a detailed description of how it is used to support space operations. For the first time on a Goddard Space Flight Center (GSFC)-managed mission, the ground attitude and orbits systems are fully integrated into a cohesive package. The executive engine of the FDS permits three levels of automation: low, medium, and high. The high-level, which will be the standard mode for LDCM, represents nearly lights-out operations. The paper provides an in-depth look at these processes within the FDS in support of LDCM in all mission phases.

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

Synthesis of recurrent neural networks for dynamical system simulation.

PubMed

Trischler, Adam P; D'Eleuterio, Gabriele M T

2016-08-01

We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Criticality and Chaos in Systems of Communities

NASA Astrophysics Data System (ADS)

Ostilli, Massimo; Figueiredo, Wagner

2016-01-01

We consider a simple model of communities interacting via bilinear terms. After analyzing the thermal equilibrium case, which can be described by an Hamiltonian, we introduce the dynamics that, for Ising-like variables, reduces to a Glauber-like dynamics. We analyze and compare four different versions of the dynamics: flow (differential equations), map (discretetime dynamics), local-time update flow, and local-time update map. The presence of only bilinear interactions prevent the flow cases to develop any dynamical instability, the system converging always to the thermal equilibrium. The situation is different for the map when unfriendly couplings are involved, where period-two oscillations arise. In the case of the map with local-time updates, oscillations of any period and chaos can arise as a consequence of the reciprocal “tension” accumulated among the communities during their sleeping time interval. The resulting chaos can be of two kinds: true chaos characterized by positive Lyapunov exponent and bifurcation cascades, or marginal chaos characterized by zero Lyapunov exponent and critical continuous regions.

Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems

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

McCullough, Michael; Iu, Herbert Ho-Ching; Small, Michael

2015-05-15

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. First, we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding. The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the Rössler system and find that periodic dynamics translate tomore » ring structures whereas chaotic time series translate to band or tube-like structures—thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore, we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length, and network diameter are highly sensitive to the interior crisis captured in this particular data set.« less

Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics

NASA Technical Reports Server (NTRS)

Rohrs, C. E.; Valavani, L.; Athans, M.; Stein, G.

1985-01-01

This paper examines the robustness properties of existing adaptive control algorithms to unmodeled plant high-frequency dynamics and unmeasurable output disturbances. It is demonstrated that there exist two infinite-gain operators in the nonlinear dynamic system which determines the time-evolution of output and parameter errors. The pragmatic implications of the existence of such infinite-gain operators is that: (1) sinusoidal reference inputs at specific frequencies and/or (2) sinusoidal output disturbances at any frequency (including dc), can cause the loop gain to increase without bound, thereby exciting the unmodeled high-frequency dynamics, and yielding an unstable control system. Hence, it is concluded that existing adaptive control algorithms as they are presented in the literature referenced in this paper, cannot be used with confidence in practical designs where the plant contains unmodeled dynamics because instability is likely to result. Further understanding is required to ascertain how the currently implemented adaptive systems differ from the theoretical systems studied here and how further theoretical development can improve the robustness of adaptive controllers.

Entrenched time delays versus accelerating opinion dynamics: are advanced democracies inherently unstable?

NASA Astrophysics Data System (ADS)

Gros, Claudius

2017-11-01

Modern societies face the challenge that the time scale of opinion formation is continuously accelerating in contrast to the time scale of political decision making. With the latter remaining of the order of the election cycle we examine here the case that the political state of a society is determined by the continuously evolving values of the electorate. Given this assumption we show that the time lags inherent in the election cycle will inevitable lead to political instabilities for advanced democracies characterized both by an accelerating pace of opinion dynamics and by high sensibilities (political correctness) to deviations from mainstream values. Our result is based on the observation that dynamical systems become generically unstable whenever time delays become comparable to the time it takes to adapt to the steady state. The time needed to recover from external shocks grows in addition dramatically close to the transition. Our estimates for the order of magnitude of the involved time scales indicate that socio-political instabilities may develop once the aggregate time scale for the evolution of the political values of the electorate falls below 7-15 months.

Dynamic density functional theory with hydrodynamic interactions: theoretical development and application in the study of phase separation in gas-liquid systems.

PubMed

Kikkinides, E S; Monson, P A

2015-03-07

Building on recent developments in dynamic density functional theory, we have developed a version of the theory that includes hydrodynamic interactions. This is achieved by combining the continuity and momentum equations eliminating velocity fields, so the resulting model equation contains only terms related to the fluid density and its time and spatial derivatives. The new model satisfies simultaneously continuity and momentum equations under the assumptions of constant dynamic or kinematic viscosity and small velocities and/or density gradients. We present applications of the theory to spinodal decomposition of subcritical temperatures for one-dimensional and three-dimensional density perturbations for both a van der Waals fluid and for a lattice gas model in mean field theory. In the latter case, the theory provides a hydrodynamic extension to the recently studied dynamic mean field theory. We find that the theory correctly describes the transition from diffusive phase separation at short times to hydrodynamic behaviour at long times.

Dynamic density functional theory with hydrodynamic interactions: Theoretical development and application in the study of phase separation in gas-liquid systems

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

Kikkinides, E. S.; Monson, P. A.

Building on recent developments in dynamic density functional theory, we have developed a version of the theory that includes hydrodynamic interactions. This is achieved by combining the continuity and momentum equations eliminating velocity fields, so the resulting model equation contains only terms related to the fluid density and its time and spatial derivatives. The new model satisfies simultaneously continuity and momentum equations under the assumptions of constant dynamic or kinematic viscosity and small velocities and/or density gradients. We present applications of the theory to spinodal decomposition of subcritical temperatures for one-dimensional and three-dimensional density perturbations for both a van dermore » Waals fluid and for a lattice gas model in mean field theory. In the latter case, the theory provides a hydrodynamic extension to the recently studied dynamic mean field theory. We find that the theory correctly describes the transition from diffusive phase separation at short times to hydrodynamic behaviour at long times.« less

Major component analysis of dynamic networks of physiologic organ interactions

NASA Astrophysics Data System (ADS)

Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

2015-09-01

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems With Control Constraints.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2016-08-31

In this paper, an event-triggered near optimal control structure is developed for nonlinear continuous-time systems with control constraints. Due to the saturating actuators, a nonquadratic cost function is introduced and the Hamilton-Jacobi-Bellman (HJB) equation for constrained nonlinear continuous-time systems is formulated. In order to solve the HJB equation, an actor-critic framework is presented. The critic network is used to approximate the cost function and the action network is used to estimate the optimal control law. In addition, in the proposed method, the control signal is transmitted in an aperiodic manner to reduce the computational and the transmission cost. Both the networks are only updated at the trigger instants decided by the event-triggered condition. Detailed Lyapunov analysis is provided to guarantee that the closed-loop event-triggered system is ultimately bounded. Three case studies are used to demonstrate the effectiveness of the proposed method.

Dynamics of Robertson–Walker spacetimes with diffusion

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

Alho, A., E-mail: aalho@math.ist.utl.pt; Calogero, S., E-mail: calogero@chalmers.se; Machado Ramos, M.P., E-mail: mpr@mct.uminho.pt

2015-03-15

We study the dynamics of spatially homogeneous and isotropic spacetimes containing a fluid undergoing microscopic velocity diffusion in a cosmological scalar field. After deriving a few exact solutions of the equations, we continue by analyzing the qualitative behavior of general solutions. To this purpose we recast the equations in the form of a two dimensional dynamical system and perform a global analysis of the flow. Among the admissible behaviors, we find solutions that are asymptotically de-Sitter both in the past and future time directions and which undergo accelerated expansion at all times.

Advances in the continuous monitoring of erosion and deposition dynamics: Developments and applications of the new PEEP-3T system

NASA Astrophysics Data System (ADS)

Lawler, D. M.

2008-01-01

In most episodic erosion and deposition systems, knowledge of the timing of geomorphological change, in relation to fluctuations in the driving forces, is crucial to strong erosion process inference, and model building, validation and development. A challenge for geomorphology, however, is that few studies have focused on geomorphological event structure (timing, magnitude, frequency and duration of individual erosion and deposition events), in relation to applied stresses, because of the absence of key monitoring methodologies. This paper therefore (a) presents full details of a new erosion and deposition measurement system — PEEP-3T — developed from the Photo-Electronic Erosion Pin sensor in five key areas, including the addition of nocturnal monitoring through the integration of the Thermal Consonance Timing (TCT) concept, to produce a continuous sensing system; (b) presents novel high-resolution datasets from the redesigned PEEP-3T system for river bank system of the Rivers Nidd and Wharfe, northern England, UK; and (c) comments on their potential for wider application throughout geomorphology to address these key measurement challenges. Relative to manual methods of erosion and deposition quantification, continuous PEEP-3T methodologies increase the temporal resolution of erosion/deposition event detection by more than three orders of magnitude (better than 1-second resolution if required), and this facility can significantly enhance process inference. Results show that river banks are highly dynamic thermally and respond quickly to radiation inputs. Data on bank retreat timing, fixed with PEEP-3T TCT evidence, confirmed that they were significantly delayed up to 55 h after flood peaks. One event occurred 13 h after emergence from the flow. This suggests that mass failure processes rather than fluid entrainment dominated the system. It is also shown how, by integrating turbidity instrumentation with TCT ideas, linkages between sediment supply and sediment flux can be forged at event timescales, and a lack of sediment exhaustion was evident here. Five challenges for wider geomorphological process investigation are discussed. This event-based dynamics approach, based on continuous monitoring methodologies, appears to have considerable wider potential for stronger process inference and model testing and validation in many areas of geomorphology.

The random continued fraction transformation

NASA Astrophysics Data System (ADS)

Kalle, Charlene; Kempton, Tom; Verbitskiy, Evgeny

2017-03-01

We introduce a random dynamical system related to continued fraction expansions. It uses random combinations of the Gauss map and the Rényi (or backwards) continued fraction map. We explore the continued fraction expansions that this system produces, as well as the dynamical properties of the system.

Operational modeling system with dynamic-wave routing

USGS Publications Warehouse

Ishii, A.L.; Charlton, T.J.; Ortel, T.W.; Vonnahme, C.C.; ,

1998-01-01

A near real-time streamflow-simulation system utilizing continuous-simulation rainfall-runoff generation with dynamic-wave routing is being developed by the U.S. Geological Survey in cooperation with the Du Page County Department of Environmental Concerns for a 24-kilometer reach of Salt Creek in Du Page County, Illinois. This system is needed in order to more effectively manage the Elmhurst Quarry Flood Control Facility, an off-line stormwater diversion reservoir located along Salt Creek. Near real time simulation capabilities will enable the testing and evaluation of potential rainfall, diversion, and return-flow scenarios on water-surface elevations along Salt Creek before implementing diversions or return-flows. The climatological inputs for the continuous-simulation rainfall-runoff model, Hydrologic Simulation Program - FORTRAN (HSPF) are obtained by Internet access and from a network of radio-telemetered precipitation gages reporting to a base-station computer. The unit area runoff time series generated from HSPF are the input for the dynamic-wave routing model. Full Equations (FEQ). The Generation and Analysis of Model Simulation Scenarios (GENSCN) interface is used as a pre- and post-processor for managing input data and displaying and managing simulation results. The GENSCN interface includes a variety of graphical and analytical tools for evaluation and quick visualization of the results of operational scenario simulations and thereby makes it possible to obtain the full benefit of the fully distributed dynamic routing results.

Stochastic thermodynamics across scales: Emergent inter-attractoral discrete Markov jump process and its underlying continuous diffusion

NASA Astrophysics Data System (ADS)

Santillán, Moisés; Qian, Hong

2013-01-01

We investigate the internal consistency of a recently developed mathematical thermodynamic structure across scales, between a continuous stochastic nonlinear dynamical system, i.e., a diffusion process with Langevin and Fokker-Planck equations, and its emergent discrete, inter-attractoral Markov jump process. We analyze how the system’s thermodynamic state functions, e.g. free energy F, entropy S, entropy production ep, free energy dissipation Ḟ, etc., are related when the continuous system is described with coarse-grained discrete variables. It is shown that the thermodynamics derived from the underlying, detailed continuous dynamics gives rise to exactly the free-energy representation of Gibbs and Helmholtz. That is, the system’s thermodynamic structure is the same as if one only takes a middle road and starts with the natural discrete description, with the corresponding transition rates empirically determined. By natural we mean in the thermodynamic limit of a large system, with an inherent separation of time scales between inter- and intra-attractoral dynamics. This result generalizes a fundamental idea from chemistry, and the theory of Kramers, by incorporating thermodynamics: while a mechanical description of a molecule is in terms of continuous bond lengths and angles, chemical reactions are phenomenologically described by a discrete representation, in terms of exponential rate laws and a stochastic thermodynamics.

Controlling aliased dynamics in motion systems? An identification for sampled-data control approach

NASA Astrophysics Data System (ADS)

Oomen, Tom

2014-07-01

Sampled-data control systems occasionally exhibit aliased resonance phenomena within the control bandwidth. The aim of this paper is to investigate the aspect of these aliased dynamics with application to a high performance industrial nano-positioning machine. This necessitates a full sampled-data control design approach, since these aliased dynamics endanger both the at-sample performance and the intersample behaviour. The proposed framework comprises both system identification and sampled-data control. In particular, the sampled-data control objective necessitates models that encompass the intersample behaviour, i.e., ideally continuous time models. Application of the proposed approach on an industrial wafer stage system provides a thorough insight and new control design guidelines for controlling aliased dynamics.

Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring.

PubMed

Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; Dalla Man, Chiara; Manohar, Chinmay; Levine, James A; Basu, Ananda; Kudva, Yogish C; Cobelli, Claudio

2013-10-01

In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

Fronts in extended systems of bistable maps coupled via convolutions

NASA Astrophysics Data System (ADS)

Coutinho, Ricardo; Fernandez, Bastien

2004-01-01

An analysis of front dynamics in discrete time and spatially extended systems with general bistable nonlinearity is presented. The spatial coupling is given by the convolution with distribution functions. It allows us to treat in a unified way discrete, continuous or partly discrete and partly continuous diffusive interactions. We prove the existence of fronts and the uniqueness of their velocity. We also prove that the front velocity depends continuously on the parameters of the system. Finally, we show that every initial configuration that is an interface between the stable phases propagates asymptotically with the front velocity.

Dealing with periodical loads and harmonics in operational modal analysis using time-varying transmissibility functions

NASA Astrophysics Data System (ADS)

Weijtjens, Wout; Lataire, John; Devriendt, Christof; Guillaume, Patrick

2014-12-01

Periodical loads, such as waves and rotating machinery, form a problem for operational modal analysis (OMA). In OMA only the vibrations of a structure of interest are measured and little to nothing is known about the loads causing these vibrations. Therefore, it is often assumed that all dynamics in the measured data are linked to the system of interest. Periodical loads defy this assumption as their periodical behavior is often visible within the measured vibrations. As a consequence most OMA techniques falsely associate the dynamics of the periodical load with the system of interest. Without additional information about the load, one is not able to correctly differentiate between structural dynamics and the dynamics of the load. In several applications, e.g. turbines and helicopters, it was observed that because of periodical loads one was unable to correctly identify one or multiple modes. Transmissibility based OMA (TOMA) is a completely different approach to OMA. By using transmissibility functions to estimate the structural dynamics of the system of interest, all influence of the load-spectrum can be eliminated. TOMA therefore allows to identify the modal parameters without being influenced by the presence of periodical loads, such as harmonics. One of the difficulties of TOMA is that the analyst is required to find two independent datasets, each associated with a different loading condition of the system of interest. This poses a dilemma for TOMA; how can an analyst identify two different loading conditions when little is known about the loads on the system? This paper tackles that problem by assuming that the loading conditions vary continuously over time, e.g. the changing wind directions. From this assumption TOMA is developed into a time-varying framework. This development allows TOMA to not only cope with the continuously changing loading conditions. The time-varying framework also enables the identification of the modal parameters from a single dataset. Moreover, the time-varying TOMA approach can be implemented in such a way that the analyst no longer has to identify different loading conditions. For these combined reasons the time-varying TOMA is less dependent on the user and requires less testing time than the earlier TOMA-technique.

Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays.

PubMed

Hu, Jin; Wang, Jun

2015-06-01

In recent years, complex-valued recurrent neural networks have been developed and analysed in-depth in view of that they have good modelling performance for some applications involving complex-valued elements. In implementing continuous-time dynamical systems for simulation or computational purposes, it is quite necessary to utilize a discrete-time model which is an analogue of the continuous-time system. In this paper, we analyse a discrete-time complex-valued recurrent neural network model and obtain the sufficient conditions on its global exponential periodicity and exponential stability. Simulation results of several numerical examples are delineated to illustrate the theoretical results and an application on associative memory is also given. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fixation, transient landscape, and diffusion dilemma in stochastic evolutionary game dynamics

NASA Astrophysics Data System (ADS)

Zhou, Da; Qian, Hong

2011-09-01

Agent-based stochastic models for finite populations have recently received much attention in the game theory of evolutionary dynamics. Both the ultimate fixation and the pre-fixation transient behavior are important to a full understanding of the dynamics. In this paper, we study the transient dynamics of the well-mixed Moran process through constructing a landscape function. It is shown that the landscape playing a central theoretical “device” that integrates several lines of inquiries: the stable behavior of the replicator dynamics, the long-time fixation, and continuous diffusion approximation associated with asymptotically large population. Several issues relating to the transient dynamics are discussed: (i) multiple time scales phenomenon associated with intra- and inter-attractoral dynamics; (ii) discontinuous transition in stochastically stationary process akin to Maxwell construction in equilibrium statistical physics; and (iii) the dilemma diffusion approximation facing as a continuous approximation of the discrete evolutionary dynamics. It is found that rare events with exponentially small probabilities, corresponding to the uphill movements and barrier crossing in the landscape with multiple wells that are made possible by strong nonlinear dynamics, plays an important role in understanding the origin of the complexity in evolutionary, nonlinear biological systems.

Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

PubMed

Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

2016-02-01

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

Chaotic trajectories in the standard map. The concept of anti-integrability

NASA Astrophysics Data System (ADS)

Aubry, Serge; Abramovici, Gilles

1990-07-01

A rigorous proof is given in the standard map (associated with a Frenkel-Kontorowa model) for the existence of chaotic trajectories with unbounded momenta for large enough coupling constant k > k0. These chaotic trajectories (with finite entropy per site) are coded by integer sequences { mi} such that the sequence bi = |m i+1 + m i-1-2m i| be bounded by some integer b. The bound k0 in k depends on b and can be lowered for coding sequences { mi} fulfilling more restrictive conditions. The obtained chaotic trajectories correspond to stationary configurations of the Frenkel-Kontorowa model with a finite (non-zero) photon gap (called gap parameter in dimensionless units). This property implies that the trajectory (or the configuration { ui}) can be uniquely continued as a uniformly continuous function of the model parameter k in some neighborhood of the initial configuration. A non-zero gap parameter implies that the Lyapunov coefficient is strictly positive (when it is defined). In addition, the existence of dilating and contracting manifolds is proven for these chaotic trajectories. “Exotic” trajectories such as ballistic trajectories are also proven to exist as a consequence of these theorems. The concept of anti-integrability emerges from these theorems. In the anti-integrable limit which can be only defined for a discrete time dynamical system, the coordinates of the trajectory at time i do not depend on the coordinates at time i - 1. Thus, at this singular limit, the existence of chaotic trajectories is trivial and the dynamical system reduces to a Bernoulli shift. It is well known that the KAM tori of symplectic dynamical originates by continuity from the invariant tori which exists in the integrible limit (under certain conditions). In a similar way, it appears that the chaotic trajectories of dynamical systems originate by continuity from those which exists at the anti-integrable limits (also under certain conditions).

Exploiting Fast-Variables to Understand Population Dynamics and Evolution

NASA Astrophysics Data System (ADS)

Constable, George W. A.; McKane, Alan J.

2018-07-01

We describe a continuous-time modelling framework for biological population dynamics that accounts for demographic noise. In the spirit of the methodology used by statistical physicists, transitions between the states of the system are caused by individual events while the dynamics are described in terms of the time-evolution of a probability density function. In general, the application of the diffusion approximation still leaves a description that is quite complex. However, in many biological applications one or more of the processes happen slowly relative to the system's other processes, and the dynamics can be approximated as occurring within a slow low-dimensional subspace. We review these time-scale separation arguments and analyse the more simple stochastic dynamics that result in a number of cases. We stress that it is important to retain the demographic noise derived in this way, and emphasise this point by showing that it can alter the direction of selection compared to the prediction made from an analysis of the corresponding deterministic model.

Exploiting Fast-Variables to Understand Population Dynamics and Evolution

NASA Astrophysics Data System (ADS)

Constable, George W. A.; McKane, Alan J.

2017-11-01

We describe a continuous-time modelling framework for biological population dynamics that accounts for demographic noise. In the spirit of the methodology used by statistical physicists, transitions between the states of the system are caused by individual events while the dynamics are described in terms of the time-evolution of a probability density function. In general, the application of the diffusion approximation still leaves a description that is quite complex. However, in many biological applications one or more of the processes happen slowly relative to the system's other processes, and the dynamics can be approximated as occurring within a slow low-dimensional subspace. We review these time-scale separation arguments and analyse the more simple stochastic dynamics that result in a number of cases. We stress that it is important to retain the demographic noise derived in this way, and emphasise this point by showing that it can alter the direction of selection compared to the prediction made from an analysis of the corresponding deterministic model.

Invariant measures in brain dynamics

NASA Astrophysics Data System (ADS)

Boyarsky, Abraham; Góra, Paweł

2006-10-01

This note concerns brain activity at the level of neural ensembles and uses ideas from ergodic dynamical systems to model and characterize chaotic patterns among these ensembles during conscious mental activity. Central to our model is the definition of a space of neural ensembles and the assumption of discrete time ensemble dynamics. We argue that continuous invariant measures draw the attention of deeper brain processes, engendering emergent properties such as consciousness. Invariant measures supported on a finite set of ensembles reflect periodic behavior, whereas the existence of continuous invariant measures reflect the dynamics of nonrepeating ensemble patterns that elicit the interest of deeper mental processes. We shall consider two different ways to achieve continuous invariant measures on the space of neural ensembles: (1) via quantum jitters, and (2) via sensory input accompanied by inner thought processes which engender a “folding” property on the space of ensembles.

Nonlinear Light Dynamics in Multi-Core Structures

DTIC Science & Technology

2017-02-27

be generated in continuous- discrete optical media such as multi-core optical fiber or waveguide arrays; localisation dynamics in a continuous... discrete nonlinear system. Detailed theoretical analysis is presented of the existence and stability of the discrete -continuous light bullets using a very...and pulse compression using wave collapse (self-focusing) energy localisation dynamics in a continuous- discrete nonlinear system, as implemented in a

Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network.

PubMed

Griffith, Mark; Courtney, Tod; Peccoud, Jean; Sanders, William H

2006-11-15

The stochastic kinetics of a well-mixed chemical system, governed by the chemical Master equation, can be simulated using the exact methods of Gillespie. However, these methods do not scale well as systems become more complex and larger models are built to include reactions with widely varying rates, since the computational burden of simulation increases with the number of reaction events. Continuous models may provide an approximate solution and are computationally less costly, but they fail to capture the stochastic behavior of small populations of macromolecules. In this article we present a hybrid simulation algorithm that dynamically partitions the system into subsets of continuous and discrete reactions, approximates the continuous reactions deterministically as a system of ordinary differential equations (ODE) and uses a Monte Carlo method for generating discrete reaction events according to a time-dependent propensity. Our approach to partitioning is improved such that we dynamically partition the system of reactions, based on a threshold relative to the distribution of propensities in the discrete subset. We have implemented the hybrid algorithm in an extensible framework, utilizing two rigorous ODE solvers to approximate the continuous reactions, and use an example model to illustrate the accuracy and potential speedup of the algorithm when compared with exact stochastic simulation. Software and benchmark models used for this publication can be made available upon request from the authors.

Three axis electronic flight motion simulator real time control system design and implementation

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

Gao, Zhiyuan; Miao, Zhonghua, E-mail: zhonghua-miao@163.com; Wang, Xiaohua

2014-12-15

A three axis electronic flight motion simulator is reported in this paper including the modelling, the controller design as well as the hardware implementation. This flight motion simulator could be used for inertial navigation test and high precision inertial navigation system with good dynamic and static performances. A real time control system is designed, several control system implementation problems were solved including time unification with parallel port interrupt, high speed finding-zero method of rotary inductosyn, zero-crossing management with continuous rotary, etc. Tests were carried out to show the effectiveness of the proposed real time control system.

Three axis electronic flight motion simulator real time control system design and implementation.

PubMed

Gao, Zhiyuan; Miao, Zhonghua; Wang, Xuyong; Wang, Xiaohua

2014-12-01

A three axis electronic flight motion simulator is reported in this paper including the modelling, the controller design as well as the hardware implementation. This flight motion simulator could be used for inertial navigation test and high precision inertial navigation system with good dynamic and static performances. A real time control system is designed, several control system implementation problems were solved including time unification with parallel port interrupt, high speed finding-zero method of rotary inductosyn, zero-crossing management with continuous rotary, etc. Tests were carried out to show the effectiveness of the proposed real time control system.

Dynamics of open quantum systems by interpolation of von Neumann and classical master equations, and its application to quantum annealing

NASA Astrophysics Data System (ADS)

Kadowaki, Tadashi

2018-02-01

We propose a method to interpolate dynamics of von Neumann and classical master equations with an arbitrary mixing parameter to investigate the thermal effects in quantum dynamics. The two dynamics are mixed by intervening to continuously modify their solutions, thus coupling them indirectly instead of directly introducing a coupling term. This maintains the quantum system in a pure state even after the introduction of thermal effects and obtains not only a density matrix but also a state vector representation. Further, we demonstrate that the dynamics of a two-level system can be rewritten as a set of standard differential equations, resulting in quantum dynamics that includes thermal relaxation. These equations are equivalent to the optical Bloch equations at the weak coupling and asymptotic limits, implying that the dynamics cause thermal effects naturally. Numerical simulations of ferromagnetic and frustrated systems support this idea. Finally, we use this method to study thermal effects in quantum annealing, revealing nontrivial performance improvements for a spin glass model over a certain range of annealing time. This result may enable us to optimize the annealing time of real annealing machines.

Self-organization of complex networks as a dynamical system

NASA Astrophysics Data System (ADS)

Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

2015-01-01

To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

Self-organization of complex networks as a dynamical system.

PubMed

Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

2015-01-01

To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

Optimal preview control for a linear continuous-time stochastic control system in finite-time horizon

NASA Astrophysics Data System (ADS)

Wu, Jiang; Liao, Fucheng; Tomizuka, Masayoshi

2017-01-01

This paper discusses the design of the optimal preview controller for a linear continuous-time stochastic control system in finite-time horizon, using the method of augmented error system. First, an assistant system is introduced for state shifting. Then, in order to overcome the difficulty of the state equation of the stochastic control system being unable to be differentiated because of Brownian motion, the integrator is introduced. Thus, the augmented error system which contains the integrator vector, control input, reference signal, error vector and state of the system is reconstructed. This leads to the tracking problem of the optimal preview control of the linear stochastic control system being transformed into the optimal output tracking problem of the augmented error system. With the method of dynamic programming in the theory of stochastic control, the optimal controller with previewable signals of the augmented error system being equal to the controller of the original system is obtained. Finally, numerical simulations show the effectiveness of the controller.

Dynamical genetic programming in XCSF.

PubMed

Preen, Richard J; Bull, Larry

2013-01-01

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.

Visibility graphlet approach to chaotic time series

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

Mutua, Stephen; Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega; Gu, Changgui, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn

Many novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems.more » Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.« less

Distributed Optimal Consensus Over Resource Allocation Network and Its Application to Dynamical Economic Dispatch.

PubMed

Li, Chaojie; Yu, Xinghuo; Huang, Tingwen; He, Xing; Chaojie Li; Xinghuo Yu; Tingwen Huang; Xing He; Li, Chaojie; Huang, Tingwen; He, Xing; Yu, Xinghuo

2018-06-01

The resource allocation problem is studied and reformulated by a distributed interior point method via a -logarithmic barrier. By the facilitation of the graph Laplacian, a fully distributed continuous-time multiagent system is developed for solving the problem. Specifically, to avoid high singularity of the -logarithmic barrier at boundary, an adaptive parameter switching strategy is introduced into this dynamical multiagent system. The convergence rate of the distributed algorithm is obtained. Moreover, a novel distributed primal-dual dynamical multiagent system is designed in a smart grid scenario to seek the saddle point of dynamical economic dispatch, which coincides with the optimal solution. The dual decomposition technique is applied to transform the optimization problem into easily solvable resource allocation subproblems with local inequality constraints. The good performance of the new dynamical systems is, respectively, verified by a numerical example and the IEEE six-bus test system-based simulations.

Stochastic Adaptive Estimation and Control.

DTIC Science & Technology

1994-10-26

Marcus, "Language Stability and Stabilizability of Discrete Event Dynamical Systems ," SIAM Journal on Control and Optimization, 31, September 1993...in the hierarchical control of flexible manufacturing systems ; in this problem, the model involves a hybrid process in continuous time whose state is...of the average cost control problem for discrete- time Markov processes. Our exposition covers from finite to Borel state and action spaces and

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

Zitney, S.E.

This presentation will examine process systems engineering R&D needs for application to advanced fossil energy (FE) systems and highlight ongoing research activities at the National Energy Technology Laboratory (NETL) under the auspices of a recently launched Collaboratory for Process & Dynamic Systems Research. The three current technology focus areas include: 1) High-fidelity systems with NETL's award-winning Advanced Process Engineering Co-Simulator (APECS) technology for integrating process simulation with computational fluid dynamics (CFD) and virtual engineering concepts, 2) Dynamic systems with R&D on plant-wide IGCC dynamic simulation, control, and real-time training applications, and 3) Systems optimization including large-scale process optimization, stochastic simulationmore » for risk/uncertainty analysis, and cost estimation. Continued R&D aimed at these and other key process systems engineering models, methods, and tools will accelerate the development of advanced gasification-based FE systems and produce increasingly valuable outcomes for DOE and the Nation.« less

On discrete control of nonlinear systems with applications to robotics

NASA Technical Reports Server (NTRS)

Eslami, Mansour

1989-01-01

Much progress has been reported in the areas of modeling and control of nonlinear dynamic systems in a continuous-time framework. From implementation point of view, however, it is essential to study these nonlinear systems directly in a discrete setting that is amenable for interfacing with digital computers. But to develop discrete models and discrete controllers for a nonlinear system such as robot is a nontrivial task. Robot is also inherently a variable-inertia dynamic system involving additional complications. Not only the computer-oriented models of these systems must satisfy the usual requirements for such models, but these must also be compatible with the inherent capabilities of computers and must preserve the fundamental physical characteristics of continuous-time systems such as the conservation of energy and/or momentum. Preliminary issues regarding discrete systems in general and discrete models of a typical industrial robot that is developed with full consideration of the principle of conservation of energy are presented. Some research on the pertinent tactile information processing is reviewed. Finally, system control methods and how to integrate these issues in order to complete the task of discrete control of a robot manipulator are also reviewed.

Single-crossover recombination in discrete time.

PubMed

von Wangenheim, Ute; Baake, Ellen; Baake, Michael

2010-05-01

Modelling the process of recombination leads to a large coupled nonlinear dynamical system. Here, we consider a particular case of recombination in discrete time, allowing only for single crossovers. While the analogous dynamics in continuous time admits a closed solution (Baake and Baake in Can J Math 55:3-41, 2003), this no longer works for discrete time. A more general model (i.e. without the restriction to single crossovers) has been studied before (Bennett in Ann Hum Genet 18:311-317, 1954; Dawson in Theor Popul Biol 58:1-20, 2000; Linear Algebra Appl 348:115-137, 2002) and was solved algorithmically by means of Haldane linearisation. Using the special formalism introduced by Baake and Baake (Can J Math 55:3-41, 2003), we obtain further insight into the single-crossover dynamics and the particular difficulties that arise in discrete time. We then transform the equations to a solvable system in a two-step procedure: linearisation followed by diagonalisation. Still, the coefficients of the second step must be determined in a recursive manner, but once this is done for a given system, they allow for an explicit solution valid for all times.

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Intermittent dynamics in complex systems driven to depletion.

PubMed

Escobar, Juan V; Pérez Castillo, Isaac

2018-03-19

When complex systems are driven to depletion by some external factor, their non-stationary dynamics can present an intermittent behaviour between relative tranquility and burst of activity whose consequences are often catastrophic. To understand and ultimately be able to predict such dynamics, we propose an underlying mechanism based on sharp thresholds of a local generalized energy density that naturally leads to negative feedback. We find a transition from a continuous regime to an intermittent one, in which avalanches can be predicted despite the stochastic nature of the process. This model may have applications in many natural and social complex systems where a rapid depletion of resources or generalized energy drives the dynamics. In particular, we show how this model accurately describes the time evolution and avalanches present in a real social system.

Third harmonic generation microscopy

NASA Astrophysics Data System (ADS)

Squier, Jeffrey A.; Muller, Michiel; Brakenhoff, G. J.; Wilson, Kent R.

1998-10-01

Third harmonic generation microscopy is used to make dynamical images of living systems for the first time. A 100 fs excitation pulse at 1.2 æm results in a 400 nm signal which is generated directly within the specimen. Chara plant rhizoids have been imaged, showing dynamic plant activity, and non-fading image characteristics even with continuous viewing, indicating prolonged viability under these THG-imaging conditions.

Predicting chaos for infinite dimensional dynamical systems: The Kuramoto-Sivashinsky equation, a case study

NASA Technical Reports Server (NTRS)

Smyrlis, Yiorgos S.; Papageorgiou, Demetrios T.

1991-01-01

The results of extensive computations are presented in order to accurately characterize transitions to chaos for the Kuramoto-Sivashinsky equation. In particular, the oscillatory dynamics in a window that supports a complete sequence of period doubling bifurcations preceding chaos is followed. As many as thirteen period doublings are followed and used to compute the Feigenbaum number for the cascade and so enable, for the first time, an accurate numerical evaluation of the theory of universal behavior of nonlinear systems, for an infinite dimensional dynamical system. Furthermore, the dynamics at the threshold of chaos exhibit a fractal behavior which is demonstrated and used to compute a universal scaling factor that enables the self-similar continuation of the solution into a chaotic regime.

Quantum control and measurement of atomic spins in polarization spectroscopy

NASA Astrophysics Data System (ADS)

Deutsch, Ivan H.; Jessen, Poul S.

2010-03-01

Quantum control and measurement are two sides of the same coin. To affect a dynamical map, well-designed time-dependent control fields must be applied to the system of interest. To read out the quantum state, information about the system must be transferred to a probe field. We study a particular example of this dual action in the context of quantum control and measurement of atomic spins through the light-shift interaction with an off-resonant optical probe. By introducing an irreducible tensor decomposition, we identify the coupling of the Stokes vector of the light field with moments of the atomic spin state. This shows how polarization spectroscopy can be used for continuous weak measurement of atomic observables that evolve as a function of time. Simultaneously, the state-dependent light shift induced by the probe field can drive nonlinear dynamics of the spin, and can be used to generate arbitrary unitary transformations on the atoms. We revisit the derivation of the master equation in order to give a unified description of spin dynamics in the presence of both nonlinear dynamics and photon scattering. Based on this formalism, we review applications to quantum control, including the design of state-to-state mappings, and quantum-state reconstruction via continuous weak measurement on a dynamically controlled ensemble.

Periodic cycles of social outbursts of activity

NASA Astrophysics Data System (ADS)

Berestycki, H.; Rossi, L.; Rodríguez, N.

2018-01-01

We study the long-time behavior of a 2 × 2 continuous dynamical system with a time-periodic source term which is either of cooperative-type or activator-inhibitor type. This system was recently introduced in the literature [2] to model the dynamics of social outbursts and consists of an explicit field measuring the level of activity and an implicit field measuring the effective tension. The system can be used to represent a general type of phenomena in which one variable exhibits self-excitement once the other variable has reached a critical value. The time-periodic source term allows one to analyze the effect that periodic external shocks to the system play in the dynamics of the outburst of activity. For cooperative systems we prove that for small shocks the level of activity dies down whereas, as the intensity of the shocks increases, the level of activity converges to a positive periodic solution (excited cycle). We further show that in some cases there is multiplicity of excited cycles. We derive a subset of these results for the activator-inhibitor system.

Dynamic autofocus for continuous-scanning time-delay-and-integration image acquisition in automated microscopy.

PubMed

Bravo-Zanoguera, Miguel E; Laris, Casey A; Nguyen, Lam K; Oliva, Mike; Price, Jeffrey H

2007-01-01

Efficient image cytometry of a conventional microscope slide means rapid acquisition and analysis of 20 gigapixels of image data (at 0.3-microm sampling). The voluminous data motivate increased acquisition speed to enable many biomedical applications. Continuous-motion time-delay-and-integrate (TDI) scanning has the potential to speed image acquisition while retaining sensitivity, but the challenge of implementing high-resolution autofocus operating simultaneously with acquisition has limited its adoption. We develop a dynamic autofocus system for this need using: 1. a "volume camera," consisting of nine fiber optic imaging conduits to charge-coupled device (CCD) sensors, that acquires images in parallel from different focal planes, 2. an array of mixed analog-digital processing circuits that measure the high spatial frequencies of the multiple image streams to create focus indices, and 3. a software system that reads and analyzes the focus data streams and calculates best focus for closed feedback loop control. Our system updates autofocus at 56 Hz (or once every 21 microm of stage travel) to collect sharply focused images sampled at 0.3x0.3 microm(2)/pixel at a stage speed of 2.3 mms. The system, tested by focusing in phase contrast and imaging long fluorescence strips, achieves high-performance closed-loop image-content-based autofocus in continuous scanning for the first time.

Consensus Algorithms for Networks of Systems with Second- and Higher-Order Dynamics

NASA Astrophysics Data System (ADS)

Fruhnert, Michael

This thesis considers homogeneous networks of linear systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilizable. We show that, in continuous-time, consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. For networks of continuous-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback. For networks of discrete-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Schur. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. We show that consensus can always be achieved for marginally stable systems and discretized systems. Simple conditions for consensus achieving controllers are obtained when the Laplacian eigenvalues are all real. For networks of continuous-time time-variant higher-order systems, we show that uniform consensus can always be achieved if systems are quadratically stabilizable. In this case, we provide a simple condition to obtain a linear feedback control. For networks of discrete-time higher-order systems, we show that constant gains can be chosen such that consensus is achieved for a variety of network topologies. First, we develop simple results for networks of time-invariant systems and networks of time-variant systems that are given in controllable canonical form. Second, we formulate the problem in terms of Linear Matrix Inequalities (LMIs). The condition found simplifies the design process and avoids the parallel solution of multiple LMIs. The result yields a modified Algebraic Riccati Equation (ARE) for which we present an equivalent LMI condition.

A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation

NASA Astrophysics Data System (ADS)

Pham, Cuong; Plötz, Thomas; Olivier, Patrick

We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.

Neural Computations in a Dynamical System with Multiple Time Scales.

PubMed

Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

2016-01-01

Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

A mobile system for assessment of physiological response to posture transitions.

PubMed

Jovanov, Emil; Milosevic, Mladen; Milenković, Aleksandar

2013-01-01

Posture changes initiate a dynamic physiological response that can be used as an indicator of the overall health status. We introduce an inconspicuous mobile wellness monitoring system (imWell) that continuously assesses the dynamic physiological response to posture transitions during activities of daily living. imWell utilizes a Zephyr BioHarness 3 physiological monitor that continually reports heart activity and physical activity via Bluetooth to a personal device (e.g. smartphone). The personal device processes the reported activity data in real-time to recognize posture transitions from the accelerometer data and to characterize dynamic heart response to posture changes. It annotates, logs, and uploads the heart activity data to our mHealth server. In this paper we present algorithms for detection of posture transitions and heart activity characterization during a sit-to-stand transition. The proposed system was tested on seven healthy subjects performing a predefined protocol. The total average and standard deviation for sit-to-stand transition time is 2.7 ± 0.69 s, resulting in the change of heart rate of 27.36 ± 9.30 bpm (from 63.3 ± 9.02 bpm to 90.66 ± 10.09 bpm).

Fuzzy model-based servo and model following control for nonlinear systems.

PubMed

Ohtake, Hiroshi; Tanaka, Kazuo; Wang, Hua O

2009-12-01

This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.

Embedding recurrent neural networks into predator-prey models.

PubMed

Moreau, Yves; Louiès, Stephane; Vandewalle, Joos; Brenig, Leon

1999-03-01

We study changes of coordinates that allow the embedding of ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models-also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form (Brenig, L. (1988). Complete factorization and analytic solutions of generalized Lotka-Volterra equations. Physics Letters A, 133(7-8), 378-382), where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoid. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network. We expect that this transformation will permit the application of existing techniques for the analysis of Lotka-Volterra systems to recurrent neural networks. Furthermore, our results show that Lotka-Volterra systems are universal approximators of dynamical systems, just as are continuous-time neural networks.

Impact of hyperbolicity on chimera states in ensembles of nonlocally coupled chaotic oscillators

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

Semenova, N.; Anishchenko, V.; Zakharova, A.

2016-06-08

In this work we analyse nonlocally coupled networks of identical chaotic oscillators. We study both time-discrete and time-continuous systems (Henon map, Lozi map, Lorenz system). We hypothesize that chimera states, in which spatial domains of coherent (synchronous) and incoherent (desynchronized) dynamics coexist, can be obtained only in networks of chaotic non-hyperbolic systems and cannot be found in networks of hyperbolic systems. This hypothesis is supported by numerical simulations for hyperbolic and non-hyperbolic cases.

Inference for dynamics of continuous variables: the extended Plefka expansion with hidden nodes

NASA Astrophysics Data System (ADS)

Bravi, B.; Sollich, P.

2017-06-01

We consider the problem of a subnetwork of observed nodes embedded into a larger bulk of unknown (i.e. hidden) nodes, where the aim is to infer these hidden states given information about the subnetwork dynamics. The biochemical networks underlying many cellular and metabolic processes are important realizations of such a scenario as typically one is interested in reconstructing the time evolution of unobserved chemical concentrations starting from the experimentally more accessible ones. We present an application to this problem of a novel dynamical mean field approximation, the extended Plefka expansion, which is based on a path integral description of the stochastic dynamics. As a paradigmatic model we study the stochastic linear dynamics of continuous degrees of freedom interacting via random Gaussian couplings. The resulting joint distribution is known to be Gaussian and this allows us to fully characterize the posterior statistics of the hidden nodes. In particular the equal-time hidden-to-hidden variance—conditioned on observations—gives the expected error at each node when the hidden time courses are predicted based on the observations. We assess the accuracy of the extended Plefka expansion in predicting these single node variances as well as error correlations over time, focussing on the role of the system size and the number of observed nodes.

Finite time convergent learning law for continuous neural networks.

PubMed

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

Crossover from antipersistent to persistent behavior in time series possessing the generalyzed dynamic scaling law

NASA Astrophysics Data System (ADS)

Balankin, Alexander S.; Morales Matamoros, Oswaldo; Gálvez M., Ernesto; Pérez A., Alfonso

2004-03-01

The behavior of crude oil price volatility is analyzed within a conceptual framework of kinetic roughening of growing interfaces. We find that the persistent long-horizon volatilities satisfy the Family-Viscek dynamic scaling ansatz, whereas the mean-reverting in time short horizon volatilities obey the generalized scaling law with continuously varying scaling exponents. Furthermore we find that the crossover from antipersistent to persistent behavior is accompanied by a change in the type of volatility distribution. These phenomena are attributed to the complex avalanche dynamics of crude oil markets and so a similar behavior may be observed in a wide variety of physical systems governed by avalanche dynamics.

Convergence Speed of a Dynamical System for Sparse Recovery

NASA Astrophysics Data System (ADS)

Balavoine, Aurele; Rozell, Christopher J.; Romberg, Justin

2013-09-01

This paper studies the convergence rate of a continuous-time dynamical system for L1-minimization, known as the Locally Competitive Algorithm (LCA). Solving L1-minimization} problems efficiently and rapidly is of great interest to the signal processing community, as these programs have been shown to recover sparse solutions to underdetermined systems of linear equations and come with strong performance guarantees. The LCA under study differs from the typical L1 solver in that it operates in continuous time: instead of being specified by discrete iterations, it evolves according to a system of nonlinear ordinary differential equations. The LCA is constructed from simple components, giving it the potential to be implemented as a large-scale analog circuit. The goal of this paper is to give guarantees on the convergence time of the LCA system. To do so, we analyze how the LCA evolves as it is recovering a sparse signal from underdetermined measurements. We show that under appropriate conditions on the measurement matrix and the problem parameters, the path the LCA follows can be described as a sequence of linear differential equations, each with a small number of active variables. This allows us to relate the convergence time of the system to the restricted isometry constant of the matrix. Interesting parallels to sparse-recovery digital solvers emerge from this study. Our analysis covers both the noisy and noiseless settings and is supported by simulation results.

Scalar and vector Keldysh models in the time domain

NASA Astrophysics Data System (ADS)

Kiselev, M. N.; Kikoin, K. A.

2009-04-01

The exactly solvable Keldysh model of disordered electron system in a random scattering field with extremely long correlation length is converted to the time-dependent model with extremely long relaxation. The dynamical problem is solved for the ensemble of two-level systems (TLS) with fluctuating well depths having the discrete Z 2 symmetry. It is shown also that the symmetric TLS with fluctuating barrier transparency may be described in terms of the vector Keldysh model with dime-dependent random planar rotations in xy plane having continuous SO(2) symmetry. Application of this model to description of dynamic fluctuations in quantum dots and optical lattices is discussed.

Systematic Computation of Nonlinear Cellular and Molecular Dynamics with Low-Power CytoMimetic Circuits: A Simulation Study

PubMed Central

Papadimitriou, Konstantinos I.; Stan, Guy-Bart V.; Drakakis, Emmanuel M.

2013-01-01

This paper presents a novel method for the systematic implementation of low-power microelectronic circuits aimed at computing nonlinear cellular and molecular dynamics. The method proposed is based on the Nonlinear Bernoulli Cell Formalism (NBCF), an advanced mathematical framework stemming from the Bernoulli Cell Formalism (BCF) originally exploited for the modular synthesis and analysis of linear, time-invariant, high dynamic range, logarithmic filters. Our approach identifies and exploits the striking similarities existing between the NBCF and coupled nonlinear ordinary differential equations (ODEs) typically appearing in models of naturally encountered biochemical systems. The resulting continuous-time, continuous-value, low-power CytoMimetic electronic circuits succeed in simulating fast and with good accuracy cellular and molecular dynamics. The application of the method is illustrated by synthesising for the first time microelectronic CytoMimetic topologies which simulate successfully: 1) a nonlinear intracellular calcium oscillations model for several Hill coefficient values and 2) a gene-protein regulatory system model. The dynamic behaviours generated by the proposed CytoMimetic circuits are compared and found to be in very good agreement with their biological counterparts. The circuits exploit the exponential law codifying the low-power subthreshold operation regime and have been simulated with realistic parameters from a commercially available CMOS process. They occupy an area of a fraction of a square-millimetre, while consuming between 1 and 12 microwatts of power. Simulations of fabrication-related variability results are also presented. PMID:23393550

The Direction of Fluid Dynamics for Liquid Propulsion at NASA Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Griffin, Lisa W.

2012-01-01

The Fluid Dynamics Branch's (ER42) at MSFC mission is to support NASA and other customers with discipline expertise to enable successful accomplishment of program/project goals. The branch is responsible for all aspects of the discipline of fluid dynamics, analysis and testing, applied to propulsion or propulsion-induced loads and environments, which includes the propellant delivery system, combustion devices, coupled systems, and launch and separation events. ER42 supports projects from design through development, and into anomaly and failure investigations. ER42 is committed to continually improving the state-of-its-practice to provide accurate, effective, and timely fluid dynamics assessments and in extending the state-of-the-art of the discipline.

Single-shot quantum state estimation via a continuous measurement in the strong backaction regime

NASA Astrophysics Data System (ADS)

Cook, Robert L.; Riofrío, Carlos A.; Deutsch, Ivan H.

2014-09-01

We study quantum tomography based on a stochastic continuous-time measurement record obtained from a probe field collectively interacting with an ensemble of identically prepared systems. In comparison to previous studies, we consider here the case in which the measurement-induced backaction has a non-negligible effect on the dynamical evolution of the ensemble. We formulate a maximum likelihood estimate for the initial quantum state given only a single instance of the continuous diffusive measurement record. We apply our estimator to the simplest problem: state tomography of a single pure qubit, which, during the course of the measurement, is also subjected to dynamical control. We identify a regime where the many-body system is well approximated at all times by a separable pure spin coherent state, whose Bloch vector undergoes a conditional stochastic evolution. We simulate the results of our estimator and show that we can achieve close to the upper bound of fidelity set by the optimal generalized measurement. This estimate is compared to, and significantly outperforms, an equivalent estimator that ignores measurement backaction.

Synthetic clock states generated in a Bose-Einstein condensate via continuous dynamical decoupling

NASA Astrophysics Data System (ADS)

Lundblad, Nathan; Trypogeorgos, Dimitrios; Valdes-Curiel, Ana; Marshall, Erin; Spielman, Ian

2017-04-01

Radiofrequency- or microwave-dressed states have been used in NV center and ion-trap experiments to extend coherence times, shielding qubits from magnetic field noise through a process known as continuous dynamical decoupling. Such field-insensitive dressed states, as applied in the context of ultracold neutral atoms, have applications related to the creation of novel phases of spin-orbit-coupled quantum matter. We present observations of such a protected dressed-state system in a Bose-Einstein condensate, including measurements of the dependence of the protection on rf coupling strength, and estimates of residual field sensitivities.

Exploiting Continuous Scanning Laser Doppler Vibrometry in timing belt dynamic characterisation

NASA Astrophysics Data System (ADS)

Chiariotti, P.; Martarelli, M.; Castellini, P.

2017-03-01

Dynamic behaviour of timing belts has always interested the engineering community over the years. Nowadays, there are several numerical methods to predict the dynamics of these systems. However, the tuning of such models by experimental approaches still represents an issue: an accurate characterisation does require a measurement in operating conditions since the belt mounting condition might severely affect its dynamic behaviour. Moreover, since the belt is constantly moving during running conditions, non-contact measurement methods are needed. Laser Doppler Vibrometry (LDV) and imaging techniques do represent valid candidates for this purpose. This paper aims at describing the use of Continuous Scanning LDV (CSLDV) as a tool for the dynamic characterisation of timing belts in IC (Internal Combustion) engines (cylinder head). The high-spatial resolution data that can be collected in short testing time makes CSLDV highly suitable for such application. The measurement on a moving surface, however, represents a challenge for CSLDV. The paper discusses how the belt in-plane speed influences CSLDV signal and how an order-based multi-harmonic excitation might affect the recovery of Operational Deflection Shapes in a CSLDV test. A comparison with a standard Discrete Scanning LDV measurement is also given in order to show that a CSLDV test, if well designed, can indeed provide the same amount of information in a drastically reduced amount of time.

Dimensional analysis of acoustically propagated signals

NASA Technical Reports Server (NTRS)

Hansen, Scott D.; Thomson, Dennis W.

1993-01-01

Traditionally, long term measurements of atmospherically propagated sound signals have consisted of time series of multiminute averages. Only recently have continuous measurements with temporal resolution corresponding to turbulent time scales been available. With modern digital data acquisition systems we now have the capability to simultaneously record both acoustical and meteorological parameters with sufficient temporal resolution to allow us to examine in detail relationships between fluctuating sound and the meteorological variables, particularly wind and temperature, which locally determine the acoustic refractive index. The atmospheric acoustic propagation medium can be treated as a nonlinear dynamical system, a kind of signal processor whose innards depend on thermodynamic and turbulent processes in the atmosphere. The atmosphere is an inherently nonlinear dynamical system. In fact one simple model of atmospheric convection, the Lorenz system, may well be the most widely studied of all dynamical systems. In this paper we report some results of our having applied methods used to characterize nonlinear dynamical systems to study the characteristics of acoustical signals propagated through the atmosphere. For example, we investigate whether or not it is possible to parameterize signal fluctuations in terms of fractal dimensions. For time series one such parameter is the limit capacity dimension. Nicolis and Nicolis were among the first to use the kind of methods we have to study the properties of low dimension global attractors.

Crossing the threshold

NASA Astrophysics Data System (ADS)

Bush, John; Tambasco, Lucas

2017-11-01

First, we summarize the circumstances in which chaotic pilot-wave dynamics gives rise to quantum-like statistical behavior. For ``closed'' systems, in which the droplet is confined to a finite domain either by boundaries or applied forces, quantum-like features arise when the persistence time of the waves exceeds the time required for the droplet to cross its domain. Second, motivated by the similarities between this hydrodynamic system and stochastic electrodynamics, we examine the behavior of a bouncing droplet above the Faraday threshold, where a stochastic element is introduced into the drop dynamics by virtue of its interaction with a background Faraday wave field. With a view to extending the dynamical range of pilot-wave systems to capture more quantum-like features, we consider a generalized theoretical framework for stochastic pilot-wave dynamics in which the relative magnitudes of the drop-generated pilot-wave field and a stochastic background field may be varied continuously. We gratefully acknowledge the financial support of the NSF through their CMMI and DMS divisions.

Detection of generalized synchronization using echo state networks

NASA Astrophysics Data System (ADS)

Ibáñez-Soria, D.; Garcia-Ojalvo, J.; Soria-Frisch, A.; Ruffini, G.

2018-03-01

Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved with unsynchronized ones. Correctly tuned, echo state networks are able to efficiently discriminate between unsynchronized and synchronized sequences even in the presence of relatively high levels of noise. Compared to other state-of-the-art techniques of synchronization detection, the online capabilities of the proposed Echo State Network based methodology make it a promising choice for real-time applications aiming to monitor dynamical synchronization changes in continuous signals.

Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input.

PubMed

Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali

2017-09-01

In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs' weights, identification error, and system states is guaranteed using Lyapunov's direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Experimental nonlinear dynamical studies in cesium magneto-optical trap using time-series analysis

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

Anwar, M., E-mail: mamalik2000@gmail.com; Islam, R.; Faisal, M.

2015-03-30

A magneto-optical trap of neutral atoms is essentially a dissipative quantum system. The fast thermal atoms continuously dissipate their energy to the environment via spontaneous emissions during the cooling. The atoms are, therefore, strongly coupled with the vacuum reservoir and the laser field. The vacuum fluctuations as well as the field fluctuations are imparted to the atoms as random photon recoils. Consequently, the external and internal dynamics of atoms becomes stochastic. In this paper, we have investigated the stochastic dynamics of the atoms in a magneto-optical trap during the loading process. The time series analysis of the fluorescence signal showsmore » that the dynamics of the atoms evolves, like all dissipative systems, from deterministic to the chaotic regime. The subsequent disappearance and revival of chaos was attributed to chaos synchronization between spatially different atoms in the magneto-optical trap.« less

Quantization improves stabilization of dynamical systems with delayed feedback

NASA Astrophysics Data System (ADS)

Stepan, Gabor; Milton, John G.; Insperger, Tamas

2017-11-01

We show that an unstable scalar dynamical system with time-delayed feedback can be stabilized by quantizing the feedback. The discrete time model corresponds to a previously unrecognized case of the microchaotic map in which the fixed point is both locally and globally repelling. In the continuous-time model, stabilization by quantization is possible when the fixed point in the absence of feedback is an unstable node, and in the presence of feedback, it is an unstable focus (spiral). The results are illustrated with numerical simulation of the unstable Hayes equation. The solutions of the quantized Hayes equation take the form of oscillations in which the amplitude is a function of the size of the quantization step. If the quantization step is sufficiently small, the amplitude of the oscillations can be small enough to practically approximate the dynamics around a stable fixed point.

Policy choices in dementia care-An exploratory analysis of the Alberta continuing care system (ACCS) using system dynamics.

PubMed

Cepoiu-Martin, Monica; Bischak, Diane P

2018-02-01

The increase in the incidence of dementia in the aging population and the decrease in the availability of informal caregivers put pressure on continuing care systems to care for a growing number of people with disabilities. Policy changes in the continuing care system need to address this shift in the population structure. One of the most effective tools for assessing policies in complex systems is system dynamics. Nevertheless, this method is underused in continuing care capacity planning. A system dynamics model of the Alberta Continuing Care System was developed using stylized data. Sensitivity analyses and policy evaluations were conducted to demonstrate the use of system dynamics modelling in this area of public health planning. We focused our policy exploration on introducing staff/resident benchmarks in both supportive living and long-term care (LTC). The sensitivity analyses presented in this paper help identify leverage points in the system that need to be acknowledged when policy decisions are made. Our policy explorations showed that the deficits of staff increase dramatically when benchmarks are introduced, as expected, but at the end of the simulation period, the difference in deficits of both nurses and health care aids are similar between the 2 scenarios tested. Modifying the benchmarks in LTC only versus in both supportive living and LTC has similar effects on staff deficits in long term, under the assumptions of this particular model. The continuing care system dynamics model can be used to test various policy scenarios, allowing decision makers to visualize the effect of a certain policy choice on different system variables and to compare different policy options. Our exploration illustrates the use of system dynamics models for policy making in complex health care systems. © 2017 John Wiley & Sons, Ltd.

Deriving the exact nonadiabatic quantum propagator in the mapping variable representation.

PubMed

Hele, Timothy J H; Ananth, Nandini

2016-12-22

We derive an exact quantum propagator for nonadiabatic dynamics in multi-state systems using the mapping variable representation, where classical-like Cartesian variables are used to represent both continuous nuclear degrees of freedom and discrete electronic states. The resulting Liouvillian is a Moyal series that, when suitably approximated, can allow for the use of classical dynamics to efficiently model large systems. We demonstrate that different truncations of the exact Liouvillian lead to existing approximate semiclassical and mixed quantum-classical methods and we derive an associated error term for each method. Furthermore, by combining the imaginary-time path-integral representation of the Boltzmann operator with the exact Liouvillian, we obtain an analytic expression for thermal quantum real-time correlation functions. These results provide a rigorous theoretical foundation for the development of accurate and efficient classical-like dynamics to compute observables such as electron transfer reaction rates in complex quantized systems.

Dynamic analysis and electronic circuit implementation of a novel 3D autonomous system without linear terms

NASA Astrophysics Data System (ADS)

Kengne, J.; Jafari, S.; Njitacke, Z. T.; Yousefi Azar Khanian, M.; Cheukem, A.

2017-11-01

Mathematical models (ODEs) describing the dynamics of almost all continuous time chaotic nonlinear systems (e.g. Lorenz, Rossler, Chua, or Chen system) involve at least a nonlinear term in addition to linear terms. In this contribution, a novel (and singular) 3D autonomous chaotic system without linear terms is introduced. This system has an especial feature of having two twin strange attractors: one ordinary and one symmetric strange attractor when the time is reversed. The complex behavior of the model is investigated in terms of equilibria and stability, bifurcation diagrams, Lyapunov exponent plots, time series and Poincaré sections. Some interesting phenomena are found including for instance, period-doubling bifurcation, antimonotonicity (i.e. the concurrent creation and annihilation of periodic orbits) and chaos while monitoring the system parameters. Compared to the (unique) case previously reported by Xu and Wang (2014) [31], the system considered in this work displays a more 'elegant' mathematical expression and experiences richer dynamical behaviors. A suitable electronic circuit (i.e. the analog simulator) is designed and used for the investigations. Pspice based simulation results show a very good agreement with the theoretical analysis.

Minimal Time Problem with Impulsive Controls

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

Kunisch, Karl, E-mail: karl.kunisch@uni-graz.at; Rao, Zhiping, E-mail: zhiping.rao@ricam.oeaw.ac.at

Time optimal control problems for systems with impulsive controls are investigated. Sufficient conditions for the existence of time optimal controls are given. A dynamical programming principle is derived and Lipschitz continuity of an appropriately defined value functional is established. The value functional satisfies a Hamilton–Jacobi–Bellman equation in the viscosity sense. A numerical example for a rider-swing system is presented and it is shown that the reachable set is enlargered by allowing for impulsive controls, when compared to nonimpulsive controls.

Finite-Time and -Size Scalings in the Evaluation of Large Deviation Functions. Numerical Analysis in Continuous Time

NASA Astrophysics Data System (ADS)

Guevara Hidalgo, Esteban; Nemoto, Takahiro; Lecomte, Vivien

Rare trajectories of stochastic systems are important to understand because of their potential impact. However, their properties are by definition difficult to sample directly. Population dynamics provide a numerical tool allowing their study, by means of simulating a large number of copies of the system, which are subjected to a selection rule that favors the rare trajectories of interest. However, such algorithms are plagued by finite simulation time- and finite population size- effects that can render their use delicate. Using the continuous-time cloning algorithm, we analyze the finite-time and finite-size scalings of estimators of the large deviation functions associated to the distribution of the rare trajectories. We use these scalings in order to propose a numerical approach which allows to extract the infinite-time and infinite-size limit of these estimators.

Effects of stochastic noise on dynamical decoupling procedures

NASA Astrophysics Data System (ADS)

Bernád, J. Z.; Frydrych, H.

2014-06-01

Dynamical decoupling is an important tool to counter decoherence and dissipation effects in quantum systems originating from environmental interactions. It has been used successfully in many experiments; however, there is still a gap between fidelity improvements achieved in practice compared to theoretical predictions. We propose a model for imperfect dynamical decoupling based on a stochastic Ito differential equation which could explain the observed gap. We discuss the impact of our model on the time evolution of various quantum systems in finite- and infinite-dimensional Hilbert spaces. Analytical results are given for the limit of continuous control, whereas we present numerical simulations and upper bounds for the case of finite control.

Out-of-equilibrium dynamics driven by localized time-dependent perturbations at quantum phase transitions

NASA Astrophysics Data System (ADS)

Pelissetto, Andrea; Rossini, Davide; Vicari, Ettore

2018-03-01

We investigate the quantum dynamics of many-body systems subject to local (i.e., restricted to a limited space region) time-dependent perturbations. If the system crosses a quantum phase transition, an off-equilibrium behavior is observed, even for a very slow driving. We show that, close to the transition, time-dependent quantities obey scaling laws. In first-order transitions, the scaling behavior is universal, and some scaling functions can be computed exactly. For continuous transitions, the scaling laws are controlled by the standard critical exponents and by the renormalization-group dimension of the perturbation at the transition. Our protocol can be implemented in existing relatively small quantum simulators, paving the way for a quantitative probe of the universal off-equilibrium scaling behavior, without the need to manipulate systems close to the thermodynamic limit.

Nonlinear dynamics of global atmospheric and earth system processes

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Verbitsky, Mikhail; Saltzman, Barry; Mann, Michael E.; Park, Jeffrey; Lall, Upmanu

1995-01-01

During the grant period, the authors continued ongoing studies aimed at enhancing their understanding of the operation of the atmosphere as a complex nonlinear system interacting with the hydrosphere, biosphere, and cryosphere in response to external radiative forcing. Five papers were completed with support from the grant, representing contributions in three main areas of study: (1) theoretical studies of the interactive atmospheric response to changed biospheric boundary conditions measurable from satellites; (2) statistical-observational studies of global-scale temperature variability on interannual to century time scales; and (3) dynamics of long-term earth system changes associated with ice sheet surges.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

PubMed

Zhang, Jian-Hua; Xia, Jia-Jun; Garibaldi, Jonathan M; Groumpos, Petros P; Wang, Ru-Bin

2017-06-01

In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller. Copyright © 2017 Elsevier B.V. All rights reserved.

Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net

NASA Technical Reports Server (NTRS)

Lerner, Uri; Moses, Brooks; Scott, Maricia; McIlraith, Sheila; Keller, Daphne

2005-01-01

The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occuring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.

A heterogenous Cournot duopoly with delay dynamics: Hopf bifurcations and stability switching curves

NASA Astrophysics Data System (ADS)

Pecora, Nicolò; Sodini, Mauro

2018-05-01

This article considers a Cournot duopoly model in a continuous-time framework and analyze its dynamic behavior when the competitors are heterogeneous in determining their output decision. Specifically the model is expressed in the form of differential equations with discrete delays. The stability conditions of the unique Nash equilibrium of the system are determined and the emergence of Hopf bifurcations is shown. Applying some recent mathematical techniques (stability switching curves) and performing numerical simulations, the paper confirms how different time delays affect the stability of the economy.

Temporal networks

NASA Astrophysics Data System (ADS)

Holme, Petter; Saramäki, Jari

2012-10-01

A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology-rather, we want to make papers readable across disciplines.

Confirming Time-reversal Symmetry of a Directed Percolation Phase Transition in a Model of Neutral Evolutionary Dynamics

NASA Astrophysics Data System (ADS)

Ordway, Stephen; King, Dawn; Bahar, Sonya

Reaction-diffusion processes, such as branching-coalescing random walks, can be used to describe the underlying dynamics of nonequilibrium phase transitions. In an agent-based, neutral model of evolutionary dynamics, we have previously shown that our system undergoes a continuous, nonequilibrium phase transition, from extinction to survival, as various system parameters were tuned. This model was shown to belong to the directed percolation (DP) universality class, by measuring the critical exponents corresponding to correlation length ξ⊥, correlation time ξ| |, and particle density β. The fourth critical exponent that defines the DP universality class is β', which measures the survival probability of growth from a single seed organism. Since DP universality is theorized to have time-reversal symmetry, it is assumed that β = β '. In order to confirm the existence of time-reversal symmetry in our model, we evaluate the system growth from a single asexually reproducing organism. Importantly, the critical exponent β' could be useful for comparison to experimental studies of phase transitions in biological systems, since observing growth of microbial populations is significantly easier than observing death. This research was supported by funding from the James S. McDonnell Foundation.

Reconstruction of the in-plane mode shape of a rotating tire with a continuous scanning measurement using the Hilbert-Huang transform.

PubMed

Lee, Jongsuh; Wang, Semyung; Pluymers, Bert; Desmet, Wim; Kindt, Peter

2015-02-01

Generally, the dynamic characteristics (natural frequency, damping, and mode shape) of a structure can be estimated by experimental modal analysis. Among these dynamic characteristics, mode shape requires multiple measurements of the structure at different positions, which increases the experimental cost and time. Recently, the Hilbert-Huang transform (HHT) method has been introduced to extract mode-shape information from a continuous measurement, which requires vibration measurements from one position to another position continuously with a non-contact sensor. In this research study, an effort has been made to estimate the mode shapes of a rolling tire with a single measurement instead of using the conventional experimental setup (i.e., measurement of the vibration of a rolling tire at multiple positions similar to the case of a non-rotating structure), which is used to estimate the dynamic behavior of a rolling tire. For this purpose, HHT, which was used in the continuous measurement of a non-rotating structure in previous research studies, has been used for the case of a rotating system in this study. Ambiguous mode combinations can occur in this rotating system, and therefore, a method to overcome this ambiguity is proposed in this study. In addition, the specific phenomenon for a rotating system is introduced, and the effect of this phenomenon with regard to the obtained results through HHT is investigated.

Nonlinear dynamics and quantum entanglement in optomechanical systems.

PubMed

Wang, Guanglei; Huang, Liang; Lai, Ying-Cheng; Grebogi, Celso

2014-03-21

To search for and exploit quantum manifestations of classical nonlinear dynamics is one of the most fundamental problems in physics. Using optomechanical systems as a paradigm, we address this problem from the perspective of quantum entanglement. We uncover strong fingerprints in the quantum entanglement of two common types of classical nonlinear dynamical behaviors: periodic oscillations and quasiperiodic motion. There is a transition from the former to the latter as an experimentally adjustable parameter is changed through a critical value. Accompanying this process, except for a small region about the critical value, the degree of quantum entanglement shows a trend of continuous increase. The time evolution of the entanglement measure, e.g., logarithmic negativity, exhibits a strong dependence on the nature of classical nonlinear dynamics, constituting its signature.

Dynamic programming methods for concurrent design and dynamic allocation of vehicles embedded in a system-of-systems

NASA Astrophysics Data System (ADS)

Nusawardhana

2007-12-01

Recent developments indicate a changing perspective on how systems or vehicles should be designed. Such transition comes from the way decision makers in defense related agencies address complex problems. Complex problems are now often posed in terms of the capabilities desired, rather than in terms of requirements for a single systems. As a result, the way to provide a set of capabilities is through a collection of several individual, independent systems. This collection of individual independent systems is often referred to as a "System of Systems'' (SoS). Because of the independent nature of the constituent systems in an SoS, approaches to design an SoS, and more specifically, approaches to design a new system as a member of an SoS, will likely be different than the traditional design approaches for complex, monolithic (meaning the constituent parts have no ability for independent operation) systems. Because a system of system evolves over time, this simultaneous system design and resource allocation problem should be investigated in a dynamic context. Such dynamic optimization problems are similar to conventional control problems. However, this research considers problems which not only seek optimizing policies but also seek the proper system or vehicle to operate under these policies. This thesis presents a framework and a set of analytical tools to solve a class of SoS problems that involves the simultaneous design of a new system and allocation of the new system along with existing systems. Such a class of problems belongs to the problems of concurrent design and control of a new systems with solutions consisting of both optimal system design and optimal control strategy. Rigorous mathematical arguments show that the proposed framework solves the concurrent design and control problems. Many results exist for dynamic optimization problems of linear systems. In contrary, results on optimal nonlinear dynamic optimization problems are rare. The proposed framework is equipped with the set of analytical tools to solve several cases of nonlinear optimal control problems: continuous- and discrete-time nonlinear problems with applications on both optimal regulation and tracking. These tools are useful when mathematical descriptions of dynamic systems are available. In the absence of such a mathematical model, it is often necessary to derive a solution based on computer simulation. For this case, a set of parameterized decision may constitute a solution. This thesis presents a method to adjust these parameters based on the principle of stochastic approximation simultaneous perturbation using continuous measurements. The set of tools developed here mostly employs the methods of exact dynamic programming. However, due to the complexity of SoS problems, this research also develops suboptimal solution approaches, collectively recognized as approximate dynamic programming solutions, for large scale problems. The thesis presents, explores, and solves problems from an airline industry, in which a new aircraft is to be designed and allocated along with an existing fleet of aircraft. Because the life cycle of an aircraft is on the order of 10 to 20 years, this problem is to be addressed dynamically so that the new aircraft design is the best design for the fleet over a given time horizon.

Phase Transitions and Scaling in Systems Far from Equilibrium

NASA Astrophysics Data System (ADS)

Täuber, Uwe C.

2017-03-01

Scaling ideas and renormalization group approaches proved crucial for a deep understanding and classification of critical phenomena in thermal equilibrium. Over the past decades, these powerful conceptual and mathematical tools were extended to continuous phase transitions separating distinct nonequilibrium stationary states in driven classical and quantum systems. In concordance with detailed numerical simulations and laboratory experiments, several prominent dynamical universality classes have emerged that govern large-scale, long-time scaling properties both near and far from thermal equilibrium. These pertain to genuine specific critical points as well as entire parameter space regions for steady states that display generic scale invariance. The exploration of nonstationary relaxation properties and associated physical aging scaling constitutes a complementary potent means to characterize cooperative dynamics in complex out-of-equilibrium systems. This review describes dynamic scaling features through paradigmatic examples that include near-equilibrium critical dynamics, driven lattice gases and growing interfaces, correlation-dominated reaction-diffusion systems, and basic epidemic models.

Causal relations among events and states in dynamic geographical phenomena

NASA Astrophysics Data System (ADS)

Huang, Zhaoqiang; Feng, Xuezhi; Xuan, Wenling; Chen, Xiuwan

2007-06-01

There is only a static state of the real world to be recorded in conventional geographical information systems. However, there is not only static information but also dynamic information in geographical phenomena. So that how to record the dynamic information and reveal the relations among dynamic information is an important issue in a spatio-temporal information system. From an ontological perspective, we can initially divide the spatio-temporal entities in the world into continuants and occurrents. Continuant entities endure through some extended (although possibly very short) interval of time (e.g., houses, roads, cities, and real-estate). Occurrent entities happen and are then gone (e.g., a house repair job, road construction project, urban expansion, real-estate transition). From an information system perspective, continuants and occurrents that have a unique identity in the system are referred to as objects and events, respectively. And the change is represented implicitly by static snapshots in current spatial temporal information systems. In the previous models, the objects can be considered as the fundamental components of the system, and the change is modeled by considering time-varying attributes of these objects. In the spatio-temporal database, the temporal information that is either interval or instant is involved and the underlying data structures and indexes for temporal are considerable investigated. However, there is the absence of explicit ways of considering events, which affect the attributes of objects or the state. So the research issue of this paper focuses on how to model events in conceptual models of dynamic geographical phenomena and how to represent the causal relations among events and the objects or states. Firstly, the paper reviews the conceptual modeling in a temporal GIS by researchers. Secondly, this paper discusses the spatio-temporal entities: objects and events. Thirdly, this paper investigates the causal relations amongst events and states. The qualitative spatiotemporal change is an important issue in the dynamic geographic-scale phenomena. In real estate transition, the events and states are needed to be represented explicitly. In our modeling the evolution of a dynamic system, it can not avoid fetching in the view of causality. The object's transition is represented by the state of object. Event causes the state of objects changing and causes other events happen. Events connect with objects closely. The basic causal relations are the state-event and event-state relationships. Lastly, the paper concludes with the overview about the causal relations amongst events and states. And this future work is pointed.

Discrete-Time Mapping for an Impulsive Goodwin Oscillator with Three Delays

NASA Astrophysics Data System (ADS)

Churilov, Alexander N.; Medvedev, Alexander; Zhusubaliyev, Zhanybai T.

A popular biomathematics model of the Goodwin oscillator has been previously generalized to a more biologically plausible construct by introducing three time delays to portray the transport phenomena arising due to the spatial distribution of the model states. The present paper addresses a similar conversion of an impulsive version of the Goodwin oscillator that has found application in mathematical modeling, e.g. in endocrine systems with pulsatile hormone secretion. While the cascade structure of the linear continuous part pertinent to the Goodwin oscillator is preserved in the impulsive Goodwin oscillator, the static nonlinear feedback of the former is substituted with a pulse modulation mechanism thus resulting in hybrid dynamics of the closed-loop system. To facilitate the analysis of the mathematical model under investigation, a discrete mapping propagating the continuous state variables through the firing times of the impulsive feedback is derived. Due to the presence of multiple time delays in the considered model, previously developed mapping derivation approaches are not applicable here and a novel technique is proposed and applied. The mapping captures the dynamics of the original hybrid system and is instrumental in studying complex nonlinear phenomena arising in the impulsive Goodwin oscillator. A simulation example is presented to demonstrate the utility of the proposed approach in bifurcation analysis.

Autonomous Information Fading and Provision to Achieve High Response Time in Distributed Information Systems

NASA Astrophysics Data System (ADS)

Lu, Xiaodong; Arfaoui, Helene; Mori, Kinji

In highly dynamic electronic commerce environment, the need for adaptability and rapid response time to information service systems has become increasingly important. In order to cope with the continuously changing conditions of service provision and utilization, Faded Information Field (FIF) has been proposed. FIF is a distributed information service system architecture, sustained by push/pull mobile agents to bring high-assurance of services through a recursive demand-oriented provision of the most popular information closer to the users to make a tradeoff between the cost of information service allocation and access. In this paper, based on the analysis of the relationship that exists among the users distribution, information provision and access time, we propose the technology for FIF design to resolve the competing requirements of users and providers to improve users' access time. In addition, to achieve dynamic load balancing with changing users preference, the autonomous information reallocation technology is proposed. We proved the effectiveness of the proposed technology through the simulation and comparison with the conventional system.

Smoothed quantum-classical states in time-irreversible hybrid dynamics

NASA Astrophysics Data System (ADS)

Budini, Adrián A.

2017-09-01

We consider a quantum system continuously monitored in time which in turn is coupled to an arbitrary dissipative classical system (diagonal reduced density matrix). The quantum and classical dynamics can modify each other, being described by an arbitrary time-irreversible hybrid Lindblad equation. Given a measurement trajectory, a conditional bipartite stochastic state can be inferred by taking into account all previous recording information (filtering). Here, we demonstrate that the joint quantum-classical state can also be inferred by taking into account both past and future measurement results (smoothing). The smoothed hybrid state is estimated without involving information from unobserved measurement channels. Its average over recording realizations recovers the joint time-irreversible behavior. As an application we consider a fluorescent system monitored by an inefficient photon detector. This feature is taken into account through a fictitious classical two-level system. The average purity of the smoothed quantum state increases over that of the (mixed) state obtained from the standard quantum jump approach.

Pseudochemotaxis in inhomogeneous active Brownian systems

NASA Astrophysics Data System (ADS)

Vuijk, Hidde D.; Sharma, Abhinav; Mondal, Debasish; Sommer, Jens-Uwe; Merlitz, Holger

2018-04-01

We study dynamical properties of confined, self-propelled Brownian particles in an inhomogeneous activity profile. Using Brownian dynamics simulations, we calculate the probability to reach a fixed target and the mean first passage time to the target of an active particle. We show that both these quantities are strongly influenced by the inhomogeneous activity. When the activity is distributed such that high-activity zone is located between the target and the starting location, the target finding probability is increased and the passage time is decreased in comparison to a uniformly active system. Moreover, for a continuously distributed profile, the activity gradient results in a drift of active particle up the gradient bearing resemblance to chemotaxis. Integrating out the orientational degrees of freedom, we derive an approximate Fokker-Planck equation and show that the theoretical predictions are in very good agreement with the Brownian dynamics simulations.

Higher-Order Hurst Signatures: Dynamical Information in Time Series

NASA Astrophysics Data System (ADS)

Ferenbaugh, Willis

2005-10-01

Understanding and comparing time series from different systems requires characteristic measures of the dynamics embedded in the series. The Hurst exponent is a second-order dynamical measure of a time series which grew up within the blossoming fractal world of Mandelbrot. This characteristic measure is directly related to the behavior of the autocorrelation, the power-spectrum, and other second-order things. And as with these other measures, the Hurst exponent captures and quantifies some but not all of the intrinsic nature of a series. The more elusive characteristics live in the phase spectrum and the higher-order spectra. This research is a continuing quest to (more) fully characterize the dynamical information in time series produced by plasma experiments or models. The goal is to supplement the series information which can be represented by a Hurst exponent, and we would like to develop supplemental techniques in analogy with Hurst's original R/S analysis. These techniques should be another way to plumb the higher-order dynamics.

Finite-dimensional modeling of network-induced delays for real-time control systems

NASA Technical Reports Server (NTRS)

Ray, Asok; Halevi, Yoram

1988-01-01

In integrated control systems (ICS), a feedback loop is closed by the common communication channel, which multiplexes digital data from the sensor to the controller and from the controller to the actuator along with the data traffic from other control loops and management functions. Due to asynchronous time-division multiplexing in the network access protocols, time-varying delays are introduced in the control loop, which degrade the system dynamic performance and are a potential source of instability. The delayed control system is represented by a finite-dimensional, time-varying, discrete-time model which is less complex than the existing continuous-time models for time-varying delays; this approach allows for simpler schemes for analysis and simulation of the ICS.

A data driven nonlinear stochastic model for blood glucose dynamics.

PubMed

Zhang, Yan; Holt, Tim A; Khovanova, Natalia

2016-03-01

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Modelling the dynamics of traits involved in fighting-predators-prey system.

PubMed

Kooi, B W

2015-12-01

We study the dynamics of a predator-prey system where predators fight for captured prey besides searching for and handling (and digestion) of the prey. Fighting for prey is modelled by a continuous time hawk-dove game dynamics where the gain depends on the amount of disputed prey while the costs for fighting is constant per fighting event. The strategy of the predator-population is quantified by a trait being the proportion of the number of predator-individuals playing hawk tactics. The dynamics of the trait is described by two models of adaptation: the replicator dynamics (RD) and the adaptive dynamics (AD). In the RD-approach a variant individual with an adapted trait value changes the population's strategy, and consequently its trait value, only when its payoff is larger than the population average. In the AD-approach successful replacement of the resident population after invasion of a rare variant population with an adapted trait value is a step in a sequence changing the population's strategy, and hence its trait value. The main aim is to compare the consequences of the two adaptation models. In an equilibrium predator-prey system this will lead to convergence to a neutral singular strategy, while in the oscillatory system to a continuous singular strategy where in this endpoint the resident population is not invasible by any variant population. In equilibrium (low prey carrying capacity) RD and AD-approach give the same results, however not always in a periodically oscillating system (high prey carrying-capacity) where the trait is density-dependent. For low costs the predator population is monomorphic (only hawks) while for high costs dimorphic (hawks and doves). These results illustrate that intra-specific trait dynamics matters in predator-prey dynamics.

Dynamics of Polydisperse Foam-like Emulsion

NASA Astrophysics Data System (ADS)

Hicock, Harry; Feitosa, Klebert

2011-10-01

Foam is a complex fluid whose relaxation properties are associated with the continuous diffusion of gas from small to large bubbles driven by differences in Laplace pressures. We study the dynamics of bubble rearrangements by tracking droplets of a clear, buoyantly neutral emulsion that coarsens like a foam. The droplets are imaged in three dimensions using confocal microscopy. Analysis of the images allows us to measure their positions and radii, and track their evolution in time. We find that the droplet size distribution fits a Weibull distribution characteristics of foam systems. Additionally, we observe that droplets undergo continuous evolution interspersed by occasional large rearrangements in par with local relaxation behavior typical of foams.

Continuous-variable quantum teleportation in bosonic structured environments

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

He Guangqiang; Zhang Jingtao; Zhu Jun

2011-09-15

The effects of dynamics of continuous-variable entanglement under the various kinds of environments on quantum teleportation are quantitatively investigated. Only under assumption of the weak system-reservoir interaction, the evolution of teleportation fidelity is analytically derived and is numerically plotted in terms of environment parameters including reservoir temperature and its spectral density, without Markovian and rotating wave approximations. We find that the fidelity of teleportation is a monotonically decreasing function for Markovian interaction in Ohmic-like environments, while it oscillates for non-Markovian ones. According to the dynamical laws of teleportation, teleportation with better performances can be implemented by selecting the appropriate time.

Dynamical Heterogeneity in Granular Fluids and Structural Glasses

NASA Astrophysics Data System (ADS)

Avila, Karina E.

Our current understanding of the dynamics of supercooled liquids and other similar slowly evolving (glassy) systems is rather limited. One aspect that is particularly poorly understood is the origin and behavior of the strong non trivial fluctuations that appear in the relaxation process toward equilibrium. Glassy systems and granular systems both present regions of particles moving cooperatively and at different rates from other regions. This phenomenon is known as spatially heterogeneous dynamics. A detailed explanation of this phenomenon may lead to a better understanding of the slow relaxation process, and perhaps it could even help to explain the presence of the glass transition. This dissertation concentrates on studying dynamical heterogeneity by analyzing simulation data for models of granular materials and structural glasses. For dissipative granular fluids, the growing behavior of dynamical heterogeneities is studied for different densities and different degrees of inelasticity in the particle collisions. The correlated regions are found to grow rapidly as the system approaches dynamical arrest. Their geometry is conserved even when probing at different cutoff length in the correlation function or when the energy dissipation in the system is increased. For structural glasses, I test a theoretical framework that models dynamical heterogeneity as originated in the presence of Goldstone modes, which emerge from a broken continuous time reparametrization symmetry. This analysis is based on quantifying the size and the spatial correlations of fluctuations in the time variable and of other kinds of fluctuations. The results obtained here agree with the predictions of the hypothesis. In particular, the fluctuations associated to the time reparametrization invariance become stronger for low temperatures, long timescales, and large coarse graining lengths. Overall, this research points to dynamical heterogeneity to be described for granular systems similarly than for other glassy systems and it provides evidence in favor of a particular theory for the origin of dynamical heterogeneity.

Building a Morbidostat: An automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition

PubMed Central

Toprak, Erdal; Veres, Adrian; Yildiz, Sadik; Pedraza, Juan M.; Chait, Remy; Paulsson, Johan; Kishony, Roy

2013-01-01

We present a protocol for building and operating an automated fluidic system for continuous culture that we call the “morbidostat”. The morbidostat is used to follow evolution of microbial drug resistance in real time. Instead of exposing bacteria to predetermined drug environments, the morbidostat constantly measures the growth rates of evolving microbial populations and dynamically adjusts drug concentrations inside culture vials in order to maintain a constant drug induced inhibition. The growth rate measurements are done using an optical detection system that is based on measuring the intensity of back-scattered light from bacterial cells suspended in the liquid culture. The morbidostat can additionally be used as a chemostat or a turbidostat. The whole system can be built from readily available components within two to three weeks, by biologists with some electronics experience or engineers familiar with basic microbiology. PMID:23429717

Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2017-01-01

Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements.

Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics

PubMed Central

2017-01-01

Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements. PMID:28727850

Quantum power functional theory for many-body dynamics

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

Schmidt, Matthias, E-mail: Matthias.Schmidt@uni-bayreuth.de

2015-11-07

We construct a one-body variational theory for the time evolution of nonrelativistic quantum many-body systems. The position- and time-dependent one-body density, particle current, and time derivative of the current act as three variational fields. The generating (power rate) functional is minimized by the true current time derivative. The corresponding Euler-Lagrange equation, together with the continuity equation for the density, forms a closed set of one-body equations of motion. Space- and time-nonlocal one-body forces are generated by the superadiabatic contribution to the functional. The theory applies to many-electron systems.

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

PubMed Central

Havlicek, Martin; Friston, Karl J.; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2011-01-01

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. PMID:21396454

Quantum circuit dynamics via path integrals: Is there a classical action for discrete-time paths?

NASA Astrophysics Data System (ADS)

Penney, Mark D.; Enshan Koh, Dax; Spekkens, Robert W.

2017-07-01

It is straightforward to compute the transition amplitudes of a quantum circuit using the sum-over-paths methodology when the gates in the circuit are balanced, where a balanced gate is one for which all non-zero transition amplitudes are of equal magnitude. Here we consider the question of whether, for such circuits, the relative phases of different discrete-time paths through the configuration space can be defined in terms of a classical action, as they are for continuous-time paths. We show how to do so for certain kinds of quantum circuits, namely, Clifford circuits where the elementary systems are continuous-variable systems or discrete systems of odd-prime dimension. These types of circuit are distinguished by having phase-space representations that serve to define their classical counterparts. For discrete systems, the phase-space coordinates are also discrete variables. We show that for each gate in the generating set, one can associate a symplectomorphism on the phase-space and to each of these one can associate a generating function, defined on two copies of the configuration space. For discrete systems, the latter association is achieved using tools from algebraic geometry. Finally, we show that if the action functional for a discrete-time path through a sequence of gates is defined using the sum of the corresponding generating functions, then it yields the correct relative phases for the path-sum expression. These results are likely to be relevant for quantizing physical theories where time is fundamentally discrete, characterizing the classical limit of discrete-time quantum dynamics, and proving complexity results for quantum circuits.

Efficient massively parallel simulation of dynamic channel assignment schemes for wireless cellular communications

NASA Technical Reports Server (NTRS)

Greenberg, Albert G.; Lubachevsky, Boris D.; Nicol, David M.; Wright, Paul E.

1994-01-01

Fast, efficient parallel algorithms are presented for discrete event simulations of dynamic channel assignment schemes for wireless cellular communication networks. The driving events are call arrivals and departures, in continuous time, to cells geographically distributed across the service area. A dynamic channel assignment scheme decides which call arrivals to accept, and which channels to allocate to the accepted calls, attempting to minimize call blocking while ensuring co-channel interference is tolerably low. Specifically, the scheme ensures that the same channel is used concurrently at different cells only if the pairwise distances between those cells are sufficiently large. Much of the complexity of the system comes from ensuring this separation. The network is modeled as a system of interacting continuous time automata, each corresponding to a cell. To simulate the model, conservative methods are used; i.e., methods in which no errors occur in the course of the simulation and so no rollback or relaxation is needed. Implemented on a 16K processor MasPar MP-1, an elegant and simple technique provides speedups of about 15 times over an optimized serial simulation running on a high speed workstation. A drawback of this technique, typical of conservative methods, is that processor utilization is rather low. To overcome this, new methods were developed that exploit slackness in event dependencies over short intervals of time, thereby raising the utilization to above 50 percent and the speedup over the optimized serial code to about 120 times.

Potentials Unbounded Below

NASA Astrophysics Data System (ADS)

Curtright, Thomas

2011-04-01

Continuous interpolates are described for classical dynamical systems defined by discrete time-steps. Functional conjugation methods play a central role in obtaining the interpolations. The interpolates correspond to particle motion in an underlying potential, V. Typically, V has no lower bound and can exhibit switchbacks wherein V changes form when turning points are encountered by the particle. The Beverton-Holt and Skellam models of population dynamics, and particular cases of the logistic map are used to illustrate these features.

Study on perception and control layer of mine CPS with mixed logic dynamic approach

NASA Astrophysics Data System (ADS)

Li, Jingzhao; Ren, Ping; Yang, Dayu

2017-01-01

Mine inclined roadway transportation system of mine cyber physical system is a hybrid system consisting of a continuous-time system and a discrete-time system, which can be divided into inclined roadway signal subsystem, error-proofing channel subsystems, anti-car subsystems, and frequency control subsystems. First, to ensure stable operation, improve efficiency and production safety, this hybrid system model with n inputs and m outputs is constructed and analyzed in detail, then its steady schedule state to be solved. Second, on the basis of the formal modeling for real-time systems, we use hybrid toolbox for system security verification. Third, the practical application of mine cyber physical system shows that the method for real-time simulation of mine cyber physical system is effective.

Parallel Multi-Step/Multi-Rate Integration of Two-Time Scale Dynamic Systems

NASA Technical Reports Server (NTRS)

Chang, Johnny T.; Ploen, Scott R.; Sohl, Garett. A,; Martin, Bryan J.

2004-01-01

Increasing demands on the fidelity of simulations for real-time and high-fidelity simulations are stressing the capacity of modern processors. New integration techniques are required that provide maximum efficiency for systems that are parallelizable. However many current techniques make assumptions that are at odds with non-cascadable systems. A new serial multi-step/multi-rate integration algorithm for dual-timescale continuous state systems is presented which applies to these systems, and is extended to a parallel multi-step/multi-rate algorithm. The superior performance of both algorithms is demonstrated through a representative example.

Dynamical inference: where phase synchronization and generalized synchronization meet.

PubMed

Stankovski, Tomislav; McClintock, Peter V E; Stefanovska, Aneta

2014-06-01

Synchronization is a widespread phenomenon that occurs among interacting oscillatory systems. It facilitates their temporal coordination and can lead to the emergence of spontaneous order. The detection of synchronization from the time series of such systems is of great importance for the understanding and prediction of their dynamics, and several methods for doing so have been introduced. However, the common case where the interacting systems have time-variable characteristic frequencies and coupling parameters, and may also be subject to continuous external perturbation and noise, still presents a major challenge. Here we apply recent developments in dynamical Bayesian inference to tackle these problems. In particular, we discuss how to detect phase slips and the existence of deterministic coupling from measured data, and we unify the concepts of phase synchronization and general synchronization. Starting from phase or state observables, we present methods for the detection of both phase and generalized synchronization. The consistency and equivalence of phase and generalized synchronization are further demonstrated, by the analysis of time series from analog electronic simulations of coupled nonautonomous van der Pol oscillators. We demonstrate that the detection methods work equally well on numerically simulated chaotic systems. In all the cases considered, we show that dynamical Bayesian inference can clearly identify noise-induced phase slips and distinguish coherence from intrinsic coupling-induced synchronization.

Generalized predictive control for a coupled four tank MIMO system using a continuous-discrete time observer.

PubMed

Gouta, Houssemeddine; Hadj Saïd, Salim; Barhoumi, Nabil; M'Sahli, Faouzi

2017-03-01

This paper deals with the problem of the observer based control design for a coupled four-tank liquid level system. For this MIMO system's dynamics, motivated by a desire to provide precise and sensorless liquid level control, a nonlinear predictive controller based on a continuous-discrete observer is presented. First, an analytical solution from the model predictive control (MPC) technique is developed for a particular class of nonlinear MIMO systems and its corresponding exponential stability is proven. Then, a high gain observer that runs in continuous-time with an output error correction time that is updated in a mixed continuous-discrete fashion is designed in order to estimate the liquid levels in the two upper tanks. The effectiveness of the designed control schemes are validated by two tests; The first one is maintaining a constant level in the first bottom tank while making the level in the second bottom tank to follow a sinusoidal reference signal. The second test is more difficult and it is made using two trapezoidal reference signals in order to see the decoupling performance of the system's outputs. Simulation and experimental results validate the objective of the paper. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Time-resolved atomic inner-shell spectroscopy

NASA Astrophysics Data System (ADS)

Drescher, M.; Hentschel, M.; Kienberger, R.; Uiberacker, M.; Yakovlev, V.; Scrinzi, A.; Westerwalbesloh, Th.; Kleineberg, U.; Heinzmann, U.; Krausz, F.

2002-10-01

The characteristic time constants of the relaxation dynamics of core-excited atoms have hitherto been inferred from the linewidths of electronic transitions measured by continuous-wave extreme ultraviolet or X-ray spectroscopy. Here we demonstrate that a laser-based sampling system, consisting of a few-femtosecond visible light pulse and a synchronized sub-femtosecond soft X-ray pulse, allows us to trace these dynamics directly in the time domain with attosecond resolution. We have measured a lifetime of 7.9-0.9^{+1.0fs of M-shell vacancies of krypton in such a pump-probe experiment.}

Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.

PubMed

Fon, Warren; Matheny, Matthew H; Li, Jarvis; Krayzman, Lev; Cross, Michael C; D'Souza, Raissa M; Crutchfield, James P; Roukes, Michael L

2017-10-11

Control of the global parameters of complex networks has been explored experimentally in a variety of contexts. Yet, the more difficult prospect of realizing arbitrary network architectures, especially analog physical networks that provide dynamical control of individual nodes and edges, has remained elusive. Given the vast hierarchy of time scales involved, it also proves challenging to measure a complex network's full internal dynamics. These span from the fastest nodal dynamics to very slow epochs over which emergent global phenomena, including network synchronization and the manifestation of exotic steady states, eventually emerge. Here, we demonstrate an experimental system that satisfies these requirements. It is based upon modular, fully controllable, nonlinear radio frequency nanomechanical oscillators, designed to form the nodes of complex dynamical networks with edges of arbitrary topology. The dynamics of these oscillators and their surrounding network are analog and continuous-valued and can be fully interrogated in real time. They comprise a piezoelectric nanomechanical membrane resonator, which serves as the frequency-determining element within an electrical feedback circuit. This embodiment permits network interconnections entirely within the electrical domain and provides unprecedented node and edge control over a vast region of parameter space. Continuous measurement of the instantaneous amplitudes and phases of every constituent oscillator node are enabled, yielding full and detailed network data without reliance upon statistical quantities. We demonstrate the operation of this platform through the real-time capture of the dynamics of a three-node ring network as it evolves from the uncoupled state to full synchronization.

Recent advances in symmetric and network dynamics

NASA Astrophysics Data System (ADS)

Golubitsky, Martin; Stewart, Ian

2015-09-01

We summarize some of the main results discovered over the past three decades concerning symmetric dynamical systems and networks of dynamical systems, with a focus on pattern formation. In both of these contexts, extra constraints on the dynamical system are imposed, and the generic phenomena can change. The main areas discussed are time-periodic states, mode interactions, and non-compact symmetry groups such as the Euclidean group. We consider both dynamics and bifurcations. We summarize applications of these ideas to pattern formation in a variety of physical and biological systems, and explain how the methods were motivated by transferring to new contexts René Thom's general viewpoint, one version of which became known as "catastrophe theory." We emphasize the role of symmetry-breaking in the creation of patterns. Topics include equivariant Hopf bifurcation, which gives conditions for a periodic state to bifurcate from an equilibrium, and the H/K theorem, which classifies the pairs of setwise and pointwise symmetries of periodic states in equivariant dynamics. We discuss mode interactions, which organize multiple bifurcations into a single degenerate bifurcation, and systems with non-compact symmetry groups, where new technical issues arise. We transfer many of the ideas to the context of networks of coupled dynamical systems, and interpret synchrony and phase relations in network dynamics as a type of pattern, in which space is discretized into finitely many nodes, while time remains continuous. We also describe a variety of applications including animal locomotion, Couette-Taylor flow, flames, the Belousov-Zhabotinskii reaction, binocular rivalry, and a nonlinear filter based on anomalous growth rates for the amplitude of periodic oscillations in a feed-forward network.

Disentangling the stochastic behavior of complex time series

NASA Astrophysics Data System (ADS)

Anvari, Mehrnaz; Tabar, M. Reza Rahimi; Peinke, Joachim; Lehnertz, Klaus

2016-10-01

Complex systems involving a large number of degrees of freedom, generally exhibit non-stationary dynamics, which can result in either continuous or discontinuous sample paths of the corresponding time series. The latter sample paths may be caused by discontinuous events - or jumps - with some distributed amplitudes, and disentangling effects caused by such jumps from effects caused by normal diffusion processes is a main problem for a detailed understanding of stochastic dynamics of complex systems. Here we introduce a non-parametric method to address this general problem. By means of a stochastic dynamical jump-diffusion modelling, we separate deterministic drift terms from different stochastic behaviors, namely diffusive and jumpy ones, and show that all of the unknown functions and coefficients of this modelling can be derived directly from measured time series. We demonstrate appli- cability of our method to empirical observations by a data-driven inference of the deterministic drift term and of the diffusive and jumpy behavior in brain dynamics from ten epilepsy patients. Particularly these different stochastic behaviors provide extra information that can be regarded valuable for diagnostic purposes.

Novel system for picosecond photoemission spectroscopy

NASA Astrophysics Data System (ADS)

Haight, R.; Silberman, J. A.; Lilie, M. I.

1988-09-01

This article describes a laser-based source and detection scheme for performing time-resolved photoemission studies of materials. The pulsed laser source produces intense picosecond pulses of coherent radiation that are nearly continuously tunable from the near infrared to photon energies up to 13 eV. To achieve high sensitivity, a novel multianode time-of-flight spectrometer has been built that generates an angularly resolved intensity versus kinetic energy spectrum with better than 100-meV resolution. The source and detector provide an opportunity to study the electronic dynamics of excited systems on a picosecond time scale.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Space station dynamics, attitude control and momentum management

NASA Technical Reports Server (NTRS)

Sunkel, John W.; Singh, Ramen P.; Vengopal, Ravi

1989-01-01

The Space Station Attitude Control System software test-bed provides a rigorous environment for the design, development and functional verification of GN and C algorithms and software. The approach taken for the simulation of the vehicle dynamics and environmental models using a computationally efficient algorithm is discussed. The simulation includes capabilities for docking/berthing dynamics, prescribed motion dynamics associated with the Mobile Remote Manipulator System (MRMS) and microgravity disturbances. The vehicle dynamics module interfaces with the test-bed through the central Communicator facility which is in turn driven by the Station Control Simulator (SCS) Executive. The Communicator addresses issues such as the interface between the discrete flight software and the continuous vehicle dynamics, and multi-programming aspects such as the complex flow of control in real-time programs. Combined with the flight software and redundancy management modules, the facility provides a flexible, user-oriented simulation platform.

Chaotic Motions in the Real Fuzzy Electronic Circuits

DTIC Science & Technology

2012-12-30

field of secure communications, the original source should be blended with other complex signals. Chaotic signals are one of the good sources to be...Takagi-Sugeno (T-S) fuzzy chaotic systems on electronic circuit. In the research field of secure communications, the original source should be blended ...model. The overall fuzzy model of the system is achieved by fuzzy blending of the linear system models. Consider a continuous-time nonlinear dynamic

Continuous system modeling

NASA Technical Reports Server (NTRS)

Cellier, Francois E.

1991-01-01

A comprehensive and systematic introduction is presented for the concepts associated with 'modeling', involving the transition from a physical system down to an abstract description of that system in the form of a set of differential and/or difference equations, and basing its treatment of modeling on the mathematics of dynamical systems. Attention is given to the principles of passive electrical circuit modeling, planar mechanical systems modeling, hierarchical modular modeling of continuous systems, and bond-graph modeling. Also discussed are modeling in equilibrium thermodynamics, population dynamics, and system dynamics, inductive reasoning, artificial neural networks, and automated model synthesis.

The Continued Demise of Columbia Glacier: Insights On Dynamic Change

NASA Astrophysics Data System (ADS)

Enderlin, E. M.; Hamilton, G. S.; O'Neel, S.; Bartholomaus, T. C.

2016-12-01

Columbia Glacier, Alaska, has served as the archetype for the retreat phase of the tidewater glacier cycle for the past three decades. Since the mid-1980s, the terminus has retreated 16 kilometers and the two major tributaries have thinned by > 400 m. This retreat and thinning led to separation of the tributaries in the late 2000s. Since their separation, the tributaries have exhibited strikingly different dynamic behaviors over seasonal to inter-annual time scales as they continue to adjust to the long-term changes in glacier geometry. Here we use a combination of ground, airborne, and satellite remote sensing datasets to characterize the dynamic behavior of the Columbia Glacier system. We focus on the time period following tributary separation, when the observational record is most abundant, but also investigate longer-term changes in dynamics such as the reorganization of ice flow in the eastern tributary (Figure 1). From the mid 2000s through 2012, the tributaries thinned at comparable rates ( 25 m/yr) based on repeat DEM differencing. Their behavior diverged in 2012, when the eastern tributary appeared to stabilize but the western tributary continued its sustained thinning trend. Thinning resumed along the eastern tributary in late 2013, and was accompanied by modest terminus retreat and acceleration. In contrast, the rate of thinning dramatically increased along the western tributary as it began to rapidly retreat in late 2013. These changes coincided with the three-fold increase in flow speed and pronounced increase in iceberg discharge from the western tributary. Although variations in the timing and magnitude of the recent dynamic changes can be at least partially explained by differences in the geometries of the tributaries, the dynamic behavior of Columbia Glacier's major tributaries is unlikely to be totally independent of environmental perturbations (i.e., entirely driven by the long-term dynamic adjustment). To assess the influence of environmental perturbations on the dynamic behavior of the glacier, we compare weekly to multi-year changes in glacier dynamics constructed from our airborne and satellite remotely-sensed datasets to time series of frontal ablation (i.e., submarine melting and iceberg calving) and surface mass balance compiled from ground-based observations.

Phase locking of a seven-channel continuous wave fibre laser system by a stochastic parallel gradient algorithm

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

Volkov, M V; Garanin, S G; Dolgopolov, Yu V

2014-11-30

A seven-channel fibre laser system operated by the master oscillator – multichannel power amplifier scheme is the phase locked using a stochastic parallel gradient algorithm. The phase modulators on lithium niobate crystals are controlled by a multichannel electronic unit with the microcontroller processing signals in real time. The dynamic phase locking of the laser system with the bandwidth of 14 kHz is demonstrated, the time of phasing is 3 – 4 ms. (fibre and integrated-optical structures)

First experimental test of a trace formula for billiard systems showing mixed dynamics.

PubMed

Dembowski, C; Gräf, H D; Heine, A; Hesse, T; Rehfeld, H; Richter, A

2001-04-09

In general, trace formulas relate the density of states for a given quantum mechanical system to the properties of the periodic orbits of its classical counterpart. Here we report for the first time on a semiclassical description of microwave spectra taken from superconducting billiards of the Limaçon family showing mixed dynamics in terms of a generalized trace formula derived by Ullmo et al. [Phys. Rev. E 54, 136 (1996)]. This expression not only describes mixed-typed behavior but also the limiting cases of fully regular and fully chaotic systems and thus presents a continuous interpolation between the Berry-Tabor and Gutzwiller formulas.

Damage-mitigating control of space propulsion systems for high performance and extended life

NASA Technical Reports Server (NTRS)

Ray, Asok; Wu, Min-Kuang; Dai, Xiaowen; Carpino, Marc; Lorenzo, Carl F.

1993-01-01

Calculations are presented showing that a substantial improvement in service life of a reusable rocket engine can be achieved by an insignificant reduction in the system dynamic performance. The paper introduces the concept of damage mitigation and formulates a continuous-time model of fatigue damage dynamics. For control of complex mechanical systems, damage prediction and damage mitigation are carried out based on the available sensory and operational information such that the plant can be inexpensively maintained and safely and efficiently steered under diverse operating conditions. The results of simulation experiments are presented for transient operations of a reusable rocket engine.

Back-propagation learning of infinite-dimensional dynamical systems.

PubMed

Tokuda, Isao; Tokunaga, Ryuji; Aihara, Kazuyuki

2003-10-01

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuous-time recurrent neural network having time delayed feedbacks and the back-propagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey-Glass equation and the Rössler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network, advantages as well as disadvantages of the DRNN are investigated.

Multiple coupled landscapes and non-adiabatic dynamics with applications to self-activating genes.

PubMed

Chen, Cong; Zhang, Kun; Feng, Haidong; Sasai, Masaki; Wang, Jin

2015-11-21

Many physical, chemical and biochemical systems (e.g. electronic dynamics and gene regulatory networks) are governed by continuous stochastic processes (e.g. electron dynamics on a particular electronic energy surface and protein (gene product) synthesis) coupled with discrete processes (e.g. hopping among different electronic energy surfaces and on and off switching of genes). One can also think of the underlying dynamics as the continuous motion on a particular landscape and discrete hoppings among different landscapes. The main difference of such systems from the intra-landscape dynamics alone is the emergence of the timescale involved in transitions among different landscapes in addition to the timescale involved in a particular landscape. The adiabatic limit when inter-landscape hoppings are fast compared to continuous intra-landscape dynamics has been studied both analytically and numerically, but the analytical treatment of the non-adiabatic regime where the inter-landscape hoppings are slow or comparable to continuous intra-landscape dynamics remains challenging. In this study, we show that there exists mathematical mapping of the dynamics on 2(N) discretely coupled N continuous dimensional landscapes onto one single landscape in 2N dimensional extended continuous space. On this 2N dimensional landscape, eddy current emerges as a sign of non-equilibrium non-adiabatic dynamics and plays an important role in system evolution. Many interesting physical effects such as the enhancement of fluctuations, irreversibility, dissipation and optimal kinetics emerge due to non-adiabaticity manifested by the eddy current illustrated for an N = 1 self-activator. We further generalize our theory to the N-gene network with multiple binding sites and multiple synthesis rates for discretely coupled non-equilibrium stochastic physical and biological systems.

A deterministic and stochastic model for the system dynamics of tumor-immune responses to chemotherapy

NASA Astrophysics Data System (ADS)

Liu, Xiangdong; Li, Qingze; Pan, Jianxin

2018-06-01

Modern medical studies show that chemotherapy can help most cancer patients, especially for those diagnosed early, to stabilize their disease conditions from months to years, which means the population of tumor cells remained nearly unchanged in quite a long time after fighting against immune system and drugs. In order to better understand the dynamics of tumor-immune responses under chemotherapy, deterministic and stochastic differential equation models are constructed to characterize the dynamical change of tumor cells and immune cells in this paper. The basic dynamical properties, such as boundedness, existence and stability of equilibrium points, are investigated in the deterministic model. Extended stochastic models include stochastic differential equations (SDEs) model and continuous-time Markov chain (CTMC) model, which accounts for the variability in cellular reproduction, growth and death, interspecific competitions, and immune response to chemotherapy. The CTMC model is harnessed to estimate the extinction probability of tumor cells. Numerical simulations are performed, which confirms the obtained theoretical results.

Optimal Trajectories Generation in Robotic Fiber Placement Systems

NASA Astrophysics Data System (ADS)

Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane

2017-06-01

The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.

JESTR: Jupiter Exploration Science in the Time Regime

NASA Technical Reports Server (NTRS)

Noll, Keith S.; Simon-Miller, A. A.; Wong, M. H.; Choi, D. S.

2012-01-01

Solar system objects are inherently time-varying with changes that occur on timescales ranging from seconds to years. For all planets other than the Earth, temporal coverage of atmospheric phenomena is limited and sparse. Many important atmospheric phenomena, especially those related to atmospheric dynamics, can be studied in only very limited ways with current data. JESTR is a mission concept that would remedy this gap in our exploration of the solar system by ncar-continuous imaging and spectral monitoring of Jupiter over a multi-year mission lifetime.

Real-Time Processing of Continuous Physiological Signals in a Neurocritical Care Unit on a Stream Data Analytics Platform.

PubMed

Bai, Yong; Sow, Daby; Vespa, Paul; Hu, Xiao

2016-01-01

Continuous high-volume and high-frequency brain signals such as intracranial pressure (ICP) and electroencephalographic (EEG) waveforms are commonly collected by bedside monitors in neurocritical care. While such signals often carry early signs of neurological deterioration, detecting these signs in real time with conventional data processing methods mainly designed for retrospective analysis has been extremely challenging. Such methods are not designed to handle the large volumes of waveform data produced by bedside monitors. In this pilot study, we address this challenge by building a prototype system using the IBM InfoSphere Streams platform, a scalable stream computing platform, to detect unstable ICP dynamics in real time. The system continuously receives electrocardiographic and ICP signals and analyzes ICP pulse morphology looking for deviations from a steady state. We also designed a Web interface to display in real time the result of this analysis in a Web browser. With this interface, physicians are able to ubiquitously check on the status of their patients and gain direct insight into and interpretation of the patient's state in real time. The prototype system has been successfully tested prospectively on live hospitalized patients.

Fluid dynamic analysis of a continuous stirred tank reactor for technical optimization of wastewater digestion.

PubMed

Hurtado, F J; Kaiser, A S; Zamora, B

2015-03-15

Continuous stirred tank reactors (CSTR) are widely used in wastewater treatment plants to reduce the organic matter and microorganism present in sludge by anaerobic digestion. The present study carries out a numerical analysis of the fluid dynamic behaviour of a CSTR in order to optimize the process energetically. The characterization of the sludge flow inside the digester tank, the residence time distribution and the active volume of the reactor under different criteria are determined. The effects of design and power of the mixing system on the active volume of the CSTR are analyzed. The numerical model is solved under non-steady conditions by examining the evolution of the flow during the stop and restart of the mixing system. An intermittent regime of the mixing system, which kept the active volume between 94% and 99%, is achieved. The results obtained can lead to the eventual energy optimization of the mixing system of the CSTR. Copyright © 2014 Elsevier Ltd. All rights reserved.

Dynamic modeling and parameter estimation of a radial and loop type distribution system network

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

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.

The Peace Mediator effect: Heterogeneous agents can foster consensus in continuous opinion models

NASA Astrophysics Data System (ADS)

Vilone, Daniele; Carletti, Timoteo; Bagnoli, Franco; Guazzini, Andrea

2016-11-01

Statistical mechanics has proven to be able to capture the fundamental rules underlying phenomena of social aggregation and opinion dynamics, well studied in disciplines like sociology and psychology. This approach is based on the underlying paradigm that the interesting dynamics of multi-agent systems emerge from the correct definition of few parameters governing the evolution of each individual. In this context, we propose a particular model of opinion dynamics based on the psychological construct named ;cognitive dissonance;. Our system is made of interacting individuals, the agents, each bearing only two dynamical variables (respectively ;opinion; and ;affinity;) self-consistently adjusted during time evolution. We also define two special classes of interacting entities, both acting for a peace mediation process but via different course of action: ;diplomats; and ;auctoritates;. The behavior of the system with and without peace mediators (PMs) is investigated and discussed with reference to corresponding psychological and social implications.

Continuing Efforts to Upgrade the Aeronautics Curriculum at Jacksonville University

ERIC Educational Resources Information Center

Terrell, Jerry L.; Merkt, Juan; Harrison, Jeffrey; Yates, Rhett

2012-01-01

The aviation industry is exceptionally dynamic. Advances in technology have enabled the industry to change drastically in a short period of time. The transition to jet propulsion advances in aerodynamics, avionics improvements, and introduction of revolutionary navigation systems have all occurred within the past 60 years. These advances have…

Real-time object detection, tracking and occlusion reasoning

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

Divakaran, Ajay; Yu, Qian; Tamrakar, Amir

A system for object detection and tracking includes technologies to, among other things, detect and track moving objects, such as pedestrians and/or vehicles, in a real-world environment, handle static and dynamic occlusions, and continue tracking moving objects across the fields of view of multiple different cameras.

A Model for Predicting Integrated Man-Machine System Reliability: Model Logic and Description

DTIC Science & Technology

1974-11-01

3. Fatigue buildup curve. The common requirement of all tests on the Dynamic Strength factor is for the muscles involved to propel, support, or...move the body repeatedly or to support it continuously over time. The tests of our Static Strength factor emphasize the lifting power of the muscles ...or the pounds of pressure which the muscles can exert. ... In contrast to Dynamic Strength the force exerted is against external objects, rather

A New High Channel-Count, High Scan-Rate, Data Acquisition System for the NASA Langley Transonic Dynamics Tunnel

NASA Technical Reports Server (NTRS)

Ivanco, Thomas G.; Sekula, Martin K.; Piatak, David J.; Simmons, Scott A.; Babel, Walter C.; Collins, Jesse G.; Ramey, James M.; Heald, Dean M.

2016-01-01

A data acquisition system upgrade project, known as AB-DAS, is underway at the NASA Langley Transonic Dynamics Tunnel. AB-DAS will soon serve as the primary data system and will substantially increase the scan-rate capabilities and analog channel count while maintaining other unique aeroelastic and dynamic test capabilities required of the facility. AB-DAS is configurable, adaptable, and enables buffet and aeroacoustic tests by synchronously scanning all analog channels and recording the high scan-rate time history values for each data quantity. AB-DAS is currently available for use as a stand-alone data system with limited capabilities while development continues. This paper describes AB-DAS, the design methodology, and the current features and capabilities. It also outlines the future work and projected capabilities following completion of the data system upgrade project.

A study on nonlinear characteristics of speech sound with reference to some languages of North East region

NASA Astrophysics Data System (ADS)

Dutta, Rashmi

INTRODUCTION : Speech science is, in fact, a sub-discipline of the Nonlinear Dynamical System [2,104 ]. There are two different types of Dynamical System. A Continuous Dynamical System may be defined for the continuous time case, by the equation: x = F (x), where x is a vector of length d, defining a point in a d- dimensional space, F is some function (linear or nonlinear) operating on x, and x is the time derivative of x. This system is deterministic, in that it is possible to completely specify its evolution or flow of trajectories in the d- dimensional space, given the initial starting conditions. A Discrete Dynamical System can be defined as a map [by the process of literations]: Xn+1 = G ( Xn ), where Xn is again a d- length vector at time step n, and G is an operator function. Given an initial state, X0, it is possible to calculate the value of xn for any n > 0. Speech has evolved as a primary form of communication between humans, i.e. speech and hearing are the man's most used means of communication [104, 114]. Analysis of human speech has been a goal of Research during the last few decades [105, 108]. With the rapid development of information technology (IT), the human-machine communication, using natural speech, has received wide attention from both academic and business communities. One highly quantitative approach of characterizing the communications potential of speech is in terms of information theory ideas as introduced by Shannon [C.E. Shannon, "A Mathematical Theory of Communication," Bell System Tech journal, Vol 27, pp623- 656, October, 1968]. According to information theory, speech can be represented in terms of its message content, or information. An alternative way of characterizing speech is in terms of the signal carrying the message information, i.e., the acoustic waveform. Although information theoretic ideas have played a major role in sophisticated communications systems, it is the speech representation based on the waveform, or some parametric model, which has been most useful in practical applications. Developing a system that can understand natural language has been a continuing goal of speech researchers. Fully automatic high quality machine translation systems are extremely difficult to build. The difficulty arises from the following reasons: In any natural language text, only part of the information to be conveyed is explicitly expressed. It is the human mind which fills up and supplements the details using contextual.

Exact folded-band chaotic oscillator.

PubMed

Corron, Ned J; Blakely, Jonathan N

2012-06-01

An exactly solvable chaotic oscillator with folded-band dynamics is shown. The oscillator is a hybrid dynamical system containing a linear ordinary differential equation and a nonlinear switching condition. Bounded oscillations are provably chaotic, and successive waveform maxima yield a one-dimensional piecewise-linear return map with segments of both positive and negative slopes. Continuous-time dynamics exhibit a folded-band topology similar to Rössler's oscillator. An exact solution is written as a linear convolution of a fixed basis pulse and a discrete binary sequence, from which an equivalent symbolic dynamics is obtained. The folded-band topology is shown to be dependent on the symbol grammar.

Comparison of cyclic fatigue and torsional resistance in reciprocating single-file systems and continuous rotary instrumentation systems.

PubMed

da Frota, Matheus F; Espir, Camila G; Berbert, Fábio L C V; Marques, André A F; Sponchiado-Junior, Emílio C; Tanomaru-Filho, Mario; Garcia, Lucas F R; Bonetti-Filho, Idomeo

2014-12-01

As compared with continuous rotary systems, reciprocating motion is believed to increase the fatigue resistance of NiTi instruments. We compared the cyclic fatigue and torsional resistance of reciprocating single-file systems and continuous rotary instrumentation systems in simulated root canals. Eighty instruments from the ProTaper Universal, WaveOne, MTwo, and Reciproc systems (n = 20) were submitted to dynamic bending testing in stainless-steel simulated curved canals. Axial displacement of the simulated canals was performed with half of the instruments (n = 10), with back-and-forth movements in a range of 1.5 mm. Time until fracture was recorded, and the number of cycles until instrument fracture was calculated. Cyclic fatigue resistance was greater for reciprocating systems than for rotary systems (P < 0.05). Instruments from the Reciproc and WaveOne systems significantly differed only when axial displacement occurred (P < 0.05). Instruments of the ProTaper Universal and MTwo systems did not significantly differ (P > 0.05). Cyclic fatigue and torsional resistance were greater for reciprocating systems than for continuous rotary systems, irrespective of axial displacement.

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

PubMed

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

The SEL Adapts to Meet Changing Times

NASA Technical Reports Server (NTRS)

Pajerski, Rose S.; Basili, Victor R.

1997-01-01

Since 1976, the Software Engineering Laboratory (SEL) has been dedicated to understanding and improving the way in which one NASA organization, the Flight Dynamics Division (FDD) at Goddard Space Flight Center, develops, maintains, and manages complex flight dynamics systems. It has done this by developing and refining a continual process improvement approach that allows an organization such as the FDD to fine-tune its process for its particular domain. Experimental software engineering and measurement play a significant role in this approach. The SEL is a partnership of NASA Goddard, its major software contractor, Computer Sciences Corporation (CSC), and the University of Maryland's (LTM) Department of Computer Science. The FDD primarily builds software systems that provide ground-based flight dynamics support for scientific satellites. They fall into two sets: ground systems and simulators. Ground systems are midsize systems that average around 250 thousand source lines of code (KSLOC). Ground system development projects typically last 1 - 2 years. Recent systems have been rehosted to workstations from IBM mainframes, and also contain significant new subsystems written in C and C++. The simulators are smaller systems averaging around 60 KSLOC that provide the test data for the ground systems. Simulator development lasts up to 1 year. Most of the simulators have been built in Ada on workstations. The SEL is responsible for the management and continual improvement of the software engineering processes used on these FDD projects.

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil

2016-01-01

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System

NASA Astrophysics Data System (ADS)

Bendjeghaba, Omar

2014-01-01

This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.

Study on the applicability of dynamic light scattering (DLS) to microemulsions including supercritical carbon dioxide-swollen micelles.

PubMed

Cadogan, Shane Patrick; Hahn, Christian Joachim; Rausch, Michael Heinrich; Fröba, Andreas Paul

2017-08-01

The applicability of dynamic light scattering (DLS) for the characterization of the size of supercritical carbon dioxide (sc-CO 2 )-swollen micelles in a polyester polyol-based multicomponent microemulsion with nonionic surfactant has been thoroughly proved for the first time in this work. Systematic experiments confirming that a hydrodynamic mode is observable in either a homodyne or a heterodyne detection scheme as well as the evaluation of the influence of the laser power applied to the slightly colored microemulsion have ensured an accurate implementation of this technique for a technically relevant system. The correlation times associated with the translational diffusion coefficient of the swollen micelles in a continuous liquid phase were measured for temperatures from (298.15 to 338.15)K at pressures of (90 and 100)bar. While there was no significant effect of pressure, it was found that the translational diffusion coefficient increases with increasing temperature as expected. We postulate this is primarily related to the effect of decreasing viscosity of the continuous phase. An estimation of the hydrodynamic diameter of the sc-CO 2 -swollen micelles is in good agreement with values for similar systems reported in the literature. For the derivation of absolute sizes for corresponding systems, also dynamic viscosity and refractive index data will be determined simultaneously in a currently developed closed experimental loop. Copyright © 2017 Elsevier Inc. All rights reserved.

Ultrafast photon counting applied to resonant scanning STED microscopy.

PubMed

Wu, Xundong; Toro, Ligia; Stefani, Enrico; Wu, Yong

2015-01-01

To take full advantage of fast resonant scanning in super-resolution stimulated emission depletion (STED) microscopy, we have developed an ultrafast photon counting system based on a multigiga sample per second analogue-to-digital conversion chip that delivers an unprecedented 450 MHz pixel clock (2.2 ns pixel dwell time in each scan). The system achieves a large field of view (∼50 × 50 μm) with fast scanning that reduces photobleaching, and advances the time-gated continuous wave STED technology to the usage of resonant scanning with hardware-based time-gating. The assembled system provides superb signal-to-noise ratio and highly linear quantification of light that result in superior image quality. Also, the system design allows great flexibility in processing photon signals to further improve the dynamic range. In conclusion, we have constructed a frontier photon counting image acquisition system with ultrafast readout rate, excellent counting linearity, and with the capacity of realizing resonant-scanning continuous wave STED microscopy with online time-gated detection. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Nonlinear stochastic exclusion financial dynamics modeling and time-dependent intrinsic detrended cross-correlation

NASA Astrophysics Data System (ADS)

Zhang, Wei; Wang, Jun

2017-09-01

In attempt to reproduce price dynamics of financial markets, a stochastic agent-based financial price model is proposed and investigated by stochastic exclusion process. The exclusion process, one of interacting particle systems, is usually thought of as modeling particle motion (with the conserved number of particles) in a continuous time Markov process. In this work, the process is utilized to imitate the trading interactions among the investing agents, in order to explain some stylized facts found in financial time series dynamics. To better understand the correlation behaviors of the proposed model, a new time-dependent intrinsic detrended cross-correlation (TDI-DCC) is introduced and performed, also, the autocorrelation analyses are applied in the empirical research. Furthermore, to verify the rationality of the financial price model, the actual return series are also considered to be comparatively studied with the simulation ones. The comparison results of return behaviors reveal that this financial price dynamics model can reproduce some correlation features of actual stock markets.

Ergodicity convergence test suggests telomere motion obeys fractional dynamics

NASA Astrophysics Data System (ADS)

Kepten, E.; Bronshtein, I.; Garini, Y.

2011-04-01

Anomalous diffusion, observed in many biological processes, is a generalized description of a wide variety of processes, all obeying the same law of mean-square displacement. Identifying the basic mechanisms of these observations is important for deducing the nature of the biophysical systems measured. We implement a previously suggested method for distinguishing between fractional Langevin dynamics, fractional Brownian motion, and continuous time random walk based on the ergodic nature of the data. We apply the method together with the recently suggested P-variation test and the displacement correlation to the lately measured dynamics of telomeres in the nucleus of mammalian cells and find strong evidence that the telomeres motion obeys fractional dynamics. The ergodic dynamics are observed experimentally to fit fractional Brownian or Langevin dynamics.

A discrete mechanics framework for real time virtual surgical simulations with application to virtual laparoscopic nephrectomy.

PubMed

Zhou, Xiangmin; Zhang, Nan; Sha, Desong; Shen, Yunhe; Tamma, Kumar K; Sweet, Robert

2009-01-01

The inability to render realistic soft-tissue behavior in real time has remained a barrier to face and content aspects of validity for many virtual reality surgical training systems. Biophysically based models are not only suitable for training purposes but also for patient-specific clinical applications, physiological modeling and surgical planning. When considering the existing approaches for modeling soft tissue for virtual reality surgical simulation, the computer graphics-based approach lacks predictive capability; the mass-spring model (MSM) based approach lacks biophysically realistic soft-tissue dynamic behavior; and the finite element method (FEM) approaches fail to meet the real-time requirement. The present development stems from physics fundamental thermodynamic first law; for a space discrete dynamic system directly formulates the space discrete but time continuous governing equation with embedded material constitutive relation and results in a discrete mechanics framework which possesses a unique balance between the computational efforts and the physically realistic soft-tissue dynamic behavior. We describe the development of the discrete mechanics framework with focused attention towards a virtual laparoscopic nephrectomy application.

Distinct timing mechanisms produce discrete and continuous movements.

PubMed

Huys, Raoul; Studenka, Breanna E; Rheaume, Nicole L; Zelaznik, Howard N; Jirsa, Viktor K

2008-04-25

The differentiation of discrete and continuous movement is one of the pillars of motor behavior classification. Discrete movements have a definite beginning and end, whereas continuous movements do not have such discriminable end points. In the past decade there has been vigorous debate whether this classification implies different control processes. This debate up until the present has been empirically based. Here, we present an unambiguous non-empirical classification based on theorems in dynamical system theory that sets discrete and continuous movements apart. Through computational simulations of representative modes of each class and topological analysis of the flow in state space, we show that distinct control mechanisms underwrite discrete and fast rhythmic movements. In particular, we demonstrate that discrete movements require a time keeper while fast rhythmic movements do not. We validate our computational findings experimentally using a behavioral paradigm in which human participants performed finger flexion-extension movements at various movement paces and under different instructions. Our results demonstrate that the human motor system employs different timing control mechanisms (presumably via differential recruitment of neural subsystems) to accomplish varying behavioral functions such as speed constraints.

A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

PubMed

Samarasinghe, S; Ling, H

In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced parameters and protein concentrations similar to the original RNN system. Results thus demonstrated the reliability of the proposed RNN method for modelling relatively large networks by modularisation for practical settings. Advantages of the method are its ability to represent accurate continuous system dynamics and ease of: parameter estimation through training with data from a practical setting, model analysis (40% faster than ODE), fine tuning parameters when more data are available, sub-model extension when new elements and/or interactions come to light and model expansion with addition of sub-models. Copyright © 2017 Elsevier B.V. All rights reserved.

Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data.

PubMed

Chen, Jin-Long; Chen, Pin-Fan; Wang, Hung-Ming

2014-07-15

Parameters of glucose dynamics recorded by the continuous glucose monitoring system (CGMS) could help in the control of glycemic fluctuations, which is important in diabetes management. Multiscale entropy (MSE) analysis has recently been developed to measure the complexity of physical and physiological time sequences. A reduced MSE complexity index indicates the increased repetition patterns of the time sequence, and, thus, a decreased complexity in this system. No study has investigated the MSE analysis of glucose dynamics in diabetes. This study was designed to compare the complexity of glucose dynamics between the diabetic patients (n = 17) and the control subjects (n = 13), who were matched for sex, age, and body mass index via MSE analysis using the CGMS data. Compared with the control subjects, the diabetic patients revealed a significant increase (P < 0.001) in the mean (diabetic patients 166.0 ± 10.4 vs. control subjects 93.3 ± 1.5 mg/dl), the standard deviation (51.7 ± 4.3 vs. 11.1 ± 0.5 mg/dl), and the mean amplitude of glycemic excursions (127.0 ± 9.2 vs. 27.7 ± 1.3 mg/dl) of the glucose levels; and a significant decrease (P < 0.001) in the MSE complexity index (5.09 ± 0.23 vs. 7.38 ± 0.28). In conclusion, the complexity of glucose dynamics is decreased in diabetes. This finding implies the reactivity of glucoregulation is impaired in the diabetic patients. Such impairment presenting as an increased regularity of glycemic fluctuating pattern could be detected by MSE analysis. Thus, the MSE complexity index could potentially be used as a biomarker in the monitoring of diabetes.

High-precision GPS autonomous platforms for sea ice dynamics and physical oceanography

NASA Astrophysics Data System (ADS)

Elosegui, P.; Wilkinson, J.; Olsson, M.; Rodwell, S.; James, A.; Hagan, B.; Hwang, B.; Forsberg, R.; Gerdes, R.; Johannessen, J.; Wadhams, P.; Nettles, M.; Padman, L.

2012-12-01

Project "Arctic Ocean sea ice and ocean circulation using satellite methods" (SATICE), is the first high-rate, high-precision, continuous GPS positioning experiment on sea ice in the Arctic Ocean. The SATICE systems collect continuous, dual-frequency carrier-phase GPS data while drifting on sea ice. Additional geophysical measurements also collected include ocean water pressure, ocean surface salinity, atmospheric pressure, snow-depth, air-ice-ocean temperature profiles, photographic imagery, and others, enabling sea ice drift, freeboard, weather, ice mass balance, and sea-level height determination. Relatively large volumes of data from each buoy are streamed over a satellite link to a central computer on the Internet in near real time, where they are processed to estimate the time-varying buoy positions. SATICE system obtains continuous GPS data at sub-minute intervals with a positioning precision of a few centimetres in all three dimensions. Although monitoring of sea ice motions goes back to the early days of satellite observations, these autonomous platforms bring out a level of spatio-temporal detail that has never been seen before, especially in the vertical axis. These high-resolution data allows us to address new polar science questions and challenge our present understanding of both sea ice dynamics and Arctic oceanography. We will describe the technology behind this new autonomous platform, which could also be adapted to other applications that require high resolution positioning information with sustained operations and observations in the polar marine environment, and present results pertaining to sea ice dynamics and physical oceanography.

High-efficiency non-uniformity correction for wide dynamic linear infrared radiometry system

NASA Astrophysics Data System (ADS)

Li, Zhou; Yu, Yi; Tian, Qi-Jie; Chang, Song-Tao; He, Feng-Yun; Yin, Yan-He; Qiao, Yan-Feng

2017-09-01

Several different integration times are always set for a wide dynamic linear and continuous variable integration time infrared radiometry system, therefore, traditional calibration-based non-uniformity correction (NUC) are usually conducted one by one, and furthermore, several calibration sources required, consequently makes calibration and process of NUC time-consuming. In this paper, the difference of NUC coefficients between different integration times have been discussed, and then a novel NUC method called high-efficiency NUC, which combines the traditional calibration-based non-uniformity correction, has been proposed. It obtains the correction coefficients of all integration times in whole linear dynamic rangesonly by recording three different images of a standard blackbody. Firstly, mathematical procedure of the proposed non-uniformity correction method is validated and then its performance is demonstrated by a 400 mm diameter ground-based infrared radiometry system. Experimental results show that the mean value of Normalized Root Mean Square (NRMS) is reduced from 3.78% to 0.24% by the proposed method. In addition, the results at 4 ms and 70 °C prove that this method has a higher accuracy compared with traditional calibration-based NUC. In the meantime, at other integration time and temperature there is still a good correction effect. Moreover, it greatly reduces the number of correction time and temperature sampling point, and is characterized by good real-time performance and suitable for field measurement.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions.

PubMed

Salis, Howard; Kaznessis, Yiannis

2005-02-01

The dynamical solution of a well-mixed, nonlinear stochastic chemical kinetic system, described by the Master equation, may be exactly computed using the stochastic simulation algorithm. However, because the computational cost scales with the number of reaction occurrences, systems with one or more "fast" reactions become costly to simulate. This paper describes a hybrid stochastic method that partitions the system into subsets of fast and slow reactions, approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the integral form of the "Next Reaction" variant of the stochastic simulation algorithm. The key innovation of this method is its mechanism of efficiently monitoring the occurrences of slow, discrete events while simultaneously simulating the dynamics of a continuous, stochastic or deterministic process. In addition, by introducing an approximation in which multiple slow reactions may occur within a time step of the numerical integration of the chemical Langevin equation, the hybrid stochastic method performs much faster with only a marginal decrease in accuracy. Multiple examples, including a biological pulse generator and a large-scale system benchmark, are simulated using the exact and proposed hybrid methods as well as, for comparison, a previous hybrid stochastic method. Probability distributions of the solutions are compared and the weak errors of the first two moments are computed. In general, these hybrid methods may be applied to the simulation of the dynamics of a system described by stochastic differential, ordinary differential, and Master equations.

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.

PubMed

Havlicek, Martin; Friston, Karl J; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D

2011-06-15

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. Copyright © 2011 Elsevier Inc. All rights reserved.

Theory of Ostwald ripening in a two-component system

NASA Technical Reports Server (NTRS)

Baird, J. K.; Lee, L. K.; Frazier, D. O.; Naumann, R. J.

1986-01-01

When a two-component system is cooled below the minimum temperature for its stability, it separates into two or more immiscible phases. The initial nucleation produces grains (if solid) or droplets (if liquid) of one of the phases dispersed in the other. The dynamics by which these nuclei proceed toward equilibrium is called Ostwald ripening. The dynamics of growth of the droplets depends upon the following factors: (1) The solubility of the droplet depends upon its radius and the interfacial energy between it and the surrounding (continuous) phase. There is a critical radius determined by the supersaturation in the continuous phase. Droplets with radii smaller than critical dissolve, while droplets with radii larger grow. (2) The droplets concentrate one component and reject the other. The rate at which this occurs is assumed to be determined by the interdiffusion of the two components in the continuous phase. (3) The Ostwald ripening is constrained by conservation of mass; e.g., the amount of materials in the droplet phase plus the remaining supersaturation in the continuous phase must equal the supersaturation available at the start. (4) There is a distribution of droplet sizes associated with a mean droplet radius, which grows continuously with time. This distribution function satisfies a continuity equation, which is solved asymptotically by a similarity transformation method.

The impact of parasitoid emergence time on host-parasitoid population dynamics.

PubMed

Cobbold, Christina A; Roland, Jens; Lewis, Mark A

2009-01-01

We investigate the effect of parasitoid phenology on host-parasitoid population cycles. Recent experimental research has shown that parasitized hosts can continue to interact with their unparasitized counterparts through competition. Parasitoid phenology, in particular the timing of emergence from the host, determines the duration of this competition. We construct a discrete-time host-parasitoid model in which within-generation dynamics associated with parasitoid timing is explicitly incorporated. We found that late-emerging parasitoids induce less severe, but more frequent, host outbreaks, independent of the choice of competition model. The competition experienced by the parasitized host reduces the parasitoids' numerical response to changes in host numbers, preventing the 'boom-bust' dynamics associated with more efficient parasitoids. We tested our findings against experimental data for the forest tent caterpillar (Malacosoma disstria Hübner) system, where a large number of consecutive years at a high host density is synonymous with severe forest damage.

Comparative analysis of the operation efficiency of the continuous and relay control systems of a multi-axle wheeled vehicle suspension

NASA Astrophysics Data System (ADS)

Zhileykin, M. M.; Kotiev, G. O.; Nagatsev, M. V.

2018-02-01

In order to improve the efficiency of the multi-axle wheeled vehicles (MWV) automotive engineers are increasing their cruising speed. One of the promising ways to improve ride comfort of the MWV is the development of the dynamic active suspension systems and control laws for such systems. Here, by the dynamic control systems we mean the systems operating in real time mode and using current (instantaneous) values of the state variables. The aim of the work is to develop the MWV suspension optimal control laws that would reduce vibrations on the driver’s seat at kinematic excitation. The authors have developed the optimal control laws for damping the oscillations of the MWV body. The developed laws allow reduction of the vibrations on the driver’s seat and increase in the maximum speed of the vehicle. The laws are characterized in that they allow generating the control inputs in real time mode. The authors have demonstrated the efficiency of the proposed control laws by means of mathematical simulation of the MWV driving over unpaved road with kinematic excitation. The proposed optimal control laws can be used in the MWV suspension control systems with magnetorheological shock absorbers or controlled hydropneumatic springs. Further evolution of the research line can be the development of the energy-efficient MWV suspension control systems with continuous control input on the vehicle body.

Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics

NASA Astrophysics Data System (ADS)

Cescon, Marzia; Johansson, Rolf; Renard, Eric; Maran, Alberto

2014-07-01

One of the main limiting factors in improving glucose control for type 1 diabetes mellitus (T1DM) subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing the magnitude and duration of such effects would be useful not only for patients and physicians, but also for the development of a controller targeting glycaemia regulation. Therefore, in this paper we focus on estimating low-complexity yet physiologically sound and individualised multi-input single-output (MISO) models of the glucose metabolism in T1DM able to reflect the basic dynamical features of the glucose-insulin metabolic system in response to a meal intake or an insulin injection. The models are continuous-time second-order transfer functions relating the amount of carbohydrate of a meal and the insulin units of the accordingly administered dose (inputs) to plasma glucose evolution (output) and consist of few parameters clinically relevant to be estimated. The estimation strategy is continuous-time data-driven system identification and exploits a database in which meals and insulin boluses are separated in time, allowing the unique identification of the model parameters.

Predicting coexistence of plants subject to a tolerance-competition trade-off.

PubMed

Haegeman, Bart; Sari, Tewfik; Etienne, Rampal S

2014-06-01

Ecological trade-offs between species are often invoked to explain species coexistence in ecological communities. However, few mathematical models have been proposed for which coexistence conditions can be characterized explicitly in terms of a trade-off. Here we present a model of a plant community which allows such a characterization. In the model plant species compete for sites where each site has a fixed stress condition. Species differ both in stress tolerance and competitive ability. Stress tolerance is quantified as the fraction of sites with stress conditions low enough to allow establishment. Competitive ability is quantified as the propensity to win the competition for empty sites. We derive the deterministic, discrete-time dynamical system for the species abundances. We prove the conditions under which plant species can coexist in a stable equilibrium. We show that the coexistence conditions can be characterized graphically, clearly illustrating the trade-off between stress tolerance and competitive ability. We compare our model with a recently proposed, continuous-time dynamical system for a tolerance-fecundity trade-off in plant communities, and we show that this model is a special case of the continuous-time version of our model.

Robust optimization with transiently chaotic dynamical systems

NASA Astrophysics Data System (ADS)

Sumi, R.; Molnár, B.; Ercsey-Ravasz, M.

2014-05-01

Efficiently solving hard optimization problems has been a strong motivation for progress in analog computing. In a recent study we presented a continuous-time dynamical system for solving the NP-complete Boolean satisfiability (SAT) problem, with a one-to-one correspondence between its stable attractors and the SAT solutions. While physical implementations could offer great efficiency, the transiently chaotic dynamics raises the question of operability in the presence of noise, unavoidable on analog devices. Here we show that the probability of finding solutions is robust to noise intensities well above those present on real hardware. We also developed a cellular neural network model realizable with analog circuits, which tolerates even larger noise intensities. These methods represent an opportunity for robust and efficient physical implementations.

Hunting stability analysis of high-speed train bogie under the frame lateral vibration active control

NASA Astrophysics Data System (ADS)

Yao, Yuan; Wu, Guosong; Sardahi, Yousef; Sun, Jian-Qiao

2018-02-01

In this paper, we study a multi-objective optimal design of three different frame vibration control configurations and compare their performances in improving the lateral stability of a high-speed train bogie. The existence of the time-delay in the control system and its impact on the bogie hunting stability are also investigated. The continuous time approximation method is used to approximate the time-delay system dynamics and then the root locus curves of the system before and after applying control are depicted. The analysis results show that the three control cases could improve the bogie hunting stability effectively. But the root locus of low- frequency hunting mode of bogie which determinates the system critical speed is different, thus affecting the system stability with the increasing of speed. Based on the stability analysis at different bogie dynamics parameters, the robustness of the control case (1) is the strongest. However, the case (2) is more suitable for the dynamic performance requirements of bogie. For the case (1), the time-delay over 10 ms may lead to instability of the control system which will affect the bogie hunting stability seriously. For the case (2) and (3), the increasing time-delay reduces the hunting stability gradually over the high-speed range. At a certain speed, such as 200 km/h, an appropriate time-delay is favourable to the bogie hunting stability. The mechanism is proposed according to the root locus analysis of time-delay system. At last, the nonlinear bifurcation characteristics of the bogie control system are studied by the numerical integration methods to verify the effects of these active control configurations and the delay on the bogie hunting stability.

Subnanosecond polarized microfluorimetry in the time domain: An instrument for studying receptor trafficking in live cells

NASA Astrophysics Data System (ADS)

Martin-Fernandez, M. L.; Tobin, M. J.; Clarke, D. T.; Gregory, C. M.; Jones, G. R.

1998-02-01

We describe an instrument designed to monitor molecular motions in multiphasic, weakly fluorescent microscopic systems. It combines synchrotron radiation, a low irradiance polarized microfluorimeter, and an automated, multiframing, single-photon-counting data acquisition system, and is capable of continually accumulating subnanosecond resolved anisotropy decays with a real-time resolution of about 60 s. The instrument has initially been built to monitor ligand-receptor interactions in living cells, but can equally be applied to the continual measurement of any dynamic process involving fluorescent molecules, that occurs over a time scale from a few minutes to several hours. As a particularly demanding demonstration of its capabilities, we have used it to monitor the environmental constraints imposed on the peptide hormone epidermal growth factor during its endocytosis and recycling to the cell surface in live cells.

Optimal perturbations for nonlinear systems using graph-based optimal transport

NASA Astrophysics Data System (ADS)

Grover, Piyush; Elamvazhuthi, Karthik

2018-06-01

We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.

Investigation on RGB laser source applied to dynamic photoelastic experiment

NASA Astrophysics Data System (ADS)

Li, Songgang; Yang, Guobiao; Zeng, Weiming

2014-06-01

When the elastomer sustains the shock load or the blast load, its internal stress state of every point will change rapidly over time. Dynamic photoelasticity method is an experimental stress analysis method, which researches the dynamic stress and the stress wave propagation. Light source is one of very important device in dynamic photoelastic experiment system, and the RGB laser light source applied in dynamic photoelastic experiment system is innovative and evolutive to the system. RGB laser is synthesized by red laser, green laser and blue laser, either as a single wavelength laser light source, also as synthesized white laser light source. RGB laser as a light source for dynamic photoelastic experiment system, the colored isochromatic can be captured in dynamic photoelastic experiment, and even the black zero-level stripe can be collected, and the isoclinics can also be collected, which conducively analysis and study of transient stress and stress wave propagation. RGB laser is highly stable and continuous output, and its power can be adjusted. The three wavelengths laser can be synthesized by different power ratio. RGB laser light source used in dynamic photoelastic experiment has overcome a number of deficiencies and shortcomings of other light sources, and simplifies dynamic photoelastic experiment, which has achieved good results.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

DCS - A high flux beamline for time resolved dynamic compression science – Design highlights

DOE PAGES

Capatina, D.; D’Amico, K.; Nudell, J.; ...

2016-07-27

The Dynamic Compression Sector (DCS) beamline, a national user facility for time resolved dynamic compression science supported by the National Nuclear Security Administration (NNSA) of the Department of Energy (DOE), has recently completed construction and is being commissioned at Sector 35 of the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). The beamline consists of a First Optics Enclosure (FOE) and four experimental enclosures. A Kirkpatrick–Baez focusing mirror system with 2.2 mrad incident angles in the FOE delivers pink beam to the experimental stations. A refocusing Kirkpatrick–Baez mirror system is situated in each of the two most downstream enclosures.more » Experiments can be conducted in either white, monochromatic, pink or monochromatic-reflected beam mode in any of the experimental stations by changing the position of two interlocked components in the FOE. The beamline Radiation Safety System (RSS) components have been designed to handle the continuous beam provided by two in-line revolver undulators with periods of 27 and 30 mm, at closed gap, 150 mA beam current, and passing through a power limiting aperture of 1.5 x 1.0 mm 2. A novel pink beam end station stop [1] is used to stop the continuous and focused pink beam which can achieve a peak heat flux of 105 kW/mm 2. Finally, a new millisecond shutter design [2] is used to deliver a quick pulse of beam to the sample, synchronized with the dynamic event, the microsecond shutter, and the storage ring clock.« less

DCS - A high flux beamline for time resolved dynamic compression science – Design highlights

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

Capatina, D., E-mail: capatina@aps.anl.gov; D’Amico, K., E-mail: kdamico@aps.anl.gov; Nudell, J., E-mail: jnudell@aps.anl.gov

2016-07-27

The Dynamic Compression Sector (DCS) beamline, a national user facility for time resolved dynamic compression science supported by the National Nuclear Security Administration (NNSA) of the Department of Energy (DOE), has recently completed construction and is being commissioned at Sector 35 of the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). The beamline consists of a First Optics Enclosure (FOE) and four experimental enclosures. A Kirkpatrick–Baez focusing mirror system with 2.2 mrad incident angles in the FOE delivers pink beam to the experimental stations. A refocusing Kirkpatrick–Baez mirror system is situated in each of the two most downstream enclosures.more » Experiments can be conducted in either white, monochromatic, pink or monochromatic-reflected beam mode in any of the experimental stations by changing the position of two interlocked components in the FOE. The beamline Radiation Safety System (RSS) components have been designed to handle the continuous beam provided by two in-line revolver undulators with periods of 27 and 30 mm, at closed gap, 150 mA beam current, and passing through a power limiting aperture of 1.5 x 1.0 mm{sup 2}. A novel pink beam end station stop [1] is used to stop the continuous and focused pink beam which can achieve a peak heat flux of 105 kW/mm{sup 2}. A new millisecond shutter design [2] is used to deliver a quick pulse of beam to the sample, synchronized with the dynamic event, the microsecond shutter, and the storage ring clock.« less

DCS - A High Flux Beamline for Time Resolved Dynamic Compression Science – Design Highlights

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

Capatina, D.; D'Amico, Kevin L.; Nudell, J.

2016-07-27

The Dynamic Compression Sector (DCS) beamline, a national user facility for time resolved dynamic compression science supported by the National Nuclear Security Administration (NNSA) of the Department of Energy (DOE), has recently completed construction and is being commissioned at Sector 35 of the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). The beamline consists of a First Optics Enclosure (FOE) and four experimental enclosures. A Kirkpatrick–Baez focusing mirror system with 2.2 mrad incident angles in the FOE delivers pink beam to the experimental stations. A refocusing Kirkpatrick–Baez mirror system is situated in each of the two most downstream enclosures.more » Experiments can be conducted in either white, monochromatic, pink or monochromatic-reflected beam mode in any of the experimental stations by changing the position of two interlocked components in the FOE. The beamline Radiation Safety System (RSS) components have been designed to handle the continuous beam provided by two in-line revolver undulators with periods of 27 and 30 mm, at closed gap, 150 mA beam current, and passing through a power limiting aperture of 1.5 x 1.0 mm2. A novel pink beam end station stop [1] is used to stop the continuous and focused pink beam which can achieve a peak heat flux of 105 kW/mm2. A new millisecond shutter design [2] is used to deliver a quick pulse of beam to the sample, synchronized with the dynamic event, the microsecond shutter, and the storage ring clock.« less

Oscillatory Dynamics of One-Dimensional Homogeneous Granular Chains

NASA Astrophysics Data System (ADS)

Starosvetsky, Yuli; Jayaprakash, K. R.; Hasan, Md. Arif; Vakakis, Alexander F.

The acoustics of the homogeneous granular chains has been studied extensively both numerically and experimentally in the references cited in the previous chapters. This chapter focuses on the oscillatory behavior of finite dimensional homogeneous granular chains. It is well known that normal vibration modes are the building blocks of the vibrations of linear systems due to the applicability of the principle of superposition. One the other hand, nonlinear theory is deprived of such a general superposition principle (although special cases of nonlinear superpositions do exist), but nonlinear normal modes ‒ NNMs still play an important role in the forced and resonance dynamics of these systems. In their basic definition [1], NNMs were defined as time-periodic nonlinear oscillations of discrete or continuous dynamical systems where all coordinates (degrees-of-freedom) oscillate in-unison with the same frequency; further extensions of this definition have been considered to account for NNMs of systems with internal resonances [2]...

Shear-stress fluctuations and relaxation in polymer glasses

NASA Astrophysics Data System (ADS)

Kriuchevskyi, I.; Wittmer, J. P.; Meyer, H.; Benzerara, O.; Baschnagel, J.

2018-01-01

We investigate by means of molecular dynamics simulation a coarse-grained polymer glass model focusing on (quasistatic and dynamical) shear-stress fluctuations as a function of temperature T and sampling time Δ t . The linear response is characterized using (ensemble-averaged) expectation values of the contributions (time averaged for each shear plane) to the stress-fluctuation relation μsf for the shear modulus and the shear-stress relaxation modulus G (t ) . Using 100 independent configurations, we pay attention to the respective standard deviations. While the ensemble-averaged modulus μsf(T ) decreases continuously with increasing T for all Δ t sampled, its standard deviation δ μsf(T ) is nonmonotonic with a striking peak at the glass transition. The question of whether the shear modulus is continuous or has a jump singularity at the glass transition is thus ill posed. Confirming the effective time-translational invariance of our systems, the Δ t dependence of μsf and related quantities can be understood using a weighted integral over G (t ) .

Identification of the structure parameters using short-time non-stationary stochastic excitation

NASA Astrophysics Data System (ADS)

Jarczewska, Kamila; Koszela, Piotr; Śniady, PaweŁ; Korzec, Aleksandra

2011-07-01

In this paper, we propose an approach to the flexural stiffness or eigenvalue frequency identification of a linear structure using a non-stationary stochastic excitation process. The idea of the proposed approach lies within time domain input-output methods. The proposed method is based on transforming the dynamical problem into a static one by integrating the input and the output signals. The output signal is the structure reaction, i.e. structure displacements due to the short-time, irregular load of random type. The systems with single and multiple degrees of freedom, as well as continuous systems are considered.

Quantitation of Cellular Dynamics in Growing Arabidopsis Roots with Light Sheet Microscopy

PubMed Central

Birnbaum, Kenneth D.; Leibler, Stanislas

2011-01-01

To understand dynamic developmental processes, living tissues have to be imaged frequently and for extended periods of time. Root development is extensively studied at cellular resolution to understand basic mechanisms underlying pattern formation and maintenance in plants. Unfortunately, ensuring continuous specimen access, while preserving physiological conditions and preventing photo-damage, poses major barriers to measurements of cellular dynamics in growing organs such as plant roots. We present a system that integrates optical sectioning through light sheet fluorescence microscopy with hydroponic culture that enables us to image, at cellular resolution, a vertically growing Arabidopsis root every few minutes and for several consecutive days. We describe novel automated routines to track the root tip as it grows, to track cellular nuclei and to identify cell divisions. We demonstrate the system's capabilities by collecting data on divisions and nuclear dynamics. PMID:21731697

A hybrid continuous-discrete method for stochastic reaction-diffusion processes.

PubMed

Lo, Wing-Cheong; Zheng, Likun; Nie, Qing

2016-09-01

Stochastic fluctuations in reaction-diffusion processes often have substantial effect on spatial and temporal dynamics of signal transductions in complex biological systems. One popular approach for simulating these processes is to divide the system into small spatial compartments assuming that molecules react only within the same compartment and jump between adjacent compartments driven by the diffusion. While the approach is convenient in terms of its implementation, its computational cost may become prohibitive when diffusive jumps occur significantly more frequently than reactions, as in the case of rapid diffusion. Here, we present a hybrid continuous-discrete method in which diffusion is simulated using continuous approximation while reactions are based on the Gillespie algorithm. Specifically, the diffusive jumps are approximated as continuous Gaussian random vectors with time-dependent means and covariances, allowing use of a large time step, even for rapid diffusion. By considering the correlation among diffusive jumps, the approximation is accurate for the second moment of the diffusion process. In addition, a criterion is obtained for identifying the region in which such diffusion approximation is required to enable adaptive calculations for better accuracy. Applications to a linear diffusion system and two nonlinear systems of morphogens demonstrate the effectiveness and benefits of the new hybrid method.

On line instrument systems for monitoring steam turbogenerators

NASA Astrophysics Data System (ADS)

Clapis, A.; Giorgetti, G.; Lapini, G. L.; Benanti, A.; Frigeri, C.; Gadda, E.; Mantino, E.

A computerized real time data acquisition and data processing for the diagnosis of malfunctioning of steam turbogenerator systems is described. Pressure, vibration and temperature measurements are continuously collected from standard or special sensors including startup or stop events. The architecture of the monitoring system is detailed. Examples of the graphics output are presented. It is shown that such a system allows accurate diagnosis and the possibility of creating a data bank to describe the dynamic characteristics of the machine park.

Intelligent optical fiber sensor system for measurement of gas concentration

NASA Astrophysics Data System (ADS)

Pan, Jingming; Yin, Zongmin

1991-08-01

A measuring, controlling, and alarming system for the concentration of a gas or transparent liquid is described. In this system, a Fabry-Perot etalon with an optical fiber is used as the sensor, a charge-coupled device (CCD) is used as the photoelectric converter, and a single- chip microcomputer 8031 along with an interface circuit is used to measure the interference ring signal. The system has such features as real-time and on-line operation, continuous dynamic handling, and intelligent control.

Dynamic dual-tracer PET reconstruction.

PubMed

Gao, Fei; Liu, Huafeng; Jian, Yiqiang; Shi, Pengcheng

2009-01-01

Although of important medical implications, simultaneous dual-tracer positron emission tomography reconstruction remains a challenging problem, primarily because the photon measurements from dual tracers are overlapped. In this paper, we propose a simultaneous dynamic dual-tracer reconstruction of tissue activity maps based on guidance from tracer kinetics. The dual-tracer reconstruction problem is formulated in a state-space representation, where parallel compartment models serve as continuous-time system equation describing the tracer kinetic processes of dual tracers, and the imaging data is expressed as discrete sampling of the system states in measurement equation. The image reconstruction problem has therefore become a state estimation problem in a continuous-discrete hybrid paradigm, and H infinity filtering is adopted as the estimation strategy. As H infinity filtering makes no assumptions on the system and measurement statistics, robust reconstruction results can be obtained for the dual-tracer PET imaging system where the statistical properties of measurement data and system uncertainty are not available a priori, even when there are disturbances in the kinetic parameters. Experimental results on digital phantoms, Monte Carlo simulations and physical phantoms have demonstrated the superior performance.

Integrated modeling of long-term vegetation and hydrologic dynamics in Rocky Mountain watersheds

Treesearch

Robert Steven Ahl

2007-01-01

Changes in forest structure resulting from natural disturbances, or managed treatments, can have negative and long lasting impacts on water resources. To facilitate integrated management of forest and water resources, a System for Long-Term Integrated Management Modeling (SLIMM) was developed. By combining two spatially explicit, continuous time models, vegetation...

Modeling and Simulation of Metallurgical Process Based on Hybrid Petri Net

NASA Astrophysics Data System (ADS)

Ren, Yujuan; Bao, Hong

2016-11-01

In order to achieve the goals of energy saving and emission reduction of iron and steel enterprises, an increasing number of modeling and simulation technologies are used to research and analyse metallurgical production process. In this paper, the basic principle of Hybrid Petri net is used to model and analyse the Metallurgical Process. Firstly, the definition of Hybrid Petri Net System of Metallurgical Process (MPHPNS) and its modeling theory are proposed. Secondly, the model of MPHPNS based on material flow is constructed. The dynamic flow of materials and the real-time change of each technological state in metallurgical process are simulated vividly by using this model. The simulation process can implement interaction between the continuous event dynamic system and the discrete event dynamic system at the same level, and play a positive role in the production decision.

A signal-flow-graph approach to on-line gradient calculation.

PubMed

Campolucci, P; Uncini, A; Piazza, F

2000-08-01

A large class of nonlinear dynamic adaptive systems such as dynamic recurrent neural networks can be effectively represented by signal flow graphs (SFGs). By this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input, one-output transformation, as in an electrical circuit. Even if graph representations are popular in the neural network community, they are often used for qualitative description rather than for rigorous representation and computational purposes. In this article, a method for both on-line and batch-backward gradient computation of a system output or cost function with respect to system parameters is derived by the SFG representation theory and its known properties. The system can be any causal, in general nonlinear and time-variant, dynamic system represented by an SFG, in particular any feedforward, time-delay, or recurrent neural network. In this work, we use discrete-time notation, but the same theory holds for the continuous-time case. The gradient is obtained in a straightforward way by the analysis of two SFGs, the original one and its adjoint (obtained from the first by simple transformations), without the complex chain rule expansions of derivatives usually employed. This method can be used for sensitivity analysis and for learning both off-line and on-line. On-line learning is particularly important since it is required by many real applications, such as digital signal processing, system identification and control, channel equalization, and predistortion.

Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions

PubMed Central

Lin, Aijing; Liu, Kang K. L.; Bartsch, Ronny P.; Ivanov, Plamen Ch.

2016-01-01

Within the framework of ‘Network Physiology’, we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain–heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain–heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems. PMID:27044991

Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions

NASA Astrophysics Data System (ADS)

Lin, Aijing; Liu, Kang K. L.; Bartsch, Ronny P.; Ivanov, Plamen Ch.

2016-05-01

Within the framework of `Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.

Real-Time Two-Dimensional Mapping of Relative Local Surface Temperatures with a Thin-Film Sensor Array

PubMed Central

Li, Gang; Wang, Zhenhai; Mao, Xinyu; Zhang, Yinghuang; Huo, Xiaoye; Liu, Haixiao; Xu, Shengyong

2016-01-01

Dynamic mapping of an object’s local temperature distribution may offer valuable information for failure analysis, system control and improvement. In this letter we present a computerized measurement system which is equipped with a hybrid, low-noise mechanical-electrical multiplexer for real-time two-dimensional (2D) mapping of surface temperatures. We demonstrate the performance of the system on a device embedded with 32 pieces of built-in Cr-Pt thin-film thermocouples arranged in a 4 × 8 matrix. The system can display a continuous 2D mapping movie of relative temperatures with a time interval around 1 s. This technique may find applications in a variety of practical devices and systems. PMID:27347969

A new class of finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2014-02-01

This paper is devoted to investigating the finite-time consensus problem for a multi-agent system in networks with undirected topology. A new class of global continuous time-invariant consensus protocols is constructed for each single-integrator agent dynamics with the aid of Lyapunov functions. In particular, it is shown that the settling time of the proposed new class of finite-time consensus protocols is upper bounded for arbitrary initial conditions. This makes it possible for network consensus problems that the convergence time is designed and estimated offline for a given undirected information flow and a group volume of agents. Finally, a numerical simulation example is presented as a proof of concept.

Real-time maritime scene simulation for ladar sensors

NASA Astrophysics Data System (ADS)

Christie, Chad L.; Gouthas, Efthimios; Swierkowski, Leszek; Williams, Owen M.

2011-06-01

Continuing interest exists in the development of cost-effective synthetic environments for testing Laser Detection and Ranging (ladar) sensors. In this paper we describe a PC-based system for real-time ladar scene simulation of ships and small boats in a dynamic maritime environment. In particular, we describe the techniques employed to generate range imagery accompanied by passive radiance imagery. Our ladar scene generation system is an evolutionary extension of the VIRSuite infrared scene simulation program and includes all previous features such as ocean wave simulation, the physically-realistic representation of boat and ship dynamics, wake generation and simulation of whitecaps, spray, wake trails and foam. A terrain simulation extension is also under development. In this paper we outline the development, capabilities and limitations of the VIRSuite extensions.

A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations

NASA Astrophysics Data System (ADS)

Zhang, Ye; Gong, Rongfang; Cheng, Xiaoliang; Gulliksson, Mårten

2018-06-01

This study considers the inverse source problem for elliptic partial differential equations with both Dirichlet and Neumann boundary data. The unknown source term is to be determined by additional boundary conditions. Unlike the existing methods found in the literature, which usually employ the first-order in time gradient-like system (such as the steepest descent methods) for numerically solving the regularized optimization problem with a fixed regularization parameter, we propose a novel method with a second-order in time dissipative gradient-like system and a dynamical selected regularization parameter. A damped symplectic scheme is proposed for the numerical solution. Theoretical analysis is given for both the continuous model and the numerical algorithm. Several numerical examples are provided to show the robustness of the proposed algorithm.

Swarming behaviors in multi-agent systems with nonlinear dynamics

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

Yu, Wenwu, E-mail: wenwuyu@gmail.com; School of Electrical and Computer Engineering, RMIT University, Melbourne VIC 3001; Chen, Guanrong

2013-12-15

The dynamic analysis of a continuous-time multi-agent swarm model with nonlinear profiles is investigated in this paper. It is shown that, under mild conditions, all agents in a swarm can reach cohesion within a finite time, where the upper bounds of the cohesion are derived in terms of the parameters of the swarm model. The results are then generalized by considering stochastic noise and switching between nonlinear profiles. Furthermore, swarm models with limited sensing range inducing changing communication topologies and unbounded repulsive interactions between agents are studied by switching system and nonsmooth analysis. Here, the sensing range of each agentmore » is limited and the possibility of collision among nearby agents is high. Finally, simulation results are presented to demonstrate the validity of the theoretical analysis.« less

Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome

PubMed Central

Pinto, Ameet J.; Schroeder, Joanna; Lunn, Mary; Sloan, William

2014-01-01

ABSTRACT Bacterial communities migrate continuously from the drinking water treatment plant through the drinking water distribution system and into our built environment. Understanding bacterial dynamics in the distribution system is critical to ensuring that safe drinking water is being supplied to customers. We present a 15-month survey of bacterial community dynamics in the drinking water system of Ann Arbor, MI. By sampling the water leaving the treatment plant and at nine points in the distribution system, we show that the bacterial community spatial dynamics of distance decay and dispersivity conform to the layout of the drinking water distribution system. However, the patterns in spatial dynamics were weaker than those for the temporal trends, which exhibited seasonal cycling correlating with temperature and source water use patterns and also demonstrated reproducibility on an annual time scale. The temporal trends were driven by two seasonal bacterial clusters consisting of multiple taxa with different networks of association within the larger drinking water bacterial community. Finally, we show that the Ann Arbor data set robustly conforms to previously described interspecific occupancy abundance models that link the relative abundance of a taxon to the frequency of its detection. Relying on these insights, we propose a predictive framework for microbial management in drinking water systems. Further, we recommend that long-term microbial observatories that collect high-resolution, spatially distributed, multiyear time series of community composition and environmental variables be established to enable the development and testing of the predictive framework. PMID:24865557

Continuous joint measurement and entanglement of qubits in remote cavities

NASA Astrophysics Data System (ADS)

Motzoi, Felix; Whaley, K. Birgitta; Sarovar, Mohan

2015-09-01

We present a first-principles theoretical analysis of the entanglement of two superconducting qubits in spatially separated microwave cavities by a sequential (cascaded) probe of the two cavities with a coherent mode, that provides a full characterization of both the continuous measurement induced dynamics and the entanglement generation. We use the SLH formalism to derive the full quantum master equation for the coupled qubits and cavities system, within the rotating wave and dispersive approximations, and conditioned equations for the cavity fields. We then develop effective stochastic master equations for the dynamics of the qubit system in both a polaronic reference frame and a reduced representation within the laboratory frame. We compare simulations with and analyze tradeoffs between these two representations, including the onset of a non-Markovian regime for simulations in the reduced representation. We provide conditions for ensuring persistence of entanglement and show that using shaped pulses enables these conditions to be met at all times under general experimental conditions. The resulting entanglement is shown to be robust with respect to measurement imperfections and loss channels. We also study the effects of qubit driving and relaxation dynamics during a weak measurement, as a prelude to modeling measurement-based feedback control in this cascaded system.

Where do we stand after twenty years of dynamic triggering studies? (Invited)

NASA Astrophysics Data System (ADS)

Prejean, S. G.; Hill, D. P.

2013-12-01

In the past two decades, remote dynamic triggering of earthquakes by other earthquakes has been explored in a variety of physical environments with a wide array of observation and modeling techniques. These studies have significantly refined our understanding of the state of the crust and the physical conditions controlling earthquake nucleation. Despite an ever growing database of dynamic triggering observations, significant uncertainties remain and vigorous debate in almost all aspects of the science continues. For example, although dynamic earthquake triggering can occur with peak dynamic stresses as small as 1 kPa, triggering thresholds and their dependence on local stress state, hydrological environment, and frictional properties of faults are not well understood. Some studies find a simple threshold based on the peak amplitude of shaking while others find dependencies on frequency, recharge time, and other parameters. Considerable debate remains over the range of physical processes responsible for dynamic triggering, and the wide variation in dynamic triggering responses and time scales suggests triggering by multiple physical processes. Although Coulomb shear failure with various friction laws can often explain dynamic triggering, particularly instantaneous triggering, delayed dynamic triggering may be dependent on fluid transport and other slowly evolving aseismic processes. Although our understanding of the global distribution of dynamic triggering has improved, it is far from complete due to spatially uneven monitoring. A major challenge involves establishing statistical significance of potentially triggered earthquakes, particularly if they are isolated events or time-delayed with respect to triggering stresses. Here we highlight these challenges and opportunities with existing data. We focus on environmental dependence of dynamic triggering by large remote earthquakes particularly in volcanic and geothermal systems, as these systems often have high rates of background seismicity. In many volcanic and geothermal systems, such as the Geysers in Northern California, dynamic triggering of micro-earthquakes is frequent and predictable. In contrast, most active and even erupting volcanoes in Alaska (with the exception of the Katmai Volcanic Cluster) do not experience dynamic triggering. We explore why.

The dynamic behaviour of data-driven Δ-M and ΔΣ-M in sliding mode control

NASA Astrophysics Data System (ADS)

Almakhles, Dhafer; Swain, Akshya K.; Nasiri, Alireza

2017-11-01

In recent years, delta (Δ-M) and delta-sigma modulators (ΔΣ-M) are increasingly being used as efficient data converters due to numerous advantages they offer. This paper investigates various dynamical features of these modulators/systems (both in continuous and discrete time domain) and derives their stability conditions using the theory of sliding mode. The upper bound of the hitting time (step) has been estimated. The equivalent mode conditions, i.e. where the outputs of the modulators are equivalent to the inputs, are established. The results of the analysis are validated through simulations considering a numerical example.

Instantons re-examined: dynamical tunneling and resonant tunneling.

PubMed

Le Deunff, Jérémy; Mouchet, Amaury

2010-04-01

Starting from trace formulas for the tunneling splittings (or decay rates) analytically continued in the complex time domain, we obtain explicit semiclassical expansions in terms of complex trajectories that are selected with appropriate complex-time paths. We show how this instantonlike approach, which takes advantage of an incomplete Wick rotation, accurately reproduces tunneling effects not only in the usual double-well potential but also in situations where a pure Wick rotation is insufficient, for instance dynamical tunneling or resonant tunneling. Even though only one-dimensional autonomous Hamiltonian systems are quantitatively studied, we discuss the relevance of our method for multidimensional and/or chaotic tunneling.

Bursts and heavy tails in temporal and sequential dynamics of foraging decisions.

PubMed

Jung, Kanghoon; Jang, Hyeran; Kralik, Jerald D; Jeong, Jaeseung

2014-08-01

A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices.

Origin and continuation of 3/2, 5/2, 3/1, 4/1 and 5/1 resonant periodic orbits in the circular and elliptic restricted three-body problem

NASA Astrophysics Data System (ADS)

Antoniadou, Kyriaki I.; Libert, Anne-Sophie

2018-06-01

We consider a planetary system consisting of two primaries, namely a star and a giant planet, and a massless secondary, say a terrestrial planet or an asteroid, which moves under their gravitational attraction. We study the dynamics of this system in the framework of the circular and elliptic restricted three-body problem, when the motion of the giant planet describes circular and elliptic orbits, respectively. Originating from the circular family, families of symmetric periodic orbits in the 3/2, 5/2, 3/1, 4/1 and 5/1 mean-motion resonances are continued in the circular and the elliptic problems. New bifurcation points from the circular to the elliptic problem are found for each of the above resonances, and thus, new families continued from these points are herein presented. Stable segments of periodic orbits were found at high eccentricity values of the already known families considered as whole unstable previously. Moreover, new isolated (not continued from bifurcation points) families are computed in the elliptic restricted problem. The majority of the new families mainly consists of stable periodic orbits at high eccentricities. The families of the 5/1 resonance are investigated for the first time in the restricted three-body problems. We highlight the effect of stable periodic orbits on the formation of stable regions in their vicinity and unveil the boundaries of such domains in phase space by computing maps of dynamical stability. The long-term stable evolution of the terrestrial planets or asteroids is dependent on the existence of regular domains in their dynamical neighbourhood in phase space, which could host them for long-time spans. This study, besides other celestial architectures that can be efficiently modelled by the circular and elliptic restricted problems, is particularly appropriate for the discovery of terrestrial companions among the single-giant planet systems discovered so far.

Path integrals and large deviations in stochastic hybrid systems.

PubMed

Bressloff, Paul C; Newby, Jay M

2014-04-01

We construct a path-integral representation of solutions to a stochastic hybrid system, consisting of one or more continuous variables evolving according to a piecewise-deterministic dynamics. The differential equations for the continuous variables are coupled to a set of discrete variables that satisfy a continuous-time Markov process, which means that the differential equations are only valid between jumps in the discrete variables. Examples of stochastic hybrid systems arise in biophysical models of stochastic ion channels, motor-driven intracellular transport, gene networks, and stochastic neural networks. We use the path-integral representation to derive a large deviation action principle for a stochastic hybrid system. Minimizing the associated action functional with respect to the set of all trajectories emanating from a metastable state (assuming that such a minimization scheme exists) then determines the most probable paths of escape. Moreover, evaluating the action functional along a most probable path generates the so-called quasipotential used in the calculation of mean first passage times. We illustrate the theory by considering the optimal paths of escape from a metastable state in a bistable neural network.

Focusing light through dynamical samples using fast continuous wavefront optimization.

PubMed

Blochet, B; Bourdieu, L; Gigan, S

2017-12-01

We describe a fast continuous optimization wavefront shaping system able to focus light through dynamic scattering media. A micro-electro-mechanical system-based spatial light modulator, a fast photodetector, and field programmable gate array electronics are combined to implement a continuous optimization of a wavefront with a single-mode optimization rate of 4.1 kHz. The system performances are demonstrated by focusing light through colloidal solutions of TiO 2 particles in glycerol with tunable temporal stability.

Real-time dynamics of typical and untypical states in nonintegrable systems

NASA Astrophysics Data System (ADS)

Richter, Jonas; Jin, Fengping; De Raedt, Hans; Michielsen, Kristel; Gemmer, Jochen; Steinigeweg, Robin

2018-05-01

Understanding (i) the emergence of diffusion from truly microscopic principles continues to be a major challenge in experimental and theoretical physics. At the same time, isolated quantum many-body systems have experienced an upsurge of interest in recent years. Since in such systems the realization of a proper initial state is the only possibility to induce a nonequilibrium process, understanding (ii) the largely unexplored role of the specific realization is vitally important. Our work reports a substantial step forward and tackles the two issues (i) and (ii) in the context of typicality, entanglement as well as integrability and nonintegrability. Specifically, we consider the spin-1/2 XXZ chain, where integrability can be broken due to an additional next-nearest neighbor interaction, and study the real-time and real-space dynamics of nonequilibrium magnetization profiles for a class of pure states. Summarizing our main results, we show that signatures of diffusion for strong interactions are equally pronounced for the integrable and nonintegrable case. In both cases, we further find a clear difference between the dynamics of states with and without internal randomness. We provide an explanation of this difference by a detailed analysis of the local density of states.

One-Time Pad as a nonlinear dynamical system

NASA Astrophysics Data System (ADS)

Nagaraj, Nithin

2012-11-01

The One-Time Pad (OTP) is the only known unbreakable cipher, proved mathematically by Shannon in 1949. In spite of several practical drawbacks of using the OTP, it continues to be used in quantum cryptography, DNA cryptography and even in classical cryptography when the highest form of security is desired (other popular algorithms like RSA, ECC, AES are not even proven to be computationally secure). In this work, we prove that the OTP encryption and decryption is equivalent to finding the initial condition on a pair of binary maps (Bernoulli shift). The binary map belongs to a family of 1D nonlinear chaotic and ergodic dynamical systems known as Generalized Luröth Series (GLS). Having established these interesting connections, we construct other perfect secrecy systems on the GLS that are equivalent to the One-Time Pad, generalizing for larger alphabets. We further show that OTP encryption is related to Randomized Arithmetic Coding - a scheme for joint compression and encryption.

Supercomputer optimizations for stochastic optimal control applications

NASA Technical Reports Server (NTRS)

Chung, Siu-Leung; Hanson, Floyd B.; Xu, Huihuang

1991-01-01

Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations.

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

A VLF-based technique in applications to digital control of nonlinear hybrid multirate systems

NASA Astrophysics Data System (ADS)

Vassilyev, Stanislav; Ulyanov, Sergey; Maksimkin, Nikolay

2017-01-01

In this paper, a technique for rigorous analysis and design of nonlinear multirate digital control systems on the basis of the reduction method and sublinear vector Lyapunov functions is proposed. The control system model under consideration incorporates continuous-time dynamics of the plant and discrete-time dynamics of the controller and takes into account uncertainties of the plant, bounded disturbances, nonlinear characteristics of sensors and actuators. We consider a class of multirate systems where the control update rate is slower than the measurement sampling rates and periodic non-uniform sampling is admitted. The proposed technique does not use the preliminary discretization of the system, and, hence, allows one to eliminate the errors associated with the discretization and improve the accuracy of analysis. The technique is applied to synthesis of digital controller for a flexible spacecraft in the fine stabilization mode and decentralized controller for a formation of autonomous underwater vehicles. Simulation results are provided to validate the good performance of the designed controllers.

Robust optimization for nonlinear time-delay dynamical system of dha regulon with cost sensitivity constraint in batch culture

NASA Astrophysics Data System (ADS)

Yuan, Jinlong; Zhang, Xu; Liu, Chongyang; Chang, Liang; Xie, Jun; Feng, Enmin; Yin, Hongchao; Xiu, Zhilong

2016-09-01

Time-delay dynamical systems, which depend on both the current state of the system and the state at delayed times, have been an active area of research in many real-world applications. In this paper, we consider a nonlinear time-delay dynamical system of dha-regulonwith unknown time-delays in batch culture of glycerol bioconversion to 1,3-propanediol induced by Klebsiella pneumonia. Some important properties and strong positive invariance are discussed. Because of the difficulty in accurately measuring the concentrations of intracellular substances and the absence of equilibrium points for the time-delay system, a quantitative biological robustness for the concentrations of intracellular substances is defined by penalizing a weighted sum of the expectation and variance of the relative deviation between system outputs before and after the time-delays are perturbed. Our goal is to determine optimal values of the time-delays. To this end, we formulate an optimization problem in which the time delays are decision variables and the cost function is to minimize the biological robustness. This optimization problem is subject to the time-delay system, parameter constraints, continuous state inequality constraints for ensuring that the concentrations of extracellular and intracellular substances lie within specified limits, a quality constraint to reflect operational requirements and a cost sensitivity constraint for ensuring that an acceptable level of the system performance is achieved. It is approximated as a sequence of nonlinear programming sub-problems through the application of constraint transcription and local smoothing approximation techniques. Due to the highly complex nature of this optimization problem, the computational cost is high. Thus, a parallel algorithm is proposed to solve these nonlinear programming sub-problems based on the filled function method. Finally, it is observed that the obtained optimal estimates for the time-delays are highly satisfactory via numerical simulations.

Bayesian dynamic mediation analysis.

PubMed

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Hybrid deterministic/stochastic simulation of complex biochemical systems.

PubMed

Lecca, Paola; Bagagiolo, Fabio; Scarpa, Marina

2017-11-21

In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by complex networks of chemical reactions involving genes, proteins, and enzymes. Accurate computational models are indispensable means for understanding the mechanisms behind the evolution of a complex system, not always explored with wet lab experiments. To serve their purpose, computational models, however, should be able to describe and simulate the complexity of a biological system in many of its aspects. Moreover, it should be implemented by efficient algorithms requiring the shortest possible execution time, to avoid enlarging excessively the time elapsing between data analysis and any subsequent experiment. Besides the features of their topological structure, the complexity of biological networks also refers to their dynamics, that is often non-linear and stiff. The stiffness is due to the presence of molecular species whose abundance fluctuates by many orders of magnitude. A fully stochastic simulation of a stiff system is computationally time-expensive. On the other hand, continuous models are less costly, but they fail to capture the stochastic behaviour of small populations of molecular species. We introduce a new efficient hybrid stochastic-deterministic computational model and the software tool MoBioS (MOlecular Biology Simulator) implementing it. The mathematical model of MoBioS uses continuous differential equations to describe the deterministic reactions and a Gillespie-like algorithm to describe the stochastic ones. Unlike the majority of current hybrid methods, the MoBioS algorithm divides the reactions' set into fast reactions, moderate reactions, and slow reactions and implements a hysteresis switching between the stochastic model and the deterministic model. Fast reactions are approximated as continuous-deterministic processes and modelled by deterministic rate equations. Moderate reactions are those whose reaction waiting time is greater than the fast reaction waiting time but smaller than the slow reaction waiting time. A moderate reaction is approximated as a stochastic (deterministic) process if it was classified as a stochastic (deterministic) process at the time at which it crosses the threshold of low (high) waiting time. A Gillespie First Reaction Method is implemented to select and execute the slow reactions. The performances of MoBios were tested on a typical example of hybrid dynamics: that is the DNA transcription regulation. The simulated dynamic profile of the reagents' abundance and the estimate of the error introduced by the fully deterministic approach were used to evaluate the consistency of the computational model and that of the software tool.

Counting and classifying attractors in high dimensional dynamical systems.

PubMed

Bagley, R J; Glass, L

1996-12-07

Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors does not reveal the combinatorial structure of the attractors. These points are illustrated in a reexamination of dynamics in a class of random Boolean networks considered previously by Kauffman. We also consider comparisons between dynamics in discrete networks and continuous analogues. A continuous analogue of a discrete network may have a different number of attractors for many different reasons. Some attractors in discrete networks may be associated with unstable dynamics, and several different attractors in a discrete network may be associated with a single attractor in the continuous case. Special problems in determining attractors in continuous systems arise when there is aperiodic dynamics associated with quasiperiodicity of deterministic chaos.

Continuous noninvasive monitoring in the neonatal ICU.

PubMed

Sahni, Rakesh

2017-04-01

Standard hemodynamic monitoring such as heart rate and systemic blood pressure may only provide a crude estimation of organ perfusion during neonatal intensive care. Pulse oximetry monitoring allows for continuous noninvasive monitoring of hemoglobin oxygenation and thus provides estimation of end-organ oxygenation. This review aims to provide an overview of pulse oximetry and discuss its current and potential clinical use during neonatal intensive care. Technological advances in continuous assessment of dynamic changes in systemic oxygenation with pulse oximetry during transition to extrauterine life and beyond provide additional details about physiological interactions among the key hemodynamic factors regulating systemic blood flow distribution along with the subtle changes that are frequently transient and undetectable with standard monitoring. Noninvasive real-time continuous systemic oxygen monitoring has the potential to serve as biomarkers for early-organ dysfunction, to predict adverse short-term and long-term outcomes in critically ill neonates, and to optimize outcomes. Further studies are needed to establish values predicting adverse outcomes and to validate targeted interventions to normalize abnormal values to improve outcomes.

Quality control of the soil moisture probe response patterns from a green infrastructure site using Dynamic Time Warping (DTW) and association rule learning

NASA Astrophysics Data System (ADS)

Yu, Z.; Bedig, A.; Quigley, M.; Montalto, F. A.

2017-12-01

In-situ field monitoring can help to improve the design and management of decentralized Green Infrastructure (GI) systems in urban areas. Because of the vast quantity of continuous data generated from multi-site sensor systems, cost-effective post-construction opportunities for real-time control are limited; and the physical processes that influence the observed phenomena (e.g. soil moisture) are hard to track and control. To derive knowledge efficiently from real-time monitoring data, there is currently a need to develop more efficient approaches to data quality control. In this paper, we employ dynamic time warping method to compare the similarity of two soil moisture patterns without ignoring the inherent autocorrelation. We also use a rule-based machine learning method to investigate the feasibility of detecting anomalous responses from soil moisture probes. The data was generated from both individual and clusters of probes, deployed in a GI site in Milwaukee, WI. In contrast to traditional QAQC methods, which seek to detect outliers at individual time steps, the new method presented here converts the continuous time series into event-based symbolic sequences from which unusual response patterns can be detected. Different Matching rules are developed on different physical characteristics for different seasons. The results suggest that this method could be used alternatively to detect sensor failure, to identify extreme events, and to call out abnormal change patterns, compared to intra-probe and inter-probe historical observations. Though this algorithm was developed for soil moisture probes, the same approach could easily be extended to advance QAQC efficiency for any continuous environmental datasets.

Method for near-real-time continuous air monitoring of phosgene, hydrogen cyanide, and cyanogen chloride

NASA Astrophysics Data System (ADS)

Lattin, Frank G.; Paul, Donald G.

1996-11-01

A sorbent-based gas chromatographic method provides continuous quantitative measurement of phosgene, hydrogen cyanide, and cyanogen chloride in ambient air. These compounds are subject to workplace exposure limits as well as regulation under terms of the Chemical Arms Treaty and Title III of the 1990 Clean Air Act amendments. The method was developed for on-sit use in a mobile laboratory during remediation operations. Incorporated into the method are automated multi-level calibrations at time weighted average concentrations, or lower. Gaseous standards are prepared in fused silica lined air sampling canisters, then transferred to the analytical system through dynamic spiking. Precision and accuracy studies performed to validate the method are described. Also described are system deactivation and passivation techniques critical to optimum method performance.

SEXTANT - Station Explorer for X-Ray Timing and Navigation Technology

NASA Technical Reports Server (NTRS)

Mitchell, Jason; Hasouneh, Monther; Winternitz, Luke; Valdez, Jennifer; Price, Sam; Semper, Sean; Yu, Wayne; Gaebler, John; Ray, Paul; Wood, Kent;

Coupling between populations of copepod taxa within an estuarine ecosystem and the adjacent offshore regions

NASA Astrophysics Data System (ADS)

McGinty, N.; Johnson, M. P.; Power, A. M.

2012-07-01

Population dynamics in open systems are complicated by the interactions of local demography and local environmental forcing with processes occurring at larger scales. A local system such as an estuary or bay may contain a zooplankton population that effectively becomes independent of regional dynamics or the local dynamics may be closely coupled to a broader scale pattern. As an alternative, the details of migration and advection may mean that dynamics in a local system are coupled to other specific areas rather than tracking the overall dynamics at a larger scale. We used a reconstructed time series (1973-1987) for copepod taxa to examine the extent to which zooplankton dynamics in Galway Bay reflect processes in broader areas of the NE Atlantic. Continuous Plankton Recorder (CPR) counts were used to establish time series for nine offshore ecoregions, with the regions themselves defined using underlying patterns of chlorophyll variability. The open nature of Galway Bay was reflected in strong associations between bay zooplankton counts and offshore CPR data in a majority of cases (7/10). For each zooplankton taxon, there were large differences among regions in the degree of association with Galway Bay time series. Akaike weights indicated that one ecoregion tended to be the dominant link for each taxon. This indicates that the zooplankton of the Bay reflect more than the local modification of a regional signal and that different zooplankton in the bay may have separate source regions. The data from Galway Bay also fall within a 'sampling shadow' of the CPR. Later years of the time series showed evidence for changes in phenology, with spring zooplankton peaks generally occurring earlier in the year for smaller species.

Solutions of burnt-bridge models for molecular motor transport.

PubMed

Morozov, Alexander Yu; Pronina, Ekaterina; Kolomeisky, Anatoly B; Artyomov, Maxim N

2007-03-01

Transport of molecular motors, stimulated by interactions with specific links between consecutive binding sites (called "bridges"), is investigated theoretically by analyzing discrete-state stochastic "burnt-bridge" models. When an unbiased diffusing particle crosses the bridge, the link can be destroyed ("burned") with a probability p , creating a biased directed motion for the particle. It is shown that for probability of burning p=1 the system can be mapped into a one-dimensional single-particle hopping model along the periodic infinite lattice that allows one to calculate exactly all dynamic properties. For the general case of p<1 a theoretical method is developed and dynamic properties are computed explicitly. Discrete-time and continuous-time dynamics for periodic distribution of bridges and different burning dynamics are analyzed and compared. Analytical predictions are supported by extensive Monte Carlo computer simulations. Theoretical results are applied for analysis of the experiments on collagenase motor proteins.

Exact Solutions of Burnt-Bridge Models for Molecular Motor Transport

NASA Astrophysics Data System (ADS)

Morozov, Alexander; Pronina, Ekaterina; Kolomeisky, Anatoly; Artyomov, Maxim

2007-03-01

Transport of molecular motors, stimulated by interactions with specific links between consecutive binding sites (called ``bridges''), is investigated theoretically by analyzing discrete-state stochastic ``burnt-bridge'' models. When an unbiased diffusing particle crosses the bridge, the link can be destroyed (``burned'') with a probability p, creating a biased directed motion for the particle. It is shown that for probability of burning p=1 the system can be mapped into one-dimensional single-particle hopping model along the periodic infinite lattice that allows one to calculate exactly all dynamic properties. For general case of p<1 a new theoretical method is developed, and dynamic properties are computed explicitly. Discrete-time and continuous-time dynamics, periodic and random distribution of bridges and different burning dynamics are analyzed and compared. Theoretical predictions are supported by extensive Monte Carlo computer simulations. Theoretical results are applied for analysis of the experiments on collagenase motor proteins.

Solutions of burnt-bridge models for molecular motor transport

NASA Astrophysics Data System (ADS)

Morozov, Alexander Yu.; Pronina, Ekaterina; Kolomeisky, Anatoly B.; Artyomov, Maxim N.

2007-03-01

Transport of molecular motors, stimulated by interactions with specific links between consecutive binding sites (called “bridges”), is investigated theoretically by analyzing discrete-state stochastic “burnt-bridge” models. When an unbiased diffusing particle crosses the bridge, the link can be destroyed (“burned”) with a probability p , creating a biased directed motion for the particle. It is shown that for probability of burning p=1 the system can be mapped into a one-dimensional single-particle hopping model along the periodic infinite lattice that allows one to calculate exactly all dynamic properties. For the general case of p<1 a theoretical method is developed and dynamic properties are computed explicitly. Discrete-time and continuous-time dynamics for periodic distribution of bridges and different burning dynamics are analyzed and compared. Analytical predictions are supported by extensive Monte Carlo computer simulations. Theoretical results are applied for analysis of the experiments on collagenase motor proteins.

Opening up closure. Semiotics across scales

PubMed

Lemke

2000-01-01

The dynamic emergence of new levels of organization in complex systems is related to the semiotic reorganization of discrete/continuous variety at the level below as continuous/discrete meaning for the level above. In this view both the semiotic and the dynamic closure of system levels is reopened to allow the development and evolution of greater complexity.

Stochastic dynamics and stable equilibrium of evolutionary optional public goods game in finite populations

NASA Astrophysics Data System (ADS)

Quan, Ji; Liu, Wei; Chu, Yuqing; Wang, Xianjia

2018-07-01

Continuous noise caused by mutation is widely present in evolutionary systems. Considering the noise effects and under the optional participation mechanism, a stochastic model for evolutionary public goods game in a finite size population is established. The evolutionary process of strategies in the population is described as a multidimensional ergodic and continuous time Markov process. The stochastic stable state of the system is analyzed by the limit distribution of the stochastic process. By numerical experiments, the influences of the fixed income coefficient for non-participants and the investment income coefficient of the public goods on the stochastic stable equilibrium of the system are analyzed. Through the numerical calculation results, we found that the optional participation mechanism can change the evolutionary dynamics and the equilibrium of the public goods game, and there is a range of parameters which can effectively promote the evolution of cooperation. Further, we obtain the accurate quantitative relationship between the parameters and the probabilities for the system to choose different stable equilibriums, which can be used to realize the control of cooperation.

Flight Control Design for an Autonomous Rotorcraft Using Pseudo-Sliding Mode Control and Waypoint Navigation

NASA Astrophysics Data System (ADS)

Mallory, Nicolas Joseph

The design of robust automated flight control systems for aircraft of varying size and complexity is a topic of continuing interest for both military and civilian industries. By merging the benefits of robustness from sliding mode control (SMC) with the familiarity and transparency of design tradeoff offered by frequency domain approaches, this thesis presents pseudo-sliding mode control as a viable option for designing automated flight control systems for complex six degree-of-freedom aircraft. The infinite frequency control switching of SMC is replaced, by necessity, with control inputs that are continuous in nature. An introduction to SMC theory is presented, followed by a detailed design of a pseudo-sliding mode control and automated flight control system for a six degree-of-freedom model of a Hughes OH6 helicopter. This model is then controlled through three different waypoint missions that demonstrate the stability of the system and the aircraft's ability to follow certain maneuvers despite time delays, large changes in model parameters and vehicle dynamics, actuator dynamics, sensor noise, and atmospheric disturbances.

A variational method for analyzing limit cycle oscillations in stochastic hybrid systems

NASA Astrophysics Data System (ADS)

Bressloff, Paul C.; MacLaurin, James

2018-06-01

Many systems in biology can be modeled through ordinary differential equations, which are piece-wise continuous, and switch between different states according to a Markov jump process known as a stochastic hybrid system or piecewise deterministic Markov process (PDMP). In the fast switching limit, the dynamics converges to a deterministic ODE. In this paper, we develop a phase reduction method for stochastic hybrid systems that support a stable limit cycle in the deterministic limit. A classic example is the Morris-Lecar model of a neuron, where the switching Markov process is the number of open ion channels and the continuous process is the membrane voltage. We outline a variational principle for the phase reduction, yielding an exact analytic expression for the resulting phase dynamics. We demonstrate that this decomposition is accurate over timescales that are exponential in the switching rate ɛ-1 . That is, we show that for a constant C, the probability that the expected time to leave an O(a) neighborhood of the limit cycle is less than T scales as T exp (-C a /ɛ ) .

Sensitivity analysis of reactive ecological dynamics.

PubMed

Verdy, Ariane; Caswell, Hal

2008-08-01

Ecological systems with asymptotically stable equilibria may exhibit significant transient dynamics following perturbations. In some cases, these transient dynamics include the possibility of excursions away from the equilibrium before the eventual return; systems that exhibit such amplification of perturbations are called reactive. Reactivity is a common property of ecological systems, and the amplification can be large and long-lasting. The transient response of a reactive ecosystem depends on the parameters of the underlying model. To investigate this dependence, we develop sensitivity analyses for indices of transient dynamics (reactivity, the amplification envelope, and the optimal perturbation) in both continuous- and discrete-time models written in matrix form. The sensitivity calculations require expressions, some of them new, for the derivatives of equilibria, eigenvalues, singular values, and singular vectors, obtained using matrix calculus. Sensitivity analysis provides a quantitative framework for investigating the mechanisms leading to transient growth. We apply the methodology to a predator-prey model and a size-structured food web model. The results suggest predator-driven and prey-driven mechanisms for transient amplification resulting from multispecies interactions.

Specialized data analysis for the Space Shuttle Main Engine and diagnostic evaluation of advanced propulsion system components

NASA Technical Reports Server (NTRS)

1993-01-01

The Marshall Space Flight Center is responsible for the development and management of advanced launch vehicle propulsion systems, including the Space Shuttle Main Engine (SSME), which is presently operational, and the Space Transportation Main Engine (STME) under development. The SSME's provide high performance within stringent constraints on size, weight, and reliability. Based on operational experience, continuous design improvement is in progress to enhance system durability and reliability. Specialized data analysis and interpretation is required in support of SSME and advanced propulsion system diagnostic evaluations. Comprehensive evaluation of the dynamic measurements obtained from test and flight operations is necessary to provide timely assessment of the vibrational characteristics indicating the operational status of turbomachinery and other critical engine components. Efficient performance of this effort is critical due to the significant impact of dynamic evaluation results on ground test and launch schedules, and requires direct familiarity with SSME and derivative systems, test data acquisition, and diagnostic software. Detailed analysis and evaluation of dynamic measurements obtained during SSME and advanced system ground test and flight operations was performed including analytical/statistical assessment of component dynamic behavior, and the development and implementation of analytical/statistical models to efficiently define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational condition. In addition, the SSME and J-2 data will be applied to develop vibroacoustic environments for advanced propulsion system components, as required. This study will provide timely assessment of engine component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. This contract will be performed through accomplishment of negotiated task orders.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

NASA Astrophysics Data System (ADS)

Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Analysis, simulation and visualization of 1D tapping via reduced dynamical models

NASA Astrophysics Data System (ADS)

Blackmore, Denis; Rosato, Anthony; Tricoche, Xavier; Urban, Kevin; Zou, Luo

2014-04-01

A low-dimensional center-of-mass dynamical model is devised as a simplified means of approximately predicting some important aspects of the motion of a vertical column comprised of a large number of particles subjected to gravity and periodic vertical tapping. This model is investigated first as a continuous dynamical system using analytical, simulation and visualization techniques. Then, by employing an approach analogous to that used to approximate the dynamics of a bouncing ball on an oscillating flat plate, it is modeled as a discrete dynamical system and analyzed to determine bifurcations and transitions to chaotic motion along with other properties. The predictions of the analysis are then compared-primarily qualitatively-with visualization and simulation results of the reduced continuous model, and ultimately with simulations of the complete system dynamics.

Two-rate periodic protocol with dynamics driven through many cycles

NASA Astrophysics Data System (ADS)

Kar, Satyaki

2017-02-01

We study the long time dynamics in closed quantum systems periodically driven via time dependent parameters with two frequencies ω1 and ω2=r ω1 . Tuning of the ratio r there can unleash plenty of dynamical phenomena to occur. Our study includes integrable models like Ising and X Y models in d =1 and the Kitaev model in d =1 and 2 and can also be extended to Dirac fermions in graphene. We witness the wave-function overlap or dynamic freezing that occurs within some small/ intermediate frequency regimes in the (ω1,r ) plane (with r ≠0 ) when the ground state is evolved through a single cycle of driving. However, evolved states soon become steady with long driving, and the freezing scenario gets rarer. We extend the formalism of adiabatic-impulse approximation for many cycle driving within our two-rate protocol and show the near-exact comparisons at small frequencies. An extension of the rotating wave approximation is also developed to gather an analytical framework of the dynamics at high frequencies. Finally we compute the entanglement entropy in the stroboscopically evolved states within the gapped phases of the system and observe how it gets tuned with the ratio r in our protocol. The minimally entangled states are found to fall within the regime of dynamical freezing. In general, the results indicate that the entanglement entropy in our driven short-ranged integrable systems follow a genuine nonarea law of scaling and show a convergence (with a r dependent pace) towards volume scaling behavior as the driving is continued for a long time.

Incorporating Dynamical Systems into the Traditional Curriculum.

ERIC Educational Resources Information Center

Natov, Jonathan

2001-01-01

Presents a brief overview of dynamical systems. Gives examples from dynamical systems and where they fit into the current curriculum. Points out that these examples are accessible to undergraduate freshmen and sophomore students, add continuity to the standard curriculum, and are worth including in classes. (MM)

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution systemmore » operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.« less

[Study for portable dynamic ECG monitor and recorder].

PubMed

Yang, Pengcheng; Li, Yongqin; Chen, Bihua

2012-09-01

This Paper presents a portable dynamic ECG monitor system based on MSP430F149 microcontroller. The electrocardiogram detecting system consists of ECG detecting circuit, man-machine interaction module, MSP430F149 and upper computer software. The ECG detecting circuit including a preamplifier, second-order Butterworth low-pass filter, high-pass filter, and 50Hz trap circuit to detects electrocardiogram and depresses various kinds of interference effectively. A microcontroller is used to collect three channel analog signals which can be displayed on TFT LCD. A SD card is used to record real-time data continuously and implement the FTA16 file system. In the end, a host computer system interface is also designed to analyze the ECG signal and the analysis results can provide diagnosis references to clinical doctors.

Assessing dry weather flow contribution in TSS and COD storm events loads in combined sewer systems.

PubMed

Métadier, M; Bertrand-Krajewski, J L

2011-01-01

Continuous high resolution long term turbidity measurements along with continuous discharge measurements are now recognised as an appropriate technique for the estimation of in sewer total suspended solids (TSS) and Chemical Oxygen Demand (COD) loads during storm events. In the combined system of the Ecully urban catchment (Lyon, France), this technique is implemented since 2003, with more than 200 storm events monitored. This paper presents a method for the estimation of the dry weather (DW) contribution to measured total TSS and COD event loads with special attention devoted to uncertainties assessment. The method accounts for the dynamics of both discharge and turbidity time series at two minutes time step. The study is based on 180 DW days monitored in 2007-2008. Three distinct classes of DW days were evidenced. Variability analysis and quantification showed that no seasonal effect and no trend over the year were detectable. The law of propagation of uncertainties is applicable for uncertainties estimation. The method has then been applied to all measured storm events. This study confirms the interest of long term continuous discharge and turbidity time series in sewer systems, especially in the perspective of wet weather quality modelling.

Model-Based Prognostics of Hybrid Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal

2015-01-01

Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

Glacier, Glacial Lake, and Ecological Response Dynamics of the Imja Glacier-Lake-Moraine System, Nepal

NASA Astrophysics Data System (ADS)

Kargel, J. S.; Shugar, D. H.; Leonard, G. J.; Haritashya, U. K.; Harrison, S.; Shrestha, A. B.; Mool, P. K.; Karki, A.; Regmi, D.

2016-12-01

Glacier response dynamics—involving a host of processes—produce a sequence of short- to long-term delayed responses to any step-wise, oscillating, or continuous trending climatic perturbation. We present analysis of Imja Lake, Nepal and examine its thinning and retreat and a sequence of the detachment of tributaries; the inception and growth of Imja Lake and concomitant glacier retreat, thinning, and stagnation, and relationships to lake dynamics; the response dynamics of the ice-cored moraine; the development of the local ecosystem; prediction of short-term dynamical responses to lake lowering (glacier lake outburst flood—GLOF—mitigation); and prospects for coming decades. The evolution of this glacier system provides a case study by which the global record of GLOFs can be assessed in terms of climate change attribution. We define three response times: glacier dynamical response time (for glacier retreat, thinning, and slowing of ice flow), limnological response time (lake growth), and GLOF trigger time (for a variety of hazardous trigger events). Lake lowering (to be completed in August 2016; see AGU abstract by D. Regmi et al.) will reduce hazards, but we expect that the elongation of the lake and retreat of the glacier will continue for decades after a pause in 2016-2017. The narrowing of the moraine dam due to thaw degradation of the ice-cored end moraine means that the hazard due to Imja Lake will soon again increase. We examine both long-term response dynamics, and two aspects of Himalayan glaciers that have very rapid responses: the area of Imja Lake fluctuates seasonally and even with subseasonal weather variations in response to changes in lake temperature and glacier meltback; and as known from other studies, glacier flow speed can vary between years and even on shorter timescales. The long-term development and stabilization of glacial moraines and small lacustrine plains in drained lake basins impacts the development of local ecosystems; satellite imaging reveals details of vegetational primary succession; and we will present an observations-constrained model of bird habitat in relationship to glacial geomorphology.

Time and frequency domain analysis of sampled data controllers via mixed operation equations

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1981-01-01

Specification of the mathematical equations required to define the dynamic response of a linear continuous plant, subject to sampled data control, is complicated by the fact that the digital components of the control system cannot be modeled via linear ordinary differential equations. This complication can be overcome by introducing two new mathematical operations; namely, the operation of zero order hold and digial delay. It is shown that by direct utilization of these operations, a set of linear mixed operation equations can be written and used to define the dynamic response characteristics of the controlled system. It also is shown how these linear mixed operation equations lead, in an automatable manner, directly to a set of finite difference equations which are in a format compatible with follow on time and frequency domain analysis methods.

The dynamic radiation environment assimilation model (DREAM)

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

Reeves, Geoffrey D; Koller, Josef; Tokar, Robert L

2010-01-01

The Dynamic Radiation Environment Assimilation Model (DREAM) is a 3-year effort sponsored by the US Department of Energy to provide global, retrospective, or real-time specification of the natural and potential nuclear radiation environments. The DREAM model uses Kalman filtering techniques that combine the strengths of new physical models of the radiation belts with electron observations from long-term satellite systems such as GPS and geosynchronous systems. DREAM includes a physics model for the production and long-term evolution of artificial radiation belts from high altitude nuclear explosions. DREAM has been validated against satellites in arbitrary orbits and consistently produces more accurate resultsmore » than existing models. Tools for user-specific applications and graphical displays are in beta testing and a real-time version of DREAM has been in continuous operation since November 2009.« less

Concordance of Interests in Dynamic Models of Social Partnership in the System of Continuing Professional Education

ERIC Educational Resources Information Center

Tarasenko, Larissa V.; Ougolnitsky, Guennady A.; Usov, Anatoly B.; Vaskov, Maksim A.; Kirik, Vladimir A.; Astoyanz, Margarita S.; Angel, Olga Y.

2016-01-01

A dynamic game theoretic model of concordance of interests in the process of social partnership in the system of continuing professional education is proposed. Non-cooperative, cooperative, and hierarchical setups are examined. Analytical solution for a linear state version of the model is provided. Nash equilibrium algorithms (for non-cooperative…

X-ray Pulsar Navigation Algorithms and Testbed for SEXTANT

NASA Technical Reports Server (NTRS)

Winternitz, Luke M. B.; Hasouneh, Monther A.; Mitchell, Jason W.; Valdez, Jennifer E.; Price, Samuel R.; Semper, Sean R.; Yu, Wayne H.; Ray, Paul S.; Wood, Kent S.; Arzoumanian, Zaven;

On the validity of Zeeman's classification for three dimensional competitive differential equations with linearly determined nullclines

NASA Astrophysics Data System (ADS)

Jiang, Jifa; Niu, Lei

2017-12-01

We study three dimensional competitive differential equations with linearly determined nullclines and prove that they always have 33 stable nullcline classes in total. Each class is given in terms of inequalities on the intrinsic growth rates and competitive coefficients and is independent of generating functions. The common characteristics are that every trajectory converges to an equilibrium in classes 1-25, that Hopf bifurcations do not occur within class 32, and that there is always a heteroclinic cycle in class 27. Nontrivial dynamical behaviors, such as the existence and multiplicity of limit cycles, only may occur in classes 26-33, but these nontrivial dynamical behaviors depend on generating functions. We show that Hopf bifurcation can occur within each of classes 26-31 for continuous-time Leslie/Gower system and Ricker system, the same as Lotka-Volterra system; but it only occurs in classes 26 and 27 for continuous-time Atkinson/Allen system and Gompertz system. There is an apparent distinction between Lotka-Volterra system and Leslie/Gower system, Ricker system, Atkinson/Allen system, and Gompertz system with the identical growth rate. Lotka-Volterra system with the identical growth rate has no limit cycle, but admits a center on the carrying simplex in classes 26 and 27. But Leslie/Gower system, Ricker system, Atkinson/Allen system, and Gompertz system with the identical growth rate do possess limit cycles. At last, we provide examples to show that Leslie/Gower system and Ricker system can also admit two limit cycles. This general classification greatly widens applications of Zeeman's method and makes it possible to investigate the existence and multiplicity of limit cycles, centers and stability of heteroclinic cycles for three dimensional competitive systems with linearly determined nullclines, as done in planar systems.

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

System Dynamics to Climate-Driven Water Budget Analysis in the Eastern Snake Plains Aquifer

NASA Astrophysics Data System (ADS)

Ryu, J.; Contor, B.; Wylie, A.; Johnson, G.; Allen, R. G.

2010-12-01

Climate variability, weather extremes and climate change continue to threaten the sustainability of water resources in the western United States. Given current climate change projections, increasing temperature is likely to modify the timing, form, and intensity of precipitation events, which consequently affect regional and local hydrologic cycles. As a result, drought, water shortage, and subsequent water conflicts may become an increasing threat in monotone hydrologic systems in arid lands, such as the Eastern Snake Plain Aquifer (ESPA). The ESPA, in particular, is a critical asset in the state of Idaho. It is known as the economic lifeblood for more than half of Idaho’s population so that water resources availability and aquifer management due to climate change is of great interest, especially over the next few decades. In this study, we apply system dynamics as a methodology with which to address dynamically complex problems in ESPA’s water resources management. Aquifer recharge and discharge dynamics are coded in STELLA modeling system as input and output, respectively to identify long-term behavior of aquifer responses to climate-driven hydrological changes.

A hybrid continuous-discrete method for stochastic reaction–diffusion processes

PubMed Central

Zheng, Likun; Nie, Qing

2016-01-01

Stochastic fluctuations in reaction–diffusion processes often have substantial effect on spatial and temporal dynamics of signal transductions in complex biological systems. One popular approach for simulating these processes is to divide the system into small spatial compartments assuming that molecules react only within the same compartment and jump between adjacent compartments driven by the diffusion. While the approach is convenient in terms of its implementation, its computational cost may become prohibitive when diffusive jumps occur significantly more frequently than reactions, as in the case of rapid diffusion. Here, we present a hybrid continuous-discrete method in which diffusion is simulated using continuous approximation while reactions are based on the Gillespie algorithm. Specifically, the diffusive jumps are approximated as continuous Gaussian random vectors with time-dependent means and covariances, allowing use of a large time step, even for rapid diffusion. By considering the correlation among diffusive jumps, the approximation is accurate for the second moment of the diffusion process. In addition, a criterion is obtained for identifying the region in which such diffusion approximation is required to enable adaptive calculations for better accuracy. Applications to a linear diffusion system and two nonlinear systems of morphogens demonstrate the effectiveness and benefits of the new hybrid method. PMID:27703710

Pathwise upper semi-continuity of random pullback attractors along the time axis

NASA Astrophysics Data System (ADS)

Cui, Hongyong; Kloeden, Peter E.; Wu, Fuke

2018-07-01

The pullback attractor of a non-autonomous random dynamical system is a time-indexed family of random sets, typically having the form {At(ṡ) } t ∈ R with each At(ṡ) a random set. This paper is concerned with the nature of such time-dependence. It is shown that the upper semi-continuity of the mapping t ↦At(ω) for each ω fixed has an equivalence relationship with the uniform compactness of the local union ∪s∈IAs(ω) , where I ⊂ R is compact. Applied to a semi-linear degenerate parabolic equation with additive noise and a wave equation with multiplicative noise we show that, in order to prove the above locally uniform compactness and upper semi-continuity, no additional conditions are required, in which sense the two properties appear to be general properties satisfied by a large number of real models.

Distributed Position-Based Consensus of Second-Order Multiagent Systems With Continuous/Intermittent Communication.

PubMed

Song, Qiang; Liu, Fang; Wen, Guanghui; Cao, Jinde; Yang, Xinsong

2017-04-24

This paper considers the position-based consensus in a network of agents with double-integrator dynamics and directed topology. Two types of distributed observer algorithms are proposed to solve the consensus problem by utilizing continuous and intermittent position measurements, respectively, where each observer does not interact with any other observers. For the case of continuous communication between network agents, some convergence conditions are derived for reaching consensus in the network with a single constant delay or multiple time-varying delays on the basis of the eigenvalue analysis and the descriptor method. When the network agents can only obtain intermittent position data from local neighbors at discrete time instants, the consensus in the network without time delay or with nonuniform delays is investigated by using the Wirtinger's inequality and the delayed-input approach. Numerical examples are given to illustrate the theoretical analysis.

Linear build-up of Fano resonance spectral profiles

NASA Astrophysics Data System (ADS)

Golovinski, P. A.; Yakovets, A. V.; Astapenko, V. A.

2018-06-01

The build-up dynamics of a continuous spectrum under the action of a weak laser field on a Fano resonance with the use of the pulses with the Lorentz spectrum and ultrashort pulses in the wavelet form is investigated. A dispersion-time excitation dependence of the Fano resonances in a He atom, in an InP impurity semiconductor, in longitudinal optical LO-phonons of a shallow donor exciton in pure ZnO crystals, and in metamaterials are calculated. The numerical simulation of the dynamics has shown time-dependent formation of a Fano spectral profile in the systems of different physical natures under the action of ultrashort pulses with attosecond and femtosecond durations.

Specialized data analysis of SSME and advanced propulsion system vibration measurements

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Swanson, Wayne L.; Jong, Yen-Yi

1993-01-01

The basic objectives of this contract were to perform detailed analysis and evaluation of dynamic data obtained during Space Shuttle Main Engine (SSME) test and flight operations, including analytical/statistical assessment of component dynamic performance, and to continue the development and implementation of analytical/statistical models to effectively define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational conditions. This study was to provide timely assessment of engine component operational status, identify probable causes of malfunction, and define feasible engineering solutions. The work was performed under three broad tasks: (1) Analysis, Evaluation, and Documentation of SSME Dynamic Test Results; (2) Data Base and Analytical Model Development and Application; and (3) Development and Application of Vibration Signature Analysis Techniques.

Scale-free memory model for multiagent reinforcement learning. Mean field approximation and rock-paper-scissors dynamics

NASA Astrophysics Data System (ADS)

Lubashevsky, I.; Kanemoto, S.

2010-07-01

A continuous time model for multiagent systems governed by reinforcement learning with scale-free memory is developed. The agents are assumed to act independently of one another in optimizing their choice of possible actions via trial-and-error search. To gain awareness about the action value the agents accumulate in their memory the rewards obtained from taking a specific action at each moment of time. The contribution of the rewards in the past to the agent current perception of action value is described by an integral operator with a power-law kernel. Finally a fractional differential equation governing the system dynamics is obtained. The agents are considered to interact with one another implicitly via the reward of one agent depending on the choice of the other agents. The pairwise interaction model is adopted to describe this effect. As a specific example of systems with non-transitive interactions, a two agent and three agent systems of the rock-paper-scissors type are analyzed in detail, including the stability analysis and numerical simulation. Scale-free memory is demonstrated to cause complex dynamics of the systems at hand. In particular, it is shown that there can be simultaneously two modes of the system instability undergoing subcritical and supercritical bifurcation, with the latter one exhibiting anomalous oscillations with the amplitude and period growing with time. Besides, the instability onset via this supercritical mode may be regarded as “altruism self-organization”. For the three agent system the instability dynamics is found to be rather irregular and can be composed of alternate fragments of oscillations different in their properties.

The Importance of Dynamic Systems Approaches for Understanding Development

ERIC Educational Resources Information Center

Howe, Mark L.; Lewis, Marc D.

2005-01-01

We outline the nature of dynamic systems, both linear and nonlinear, and we review dynamic systems principles that apply well to various aspects of human development, including the emergence of new forms, phases of stability and instability, continuous and discontinuous change, and differentiation among individual trajectories. We then document…

Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions

PubMed Central

Jung, Kanghoon; Jang, Hyeran; Kralik, Jerald D.; Jeong, Jaeseung

2014-01-01

A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices. PMID:25122498

State dragging using the quantum Zeno effect

NASA Astrophysics Data System (ADS)

Hacohen-Gourgy, Shay; Martin, Leigh; GarcíA-Pintos, Luis Pedro; Dressel, Justin; Siddiqi, Irfan

The quantum Zeno effect is the suppression of Hamiltonian evolution by continuous measurement. It arises as a consequence of the quantum back-action pushing the state towards an eigenstate of the measurement operator. Rotating the operator at a rate much slower than the measurement rate will effectively drag the state with it. We use our recently developed scheme, which enables dynamic control of the measurement operator, to demonstrate this dragging effect on a superconducting transmon qubit. Since the system is continuously measured, the deterministic trajectory can be monitored, and quantum jumps can be detected in real-time. Furthermore, we perform this with two observables that are set to be either commuting or non-commuting, demonstrating new quantum dynamics. This work was supported by the Army Research Office and the Air Force Research Laboratory.

An integrated, multisensor system for the continuous monitoring of water dynamics in rice fields under different irrigation regimes.

PubMed

Chiaradia, Enrico Antonio; Facchi, Arianna; Masseroni, Daniele; Ferrari, Daniele; Bischetti, Gian Battista; Gharsallah, Olfa; Cesari de Maria, Sandra; Rienzner, Michele; Naldi, Ezio; Romani, Marco; Gandolfi, Claudio

2015-09-01

The cultivation of rice, one of the most important staple crops worldwide, has very high water requirements. A variety of irrigation practices are applied, whose pros and cons, both in terms of water productivity and of their effects on the environment, are not completely understood yet. The continuous monitoring of irrigation and rainfall inputs, as well as of soil water dynamics, is a very important factor in the analysis of these practices. At the same time, however, it represents a challenging and costly task because of the complexity of the processes involved, of the difference in nature and magnitude of the driving variables and of the high variety of field conditions. In this paper, we present the prototype of an integrated, multisensor system for the continuous monitoring of water dynamics in rice fields under different irrigation regimes. The system consists of the following: (1) flow measurement devices for the monitoring of irrigation supply and tailwater drainage; (2) piezometers for groundwater level monitoring; (3) level gauges for monitoring the flooding depth; (4) multilevel tensiometers and moisture sensor clusters to monitor soil water status; (5) eddy covariance station for the estimation of evapotranspiration fluxes and (6) wireless transmission devices and software interface for data transfer, storage and control from remote computer. The system is modular and it is replicable in different field conditions. It was successfully applied over a 2-year period in three experimental plots in Northern Italy, each one with a different water management strategy. In the paper, we present information concerning the different instruments selected, their interconnections and their integration in a common remote control scheme. We also provide considerations and figures on the material and labour costs of the installation and management of the system.

System-level power optimization for real-time distributed embedded systems

NASA Astrophysics Data System (ADS)

Luo, Jiong

Power optimization is one of the crucial design considerations for modern electronic systems. In this thesis, we present several system-level power optimization techniques for real-time distributed embedded systems, based on dynamic voltage scaling, dynamic power management, and management of peak power and variance of the power profile. Dynamic voltage scaling has been widely acknowledged as an important and powerful technique to trade off dynamic power consumption and delay. Efficient dynamic voltage scaling requires effective variable-voltage scheduling mechanisms that can adjust voltages and clock frequencies adaptively based on workloads and timing constraints. For this purpose, we propose static variable-voltage scheduling algorithms utilizing criticalpath driven timing analysis for the case when tasks are assumed to have uniform switching activities, as well as energy-gradient driven slack allocation for a more general scenario. The proposed techniques can achieve closeto-optimal power savings with very low computational complexity, without violating any real-time constraints. We also present algorithms for power-efficient joint scheduling of multi-rate periodic task graphs along with soft aperiodic tasks. The power issue is addressed through both dynamic voltage scaling and power management. Periodic task graphs are scheduled statically. Flexibility is introduced into the static schedule to allow the on-line scheduler to make local changes to PE schedules through resource reclaiming and slack stealing, without interfering with the validity of the global schedule. We provide a unified framework in which the response times of aperiodic tasks and power consumption are dynamically optimized simultaneously. Interconnection network fabrics point to a new generation of power-efficient and scalable interconnection architectures for distributed embedded systems. As the system bandwidth continues to increase, interconnection networks become power/energy limited as well. Variable-frequency links have been designed by circuit designers for both parallel and serial links, which can adaptively regulate the supply voltage of transceivers to a desired link frequency, to exploit the variations in bandwidth requirement for power savings. We propose solutions for simultaneous dynamic voltage scaling of processors and links. The proposed solution considers real-time scheduling, flow control, and packet routing jointly. It can trade off the power consumption on processors and communication links via efficient slack allocation, and lead to more power savings than dynamic voltage scaling on processors alone. For battery-operated systems, the battery lifespan is an important concern. Due to the effects of discharge rate and battery recovery, the discharge pattern of batteries has an impact on the battery lifespan. Battery models indicate that even under the same average power consumption, reducing peak power current and variance in the power profile can increase the battery efficiency and thereby prolong battery lifetime. To take advantage of these effects, we propose battery-driven scheduling techniques for embedded applications, to reduce the peak power and the variance in the power profile of the overall system under real-time constraints. The proposed scheduling algorithms are also beneficial in addressing reliability and signal integrity concerns by effectively controlling peak power and variance of the power profile.

Analysis of continuous-time switching networks

NASA Astrophysics Data System (ADS)

Edwards, R.

2000-11-01

Models of a number of biological systems, including gene regulation and neural networks, can be formulated as switching networks, in which the interactions between the variables depend strongly on thresholds. An idealized class of such networks in which the switching takes the form of Heaviside step functions but variables still change continuously in time has been proposed as a useful simplification to gain analytic insight. These networks, called here Glass networks after their originator, are simple enough mathematically to allow significant analysis without restricting the range of dynamics found in analogous smooth systems. A number of results have been obtained before, particularly regarding existence and stability of periodic orbits in such networks, but important cases were not considered. Here we present a coherent method of analysis that summarizes previous work and fills in some of the gaps as well as including some new results. Furthermore, we apply this analysis to a number of examples, including surprising long and complex limit cycles involving sequences of hundreds of threshold transitions. Finally, we show how the above methods can be extended to investigate aperiodic behaviour in specific networks, though a complete analysis will have to await new results in matrix theory and symbolic dynamics.

Chaotic oscillation and random-number generation based on nanoscale optical-energy transfer.

PubMed

Naruse, Makoto; Kim, Song-Ju; Aono, Masashi; Hori, Hirokazu; Ohtsu, Motoichi

2014-08-12

By using nanoscale energy-transfer dynamics and density matrix formalism, we demonstrate theoretically and numerically that chaotic oscillation and random-number generation occur in a nanoscale system. The physical system consists of a pair of quantum dots (QDs), with one QD smaller than the other, between which energy transfers via optical near-field interactions. When the system is pumped by continuous-wave radiation and incorporates a timing delay between two energy transfers within the system, it emits optical pulses. We refer to such QD pairs as nano-optical pulsers (NOPs). Irradiating an NOP with external periodic optical pulses causes the oscillating frequency of the NOP to synchronize with the external stimulus. We find that chaotic oscillation occurs in the NOP population when they are connected by an external time delay. Moreover, by evaluating the time-domain signals by statistical-test suites, we confirm that the signals are sufficiently random to qualify the system as a random-number generator (RNG). This study reveals that even relatively simple nanodevices that interact locally with each other through optical energy transfer at scales far below the wavelength of irradiating light can exhibit complex oscillatory dynamics. These findings are significant for applications such as ultrasmall RNGs.

The real-time link between person perception and action: Brain potential evidence for dynamic continuity

PubMed Central

Freeman, Jonathan B.; Ambady, Nalini; Midgley, Katherine J.; Holcomb, Phillip J.

2010-01-01

Using event-related potentials, we investigated how the brain extracts information from another’s face and translates it into relevant action in real-time. In Study 1, participants made between-hand sex categorizations of sex-typical and sex-atypical faces. Sex-atypical faces evoked negativity between 250-550 ms (N300/N400 effects), reflecting the integration of accumulating sex-category knowledge into a coherent sex-category interpretation. Additionally, the lateralized readiness potential (LRP) revealed that the motor cortex began preparing for a correct hand response while social category knowledge was still gradually evolving in parallel. In Study 2, participants made between-hand eye-color categorizations as part of go/no-go trials that were contingent on a target’s sex. On no-go trials, although the hand did not actually move, information about eye color partially prepared the motor cortex to move the hand before perception of sex had finalized. Together, these findings demonstrate the dynamic continuity between person perception and action, such that ongoing results from face processing are immediately and continuously cascaded into the motor system over time. The preparation of action begins based on tentative perceptions of another’s face before perceivers have finished interpreting what they just saw. PMID:20602284

The real-time link between person perception and action: brain potential evidence for dynamic continuity.

PubMed

Freeman, Jonathan B; Ambady, Nalini; Midgley, Katherine J; Holcomb, Phillip J

2011-01-01

Using event-related potentials, we investigated how the brain extracts information from another's face and translates it into relevant action in real time. In Study 1, participants made between-hand sex categorizations of sex-typical and sex-atypical faces. Sex-atypical faces evoked negativity between 250 and 550 ms (N300/N400 effects), reflecting the integration of accumulating sex-category knowledge into a coherent sex-category interpretation. Additionally, the lateralized readiness potential revealed that the motor cortex began preparing for a correct hand response while social category knowledge was still gradually evolving in parallel. In Study 2, participants made between-hand eye-color categorizations as part of go/no-go trials that were contingent on a target's sex. On no-go trials, although the hand did not actually move, information about eye color partially prepared the motor cortex to move the hand before perception of sex had finalized. Together, these findings demonstrate the dynamic continuity between person perception and action, such that ongoing results from face processing are immediately and continuously cascaded into the motor system over time. The preparation of action begins based on tentative perceptions of another's face before perceivers have finished interpreting what they just saw. © 2010 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business

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

Dumitru, Irina, E-mail: aniri-dum@yahoo.com; Isar, Aurelian

In the framework of the theory of open systems based on completely positive quantum dynamical semigroups, we give a description of the continuous variable entanglement for a system consisting of two non-interacting bosonic modes embedded in a thermal environment. The calculated measure of entanglement is entanglement of formation. We describe the evolution of entanglement in terms of the covariance matrix for symmetric Gaussian input states. In the case of an entangled initial squeezed thermal state, entanglement suppression (entanglement sudden death) takes place, for all non-zero temperatures of the thermal bath. After that, the system remains for all times in amore » separable state. For a zero temperature of the thermal bath, the system remains entangled for all finite times, but in the limit of asymptotic large times the state becomes separable.« less

Brownian dynamics simulations on a hypersphere in 4-space

NASA Astrophysics Data System (ADS)

Nissfolk, Jarl; Ekholm, Tobias; Elvingson, Christer

2003-10-01

We describe an algorithm for performing Brownian dynamics simulations of particles diffusing on S3, a hypersphere in four dimensions. The system is chosen due to recent interest in doing computer simulations in a closed space where periodic boundary conditions can be avoided. We specifically address the question how to generate a random walk on the 3-sphere, starting from the solution of the corresponding diffusion equation, and we also discuss an efficient implementation based on controlled approximations. Since S3 is a closed manifold (space), the average square displacement during a random walk is no longer proportional to the elapsed time, as in R3. Instead, its time rate of change is continuously decreasing, and approaches zero as time becomes large. We show, however, that the effective diffusion coefficient can still be obtained from the time dependence of the square displacement.

Floquet scalar dynamics in global AdS

NASA Astrophysics Data System (ADS)

Biasi, Anxo; Carracedo, Pablo; Mas, Javier; Musso, Daniele; Serantes, Alexandre

2018-04-01

We study periodically driven scalar fields and the resulting geometries with global AdS asymptotics. These solutions describe the strongly coupled dynamics of dual finite-size quantum systems under a periodic driving which we interpret as Floquet condensates. They span a continuous two-parameter space that extends the linearized solutions on AdS. We map the regions of stability in the solution space. In a significant portion of the unstable subspace, two very different endpoints are reached depending upon the sign of the perturbation. Collapse into a black hole occurs for one sign. For the opposite sign instead one attains a regular solution with periodic modulation. We also construct quenches where the driving frequency and amplitude are continuously varied. Quasistatic quenches can interpolate between pure AdS and sourced solutions with time periodic vev. By suitably choosing the quasistatic path one can obtain boson stars dual to Floquet condensates at zero driving field. We characterize the adiabaticity of the quenching processes. Besides, we speculate on the possible connections of this framework with time crystals.

Hydrodynamic comparison of the Penumbra system and commonly available syringes in forced-suction thrombectomy.

PubMed

Simon, Scott Douglas; Grey, Casey Paul

2014-04-01

The Penumbra system uses a coaxial separator and continuous extracorporeal suction to remove a clot from a cerebral artery. Forced-suction thrombectomy (FST) involves aspirating clots through the same reperfusion catheter using only a syringe, decreasing the procedure time and supplies needed. To evaluate multiple combinations of catheters and syringes to determine the optimal pairing for use in FST. Tests were performed using both the Penumbra system and syringes to aspirate water through Penumbra 0.041 inch (041), 4Max, 0.054 inch (054) and 5Max reperfusion catheters and a shuttle sheath. Dynamic pressure and flow at the catheter tip were calculated from the fill times for each system. Static pressure and force for each aspiration source were determined with a vacuum gauge. All syringes provided significantly higher dynamic pressure at the catheter tip than the Penumbra system (p<0.001). Increasing syringe volume significantly increased static pressure (p<0.001). Both flow and aspiration force significantly increased with catheter size (p<0.001). Cases are presented to demonstrate the clinical value of the laboratory principles. Maximizing static and dynamic pressure when performing FST is achieved by aspirating with a syringe possessing both the largest volume and the largest inlet diameter available. Maximizing aspiration force and flow rate is achieved by using the largest catheter possible.

A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation

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

Zhang, Jinghe; Welch, Greg; Bishop, Gary

2014-04-01

As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However in practicemore » the process and measurement noise models are usually difficult to obtain. Thus we have developed the Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.« less

Exponential stability preservation in semi-discretisations of BAM networks with nonlinear impulses

NASA Astrophysics Data System (ADS)

Mohamad, Sannay; Gopalsamy, K.

2009-01-01

This paper demonstrates the reliability of a discrete-time analogue in preserving the exponential convergence of a bidirectional associative memory (BAM) network that is subject to nonlinear impulses. The analogue derived from a semi-discretisation technique with the value of the time-step fixed is treated as a discrete-time dynamical system while its exponential convergence towards an equilibrium state is studied. Thereby, a family of sufficiency conditions governing the network parameters and the impulse magnitude and frequency is obtained for the convergence. As special cases, one can obtain from our results, those corresponding to the non-impulsive discrete-time BAM networks and also those corresponding to continuous-time (impulsive and non-impulsive) systems. A relation between the Lyapunov exponent of the non-impulsive system and that of the impulsive system involving the size of the impulses and the inter-impulse intervals is obtained.

Will a category cue attract you? Motor output reveals dynamic competition across person construal.

PubMed

Freeman, Jonathan B; Ambady, Nalini; Rule, Nicholas O; Johnson, Kerri L

2008-11-01

People use social categories to perceive others, extracting category cues to glean membership. Growing evidence for continuous dynamics in real-time cognition suggests, contrary to prevailing social psychological accounts, that person construal may involve dynamic competition between simultaneously active representations. To test this, the authors examined social categorization in real-time by streaming the x, y coordinates of hand movements as participants categorized typical and atypical faces by sex. Though judgments of atypical targets were largely accurate, online motor output exhibited a continuous spatial attraction toward the opposite sex category, indicating dynamic competition between multiple social category alternatives. The authors offer a dynamic continuity account of social categorization and provide converging evidence across categorizations of real male and female faces (containing a typical or an atypical sex-specifying cue) and categorizations of computer-generated male and female faces (with subtly morphed sex-typical or sex-atypical features). In 3 studies, online motor output revealed continuous dynamics underlying person construal, in which multiple simultaneously and partially active category representations gradually cascade into social categorical judgments. Such evidence is challenging for discrete stage-based accounts. (c) 2008 APA, all rights reserved

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

NASA Astrophysics Data System (ADS)

Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

2013-05-01

The collective dynamics of excitatory pulse coupled neurons with spike-timing dependent plasticity is studied. The introduction of spike-timing dependent plasticity induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.

Optimal nonlinear filtering using the finite-volume method

NASA Astrophysics Data System (ADS)

Fox, Colin; Morrison, Malcolm E. K.; Norton, Richard A.; Molteno, Timothy C. A.

2018-01-01

Optimal sequential inference, or filtering, for the state of a deterministic dynamical system requires simulation of the Frobenius-Perron operator, that can be formulated as the solution of a continuity equation. For low-dimensional, smooth systems, the finite-volume numerical method provides a solution that conserves probability and gives estimates that converge to the optimal continuous-time values, while a Courant-Friedrichs-Lewy-type condition assures that intermediate discretized solutions remain positive density functions. This method is demonstrated in an example of nonlinear filtering for the state of a simple pendulum, with comparison to results using the unscented Kalman filter, and for a case where rank-deficient observations lead to multimodal probability distributions.

SAVA 3: A testbed for integration and control of visual processes

NASA Technical Reports Server (NTRS)

Crowley, James L.; Christensen, Henrik

1994-01-01

The development of an experimental test-bed to investigate the integration and control of perception in a continuously operating vision system is described. The test-bed integrates a 12 axis robotic stereo camera head mounted on a mobile robot, dedicated computer boards for real-time image acquisition and processing, and a distributed system for image description. The architecture was designed to: (1) be continuously operating, (2) integrate software contributions from geographically dispersed laboratories, (3) integrate description of the environment with 2D measurements, 3D models, and recognition of objects, (4) capable of supporting diverse experiments in gaze control, visual servoing, navigation, and object surveillance, and (5) dynamically reconfiguarable.

Extensional channel flow revisited: a dynamical systems perspective

PubMed Central

Meseguer, Alvaro; Mellibovsky, Fernando; Weidman, Patrick D.

2017-01-01

Extensional self-similar flows in a channel are explored numerically for arbitrary stretching–shrinking rates of the confining parallel walls. The present analysis embraces time integrations, and continuations of steady and periodic solutions unfolded in the parameter space. Previous studies focused on the analysis of branches of steady solutions for particular stretching–shrinking rates, although recent studies focused also on the dynamical aspects of the problems. We have adopted a dynamical systems perspective, analysing the instabilities and bifurcations the base state undergoes when increasing the Reynolds number. It has been found that the base state becomes unstable for small Reynolds numbers, and a transitional region including complex dynamics takes place at intermediate Reynolds numbers, depending on the wall acceleration values. The base flow instabilities are constitutive parts of different codimension-two bifurcations that control the dynamics in parameter space. For large Reynolds numbers, the restriction to self-similarity results in simple flows with no realistic behaviour, but the flows obtained in the transition region can be a valuable tool for the understanding of the dynamics of realistic Navier–Stokes solutions. PMID:28690413

ORACLS- OPTIMAL REGULATOR ALGORITHMS FOR THE CONTROL OF LINEAR SYSTEMS (CDC VERSION)

NASA Technical Reports Server (NTRS)

Armstrong, E. S.

1994-01-01

This control theory design package, called Optimal Regulator Algorithms for the Control of Linear Systems (ORACLS), was developed to aid in the design of controllers and optimal filters for systems which can be modeled by linear, time-invariant differential and difference equations. Optimal linear quadratic regulator theory, currently referred to as the Linear-Quadratic-Gaussian (LQG) problem, has become the most widely accepted method of determining optimal control policy. Within this theory, the infinite duration time-invariant problems, which lead to constant gain feedback control laws and constant Kalman-Bucy filter gains for reconstruction of the system state, exhibit high tractability and potential ease of implementation. A variety of new and efficient methods in the field of numerical linear algebra have been combined into the ORACLS program, which provides for the solution to time-invariant continuous or discrete LQG problems. The ORACLS package is particularly attractive to the control system designer because it provides a rigorous tool for dealing with multi-input and multi-output dynamic systems in both continuous and discrete form. The ORACLS programming system is a collection of subroutines which can be used to formulate, manipulate, and solve various LQG design problems. The ORACLS program is constructed in a manner which permits the user to maintain considerable flexibility at each operational state. This flexibility is accomplished by providing primary operations, analysis of linear time-invariant systems, and control synthesis based on LQG methodology. The input-output routines handle the reading and writing of numerical matrices, printing heading information, and accumulating output information. The basic vector-matrix operations include addition, subtraction, multiplication, equation, norm construction, tracing, transposition, scaling, juxtaposition, and construction of null and identity matrices. The analysis routines provide for the following computations: the eigenvalues and eigenvectors of real matrices; the relative stability of a given matrix; matrix factorization; the solution of linear constant coefficient vector-matrix algebraic equations; the controllability properties of a linear time-invariant system; the steady-state covariance matrix of an open-loop stable system forced by white noise; and the transient response of continuous linear time-invariant systems. The control law design routines of ORACLS implement some of the more common techniques of time-invariant LQG methodology. For the finite-duration optimal linear regulator problem with noise-free measurements, continuous dynamics, and integral performance index, a routine is provided which implements the negative exponential method for finding both the transient and steady-state solutions to the matrix Riccati equation. For the discrete version of this problem, the method of backwards differencing is applied to find the solutions to the discrete Riccati equation. A routine is also included to solve the steady-state Riccati equation by the Newton algorithms described by Klein, for continuous problems, and by Hewer, for discrete problems. Another routine calculates the prefilter gain to eliminate control state cross-product terms in the quadratic performance index and the weighting matrices for the sampled data optimal linear regulator problem. For cases with measurement noise, duality theory and optimal regulator algorithms are used to calculate solutions to the continuous and discrete Kalman-Bucy filter problems. Finally, routines are included to implement the continuous and discrete forms of the explicit (model-in-the-system) and implicit (model-in-the-performance-index) model following theory. These routines generate linear control laws which cause the output of a dynamic time-invariant system to track the output of a prescribed model. In order to apply ORACLS, the user must write an executive (driver) program which inputs the problem coefficients, formulates and selects the routines to be used to solve the problem, and specifies the desired output. There are three versions of ORACLS source code available for implementation: CDC, IBM, and DEC. The CDC version has been implemented on a CDC 6000 series computer with a central memory of approximately 13K (octal) of 60 bit words. The CDC version is written in FORTRAN IV, was developed in 1978, and last updated in 1989. The IBM version has been implemented on an IBM 370 series computer with a central memory requirement of approximately 300K of 8 bit bytes. The IBM version is written in FORTRAN IV and was generated in 1981. The DEC version has been implemented on a VAX series computer operating under VMS. The VAX version is written in FORTRAN 77 and was generated in 1986.

ORACLS- OPTIMAL REGULATOR ALGORITHMS FOR THE CONTROL OF LINEAR SYSTEMS (DEC VAX VERSION)

NASA Technical Reports Server (NTRS)

Frisch, H.

1994-01-01

This control theory design package, called Optimal Regulator Algorithms for the Control of Linear Systems (ORACLS), was developed to aid in the design of controllers and optimal filters for systems which can be modeled by linear, time-invariant differential and difference equations. Optimal linear quadratic regulator theory, currently referred to as the Linear-Quadratic-Gaussian (LQG) problem, has become the most widely accepted method of determining optimal control policy. Within this theory, the infinite duration time-invariant problems, which lead to constant gain feedback control laws and constant Kalman-Bucy filter gains for reconstruction of the system state, exhibit high tractability and potential ease of implementation. A variety of new and efficient methods in the field of numerical linear algebra have been combined into the ORACLS program, which provides for the solution to time-invariant continuous or discrete LQG problems. The ORACLS package is particularly attractive to the control system designer because it provides a rigorous tool for dealing with multi-input and multi-output dynamic systems in both continuous and discrete form. The ORACLS programming system is a collection of subroutines which can be used to formulate, manipulate, and solve various LQG design problems. The ORACLS program is constructed in a manner which permits the user to maintain considerable flexibility at each operational state. This flexibility is accomplished by providing primary operations, analysis of linear time-invariant systems, and control synthesis based on LQG methodology. The input-output routines handle the reading and writing of numerical matrices, printing heading information, and accumulating output information. The basic vector-matrix operations include addition, subtraction, multiplication, equation, norm construction, tracing, transposition, scaling, juxtaposition, and construction of null and identity matrices. The analysis routines provide for the following computations: the eigenvalues and eigenvectors of real matrices; the relative stability of a given matrix; matrix factorization; the solution of linear constant coefficient vector-matrix algebraic equations; the controllability properties of a linear time-invariant system; the steady-state covariance matrix of an open-loop stable system forced by white noise; and the transient response of continuous linear time-invariant systems. The control law design routines of ORACLS implement some of the more common techniques of time-invariant LQG methodology. For the finite-duration optimal linear regulator problem with noise-free measurements, continuous dynamics, and integral performance index, a routine is provided which implements the negative exponential method for finding both the transient and steady-state solutions to the matrix Riccati equation. For the discrete version of this problem, the method of backwards differencing is applied to find the solutions to the discrete Riccati equation. A routine is also included to solve the steady-state Riccati equation by the Newton algorithms described by Klein, for continuous problems, and by Hewer, for discrete problems. Another routine calculates the prefilter gain to eliminate control state cross-product terms in the quadratic performance index and the weighting matrices for the sampled data optimal linear regulator problem. For cases with measurement noise, duality theory and optimal regulator algorithms are used to calculate solutions to the continuous and discrete Kalman-Bucy filter problems. Finally, routines are included to implement the continuous and discrete forms of the explicit (model-in-the-system) and implicit (model-in-the-performance-index) model following theory. These routines generate linear control laws which cause the output of a dynamic time-invariant system to track the output of a prescribed model. In order to apply ORACLS, the user must write an executive (driver) program which inputs the problem coefficients, formulates and selects the routines to be used to solve the problem, and specifies the desired output. There are three versions of ORACLS source code available for implementation: CDC, IBM, and DEC. The CDC version has been implemented on a CDC 6000 series computer with a central memory of approximately 13K (octal) of 60 bit words. The CDC version is written in FORTRAN IV, was developed in 1978, and last updated in 1986. The IBM version has been implemented on an IBM 370 series computer with a central memory requirement of approximately 300K of 8 bit bytes. The IBM version is written in FORTRAN IV and was generated in 1981. The DEC version has been implemented on a VAX series computer operating under VMS. The VAX version is written in FORTRAN 77 and was generated in 1986.

A Dynamic Anesthesia System for Long-Term Imaging in Adult Zebrafish

PubMed Central

Wynd, Brenen M.; Watson, Claire J.; Patil, Karuna; Sanders, George E.

2017-01-01

Abstract Long-term in vivo imaging in adult zebrafish (i.e., 1–24 h) has been limited by the fact that regimens for long-term anesthesia in embryos and larvae are ineffective in adults. Here, we examined the potential for dynamic administration of benzocaine to enable long-term anesthesia in adult zebrafish. We developed a computer-controlled perfusion system comprised of programmable peristaltic pumps that enabled automatic exchange between anesthetic and system water. Continuous administration of benzocaine in adult zebrafish resulted in a mean time to respiratory arrest of 5.0 h and 8-h survival of 14.3%. We measured characteristic sedation and recovery times in response to benzocaine, and used them to devise an intermittent dosing regimen consisting of 14.5 min of benzocaine followed by 5.5 min of system water. Intermittent benzocaine administration in adult zebrafish resulted in a mean time to respiratory arrest of 7.6 h and 8-h survival of 71.4%. Finally, we performed a single 24-h trial and found that intermittent dosing maintained anesthesia in an adult zebrafish over the entire 24-h period. In summary, our studies demonstrate the potential for dynamic administration of benzocaine to enable prolonged anesthesia in adult zebrafish, expanding the potential for imaging in adult physiologies that unfold over 1–24 h. PMID:27409411

A Dynamic Anesthesia System for Long-Term Imaging in Adult Zebrafish.

PubMed

Wynd, Brenen M; Watson, Claire J; Patil, Karuna; Sanders, George E; Kwon, Ronald Y

2017-02-01

Long-term in vivo imaging in adult zebrafish (i.e., 1-24 h) has been limited by the fact that regimens for long-term anesthesia in embryos and larvae are ineffective in adults. Here, we examined the potential for dynamic administration of benzocaine to enable long-term anesthesia in adult zebrafish. We developed a computer-controlled perfusion system comprised of programmable peristaltic pumps that enabled automatic exchange between anesthetic and system water. Continuous administration of benzocaine in adult zebrafish resulted in a mean time to respiratory arrest of 5.0 h and 8-h survival of 14.3%. We measured characteristic sedation and recovery times in response to benzocaine, and used them to devise an intermittent dosing regimen consisting of 14.5 min of benzocaine followed by 5.5 min of system water. Intermittent benzocaine administration in adult zebrafish resulted in a mean time to respiratory arrest of 7.6 h and 8-h survival of 71.4%. Finally, we performed a single 24-h trial and found that intermittent dosing maintained anesthesia in an adult zebrafish over the entire 24-h period. In summary, our studies demonstrate the potential for dynamic administration of benzocaine to enable prolonged anesthesia in adult zebrafish, expanding the potential for imaging in adult physiologies that unfold over 1-24 h.

Wavelet and adaptive methods for time dependent problems and applications in aerosol dynamics

NASA Astrophysics Data System (ADS)

Guo, Qiang

Time dependent partial differential equations (PDEs) are widely used as mathematical models of environmental problems. Aerosols are now clearly identified as an important factor in many environmental aspects of climate and radiative forcing processes, as well as in the health effects of air quality. The mathematical models for the aerosol dynamics with respect to size distribution are nonlinear partial differential and integral equations, which describe processes of condensation, coagulation and deposition. Simulating the general aerosol dynamic equations on time, particle size and space exhibits serious difficulties because the size dimension ranges from a few nanometer to several micrometer while the spatial dimension is usually described with kilometers. Therefore, it is an important and challenging task to develop efficient techniques for solving time dependent dynamic equations. In this thesis, we develop and analyze efficient wavelet and adaptive methods for the time dependent dynamic equations on particle size and further apply them to the spatial aerosol dynamic systems. Wavelet Galerkin method is proposed to solve the aerosol dynamic equations on time and particle size due to the fact that aerosol distribution changes strongly along size direction and the wavelet technique can solve it very efficiently. Daubechies' wavelets are considered in the study due to the fact that they possess useful properties like orthogonality, compact support, exact representation of polynomials to a certain degree. Another problem encountered in the solution of the aerosol dynamic equations results from the hyperbolic form due to the condensation growth term. We propose a new characteristic-based fully adaptive multiresolution numerical scheme for solving the aerosol dynamic equation, which combines the attractive advantages of adaptive multiresolution technique and the characteristics method. On the aspect of theoretical analysis, the global existence and uniqueness of solutions of continuous time wavelet numerical methods for the nonlinear aerosol dynamics are proved by using Schauder's fixed point theorem and the variational technique. Optimal error estimates are derived for both continuous and discrete time wavelet Galerkin schemes. We further derive reliable and efficient a posteriori error estimate which is based on stable multiresolution wavelet bases and an adaptive space-time algorithm for efficient solution of linear parabolic differential equations. The adaptive space refinement strategies based on the locality of corresponding multiresolution processes are proved to converge. At last, we develop efficient numerical methods by combining the wavelet methods proposed in previous parts and the splitting technique to solve the spatial aerosol dynamic equations. Wavelet methods along the particle size direction and the upstream finite difference method along the spatial direction are alternately used in each time interval. Numerical experiments are taken to show the effectiveness of our developed methods.

Modeling US Adult Obesity Trends: A System Dynamics Model for Estimating Energy Imbalance Gap

PubMed Central

Rahmandad, Hazhir; Huang, Terry T.-K.; Bures, Regina M.; Glass, Thomas A.

2014-01-01

Objectives. We present a system dynamics model that quantifies the energy imbalance gap responsible for the US adult obesity epidemic among gender and racial subpopulations. Methods. We divided the adult population into gender–race/ethnicity subpopulations and body mass index (BMI) classes. We defined transition rates between classes as a function of metabolic dynamics of individuals within each class. We estimated energy intake in each BMI class within the past 4 decades as a multiplication of the equilibrium energy intake of individuals in that class. Through calibration, we estimated the energy gap multiplier for each gender–race–BMI group by matching simulated BMI distributions for each subpopulation against national data with maximum likelihood estimation. Results. No subpopulation showed a negative or zero energy gap, suggesting that the obesity epidemic continues to worsen, albeit at a slower rate. In the past decade the epidemic has slowed for non-Hispanic Whites, is starting to slow for non-Hispanic Blacks, but continues to accelerate among Mexican Americans. Conclusions. The differential energy balance gap across subpopulations and over time suggests that interventions should be tailored to subpopulations’ needs. PMID:24832405

Experimental Chaos - Proceedings of the 3rd Conference

NASA Astrophysics Data System (ADS)

Harrison, Robert G.; Lu, Weiping; Ditto, William; Pecora, Lou; Spano, Mark; Vohra, Sandeep

1996-10-01

The Table of Contents for the full book PDF is as follows: * Preface * Spatiotemporal Chaos and Patterns * Scale Segregation via Formation of Domains in a Nonlinear Optical System * Laser Dynamics as Hydrodynamics * Spatiotemporal Dynamics of Human Epileptic Seizures * Experimental Transition to Chaos in a Quasi 1D Chain of Oscillators * Measuring Coupling in Spatiotemporal Dynamical Systems * Chaos in Vortex Breakdown * Dynamical Analysis * Radial Basis Function Modelling and Prediction of Time Series * Nonlinear Phenomena in Polyrhythmic Hand Movements * Using Models to Diagnose, Test and Control Chaotic Systems * New Real-Time Analysis of Time Series Data with Physical Wavelets * Control and Synchronization * Measuring and Controlling Chaotic Dynamics in a Slugging Fluidized Bed * Control of Chaos in a Laser with Feedback * Synchronization and Chaotic Diode Resonators * Control of Chaos by Continuous-time Feedback with Delay * A Framework for Communication using Chaos Sychronization * Control of Chaos in Switching Circuits * Astrophysics, Meteorology and Oceanography * Solar-Wind-Magnetospheric Dynamics via Satellite Data * Nonlinear Dynamics of the Solar Atmosphere * Fractal Dimension of Scalar and Vector Variables from Turbulence Measurements in the Atmospheric Surface Layer * Mechanics * Escape and Overturning: Subtle Transient Behavior in Nonlinear Mechanical Models * Organising Centres in the Dynamics of Parametrically Excited Double Pendulums * Intermittent Behaviour in a Heating System Driven by Phase Transitions * Hydrodynamics * Size Segregation in Couette Flow of Granular Material * Routes to Chaos in Rotational Taylor-Couette Flow * Experimental Study of the Laminar-Turbulent Transition in an Open Flow System * Chemistry * Order and Chaos in Excitable Media under External Forcing * A Chemical Wave Propagation with Accelerating Speed Accompanied by Hydrodynamic Flow * Optics * Instabilities in Semiconductor Lasers with Optical Injection * Spatio-Temporal Dynamics of a Bimode CO2 Laser with Saturable Absorber * Chaotic Homoclinic Phenomena in Opto-Thermal Devices * Observation and Characterisation of Low-Frequency Chaos in Semiconductor Lasers with External Feedback * Condensed Matter * The Application of Nonlinear Dynamics in the Study of Ferroelectric Materials * Cellular Convection in a Small Aspect Ratio Liquid Crystal Device * Driven Spin-Wave Dynamics in YIG Films * Quantum Chaology in Quartz * Small Signal Amplification Caused by Nonlinear Properties of Ferroelectrics * Composite Materials Evolved from Chaos * Electronics and Circuits * Controlling a Chaotic Array of Pulse-Coupled Fitzhugh-Nagumo Circuits * Experimental Observation of On-Off Intermittency * Phase Lock-In of Chaotic Relaxation Oscillators * Biology and Medicine * Singular Value Decomposition and Circuit Structure in Invertebrate Ganglia * Nonlinear Forecasting of Spike Trains from Neurons of a Mollusc * Ultradian Rhythm in the Sensitive Plants: Chaos or Coloured Noise? * Chaos and the Crayfish Sixth Ganglion * Hardware Coupled Nonlinear Oscillators as a Model of Retina

Time-optimal thermalization of single-mode Gaussian states

NASA Astrophysics Data System (ADS)

Carlini, Alberto; Mari, Andrea; Giovannetti, Vittorio

2014-11-01

We consider the problem of time-optimal control of a continuous bosonic quantum system subject to the action of a Markovian dissipation. In particular, we consider the case of a one-mode Gaussian quantum system prepared in an arbitrary initial state and which relaxes to the steady state due to the action of the dissipative channel. We assume that the unitary part of the dynamics is represented by Gaussian operations which preserve the Gaussian nature of the quantum state, i.e., arbitrary phase rotations, bounded squeezing, and unlimited displacements. In the ideal ansatz of unconstrained quantum control (i.e., when the unitary phase rotations, squeezing, and displacement of the mode can be performed instantaneously), we study how control can be optimized for speeding up the relaxation towards the fixed point of the dynamics and we analytically derive the optimal relaxation time. Our model has potential and interesting applications to the control of modes of electromagnetic radiation and of trapped levitated nanospheres.

Control of linear uncertain systems utilizing mismatched state observers

NASA Technical Reports Server (NTRS)

Goldstein, B.

1972-01-01

The control of linear continuous dynamical systems is investigated as a problem of limited state feedback control. The equations which describe the structure of an observer are developed constrained to time-invarient systems. The optimal control problem is formulated, accounting for the uncertainty in the design parameters. Expressions for bounds on closed loop stability are also developed. The results indicate that very little uncertainty may be tolerated before divergence occurs in the recursive computation algorithms, and the derived stability bound yields extremely conservative estimates of regions of allowable parameter variations.

Terminal attractors for addressable memory in neural networks

NASA Technical Reports Server (NTRS)

Zak, Michail

1988-01-01

A new type of attractors - terminal attractors - for an addressable memory in neural networks operating in continuous time is introduced. These attractors represent singular solutions of the dynamical system. They intersect (or envelope) the families of regular solutions while each regular solution approaches the terminal attractor in a finite time period. It is shown that terminal attractors can be incorporated into neural networks such that any desired set of these attractors with prescribed basins is provided by an appropriate selection of the weight matrix.

Non-smooth saddle-node bifurcations III: Strange attractors in continuous time

NASA Astrophysics Data System (ADS)

Fuhrmann, G.

2016-08-01

Non-smooth saddle-node bifurcations give rise to minimal sets of interesting geometry built of so-called strange non-chaotic attractors. We show that certain families of quasiperiodically driven logistic differential equations undergo a non-smooth bifurcation. By a previous result on the occurrence of non-smooth bifurcations in forced discrete time dynamical systems, this yields that within the class of families of quasiperiodically driven differential equations, non-smooth saddle-node bifurcations occur in a set with non-empty C2-interior.

On continuous user authentication via typing behavior.

PubMed

Roth, Joseph; Liu, Xiaoming; Metaxas, Dimitris

2014-10-01

We hypothesize that an individual computer user has a unique and consistent habitual pattern of hand movements, independent of the text, while typing on a keyboard. As a result, this paper proposes a novel biometric modality named typing behavior (TB) for continuous user authentication. Given a webcam pointing toward a keyboard, we develop real-time computer vision algorithms to automatically extract hand movement patterns from the video stream. Unlike the typical continuous biometrics, such as keystroke dynamics (KD), TB provides a reliable authentication with a short delay, while avoiding explicit key-logging. We collect a video database where 63 unique subjects type static text and free text for multiple sessions. For one typing video, the hands are segmented in each frame and a unique descriptor is extracted based on the shape and position of hands, as well as their temporal dynamics in the video sequence. We propose a novel approach, named bag of multi-dimensional phrases, to match the cross-feature and cross-temporal pattern between a gallery sequence and probe sequence. The experimental results demonstrate a superior performance of TB when compared with KD, which, together with our ultrareal-time demo system, warrant further investigation of this novel vision application and biometric modality.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

PubMed

Suh, Myung-Kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2012-09-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems

PubMed Central

Suh, Myung-kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2016-01-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %. PMID:27617297

Critical Slowing Down in Time-to-Extinction: An Example of Critical Phenomena in Ecology

NASA Technical Reports Server (NTRS)

Gandhi, Amar; Levin, Simon; Orszag, Steven

1998-01-01

We study a model for two competing species that explicitly accounts for effects due to discreteness, stochasticity and spatial extension of populations. The two species are equally preferred by the environment and do better when surrounded by others of the same species. We observe that the final outcome depends on the initial densities (uniformly distributed in space) of the two species. The observed phase transition is a continuous one and key macroscopic quantities like the correlation length of clusters and the time-to-extinction diverge at a critical point. Away from the critical point, the dynamics can be described by a mean-field approximation. Close to the critical point, however, there is a crossover to power-law behavior because of the gross mismatch between the largest and smallest scales in the system. We have developed a theory based on surface effects, which is in good agreement with the observed behavior. The course-grained reaction-diffusion system obtained from the mean-field dynamics agrees well with the particle system.

Continuous-time random walks with reset events. Historical background and new perspectives

NASA Astrophysics Data System (ADS)

Montero, Miquel; Masó-Puigdellosas, Axel; Villarroel, Javier

2017-09-01

In this paper, we consider a stochastic process that may experience random reset events which relocate the system to its starting position. We focus our attention on a one-dimensional, monotonic continuous-time random walk with a constant drift: the process moves in a fixed direction between the reset events, either by the effect of the random jumps, or by the action of a deterministic bias. However, the orientation of its motion is randomly determined after each restart. As a result of these alternating dynamics, interesting properties do emerge. General formulas for the propagator as well as for two extreme statistics, the survival probability and the mean first-passage time, are also derived. The rigor of these analytical results is verified by numerical estimations, for particular but illuminating examples.

X-ray transmission movies of spontaneous dynamic events

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

Smilowitz, L.; Henson, B. F.; Holmes, M.

2014-11-15

We describe a new x-ray radiographic imaging system which allows for continuous x-ray transmission imaging of spontaneous dynamic events. We demonstrate this method on thermal explosions in three plastic bonded formulations of the energetic material octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine. We describe the x-ray imaging system and triggering developed to enable the continuous imaging of a thermal explosion.

Characterizing the continuously acquired cardiovascular time series during hemodialysis, using median hybrid filter preprocessing noise reduction.

PubMed

Wilson, Scott; Bowyer, Andrea; Harrap, Stephen B

2015-01-01

The clinical characterization of cardiovascular dynamics during hemodialysis (HD) has important pathophysiological implications in terms of diagnostic, cardiovascular risk assessment, and treatment efficacy perspectives. Currently the diagnosis of significant intradialytic systolic blood pressure (SBP) changes among HD patients is imprecise and opportunistic, reliant upon the presence of hypotensive symptoms in conjunction with coincident but isolated noninvasive brachial cuff blood pressure (NIBP) readings. Considering hemodynamic variables as a time series makes a continuous recording approach more desirable than intermittent measures; however, in the clinical environment, the data signal is susceptible to corruption due to both impulsive and Gaussian-type noise. Signal preprocessing is an attractive solution to this problem. Prospectively collected continuous noninvasive SBP data over the short-break intradialytic period in ten patients was preprocessed using a novel median hybrid filter (MHF) algorithm and compared with 50 time-coincident pairs of intradialytic NIBP measures from routine HD practice. The median hybrid preprocessing technique for continuously acquired cardiovascular data yielded a dynamic regression without significant noise and artifact, suitable for high-level profiling of time-dependent SBP behavior. Signal accuracy is highly comparable with standard NIBP measurement, with the added clinical benefit of dynamic real-time hemodynamic information.

Reversing the irreversible: From limit cycles to emergent time symmetry

NASA Astrophysics Data System (ADS)

Cortês, Marina; Smolin, Lee

2018-01-01

In 1979 Penrose hypothesized that the arrows of time are explained by the hypothesis that the fundamental laws are time irreversible [R. Penrose, in General Relativity: An Einstein Centenary Survey (1979)]. That is, our reversible laws, such as the standard model and general relativity are effective, and emerge from an underlying fundamental theory which is time irreversible. In [M. Cortês and L. Smolin, Phys. Rev. D 90, 084007 (2014), 10.1103/PhysRevD.90.084007; 90, 044035 (2014), 10.1103/PhysRevD.90.044035; 93, 084039 (2016), 10.1103/PhysRevD.93.084039] we put forward a research program aiming at realizing just this. The aim is to find a fundamental description of physics above the Planck scale, based on irreversible laws, from which will emerge the apparently reversible dynamics we observe on intermediate scales. Here we continue that program and note that a class of discrete dynamical systems are known to exhibit this very property: they have an underlying discrete irreversible evolution, but in the long term exhibit the properties of a time reversible system, in the form of limit cycles. We connect this to our original model proposal in [M. Cortês and L. Smolin, Phys. Rev. D 90, 084007 (2014), 10.1103/PhysRevD.90.084007], and show that the behaviors obtained there can be explained in terms of the same phenomenon: the attraction of the system to a basin of limit cycles, where the dynamics appears to be time reversible. Further than that, we show that our original models exhibit the very same feature: the emergence of quasiparticle excitations obtained in the earlier work in the space-time description is an expression of the system's convergence to limit cycles when seen in the causal set description.

76 FR 66057 - North American Electric Reliability Corporation; Order Approving Regional Reliability Standard

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-25

... system conditions when the system experiences dynamic events such as low frequency oscillations, or... R8 requires that dynamic disturbance recorders function continuously. To capture system disturbance... recording capability necessary to monitor the response of the Bulk-Power System to system disturbances...

Discrete and continuous dynamics modeling of a mass moving on a flexible structure

NASA Technical Reports Server (NTRS)

Herman, Deborah Ann

1992-01-01

A general discrete methodology for modeling the dynamics of a mass that moves on the surface of a flexible structure is developed. This problem was motivated by the Space Station/Mobile Transporter system. A model reduction approach is developed to make the methodology applicable to large structural systems. To validate the discrete methodology, continuous formulations are also developed. Three different systems are examined: (1) simply-supported beam, (2) free-free beam, and (3) free-free beam with two points of contact between the mass and the flexible beam. In addition to validating the methodology, parametric studies were performed to examine how the system's physical properties affect its dynamics.

Single molecule translocation in smectics illustrates the challenge for time-mapping in simulations on multiple scales

NASA Astrophysics Data System (ADS)

Mukherjee, Biswaroop; Peter, Christine; Kremer, Kurt

2017-09-01

Understanding the connections between the characteristic dynamical time scales associated with a coarse-grained (CG) and a detailed representation is central to the applicability of the coarse-graining methods to understand molecular processes. The process of coarse graining leads to an accelerated dynamics, owing to the smoothening of the underlying free-energy landscapes. Often a single time-mapping factor is used to relate the time scales associated with the two representations. We critically examine this idea using a model system ideally suited for this purpose. Single molecular transport properties are studied via molecular dynamics simulations of the CG and atomistic representations of a liquid crystalline, azobenzene containing mesogen, simulated in the smectic and the isotropic phases. The out-of-plane dynamics in the smectic phase occurs via molecular hops from one smectic layer to the next. Hopping can occur via two mechanisms, with and without significant reorientation. The out-of-plane transport can be understood as a superposition of two (one associated with each mode of transport) independent continuous time random walks for which a single time-mapping factor would be rather inadequate. A comparison of the free-energy surfaces, relevant to the out-of-plane transport, qualitatively supports the above observations. Thus, this work underlines the need for building CG models that exhibit both structural and dynamical consistency to the underlying atomistic model.

Project Echo: Antenna Steering System

NASA Technical Reports Server (NTRS)

Klahn, R.; Norton, J. A.; Githens, J. A.

1961-01-01

The Project Echo communications experiment employed large, steerable,transmitting and receiving antennas at the ground terminals. It was necessary that these highly directional antennas be continuously and accurately pointed at the passing satellite. This paper describes a new type of special purpose data converter for directing narrow-beam communication antennas on the basis of predicted information. The system is capable of converting digital input data into real-time analog voltage commands with a dynamic accuracy of +/- 0.05 degree, which meets the requirements of the present antennas.

Chaotic Motions in the Real Fuzzy Electronic Circuits (Preprint)

DTIC Science & Technology

2012-12-01

the research field of secure communications, the original source should be blended with other complex signals. Chaotic signals are one of the good... blending of the linear system models. Consider a continuous-time nonlinear dynamic system as follows: Rule i: IF )(1 tx is ...1iM and )(txn is...Chaos Solitons Fractals, vol. 21, no. 4, pp. 957–965, 2004. 29. L. M. Tam and W. M. SiTou, “Parametric study of the fractional order Chen–Lee

Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays.

PubMed

Wang, Min; Ge, Shuzhi Sam; Hong, Keum-Shik

2010-11-01

This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables x(i) as virtual controls α(i), and of the actual control u. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of u and [Formula: see text] . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.

Flow networks for Ocean currents

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkenthin, Nora; Marwan, Norbert; Kurths, Jürgen

2014-05-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e., by its high computational complexity, we here introduce a new, discrete construction of flow-networks, which is then applied to static and dynamic velocity fields. Analyzing the flow-networks of prototypical flows we find that our approach can highlight the zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. We also apply the method to time series data of the Equatorial Pacific Ocean Current and the Gulf Stream ocean current for the changing velocity fields, which could not been done before, and analyse the properties of the dynamical system. Flow-networks can be powerful tools to theoretically understand the step from system's dynamics to network's topology that can be analyzed using network measures and is used for shading light on different climatic phenomena.

Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

PubMed Central

Ye, Hao; Beamish, Richard J.; Glaser, Sarah M.; Grant, Sue C. H.; Hsieh, Chih-hao; Richards, Laura J.; Schnute, Jon T.; Sugihara, George

2015-01-01

It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts. PMID:25733874

Numerical research of dynamic characteristics in tower solar cavity receiver based on step-change radiation flux

NASA Astrophysics Data System (ADS)

Chen, Zhengwei; Wang, Yueshe; Hao, Yun; Wang, Qizhi

2013-07-01

The solar cavity receiver is an important light-energy to thermal-energy convector in the tower solar thermal power plant system. The heat flux in the inner surface of the cavity will show the characteristics of non-continuous step change especially in non-normal and transient weather conditions, which may result in a continuous dynamic variation of the characteristic parameters. Therefore, the research of dynamic characteristics of the receiver plays a very important role in the operation and the control safely in solar cavity receiver system. In this paper, based on the non-continuous step change of radiation flux, a non-linear dynamic model is put forward to obtain the effects of the non-continuous step change radiation flux and step change feed water flow on the receiver performance by sequential modular approach. The subject investigated in our study is a 1MW solar power station constructed in Yanqing County, Beijing. This study has obtained the dynamic responses of the characteristic parameters in the cavity receiver, such as drum pressure, drum water level, main steam flow and main steam enthalpy under step change radiation flux. And the influence law of step-change feed water flow to the dynamic characteristics in the receiver also has been analyzed. The results have a reference value for the safe operation and the control in solar cavity receiver system.

Coarse-grained molecular dynamics simulations for giant protein-DNA complexes

NASA Astrophysics Data System (ADS)

Takada, Shoji

Biomolecules are highly hierarchic and intrinsically flexible. Thus, computational modeling calls for multi-scale methodologies. We have been developing a coarse-grained biomolecular model where on-average 10-20 atoms are grouped into one coarse-grained (CG) particle. Interactions among CG particles are tuned based on atomistic interactions and the fluctuation matching algorithm. CG molecular dynamics methods enable us to simulate much longer time scale motions of much larger molecular systems than fully atomistic models. After broad sampling of structures with CG models, we can easily reconstruct atomistic models, from which one can continue conventional molecular dynamics simulations if desired. Here, we describe our CG modeling methodology for protein-DNA complexes, together with various biological applications, such as the DNA duplication initiation complex, model chromatins, and transcription factor dynamics on chromatin-like environment.

A living mesoscopic cellular automaton made of skin scales.

PubMed

Manukyan, Liana; Montandon, Sophie A; Fofonjka, Anamarija; Smirnov, Stanislav; Milinkovitch, Michel C

2017-04-12

In vertebrates, skin colour patterns emerge from nonlinear dynamical microscopic systems of cell interactions. Here we show that in ocellated lizards a quasi-hexagonal lattice of skin scales, rather than individual chromatophore cells, establishes a green and black labyrinthine pattern of skin colour. We analysed time series of lizard scale colour dynamics over four years of their development and demonstrate that this pattern is produced by a cellular automaton (a grid of elements whose states are iterated according to a set of rules based on the states of neighbouring elements) that dynamically computes the colour states of individual mesoscopic skin scales to produce the corresponding macroscopic colour pattern. Using numerical simulations and mathematical derivation, we identify how a discrete von Neumann cellular automaton emerges from a continuous Turing reaction-diffusion system. Skin thickness variation generated by three-dimensional morphogenesis of skin scales causes the underlying reaction-diffusion dynamics to separate into microscopic and mesoscopic spatial scales, the latter generating a cellular automaton. Our study indicates that cellular automata are not merely abstract computational systems, but can directly correspond to processes generated by biological evolution.

A living mesoscopic cellular automaton made of skin scales

NASA Astrophysics Data System (ADS)

Manukyan, Liana; Montandon, Sophie A.; Fofonjka, Anamarija; Smirnov, Stanislav; Milinkovitch, Michel C.

2017-04-01

In vertebrates, skin colour patterns emerge from nonlinear dynamical microscopic systems of cell interactions. Here we show that in ocellated lizards a quasi-hexagonal lattice of skin scales, rather than individual chromatophore cells, establishes a green and black labyrinthine pattern of skin colour. We analysed time series of lizard scale colour dynamics over four years of their development and demonstrate that this pattern is produced by a cellular automaton (a grid of elements whose states are iterated according to a set of rules based on the states of neighbouring elements) that dynamically computes the colour states of individual mesoscopic skin scales to produce the corresponding macroscopic colour pattern. Using numerical simulations and mathematical derivation, we identify how a discrete von Neumann cellular automaton emerges from a continuous Turing reaction-diffusion system. Skin thickness variation generated by three-dimensional morphogenesis of skin scales causes the underlying reaction-diffusion dynamics to separate into microscopic and mesoscopic spatial scales, the latter generating a cellular automaton. Our study indicates that cellular automata are not merely abstract computational systems, but can directly correspond to processes generated by biological evolution.

Continuous model for the rock-scissors-paper game between bacteriocin producing bacteria.

PubMed

Neumann, Gunter; Schuster, Stefan

2007-06-01

In this work, important aspects of bacteriocin producing bacteria and their interplay are elucidated. Various attempts to model the resistant, producer and sensitive Escherichia coli strains in the so-called rock-scissors-paper (RSP) game had been made in the literature. The question arose whether there is a continuous model with a cyclic structure and admitting an oscillatory dynamics as observed in various experiments. The May-Leonard system admits a Hopf bifurcation, which is, however, degenerate and hence inadequate. The traditional differential equation model of the RSP-game cannot be applied either to the bacteriocin system because it involves positive interaction terms. In this paper, a plausible competitive Lotka-Volterra system model of the RSP game is presented and the dynamics generated by that model is analyzed. For the first time, a continuous, spatially homogeneous model that describes the competitive interaction between bacteriocin-producing, resistant and sensitive bacteria is established. The interaction terms have negative coefficients. In some experiments, for example, in mice cultures, migration seemed to be essential for the reinfection in the RSP cycle. Often statistical and spatial effects such as migration and mutation are regarded to be essential for periodicity. Our model gives rise to oscillatory dynamics in the RSP game without such effects. Here, a normal form description of the limit cycle and conditions for its stability are derived. The toxicity of the bacteriocin is used as a bifurcation parameter. Exact parameter ranges are obtained for which a stable (robust) limit cycle and a stable heteroclinic cycle exist in the three-species game. These parameters are in good accordance with the observed relations for the E. coli strains. The roles of growth rate and growth yield of the three strains are discussed. Numerical calculations show that the sensitive, which might be regarded as the weakest, can have the longest sojourn times.

Visual-motor response of crewmen during a simulated 90-day space mission as measured by the critical task battery

NASA Technical Reports Server (NTRS)

Allen, R. W.; Jex, H. R.

1973-01-01

In order to test various components of a regenerative life support system and to obtain data on the physiological and psychological effects of long duration exposure to confinement in a space station atmosphere, four carefully screened young men were sealed in a space station simulator for 90 days and administered a tracking test battery. The battery included a clinical test (Critical Instability Task) designed to measure a subject's dynamic time delay, and a more conventional steady tracking task, during which dynamic response (describing functions) and performance measures were obtained. Good correlation was noted between the clinical critical instability scores and more detailed tracking parameters such as dynamic time delay and gain-crossover frequency. The levels of each parameter span the range observed with professional pilots and astronaut candidates tested previously. The chamber environment caused no significant decrement on the average crewman's dynamic response behavior, and the subjects continued to improve slightly in their tracking skills during the 90-day confinement period.

Digital computer program for generating dynamic turbofan engine models (DIGTEM)

NASA Technical Reports Server (NTRS)

Daniele, C. J.; Krosel, S. M.; Szuch, J. R.; Westerkamp, E. J.

1983-01-01

This report describes DIGTEM, a digital computer program that simulates two spool, two-stream turbofan engines. The turbofan engine model in DIGTEM contains steady-state performance maps for all of the components and has control volumes where continuity and energy balances are maintained. Rotor dynamics and duct momentum dynamics are also included. Altogether there are 16 state variables and state equations. DIGTEM features a backward-differnce integration scheme for integrating stiff systems. It trims the model equations to match a prescribed design point by calculating correction coefficients that balance out the dynamic equations. It uses the same coefficients at off-design points and iterates to a balanced engine condition. Transients can also be run. They are generated by defining controls as a function of time (open-loop control) in a user-written subroutine (TMRSP). DIGTEM has run on the IBM 370/3033 computer using implicit integration with time steps ranging from 1.0 msec to 1.0 sec. DIGTEM is generalized in the aerothermodynamic treatment of components.

The Dynamics of the Atmospheric Radiation Environment at Aviation Altitudes

NASA Technical Reports Server (NTRS)

Stassinopoulos, Epaminondas G.

2004-01-01

Single Event Effects vulnerability of on-board computers that regulate the: navigational, flight control, communication, and life support systems has become an issue in advanced modern aircraft, especially those that may be equipped with new technology devices in terabit memory banks (low voltage, nanometer feature size, gigabit integration). To address this concern, radiation spectrometers need to fly continually on a multitude of carriers over long periods of time so as to accumulate sufficient information that will broaden our understanding of the very dynamic and complex nature of the atmospheric radiation environment regarding: composition, spectral distribution, intensity, temporal variation, and spatial variation.

Use of GIS Mapping as a Public Health Tool—From Cholera to Cancer

PubMed Central

Musa, George J.; Chiang, Po-Huang; Sylk, Tyler; Bavley, Rachel; Keating, William; Lakew, Bereketab; Tsou, Hui-Chen; Hoven, Christina W.

2013-01-01

The field of medical geographic information systems (Medical GIS) has become extremely useful in understanding the bigger picture of public health. The discipline holds a substantial capacity to understand not only differences, but also similarities in population health all over the world. The main goal of marrying the disciplines of medical geography, public health and informatics is to understand how countless health issues impact populations, and the trends by which these populations are affected. From the 1990s to today, this practical approach has become a valued and progressive system in analyzing medical and epidemiological phenomena ranging from cholera to cancer. The instruments supporting this field include geographic information systems (GIS), disease surveillance, big data, and analytical approaches like the Geographical Analysis Machine (GAM), Dynamic Continuous Area Space Time Analysis (DYCAST), cellular automata, agent-based modeling, spatial statistics and self-organizing maps. The positive effects on disease mapping have proven to be tremendous as these instruments continue to have a great impact on the mission to improve worldwide health care. While traditional uses of GIS in public health are static and lacking real-time components, implementing a space-time animation in these instruments will be monumental as technology and data continue to grow. PMID:25114567

Use of GIS Mapping as a Public Health Tool-From Cholera to Cancer.

PubMed

Musa, George J; Chiang, Po-Huang; Sylk, Tyler; Bavley, Rachel; Keating, William; Lakew, Bereketab; Tsou, Hui-Chen; Hoven, Christina W

2013-01-01

The field of medical geographic information systems (Medical GIS) has become extremely useful in understanding the bigger picture of public health. The discipline holds a substantial capacity to understand not only differences, but also similarities in population health all over the world. The main goal of marrying the disciplines of medical geography, public health and informatics is to understand how countless health issues impact populations, and the trends by which these populations are affected. From the 1990s to today, this practical approach has become a valued and progressive system in analyzing medical and epidemiological phenomena ranging from cholera to cancer. The instruments supporting this field include geographic information systems (GIS), disease surveillance, big data, and analytical approaches like the Geographical Analysis Machine (GAM), Dynamic Continuous Area Space Time Analysis (DYCAST), cellular automata, agent-based modeling, spatial statistics and self-organizing maps. The positive effects on disease mapping have proven to be tremendous as these instruments continue to have a great impact on the mission to improve worldwide health care. While traditional uses of GIS in public health are static and lacking real-time components, implementing a space-time animation in these instruments will be monumental as technology and data continue to grow.

A Parametric Computational Model of the Action Potential of Pacemaker Cells.

PubMed

Ai, Weiwei; Patel, Nitish D; Roop, Partha S; Malik, Avinash; Andalam, Sidharta; Yip, Eugene; Allen, Nathan; Trew, Mark L

2018-01-01

A flexible, efficient, and verifiable pacemaker cell model is essential to the design of real-time virtual hearts that can be used for closed-loop validation of cardiac devices. A new parametric model of pacemaker action potential is developed to address this need. The action potential phases are modeled using hybrid automaton with one piecewise-linear continuous variable. The model can capture rate-dependent dynamics, such as action potential duration restitution, conduction velocity restitution, and overdrive suppression by incorporating nonlinear update functions. Simulated dynamics of the model compared well with previous models and clinical data. The results show that the parametric model can reproduce the electrophysiological dynamics of a variety of pacemaker cells, such as sinoatrial node, atrioventricular node, and the His-Purkinje system, under varying cardiac conditions. This is an important contribution toward closed-loop validation of cardiac devices using real-time heart models.

Maternal Depressive Symptomatology and Child Behavior: Transactional Relationship with Simultaneous Bidirectional Coupling

PubMed Central

Nicholson, Jody S.; Deboeck, Pascal; Farris, Jaelyn R.; Boker, Steven M.; Borkowski, John G.

2011-01-01

The present study investigated reciprocal relationships between adolescent mothers and their children’s well-being through an analysis of the coupling relationship of mothers’ depressive symptomatology and children’s internalizing and externalizing behaviors. Unlike studies using discrete time analyses, the present study used dynamical systems to model time continuously, which allowed for the study of dynamic, transactional effects between members of each dyad. Findings provided evidence of coupling between maternal depressive symptoms and children’s behaviors. The most robust finding was that as maternal depressive symptoms became more or less severe, children’s behavior problems increased or decreased in a reciprocal manner. Results from this study extended upon theoretical contributions of authors such as Richters (1997) and Granic and Hollenstein (2003), providing empirical validation from a longitudinal study for understanding the ongoing, dynamic relationships between at-risk mothers and their children. PMID:21639624

Visualization of Notch signaling oscillation in cells and tissues.

PubMed

Shimojo, Hiromi; Harima, Yukiko; Kageyama, Ryoichiro

2014-01-01

The Notch signaling effectors Hes1 and Hes7 exhibit oscillatory expression with a period of about 2-3 h during embryogenesis. Hes1 oscillation is important for proliferation and differentiation of neural stem cells, whereas Hes7 oscillation regulates periodic formation of somites. Continuous expression of Hes1 and Hes7 inhibits these developmental processes. Thus, expression dynamics are very important for gene functions, but it is difficult to distinguish between oscillatory and persistent expression by conventional methods such as in situ hybridization and immunostaining. Here, we describe time-lapse imaging methods using destabilized luciferase reporters and a highly sensitive cooled charge-coupled device camera, which can monitor dynamic gene expression. Furthermore, the expression of two genes can be examined simultaneously by a dual reporter system using two-color luciferase reporters. Time-lapse imaging analyses reveal how dynamically gene expression changes in many biological events.

Application of dynamical systems theory to the high angle of attack dynamics of the F-14

NASA Technical Reports Server (NTRS)

Jahnke, Craig C.; Culick, Fred E. C.

1990-01-01

Dynamical systems theory has been used to study the nonlinear dynamics of the F-14. An eight degree of freedom model that does not include the control system present in operational F-14s has been analyzed. The aerodynamic model, supplied by NASA, includes nonlinearities as functions of the angles of attack and sideslip, the rotation rate, and the elevator deflection. A continuation method has been used to calculate the steady states of the F-14 as continuous functions of the control surface deflections. Bifurcations of these steady states have been used to predict the onset of wing rock, spiral divergence, and jump phenomena which cause the aircraft to enter a spin. A simple feedback control system was designed to eliminate the wing rock and spiral divergence instabilities. The predictions were verified with numerical simulations.

Practical performance of real-time shot-noise measurement in continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Wang, Tao; Huang, Peng; Zhou, Yingming; Liu, Weiqi; Zeng, Guihua

2018-01-01

In a practical continuous-variable quantum key distribution (CVQKD) system, real-time shot-noise measurement (RTSNM) is an essential procedure for preventing the eavesdropper exploiting the practical security loopholes. However, the performance of this procedure itself is not analyzed under the real-world condition. Therefore, we indicate the RTSNM practical performance and investigate its effects on the CVQKD system. In particular, due to the finite-size effect, the shot-noise measurement at the receiver's side may decrease the precision of parameter estimation and consequently result in a tight security bound. To mitigate that, we optimize the block size for RTSNM under the ensemble size limitation to maximize the secure key rate. Moreover, the effect of finite dynamics of amplitude modulator in this scheme is studied and its mitigation method is also proposed. Our work indicates the practical performance of RTSNM and provides the real secret key rate under it.

Automated analysis of two-dimensional positions and body lengths of earthworms (Oligochaeta); MimizuTrack.

PubMed

Kodama, Naomi; Kimura, Toshifumi; Yonemura, Seiichiro; Kaneda, Satoshi; Ohashi, Mizue; Ikeno, Hidetoshi

2014-01-01

Earthworms are important soil macrofauna inhabiting almost all ecosystems. Their biomass is large and their burrowing and ingestion of soils alters soil physicochemical properties. Because of their large biomass, earthworms are regarded as an indicator of "soil heath". However, primarily because the difficulties in quantifying their behavior, the extent of their impact on soil material flow dynamics and soil health is poorly understood. Image data, with the aid of image processing tools, are a powerful tool in quantifying the movements of objects. Image data sets are often very large and time-consuming to analyze, especially when continuously recorded and manually processed. We aimed to develop a system to quantify earthworm movement from video recordings. Our newly developed program successfully tracked the two-dimensional positions of three separate parts of the earthworm and simultaneously output the change in its body length. From the output data, we calculated the velocity of the earthworm's movement. Our program processed the image data three times faster than the manual tracking system. To date, there are no existing systems to quantify earthworm activity from continuously recorded image data. The system developed in this study will reduce input time by a factor of three compared with manual data entry and will reduce errors involved in quantifying large data sets. Furthermore, it will provide more reliable measured values, although the program is still a prototype that needs further testing and improvement. Combined with other techniques, such as measuring metabolic gas emissions from earthworm bodies, this program could provide continuous observations of earthworm behavior in response to environmental variables under laboratory conditions. In the future, this standardized method will be applied to other animals, and the quantified earthworm movement will be incorporated into models of soil material flow dynamics or behavior in response to chemical substances present in the soil.

On the stability of motion of N-body systems: a geometric approach.

NASA Astrophysics Data System (ADS)

El-Zant, A. A.

1997-10-01

Much of standard galaxy dynamics rests on the implicit assumption that the corresponding N-body problem is (near) integrable. This notion although leading to great simplification is by no means a fact. In particular, this assumption is unlikely to be satisfied for systems which display chaotic behaviour which manifests itself on short time-scales and for most initial conditions. It is therefore important to develop and test methods that can characterize this kind of behaviour in realistic situations. We examine here a method, pioneered by Krylov (1950, Studies on the Foundation of statistical Physics. Publ AN SSSR, Leningrad Eng. Trans. Princeton University Press. 1980) and first introduced to gravitational systems by Gurzadyan & Savvidy (1984SPhD...29..520G, 1986A&A...160..203G). It involves a metric on the configuration manifold which is then used to find local quantification of the divergence of trajectories and therefore appears to be suitable for short time characterization of chaotic behaviour. We present results of high precision N-body simulations of the dynamics of systems of 231 point particles over a few dynamical times. The Ricci (or mean) curvature is calculated along the trajectories. Once fluctuations due to close encounters are removed this quantity is found to be almost always negative and therefore all systems studied display local instability to random perturbations along their trajectories. However it is found that when significant softening is present the Ricci curvature is no longer negative. This suggests that smoothing significantly changes the structure of the 6N phase space of gravitational systems and casts doubts on the continuity of the transition from the large-N limit to the continuum limit. From the value of the negative curvature, evolution time-scales of systems displaying clear instabilities (for example collective instabilities or violent relaxation) are derived. We compare the predictions obtained from these calculations with the time-scales of the observed spatial evolution of the different systems and deduce that this is fairly well described. In all cases the results based on calculations of the scalar curvature qualitatively agree. These results suggest that future applications of these methods to realistic systems may be useful in characterizing their stability properties. One has to be careful however in relating the time-scales obtained to the time-scales of energy relaxation since different dynamical quantities may relax at different rates.

Unraveling the sub-processes of selective attention: insights from dynamic modeling and continuous behavior.

PubMed

Frisch, Simon; Dshemuchadse, Maja; Görner, Max; Goschke, Thomas; Scherbaum, Stefan

2015-11-01

Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.

Design and Test of an Event Detector for the ReflectoActive Seals System

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

Stinson, Brad J

2006-05-01

The purpose of this thesis was to research, design, develop and test a novel instrument for detecting fiber optic loop continuity and spatially locating fiber optic breaches. The work is for an active seal system called ReflectoActive Seals whose purpose is to provide real time container tamper indication. A Field Programmable Gate Array was used to implement a loop continuity detector and a spatial breach locator based on a high acquisition speed single photon counting optical time domain reflectometer. Communication and other control features were added in order to create a usable instrument that met defined requirements. A host graphicalmore » user interface was developed to illustrate system use and performance. The resulting device meets performance specifications by exhibiting a dynamic range of 27dB and a spatial resolution of 1.5 ft. The communication scheme used expands installation options and allows the device to communicate to a central host via existing Local Area Networks and/or the Internet.« less

Design and Test of an Event Detector and Locator for the ReflectoActive Seals System

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

Stinson, Brad J

2006-06-01

The purpose of this work was to research, design, develop and test a novel instrument for detecting fiber optic loop continuity and spatially locating fiber optic breaches. The work is for an active seal system called ReflectoActive{trademark} Seals whose purpose is to provide real time container tamper indication. A Field Programmable Gate Array was used to implement a loop continuity detector and a spatial breach locator based on a high acquisition speed single photon counting optical time domain reflectometer. Communication and other control features were added in order to create a usable instrument that met defined requirements. A host graphicalmore » user interface was developed to illustrate system use and performance. The resulting device meets performance specifications by exhibiting a dynamic range of 27dB and a spatial resolution of 1.5 ft. The communication scheme used expands installation options and allows the device to communicate to a central host via existing Local Area Networks and/or the Internet.« less

Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach.

PubMed

Liu, Derong; Wang, Ding; Li, Hongliang

2014-02-01

In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme.

Effect of silica nanoparticle filler on microscopic polymer α-relaxation dynamics

NASA Astrophysics Data System (ADS)

Saito, Makina; Mashita, Ryo; Kishimoto, Hiroyuki; Masuda, Ryo; Yoda, Yoshitaka; Seto, Makoto

2017-11-01

Tyre rubber has been continuously developed to improve its performance, but the microscopic mechanisms behind these improvements, e.g. by adding nanoparticles to the rubber, are still not fully understood. We study the microscopic polymer dynamics of a rubber nanocomposite system consisting of polymer polybutadiene with 20 volume% of silica nanoparticles with diameters of 100 nm via quasi-elastic scattering experiments using gamma-ray time-domain interferometry. The result shows that the presence of silica nanoparticles caused the inter-chain α-relaxation dynamics to slow down in a shallowly supercooled state suggesting that the presence of the nanoparticles that came in contact with the polymer controlled the timescale of the polymer's α-relaxation dynamics. Conversely, the presence of nanoparticles less affects the dynamics in a lower temperature region near T g. It is consistent with the result of the differential scanning calorimetry study showing negligible T g difference among the pure polymer and the nanocomposite system. It also shows that the quasi-elastic scattering experiment can be used to reveal the polymer dynamics in nanocomposites and is appropriate for characterising their microscopic dynamics for the purpose of improving tyre performance.

A Dynamic Interactive Theory of Person Construal

ERIC Educational Resources Information Center

Freeman, Jonathan B.; Ambady, Nalini

2011-01-01

A dynamic interactive theory of person construal is proposed. It assumes that the perception of other people is accomplished by a dynamical system involving continuous interaction between social categories, stereotypes, high-level cognitive states, and the low-level processing of facial, vocal, and bodily cues. This system permits lower-level…

Dynamic Transitions and Baroclinic Instability for 3D Continuously Stratified Boussinesq Flows

NASA Astrophysics Data System (ADS)

Şengül, Taylan; Wang, Shouhong

2018-02-01

The main objective of this article is to study the nonlinear stability and dynamic transitions of the basic (zonal) shear flows for the three-dimensional continuously stratified rotating Boussinesq model. The model equations are fundamental equations in geophysical fluid dynamics, and dynamics associated with their basic zonal shear flows play a crucial role in understanding many important geophysical fluid dynamical processes, such as the meridional overturning oceanic circulation and the geophysical baroclinic instability. In this paper, first we derive a threshold for the energy stability of the basic shear flow, and obtain a criterion for local nonlinear stability in terms of the critical horizontal wavenumbers and the system parameters such as the Froude number, the Rossby number, the Prandtl number and the strength of the shear flow. Next, we demonstrate that the system always undergoes a dynamic transition from the basic shear flow to either a spatiotemporal oscillatory pattern or circle of steady states, as the shear strength of the basic flow crosses a critical threshold. Also, we show that the dynamic transition can be either continuous or catastrophic, and is dictated by the sign of a transition number, fully characterizing the nonlinear interactions of different modes. Both the critical shear strength and the transition number are functions of the system parameters. A systematic numerical method is carried out to explore transition in different flow parameter regimes. In particular, our numerical investigations show the existence of a hypersurface which separates the parameter space into regions where the basic shear flow is stable and unstable. Numerical investigations also yield that the selection of horizontal wave indices is determined only by the aspect ratio of the box. We find that the system admits only critical eigenmodes with roll patterns aligned with the x-axis. Furthermore, numerically we encountered continuous transitions to multiple steady states, as well as continuous and catastrophic transitions to spatiotemporal oscillations.

An intelligent factory-wide optimal operation system for continuous production process

NASA Astrophysics Data System (ADS)

Ding, Jinliang; Chai, Tianyou; Wang, Hongfeng; Wang, Junwei; Zheng, Xiuping

2016-03-01

In this study, a novel intelligent factory-wide operation system for a continuous production process is designed to optimise the entire production process, which consists of multiple units; furthermore, this system is developed using process operational data to avoid the complexity of mathematical modelling of the continuous production process. The data-driven approach aims to specify the structure of the optimal operation system; in particular, the operational data of the process are used to formulate each part of the system. In this context, the domain knowledge of process engineers is utilised, and a closed-loop dynamic optimisation strategy, which combines feedback, performance prediction, feed-forward, and dynamic tuning schemes into a framework, is employed. The effectiveness of the proposed system has been verified using industrial experimental results.

A Fiber Bragg Grating-Based Dynamic Tension Detection System for Overhead Transmission Line Galloping.

PubMed

Ma, Guo-Ming; Li, Ya-Bo; Mao, Nai-Qiang; Shi, Cheng; Zhang, Bo; Li, Cheng-Rong

2018-01-26

Galloping of overhead transmission lines (OHTLs) may induce conductor breakage and tower collapse, and there is no effective method for long distance distribution on-line galloping monitoring. To overcome the drawbacks of the conventional galloping monitoring systems, such as sensitivity to electromagnetic interference, the need for onsite power, and short lifetimes, a novel optical remote passive measuring system is proposed in the paper. Firstly, to solve the hysteresis and eccentric load problem in tension sensing, and to extent the dynamic response range, an 'S' type elastic element structure with flanges was proposed. Then, a tension experiment was carried out to demonstrate the dynamic response characteristics. Moreover, the designed tension sensor was stretched continuously for 30 min to observe its long time stability. Last but not the least, the sensor was mounted on a 70 m conductor model, and the conductor was oscillated at different frequencies to investigate the dynamic performance of the sensor. The experimental results demonstrate the sensor is suitable for the OHTL galloping detection. Compared with the conventional sensors for OHTL monitoring, the system has many advantages, such as easy installation, no flashover risk, distribution monitoring, better bandwidth, improved accuracy and higher reliability.

Simulation-based decision support framework for dynamic ambulance redeployment in Singapore.

PubMed

Lam, Sean Shao Wei; Ng, Clarence Boon Liang; Nguyen, Francis Ngoc Hoang Long; Ng, Yih Yng; Ong, Marcus Eng Hock

2017-10-01

Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper. The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage. Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases. This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated. Copyright © 2017 Elsevier B.V. All rights reserved.

46 CFR 56.20-1 - General.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., and II-L systems receiving ship motion dynamic analysis and nondestructive examination. For Class I, I-L, or II-L systems not receiving ship motion dynamic analysis and nondestructive examination under..., DEPARTMENT OF HOMELAND SECURITY (CONTINUED) MARINE ENGINEERING PIPING SYSTEMS AND APPURTENANCES Valves § 56...

Research on calibration method of downhole optical fiber temperature measurement and its application in SAGD well

NASA Astrophysics Data System (ADS)

Lu, Zhiwei; Han, Li; Hu, Chengjun; Pan, Yong; Duan, Shengnan; Wang, Ningbo; Li, Shijian; Nuer, Maimaiti

2017-10-01

With the development of oil and gas fields, the accuracy and quantity requirements of real-time dynamic monitoring data needed for well dynamic analysis and regulation are increasing. Permanent, distributed downhole optical fiber temperature and pressure monitoring and other online real-time continuous data monitoring has become an important data acquisition and transmission technology in digital oil field and intelligent oil field construction. Considering the requirement of dynamic analysis of steam chamber developing state in SAGD horizontal wells in F oil reservoir in Xinjiang oilfield, it is necessary to carry out real-time and continuous temperature monitoring in horizontal section. Based on the study of the principle of optical fiber temperature measurement, the factors that cause the deviation of optical fiber temperature sensing are analyzed, and the method of fiber temperature calibration is proposed to solve the problem of temperature deviation. Field application in three wells showed that it could attain accurate measurement of downhole temperature by temperature correction. The real-time and continuous downhole distributed fiber temperature sensing technology has higher application value in the reservoir management of SAGD horizontal wells. It also has a reference for similar dynamic monitoring in reservoir production.

Method and apparatus for creating time-optimal commands for linear systems

NASA Technical Reports Server (NTRS)

Seering, Warren P. (Inventor); Tuttle, Timothy D. (Inventor)

2004-01-01

A system for and method of determining an input command profile for substantially any dynamic system that can be modeled as a linear system, the input command profile for transitioning an output of the dynamic system from one state to another state. The present invention involves identifying characteristics of the dynamic system, selecting a command profile which defines an input to the dynamic system based on the identified characteristics, wherein the command profile comprises one or more pulses which rise and fall at switch times, imposing a plurality of constraints on the dynamic system, at least one of the constraints being defined in terms of the switch times, and determining the switch times for the input to the dynamic system based on the command profile and the plurality of constraints. The characteristics may be related to poles and zeros of the dynamic system, and the plurality of constraints may include a dynamics cancellation constraint which specifies that the input moves the dynamic system from a first state to a second state such that the dynamic system remains substantially at the second state.

Distributed Leader-Following Finite-Time Consensus Control for Linear Multiagent Systems under Switching Topology

PubMed Central

Xu, Xiaole; Chen, Shengyong

2014-01-01

This paper investigates the finite-time consensus problem of leader-following multiagent systems. The dynamical models for all following agents and the leader are assumed the same general form of linear system, and the interconnection topology among the agents is assumed to be switching and undirected. We mostly consider the continuous-time case. By assuming that the states of neighbouring agents are known to each agent, a sufficient condition is established for finite-time consensus via a neighbor-based state feedback protocol. While the states of neighbouring agents cannot be available and only the outputs of neighbouring agents can be accessed, the distributed observer-based consensus protocol is proposed for each following agent. A sufficient condition is provided in terms of linear matrix inequalities to design the observer-based consensus protocol, which makes the multiagent systems achieve finite-time consensus under switching topologies. Then, we discuss the counterparts for discrete-time case. Finally, we provide an illustrative example to show the effectiveness of the design approach. PMID:24883367

Dynamic Voltage-Frequency and Workload Joint Scaling Power Management for Energy Harvesting Multi-Core WSN Node SoC

PubMed Central

Li, Xiangyu; Xie, Nijie; Tian, Xinyue

2017-01-01

This paper proposes a scheduling and power management solution for energy harvesting heterogeneous multi-core WSN node SoC such that the system continues to operate perennially and uses the harvested energy efficiently. The solution consists of a heterogeneous multi-core system oriented task scheduling algorithm and a low-complexity dynamic workload scaling and configuration optimization algorithm suitable for light-weight platforms. Moreover, considering the power consumption of most WSN applications have the characteristic of data dependent behavior, we introduce branches handling mechanism into the solution as well. The experimental result shows that the proposed algorithm can operate in real-time on a lightweight embedded processor (MSP430), and that it can make a system do more valuable works and make more than 99.9% use of the power budget. PMID:28208730

Dynamic Voltage-Frequency and Workload Joint Scaling Power Management for Energy Harvesting Multi-Core WSN Node SoC.

PubMed

Li, Xiangyu; Xie, Nijie; Tian, Xinyue

2017-02-08

This paper proposes a scheduling and power management solution for energy harvesting heterogeneous multi-core WSN node SoC such that the system continues to operate perennially and uses the harvested energy efficiently. The solution consists of a heterogeneous multi-core system oriented task scheduling algorithm and a low-complexity dynamic workload scaling and configuration optimization algorithm suitable for light-weight platforms. Moreover, considering the power consumption of most WSN applications have the characteristic of data dependent behavior, we introduce branches handling mechanism into the solution as well. The experimental result shows that the proposed algorithm can operate in real-time on a lightweight embedded processor (MSP430), and that it can make a system do more valuable works and make more than 99.9% use of the power budget.

Unbinding Transition of Probes in Single-File Systems

NASA Astrophysics Data System (ADS)

Bénichou, Olivier; Démery, Vincent; Poncet, Alexis

2018-02-01

Single-file transport, arising in quasi-one-dimensional geometries where particles cannot pass each other, is characterized by the anomalous dynamics of a probe, notably its response to an external force. In these systems, the motion of several probes submitted to different external forces, although relevant to mixtures of charged and neutral or active and passive objects, remains unexplored. Here, we determine how several probes respond to external forces. We rely on a hydrodynamic description of the symmetric exclusion process to obtain exact analytical results at long times. We show that the probes can either move as a whole, or separate into two groups moving away from each other. In between the two regimes, they separate with a different dynamical exponent, as t1 /4. This unbinding transition also occurs in several continuous single-file systems and is expected to be observable.

Parameterizing Coefficients of a POD-Based Dynamical System

NASA Technical Reports Server (NTRS)

Kalb, Virginia L.

2010-01-01

A method of parameterizing the coefficients of a dynamical system based of a proper orthogonal decomposition (POD) representing the flow dynamics of a viscous fluid has been introduced. (A brief description of POD is presented in the immediately preceding article.) The present parameterization method is intended to enable construction of the dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers. The need for this or a similar method arises as follows: A procedure that includes direct numerical simulation followed by POD, followed by Galerkin projection to a dynamical system has been proven to enable representation of flow dynamics by a low-dimensional model at the Reynolds number of the simulation. However, a more difficult task is to obtain models that are valid over a range of Reynolds numbers. Extrapolation of low-dimensional models by use of straightforward Reynolds-number-based parameter continuation has proven to be inadequate for successful prediction of flows. A key part of the problem of constructing a dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers is the problem of understanding and providing for the variation of the coefficients of the dynamical system with the Reynolds number. Prior methods do not enable capture of temporal dynamics over ranges of Reynolds numbers in low-dimensional models, and are not even satisfactory when large numbers of modes are used. The basic idea of the present method is to solve the problem through a suitable parameterization of the coefficients of the dynamical system. The parameterization computations involve utilization of the transfer of kinetic energy between modes as a function of Reynolds number. The thus-parameterized dynamical system accurately predicts the flow dynamics and is applicable to a range of flow problems in the dynamical regime around the Hopf bifurcation. Parameter-continuation software can be used on the parameterized dynamical system to derive a bifurcation diagram that accurately predicts the temporal flow behavior.

Dynamical quantum phase transitions in discrete time crystals

NASA Astrophysics Data System (ADS)

Kosior, Arkadiusz; Sacha, Krzysztof

2018-05-01

Discrete time crystals are related to nonequilibrium dynamics of periodically driven quantum many-body systems where the discrete time-translation symmetry of the Hamiltonian is spontaneously broken into another discrete symmetry. Recently, the concept of phase transitions has been extended to nonequilibrium dynamics of time-independent systems induced by a quantum quench, i.e., a sudden change of some parameter of the Hamiltonian. There, the return probability of a system to the ground state reveals singularities in time which are dubbed dynamical quantum phase transitions. We show that the quantum quench in a discrete time crystal leads to dynamical quantum phase transitions where the return probability of a periodically driven system to a Floquet eigenstate before the quench reveals singularities in time. It indicates that dynamical quantum phase transitions are not restricted to time-independent systems and can be also observed in systems that are periodically driven. We discuss how the phenomenon can be observed in ultracold atomic gases.

Periodic orbit analysis of a system with continuous symmetry--A tutorial.

PubMed

Budanur, Nazmi Burak; Borrero-Echeverry, Daniel; Cvitanović, Predrag

2015-07-01

Dynamical systems with translational or rotational symmetry arise frequently in studies of spatially extended physical systems, such as Navier-Stokes flows on periodic domains. In these cases, it is natural to express the state of the fluid in terms of a Fourier series truncated to a finite number of modes. Here, we study a 4-dimensional model with chaotic dynamics and SO(2) symmetry similar to those that appear in fluid dynamics problems. A crucial step in the analysis of such a system is symmetry reduction. We use the model to illustrate different symmetry-reduction techniques. The system's relative equilibria are conveniently determined by rewriting the dynamics in terms of a symmetry-invariant polynomial basis. However, for the analysis of its chaotic dynamics, the "method of slices," which is applicable to very high-dimensional problems, is preferable. We show that a Poincaré section taken on the "slice" can be used to further reduce this flow to what is for all practical purposes a unimodal map. This enables us to systematically determine all relative periodic orbits and their symbolic dynamics up to any desired period. We then present cycle averaging formulas adequate for systems with continuous symmetry and use them to compute dynamical averages using relative periodic orbits. The convergence of such computations is discussed.

Phase Transitions in Geomorphology

NASA Astrophysics Data System (ADS)

Ortiz, C. P.; Jerolmack, D. J.

2015-12-01

Landscapes are patterns in a dynamic steady-state, due to competing processes that smooth or sharpen features over large distances and times. Geomorphic transport laws have been developed to model the mass-flux due to different processes, but are unreasonably effective at recovering the scaling relations of landscape features. Using a continuum approximation to compare experimental landscapes and the observed landscapes of the earth, one finds they share similar morphodynamics despite a breakdown of classical dynamical similarity between the two. We propose the origin of this effectiveness is a different kind of dynamic similarity in the statistics of initiation and cessation of motion of groups of grains, which is common to disordered systems of grains under external driving. We will show how the existing data of sediment transport points to common signatures with dynamical phase transitions between "mobile" and "immobile" phases in other disordered systems, particularly granular materials, colloids, and foams. Viewing landscape evolution from the lens of non-equilibrium statistical physics of disordered systems leads to predictions that the transition of bulk measurements such as particle flux is continuous from one phase to another, that the collective nature of the particle dynamics leads to very slow aging of bulk properties, and that the dynamics are history-dependent. Recent results from sediment transport experiments support these predictions, suggesting that existing geomorphic transport laws may need to be replaced by a new generation of stochastic models with ingredients based on the physics of disordered phase transitions. We discuss possible strategies for extracting the necessary information to develop these models from measurements of geomorphic transport noise by connecting particle-scale collective dynamics and space-time fluctuations over landscape features.

Continuous-Time Finance and the Waiting Time Distribution: Multiple Characteristic Times

NASA Astrophysics Data System (ADS)

Fa, Kwok Sau

2012-09-01

In this paper, we model the tick-by-tick dynamics of markets by using the continuous-time random walk (CTRW) model. We employ a sum of products of power law and stretched exponential functions for the waiting time probability distribution function; this function can fit well the waiting time distribution for BUND futures traded at LIFFE in 1997.

Long series of geomagnetic measurements - unique at satellite era

NASA Astrophysics Data System (ADS)

Mandea, Mioara; Balasis, Georgios

2017-04-01

We have long appreciated that magnetic measurements obtained at Earth's surface are of great value in characterizing geomagnetic field behavior and then probing the deep interior of our Planet. The existence of new magnetic satellite missions data offer a new detailed global understanding of the geomagnetic field. However, when our interest moves over long-time scales, the very long series of measurements play an important role. Here, we firstly provide an updated series of geomagnetic declination in Paris, shortly after a very special occasion: its value has reached zero after some 350 years of westerly values. We take this occasion to emphasize the importance of long series of continuous measurements, mainly when various techniques are used to detect the abrupt changes in geomagnetic field, the geomagnetic jerks. Many novel concepts originated in dynamical systems or information theory have been developed, partly motivated by specific research questions from the geosciences. This continuously extending toolbox of nonlinear time series analysis is a key to understand the complexity of geomagnetic field. Here, motivated by these efforts, a series of entropy analysis are applied to geomagnetic field time series aiming to detect dynamical complex changes associated with geomagnetic jerks.

A challenge to chaotic itinerancy from brain dynamics

NASA Astrophysics Data System (ADS)

Kay, Leslie M.

2003-09-01

Brain hermeneutics and chaotic itinerancy proposed by Tsuda are attractive characterizations of perceptual dynamics in the mammalian olfactory system. This theory proposes that perception occurs at the interface between itinerant neural representation and interaction with the environment. Quantifiable application of these dynamics has been hampered by the lack of definable history and action processes which characterize the changes induced by behavioral state, attention, and learning. Local field potentials measured from several brain areas were used to characterize dynamic activity patterns for their use as representations of history and action processes. The signals were recorded from olfactory areas (olfactory bulb, OB, and pyriform cortex) and hippocampal areas (entorhinal cortex and dentate gyrus, DG) in the brains of rats. During odor-guided behavior the system shows dynamics at three temporal scales. Short time-scale changes are system-wide and can occur in the space of a single sniff. They are predictable, associated with learned shifts in behavioral state and occur periodically on the scale of the intertrial interval. These changes occupy the theta (2-12 Hz), beta (15-30 Hz), and gamma (40-100 Hz) frequency bands within and between all areas. Medium time-scale changes occur relatively unpredictably, manifesting in these data as alterations in connection strength between the OB and DG. These changes are strongly correlated with performance in associated trial blocks (5-10 min) and may be due to fluctuations in attention, mood, or amount of reward received. Long time-scale changes are likely related to learning or decline due to aging or disease. These may be modeled as slow monotonic processes that occur within or across days or even weeks or years. The folding of different time scales is proposed as a mechanism for chaotic itinerancy, represented by dynamic processes instead of static connection strengths. Thus, the individual maintains continuity of experience within the stability of fast periodic and slow monotonic processes, while medium scale events alter experience and performance dramatically but temporarily. These processes together with as yet to be determined action effects from motor system feedback are proposed as an instantiation of brain hermeneutics and chaotic itinerancy.

Eternal non-Markovianity: from random unitary to Markov chain realisations.

PubMed

Megier, Nina; Chruściński, Dariusz; Piilo, Jyrki; Strunz, Walter T

2017-07-25

The theoretical description of quantum dynamics in an intriguing way does not necessarily imply the underlying dynamics is indeed intriguing. Here we show how a known very interesting master equation with an always negative decay rate [eternal non-Markovianity (ENM)] arises from simple stochastic Schrödinger dynamics (random unitary dynamics). Equivalently, it may be seen as arising from a mixture of Markov (semi-group) open system dynamics. Both these approaches lead to a more general family of CPT maps, characterized by a point within a parameter triangle. Our results show how ENM quantum dynamics can be realised easily in the laboratory. Moreover, we find a quantum time-continuously measured (quantum trajectory) realisation of the dynamics of the ENM master equation based on unitary transformations and projective measurements in an extended Hilbert space, guided by a classical Markov process. Furthermore, a Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) representation of the dynamics in an extended Hilbert space can be found, with a remarkable property: there is no dynamics in the ancilla state. Finally, analogous constructions for two qubits extend these results from non-CP-divisible to non-P-divisible dynamics.

Lorentz quantum mechanics

NASA Astrophysics Data System (ADS)

Zhang, Qi; Wu, Biao

2018-01-01

We present a theoretical framework for the dynamics of bosonic Bogoliubov quasiparticles. We call it Lorentz quantum mechanics because the dynamics is a continuous complex Lorentz transformation in complex Minkowski space. In contrast, in usual quantum mechanics, the dynamics is the unitary transformation in Hilbert space. In our Lorentz quantum mechanics, three types of state exist: space-like, light-like and time-like. Fundamental aspects are explored in parallel to the usual quantum mechanics, such as a matrix form of a Lorentz transformation, and the construction of Pauli-like matrices for spinors. We also investigate the adiabatic evolution in these mechanics, as well as the associated Berry curvature and Chern number. Three typical physical systems, where bosonic Bogoliubov quasi-particles and their Lorentz quantum dynamics can arise, are presented. They are a one-dimensional fermion gas, Bose-Einstein condensate (or superfluid), and one-dimensional antiferromagnet.

High dynamic range infrared radiometry and imaging

NASA Technical Reports Server (NTRS)

Coon, Darryl D.; Karunasiri, R. P. G.; Bandara, K. M. S. V.

1988-01-01

The use is described of cryogenically cooled, extrinsic silicon infrared detectors in an unconventional mode of operation which offers an unusually large dynamic range. The system performs intensity-to-frequency conversion at the focal plane via simple circuits with very low power consumption. The incident IR intensity controls the repetition rate of short duration output pulses over a pulse rate dynamic range of about 10(6). Theory indicates the possibility of monotonic and approx. linear response over the full dynamic range. A comparison between the theoretical and the experimental results shows that the model provides a reasonably good description of experimental data. Some measurements of survivability with a very intense IR source were made on these devices and found to be very encouraging. Evidence continues to indicate that some variations in interpulse time intervals are deterministic rather than probabilistic.

A Java-based enterprise system architecture for implementing a continuously supported and entirely Web-based exercise solution.

PubMed

Wang, Zhihui; Kiryu, Tohru

2006-04-01

Since machine-based exercise still uses local facilities, it is affected by time and place. We designed a web-based system architecture based on the Java 2 Enterprise Edition that can accomplish continuously supported machine-based exercise. In this system, exercise programs and machines are loosely coupled and dynamically integrated on the site of exercise via the Internet. We then extended the conventional health promotion model, which contains three types of players (users, exercise trainers, and manufacturers), by adding a new player: exercise program creators. Moreover, we developed a self-describing strategy to accommodate a variety of exercise programs and provide ease of use to users on the web. We illustrate our novel design with examples taken from our feasibility study on a web-based cycle ergometer exercise system. A biosignal-based workload control approach was introduced to ensure that users performed appropriate exercise alone.

46 CFR 56.15-5 - Fluid-conditioner fittings.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Class I, I-L, and II-L systems receiving ship motion dynamic analysis and nondestructive examination. For Class I, I-L, or II-L systems not receiving ship motion dynamic analysis and nondestructive... Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) MARINE ENGINEERING PIPING SYSTEMS AND...

46 CFR 56.15-1 - Pipe joining fittings.

Code of Federal Regulations, 2010 CFR

2010-10-01

... for all Class I, I-L, and II-L systems receiving ship motion dynamic analysis and nondestructive examination. For Class I, I-L, or II-L systems not receiving ship motion dynamic analysis and nondestructive... COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) MARINE ENGINEERING PIPING SYSTEMS AND...

Network Physiology: How Organ Systems Dynamically Interact

PubMed Central

Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

2015-01-01

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

Quantum spin chains with multiple dynamics

NASA Astrophysics Data System (ADS)

Chen, Xiao; Fradkin, Eduardo; Witczak-Krempa, William

2017-11-01

Many-body systems with multiple emergent time scales arise in various contexts, including classical critical systems, correlated quantum materials, and ultracold atoms. We investigate such nontrivial quantum dynamics in a different setting: a spin-1 bilinear-biquadratic chain. It has a solvable entangled ground state, but a gapless excitation spectrum that is poorly understood. By using large-scale density matrix renormalization group simulations, we find that the lowest excitations have a dynamical exponent z that varies from 2 to 3.2 as we vary a coupling in the Hamiltonian. We find an additional gapless mode with a continuously varying exponent 2 ≤z <2.7 , which establishes the presence of multiple dynamics. In order to explain these striking properties, we construct a continuum wave function for the ground state, which correctly describes the correlations and entanglement properties. We also give a continuum parent Hamiltonian, but show that additional ingredients are needed to capture the excitations of the chain. By using an exact mapping to the nonequilibrium dynamics of a classical spin chain, we find that the large dynamical exponent is due to subdiffusive spin motion. Finally, we discuss the connections to other spin chains and to a family of quantum critical models in two dimensions.

Cascaded Optimization for a Persistent Data Ferrying Unmanned Aircraft

NASA Astrophysics Data System (ADS)

Carfang, Anthony

This dissertation develops and assesses a cascaded method for designing optimal periodic trajectories and link schedules for an unmanned aircraft to ferry data between stationary ground nodes. This results in a fast solution method without the need to artificially constrain system dynamics. Focusing on a fundamental ferrying problem that involves one source and one destination, but includes complex vehicle and Radio-Frequency (RF) dynamics, a cascaded structure to the system dynamics is uncovered. This structure is exploited by reformulating the nonlinear optimization problem into one that reduces the independent control to the vehicle's motion, while the link scheduling control is folded into the objective function and implemented as an optimal policy that depends on candidate motion control. This formulation is proven to maintain optimality while reducing computation time in comparison to traditional ferry optimization methods. The discrete link scheduling problem takes the form of a combinatorial optimization problem that is known to be NP-Hard. A derived necessary condition for optimality guides the development of several heuristic algorithms, specifically the Most-Data-First Algorithm and the Knapsack Adaptation. These heuristics are extended to larger ferrying scenarios, and assessed analytically and through Monte Carlo simulation, showing better throughput performance in the same order of magnitude of computation time in comparison to other common link scheduling policies. The cascaded optimization method is implemented with a novel embedded software system on a small, unmanned aircraft to validate the simulation results with field experiments. To address the sensitivity of results on trajectory tracking performance, a system that combines motion and link control with waypoint-based navigation is developed and assessed through field experiments. The data ferrying algorithms are further extended by incorporating a Gaussian process to opportunistically learn the RF environment. By continuously improving RF models, the cascaded planner can continually improve the ferrying system's overall performance.

Minimum fuel control of a vehicle with a continuously variable transmission. [control system simulation

NASA Technical Reports Server (NTRS)

Burghart, J. H.; Donoghue, J. F.

1980-01-01

The design and evaluation of a control system for a sedan with a heat engine and a continuously variable transmission, is considered in a effort to minimize fuel consumption and achieve satisfactory dynamic response of vehicle variables as the vehicle is driven over a standard driving cycle. Even though the vehicle system was highly nonlinear, attention was restricted to linear control algorithms which could be easily understood and implemented demonstrated by simulation. Simulation results also revealed that the vehicle could exhibit unexpected dynamic behavior which must be taken into account in any control system design.

Trajectory phase transitions and dynamical Lee-Yang zeros of the Glauber-Ising chain.

PubMed

Hickey, James M; Flindt, Christian; Garrahan, Juan P

2013-07-01

We examine the generating function of the time-integrated energy for the one-dimensional Glauber-Ising model. At long times, the generating function takes on a large-deviation form and the associated cumulant generating function has singularities corresponding to continuous trajectory (or "space-time") phase transitions between paramagnetic trajectories and ferromagnetically or antiferromagnetically ordered trajectories. In the thermodynamic limit, the singularities make up a whole curve of critical points in the complex plane of the counting field. We evaluate analytically the generating function by mapping the generator of the biased dynamics to a non-Hermitian Hamiltonian of an associated quantum spin chain. We relate the trajectory phase transitions to the high-order cumulants of the time-integrated energy which we use to extract the dynamical Lee-Yang zeros of the generating function. This approach offers the possibility to detect continuous trajectory phase transitions from the finite-time behavior of measurable quantities.

Multiscaling for systems with a broad continuum of characteristic lengths and times: Structural transitions in nanocomposites.

PubMed

Pankavich, S; Ortoleva, P

2010-06-01

The multiscale approach to N-body systems is generalized to address the broad continuum of long time and length scales associated with collective behaviors. A technique is developed based on the concept of an uncountable set of time variables and of order parameters (OPs) specifying major features of the system. We adopt this perspective as a natural extension of the commonly used discrete set of time scales and OPs which is practical when only a few, widely separated scales exist. The existence of a gap in the spectrum of time scales for such a system (under quasiequilibrium conditions) is used to introduce a continuous scaling and perform a multiscale analysis of the Liouville equation. A functional-differential Smoluchowski equation is derived for the stochastic dynamics of the continuum of Fourier component OPs. A continuum of spatially nonlocal Langevin equations for the OPs is also derived. The theory is demonstrated via the analysis of structural transitions in a composite material, as occurs for viral capsids and molecular circuits.

PAQ: Persistent Adaptive Query Middleware for Dynamic Environments

NASA Astrophysics Data System (ADS)

Rajamani, Vasanth; Julien, Christine; Payton, Jamie; Roman, Gruia-Catalin

Pervasive computing applications often entail continuous monitoring tasks, issuing persistent queries that return continuously updated views of the operational environment. We present PAQ, a middleware that supports applications' needs by approximating a persistent query as a sequence of one-time queries. PAQ introduces an integration strategy abstraction that allows composition of one-time query responses into streams representing sophisticated spatio-temporal phenomena of interest. A distinguishing feature of our middleware is the realization that the suitability of a persistent query's result is a function of the application's tolerance for accuracy weighed against the associated overhead costs. In PAQ, programmers can specify an inquiry strategy that dictates how information is gathered. Since network dynamics impact the suitability of a particular inquiry strategy, PAQ associates an introspection strategy with a persistent query, that evaluates the quality of the query's results. The result of introspection can trigger application-defined adaptation strategies that alter the nature of the query. PAQ's simple API makes developing adaptive querying systems easily realizable. We present the key abstractions, describe their implementations, and demonstrate the middleware's usefulness through application examples and evaluation.

Molecular dynamics simulations investigating consecutive nucleation, solidification and grain growth in a twelve-million-atom Fe-system

NASA Astrophysics Data System (ADS)

Okita, Shin; Verestek, Wolfgang; Sakane, Shinji; Takaki, Tomohiro; Ohno, Munekazu; Shibuta, Yasushi

2017-09-01

Continuous processes of homogeneous nucleation, solidification and grain growth are spontaneously achieved from an undercooled iron melt without any phenomenological parameter in the molecular dynamics (MD) simulation with 12 million atoms. The nucleation rate at the critical temperature is directly estimated from the atomistic configuration by cluster analysis to be of the order of 1034 m-3 s-1. Moreover, time evolution of grain size distribution during grain growth is obtained by the combination of Voronoi and cluster analyses. The grain growth exponent is estimated to be around 0.3 from the geometric average of the grain size distribution. Comprehensive understanding of kinetic properties during continuous processes is achieved in the large-scale MD simulation by utilizing the high parallel efficiency of a graphics processing unit (GPU), which is shedding light on the fundamental aspects of production processes of materials from the atomistic viewpoint.

Xcas as a Programming Environment for Stability Conditions for a Class of Differential Equation Models in Economics

NASA Astrophysics Data System (ADS)

Halkos, George E.; Tsilika, Kyriaki D.

2011-09-01

In this paper we examine the property of asymptotic stability in several dynamic economic systems, modeled in ordinary differential equation formulations of time parameter t. Asymptotic stability ensures intertemporal equilibrium for the economic quantity the solution stands for, regardless of what the initial conditions happen to be. Existence of economic equilibrium in continuous time models is checked via a Symbolic language, the Xcas program editor. Using stability theorems of differential equations as background a brief overview of symbolic capabilities of free software Xcas is given. We present computational experience with a programming style for stability results of ordinary linear and nonlinear differential equations. Numerical experiments on traditional applications of economic dynamics exhibit the simplicity clarity and brevity of input and output of our computer codes.

Time Varying Compensator Design for Reconfigurable Structures Using Non-Collocated Feedback

NASA Technical Reports Server (NTRS)

Scott, Michael A.

1996-01-01

Analysis and synthesis tools are developed to improved the dynamic performance of reconfigurable nonminimum phase, nonstrictly positive real-time variant systems. A novel Spline Varying Optimal (SVO) controller is developed for the kinematic nonlinear system. There are several advantages to using the SVO controller, in which the spline function approximates the system model, observer, and controller gain. They are: The spline function approximation is simply connected, thus the SVO controller is more continuous than traditional gain scheduled controllers when implemented on a time varying plant; ft is easier for real-time implementations in storage and computational effort; where system identification is required, the spline function requires fewer experiments, namely four experiments; and initial startup estimator transients are eliminated. The SVO compensator was evaluated on a high fidelity simulation of the Shuttle Remote Manipulator System. The SVO controller demonstrated significant improvement over the present arm performance: (1) Damping level was improved by a factor of 3; and (2) Peak joint torque was reduced by a factor of 2 following Shuttle thruster firings.

Linearized semiclassical initial value time correlation functions with maximum entropy analytic continuation.

PubMed

Liu, Jian; Miller, William H

2008-09-28

The maximum entropy analytic continuation (MEAC) method is used to extend the range of accuracy of the linearized semiclassical initial value representation (LSC-IVR)/classical Wigner approximation for real time correlation functions. LSC-IVR provides a very effective "prior" for the MEAC procedure since it is very good for short times, exact for all time and temperature for harmonic potentials (even for correlation functions of nonlinear operators), and becomes exact in the classical high temperature limit. This combined MEAC+LSC/IVR approach is applied here to two highly nonlinear dynamical systems, a pure quartic potential in one dimensional and liquid para-hydrogen at two thermal state points (25 and 14 K under nearly zero external pressure). The former example shows the MEAC procedure to be a very significant enhancement of the LSC-IVR for correlation functions of both linear and nonlinear operators, and especially at low temperature where semiclassical approximations are least accurate. For liquid para-hydrogen, the LSC-IVR is seen already to be excellent at T=25 K, but the MEAC procedure produces a significant correction at the lower temperature (T=14 K). Comparisons are also made as to how the MEAC procedure is able to provide corrections for other trajectory-based dynamical approximations when used as priors.

Adaptive feedback synchronisation of complex dynamical network with discrete-time communications and delayed nodes

NASA Astrophysics Data System (ADS)

Wang, Tong; Ding, Yongsheng; Zhang, Lei; Hao, Kuangrong

2016-08-01

This paper considered the synchronisation of continuous complex dynamical networks with discrete-time communications and delayed nodes. The nodes in the dynamical networks act in the continuous manner, while the communications between nodes are discrete-time; that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communication instants, we investigate the adaptive feedback synchronisation and a criterion is derived to guarantee the existence of the desired controllers. The globally exponential synchronisation can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.

A string of Peregrine rogue waves in the nonlocal nonlinear Schrödinger equation with parity-time symmetric self-induced potential

NASA Astrophysics Data System (ADS)

Gupta, Samit Kumar

2018-03-01

Dynamic wave localization phenomena draw fundamental and technological interests in optics and photonics. Based on the recently proposed (Ablowitz and Musslimani, 2013) continuous nonlocal nonlinear Schrödinger system with parity-time symmetric Kerr nonlinearity (PTNLSE), a numerical investigation has been carried out for two first order Peregrine solitons as the initial ansatz. Peregrine soliton, as an exact solution to the PTNLSE, evokes a very potent question: what effects does the interaction of two first order Peregrine solitons have on the overall optical field dynamics. Upon numerical computation, we observe the appearance of Kuznetsov-Ma (KM) soliton trains in the unbroken PT-phase when the initial Peregrine solitons are in phase. In the out of phase condition, it shows repulsive nonlinear waves. Quite interestingly, our study shows that within a specific range of the interval factor in the transverse co-ordinate there exists a string of high intensity well-localized Peregrine rogue waves in the PT unbroken phase. We note that the interval factor as well as the transverse shift parameter play important roles in the nonlinear interaction and evolution dynamics of the optical fields. This could be important in developing fundamental understanding of nonlocal non-Hermitian NLSE systems and dynamic wave localization behaviors.

Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Multigrid Methods: Proceedings of the Copper Mountain Conference on Multigrid Methods (3rd) Held in Copper Mountain, Colorado on April 5-10, 1987

DTIC Science & Technology

1988-08-01

Time Series 53. J. Barros-Neto and R. A. Artino, Hypoelliptic Boundary-Value Problems 54. R. L. Sternberg, A. J. Kalinowski, and J. S. Papadakis... Systems 95. C E. AuL Rings of Continuous Functions 96. R. Chuaqui, Analysis , Geometry, and Probability 97. L. Fuchs and L. Sace, Modules Over...Local Refinements for a Class of Nonshared Memory Systems 449 Hermann Mierendorif Analysis of a Multigrid Method for the Euler Equations of Gas Dynamics

Dynamical continuous time random Lévy flights

NASA Astrophysics Data System (ADS)

Liu, Jian; Chen, Xiaosong

2016-03-01

The Lévy flights' diffusive behavior is studied within the framework of the dynamical continuous time random walk (DCTRW) method, while the nonlinear friction is introduced in each step. Through the DCTRW method, Lévy random walker in each step flies by obeying the Newton's Second Law while the nonlinear friction f(v) = - γ0v - γ2v3 being considered instead of Stokes friction. It is shown that after introducing the nonlinear friction, the superdiffusive Lévy flights converges, behaves localization phenomenon with long time limit, but for the Lévy index μ = 2 case, it is still Brownian motion.

The Dynamics of Information Search Services.

ERIC Educational Resources Information Center

Lindquist, Mats G.

Computer-based information search services (ISSs) of the type that provide online literature searches are analyzed from a systems viewpoint using a continuous simulation model. The methodology applied is "system dynamics," and the system language is DYNAMO. The analysis reveals that the observed growth and stagnation of a typical ISS can…

Cascading Failures as Continuous Phase-Space Transitions

DOE PAGES

Yang, Yang; Motter, Adilson E.

2017-12-14

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

Cascading Failures as Continuous Phase-Space Transitions

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

Yang, Yang; Motter, Adilson E.

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

Assessing the future of air freight

NASA Technical Reports Server (NTRS)

Dajani, J. S.

1977-01-01

The role of air cargo in the current transportation system in the United States is explored. Methods for assessing the future role of this mode of transportation include the use of continuous-time recursive systems modeling for the simulation of different components of the air freight system, as well as for the development of alternative future scenarios which may result from different policy actions. A basic conceptual framework for conducting such a dynamic simulation is presented within the context of the air freight industry. Some research needs are identified and recommended for further research. The benefits, limitations, pitfalls, and problems usually associated with large scale systems models are examined.

Graph determined symbolic dynamics and hybrid systems

NASA Astrophysics Data System (ADS)

Ayers, Kimberly Danielle

In this paper we explore the concept of symbolic dynamical systems whose structure is determined by a directed graph, and then discrete-continuous hybrid systems that arise from such dynamical systems. Typically, symbolic dynamics involve the study of a left shift of a bi-infinite sequence. We examine the case when the bi-infinite system is dictated by a graph; that is, the sequence is a bi-infinite path of a directed graph. We then use the concept to study a system of dynamical systems all on the same compact space M, where "switching" between the systems occurs as given by the bi-infinite sequence in question. The concepts of limit sets, chain recurrent sets, chaos, and Morse sets for these systems are explored.

Continuous real-time in vivo measurement of cerebral nitric oxide supports theoretical predictions of an irreversible switching in cerebral ROS after sufficient exposure to external toxins.

PubMed

Finnerty, Niall J; O'Riordan, Saidhbhe L; Lowry, John P; Cloutier, Mathieu; Wellstead, Peter

2013-01-01

Mathematical models of the interactions between alphasynuclein (αS) and reactive oxygen species (ROS) predict a systematic and irreversible switching to damagingly high levels of ROS after sufficient exposure to risk factors associated with Parkinson's disease (PD). We tested this prediction by continuously monitoring real-time changes in neurochemical levels over periods of several days in animals exposed to a toxin known to cause Parkinsonian symptoms. Nitric oxide (NO) sensors were implanted in the brains of freely moving rats and the NO levels continuously recorded while the animals were exposed to paraquat (PQ) injections of various amounts and frequencies. Long-term, real-time measurement of NO in a cohort of animals showed systematic switching in levels when PQ injections of sufficient size and frequency were administered. The experimental observations of changes in NO imply a corresponding switching in endogenous ROS levels and support theoretical predictions of an irreversible change to damagingly high levels of endogenous ROS when PD risks are sufficiently large. Our current results only consider one form of PD risk, however, we are sufficiently confident in them to conclude that: (i) continuous long-term measurement of neurochemical dynamics provide a novel way to measure the temporal change and system dynamics which determine Parkinsonian damage, and (ii) the bistable feedback switching predicted by mathematical modelling seems to exist and that a deeper analysis of its characteristics would provide a way of understanding the pathogenic mechanisms that initiate Parkinsonian cell damage.

Respiratory system dynamical mechanical properties: modeling in time and frequency domain.

PubMed

Carvalho, Alysson Roncally; Zin, Walter Araujo

2011-06-01

The mechanical properties of the respiratory system are important determinants of its function and can be severely compromised in disease. The assessment of respiratory system mechanical properties is thus essential in the management of some disorders as well as in the evaluation of respiratory system adaptations in response to an acute or chronic process. Most often, lungs and chest wall are treated as a linear dynamic system that can be expressed with differential equations, allowing determination of the system's parameters, which will reflect the mechanical properties. However, different models that encompass nonlinear characteristics and also multicompartments have been used in several approaches and most specifically in mechanically ventilated patients with acute lung injury. Additionally, the input impedance over a range of frequencies can be assessed with a convenient excitation method allowing the identification of the mechanical characteristics of the central and peripheral airways as well as lung periphery impedance. With the evolution of computational power, the airway pressure and flow can be recorded and stored for hours, and hence continuous monitoring of the respiratory system mechanical properties is already available in some mechanical ventilators. This review aims to describe some of the most frequently used models for the assessment of the respiratory system mechanical properties in both time and frequency domain.

State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm.

PubMed

Elenchezhiyan, M; Prakash, J

2015-09-01

In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Stability Analysis of Continuous-Time and Discrete-Time Quaternion-Valued Neural Networks With Linear Threshold Neurons.

PubMed

Chen, Xiaofeng; Song, Qiankun; Li, Zhongshan; Zhao, Zhenjiang; Liu, Yurong

2018-07-01

This paper addresses the problem of stability for continuous-time and discrete-time quaternion-valued neural networks (QVNNs) with linear threshold neurons. Applying the semidiscretization technique to the continuous-time QVNNs, the discrete-time analogs are obtained, which preserve the dynamical characteristics of their continuous-time counterparts. Via the plural decomposition method of quaternion, homeomorphic mapping theorem, as well as Lyapunov theorem, some sufficient conditions on the existence, uniqueness, and global asymptotical stability of the equilibrium point are derived for the continuous-time QVNNs and their discrete-time analogs, respectively. Furthermore, a uniform sufficient condition on the existence, uniqueness, and global asymptotical stability of the equilibrium point is obtained for both continuous-time QVNNs and their discrete-time version. Finally, two numerical examples are provided to substantiate the effectiveness of the proposed results.

Quantification of resilience to water scarcity, a dynamic measure in time and space

NASA Astrophysics Data System (ADS)

Simonovic, S. P.; Arunkumar, R.

2016-05-01

There are practical links between water resources management, climate change adaptation and sustainable development leading to reduction of water scarcity risk and re-enforcing resilience as a new development paradigm. Water scarcity, due to the global change (population growth, land use change and climate change), is of serious concern since it can cause loss of human lives and serious damage to the economy of a region. Unfortunately, in many regions of the world, water scarcity is, and will be unavoidable in the near future. As the scarcity is increasing, at the same time it erodes resilience, therefore global change has a magnifying effect on water scarcity risk. In the past, standard water resources management planning considered arrangements for prevention, mitigation, preparedness and recovery, as well as response. However, over the last ten years substantial progress has been made in establishing the role of resilience in sustainable development. Dynamic resilience is considered as a novel measure that provides for better understanding of temporal and spatial dynamics of water scarcity. In this context, a water scarcity is seen as a disturbance in a complex physical-socio-economic system. Resilience is commonly used as a measure to assess the ability of a system to respond and recover from a failure. However, the time independent static resilience without consideration of variability in space does not provide sufficient insight into system's ability to respond and recover from the failure state and was mostly used as a damage avoidance measure. This paper provides an original systems framework for quantification of resilience. The framework is based on the definition of resilience as the ability of physical and socio-economic systems to absorb disturbance while still being able to continue functioning. The disturbance depends on spatial and temporal perspectives and direct interaction between impacts of disturbance (social, health, economic, and other) and adaptive capacity of the system to absorb disturbance. Utility of the dynamic resilience is demonstrated through a single-purpose reservoir operation subject to different failure (water scarcity) scenarios. The reservoir operation is simulated using the system dynamics (SD) feedback-based object-oriented simulation approach.

Turbulent complex (dusty) plasma

NASA Astrophysics Data System (ADS)

Zhdanov, Sergey; Schwabe, Mierk

2017-04-01

As a paradigm of complex system dynamics, solid particles immersed into a weakly ionized plasma, so called complex (dusty) plasmas, were (and continue to be) a subject of many detailed studies. Special types of dynamical activity have been registered, in particular, spontaneous pairing, entanglement and cooperative action of a great number of particles resulting in formation of vortices, self-propelling, tunneling, and turbulent movements. In the size domain of 1-10 mkm normally used in experiments with complex plasmas, the characteristic dynamic time-scale is of the order of 0.01-0.1 s, and these particles can be visualized individually in real time, providing an atomistic (kinetic) level of investigations. The low-R turbulent flow induced either by the instability in a complex plasma cloud or formed behind a projectile passing through the cloud is a typical scenario. Our simulations showed formation of a fully developed system of vortices and demonstrated that the velocity structure functions scale very close to the theoretical predictions. As an important element of self-organization, cooperative and turbulent particle motions are present in many physical, astrophysical, and biological systems. Therefore, experiments with turbulent wakes and turbulent complex plasma oscillations are a promising mean to observe and study in detail the anomalous transport on the level of individual particles.

Real-time modulated nanoparticle separation with an ultra-large dynamic range.

PubMed

Zeming, Kerwin Kwek; Thakor, Nitish V; Zhang, Yong; Chen, Chia-Hung

2016-01-07

Nanoparticles exhibit size-dependent properties which make size-selective purification of proteins, DNA or synthetic nanoparticles essential for bio-analytics, clinical medicine, nano-plasmonics and nano-material sciences. Current purification methods of centrifugation, column chromatography and continuous-flow techniques suffer from particle aggregation, multi-stage process, complex setups and necessary nanofabrication. These increase process costs and time, reduce efficiency and limit dynamic range. Here, we achieve an unprecedented real-time nanoparticle separation (51-1500 nm) using a large-pore (2 μm) deterministic lateral displacement (DLD) device. No external force fields or nanofabrication are required. Instead, we investigated innate long-range electrostatic influences on nanoparticles within a fluid medium at different NaCl ionic concentrations. In this study we account for the electrostatic forces beyond Debye length and showed that they cannot be assumed as negligible especially for precise nanoparticle separation methods such as DLD. Our findings have enabled us to develop a model to simultaneously quantify and modulate the electrostatic force interactions between nanoparticle and micropore. By simply controlling buffer solutions, we achieve dynamic nanoparticle size separation on a single device with a rapid response time (<20 s) and an enlarged dynamic range (>1200%), outperforming standard benchtop centrifuge systems. This novel method and model combines device simplicity, isolation precision and dynamic flexibility, opening opportunities for high-throughput applications in nano-separation for industrial and biological applications.

Examining Extreme Events Using Dynamically Downscaled 12-km WRF Simulations

EPA Science Inventory

Continued improvements in the speed and availability of computational resources have allowed dynamical downscaling of global climate model (GCM) projections to be conducted at increasingly finer grid scales and over extended time periods. The implementation of dynamical downscal...

Discrete Events as Units of Perceived Time

ERIC Educational Resources Information Center

Liverence, Brandon M.; Scholl, Brian J.

2012-01-01

In visual images, we perceive both space (as a continuous visual medium) and objects (that inhabit space). Similarly, in dynamic visual experience, we perceive both continuous time and discrete events. What is the relationship between these units of experience? The most intuitive answer may be similar to the spatial case: time is perceived as an…

Breaking time reversal in a simple smooth chaotic system.

PubMed

Tomsovic, Steven; Ullmo, Denis; Nagano, Tatsuro

2003-06-01

Within random matrix theory, the statistics of the eigensolutions depend fundamentally on the presence (or absence) of time reversal symmetry. Accepting the Bohigas-Giannoni-Schmit conjecture, this statement extends to quantum systems with chaotic classical analogs. For practical reasons, much of the supporting numerical studies of symmetry breaking have been done with billiards or maps, and little with simple, smooth systems. There are two main difficulties in attempting to break time reversal invariance in a continuous time system with a smooth potential. The first is avoiding false time reversal breaking. The second is locating a parameter regime in which the symmetry breaking is strong enough to transform the fluctuation properties fully to the broken symmetry case, and yet remain weak enough so as not to regularize the dynamics sufficiently that the system is no longer chaotic. We give an example of a system of two coupled quartic oscillators whose energy level statistics closely match with those of the Gaussian unitary ensemble, and which possesses only a minor proportion of regular motion in its phase space.

A Second Order Semi-Discrete Cosserat Rod Model Suitable for Dynamic Simulations in Real Time

NASA Astrophysics Data System (ADS)

Lang, Holger; Linn, Joachim

2009-09-01

We present an alternative approach for a semi-discrete viscoelastic Cosserat rod model that allows both fast dynamic computations within milliseconds and accurate results compared to detailed finite element solutions. The model is able to represent extension, shearing, bending and torsion. For inner dissipation, a consistent damping potential from Antman is chosen. The continuous equations of motion, which consist a system of nonlinear hyperbolic partial differential algebraic equations, are derived from a two dimensional variational principle. The semi-discrete balance equations are obtained by spatial finite difference schemes on a staggered grid and standard index reduction techniques. The right-hand side of the model and its Jacobian can be chosen free of higher algebraic (e.g. root) or transcendent (e.g. trigonometric or exponential) functions and is therefore extremely cheap to evaluate numerically. For the time integration of the system, we use well established stiff solvers. As our model yields computational times within milliseconds, it is suitable for interactive manipulation. It reflects structural mechanics solutions sufficiently correct, as comparison with detailed finite element results shows.

Model-Driven Safety Analysis of Closed-Loop Medical Systems

PubMed Central

Pajic, Miroslav; Mangharam, Rahul; Sokolsky, Oleg; Arney, David; Goldman, Julian; Lee, Insup

2013-01-01

In modern hospitals, patients are treated using a wide array of medical devices that are increasingly interacting with each other over the network, thus offering a perfect example of a cyber-physical system. We study the safety of a medical device system for the physiologic closed-loop control of drug infusion. The main contribution of the paper is the verification approach for the safety properties of closed-loop medical device systems. We demonstrate, using a case study, that the approach can be applied to a system of clinical importance. Our method combines simulation-based analysis of a detailed model of the system that contains continuous patient dynamics with model checking of a more abstract timed automata model. We show that the relationship between the two models preserves the crucial aspect of the timing behavior that ensures the conservativeness of the safety analysis. We also describe system design that can provide open-loop safety under network failure. PMID:24177176

Model-Driven Safety Analysis of Closed-Loop Medical Systems.

PubMed

Pajic, Miroslav; Mangharam, Rahul; Sokolsky, Oleg; Arney, David; Goldman, Julian; Lee, Insup

2012-10-26

In modern hospitals, patients are treated using a wide array of medical devices that are increasingly interacting with each other over the network, thus offering a perfect example of a cyber-physical system. We study the safety of a medical device system for the physiologic closed-loop control of drug infusion. The main contribution of the paper is the verification approach for the safety properties of closed-loop medical device systems. We demonstrate, using a case study, that the approach can be applied to a system of clinical importance. Our method combines simulation-based analysis of a detailed model of the system that contains continuous patient dynamics with model checking of a more abstract timed automata model. We show that the relationship between the two models preserves the crucial aspect of the timing behavior that ensures the conservativeness of the safety analysis. We also describe system design that can provide open-loop safety under network failure.

Nonuniform dependence on initial data for compressible gas dynamics: The periodic Cauchy problem

NASA Astrophysics Data System (ADS)

Keyfitz, B. L.; Tığlay, F.

2017-11-01

We start with the classic result that the Cauchy problem for ideal compressible gas dynamics is locally well posed in time in the sense of Hadamard; there is a unique solution that depends continuously on initial data in Sobolev space Hs for s > d / 2 + 1 where d is the space dimension. We prove that the data to solution map for periodic data in two dimensions although continuous is not uniformly continuous.

Nonlinear dynamics of global atmospheric and Earth-system processes

NASA Technical Reports Server (NTRS)

Saltzman, Barry; Ebisuzaki, Wesley; Maasch, Kirk A.; Oglesby, Robert; Pandolfo, Lionel

1990-01-01

Researchers are continuing their studies of the nonlinear dynamics of global weather systems. Sensitivity analyses of large-scale dynamical models of the atmosphere (i.e., general circulation models i.e., GCM's) were performed to establish the role of satellite-signatures of soil moisture, sea surface temperature, snow cover, and sea ice as crucial boundary conditions determining global weather variability. To complete their study of the bimodality of the planetary wave states, they are using the dynamical systems approach to construct a low-order theoretical explanation of this phenomenon. This work should have important implications for extended range forecasting of low-frequency oscillations, elucidating the mechanisms for the transitions between the two wave modes. Researchers are using the methods of jump analysis and attractor dimension analysis to examine the long-term satellite records of significant variables (e.g., long wave radiation, and cloud amount), to explore the nature of mode transitions in the atmosphere, and to determine the minimum number of equations needed to describe the main weather variations with a low-order dynamical system. Where feasible they will continue to explore the applicability of the methods of complex dynamical systems analysis to the study of the global earth-system from an integrative viewpoint involving the roles of geochemical cycling and the interactive behavior of the atmosphere, hydrosphere, and biosphere.

Theory of Turing Patterns on Time Varying Networks.

PubMed

Petit, Julien; Lauwens, Ben; Fanelli, Duccio; Carletti, Timoteo

2017-10-06

The process of pattern formation for a multispecies model anchored on a time varying network is studied. A nonhomogeneous perturbation superposed to an homogeneous stable fixed point can be amplified following the Turing mechanism of instability, solely instigated by the network dynamics. By properly tuning the frequency of the imposed network evolution, one can make the examined system behave as its averaged counterpart, over a finite time window. This is the key observation to derive a closed analytical prediction for the onset of the instability in the time dependent framework. Continuously and piecewise constant periodic time varying networks are analyzed, setting the framework for the proposed approach. The extension to nonperiodic settings is also discussed.

Nonlinear Socio-Ecological Dynamics and First Principles ofCollective Choice Behavior of ``Homo Socialis"

NASA Astrophysics Data System (ADS)

Sonis, M.

Socio-ecological dynamics emerged from the field of Mathematical SocialSciences and opened up avenues for re-examination of classical problems of collective behavior in Social and Spatial sciences. The ``engine" of this collective behavior is the subjective mental evaluation of level of utilities in the future, presenting sets of composite socio-economic-temporal-locational advantages. These dynamics present new laws of collective multi-population behavior which are the meso-level counterparts of the utility optimization individual behavior. The central core of the socio-ecological choice dynamics includes the following first principle of the collective choice behavior of ``Homo Socialis" based on the existence of ``collective consciousness": the choice behavior of ``Homo Socialis" is a collective meso-level choice behavior such that the relative changes in choice frequencies depend on the distribution of innovation alternatives between adopters of innovations. The mathematical basis of the Socio-Ecological Dynamics includes two complementary analytical approaches both based on the use of computer modeling as a theoretical and simulation tool. First approach is the ``continuous approach" --- the systems of ordinary and partial differential equations reflecting the continuous time Volterra ecological formalism in a form of antagonistic and/or cooperative collective hyper-games between different sub-sets of choice alternatives. Second approach is the ``discrete approach" --- systems of difference equations presenting a new branch of the non-linear discrete dynamics --- the Discrete Relative m-population/n-innovations Socio-Spatial Dynamics (Dendrinos and Sonis, 1990). The generalization of the Volterra formalism leads further to the meso-level variational principle of collective choice behavior determining the balance between the resulting cumulative social spatio-temporal interactions among the population of adopters susceptible to the choice alternatives and the cumulative equalization of the power of elites supporting different choice alternatives. This balance governs the dynamic innovation choice process and constitutes the dynamic meso-level counterpart of the micro-economic individual utility maximization principle.

Discrete-time systems with random switches: From systems stability to networks synchronization.

PubMed

Guo, Yao; Lin, Wei; Ho, Daniel W C

2016-03-01

In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developed approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks.

Discrete-time systems with random switches: From systems stability to networks synchronization

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

Guo, Yao; Lin, Wei, E-mail: wlin@fudan.edu.cn; Shanghai Key Laboratory of Contemporary Applied Mathematics, LMNS, and Shanghai Center for Mathematical Sciences, Shanghai 200433

2016-03-15

In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developedmore » approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks.« less

Soil Moisture Dynamics under Corn, Soybean, and Perennial Kura Clover

NASA Astrophysics Data System (ADS)

Ochsner, T.; Venterea, R. T.

2009-12-01

Rising global food and energy consumption call for increased agricultural production, whereas rising concerns for environmental quality call for farming systems with more favorable environmental impacts. Improved understanding and management of plant-soil water interactions are central to meeting these twin challenges. The objective of this research was to compare the temporal dynamics of soil moisture under contrasting cropping systems suited for the Midwestern region of the United States. Precipitation, infiltration, drainage, evapotranspiration, soil water storage, and freeze/thaw processes were measured hourly for three years in field plots of continuous corn (Zea mays L.), corn/soybean [Glycine max (L.) Merr.] rotation, and perennial kura clover (Trifolium ambiguum M. Bieb.) in southeastern Minnesota. The evapotranspiration from the perennial clover most closely followed the temporal dynamics of precipitation, resulting in deep drainage which was reduced up to 50% relative to the annual crops. Soil moisture utilization also continued later into the fall under the clover than under the annual crops. In the annual cropping systems, crop sequence influenced the soil moisture dynamics. Soybean following corn and continuous corn exhibited evapotranspiration which was 80 mm less than and deep drainage which was 80 mm greater than that of corn following soybean. These differences occurred primarily during the spring and were associated with differences in early season plant growth between the systems. In the summer, soil moisture depletion was up to 30 mm greater under corn than soybean. Crop residue also played an important role in the soil moisture dynamics. Higher amounts of residue were associated with reduced soil freezing. This presentation will highlight key aspects of the soil moisture dynamics for these contrasting cropping systems across temporal scales ranging from hours to years. The links between soil moisture dynamics, crop yields, and nutrient leaching will also be examined.

MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

PubMed

Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

2012-01-01

Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

Adaptive control of periodic systems

NASA Astrophysics Data System (ADS)

Tian, Zhiling

2009-12-01

Adaptive control is needed to cope with parametric uncertainty in dynamical systems. The adaptive control of LTI systems in both discrete and continuous time has been studied for four decades and the results are currently used widely in many different fields. In recent years, interest has shifted to the adaptive control of time-varying systems. It is known that the adaptive control of arbitrarily rapidly time-varying systems is in general intractable, but systems with periodically time-varying parameters (LTP systems) which have much more structure, are amenable to mathematical analysis. Further, there is also a need for such control in practical problems which have arisen in industry during the past twenty years. This thesis is the first attempt to deal with the adaptive control of LTP systems. Adaptive Control involves estimation of unknown parameters, adjusting the control parameters based on the estimates, and demonstrating that the overall system is stable. System theoretic properties such as stability, controllability, and observability play an important role both in formulating of the problems, as well as in generating solutions for them. For LTI systems, these properties have been studied since 1960s, and algebraic conditions that have to be satisfied to assure these properties are now well established. In the case of LTP systems, these properties can be expressed only in terms of transition matrices that are much more involved than those for LTI systems. Since adaptive control problems can be formulated only when these properties are well understood, it is not surprising that systematic efforts have not been made thus far for formulating and solving adaptive control problems that arise in LTP systems. Even in the case of LTI systems, it is well recognized that problems related to adaptive discrete-time system are not as difficult as those that arise in the continuous-time systems. This is amply evident in the solutions that were derived in the 1980s and 1990s for all the important problems. These differences are even more amplified in the LTP case; some problems in continuous time cannot even be formulated precisely. This thesis consequently focuses primarily on the adaptive identification and control of discrete-time systems, and derives most of the results that currently exist in the literature for LTI systems. Based on these investigations of discrete-time adaptive systems, attempts are made in the thesis to examine their continuous-time counterparts, and discuss the principal difficulties encountered. The dissertation examines critically the system theoretic properties of LTP systems in Chapter 2, and the mathematical framework provided for their analysis by Floquet theory in Chapter 3. Assuming that adaptive identification and control problems can be formulated precisely, a unified method of developing stable adaptive laws using error models is treated in Chapter 4. Chapter 5 presents a detailed study of the adaptation in SISO discrete-time LTP systems, and represents the core of the thesis. The important problems of identification, stabilization, regulation, and tracking of arbitrary signals are investigated, and practically implementable stable adaptive laws are derived. The dissertation concludes with a discussion of continuous-time adaptive control in Chapter 6 and discrete multivariable systems in Chapter 7. Directions for future research are indicated towards the end of the dissertation.

A Full Dynamic Compound Inverse Method for output-only element-level system identification and input estimation from earthquake response signals

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Rizzi, Egidio

2016-08-01

This paper proposes a new output-only element-level system identification and input estimation technique, towards the simultaneous identification of modal parameters, input excitation time history and structural features at the element-level by adopting earthquake-induced structural response signals. The method, named Full Dynamic Compound Inverse Method (FDCIM), releases strong assumptions of earlier element-level techniques, by working with a two-stage iterative algorithm. Jointly, a Statistical Average technique, a modification process and a parameter projection strategy are adopted at each stage to achieve stronger convergence for the identified estimates. The proposed method works in a deterministic way and is completely developed in State-Space form. Further, it does not require continuous- to discrete-time transformations and does not depend on initialization conditions. Synthetic earthquake-induced response signals from different shear-type buildings are generated to validate the implemented procedure, also with noise-corrupted cases. The achieved results provide a necessary condition to demonstrate the effectiveness of the proposed identification method.

Feynman-Kac formula for stochastic hybrid systems.

PubMed

Bressloff, Paul C

2017-01-01

We derive a Feynman-Kac formula for functionals of a stochastic hybrid system evolving according to a piecewise deterministic Markov process. We first derive a stochastic Liouville equation for the moment generator of the stochastic functional, given a particular realization of the underlying discrete Markov process; the latter generates transitions between different dynamical equations for the continuous process. We then analyze the stochastic Liouville equation using methods recently developed for diffusion processes in randomly switching environments. In particular, we obtain dynamical equations for the moment generating function, averaged with respect to realizations of the discrete Markov process. The resulting Feynman-Kac formula takes the form of a differential Chapman-Kolmogorov equation. We illustrate the theory by calculating the occupation time for a one-dimensional velocity jump process on the infinite or semi-infinite real line. Finally, we present an alternative derivation of the Feynman-Kac formula based on a recent path-integral formulation of stochastic hybrid systems.

Planets Around Neutron Stars

NASA Technical Reports Server (NTRS)

Wolszczan, Alexander; Kulkarni, Shrinivas R; Anderson, Stuart B.

2003-01-01

The objective of this proposal was to continue investigations of neutron star planetary systems in an effort to describe and understand their origin, orbital dynamics, basic physical properties and their relationship to planets around normal stars. This research represents an important element of the process of constraining the physics of planet formation around various types of stars. The research goals of this project included long-term timing measurements of the planets pulsar, PSR B1257+12, to search for more planets around it and to study the dynamics of the whole system, and sensitive searches for millisecond pulsars to detect further examples of old, rapidly spinning neutron stars with planetary systems. The instrumentation used in our project included the 305-m Arecibo antenna with the Penn State Pulsar Machine (PSPM), the 100-m Green Bank Telescope with the Berkeley- Caltech Pulsar Machine (BCPM), and the 100-m Effelsberg and 64-m Parkes telescopes equipped with the observatory supplied backend hardware.

Periodic orbit analysis of a system with continuous symmetry—A tutorial

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

Budanur, Nazmi Burak, E-mail: budanur3@gatech.edu; Cvitanović, Predrag; Borrero-Echeverry, Daniel

2015-07-15

Dynamical systems with translational or rotational symmetry arise frequently in studies of spatially extended physical systems, such as Navier-Stokes flows on periodic domains. In these cases, it is natural to express the state of the fluid in terms of a Fourier series truncated to a finite number of modes. Here, we study a 4-dimensional model with chaotic dynamics and SO(2) symmetry similar to those that appear in fluid dynamics problems. A crucial step in the analysis of such a system is symmetry reduction. We use the model to illustrate different symmetry-reduction techniques. The system's relative equilibria are conveniently determined bymore » rewriting the dynamics in terms of a symmetry-invariant polynomial basis. However, for the analysis of its chaotic dynamics, the “method of slices,” which is applicable to very high-dimensional problems, is preferable. We show that a Poincaré section taken on the 'slice' can be used to further reduce this flow to what is for all practical purposes a unimodal map. This enables us to systematically determine all relative periodic orbits and their symbolic dynamics up to any desired period. We then present cycle averaging formulas adequate for systems with continuous symmetry and use them to compute dynamical averages using relative periodic orbits. The convergence of such computations is discussed.« less

Dynamic sensitivity analysis of biological systems

PubMed Central

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2008-01-01

Background A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains. Results We present an algorithm with an adaptive step size control that can be used for computing the solution and dynamic sensitivities of an autonomous ODE system simultaneously. Although our algorithm is one of the decouple direct methods in computing dynamic sensitivities of an ODE system, the step size determined by model equations can be used on the computations of the time profile and dynamic sensitivities with moderate accuracy even when sensitivity equations are more stiff than model equations. To show this algorithm can perform the dynamic sensitivity analysis on very stiff ODE systems with moderate accuracy, it is implemented and applied to two sets of chemical reactions: pyrolysis of ethane and oxidation of formaldehyde. The accuracy of this algorithm is demonstrated by comparing the dynamic parameter sensitivities obtained from this new algorithm and from the direct method with Rosenbrock stiff integrator based on the indirect method. The same dynamic sensitivity analysis was performed on an ethanol fed-batch fermentation system with a time-varying feed rate to evaluate the applicability of the algorithm to realistic models with time-dependent admissible input. Conclusion By combining the accuracy we show with the efficiency of being a decouple direct method, our algorithm is an excellent method for computing dynamic parameter sensitivities in stiff problems. We extend the scope of classical dynamic sensitivity analysis to the investigation of dynamic log gains of models with time-dependent admissible input. PMID:19091016

Model reduction of multiscale chemical langevin equations: a numerical case study.

PubMed

Sotiropoulos, Vassilios; Contou-Carrere, Marie-Nathalie; Daoutidis, Prodromos; Kaznessis, Yiannis N

2009-01-01

Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models. Second, models must take into consideration the disparate spectrum of time scales observed in biological phenomena, such as slow transcription events and fast dimerization reactions. In the last decade, significant efforts have been expended on the development of stochastic chemical kinetics models to capture the dynamics of biomolecular systems, and on the development of robust multiscale algorithms, able to handle stiffness. In this paper, the focus is on the dynamics of reaction sets governed by stiff chemical Langevin equations, i.e., stiff stochastic differential equations. These are particularly challenging systems to model, requiring prohibitively small integration step sizes. We describe and illustrate the application of a semianalytical reduction framework for chemical Langevin equations that results in significant gains in computational cost.

Continuation Methods for Qualitative Analysis of Aircraft Dynamics

NASA Technical Reports Server (NTRS)

Cummings, Peter A.

2004-01-01

A class of numerical methods for constructing bifurcation curves for systems of coupled, non-linear ordinary differential equations is presented. Foundations are discussed, and several variations are outlined along with their respective capabilities. Appropriate background material from dynamical systems theory is presented.

A generalized computer code for developing dynamic gas turbine engine models (DIGTEM)

NASA Technical Reports Server (NTRS)

Daniele, C. J.

1984-01-01

This paper describes DIGTEM (digital turbofan engine model), a computer program that simulates two spool, two stream (turbofan) engines. DIGTEM was developed to support the development of a real time multiprocessor based engine simulator being designed at the Lewis Research Center. The turbofan engine model in DIGTEM contains steady state performance maps for all the components and has control volumes where continuity and energy balances are maintained. Rotor dynamics and duct momentum dynamics are also included. DIGTEM features an implicit integration scheme for integrating stiff systems and trims the model equations to match a prescribed design point by calculating correction coefficients that balance out the dynamic equations. It uses the same coefficients at off design points and iterates to a balanced engine condition. Transients are generated by defining the engine inputs as functions of time in a user written subroutine (TMRSP). Closed loop controls can also be simulated. DIGTEM is generalized in the aerothermodynamic treatment of components. This feature, along with DIGTEM's trimming at a design point, make it a very useful tool for developing a model of a specific turbofan engine.

A generalized computer code for developing dynamic gas turbine engine models (DIGTEM)

NASA Technical Reports Server (NTRS)

Daniele, C. J.

1983-01-01

This paper describes DIGTEM (digital turbofan engine model), a computer program that simulates two spool, two stream (turbofan) engines. DIGTEM was developed to support the development of a real time multiprocessor based engine simulator being designed at the Lewis Research Center. The turbofan engine model in DIGTEM contains steady state performance maps for all the components and has control volumes where continuity and energy balances are maintained. Rotor dynamics and duct momentum dynamics are also included. DIGTEM features an implicit integration scheme for integrating stiff systems and trims the model equations to match a prescribed design point by calculating correction coefficients that balance out the dynamic equations. It uses the same coefficients at off design points and iterates to a balanced engine condition. Transients are generated by defining the engine inputs as functions of time in a user written subroutine (TMRSP). Closed loop controls can also be simulated. DIGTEM is generalized in the aerothermodynamic treatment of components. This feature, along with DIGTEM's trimming at a design point, make it a very useful tool for developing a model of a specific turbofan engine.

On the interpretation of continuous wave electron spin resonance spectra of tempo-palmitate in 5-cyanobiphenyl.

PubMed

Zerbetto, Mirco; Polimeno, Antonino; Cimino, Paola; Barone, Vincenzo

2008-01-14

Electron spin resonance (ESR) measurements are highly informative on the dynamic behavior of molecules, which is of fundamental importance to understand their stability, biological functions and activities, and catalytic action. The wealth of dynamic information which can be extracted from a continuous wave electron spin resonance (cw-ESR) spectrum can be inferred by a basic theoretical approach defined within the stochastic Liouville equation formalism, i.e., the direct inclusion of motional dynamics in the form of stochastic (Fokker-Planck/diffusive) operators in the super Hamiltonian H governing the time evolution of the system. Modeling requires the characterization of magnetic parameters (e.g., hyperfine and Zeeman tensors) and the calculation of ESR observables in terms of spectral densities. The magnetic observables can be pursued by the employment of density functional theory which is apt, provided that hybrid functionals are employed, for the accurate computation of structural properties of molecular systems. Recently, an ab initio integrated computational approach to the in silico interpretation of cw-ESR spectra of multilabeled systems in isotropic fluids has been discussed. In this work we present the extension to the case of nematic liquid crystalline environments by performing simulations of the ESR spectra of the prototypical nitroxide probe 4-(hexadecanoyloxy)-2,2,6,6-tetramethylpiperidine-1-oxy in isotropic and nematic phases of 5-cyanobiphenyl. We first discuss the basic ingredients of the integrated approach, i.e., (1) determination of geometric and local magnetic parameters by quantum-mechanical calculations, taking into account the solvent and, when needed, the vibrational averaging contributions; (2) numerical solution of a stochastic Liouville equation in the presence of diffusive rotational dynamics, based on (3) parameterization of diffusion rotational tensor provided by a hydrodynamic model. Next we present simulated spectra with minimal resorting to fitting procedures, proving that the combination of sensitive ESR spectroscopy and sophisticated modeling can be highly helpful in providing three-dimensional structural and dynamic information on molecular systems in anisotropic environments.

Effect of Parametric Dichotomic Markov Noise on the Properties of Chaotic Transitions in Dynamical Systems

NASA Astrophysics Data System (ADS)

Gac, J. M.; Żebrowski, J. J.

A chaotic transition occurs when a continuous change of one of the parameters of the system causes a discontinuous change in the properties of the chaotic attractor of the system. Such phenomena are present in many dynamical systems, in which a chaotic behavior occurs. The best known of these transitions are: the period-doubling bifurcation cascade, intermittency and crises. The effect of dichotomous Markov noise (DMN) on the properties of systems with chaotic transitions is discussed. DMN is a very simple two-valued stochastic process, with constant transition rates between the two states. In spite of its simplicity, this kind of noise is a very powerful tool to describe various phenomena present in many physical, chemical or biological systems. Many interesting phenomena induced by DMN are known. However, there is no research on the effect of this kind of noise on intermittency or crises. We present the change of the mean laminar phase length and of laminar phase length distribution caused by DMN modulating the parameters of a system with intermittency and the modification of the mean life time on the pre-crisis attractor in the case of a boundary crisis. The results obtained analytically are compared with numerical simulations for several simple dynamical systems.

Plant Phenotyping through the Eyes of Complex Systems: Theoretical Considerations

NASA Astrophysics Data System (ADS)

Kim, J.

2017-12-01

Plant phenotyping is an emerging transdisciplinary research which necessitates not only the communication and collaboration of scientists from different disciplines but also the paradigm shift to a holistic approach. Complex system is defined as a system having a large number of interacting parts (or particles, agents), whose interactions give rise to non-trivial properties like self-organization and emergence. Plant ecosystems are complex systems which are continually morphing dynamical systems, i.e. self-organizing hierarchical open systems. Such systems are composed of many subunits/subsystems with nonlinear interactions and feedback. The throughput such as the flow of energy, matter and information is the key control parameter in complex systems. Information theoretic approaches can be used to understand and identify such interactions, structures and dynamics through reductions in uncertainty (i.e. entropy). The theoretical considerations based on network and thermodynamic thinking and exemplary analyses (e.g. dynamic process network, spectral entropy) of the throughput time series will be presented. These can be used as a framework to develop more discipline-specific fundamental approaches to provide tools for the transferability of traits between measurement scales in plant phenotyping. Acknowledgment: This work was funded by the Weather Information Service Engine Program of the Korea Meteorological Administration under Grant KMIPA-2012-0001.

Urban Dynamics: Analyzing Land Use Change in Urban Environments

NASA Technical Reports Server (NTRS)

Acevedo, William; Richards, Lora R.; Buchanan, Janis T.; Wegener, Whitney R.

2000-01-01

In FY99, the Earth Resource Observation System (EROS) staff at Ames continued managing the U.S. Geological Survey's (USGS) Urban Dynamics Research program, which has mapping and analysis activities at five USGS mapping centers. Historic land use reconstruction work continued while activities in geographic analysis and modeling were expanded. Retrospective geographic information system (GIS) development - the spatial reconstruction of a region's urban land-use history - focused on the Detroit River Corridor, California's Central Valley, and the city of Sioux Falls, South Dakota.

Continuous quantum measurement in spin environments

NASA Astrophysics Data System (ADS)

Xie, Dong; Wang, An Min

2015-08-01

We derive a stochastic master equation (SME) which describes the decoherence dynamics of a system in spin environments conditioned on the measurement record. Markovian and non-Markovian nature of environment can be revealed by a spectroscopy method based on weak continuous quantum measurement. On account of that correlated environments can lead to a non-local open system which exhibits strong non-Markovian effects although the local dynamics are Markovian, the spectroscopy method can be used to demonstrate that there is correlation between two environments.

Portable Just-in-Time Specialization of Dynamically Typed Scripting Languages

NASA Astrophysics Data System (ADS)

Williams, Kevin; McCandless, Jason; Gregg, David

In this paper, we present a portable approach to JIT compilation for dynamically typed scripting languages. At runtime we generate ANSI C code and use the system's native C compiler to compile this code. The C compiler runs on a separate thread to the interpreter allowing program execution to continue during JIT compilation. Dynamic languages have variables which may change type at any point in execution. Our interpreter profiles variable types at both whole method and partial method granularity. When a frequently executed region of code is discovered, the compilation thread generates a specialized version of the region based on the profiled types. In this paper, we evaluate the level of instruction specialization achieved by our profiling scheme as well as the overall performance of our JIT.

Quantitative phase-contrast digital holographic microscopy for cell dynamic evaluation

NASA Astrophysics Data System (ADS)

Yu, Lingfeng; Mohanty, Samarendra; Berns, Michael W.; Chen, Zhongping

2009-02-01

The laser microbeam uses lasers to alter and/or to ablate intracellular organelles and cellular and tissue samples, and, today, has become an important tool for cell biologists to study the molecular mechanism of complex biological systems by removing individual cells or sub-cellular organelles. However, absolute quantitation of the localized alteration/damage to transparent phase objects, such as the cell membrane or chromosomes, was not possible using conventional phase-contrast or differential interference contrast microscopy. We report the development of phase-contrast digital holographic microscopy for quantitative evaluation of cell dynamic changes in real time during laser microsurgery. Quantitative phase images are recorded during the process of laser microsurgery and thus, the dynamic change in phase can be continuously evaluated. Out-of-focus organelles are re-focused by numerical reconstruction algorithms.

Random walk to a nonergodic equilibrium concept

NASA Astrophysics Data System (ADS)

Bel, G.; Barkai, E.

2006-01-01

Random walk models, such as the trap model, continuous time random walks, and comb models, exhibit weak ergodicity breaking, when the average waiting time is infinite. The open question is, what statistical mechanical theory replaces the canonical Boltzmann-Gibbs theory for such systems? In this paper a nonergodic equilibrium concept is investigated, for a continuous time random walk model in a potential field. In particular we show that in the nonergodic phase the distribution of the occupation time of the particle in a finite region of space approaches U- or W-shaped distributions related to the arcsine law. We show that when conditions of detailed balance are applied, these distributions depend on the partition function of the problem, thus establishing a relation between the nonergodic dynamics and canonical statistical mechanics. In the ergodic phase the distribution function of the occupation times approaches a δ function centered on the value predicted based on standard Boltzmann-Gibbs statistics. The relation of our work to single-molecule experiments is briefly discussed.

The Effects of Hearing Aid Compression Parameters on the Short-Term Dynamic Range of Continuous Speech

ERIC Educational Resources Information Center

Henning, Rebecca L. Warner; Bentler, Ruth A.

2008-01-01

Purpose: The purpose of this study was to evaluate and quantitatively model the independent and interactive effects of compression ratio, number of compression channels, and release time on the dynamic range of continuous speech. Method: A CD of the Rainbow Passage (J. E. Bernthal & N. W. Bankson, 1993) was used. The hearing aid was a…

Dynamic value assessments in oncology supported by the PACE Continuous Innovation Indicators.

PubMed

Paddock, Silvia; Goodman, Clifford; Shortenhaus, Scott; Grainger, David; Zummo, Jacqueline; Thomas, Samuel

2017-10-01

Several recently developed frameworks aim to assess the value of cancer treatments, but the most appropriate metrics remain uncertain. We use data from the Patient Access to Cancer care Excellence Continuous Innovation Indicators to examine the relationship between hazard ratios (HRs) from clinical trials and dynamic therapeutic value accumulating over time. Our analysis shows that HRs from initial clinical trials poorly predict the eventual therapeutic value of cancer treatments. Relying strongly on HRs from registration trials to predict the long-term success of treatments leaves a lot of the variance unexplained. The Continuous Innovation Indicators offer a complementing, dynamic method to track the therapeutic value of cancer treatments and continuously update value assessments as additional evidence accumulates.

Joint action syntax in Japanese martial arts.

PubMed

Yamamoto, Yuji; Yokoyama, Keiko; Okumura, Motoki; Kijima, Akifumi; Kadota, Koji; Gohara, Kazutoshi

2013-01-01

Participation in interpersonal competitions, such as fencing or Japanese martial arts, requires players to make instantaneous decisions and execute appropriate motor behaviors in response to various situations. Such actions can be understood as complex phenomena emerging from simple principles. We examined the intentional switching dynamics associated with continuous movement during interpersonal competition in terms of their emergence from a simple syntax. Linear functions on return maps identified two attractors as well as the transitions between them. The effects of skill differences were evident in the second- and third-order state-transition diagrams for these two attractors. Our results suggest that abrupt switching between attractors is related to the diverse continuous movements resulting from quick responses to sudden changes in the environment. This abrupt-switching-quick-response behavior is characterized by a joint action syntax. The resulting hybrid dynamical system is composed of a higher module with discrete dynamics and a lower module with continuous dynamics. Our results suggest that intelligent human behavior and robust autonomy in real-life scenarios are based on this hybrid dynamical system, which connects interpersonal coordination and competition.

CSM solutions of rotating blade dynamics using integrating matrices

NASA Technical Reports Server (NTRS)

Lakin, William D.

1992-01-01

The dynamic behavior of flexible rotating beams continues to receive considerable research attention as it constitutes a fundamental problem in applied mechanics. Further, beams comprise parts of many rotating structures of engineering significance. A topic of particular interest at the present time involves the development of techniques for obtaining the behavior in both space and time of a rotor acted upon by a simple airload loading. Most current work on problems of this type use solution techniques based on normal modes. It is certainly true that normal modes cannot be disregarded, as knowledge of natural blade frequencies is always important. However, the present work has considered a computational structural mechanics (CSM) approach to rotor blade dynamics problems in which the physical properties of the rotor blade provide input for a direct numerical solution of the relevant boundary-and-initial-value problem. Analysis of the dynamics of a given rotor system may require solution of the governing equations over a long time interval corresponding to many revolutions of the loaded flexible blade. For this reason, most of the common techniques in computational mechanics, which treat the space-time behavior concurrently, cannot be applied to the rotor dynamics problem without a large expenditure of computational resources. By contrast, the integrating matrix technique of computational mechanics has the ability to consistently incorporate boundary conditions and 'remove' dependence on a space variable. For problems involving both space and time, this feature of the integrating matrix approach thus can generate a 'splitting' which forms the basis of an efficient CSM method for numerical solution of rotor dynamics problems.

The resonant system: Linking brain-body-environment in sport performance☆.

PubMed

Teques, Pedro; Araújo, Duarte; Seifert, Ludovic; Del Campo, Vicente L; Davids, Keith

2017-01-01

The ecological dynamics approach offers new insights to understand how athlete nervous systems are embedded within the body-environment system in sport. Cognitive neuroscience focuses on the neural bases of athlete behaviors in terms of perceptual, cognitive, and motor functions defined within specific brain structures. Here, we discuss some limitations of this traditional perspective, addressing how athletes functionally adapt perception and action to the dynamics of complex performance environments by continuously perceiving information to regulate goal-directed actions. We examine how recent neurophysiological evidence of functioning in diverse cortical and subcortical regions appears more compatible with an ecological dynamics perspective, than traditional views in cognitive neuroscience. We propose how athlete behaviors in sports may be related to the tuning of resonant mechanisms indicating that perception is a dynamic process involving the whole body of the athlete. We emphasize the important role of metastable dynamics in the brain-body-environment system facilitating continuous interactions with a landscape of affordances (opportunities for action) in a performance environment. We discuss implications of these ideas for performance preparation and practice design in sport. © 2017 Elsevier B.V. All rights reserved.

The Concept of Limitation of the Vibration Generated by Rail-Vehicles at Railway Stations and Railway Crossings

NASA Astrophysics Data System (ADS)

Adamczyk, Jan; Targosz, Jan

2011-03-01

One of the possibilities of limitation of effects of dynamic influence of the rail-vehicles is the application of the complex objects of vibroinsulation when the mass of the vibroinsulating element is significant, and that is the case of the transporting machines and devices, when the geometric dimensions of the elements of vibroinsulation system are similar to the slab, where the process of modelling of the vibroinsulation mechanism as a discrete system, creates extreme hazards. The article presents the concept of limitation of effects of dynamic influence of the rail-vehicles and tram-vehicles, mainly in the railway tracks located at the railway stations, tram-stops and other engineering structures. The digital model was developed for simulation regarding the propagation of the vibration to the environment. The results of simulation were the basis for development of the vibroinsulation system for the rail-tracks located at the engineering structures such as railway stations, viaducts. The second part of the article presents the approach for controlling of the tension as a function of load of the railway crossing, which was modelled as discrete-continous model. The continuous systems consist of two elements, that is of the support made of elastomer and of the tension members with controlled tension depending on the crossing load. Together with development and more popular application of tension member systems in engineering structures, among others in vibroinsulation systems, it is important to include into calculations and experiments the dynamic loads of the tension member with the mass attached to it. In case of complex objects of vibroinsulation when the mass of the vibroinsulator is significant, and that is the case of the transporting machines and devices, when the geometric dimensions of the elements of vibroinsulation system are similar to the slab, where the process of modelling of the vibroinsulation mechanism as a discrete system, creates extreme hazards when the vibroinsulation is chosen without consideration of its mass. The most serious of the hazards is occurrence of the wave effect of the springdumper elements, since it cannot be assumed that the elements are weight free. In such an elastic element wave phenomena might occur, which in turn might cause that the effect of vibroinsulation is opposite to the expected, that is to the limitation of the dynamic influence on the environment. To prevent such a possibility it is necessary to estimate the natural frequency of the vibroinsulating system based on the consideration of the system as a continuous model and discrete-continuous model. In case when the vibroinsulating elements (rubber or tension member) are characterised by their mass distributed evenly, the frequencies for uniform prismatic systems, e.g. rubber systems, might be estimated based on the method presented in the article. Based on the presented analysis of the proposed control system it can be stated that there exists the possibility of application of that type of control for controlling of the rigidity of the vibroinsulation system of the subgrade. Based on the numerous simulations with different weights of the crossing vehicles and different times of crossing it should be considered to use experimental method for calculation of the PID coefficients for different configurations of the weight and crossing time to dynamically adjust the coefficients based on the information on the speed and weight of the vehicle.

Discovering governing equations from data by sparse identification of nonlinear dynamics

NASA Astrophysics Data System (ADS)

Brunton, Steven

The ability to discover physical laws and governing equations from data is one of humankind's greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technology, including aircraft, combustion engines, satellites, and electrical power. There are many more critical data-driven problems, such as understanding cognition from neural recordings, inferring patterns in climate, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in these efforts. This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time closed-loop feedback control of complex systems. This is joint work with Joshua L. Proctor and J. Nathan Kutz. Video Abstract: https://www.youtube.com/watch?v=gSCa78TIldg

Fluctuations around equilibrium laws in ergodic continuous-time random walks.

PubMed

Schulz, Johannes H P; Barkai, Eli

2015-06-01

We study occupation time statistics in ergodic continuous-time random walks. Under thermal detailed balance conditions, the average occupation time is given by the Boltzmann-Gibbs canonical law. But close to the nonergodic phase, the finite-time fluctuations around this mean are large and nontrivial. They exhibit dual time scaling and distribution laws: the infinite density of large fluctuations complements the Lévy-stable density of bulk fluctuations. Neither of the two should be interpreted as a stand-alone limiting law, as each has its own deficiency: the infinite density has an infinite norm (despite particle conservation), while the stable distribution has an infinite variance (although occupation times are bounded). These unphysical divergences are remedied by consistent use and interpretation of both formulas. Interestingly, while the system's canonical equilibrium laws naturally determine the mean occupation time of the ergodic motion, they also control the infinite and Lévy-stable densities of fluctuations. The duality of stable and infinite densities is in fact ubiquitous for these dynamics, as it concerns the time averages of general physical observables.

Analysis of discrete and continuous distributions of ventilatory time constants from dynamic computed tomography

NASA Astrophysics Data System (ADS)

Doebrich, Marcus; Markstaller, Klaus; Karmrodt, Jens; Kauczor, Hans-Ulrich; Eberle, Balthasar; Weiler, Norbert; Thelen, Manfred; Schreiber, Wolfgang G.

2005-04-01

In this study, an algorithm was developed to measure the distribution of pulmonary time constants (TCs) from dynamic computed tomography (CT) data sets during a sudden airway pressure step up. Simulations with synthetic data were performed to test the methodology as well as the influence of experimental noise. Furthermore the algorithm was applied to in vivo data. In five pigs sudden changes in airway pressure were imposed during dynamic CT acquisition in healthy lungs and in a saline lavage ARDS model. The fractional gas content in the imaged slice (FGC) was calculated by density measurements for each CT image. Temporal variations of the FGC were analysed assuming a model with a continuous distribution of exponentially decaying time constants. The simulations proved the feasibility of the method. The influence of experimental noise could be well evaluated. Analysis of the in vivo data showed that in healthy lungs ventilation processes can be more likely characterized by discrete TCs whereas in ARDS lungs continuous distributions of TCs are observed. The temporal behaviour of lung inflation and deflation can be characterized objectively using the described new methodology. This study indicates that continuous distributions of TCs reflect lung ventilation mechanics more accurately compared to discrete TCs.

Scaling view by the Virtual Nature Systems

NASA Astrophysics Data System (ADS)

Klenov, Valeriy

2010-05-01

The Actual Nature Systems (ANS) continually are under spatial-temporal governing external influences from other systems (Meteorology and Geophysics). This influences provide own spatial temporal patterns on the Earth Nature Systems, which reforms these influences by own manner and scales. These at last three systems belong to the Open Non Equilibrium Nature Systems (ONES). The Geophysics and Meteorology Systems are both governing for the ANS on the Earth. They provide as continual energetic pressure and impacts, and direct Extremes from the both systems to the ANS on Earth surface (earthquakes, storms, and others). The Geodynamics of the ANS is under mixing of influence for both systems, on their scales and on dynamics of their spatial-temporal structures, and by own ANS properties, as the ONES. To select influences of external systems on the Earth systems always is among major tasks of the Geomorphology. Mixing of the Systems scales and dynamics provide specific properties for the memory of Earth system. The memory of the ANS has practical value for their multi-purpose management. The knowledge of these properties is the key for research spatial-temporal GeoDynamics and Trends of Earth Nature Systems. Selection of the influences in time and space requires for special tool, requires elaboration and action of the Virtual Nature Systems (VNS), which are enliven computer doubles for analysis Geodynamics of the ANS. The Experience on the VNS enables to assess influence of each and both external factors on the ANS. It is source of knowledge for regional tectonic and climate oscillations, trends, and threats. Research by the VNS for spatial-temporal dynamics and structures of stochastic regimes of governing systems and processes results in stochastic GeoDynamics of environmental processes, in forming of false trends and blanks in natural records. This ‘wild dance' of 2D stochastic patterns and their interaction each other and generates acting structures of river nets, and of river basins, in multi-layer, multi-scale, and multi-driven structures of surface processes. It results in the Information Loss Law for observed memory of the VNS (and of external drivers) which gradually cut off own Past and distort own history. This view on the GeoDynamics appeared after long time field measurements thousand of terrace levels, hundreds of terrace ranks, and many terrace complexes in river basins of all scales - for the purpose to recognize their deforming by climatic and tectonic spatial-temporal influences. The method for following up of terrace levels along valleys was used in the Geomorphology and Geology for a long time, by linking fragments of level to ‘cycles'. It gradually linked them by heights above riverbed. The understanding of this logical mistake was happened (as insight) during observing from upstream a valley. All fragmental levels downstream were good visible, without chances for their correlation ‘by height' or ‘by number'. Instead of link of fragments, this explains process of river valleys' stochastic GeoDynamics by properties of the ONES (I. Prigogine et al., 1984) to generate oscillations. Is only first view, but later it turned to simple mechanic of Information Loss Law action in the GeoInformatics for Nature Systems (Klenov, 1980, et al.). The Information Loss distorts and destroys natural records (sources for data on the Past exogenous and endogenous rivers). This simple equation was received by multiple measures of terrace rank, and other natural records. It explains origin of false trend in natural records, destroys most own history by stochastic dynamics of the ONES. It prevents to restore of nature records as a memory of the Past. Non-disturbed is only small time between the Past and the Future, which looks like a peak between two non-linear losses. The history of Past (of the ANS, and of external drivers) are destroyed by the ANS. The Future becomes none determined due unknown 2D data of future external influences. However, the effect is the reliable Outstripping Monitoring for impending disasters and of other processes with satisfactory exactness. It was proved by direct validations (by use observed records). The conclusions are as follows: The ILL is mechanics for dissipation the Past and indeterminism the Future of the Nature. Moving back along the VNS' Phase Trajectory changes a view on natural records, and is chance to restore history of the ANS and its external drivers.

Dynamically accumulated dose and 4D accumulated dose for moving tumors

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

Li Heng; Li Yupeng; Zhang Xiaodong

2012-12-15

Purpose: The purpose of this work was to investigate the relationship between dynamically accumulated dose (dynamic dose) and 4D accumulated dose (4D dose) for irradiation of moving tumors, and to quantify the dose uncertainty induced by tumor motion. Methods: The authors established that regardless of treatment modality and delivery properties, the dynamic dose will converge to the 4D dose, instead of the 3D static dose, after multiple deliveries. The bounds of dynamic dose, or the maximum estimation error using 4D or static dose, were established for the 4D and static doses, respectively. Numerical simulations were performed (1) to prove themore » principle that for each phase, after multiple deliveries, the average number of deliveries for any given time converges to the total number of fractions (K) over the number of phases (N); (2) to investigate the dose difference between the 4D and dynamic doses as a function of the number of deliveries for deliveries of a 'pulsed beam'; and (3) to investigate the dose difference between 4D dose and dynamic doses as a function of delivery time for deliveries of a 'continuous beam.' A Poisson model was developed to estimate the mean dose error as a function of number of deliveries or delivered time for both pulsed beam and continuous beam. Results: The numerical simulations confirmed that the number of deliveries for each phase converges to K/N, assuming a random starting phase. Simulations for the pulsed beam and continuous beam also suggested that the dose error is a strong function of the number of deliveries and/or total deliver time and could be a function of the breathing cycle, depending on the mode of delivery. The Poisson model agrees well with the simulation. Conclusions: Dynamically accumulated dose will converge to the 4D accumulated dose after multiple deliveries, regardless of treatment modality. Bounds of the dynamic dose could be determined using quantities derived from 4D doses, and the mean dose difference between the dynamic dose and 4D dose as a function of number of deliveries and/or total deliver time was also established.« less

Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors

PubMed Central

Zhang, Yu Shrike; Aleman, Julio; Shin, Su Ryon; Kim, Duckjin; Mousavi Shaegh, Seyed Ali; Massa, Solange; Riahi, Reza; Chae, Sukyoung; Hu, Ning; Avci, Huseyin; Zhang, Weijia; Silvestri, Antonia; Sanati Nezhad, Amir; Manbohi, Ahmad; De Ferrari, Fabio; Polini, Alessandro; Calzone, Giovanni; Shaikh, Noor; Alerasool, Parissa; Budina, Erica; Kang, Jian; Bhise, Nupura; Pourmand, Adel; Skardal, Aleksander; Shupe, Thomas; Bishop, Colin E.; Dokmeci, Mehmet Remzi; Atala, Anthony; Khademhosseini, Ali

2017-01-01

Organ-on-a-chip systems are miniaturized microfluidic 3D human tissue and organ models designed to recapitulate the important biological and physiological parameters of their in vivo counterparts. They have recently emerged as a viable platform for personalized medicine and drug screening. These in vitro models, featuring biomimetic compositions, architectures, and functions, are expected to replace the conventional planar, static cell cultures and bridge the gap between the currently used preclinical animal models and the human body. Multiple organoid models may be further connected together through the microfluidics in a similar manner in which they are arranged in vivo, providing the capability to analyze multiorgan interactions. Although a wide variety of human organ-on-a-chip models have been created, there are limited efforts on the integration of multisensor systems. However, in situ continual measuring is critical in precise assessment of the microenvironment parameters and the dynamic responses of the organs to pharmaceutical compounds over extended periods of time. In addition, automated and noninvasive capability is strongly desired for long-term monitoring. Here, we report a fully integrated modular physical, biochemical, and optical sensing platform through a fluidics-routing breadboard, which operates organ-on-a-chip units in a continual, dynamic, and automated manner. We believe that this platform technology has paved a potential avenue to promote the performance of current organ-on-a-chip models in drug screening by integrating a multitude of real-time sensors to achieve automated in situ monitoring of biophysical and biochemical parameters. PMID:28265064

Universality of phase transition dynamics: topological defects from symmetry breaking

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

Zurek, Wojciech H.; Del Campo, Adolfo

In the course of a non-equilibrium continuous phase transition, the dynamics ceases to be adiabatic in the vicinity of the critical point as a result of the critical slowing down (the divergence of the relaxation time in the neighborhood of the critical point). This enforces a local choice of the broken symmetry and can lead to the formation of topological defects. The Kibble-Zurek mechanism (KZM) was developed to describe the associated nonequilibrium dynamics and to estimate the density of defects as a function of the quench rate through the transition. During recent years, several new experiments investigating formation of defectsmore » in phase transitions induced by a quench both in classical and quantum mechanical systems were carried out. At the same time, some established results were called into question. We review and analyze the Kibble-Zurek mechanism focusing in particular on this surge of activity, and suggest possible directions for further progress.« less

Extended slow dynamical regime close to the many-body localization transition

NASA Astrophysics Data System (ADS)

Luitz, David J.; Laflorencie, Nicolas; Alet, Fabien

2016-02-01

Many-body localization is characterized by a slow logarithmic growth of the entanglement entropy after a global quantum quench while the local memory of an initial density imbalance remains at infinite time. We investigate how much the proximity of a many-body localized phase can influence the dynamics in the delocalized ergodic regime where thermalization is expected. Using an exact Krylov space technique, the out-of-equilibrium dynamics of the random-field Heisenberg chain is studied up to L =28 sites, starting from an initially unentangled high-energy product state. Within most of the delocalized phase, we find a sub-ballistic entanglement growth S (t ) ∝t1 /z with a disorder-dependent exponent z ≥1 , in contrast with the pure ballistic growth z =1 of clean systems. At the same time, anomalous relaxation is also observed for the spin imbalance I (t ) ∝t-ζ with a continuously varying disorder-dependent exponent ζ , vanishing at the transition. This provides a clear experimental signature for detecting this nonconventional regime.

Real-time feedback control of twin-screw wet granulation based on image analysis.

PubMed

Madarász, Lajos; Nagy, Zsombor Kristóf; Hoffer, István; Szabó, Barnabás; Csontos, István; Pataki, Hajnalka; Démuth, Balázs; Szabó, Bence; Csorba, Kristóf; Marosi, György

2018-06-04

The present paper reports the first dynamic image analysis-based feedback control of continuous twin-screw wet granulation process. Granulation of the blend of lactose and starch was selected as a model process. The size and size distribution of the obtained particles were successfully monitored by a process camera coupled with an image analysis software developed by the authors. The validation of the developed system showed that the particle size analysis tool can determine the size of the granules with an error of less than 5 µm. The next step was to implement real-time feedback control of the process by controlling the liquid feeding rate of the pump through a PC, based on the real-time determined particle size results. After the establishment of the feedback control, the system could correct different real-life disturbances, creating a Process Analytically Controlled Technology (PACT), which guarantees the real-time monitoring and controlling of the quality of the granules. In the event of changes or bad tendencies in the particle size, the system can automatically compensate the effect of disturbances, ensuring proper product quality. This kind of quality assurance approach is especially important in the case of continuous pharmaceutical technologies. Copyright © 2018 Elsevier B.V. All rights reserved.

PMU-Aided Voltage Security Assessment for a Wind Power Plant

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

Jiang, Huaiguang; Zhang, Yingchen; Zhang, Jun Jason

2015-10-05

Because wind power penetration levels in electric power systems are continuously increasing, voltage stability is a critical issue for maintaining power system security and operation. The traditional methods to analyze voltage stability can be classified into two categories: dynamic and steady-state. Dynamic analysis relies on time-domain simulations of faults at different locations; however, this method needs to exhaust faults at all locations to find the security region for voltage at a single bus. With the widely located phasor measurement units (PMUs), the Thevenin equivalent matrix can be calculated by the voltage and current information collected by the PMUs. This papermore » proposes a method based on a Thevenin equivalent matrix to identify system locations that will have the greatest impact on the voltage at the wind power plant's point of interconnection. The number of dynamic voltage stability analysis runs is greatly reduced by using the proposed method. The numerical results demonstrate the feasibility, effectiveness, and robustness of the proposed approach for voltage security assessment for a wind power plant.« less

Effects of dispersal on total biomass in a patchy, heterogeneous system: analysis and experiment.

USGS Publications Warehouse

Zhang, Bo; Liu, Xin; DeAngelis, Donald L.; Ni, Wei-Ming; Wang, G Geoff

2015-01-01

An intriguing recent result from mathematics is that a population diffusing at an intermediate rate in an environment in which resources vary spatially will reach a higher total equilibrium biomass than the population in an environment in which the same total resources are distributed homogeneously. We extended the current mathematical theory to apply to logistic growth and also showed that the result applies to patchy systems with dispersal among patches, both for continuous and discrete time. This allowed us to make specific predictions, through simulations, concerning the biomass dynamics, which were verified by a laboratory experiment. The experiment was a study of biomass growth of duckweed (Lemna minor Linn.), where the resources (nutrients added to water) were distributed homogeneously among a discrete series of water-filled containers in one treatment, and distributed heterogeneously in another treatment. The experimental results showed that total biomass peaked at an intermediate, relatively low, diffusion rate, higher than the total carrying capacity of the system and agreeing with the simulation model. The implications of the experiment to dynamics of source, sink, and pseudo-sink dynamics are discussed.

Load evaluation of the da Vinci surgical system for transoral robotic surgery.

PubMed

Fujiwara, Kazunori; Fukuhara, Takahiro; Niimi, Koji; Sato, Takahiro; Kitano, Hiroya

2015-12-01

Transoral robotic surgery, performed with the da Vinci surgical system (da Vinci), is a surgical approach for benign and malignant lesions of the oral cavity and laryngopharynx. It provides several unique advantages, which include a 3-dimensional magnified view and ability to see and work around curves or angles. However, the current da Vinci surgical system does not provide haptic feedback. This is problematic because the potential risks specific to the transoral use of the da Vinci include tooth injury, mucosal laceration, ocular injury and mandibular fracture. To assess the potential for intraoperative injuries, we measured the load of the endoscope and the instrument of the da Vinci Si surgical system. We pressed the endoscope and instrument of the da Vinci Si against Load cell six times each and measured the dynamic load and the time-to-maximum load. We also struck the da Vinci Si endoscope and instrument against the Load cell six times each and measured the impact load. The maximum dynamic load was 7.27 ± 1.31 kg for the endoscope and 1.90 ± 0.72 for the instrument. The corresponding time-to-maximum loads were 1.72 ± 0.22 and 1.29 ± 0.34 s, but the impact loads were significantly lower than the dynamic load. It remains possible that a major load is exerted on adjacent structures by continuous contact with the endoscope and instrument of da Vinci Si. However, there is a minor delay in reaching the maximum load. Careful monitoring by an on-site assistant may, therefore, help prevent contiguous injury.