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Sample records for distributed time-varying delays

  1. Adaptive synchronization of neural networks with time-varying delay and distributed delay

    NASA Astrophysics Data System (ADS)

    Wang, Kai; Teng, Zhidong; Jiang, Haijun

    2008-01-01

    In this paper, the adaptive synchronization of neural networks with time-varying delay and distributed delay is discussed. Based on the LaSalle invariant principle of functional differential equations and the adaptive feedback control technique, some sufficient conditions for adaptive synchronization of such a system are obtained. Finally, a numerical example is given to show the effectiveness of the proposed synchronization method.

  2. Analysis of distributed power control under constant and time-varying delays

    NASA Astrophysics Data System (ADS)

    Campos-delgado, Daniel U.; Luna-rivera, J. Martin; Bonilla, Isela

    2013-10-01

    This work studies the distributed power control algorithm proposed in 1993 by Foschini-Miljanic, standardised for universal mobile telecommunication systems. Continuous and discrete time versions of this algorithm are analysed. First, the stability of the distributed power allocation schemes was studied, where sufficient conditions to guarantee stability and convergence to a desired quality of service were provided. In this study, the channel gains are assumed to be slowly time-varying or piece-wise constant. For closed-loop control, a proportional controller is then employed under integral action in order to achieve good tracking despite time-varying and unknown channel gains. Next, the effects of constant and time-varying time delays in the closed-loop structure are studied. Explicit stability regions for the control gains in the Foschini-Miljanic scheme are derived for both the continuous and discrete-time versions of the algorithm, under constant and time-varying delays. For time-varying scenario, the resulting stability regions do not impose limitations on the rate change of the time-varying profiles. A comprehensive evaluation using simulations is performed to validate the analytical derivations described in the paper.

  3. Delay-dependent exponential passivity of uncertain cellular neural networks with discrete and distributed time-varying delays.

    PubMed

    Du, Yuanhua; Zhong, Shouming; Xu, Jia; Zhou, Nan

    2015-05-01

    This paper is concerned with the delay-dependent exponential passivity analysis issue for uncertain cellular neural networks with discrete and distributed time-varying delays. By decomposing the delay interval into multiple equidistant subintervals and multiple nonuniform subintervals, a suitable augmented Lyapunov-Krasovskii functionals are constructed on these intervals. A set of novel sufficient conditions are obtained to guarantee the exponential passivity analysis issue for the considered system. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed results.

  4. Adaptive synchronization under almost every initial data for stochastic neural networks with time-varying delays and distributed delays

    NASA Astrophysics Data System (ADS)

    Zhu, Quanxin; Cao, Jinde

    2011-04-01

    This paper is concerned with the adaptive synchronization problem for a class of stochastic delayed neural networks. Based on the LaSalle invariant principle of stochastic differential delay equations and the stochastic analysis theory as well as the adaptive feedback control technique, a linear matrix inequality approach is developed to derive some novel sufficient conditions achieving complete synchronization of unidirectionally coupled stochastic delayed neural networks. In particular, the synchronization criterion considered in this paper is the globally almost surely asymptotic stability of the error dynamical system, which has seldom been applied to investigate the synchronization problem. Moreover, the delays proposed in this paper are time-varying delays and distributed delays, which have rarely been used to study the synchronization problem for coupled stochastic delayed neural networks. Therefore, the results obtained in this paper are more general and useful than those given in the previous literature. Finally, two numerical examples and their simulations are provided to demonstrate the effectiveness of the theoretical results.

  5. Almost periodic solutions for a memristor-based neural networks with leakage, time-varying and distributed delays.

    PubMed

    Jiang, Ping; Zeng, Zhigang; Chen, Jiejie

    2015-08-01

    In this paper, we study the existence and global exponential stability of almost periodic solution for memristor-based neural networks with leakage, time-varying and distributed delays. Using a new Lyapunov function method, we prove that this delayed neural network has a unique almost periodic solution, which is globally exponentially stable. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for this delayed neural network with periodic coefficients (or constant coefficients).

  6. Global exponential stability of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays.

    PubMed

    Song, Qiankun; Yan, Huan; Zhao, Zhenjiang; Liu, Yurong

    2016-09-01

    This paper investigates the stability problem for a class of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays. By employing the idea of vector Lyapunov function, M-matrix theory and inequality technique, several sufficient conditions are obtained to ensure the global exponential stability of equilibrium point. When the impulsive effects are not considered, several sufficient conditions are also given to guarantee the existence, uniqueness and global exponential stability of equilibrium point. Two examples are given to illustrate the effectiveness and lower level of conservatism of the proposed criteria in comparison with some existing results.

  7. Design of delay-dependent state estimator for discrete-time recurrent neural networks with interval discrete and infinite-distributed time-varying delays.

    PubMed

    Liao, Chin-Wen; Lu, Chien-Yu

    2011-06-01

    The state estimation problem for discrete-time recurrent neural networks with both interval discrete and infinite-distributed time-varying delays is studied in this paper, where interval discrete time-varying delay is in a given range. The activation functions are assumed to be globally Lipschitz continuous. A delay-dependent condition for the existence of state estimators is proposed based on new bounding techniques. Via solutions to certain linear matrix inequalities, general full-order state estimators are designed that ensure globally asymptotic stability. The significant feature is that no inequality is needed for seeking upper bounds for the inner product between two vectors, which can reduce the conservatism of the criterion by employing the new bounding techniques. Two illustrative examples are given to demonstrate the effectiveness and applicability of the proposed approach.

  8. Delay-dependent fuzzy static output feedback control for discrete-time fuzzy stochastic systems with distributed time-varying delays.

    PubMed

    Xia, ZhiLe; Li, JunMin; Li, JiangRong

    2012-11-01

    This paper is concerned with the delay-dependent H(∞) fuzzy static output feedback control scheme for discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems with distributed time-varying delays. To begin with, the T-S fuzzy stochastic system is transformed to an equivalent switching fuzzy stochastic system. Then, based on novel matrix decoupling technique, improved free-weighting matrix technique and piecewise Lyapunov-Krasovskii function (PLKF), a new delay-dependent H(∞) fuzzy static output feedback controller design approach is first derived for the switching fuzzy stochastic system. Some drawbacks existing in the previous papers such as matrix equalities constraint, coordinate transformation, the same output matrices, diagonal structure constraint on Lyapunov matrices and BMI problem have been eliminated. Since only a set of LMIs is involved, the controller parameters can be solved directly by the Matlab LMI toolbox. Finally, two examples are provided to illustrate the validity of the proposed method.

  9. Dissipativity analysis of stochastic memristor-based recurrent neural networks with discrete and distributed time-varying delays.

    PubMed

    Radhika, Thirunavukkarasu; Nagamani, Gnaneswaran

    2016-01-01

    In this paper, based on the knowledge of memristor-based recurrent neural networks (MRNNs), the model of the stochastic MRNNs with discrete and distributed delays is established. In real nervous systems and in the implementation of very large-scale integration (VLSI) circuits, noise is unavoidable, which leads to the stochastic model of the MRNNs. In this model, the delay interval is decomposed into two subintervals by using the tuning parameter α such that 0 < α < 1. By constructing proper Lyapunov-Krasovskii functional and employing direct delay decomposition technique, several sufficient conditions are given to guarantee the dissipativity and passivity of the stochastic MRNNs with discrete and distributed delays in the sense of Filippov solutions. Using the stochastic analysis theory and Itô's formula for stochastic differential equations, we establish sufficient conditions for dissipativity criterion. The dissipativity and passivity conditions are presented in terms of linear matrix inequalities, which can be easily solved by using Matlab Tools. Finally, three numerical examples with simulations are presented to demonstrate the effectiveness of the theoretical results.

  10. New stability conditions for nonlinear time varying delay systems

    NASA Astrophysics Data System (ADS)

    Elmadssia, S.; Saadaoui, K.; Benrejeb, M.

    2016-07-01

    In this paper, new practical stability conditions for a class of nonlinear time varying delay systems are proposed. The study is based on the use of a specific state space description, known as the Benrejeb characteristic arrow form matrix, and aggregation techniques to obtain delay-dependent stability conditions. Application of this method to delayed Lurie-Postnikov nonlinear systems is given. Illustrative examples are presented to show the effectiveness of the proposed approach.

  11. Exponential stabilization of neural networks with various activation functions and mixed time-varying delays.

    PubMed

    Phat, V N; Trinh, H

    2010-07-01

    This paper presents some results on the global exponential stabilization for neural networks with various activation functions and time-varying continuously distributed delays. Based on augmented time-varying Lyapunov-Krasovskii functionals, new delay-dependent conditions for the global exponential stabilization are obtained in terms of linear matrix inequalities. A numerical example is given to illustrate the feasibility of our results.

  12. Distributed Tracking with Consensus on Noisy Time-varying Graphs: Convergence Results and Applications

    DTIC Science & Technology

    2010-12-01

    R. M. Murray, “ Consensus problems in networks of agents with switching topology and time -delays,” IEEE Trans. Autom. Control, vol. 49, no. 9, pp... Distributed Tracking with Consensus on Noisy Time -varying Graphs: Convergence Results and Applications Sudharman K. Jayaweera and Y. Ruan ECE... distributed tracking with consensus on a time -varying graph with noisy communications links and sensing constraints. We develop a framework to handle the

  13. Stability Analysis of Uncertain Switched Delay Systems: A Time-Varying Lyapunov Function Approach

    NASA Astrophysics Data System (ADS)

    Huang, Ganji; Luo, Shixian; Chen, Wu-Hua

    Exponential stability for switched systems with uncertain parameters and time-varying delay is considered in this paper. The parametric uncertainties are assumed to be time-varying and norm-bounded. By introducing a novel piecewise time-varying Lyapunov function and using Razumikhin techniques, some linear matrix inequalities (LMIs) stability criteria are derived to guarantee the exponential stability of the switched delay systems. A numerical example is presented to demonstrate the effectiveness of the proposed method.

  14. Reliable dissipative control of high-speed train with probabilistic time-varying delays

    NASA Astrophysics Data System (ADS)

    Kaviarasan, B.; Sakthivel, R.; Shi, Y.

    2016-12-01

    This paper investigates the reliable dissipative control problem for high-speed trains (HSTs) under probabilistic time-varying sampling with a known upper bound on the sampling intervals. In particular, random variables obeying the Bernoulli distribution are considered to account for the probabilistic time-varying delays. Based on Lyapunov-Krasovskii functional approach which considers full use of the available information about actual sampling pattern, a new set of sufficient condition is established to guarantee that the HST can well track the desired speed and the relative spring displacement between the two neighbouring carriages is asymptotically stable and the corresponding error system is strictly ?-dissipative. The existence condition of the dissipativity-based reliable sampled-data controller is obtained in terms of a set of linear matrix inequalities which are delay-distribution-dependent, i.e. the solvability of the condition depends on not only the variation range of the delay but also the probability distribution of it. Moreover, different control processes for the HST system can be obtained from the proposed design procedure and hence it can reduce the time and cost. Finally, the effectiveness and benefits of the proposed control law is demonstrated through a numerical example by taking the experimental values of Japan Shinkansen HST.

  15. Delay-dependent global exponential robust stability for delayed cellular neural networks with time-varying delay.

    PubMed

    Liu, Pin-Lin

    2013-11-01

    This paper investigates a class of delayed cellular neural networks (DCNN) with time-varying delay. Based on the Lyapunov-Krasovski functional and integral inequality approach (IIA), a uniformly asymptotic stability criterion in terms of only one simple linear matrix inequality (LMI) is addressed, which guarantees stability for such time-varying delay systems. This LMI can be easily solved by convex optimization techniques. Unlike previous methods, the upper bound of the delay derivative is taken into consideration, even if larger than or equal to 1. It is proven that results obtained are less conservative than existing ones. Four numerical examples illustrate efficacy of the proposed methods.

  16. Exponential synchronization of a class of neural networks with time-varying delays.

    PubMed

    Cheng, Chao-Jung; Liao, Teh-Lu; Yan, Jun-Juh; Hwang, Chi-Chuan

    2006-02-01

    This paper aims to present a synchronization scheme for a class of delayed neural networks, which covers the Hopfield neural networks and cellular neural networks with time-varying delays. A feedback control gain matrix is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory, and its exponential synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalues on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. Both the cellular neural networks and Hopfield neural networks with time-varying delays are given as examples for illustration.

  17. Consensus for Linear Multiagent Systems With Time-Varying Delays: A Frequency Domain Perspective.

    PubMed

    Chen, Yuanye; Shi, Yang

    2016-07-27

    This paper investigates the consensus problem for multiagent systems with time-varying delays. The bounded delays can be arbitrarily fast time-varying. The communication topology is assumed to be undirected and fixed. With general linear dynamics under average state feedback protocols, the consensus problem is then transformed into the robust control problem. Further, sufficient frequency domain criteria are established in terms of small gain theorem by analyzing the delay dependent gains for both continuous-time and discrete-time systems. The controller synthesis problems can be solved by applying the frequency domain design methods. Numerical examples are demonstrated to verify the effectiveness of the proposed approaches.

  18. Containment consensus with measurement noises and time-varying communication delays

    NASA Astrophysics Data System (ADS)

    Zhou, Feng; Wang, Zheng-Jie; Fan, Ning-Jun

    2015-02-01

    In this paper, we consider the containment consensus control problem for multi-agent systems with measurement noises and time-varying communication delays under directed networks. By using stochastic analysis tools and algebraic graph theory, we prove that the followers can converge to the convex hull spanned by the leaders in the sense of mean square if the allowed upper bound of the time-varying delays satisfies a certain sufficient condition. Moreover, the time-varying delays are asymmetric for each follower agent, and the time-delay-dependent consensus condition is derived. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results. Project supported by the National Natural Science Foundation of China (Grant No. 11102019), the Aeronautical Science Foundation of China (Grant No. 2013ZC72006), and the Research Foundation of Beijing Institute of Technology, China.

  19. Stability of uncertain impulsive complex-variable chaotic systems with time-varying delays.

    PubMed

    Zheng, Song

    2015-09-01

    In this paper, the robust exponential stabilization of uncertain impulsive complex-variable chaotic delayed systems is considered with parameters perturbation and delayed impulses. It is assumed that the considered complex-variable chaotic systems have bounded parametric uncertainties together with the state variables on the impulses related to the time-varying delays. Based on the theories of adaptive control and impulsive control, some less conservative and easily verified stability criteria are established for a class of complex-variable chaotic delayed systems with delayed impulses. Some numerical simulations are given to validate the effectiveness of the proposed criteria of impulsive stabilization for uncertain complex-variable chaotic delayed systems.

  20. Mean square stability of uncertain stochastic BAM neural networks with interval time-varying delays.

    PubMed

    Wu, Haixia; Liao, Xiaofeng; Feng, Wei; Guo, Songtao

    2012-10-01

    The robust asymptotic stability analysis for uncertain BAM neural networks with both interval time-varying delays and stochastic disturbances is considered. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which takes into account the ranges for delays, some new stability criteria are established to guarantee the delayed BAM neural networks to be robustly asymptotically stable in the mean square. Unlike the most existing mean square stability conditions for BAM neural networks, the supplementary requirements that the time derivatives of time-varying delays must be smaller than 1 are released and the lower bounds of time varying delays are not restricted to be 0. Furthermore, in the proposed scheme, the stability conditions are delay-range-dependent and rate-dependent/independent. As a result, the new criteria are applicable to both fast and slow time-varying delays. Three numerical examples are given to illustrate the effectiveness of the proposed criteria.

  1. An observer for a velocity-sensorless VTOL aircraft with time-varying measurement delay

    NASA Astrophysics Data System (ADS)

    He, Qing; Liu, Jinkun

    2016-02-01

    This paper presents a kind of state observer for a velocity-sensorless vertical take-off and landing (VTOL) aircraft with bounded time-varying delay in its measurement outputs. The proposed observer predicts current state variables based on the delayed outputs, and the estimated state variables can be considered as the actual state variables for feedback control scheme design. Since the delay is time-varying, compared to the constant delay case, different analysis theory must be employed. Under the assumption that the delays are identical for different outputs and bounded input, the asymptotic convergence property of the estimation error based on Lyapunov-Razumikhin theorem is proved. A relative large time delay for the VTOL aircraft in the outputs has been tested in the numerical simulation, and the simulation results show the effectiveness of the proposed observer.

  2. Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays.

    PubMed

    Wen, Shiping; Bao, Gang; Zeng, Zhigang; Chen, Yiran; Huang, Tingwen

    2013-12-01

    This paper deals with the problem of global exponential synchronization of a class of memristor-based recurrent neural networks with time-varying delays based on the fuzzy theory and Lyapunov method. First, a memristor-based recurrent neural network is designed. Then, considering the state-dependent properties of the memristor, a new fuzzy model employing parallel distributed compensation (PDC) gives a new way to analyze the complicated memristor-based neural networks with only two subsystems. Comparisons between results in this paper and in the previous ones have been made. They show that the results in this paper improve and generalized the results derived in the previous literature. An example is also given to illustrate the effectiveness of the results.

  3. New Stabilization for Dynamical System with Two Additive Time-Varying Delays

    PubMed Central

    Yang, Fan; Chen, Xiaozhou

    2014-01-01

    This paper provides a new delay-dependent stabilization criterion for systems with two additive time-varying delays. The novel functional is constructed, a tighter upper bound of the derivative of the Lyapunov functional is obtained. These results have advantages over some existing ones because the combination of the delay decomposition technique and the reciprocally convex approach. Two examples are provided to demonstrate the less conservatism and effectiveness of the results in this paper. PMID:24701159

  4. Improved results for linear discrete-time systems with an interval time-varying input delay

    NASA Astrophysics Data System (ADS)

    Zhang, Jin; Peng, Chen; Zheng, Min

    2016-01-01

    This paper addresses the problem of delay-dependent stability analysis and controller synthesis for a discrete-time system with an interval time-varying input delay. By dividing delay interval into multiple parts and constructing a novel piecewise Lyapunov-Krasovskii functional, an improved delay-partitioning-dependent stability criterion and a stabilisation criterion are obtained in terms of matrix inequalities. Compared with some existing results, since a tighter bounding inequality is employed to deal with the integral items, our results depend on less number of linear matrix inequality scalar decision variables while obtaining same or better allowable upper delay bound. Numerical examples show the effectiveness of the proposed method.

  5. Exponential convergence analysis of uncertain genetic regulatory networks with time-varying delays.

    PubMed

    Wang, Wenqin; Nguang, Sing Kiong; Zhong, Shouming; Liu, Feng

    2014-09-01

    This study is concerned with the problem of exponential convergence of uncertain genetic regulatory networks with time-varying delays in the case of the unknown equilibrium point. The system׳s uncertainties are modeled as a structured linear fractional form. Novel stability criteria are obtained by using the lower bound lemma together with Jensen inequality lemma. In order to get rid of the rigorous constraint that the derivatives of time-varying delays must be less than one, a new approach is introduced by improving Lyapunov-Krasovskii functional rather than using the traditional free-weighting matrices. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results.

  6. Synchronization of Uncertain Euler-Lagrange Systems With Uncertain Time-Varying Communication Delays.

    PubMed

    Klotz, Justin R; Obuz, Serhat; Kan, Zhen; Dixon, Warren E

    2017-02-07

    A decentralized controller is designed for leader-based synchronization of communication-delayed networked agents. The agents have heterogeneous dynamics modeled by uncertain, nonlinear Euler-Lagrange equations of motion affected by heterogeneous, unknown, exogenous disturbances. The developed controller requires only one-hop (delayed) communication from network neighbors and the communication delays are assumed to be heterogeneous, uncertain, and time-varying. Each agent uses an estimate of communication delay to provide feedback of estimated recent tracking error. Simulation results are provided to demonstrate the improved performance of the developed controller over other popular control designs.

  7. Complete stability of cellular neural networks with unbounded time-varying delays.

    PubMed

    Wang, Lili; Chen, Tianping

    2012-12-01

    In this paper, we are concerned with the delayed cellular neural networks (DCNNs) in the case that the time-varying delays are unbounded. Under some conditions, it shows that the DCNNs can exhibit 3(n) equilibrium points. Then, we track the dynamics of u(t)(t>0) in two cases with respect to different types of subset regions in which u(0) is located. It concludes that every solution trajectory u(t) would converge to one of the equilibrium points despite the time-varying delays, that is, the delayed cellular neural networks are completely stable. The method is novel and the results obtained extend the existing ones. In addition, two illustrative examples are presented to verify the effectiveness of our results.

  8. Stability Analysis of Networked Control Systems With Aperiodic Sampling and Time-Varying Delay.

    PubMed

    Chen, Jie; Meng, Su; Sun, Jian

    2016-12-01

    This paper addresses the stability of networked control systems with aperiodic sampling and time-varying network-induced delay. The sampling intervals are assumed to vary within a known interval. The transmission delay is assumed to belong to a given interval. The closed-loop system is first converted to a discrete-time system with multiple time-varying delays and norm-bounded uncertainties resulting from the variation of the sampling intervals. And then, it is transformed into a delay-free system being form of an interconnection of two subsystems. By utilizing scaled small gain theorem, an asymptotic stability criterion for the closed-loop system is proposed in terms of linear matrix inequality. Finally, numerical examples demonstrate the effectiveness of the proposed method and its advantages over existing methods.

  9. Nonlinear adaptive control for teleoperation systems with symmetrical and unsymmetrical time-varying delay

    NASA Astrophysics Data System (ADS)

    Islam, S.; Liu, P. X.; El Saddik, A.

    2015-12-01

    The stability and trajectory tracking control problem of passive teleoperation systems with the presence of the symmetrical and unsymmetrical time-varying communication delay is addressed in this paper. The proposed teleoperator is designed by coupling local and remote sites by delaying position signals of the master and slave manipulator. The design also comprises local proportional and derivative signals with nonlinear adaptive terms to cope with parametric uncertainty associated with the master and slave dynamics. The Lyapunov-Krasovskii function is employed to establish stability conditions for the closed-loop teleoperators under both symmetrical and unsymmetrical time-varying communication delay. These delay-dependent conditions allow the designer to estimate the control gains a priori in order to achieve asymptotic property of the position, velocity and synchronisation errors of the master and slave systems. Finally, simulation results along with comparative studies are presented to illustrate the effectiveness of the proposed method.

  10. New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations.

    PubMed

    Cai, Zuowei; Huang, Lihong; Zhang, Lingling

    2015-05-01

    This paper investigates the problem of exponential synchronization of time-varying delayed neural networks with discontinuous neuron activations. Under the extended Filippov differential inclusion framework, by designing discontinuous state-feedback controller and using some analytic techniques, new testable algebraic criteria are obtained to realize two different kinds of global exponential synchronization of the drive-response system. Moreover, we give the estimated rate of exponential synchronization which depends on the delays and system parameters. The obtained results extend some previous works on synchronization of delayed neural networks not only with continuous activations but also with discontinuous activations. Finally, numerical examples are provided to show the correctness of our analysis via computer simulations. Our method and theoretical results have a leading significance in the design of synchronized neural network circuits involving discontinuous factors and time-varying delays.

  11. Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals.

    PubMed

    Rajchakit, G; Saravanakumar, R; Ahn, Choon Ki; Karimi, Hamid Reza

    2017-02-01

    This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples.

  12. Improved conditions for global exponential stability of recurrent neural networks with time-varying delays.

    PubMed

    Zeng, Zhigang; Wang, Jun

    2006-05-01

    This paper presents new theoretical results on global exponential stability of recurrent neural networks with bounded activation functions and time-varying delays. The stability conditions depend on external inputs, connection weights, and time delays of recurrent neural networks. Using these results, the global exponential stability of recurrent neural networks can be derived, and the estimated location of the equilibrium point can be obtained. As typical representatives, the Hopfield neural network (HNN) and the cellular neural network (CNN) are examined in detail.

  13. Almost automorphic solutions for shunting inhibitory cellular neural networks with time-varying delays.

    PubMed

    Xu, Changjin; Liao, Maoxin

    2015-01-01

    This paper is concerned with the shunting inhibitory cellular neural networks with time-varying delays. Under some suitable conditions, we establish some criteria on the existence and global exponential stability of the almost automorphic solutions of the networks. Numerical simulations are given to support the theoretical findings.

  14. Passivity analysis of uncertain stochastic neural networks with time-varying delays and Markovian jumping parameters.

    PubMed

    Ali, M Syed; Rani, M Esther

    2015-01-01

    This paper investigates the problem of robust passivity of uncertain stochastic neural networks with time-varying delays and Markovian jumping parameters. To reflect most of the dynamical behaviors of the system, both parameter uncertainties and stochastic disturbances are considered; stochastic disturbances are given in the form of a Brownian motion. By utilizing the Lyapunov functional method, the Itô differential rule, and matrix analysis techniques, we establish a sufficient criterion such that, for all admissible parameter uncertainties and stochastic disturbances, the stochastic neural network is robustly passive in the sense of expectation. A delay-dependent stability condition is formulated, in which the restriction of the derivative of the time-varying delay should be less than 1 is removed. The derived criteria are expressed in terms of linear matrix inequalities that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results.

  15. Stabilisation for switched linear systems with time-varying delay and input saturation

    NASA Astrophysics Data System (ADS)

    Chen, Yonggang; Fei, Shumin; Zhang, Kanjian

    2014-03-01

    This article investigates the stabilisation problems for continuous-time and discrete-time switched systems with time-varying delay and saturated control input. Based on dwell time switching signals and multiple Lyapunov functional method, stabilisation conditions are well obtained in the context of linear matrix inequalities. To estimate attractive regions as large as possible, the feasibility problems are translated into optimisation problems. In addition, the corresponding results are presented for linear time-delay systems and switched delay-free systems, which improve and supplement some existing ones in the literature. Finally, numerical examples and simulations are given to illustrate the effectiveness and values of the proposed results.

  16. Stability of Nonlinear Systems with Unknown Time-varying Feedback Delay

    NASA Astrophysics Data System (ADS)

    Chunodkar, Apurva A.; Akella, Maruthi R.

    2013-12-01

    This paper considers the problem of stabilizing a class of nonlinear systems with unknown bounded delayed feedback wherein the time-varying delay is 1) piecewise constant 2) continuous with a bounded rate. We also consider application of these results to the stabilization of rigid-body attitude dynamics. In the first case, the time-delay in feedback is modeled specifically as a switch among an arbitrarily large set of unknown constant values with a known strict upper bound. The feedback is a linear function of the delayed states. In the case of linear systems with switched delay feedback, a new sufficiency condition for average dwell time result is presented using a complete type Lyapunov-Krasovskii (L-K) functional approach. Further, the corresponding switched system with nonlinear perturbations is proven to be exponentially stable inside a well characterized region of attraction for an appropriately chosen average dwell time. In the second case, the concept of the complete type L-K functional is extended to a class of nonlinear time-delay systems with unknown time-varying time-delay. This extension ensures stability robustness to time-delay in the control design for all values of time-delay less than the known upper bound. Model-transformation is used in order to partition the nonlinear system into a nominal linear part that is exponentially stable with a bounded perturbation. We obtain sufficient conditions which ensure exponential stability inside a region of attraction estimate. A constructive method to evaluate the sufficient conditions is presented together with comparison with the corresponding constant and piecewise constant delay. Numerical simulations are performed to illustrate the theoretical results of this paper.

  17. Robust stabilisation of time-varying delay systems with probabilistic uncertainties

    NASA Astrophysics Data System (ADS)

    Jiang, Ning; Xiong, Junlin; Lam, James

    2016-09-01

    For robust stabilisation of time-varying delay systems, only sufficient conditions are available to date. A natural question is as follows: if the existing sufficient conditions are not satisfied, and hence no controllers can be found, what can one do to improve the stability performance of time-varying delay systems? This question is addressed in this paper when there is a probabilistic structure on the parameter uncertainty set. A randomised algorithm is proposed to design a state-feedback controller, which stabilises the system over the uncertainty domain in a probabilistic sense. The capability of the designed controller is quantified by the probability of stability of the resulting closed-loop system. The accuracy of the solution obtained from the randomised algorithm is also analysed. Finally, numerical examples are used to illustrate the effectiveness and advantages of the developed controller design approach.

  18. Global exponential stability for switched memristive neural networks with time-varying delays.

    PubMed

    Xin, Youming; Li, Yuxia; Cheng, Zunshui; Huang, Xia

    2016-08-01

    This paper considers the problem of exponential stability for switched memristive neural networks (MNNs) with time-varying delays. Different from most of the existing papers, we model a memristor as a continuous system, and view switched MNNs as switched neural networks with uncertain time-varying parameters. Based on average dwell time technique, mode-dependent average dwell time technique and multiple Lyapunov-Krasovskii functional approach, two conditions are derived to design the switching signal and guarantee the exponential stability of the considered neural networks, which are delay-dependent and formulated by linear matrix inequalities (LMIs). Finally, the effectiveness of the theoretical results is demonstrated by two numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals.

    PubMed

    Manivannan, R; Samidurai, R; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-03-01

    This paper investigates the problems of exponential stability and dissipativity of generalized neural networks (GNNs) with time-varying delay signals. By constructing a novel Lyapunov-Krasovskii functionals (LKFs) with triple integral terms that contain more advantages of the state vectors of the neural networks, and the upper bound on the time-varying delay signals are formulated. We employ a new integral inequality technique (IIT), free-matrix-based (FMB) integral inequality approach, and Wirtinger double integral inequality (WDII) technique together with the reciprocally convex combination (RCC) approach to bound the time derivative of the LKFs. An improved exponential stability and strictly (Q,S,R)-γ-dissipative conditions of the addressed systems are represented by the linear matrix inequalities (LMIs). Finally, four interesting numerical examples are developed to verify the usefulness of the proposed method with a practical application to a biological network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. From dynamical systems with time-varying delay to circle maps and Koopman operators

    NASA Astrophysics Data System (ADS)

    Müller, David; Otto, Andreas; Radons, Günter

    2017-06-01

    In this paper, we investigate the influence of the retarded access by a time-varying delay on the dynamics of delay systems. We show that there are two universality classes of delays, which lead to fundamental differences in dynamical quantities such as the Lyapunov spectrum. Therefore, we introduce an operator theoretic framework, where the solution operator of the delay system is decomposed into the Koopman operator describing the delay access and an operator similar to the solution operator known from systems with constant delay. The Koopman operator corresponds to an iterated map, called access map, which is defined by the iteration of the delayed argument of the delay equation. The dynamics of this one-dimensional iterated map determines the universality classes of the infinite-dimensional state dynamics governed by the delay differential equation. In this way, we connect the theory of time-delay systems with the theory of circle maps and the framework of the Koopman operator. In this paper, we extend our previous work [A. Otto, D. Müller, and G. Radons, Phys. Rev. Lett. 118, 044104 (2017), 10.1103/PhysRevLett.118.044104] by elaborating the mathematical details and presenting further results also on the Lyapunov vectors.

  1. Global output feedback stabilisation of high-order nonlinear systems with multiple time-varying delays

    NASA Astrophysics Data System (ADS)

    Gao, Fangzheng; Wu, Yuqiang; Yuan, Fushun

    2016-07-01

    This paper investigates the problem of global output feedback stabilisation for a class of high-order nonlinear systems with multiple time-varying delays. By using backstepping recursive technique and the homogeneous domination approach, a continuous output feedback controller is successfully designed, and the global asymptotic stability of the resulting closed-loop system is proven with the help of an appropriate Lyapunov- Krasovskii functional. Two simulation examples are given to illustrate the effectiveness of the proposed approach.

  2. On global exponential stability of positive neural networks with time-varying delay.

    PubMed

    Hien, Le Van

    2017-03-01

    This paper presents a new result on the existence, uniqueness and global exponential stability of a positive equilibrium of positive neural networks in the presence of bounded time-varying delay. Based on some novel comparison techniques, a testable condition is derived to ensure that all the state trajectories of the system converge exponentially to a unique positive equilibrium. The effectiveness of the obtained results is illustrated by a numerical example.

  3. Fuzzy neural-based control for nonlinear time-varying delay systems.

    PubMed

    Hwang, Chih-Lyang; Chang, Li-Jui

    2007-12-01

    In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by N fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control.

  4. H∞ state estimation of generalised neural networks with interval time-varying delays

    NASA Astrophysics Data System (ADS)

    Saravanakumar, R.; Syed Ali, M.; Cao, Jinde; Huang, He

    2016-12-01

    This paper focuses on studying the H∞ state estimation of generalised neural networks with interval time-varying delays. The integral terms in the time derivative of the Lyapunov-Krasovskii functional are handled by the Jensen's inequality, reciprocally convex combination approach and a new Wirtinger-based double integral inequality. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with H∞ performance. The proposed conditions are represented by linear matrix inequalities. Optimal H∞ norm bounds are obtained easily by solving convex problems in terms of linear matrix inequalities. The advantage of employing the proposed inequalities is illustrated by numerical examples.

  5. Stability analysis of switched stochastic neural networks with time-varying delays.

    PubMed

    Wu, Xiaotai; Tang, Yang; Zhang, Wenbing

    2014-03-01

    This paper is concerned with the global exponential stability of switched stochastic neural networks with time-varying delays. Firstly, the stability of switched stochastic delayed neural networks with stable subsystems is investigated by utilizing the mathematical induction method, the piecewise Lyapunov function and the average dwell time approach. Secondly, by utilizing the extended comparison principle from impulsive systems, the stability of stochastic switched delayed neural networks with both stable and unstable subsystems is analyzed and several easy to verify conditions are derived to ensure the exponential mean square stability of switched delayed neural networks with stochastic disturbances. The effectiveness of the proposed results is illustrated by two simulation examples. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. A dynamic feedback control strategy for control loops with time-varying delay

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Behrouz; Tafreshi, Reza; Franchek, Matthew; Grigoriadis, Karolos; Mohammadpour, Javad

    2014-05-01

    Dynamic systems of nth order with time-varying delay in the control loop are examined in this paper. The infinite-dimensional pure delay problem is approximated using a jth-order Padé approximation. Although the approximation provides a well-matched finite-dimensional configuration, it poses a new challenge in terms of unstable internal dynamics for the resulted non-minimum phase system. Such a non-minimum phase characteristic limits the closed-loop system bandwidth and leads to an imperfect tracking performance. To circumvent this problem, the unstable internal dynamics of the system is captured and a new dynamic compensator is proposed to stabilise it in a systematic framework. A dynamic controller is developed, which provides the overall system stability against unmatched perturbation and meets the desired tracking error dynamics. The proposed approach is then applied to fuelling control in gasoline engines addressing the varying transport delay of the oxygen-sensor measurement in the exhaust. The developed methodology is finally validated on a Ford F-150 SI lean-burn engine model with large time-varying delay in the control loop.

  7. Adaptive control for a class of MIMO nonlinear time delay systems against time varying actuator failures.

    PubMed

    Hashemi, Mahnaz; Ghaisari, Jafar; Askari, Javad

    2015-07-01

    This paper investigates an adaptive controller for a class of Multi Input Multi Output (MIMO) nonlinear systems with unknown parameters, bounded time delays and in the presence of unknown time varying actuator failures. The type of considered actuator failure is one in which some inputs may be stuck at some time varying values where the values, times and patterns of the failures are unknown. The proposed approach is constructed based on a backstepping design method. The boundedness of all the closed-loop signals is guaranteed and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark and a chemical reactor system. The simulation results show the effectiveness of the proposed method.

  8. Robust Adaptive Control for a Class of Uncertain Nonlinear Systems with Time-Varying Delay

    PubMed Central

    Wang, Ruliang; Li, Jie; Zhang, Shanshan; Gao, Dongmei; Sun, Huanlong

    2013-01-01

    We present adaptive neural control design for a class of perturbed nonlinear MIMO time-varying delay systems in a block-triangular form. Based on a neural controller, it is obtained by constructing a quadratic-type Lyapunov-Krasovskii functional, which efficiently avoids the controller singularity. The proposed control guarantees that all closed-loop signals remain bounded, while the output tracking error dynamics converge to a neighborhood of the desired trajectories. The simulation results demonstrate the effectiveness of the proposed control scheme. PMID:23853544

  9. Robust adaptive control for a class of uncertain nonlinear systems with time-varying delay.

    PubMed

    Wang, Ruliang; Li, Jie; Zhang, Shanshan; Gao, Dongmei; Sun, Huanlong

    2013-01-01

    We present adaptive neural control design for a class of perturbed nonlinear MIMO time-varying delay systems in a block-triangular form. Based on a neural controller, it is obtained by constructing a quadratic-type Lyapunov-Krasovskii functional, which efficiently avoids the controller singularity. The proposed control guarantees that all closed-loop signals remain bounded, while the output tracking error dynamics converge to a neighborhood of the desired trajectories. The simulation results demonstrate the effectiveness of the proposed control scheme.

  10. Periodic solution for state-dependent impulsive shunting inhibitory CNNs with time-varying delays.

    PubMed

    Şaylı, Mustafa; Yılmaz, Enes

    2015-08-01

    In this paper, we consider existence and global exponential stability of periodic solution for state-dependent impulsive shunting inhibitory cellular neural networks with time-varying delays. By means of B-equivalence method, we reduce these state-dependent impulsive neural networks system to an equivalent fix time impulsive neural networks system. Further, by using Mawhin's continuation theorem of coincide degree theory and employing a suitable Lyapunov function some new sufficient conditions for existence and global exponential stability of periodic solution are obtained. Previous results are improved and extended. Finally, we give an illustrative example with numerical simulations to demonstrate the effectiveness of our theoretical results.

  11. Attitude tracking control for spacecraft formation with time-varying delays and switching topology

    NASA Astrophysics Data System (ADS)

    Yang, Hongjiu; You, Xiu; Hua, Changchun

    2016-09-01

    This paper investigates attitude dynamic tracking control for spacecraft formation in the presence of unmeasurable velocity information with time-varying delays and switching topology. Based on an extended state observer, a nonlinear attitude tracking control approach is developed for spacecraft attitude model formulated by Euler-Lagrangian equations. The attitude tracking controller allows for external disturbances and absence of angular velocity information. Both auto-stable region techniques and a Lyapunov function approach are developed to prove ultimately bounded tracking. Simulation results demonstrate effectiveness of the nonlinear control techniques proposed in this paper.

  12. Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays

    PubMed Central

    Zhu, Qing; Song, Aiguo; Fei, Shumin; Yang, Yuequan; Cao, Zhiqiang

    2014-01-01

    Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results. PMID:25110747

  13. Finite-time boundedness and stabilization of uncertain switched neural networks with time-varying delay.

    PubMed

    Wu, Yuanyuan; Cao, Jinde; Alofi, Abdulaziz; Al-Mazrooei, Abdullah; Elaiw, Ahmed

    2015-09-01

    This paper deals with the finite-time boundedness and stabilization problem for a class of switched neural networks with time-varying delay and parametric uncertainties. Based on Lyapunov-like function method and average dwell time technique, some sufficient conditions are derived to guarantee the finite-time boundedness of considered uncertain switched neural networks. Furthermore, the state feedback controller is designed to solve the finite-time stabilization problem. Moreover, the proposed sufficient conditions can be simplified into the form of linear matrix equalities for conveniently using Matlab LMI toolbox. Finally, two numerical examples are given to show the effectiveness of the main results.

  14. Non-fragile control for a class of uncertain systems with time-varying delay

    NASA Astrophysics Data System (ADS)

    Yao, Hejun; Yuan, Fushun; Qiao, Yue

    2017-01-01

    The problem of exponential stability non-fragile control for uncertain systems with time-varying delay is considered in this paper. Based on the Lyapunov stability theorem, and by using linear matrix inequality approach, a new approach is obtained to design the state feedback exponential stability non-fragile controller. By introducing a new Lyapunov functional, a sufficient exponential stability condition is given in terms of linear matrix inequality. With the non-fragile controller and the linear matrix inequality Control Toolbox in MATLAB, the simulation results are easier obtained.

  15. Mixed outer synchronization of coupled complex networks with time-varying coupling delay.

    PubMed

    Wang, Jun-Wei; Ma, Qinghua; Zeng, Li; Abd-Elouahab, Mohammed Salah

    2011-03-01

    In this paper, the problem of outer synchronization between two complex networks with the same topological structure and time-varying coupling delay is investigated. In particular, we introduce a new type of outer synchronization behavior, i.e., mixed outer synchronization (MOS), in which different state variables of the corresponding nodes can evolve into complete synchronization, antisynchronization, and even amplitude death simultaneously for an appropriate choice of the scaling matrix. A novel nonfragile linear state feedback controller is designed to realize the MOS between two networks and proved analytically by using Lyapunov-Krasovskii stability theory. Finally, numerical simulations are provided to demonstrate the feasibility and efficacy of our proposed control approach.

  16. Decentralized adaptive fuzzy output feedback control of nonlinear interconnected systems with time-varying delay

    NASA Astrophysics Data System (ADS)

    Wang, Qin; Chen, Zuwen; Song, Aiguo

    2017-01-01

    A robust adaptive output-feedback control scheme based on K-filters is proposed for a class of nonlinear interconnected time-varying delay systems with immeasurable states. It is difficult to design the controller due to the existence of the immeasurable states and the time-delay couplings among interconnected subsystems. This difficulty is overcome by use of the fuzzy system, the K-filters and the appropriate Lyapunov-Krasovskii functional. Based on Lyapunov theory, the closed-loop control system is proved to be semi-global uniformly ultimately bounded (SGUUB), and the output tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the approach.

  17. H∞ control problem for Hopfield neural networks with interval time-varying delay

    NASA Astrophysics Data System (ADS)

    Emharuethai, Chanikan

    2016-02-01

    In this paper, we consider H∞ control problem for a class Hopfield neural networks with interval time-varying delay. The time delay is a continuous function belonging to a given interval, but not necessariry differentiable. The stabilizing controllers to be designed must satisfy some exponential stability constraints on the closed-loop poles. Based on the construction of improved Lyapunov-Krasovskii functionals combined with Newton-Leibniz formula. H∞ controller is designed via memoryless state feedback control and new sufficient conditions for the existence of the H∞ state-feedback for the system are given in terms of linear matrix inequalities (LMIs). Numerical examples are given to illustrate the effectiveness of the obtained result.

  18. Bilateral shared autonomous systems with passive and nonpassive input forces under time varying delay.

    PubMed

    Islam, Shafiqul; Liu, Peter X; El Saddik, Abdulmotaleb; Dias, J; Seneviratne, Lakmal

    2015-01-01

    In this paper, we address stability and tracking control problem of bilateral shared autonomous systems in the presence of passive and nonpassive input interaction forces. The design comprises delayed position and position-velocity signals with the known and unknown structures of the master and slave manipulator dynamics. Using novel Lyapunov-Krasovskii functional, stability and tracking conditions of the coupled master-slave shared autonomous systems are developed under symmetrical and unsymmetrical time varying data transmission delays. This condition allows the designer to estimate the control design parameters to ensure position, velocity and synchronizing errors of the master and slave manipulators. Finally, evaluation results are presented to demonstrate the validity of the proposed design for real-time teleoperation applications.

  19. Stability analysis for uncertain switched neural networks with time-varying delay.

    PubMed

    Shen, Wenwen; Zeng, Zhigang; Wang, Leimin

    2016-11-01

    In this paper, stability for a class of uncertain switched neural networks with time-varying delay is investigated. By exploring the mode-dependent properties of each subsystem, all the subsystems are categorized into stable and unstable ones. Based on Lyapunov-like function method and average dwell time technique, some delay-dependent sufficient conditions are derived to guarantee the exponential stability of considered uncertain switched neural networks. Compared with general results, our proposed approach distinguishes the stable and unstable subsystems rather than viewing all subsystems as being stable, thus getting less conservative criteria. Finally, two numerical examples are provided to show the validity and the advantages of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. New Results on Passivity Analysis of Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

    PubMed Central

    Li, YaJun; Huang, Zhaowen

    2015-01-01

    The passivity problem for a class of stochastic neural networks systems (SNNs) with varying delay and leakage delay has been further studied in this paper. By constructing a more effective Lyapunov functional, employing the free-weighting matrix approach, and combining with integral inequality technic and stochastic analysis theory, the delay-dependent conditions have been proposed such that SNNs are asymptotically stable with guaranteed performance. The time-varying delay is divided into several subintervals and two adjustable parameters are introduced; more information about time delay is utilised and less conservative results have been obtained. Examples are provided to illustrate the less conservatism of the proposed method and simulations are given to show the impact of leakage delay on stability of SNNs. PMID:26366165

  1. Multiple μ-stability of neural networks with unbounded time-varying delays.

    PubMed

    Wang, Lili; Chen, Tianping

    2014-05-01

    In this paper, we are concerned with a class of recurrent neural networks with unbounded time-varying delays. Based on the geometrical configuration of activation functions, the phase space R(n) can be divided into several Φη-type subsets. Accordingly, a new set of regions Ωη are proposed, and rigorous mathematical analysis is provided to derive the existence of equilibrium point and its local μ-stability in each Ωη. It concludes that the n-dimensional neural networks can exhibit at least 3(n) equilibrium points and 2(n) of them are μ-stable. Furthermore, due to the compatible property, a set of new conditions are presented to address the dynamics in the remaining 3(n)-2(n) subset regions. As direct applications of these results, we can get some criteria on the multiple exponential stability, multiple power stability, multiple log-stability, multiple log-log-stability and so on. In addition, the approach and results can also be extended to the neural networks with K-level nonlinear activation functions and unbounded time-varying delays, in which there can store (2K+1)(n) equilibrium points, (K+1)(n) of them are locally μ-stable. Numerical examples are given to illustrate the effectiveness of our results.

  2. A delay-range-partition approach to analyse stability of linear systems with time-varying delays

    NASA Astrophysics Data System (ADS)

    Xue, Y.; Zhang, X.; Han, Y. Y.; Shi, M.

    2016-12-01

    In this paper, the stability analysis of linear systems with an interval time-varying delay is investigated. First, augmented Lyapunov-Krasovskii functionals are constructed, which include more information of the delay's range and the delay's derivative. Second, two improved integral inequalities, which are less conservative than Jensen's integral inequalities, and delay-range-partition approach are utilised to estimate the upper bounds of the derivatives of the augmented Lyapunov-Krasovskii functionals. Then, less conservative stability criteria are proposed no matter whether the lower bound of delay is zero or not. Finally, to illustrate the effectiveness of the stability criteria proposed in this paper, two numerical examples are given and their results are compared with the existing results.

  3. Exponential synchronization of discontinuous neural networks with time-varying mixed delays via state feedback and impulsive control.

    PubMed

    Yang, Xinsong; Cao, Jinde; Ho, Daniel W C

    2015-04-01

    This paper investigates drive-response synchronization for a class of neural networks with time-varying discrete and distributed delays (mixed delays) as well as discontinuous activations. Strict mathematical proof shows the global existence of Filippov solutions to neural networks with discontinuous activation functions and the mixed delays. State feedback controller and impulsive controller are designed respectively to guarantee global exponential synchronization of the neural networks. By using Lyapunov function and new analysis techniques, several new synchronization criteria are obtained. Moreover, lower bound on the convergence rate is explicitly estimated when state feedback controller is utilized. Results of this paper are new and some existing ones are extended and improved. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.

  4. Containment Control for First-Order Multi-Agent Systems with Time-Varying Delays and Uncertain Topologies

    NASA Astrophysics Data System (ADS)

    Wang, Fu-Yong; Yang, Hong-Yong; Zhang, Shu-Ning; Han, Fu-Jun

    2016-08-01

    Containment control of first-order multi-agent systems with uncertain topologies and communication time-delays is studied. Suppose system topologies are dynamically changed, a containment control algorithm with time-varying delays is presented. The stability of the control algorithm is studied under the assumption that communication topologies are jointly-connected, and constraint condition of distributed containment control for delayed multi-agent systems is derived with the aid of Lyapunov-Krasovskii function. Simulation results are provided to prove the correctness and effectiveness of the conclusion. Supported by the National Natural Science Foundation of China under Grant Nos. 61273152, 61304052, 51407088, the Science Foundation of Education Office of Shandong Province of China under Grant Nos. ZR2011FM07, BS2015DX018

  5. L2-Stable Bilateral Control System with Time-Varying Communication Delay

    NASA Astrophysics Data System (ADS)

    Yashiro, Daisuke; Natori, Kenji; Ohnishi, Kouhei

    Passivity theory is a very useful tool for the stability analysis of bilateral teleoperation with communication delay. Many researchers use passivity-based stabilization method such as those based on scattering theory or wave variables. However, passivity-based stabilization is too conservative for the design of a high-performance teleoperation system. The term “high-performance” implies that people who operate this system can experience a highly accurate reaction force from environments. In this study, we analyze the L2-stability of some bilateral control systems with time-varying communication delay by using the small-gain theorem. The small-gain theorem is known to be a stability condition for multi-input multi-output feedback systems. Additionally, a novel control structure for the bilateral control system with communication delay is proposed. The bilateral control system with the proposed structure satisfies the L2-stability condition of the small-gain theorem. The experimental results show the utility of the proposed control structure.

  6. Delay-dependent finite-time boundedness of a class of Markovian switching neural networks with time-varying delays.

    PubMed

    Zhong, Qishui; Cheng, Jun; Zhao, Yuqing

    2015-07-01

    In this paper, a novel method is developed for delay-dependent finite-time boundedness of a class of Markovian switching neural networks with time-varying delays. New sufficient condition for stochastic boundness of Markovian jumping neural networks is presented and proved by an newly augmented stochastic Lyapunov-Krasovskii functional and novel activation function conditions, the state trajectory remains in a bounded region of the state space over a given finite-time interval. Finally, a numerical example is given to illustrate the efficiency and less conservative of the proposed method.

  7. μ-Stability of Nonlinear Positive Systems With Unbounded Time-Varying Delays.

    PubMed

    Chen, Tianping; Liu, Xiwei

    2016-03-11

    The stability of the zero solution plays an important role in the investigation of positive systems. In this brief, we discuss the μ-stability of positive nonlinear systems with unbounded time-varying delays. The system is modeled by the continuous-time ordinary differential equation. Under some assumptions on the nonlinear functions, such as homogeneous, cooperative, and nondecreasing, we propose a novel transform, by which the nonlinear system reduces to a new system. Thus, we analyze its dynamics, which can simplify the nonlinear homogenous functions with respect to the arbitrary dilation map to those with respect to the standard dilation map. We finally get some new criteria for the global μ-stability taking the degree into consideration. A numerical example is given to demonstrate the validity of obtained results.

  8. Extended dissipative state estimation for memristive neural networks with time-varying delay.

    PubMed

    Xiao, Jianying; Li, Yongtao; Zhong, Shouming; Xu, Fang

    2016-09-01

    This paper investigates the problem of extended dissipative state estimation for memristor-based neural networks (MNNs) with time-varying delay. Based on both nonsmooth analysis and the construction of a new Lyapunov-Krasovskii functional, the extended dissipative state estimation criteria are obtained by mainly applying differential inclusions, set-valued maps and many new integral inequalities. The extended dissipative state estimation can be adopted to deal with l2-l∞ state estimation, H∞ state estimation, passive state estimation and dissipative state estimation by valuing the corresponding weighting matrices. Finally, two numerical examples are given to show the effectiveness and less conservatism of the proposed criteria. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  9. New passivity criteria for memristive uncertain neural networks with leakage and time-varying delays.

    PubMed

    Xiao, Jianying; Zhong, Shouming; Li, Yongtao

    2015-11-01

    In this paper, the problem of passivity analysis is studied for memristor-based uncertain neural networks with leakage and time-varying delays. By combining differential inclusions with set-valued maps, the system of memristive neural networks is changed into the conventional one. By adding a triple quadratic integral and relaxing the requirement for the positive definiteness of some matrices, a proper Lyapunov-Krasovskii functional is constructed. Based on the establishment of the novel Lyapunov-Krasovskii functional, the new passivity criteria are derived by mainly applying Wirtinger-based double integral inequality, S-procedure and so on. Moreover, the conservatism of passivity conditions can be reduced. Finally, four numerical examples are given to show the effectiveness and less conservatism of the proposed criteria.

  10. Bifurcation onset delay in magnetic bearing systems by time varying stiffness

    NASA Astrophysics Data System (ADS)

    Ghazavi, M. R.; Sun, Q.

    2017-06-01

    We study the nonlinear dynamics behaviours of a rigid rotor supported by magnetic bearings. In particular, we consider the effect of rotor unbalanced mass and geometric coupling. Existing works in literature have mostly focused on a single value of parameter or a smaller range of the nonlinearities introduced by rotor imbalance and geometric coupling. This is partly due to the use of a linear PD controller which limits the system performance. In this paper, we use a nonlinear PD controller by adopting a time varying stiffness term. The control gains are chosen according to the stability chart for a Mathieu's equation. Consequently, we observe a delay in the onset of bifurcation indicating an improved rotor performance.

  11. Robust Reliable Control for Neutral-Type Nonlinear Systems with Time-Varying Delays

    NASA Astrophysics Data System (ADS)

    Mathiyalagan, K.; Sakthivel, R.; Park, Ju. H.

    2014-10-01

    The problem of robust reliable stabilization against actuator failures for a class of uncertain nonlinear neutral systems with time-varying delays is considered. Based on a new Lyapunov-Krasovskii functional, by employing linear matrix inequality technique and free weighting matrix approach, we derived a set of sufficient conditions for the existence of a reliable controller. The derived controller is applied for the robust stabilization of the nonlinear neutral system in the presence of known actuator failure matrix and uncertainties. Further, the results are extended to study the stabilization of neutral systems with unknown actuator failure matrix. The failure of actuators are considered by variables, which are varying in a given interval. The developed theoretical results are established in the form of linear matrix inequalities, which can be easily solved via standard numerical software. Finally, two numerical examples are presented to demonstrate the validity and less conservatism of the obtained results.

  12. Pinning synchronization of memristor-based neural networks with time-varying delays.

    PubMed

    Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng

    2017-09-01

    In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays

    PubMed Central

    Delgado, Emma; Barreiro, Antonio; Falcón, Pablo; Díaz-Cacho, Miguel

    2016-01-01

    We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C) control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving rise to mu-test conditions for robust stability and offering an appealing frequency technique to deal with the stability robustness of the architecture. We discuss ideal transparency problems and we adapt classical solutions so that controllers are proper, without single or double differentiators, and thus avoid the negative effects of noise. The control scheme was fine-tuned and tested for complete stability to zero of the whole state, while seeking a practical solution to the trade-off between stability and transparency in the Internet-based teleoperation. These ideas were tested on an Internet-based application with two Omni devices at remote laboratory locations via simulations and real remote experiments that achieved robust stability, while performing well in terms of position synchronization and force transparency. PMID:27128914

  14. Event-Triggered Generalized Dissipativity Filtering for Neural Networks With Time-Varying Delays.

    PubMed

    Wang, Jia; Zhang, Xian-Ming; Han, Qing-Long

    2016-01-01

    This paper is concerned with event-triggered generalized dissipativity filtering for a neural network (NN) with a time-varying delay. The signal transmission from the NN to its filter is completed through a communication channel. It is assumed that the network measurement of the NN is sampled periodically. An event-triggered communication scheme is introduced to design a suitable filter such that precious communication resources can be saved significantly while certain filtering performance can be ensured. On the one hand, the event-triggered communication scheme is devised to select only those sampled signals violating a certain threshold to be transmitted, which directly leads to saving of precious communication resources. On the other hand, the filtering error system is modeled as a time-delay system closely dependent on the parameters of the event-triggered scheme. Based on this model, a suitable filter is designed such that certain filtering performance can be ensured, provided that a set of linear matrix inequalities are satisfied. Furthermore, since a generalized dissipativity performance index is introduced, several kinds of event-triggered filtering issues, such as H∞ filtering, passive filtering, mixed H∞ and passive filtering, (Q,S,R) -dissipative filtering, and L2 - L∞ filtering, are solved in a unified framework. Finally, two examples are given to illustrate the effectiveness of the proposed method.

  15. Delay decomposition approach to [Formula: see text] filtering analysis of genetic oscillator networks with time-varying delays.

    PubMed

    Revathi, V M; Balasubramaniam, P

    2016-04-01

    In this paper, the [Formula: see text] filtering problem is treated for N coupled genetic oscillator networks with time-varying delays and extrinsic molecular noises. Each individual genetic oscillator is a complex dynamical network that represents the genetic oscillations in terms of complicated biological functions with inner or outer couplings denote the biochemical interactions of mRNAs, proteins and other small molecules. Throughout the paper, first, by constructing appropriate delay decomposition dependent Lyapunov-Krasovskii functional combined with reciprocal convex approach, improved delay-dependent sufficient conditions are obtained to ensure the asymptotic stability of the filtering error system with a prescribed [Formula: see text] performance. Second, based on the above analysis, the existence of the designed [Formula: see text] filters are established in terms of linear matrix inequalities with Kronecker product. Finally, numerical examples including a coupled Goodwin oscillator model are inferred to illustrate the effectiveness and less conservatism of the proposed techniques.

  16. Dynamic programming based time-delay estimation technique for analysis of time-varying time-delay

    SciTech Connect

    Gupta, Deepak K.; McKee, George R.; Fonck, Raymond J.

    2010-01-15

    A new time-delay estimation (TDE) technique based on dynamic programming is developed to measure the time-varying time-delay between two signals. The dynamic programming based TDE technique provides a frequency response five to ten times better than previously known TDE techniques, namely, those based on time-lag cross-correlation or wavelet analysis. Effects of frequency spectrum, signal-to-noise ratio, and amplitude of time-delay on response of the TDE technique (represented as transfer function) are studied using simulated data signals. The transfer function for the technique decreases with increase in noise in signal; however it is independent of signal spectrum shape. The dynamic programming based TDE technique is applied to the beam emission spectroscopy diagnostic data to measure poloidal velocity fluctuations, which led to the observation of theoretically predicted zonal flows in high-temperature tokamak plasmas.

  17. Robust asymptotic stability of fuzzy Markovian jumping genetic regulatory networks with time-varying delays by delay decomposition approach

    NASA Astrophysics Data System (ADS)

    Balasubramaniam, P.; Sathy, R.

    2011-02-01

    In this paper, the robust asymptotic stability problem is considered for a class of fuzzy Markovian jumping genetic regulatory networks with uncertain parameters and switching probabilities by delay decomposition approach. The purpose of the addressed stability analysis problem is to establish an easy-to-verify condition under which the dynamics of the true concentrations of the messenger ribonucleic acid (mRNA) and protein is asymptotically stable irrespective of the norm-bounded modeling errors. A new Lyapunov-Krasovskii functional (LKF) is constructed by nonuniformly dividing the delay interval into multiple subinterval, and choosing proper functionals with different weighting matrices corresponding to different subintervals in the LKFs. Employing these new LKFs for the time-varying delays, a new delay-dependent stability criterion is established with Markovian jumping parameters by T-S fuzzy model. Note that the obtained results are formulated in terms of linear matrix inequality (LMI) that can efficiently solved by the LMI toolbox in Matlab. Numerical examples are exploited to illustrate the effectiveness of the proposed design procedures.

  18. Stability and Synchronization for Discrete-Time Complex-Valued Neural Networks with Time-Varying Delays

    PubMed Central

    Zhang, Hao; Wang, Xing-yuan; Lin, Xiao-hui; Liu, Chong-xin

    2014-01-01

    In this paper, the synchronization problem for a class of discrete-time complex-valued neural networks with time-varying delays is investigated. Compared with the previous work, the time delay and parameters are assumed to be time-varying. By separating the real part and imaginary part, the discrete-time model of complex-valued neural networks is derived. Moreover, by using the complex-valued Lyapunov-Krasovskii functional method and linear matrix inequality as tools, sufficient conditions of the synchronization stability are obtained. In numerical simulation, examples are presented to show the effectiveness of our method. PMID:24714386

  19. Global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays.

    PubMed

    Chen, Boshan; Wang, Jun

    2005-11-01

    In this paper, we present the analytical results on the global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays. Sufficient conditions are derived for ascertaining the existence, uniqueness and global exponential periodicity of the oscillatory solution of such recurrent neural networks by using the comparison principle and mixed monotone operator method. The periodicity results extend or improve existing stability results for the class of recurrent neural networks with and without time delays.

  20. Stability Analysis of Discrete-Time Neural Networks With Time-Varying Delay via an Extended Reciprocally Convex Matrix Inequality.

    PubMed

    Zhang, Chuan-Ke; He, Yong; Jiang, Lin; Wang, Qing-Guo; Wu, Min

    2017-02-17

    This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequality is developed to replace the popular reciprocally convex combination lemma (RCCL). It has potential to reduce the conservatism of the RCCL-based criteria without introducing any extra decision variable due to its advantage of reduced estimation gap using the same decision variables. Moreover, a delay-product-type term is introduced for the first time into the Lyapunov function candidate such that a delay-variation-dependent stability criterion with the bounds of delay change rate is established. Finally, the advantages of the proposed criteria are demonstrated through two numerical examples.

  1. Mixed-mode oscillations in a nonlinear time delay oscillator with time varying parameters

    NASA Astrophysics Data System (ADS)

    Yu, Yue; Han, Xiujing; Zhang, Chun; Bi, Qinsheng

    2017-06-01

    In this study, the mechanism for the action of time-invariant delay on a non-autonomous system with slow parametric excitation is investigated. The complex mix-mode oscillations (MMOs) are presented when the parametric excitation item slowly passes through critical bifurcation values of this nonlinear time delay oscillator. We use bifurcation theory to clarify certain generation mechanism related to three complex spiking formations, i.e., ``symmetric sup-pitchfork bifurcation'', ``symmetric sup-pitchfork/sup-Hopf bifurcation'', and ``symmetric sup-pitchfork/sup-Hopf/homoclinic orbit bifurcation''. Such bifurcation behaviors result in various hysteresis loops between the spiking attractor and the quasi-stationary process, which are responsible for the generation of MMOs. We further identify that the occurrence and evolution of such complex MMOs depend on the magnitude of the delay. Specifically, with the increase of time delay, the two limit cycles bifurcated from Hopf bifurcations may merge into an enlarged cycle, which is caused by a saddle homoclinic orbit bifurcation. We can conclude that time delay plays a vital role in the generation of MMOs. Our findings enrich the routes to spiking process and deepen the understanding of MMOs in time delay systems.

  2. Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays.

    PubMed

    Guo, Zhenyuan; Wang, Jun; Yan, Zheng

    2013-12-01

    This paper addresses the global exponential dissipativity of memristor-based recurrent neural networks with time-varying delays. By constructing proper Lyapunov functionals and using M-matrix theory and LaSalle invariant principle, the sets of global exponentially dissipativity are characterized parametrically. It is proven herein that there are 2(2n(2)-n) equilibria for an n-neuron memristor-based neural network and they are located in the derived globally attractive sets. It is also shown that memristor-based recurrent neural networks with time-varying delays are stabilizable at the origin of the state space by using a linear state feedback control law with appropriate gains. Finally, two numerical examples are discussed in detail to illustrate the characteristics of the results. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Synchronization and State Estimation of a Class of Hierarchical Hybrid Neural Networks With Time-Varying Delays.

    PubMed

    Zhang, Lixian; Zhu, Yanzheng; Zheng, Wei Xing

    2016-02-01

    This paper addresses the problems of synchronization and state estimation for a class of discrete-time hierarchical hybrid neural networks (NNs) with time-varying delays. The hierarchical hybrid feature consists of a higher level nondeterministic switching and a lower level stochastic switching. The latter is used to describe the NNs subject to Markovian modes transitions, whereas the former is of the average dwell-time switching regularity to model the supervisory orchestrating mechanism among these Markov jump NNs. The considered time delays are not only time-varying but also dependent on the mode of NNs on the lower layer in the hierarchical structure. Despite quantization and random data missing, the synchronized controllers and state estimators are designed such that the resulting error system is exponentially stable with an expected decay rate and has a prescribed H∞ disturbance attenuation level. Two numerical examples are provided to show the validity and potential of the developed results.

  4. On antiperiodic solutions for Cohen-Grossberg shunting inhibitory neural networks with time-varying delays and impulses.

    PubMed

    Xu, Changjin; Zhang, Qiming

    2014-10-01

    In this letter, a class of Cohen-Grossberg shunting inhibitory neural networks with time-varying delays and impulses is investigated. Sufficient conditions for the existence and exponential stability of antiperiodic solutions of such a class of neural networks are established. Our results are new and complementary to previously known results. An example is given to illustrate the feasibility and effectiveness of our main results.

  5. Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.

    PubMed

    Li, Da-Peng; Li, Dong-Juan; Liu, Yan-Jun; Tong, Shaocheng; Chen, C L Philip

    2017-10-01

    This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.

  6. Synchronization analysis of delayed complex networks with time-varying couplings

    NASA Astrophysics Data System (ADS)

    Li, Ping; Yi, Zhang

    2008-06-01

    In this paper, a new method is presented to analyze the linear stability of the synchronized state in arbitrarily coupled complex dynamical systems with time delays. The coupling configurations are not restricted to the symmetric and irreducible connections or the non-negative off-diagonal links. The stability criteria are obtained by using Lyapunov-Krasovskii functional method and subspace projection method. These criteria reveal the relationship between coupling matrices and stability of the dynamical networks.

  7. DDI-based finite-time stability analysis for nonlinear switched systems with time-varying delays

    NASA Astrophysics Data System (ADS)

    Xue, Wenping; Li, Kangji; Liu, Guohai

    2016-09-01

    This paper investigates the finite-time stability (FTS) analysis problem for switched systems with both nonlinear perturbation and time-varying delays. For the system to be finite-time stable, a sufficient condition is proposed based on some delay differential inequalities (DDIs), rather than the Lyapunov-like functions which are commonly used in the FTS analysis of switched systems. Compared with the Lyapunov-like function method, the FTS conditions based on the DDI method are easier for checking and do not require FTS of each subsystem. Two examples are given to illustrate the effectiveness of the developed theory.

  8. An Improved Integral Inequality to Stability Analysis of Genetic Regulatory Networks With Interval Time-Varying Delays.

    PubMed

    Zhang, Xian; Wu, Ligang; Cui, Shaochun

    2015-01-01

    This paper focuses on stability analysis for a class of genetic regulatory networks with interval time-varying delays. An improved integral inequality concerning on double-integral items is first established. Then, we use the improved integral inequality to deal with the resultant double-integral items in the derivative of the involved Lyapunov-Krasovskii functional. As a result, a delay-range-dependent and delay-rate-dependent asymptotical stability criterion is established for genetic regulatory networks with differential time-varying delays. Furthermore, it is theoretically proven that the stability criterion proposed here is less conservative than the corresponding one in [Neurocomputing, 2012, 93: 19-26]. Based on the obtained result, another stability criterion is given under the case that the information of the derivatives of delays is unknown. Finally, the effectiveness of the approach proposed in this paper is illustrated by a pair of numerical examples which give the comparisons of stability criteria proposed in this paper and some literature.

  9. Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

    PubMed

    Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

    2017-11-01

    In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Delay-dependent stability and stabilization criteria for T-S fuzzy singular systems with interval time-varying delay by improved delay partitioning approach.

    PubMed

    Sun, Chao; Wang, Fuli; He, Xiqin

    2016-01-01

    This paper deals with the stability analysis and fuzzy stabilizing controller design for a class of Takagi-Sugeno fuzzy singular systems with interval time-varying delay and linear fractional uncertainties. By decomposing the delay interval into two unequal subintervals and seeking a appropriate ρ, a new Lyapunov-Krasovskii functional is constructed to develop the improved delay-dependent stability criteria, which ensures the considered system to be regular, impulse-free and stable. Furthermore, the desired fuzzy controller gains are also presented by solving a set of strict linear matrix inequalities. Compared with some existing results, the obtained ones give the result with less conservatism. Finally, some examples are given to show the improvement and the effectiveness of the proposed method.

  11. Non-predictor control of a class of feedforward nonlinear systems with unknown time-varying delays

    NASA Astrophysics Data System (ADS)

    Koo, Min-Sung; Choi, Ho-Lim

    2016-08-01

    This paper generalises the several recent results on the control of feedforward time-delay nonlinear systems. First, in view of system formulation, there are unknown time-varying delays in both states and main control input. Also, the considered nonlinear system has extended feedforward nonlinearities. Second, in view of control solution, our proposed controller is a non-predictor feedback controller whereas smith-predictor type controllers are used in the several existing results. Moreover, our controller does not need any information on the unknown delays except their upper bounds. Thus, our result has certain merits in both system formulation and control solution perspective. The analysis and example are given for clear illustration.

  12. Time varying feedbacks and coevolution of alluvial channel morphology, flow distribution and vegetation.

    NASA Astrophysics Data System (ADS)

    Rodriguez, Jose F.; Bayat, Esmaeel; Vahidi, Elham; de Almeida, Gustavo; Gorrick, Sam

    2016-04-01

    Alluvial channel dynamics is the result of the coevolution of channel morphology, flow distribution and vegetation. Even though these processes interact on a variety of time and spatial scales, local detailed analysis can provide valuable insights on mechanisms and feedbacks that can affect the long-term evolution of the system. The three-dimensional, time varying flow distribution can be considered the main driving force, but is affected by the channel geometry as a result of stream curvature, stream width changes and in-stream topographic steering. In-stream vegetation also affects flow distribution in a similar way, either reinforcing or attenuating geometric forcing. But as these systems are the result of coevolution, flow patterns in turn affect sediment transport fluxes, erosion and deposition which eventually modify some aspects of the topography, sediment size distribution and vegetative cover. This contribution presents different studies in which a variety of situations are covered, where the interplay and feedbacks between flow mechanisms are different. We analyse the effects of curvature, width changes and bedform and vegetation steering on sediment transport and sorting and the resulting changes in flow patterns. We study how three-dimensional flow patterns are stage dependent and how that impacts sediment transport and vegetation distribution. We also analyse self-maintenance feedbacks of flow-related features under different time-varying flow conditions. We present cases of straight and meandering reaches, reaches with pools and riffles, and reaches with riparian vegetation within a common framework. We cover both gravel-bed and sand-bed streams.

  13. Delay Analysis of Max-Weight Queue Algorithm for Time-Varying Wireless Ad hoc Networks—Control Theoretical Approach

    NASA Astrophysics Data System (ADS)

    Chen, Junting; Lau, Vincent K. N.

    2013-01-01

    Max weighted queue (MWQ) control policy is a widely used cross-layer control policy that achieves queue stability and a reasonable delay performance. In most of the existing literature, it is assumed that optimal MWQ policy can be obtained instantaneously at every time slot. However, this assumption may be unrealistic in time varying wireless systems, especially when there is no closed-form MWQ solution and iterative algorithms have to be applied to obtain the optimal solution. This paper investigates the convergence behavior and the queue delay performance of the conventional MWQ iterations in which the channel state information (CSI) and queue state information (QSI) are changing in a similar timescale as the algorithm iterations. Our results are established by studying the stochastic stability of an equivalent virtual stochastic dynamic system (VSDS), and an extended Foster-Lyapunov criteria is applied for the stability analysis. We derive a closed form delay bound of the wireless network in terms of the CSI fading rate and the sensitivity of MWQ policy over CSI and QSI. Based on the equivalent VSDS, we propose a novel MWQ iterative algorithm with compensation to improve the tracking performance. We demonstrate that under some mild conditions, the proposed modified MWQ algorithm converges to the optimal MWQ control despite the time-varying CSI and QSI.

  14. Convergence Rate for Discrete-Time Multiagent Systems With Time-Varying Delays and General Coupling Coefficients.

    PubMed

    Chen, Yao; Ho, Daniel W C; Lü, Jinhu; Lin, Zongli

    2016-01-01

    Multiagent systems (MASs) are ubiquitous in our real world. There is an increasing attention focusing on the consensus (or synchronization) problem of MASs over the past decade. Although there are numerous results reported on the convergence of a discrete-time MAS based on the infinite products of matrices, few results are on the convergence rate. Because of the switching topology, the traditional eigenvalue analysis and the Lyapunov function methods are both invalid for the convergence rate analysis of an MAS with a switching topology. Therefore, the estimation of the convergence rate for a discrete-time MAS with time-varying delays remains a difficult problem. To overcome the essential difficulty of switching topology, this paper aims at developing a contractive-set approach to analyze the convergence rate of a discrete-time MAS in the presence of time-varying delays and generalized coupling coefficients. Using the proposed approach, we obtain an upper bound of the convergence rate under the condition of joint connectivity. In particular, the proposed method neither requires the nonnegative property of the coupling coefficients nor the basic assumption of a uniform lower bound for all positive coupling coefficients, which have been widely applied in the existing works on this topic. As an application of the main results, we will show that the classical Vicsek model with time delays can realize synchronization if the initial topology is connected.

  15. Time Varying Apparent Volume of Distribution and Drug Half-Lives Following Intravenous Bolus Injections

    PubMed Central

    Wesolowski, Carl A.; Wesolowski, Michal J.; Babyn, Paul S.

    2016-01-01

    We present a model that generalizes the apparent volume of distribution and half-life as functions of time following intravenous bolus injection. This generalized model defines a time varying apparent volume of drug distribution. The half-lives of drug remaining in the body vary in time and become longer as time elapses, eventually converging to the terminal half-life. Two example fit models were substituted into the general model: biexponential models from the least relative concentration error, and gamma variate models using adaptive regularization for least relative error of clearance. Using adult population parameters from 41 studies of the renal glomerular filtration marker 169Yb-DTPA, simulations of extracellular fluid volumes of 5, 10, 15 and 20 litres and plasma clearances of 40 and 100 ml/min were obtained. Of these models, the adaptively obtained gamma variate models had longer times to 95% of terminal volume and longer half-lives. PMID:27403663

  16. From maps to movies: High resolution time-varying sensitivity analysis for spatially distributed watershed models

    NASA Astrophysics Data System (ADS)

    Herman, J. D.; Kollat, J. B.; Reed, P. M.; Wagener, T.

    2013-12-01

    Distributed watershed models are now widely used in practice to simulate runoff responses at high spatial and temporal resolutions. Counter to this purpose, diagnostic analyses of distributed models currently aggregate performance measures in space and/or time and are thus disconnected from the models' operational and scientific goals. To address this disconnect, this study contributes a novel approach for computing and visualizing time-varying global sensitivity indices for spatially distributed model parameters. The high-resolution model diagnostics employ the method of Morris to identify evolving patterns in dominant model processes at sub-daily timescales over a six-month period. The method is demonstrated on the United States National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events are selected from within the six-month period to investigate the patterns in spatiotemporal sensitivities that emerge as a function of forcing patterns as well as wet-to-dry transitions. Surprisingly, events with similar magnitudes and durations exhibit significantly different performance controls in space and time, indicating that the diagnostic inferences drawn from representative events will be heavily biased by the a priori selection of those events. By contrast, this study demonstrates high-resolution time-varying sensitivity analysis, requiring no assumptions regarding representative events and allowing modelers to identify transitions between modeled hydrologic regimes a posteriori. The proposed approach details the dynamics of parameter sensitivity in nearly continuous time, providing critical diagnostic insights into the underlying model processes driving predictions. Furthermore, the approach offers the potential to identify transition points between hydrologic regimes under nonstationarity.

  17. From maps to movies: high resolution time-varying sensitivity analysis for spatially distributed watershed models

    NASA Astrophysics Data System (ADS)

    Herman, J. D.; Kollat, J. B.; Reed, P. M.; Wagener, T.

    2013-08-01

    Distributed watershed models are now widely used in practice to simulate runoff responses at high spatial and temporal resolutions. Counter to this purpose, diagnostic analyses of distributed models currently aggregate performance measures in space and/or time and are thus disconnected from the models' operational and scientific goals. To address this disconnect, this study contributes a novel approach for computing and visualizing time-varying global sensitivity indices for spatially distributed model parameters. The high-resolution model diagnostics employ the method of Morris to identify evolving patterns in dominant model processes at sub-daily timescales over a six-month period. The method is demonstrated on the United States National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events are selected from within the six-month period to investigate the patterns in spatiotemporal sensitivities that emerge as a function of forcing patterns as well as wet-to-dry transitions. Surprisingly, events with similar magnitudes and durations exhibit significantly different performance controls in space and time, indicating that the diagnostic inferences drawn from representative events will be heavily biased by the a priori selection of those events. By contrast, this study demonstrates high-resolution time-varying sensitivity analysis, requiring no assumptions regarding representative events and allowing modelers to identify transitions between modeled hydrologic regimes a posteriori. The proposed approach details the dynamics of parameter sensitivity in nearly continuous time, providing critical diagnostic insights into the underlying model processes driving predictions. Furthermore, the approach offers the potential to identify transition points between hydrologic regimes under nonstationarity.

  18. Exponential Stabilization of Memristor-based Chaotic Neural Networks with Time-Varying Delays via Intermittent Control.

    PubMed

    Zhang, Guodong; Shen, Yi

    2015-07-01

    This paper is concerned with the global exponential stabilization of memristor-based chaotic neural networks with both time-varying delays and general activation functions. Here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new sufficient conditions ensuring exponential stabilization of memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the proposed results here are easy to verify and they also extend the earlier publications. Finally, numerical simulations illustrate the effectiveness of the obtained results.

  19. Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli.

    PubMed

    Zeng, Zhigang; Wang, Jun

    2006-12-01

    This paper presents new theoretical results on the global exponential stability of recurrent neural networks with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown that the Cohen-Grossberg neural network is globally exponentially stable, if the absolute value of the input vector exceeds a criterion. As special cases, the Hopfield neural network and the cellular neural network are examined in detail. In addition, it is shown that criteria herein, if partially satisfied, can still be used in combination with existing stability conditions. Simulation results are also discussed in two illustrative examples.

  20. Matrix measure method for global exponential stability of complex-valued recurrent neural networks with time-varying delays.

    PubMed

    Gong, Weiqiang; Liang, Jinling; Cao, Jinde

    2015-10-01

    In this paper, based on the matrix measure method and the Halanay inequality, global exponential stability problem is investigated for the complex-valued recurrent neural networks with time-varying delays. Without constructing any Lyapunov functions, several sufficient criteria are obtained to ascertain the global exponential stability of the addressed complex-valued neural networks under different activation functions. Here, the activation functions are no longer assumed to be derivative which is always demanded in relating references. In addition, the obtained results are easy to be verified and implemented in practice. Finally, two examples are given to illustrate the effectiveness of the obtained results.

  1. Global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects.

    PubMed

    Song, Qiankun; Yan, Huan; Zhao, Zhenjiang; Liu, Yurong

    2016-07-01

    In this paper, the global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects is discussed. By employing Lyapunov functional method and using matrix inequality technique, several sufficient conditions in complex-valued linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the considered neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed stability results are less conservative than some recently known ones in the literatures, which is demonstrated via two examples with simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Further results for global exponential stability of stochastic memristor-based neural networks with time-varying delays

    NASA Astrophysics Data System (ADS)

    Zhong, Kai; Zhu, Song; Yang, Qiqi

    2016-11-01

    In recent years, the stability problems of memristor-based neural networks have been studied extensively. This paper not only takes the unavoidable noise into consideration but also investigates the global exponential stability of stochastic memristor-based neural networks with time-varying delays. The obtained criteria are essentially new and complement previously known ones, which can be easily validated with the parameters of system itself. In addition, the study of the nonlinear dynamics for the addressed neural networks may be helpful in qualitative analysis for general stochastic systems. Finally, two numerical examples are provided to substantiate our results.

  3. Position and force tracking in nonlinear teleoperation systems with sandwich linearity in actuators and time-varying delay

    NASA Astrophysics Data System (ADS)

    Ganjefar, Soheil; Rezaei, Sara; Hashemzadeh, Farzad

    2017-03-01

    In this paper, a new bounded force feedback control law is proposed to guarantee position and force tracking in nonlinear teleoperation systems in the presence of passive and nonpassive input interaction forces, time varying delay in their communication channels and sandwich linearity in their actuators. The proposed control is a nonlinear-proportional plus nonlinear damping (nP+nD) controller with the addition of a nonlinear function of the environment force on the slave side and nonlinear function of the human force and force error on the master side, the transparency of the proposed scheme will be improved. The controller prevents the inputs from reaching their usual actuator bounds. Using a novel Lyapunov-Krasovskii functional, the asymptotic stability and tracking performance of the teleoperation system are established under some conditions on the controller parameters, actuator saturation characteristics and maximum allowable time delays.

  4. Synchronization-based topology identification of weighted general complex dynamical networks with time-varying coupling delay

    NASA Astrophysics Data System (ADS)

    Wu, Xiaoqun

    2008-02-01

    Many existing papers investigated the geometric features, control and synchronization of complex dynamical networks provided with certain topology. However, the exact topology of a network is sometimes unknown or uncertain. Based on LaSalle’s invariance principle, we propose an adaptive feedback technique to identify the exact topology of a weighted general complex dynamical network model with time-varying coupling delay. By receiving the network nodes evolution, the topology of such a kind of network with identical or different nodes, or even with varying topology can be monitored. In comparison with previous methods, time delay is taken into account in this simple, analytical and systematic synchronization-based technique. Particularly, the weight configuration matrix is not necessarily symmetric or irreducible, and the inner-coupling matrix need not be symmetric. Illustrative simulations are provided to verify the correctness and effectiveness of the proposed scheme.

  5. State Estimation for Static Neural Networks With Time-Varying Delays Based on an Improved Reciprocally Convex Inequality.

    PubMed

    Zhang, Xian-Ming; Han, Qing-Long

    2017-02-16

    This brief is concerned with the problem of neural state estimation for static neural networks with time-varying delays. Notice that a Luenberger estimator can produce an estimation error irrespective of the neuron state trajectory. This brief provides a method for designing such an estimator for static neural networks with time-varying delays. First, in-depth analysis on a well-used reciprocally convex approach is made, leading to an improved reciprocally convex inequality. Second, the improved reciprocally convex inequality and some integral inequalities are employed to provide a tight upper bound on the time-derivative of some Lyapunov-Krasovskii functional. As a result, a novel bounded real lemma (BRL) for the resultant error system is derived. Third, the BRL is applied to present a method for designing suitable Luenberger estimators in terms of solutions of linear matrix inequalities with two tuning parameters. Finally, it is shown through a numerical example that the proposed method can derive less conservative results than some existing ones.

  6. Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays.

    PubMed

    Nie, Xiaobing; Zheng, Wei Xing

    2015-05-01

    This paper is concerned with the problem of coexistence and dynamical behaviors of multiple equilibrium points for neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays. The fixed point theorem and other analytical tools are used to develop certain sufficient conditions that ensure that the n-dimensional discontinuous neural networks with time-varying delays can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable. The importance of the derived results is that it reveals that the discontinuous neural networks can have greater storage capacity than the continuous ones. Moreover, different from the existing results on multistability of neural networks with discontinuous activation functions, the 3(n) locally stable equilibrium points obtained in this paper are located in not only saturated regions, but also unsaturated regions, due to the non-monotonic structure of discontinuous activation functions. A numerical simulation study is conducted to illustrate and support the derived theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Synchronization of generalized reaction-diffusion neural networks with time-varying delays based on general integral inequalities and sampled-data control approach.

    PubMed

    Dharani, S; Rakkiyappan, R; Cao, Jinde; Alsaedi, Ahmed

    2017-08-01

    This paper explores the problem of synchronization of a class of generalized reaction-diffusion neural networks with mixed time-varying delays. The mixed time-varying delays under consideration comprise of both discrete and distributed delays. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Using a newly introduced integral inequality, less conservative synchronization criteria that assure the global asymptotic synchronization of the considered generalized reaction-diffusion neural network and mixed delays are established in terms of linear matrix inequalities (LMIs). The obtained easy-to-test LMI-based synchronization criteria depends on the delay bounds in addition to the reaction-diffusion terms, which is more practicable. Upon solving these LMIs by using Matlab LMI control toolbox, a desired sampled-data controller gain can be acuqired without any difficulty. Finally, numerical examples are exploited to express the validity of the derived LMI-based synchronization criteria.

  8. Existence and stability of pseudo almost periodic solutions for shunting inhibitory cellular neural networks with neutral type delays and time-varying leakage delays.

    PubMed

    Xu, Changjin; Zhang, Qiming; Wu, Yusen

    2014-01-01

    In this paper, shunting inhibitory cellular neural networks(SICNNs) with neutral type delays and time-varying leakage delays are investigated. By applying Lyapunov functional method and differential inequality techniques, a set of sufficient conditions are obtained for the existence and exponential stability of pseudo almost periodic solutions of the model. An example is given to support the theoretical findings. Our results improve and generalize those of the previous studies.

  9. Nonlinear Reduced-Order Analysis with Time-Varying Spatial Loading Distributions

    NASA Technical Reports Server (NTRS)

    Prezekop, Adam

    2008-01-01

    Oscillating shocks acting in combination with high-intensity acoustic loadings present a challenge to the design of resilient hypersonic flight vehicle structures. This paper addresses some features of this loading condition and certain aspects of a nonlinear reduced-order analysis with emphasis on system identification leading to formation of a robust modal basis. The nonlinear dynamic response of a composite structure subject to the simultaneous action of locally strong oscillating pressure gradients and high-intensity acoustic loadings is considered. The reduced-order analysis used in this work has been previously demonstrated to be both computationally efficient and accurate for time-invariant spatial loading distributions, provided that an appropriate modal basis is used. The challenge of the present study is to identify a suitable basis for loadings with time-varying spatial distributions. Using a proper orthogonal decomposition and modal expansion, it is shown that such a basis can be developed. The basis is made more robust by incrementally expanding it to account for changes in the location, frequency and span of the oscillating pressure gradient.

  10. Input-output method to fault detection for discrete-time fuzzy networked systems with time-varying delay and multiple packet losses

    NASA Astrophysics Data System (ADS)

    Wang, Shenquan; Feng, Jian; Jiang, Yulian

    2016-05-01

    The fault detection (FD) problem for discrete-time fuzzy networked systems with time-varying delay and multiple packet losses is investigated in this paper. The communication links between the plant and the FD filter (FDF) are assumed to be imperfect, and the missing probability is governed by an individual random variable satisfying a certain probabilistic distribution over the interval [0 1]. The discrete-time delayed fuzzy networked system is first transformed into the form of interconnect ion of two subsystems by applying an input-output method and a two-term approximation approach, which are employed to approximate the time-varying delay. Our attention is focused on the design of fuzzy FDF (FFDF) such that, for all data missing conditions, the overall FD dynamics are input-output stable in mean square and preserves a guaranteed performance. Sufficient conditions are first established via H∞ performance analysis for the existence of the desired FFDF; meanwhile, the corresponding solvability conditions for the desired FFDF gains are characterised in terms of the feasibility of a convex optimisation problem. Moreover, we show that the obtained criteria based on the input-output approach can also be established by applying the direct Lyapunov method to the original time-delay systems. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed approaches.

  11. Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction.

    PubMed

    Wen, Yuntong; Ren, Xuemei

    2011-10-01

    This paper investigates a neural network (NN) state observer-based adaptive control for a class of time-varying delays nonlinear systems with unknown control direction. An adaptive neural memoryless observer, in which the knowledge of time-delay is not used, is designed to estimate the system states. Furthermore, by applying the property of the function tanh(2)(ϑ/ε)/ϑ (the function can be defined at ϑ = 0) and introducing a novel type appropriate Lyapunov-Krasovskii functional, an adaptive output feedback controller is constructed via backstepping method which can efficiently avoid the problem of controller singularity and compensate for the time-delay. It is highly proven that the closed-loop systems controller designed by the NN-basis function property, new kind parameter adaptive law and Nussbaum function in detecting the control direction is able to guarantee the semi-global uniform ultimate boundedness of all signals and the tracking error can converge to a small neighborhood of zero. The characteristic of the proposed approach is that it relaxes any restrictive assumptions of Lipschitz condition for the unknown nonlinear continuous functions. And the proposed scheme is suitable for the systems with mismatching conditions and unmeasurable states. Finally, two simulation examples are given to illustrate the effectiveness and applicability of the proposed approach. © 2011 IEEE

  12. Synchronization of a Class of Switched Neural Networks with Time-Varying Delays via Nonlinear Feedback Control.

    PubMed

    Wang, Leimin; Shen, Yi; Zhang, Guodong

    2016-10-01

    This paper is concerned with the synchronization problem for a class of switched neural networks (SNNs) with time-varying delays. First, a new crucial lemma which includes and extends the classical exponential stability theorem is constructed. Then by using the lemma, new algebraic criteria of ψ -type synchronization (synchronization with general decay rate) for SNNs are established via the designed nonlinear feedback control. The ψ -type synchronization which is in a general framework is obtained by introducing a ψ -type function. It contains exponential synchronization, polynomial synchronization, and other synchronization as its special cases. The results of this paper are general, and they also complement and extend some previous results. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

  13. Consensus with guaranteed convergence rate of high-order integrator agents in the presence of time-varying delays

    NASA Astrophysics Data System (ADS)

    Savino, H. J.; Souza, F. O.; Pimenta, L. C. A.

    2016-07-01

    This paper aims to study the consensus problem in directed networks of agents with high-order integrator dynamics and fixed topology. It is considered the existence of non-uniform time-varying delays in the agents control laws for each interaction between agents and their neighbours. Based on Lyapunov-Krasovskii stability theory and algebraic graph theory, sufficient conditions, in terms of linear matrix inequalities, are given to verify if consensus is achieved with guaranteed exponential convergence rate. The efficiency of the proposed method is verified by numerical simulations. The simulations reveal that the conditions established in this work outperformed the similar existing ones in all numerical tests accomplished in this paper.

  14. Well-posedness and exponential stability for a plate equation with time-varying delay and past history

    NASA Astrophysics Data System (ADS)

    Feng, Baowei

    2017-02-01

    This paper is concerned with a class of plate equation with past history and time-varying delay in the internal feedback u_{tt}+α Δ ^2 u-int limits ^t_{-∞}g(t-s)Δ ^2 u(s)ds+μ _1u_t+μ _2u_t(t-τ (t))+f(u)=h(x), defined in a bounded domain of {R}^n (n≥1) with some suitable initial data and boundary conditions. For arbitrary real numbers μ _1 and μ _2, we proved the global well-posedness of the problem. Results on stability of energy are also proved under some restrictions on μ _1, μ _2 and h(x)=0.

  15. Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays.

    PubMed

    Li, Hongfei; Jiang, Haijun; Hu, Cheng

    2016-03-01

    In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. On the periodic dynamics of a class of time-varying delayed neural networks via differential inclusions.

    PubMed

    Cai, Zuowei; Huang, Lihong; Guo, Zhenyuan; Chen, Xiaoyan

    2012-09-01

    This paper investigates the periodic dynamics of a general class of time-varying delayed neural networks with discontinuous right-hand sides. By employing the topological degree theory in set-valued analysis, differential inclusions theory and Lyapunov-like approach, we perform a thorough analysis of the existence, uniqueness and global exponential stability of the periodic solution for the neural networks. Especially, some sufficient conditions are derived to guarantee the existence, uniqueness and global exponential stability of the equilibrium point for the autonomous systems corresponding to the non-autonomous neural networks. Furthermore, the global convergence of the output and the convergence in finite time of the state are also discussed. Without assuming the boundedness or monotonicity of the discontinuous neuron activation functions, the obtained results improve and extend previous works on discontinuous or continuous neural network dynamical systems. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results.

  17. Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay.

    PubMed

    Hwang, Chih-Lyang; Jan, Chau

    2016-02-01

    At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.

  18. Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication.

    PubMed

    Li, Huaqing; Huang, Chicheng; Chen, Guo; Liao, Xiaofeng; Huang, Tingwen

    2017-03-31

    This paper considers solving a class of optimization problems which are modeled as the sum of all agents' convex cost functions and each agent is only accessible to its individual function. Communication between agents in multiagent networks is assumed to be limited: each agent can only interact information with its neighbors by using time-varying communication channels with limited capacities. A technique which overcomes the limitation is to implement a quantization process to the interacted information. The quantized information is first encoded as a binary sequence at the side of each agent before sending. After the binary sequence is received by the neighboring agent, corresponding decoding scheme is utilized to resume the original information with a certain degree of error which is caused by the quantization process. With the availability of each agent's encoding states (associated with its out-channels) and decoding states (associated with its in-channels), we devise a set of distributed optimization algorithms that generate two iterative sequences, one of which converges to the optimal solution and the other of which reaches to the optimal value. We prove that if the parameters satisfy some mild conditions, the quantization errors are bounded and the consensus optimization can be achieved. How to minimize the number of quantization level of each connected communication channel in fixed networks is also explored thoroughly. It is found that, by properly choosing system parameters, one bit information exchange suffices to ensure consensus optimization. Finally, we present two numerical simulation experiments to illustrate the efficacy of the algorithms as well as to validate the theoretical findings.

  19. Adaptive Control of Semi-Autonomous Teleoperation System With Asymmetric Time-Varying Delays and Input Uncertainties.

    PubMed

    Zhai, Di-Hua; Xia, Yuanqing

    2016-06-07

    This paper addresses the adaptive task-space bilateral teleoperation for heterogeneous master and slave robots to guarantee stability and tracking performance, where a novel semi-autonomous teleoperation framework is developed to ensure the safety and enhance the efficiency of the robot in remote site. The basic idea is to stabilize the tracking error in task space while enhancing the efficiency of complex teleoperation by using redundant slave robot with subtask control. To unify the study of the asymmetric time-varying delays, passive/nonpassive exogenous forces, dynamic parameter uncertainties and dead-zone input in the same framework, a novel switching technique-based adaptive control scheme is investigated, where a special switched error filter is developed. By replacing the derivatives of position errors with their filtered outputs in the coordinate torque design, and employing the multiple Lyapunov-Krasovskii functionals method, the complete closed-loop master (slave) system is proven to be state-independent input-to-output stable. It is shown that both the position tracking errors in task space and the adaptive parameter estimation errors remain bounded for any bounded exogenous forces. Moreover, by using the redundancy of the slave robot, the proposed teleoperation framework can autonomously achieve additional subtasks in the remote environment. Finally, the obtained results are demonstrated by the simulation.

  20. Analysis of global O(t(-α)) stability and global asymptotical periodicity for a class of fractional-order complex-valued neural networks with time varying delays.

    PubMed

    Rakkiyappan, R; Sivaranjani, R; Velmurugan, G; Cao, Jinde

    2016-05-01

    In this paper, the problem of the global O(t(-α)) stability and global asymptotic periodicity for a class of fractional-order complex-valued neural networks (FCVNNs) with time varying delays is investigated. By constructing suitable Lyapunov functionals and a Leibniz rule for fractional differentiation, some new sufficient conditions are established to ensure that the addressed FCVNNs are globally O(t(-α)) stable. Moreover, some sufficient conditions for the global asymptotic periodicity of the addressed FCVNNs with time varying delays are derived, showing that all solutions converge to the same periodic function. Finally, numerical examples are given to demonstrate the effectiveness and usefulness of our theoretical results.

  1. M-matrix based robust stability and stabilization for uncertain discrete-time switched TS fuzzy systems with time-varying delays.

    PubMed

    Jaballi, Ahmed; Sakly, Anis; Hajjaji, Ahmed El

    2016-07-01

    This paper provides novel sufficient conditions on robust asymptotic stability and stabilization for a class of uncertain discrete-time switched fuzzy with time-varying delays. The attention is focused on developing new algebraic criteria to break with classical criteria in terms of Linear Matrix Inequalities (LMIs). Firstly, based on the M-matrix proprieties and through l1,∞ induced norms notion, new delay-dependent sufficient conditions are derived to ensure the asymptotic stability and stabilization for a class of uncertain discrete-time switched fuzzy systems with time-varying delay. Secondly, these results are extended for a class of uncertain discrete-time switched fuzzy systems with time delays, modeled by difference equations. Finally, two numerical examples and practical example (a robot arm) are provided to demonstrate the advantage and the effectiveness of our results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

    PubMed

    Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

    2017-05-18

    This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.

  3. The time-varying electron energy distribution function in the plume of a Hall thruster

    NASA Astrophysics Data System (ADS)

    Dannenmayer, K.; Mazouffre, S.; Kudrna, P.; Tichý, M.

    2014-12-01

    Time-resolved Langmuir probe measurements have been performed in the plume of the 1.5 kW class PPS®1350-ML Hall thruster. The time-dependent electron energy distribution function (EEDF) has been inferred from the probe current-voltage characteristic curves obtained after active stabilization of the discharge. The distribution function changes in the course of time at the breathing oscillation frequency (13.8 kHz). The EEDF is Maxwellian with a depleted tail above the xenon ionization energy whatever the location and the time. The electron density and temperature computed from the EEDF also oscillate at the breathing mode frequency. Experimental outcomes indicate the existence of a low-frequency plasma wave that propagates axially. The wave front speed (2700 m s-1) was found to be compatible with the ion acoustic speed (2300 m s-1).

  4. Modeling hyporheic exchange and in-stream transport with time-varying transit time distributions

    NASA Astrophysics Data System (ADS)

    Ball, A.; Harman, C. J.; Ward, A. S.

    2014-12-01

    Transit time distributions (TTD) are used to understand in-stream transport and exchange with the hyporheic zone by quantifying the probability of water (and of dissolved material) taking time T to traverse the stream reach control volume. However, many studies using this method assume a TTD that is time-invariant, despite the time-variability of the streamflow. Others assume that storage is 'randomly sampled' or 'well-mixed' with a fixed volume or fixed exchange rate. Here we present a formulation for a time-variable TTD that relaxes both the time-invariant and 'randomly sampled' assumptions and only requires a few parameters. The framework is applied to transient storage, representing some combination of in-stream and hyporheic storage, along a stream reach. This approach does not assume that hyporheic and dead-zone storage is fixed or temporally-invariant, and allows for these stores to be sampled in more physically representative ways determined by the system itself. Instead of using probability distributions of age, probability distributions of storage (ranked by age) called Ω functions are used to describe how the off-stream storage is sampled in the outflow. Here the Ω function approach is used to describe hyporheic exchange during diurnal fluctuations in streamflow in a gaining reach of the H.J. Andrews Experimental Forest. The breakthrough curves of salt slugs injected four hours apart over a 28-hour period show a systematic variation in transit time distribution. This new approach allows us to relate these salt slug TTDs to a corresponding time-variation in the Ω function, which can then be related to changes in in-stream storage and hyporheic zone mobilization under varying flow conditions. Thus, we can gain insights into how channel storage and hyporheic exchange are changing through time without having to specify difficult to measure or unmeasurable quantities of our system, such as total storage.

  5. Modeling unsteady lumped transport with time-varying transit time distributions

    NASA Astrophysics Data System (ADS)

    Harman, Ciaran

    2014-05-01

    Transit time distributions (TTD) offer a powerful tool for characterizing 'lumped' hydrologic transport (i.e. with few parameters, and without resolving the internal dynamics), but their general application for transport modeling has been hampered by the challenge of dealing with time-variable TTD. A way forward has emerged with the development of the 'age function' approach, but it has not been clear how to parameterize the age function, or how to interpret it physically and compare it to perceptual models. It also requires specification of the total storage, which is not possible in many cases of interest. This paper presents a more general formulation for TTD modeling that addresses these limitations. Transport is parameterized in terms of a probability density function Ω that represents the relative contribution of age-ranked water in storage to the flux out. Other frameworks are shown to be a special case of this one if the total storage is known. A new equation is obtained describing the time-evolution of the TTD that does not require specification of the total storage. In fact, the storage can be indefinitely large, allowing pdfs with semi-infinite support to parameterize Ω. Classical equations for random-sampling ('completely mixed') and piston-flow type transport fall out as special cases of Ω at steady-state. Other choices for Ω yield TTD capable of replicating observed transport phenomena like heavy tails and fractal 1/f-noise. Application of the model to long term and high frequency passive tracer datasets demonstrates its promise as a framework for new models of transport in time-variable landscape hydrologic systems with a unique ability to capture these important features.

  6. Well-posedness and exponential decay for a porous thermoelastic system with second sound and a time-varying delay term in the internal feedback

    NASA Astrophysics Data System (ADS)

    Liu, Wenjun; Chen, Miaomiao

    2017-05-01

    In this paper, we study the well-posedness and exponential decay for the porous thermoelastic system with the heat conduction given by Cattaneo's law and a time-varying delay term, the coefficient of which is not necessarily positive. Using the semigroup arguments and variable norm technique of Kato, we first prove that the system is well-posed under a certain condition on the weight of the delay term, the weight of the elastic damping term and the speed of the delay function. By introducing a suitable energy and an appropriate Lyapunov functional, we then establish an exponential decay rate result.

  7. Central suboptimal H ∞ filter design for linear time-varying systems with state and measurement delays

    NASA Astrophysics Data System (ADS)

    Basin, Michael; Shi, Peng; Calderon-Alvarez, Dario

    2010-04-01

    This article presents the central finite-dimensional H ∞ filters for linear systems with state and measurement delay that are suboptimal for a given threshold γ with respect to a modified Bolza-Meyer quadratic criterion including the attenuation control term with the opposite sign. In contrast to the results previously obtained for linear time delay systems, this article reduces the original H ∞ filtering problem to H 2 (optimal mean-square) filtering problem using the technique proposed in Doyle, Glover, Khargonekar, and Francis (1989 'State-space Solutions to Standard H 2 and H ∞ Control Problems', IEEE Transactions on Automatic Control, 34, 831-847). Application of the reduction technique becomes possible, since the optimal closed-form filtering equations solving the H 2 (mean-square) filtering problem have been obtained for linear systems with state and measurement delays. This article first presents the central suboptimal H ∞ filter for linear systems with state and measurement delays, based on the optimal H 2 filter from Basin, Alcorta-Garcia, and Rodriguez-Gonzalez (2005, 'Optimal Filtering for Linear Systems with State and Observation Delays', International Journal of Robust and Nonlinear Control, 15, 859-871), which consists, in the general case, of an infinite set of differential equations. Then, the finite-dimensional central suboptimal H ∞ filter is designed in case of linear systems with commensurable state and measurement delays, which contains a finite number of equations for any fixed filtering horizon; however, this number still grows unboundedly as time goes to infinity. To overcome that difficulty, the alternative central suboptimal H ∞ filter is designed for linear systems with state and measurement delays, which is based on the alternative optimal H 2 filter from Basin, Perez, and Martinez-Zuniga (2006, 'Alternative Optimal Filter for Linear State Delay Systmes', International Journal of Adaptive Control and Signal Processing, 20

  8. Finite-Time Stability Analysis of Reaction-Diffusion Genetic Regulatory Networks with Time-Varying Delays.

    PubMed

    Fan, Xiaofei; Zhang, Xian; Wu, Ligang; Shi, Michael

    2016-04-11

    This paper is concerned with the finite-time stability problem of the delayed genetic regulatory networks (GRNs) with reaction-diffusion terms under Dirichlet boundary conditions. By constructing a Lyapunov-Krasovskii functional including quad- slope integrations, we establish delay-dependent finite-time stabil- ity criteria by employing the Wirtinger-type integral inequality, Gronwall inequality, convex technique, and reciprocally convex technique. In addition, the obtained criteria are also reaction- diffusion-dependent. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.

  9. Novel switching design for finite-time stabilization: Applications to memristor-based neural networks with time-varying delay

    NASA Astrophysics Data System (ADS)

    Cai, Zuo-Wei; Huang, Jian-Hua; Huang, Li-Hong

    2017-02-01

    The aim of this paper is to provide a novel switching control design to solve finite-time stabilization issues of a discontinuous or switching dynamical system. In order to proceed with our analysis, we first design two kinds of switching controllers: switching adaptive controller and switching state-feedback controller. Then, we apply the proposed switching control technique to stabilize the states of delayed memristor-based neural networks (DMNNs) in finite time. Based on a famous finite-time stability theorem, the theory of differential inclusion and the generalized Lyapunov functional method, some sufficient conditions are obtained to guarantee the finite-time stabilization control of DMNNs. The feedback functions of our model are allowed to be unbounded, and the upper bounds of the settling time for stabilization are also given. Finally, the validity of designed method and the theoretical results are illustrated by numerical examples.

  10. Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay.

    PubMed

    Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

    2017-09-11

    This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Event-based state estimation for a class of complex networks with time-varying delays: A comparison principle approach

    NASA Astrophysics Data System (ADS)

    Zhang, Wenbing; Wang, Zidong; Liu, Yurong; Ding, Derui; Alsaadi, Fuad E.

    2017-01-01

    The paper is concerned with the state estimation problem for a class of time-delayed complex networks with event-triggering communication protocol. A novel event generator function, which is dependent not only on the measurement output but also on a predefined positive constant, is proposed with hope to reduce the communication burden. A new concept of exponentially ultimate boundedness is provided to quantify the estimation performance. By means of the comparison principle, some sufficient conditions are obtained to guarantee that the estimation error is exponentially ultimately bounded, and then the estimator gains are obtained in terms of the solution of certain matrix inequalities. Furthermore, a rigorous proof is proposed to show that the designed triggering condition is free of the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed event-based estimator.

  12. From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models

    NASA Astrophysics Data System (ADS)

    Herman, J. D.; Kollat, J. B.; Reed, P. M.; Wagener, T.

    2013-12-01

    Distributed watershed models are now widely used in practice to simulate runoff responses at high spatial and temporal resolutions. Counter to this purpose, diagnostic analyses of distributed models currently aggregate performance measures in space and/or time and are thus disconnected from the models' operational and scientific goals. To address this disconnect, this study contributes a novel approach for computing and visualizing time-varying global sensitivity indices for spatially distributed model parameters. The high-resolution model diagnostics employ the method of Morris to identify evolving patterns in dominant model processes at sub-daily timescales over a six-month period. The method is demonstrated on the United States National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events are selected from within the six-month period to investigate the patterns in spatiotemporal sensitivities that emerge as a function of forcing patterns as well as wet-to-dry transitions. Events with similar magnitudes and durations exhibit significantly different performance controls in space and time, indicating that the diagnostic inferences drawn from representative events will be heavily biased by the a priori selection of those events. By contrast, this study demonstrates high-resolution time-varying sensitivity analysis, requiring no assumptions regarding representative events and allowing modelers to identify transitions between sets of dominant parameters or processes a posteriori. The proposed approach details the dynamics of parameter sensitivity in nearly continuous time, providing critical diagnostic insights into the underlying model processes driving predictions. Furthermore, the approach offers the potential to identify transition points between dominant parameters and processes in the absence of observations, such as under nonstationarity.

  13. Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

    PubMed

    Li, Zhijun; Su, Chun-Yi

    2013-09-01

    In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

  14. Finite-time H∞ control for a class of Markovian jump systems with mode-dependent time-varying delays via new Lyapunov functionals.

    PubMed

    Cheng, Jun; Zhu, Hong; Zhong, Shouming; Zeng, Yong; Dong, Xiucheng

    2013-11-01

    This paper is concerned with the problem of finite-time H∞ control for a class of Markovian jump systems with mode-dependent time-varying delays via new Lyapunov functionals. In order to reduce conservatism, a new Lyapunov-Krasovskii functional is constructed. Based on the derived condition, the reliable H∞ control problem is solved, and the system trajectory stays within a prescribed bound during a specified time interval. Finally, numerical examples are given to demonstrate the proposed approach is more effective than some existing ones.

  15. Existence of periodic solutions for the discrete-time counterpart of a neutral-type cellular neural network with time-varying delays and impulses

    NASA Astrophysics Data System (ADS)

    Akça, Haydar; Al-Zahrani, Eadah; Covachev, Valéry; Covacheva, Zlatinka

    2017-07-01

    From the mathematical point of view, a cellular neural network (CNN) can be characterized by an array of identical nonlinear dynamical systems called cells (neurons) that are locally interconnected. Using the semi-discretization method, in the present talk we construct a discrete-time counterpart of a neutral-type CNN with time-varying delays and impulses. Sufficient conditions for the existence of periodic solutions of the discrete-time system thus obtained are found by using the continuation theorem of coincidence degree theory.

  16. Exponential synchronization of generalized neural networks with mixed time-varying delays and reaction-diffusion terms via aperiodically intermittent control

    NASA Astrophysics Data System (ADS)

    Gan, Qintao

    2017-01-01

    In this paper, the exponential synchronization problem of generalized reaction-diffusion neural networks with mixed time-varying delays is investigated concerning Dirichlet boundary conditions in terms of p-norm. Under the framework of the Lyapunov stability method, stochastic theory, and mathematical analysis, some novel synchronization criteria are derived, and an aperiodically intermittent control strategy is proposed simultaneously. Moreover, the effects of diffusion coefficients, diffusion space, and stochastic perturbations on the synchronization process are explicitly expressed under the obtained conditions. Finally, some numerical simulations are performed to illustrate the feasibility of the proposed control strategy and show different synchronization dynamics under a periodically/aperiodically intermittent control.

  17. Existence and global exponential stability of almost periodic solution for cellular neural networks with variable coefficients and time-varying delays.

    PubMed

    Jiang, Haijun; Zhang, Long; Teng, Zhidong

    2005-11-01

    In this paper, we study cellular neural networks with almost periodic variable coefficients and time-varying delays. By using the existence theorem of almost periodic solution for general functional differential equations, introducing many real parameters and applying the Lyapunov functional method and the technique of Young inequality, we obtain some sufficient conditions to ensure the existence, uniqueness, and global exponential stability of almost periodic solution. The results obtained in this paper are new, useful, and extend and improve the existing ones in previous literature.

  18. Time-varying BRDFs.

    PubMed

    Sun, Bo; Sunkavalli, Kalyan; Ramamoorthi, Ravi; Belhumeur, Peter N; Nayar, Shree K

    2007-01-01

    The properties of virtually all real-world materials change with time, causing their bidirectional reflectance distribution functions (BRDFs) to be time varying. However, none of the existing BRDF models and databases take time variation into consideration; they represent the appearance of a material at a single time instance. In this paper, we address the acquisition, analysis, modeling, and rendering of a wide range of time-varying BRDFs (TVBRDFs). We have developed an acquisition system that is capable of sampling a material's BRDF at multiple time instances, with each time sample acquired within 36 sec. We have used this acquisition system to measure the BRDFs of a wide range of time-varying phenomena, which include the drying of various types of paints (watercolor, spray, and oil), the drying of wet rough surfaces (cement, plaster, and fabrics), the accumulation of dusts (household and joint compound) on surfaces, and the melting of materials (chocolate). Analytic BRDF functions are fit to these measurements and the model parameters' variations with time are analyzed. Each category exhibits interesting and sometimes nonintuitive parameter trends. These parameter trends are then used to develop analytic TVBRDF models. The analytic TVBRDF models enable us to apply effects such as paint drying and dust accumulation to arbitrary surfaces and novel materials.

  19. Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers.

    PubMed

    Stamova, Ivanka; Stamov, Gani

    2017-09-08

    In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fractional Lyapunov method sufficient conditions are given. We also study the global Mittag-Leffler synchronization of two identical fractional impulsive reaction-diffusion neural networks using linear controllers, which was an open problem even for integer-order models. Since the Mittag-Leffler stability notion is a generalization of the exponential stability concept for fractional-order systems, our results extend and improve the exponential impulsive control theory of neural network system with time-varying delays and reaction-diffusion terms to the fractional-order case. The fractional-order derivatives allow us to model the long-term memory in the neural networks, and thus the present research provides with a conceptually straightforward mathematical representation of rather complex processes. Illustrative examples are presented to show the validity of the obtained results. We show that by means of appropriate impulsive controllers we can realize the stability goal and to control the qualitative behavior of the states. An image encryption scheme is extended using fractional derivatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. State Estimation for Discrete-Time Dynamical Networks With Time-Varying Delays and Stochastic Disturbances Under the Round-Robin Protocol.

    PubMed

    Zou, Lei; Wang, Zidong; Gao, Huijun; Liu, Xiaohui

    2016-02-19

    This paper is concerned with the state estimation problem for a class of nonlinear dynamical networks with time-varying delays subject to the round-robin protocol. The communication between the state estimator and the nodes of the dynamical networks is implemented through a shared constrained network, in which only one node is allowed to send data at each time instant. The round-robin protocol is utilized to orchestrate the transmission order of nodes. By using a switch-based approach, the dynamics of the estimation error is modeled by a periodic parameter-switching system with time-varying delays. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded with a certain asymptotic upper bound in mean square subject to the process noise and exogenous disturbance. Furthermore, such a bound is subsequently minimized by the designed estimator parameters. A novel Lyapunov-like functional is employed to deal with the dynamics analysis issue of the estimation error. Sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square by applying the stochastic analysis approach. Then, the desired estimator gains are characterized by solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the estimator design scheme.

  1. Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays.

    PubMed

    Rakkiyappan, R; Sakthivel, N; Cao, Jinde

    2015-06-01

    This study examines the exponential synchronization of complex dynamical networks with control packet loss and additive time-varying delays. Additionally, sampled-data controller with time-varying sampling period is considered and is assumed to switch between m different values in a random way with given probability. Then, a novel Lyapunov-Krasovskii functional (LKF) with triple integral terms is constructed and by using Jensen's inequality and reciprocally convex approach, sufficient conditions under which the dynamical network is exponentially mean-square stable are derived. When applying Jensen's inequality to partition double integral terms in the derivation of linear matrix inequality (LMI) conditions, a new kind of linear combination of positive functions weighted by the inverses of squared convex parameters appears. In order to handle such a combination, an effective method is introduced by extending the lower bound lemma. To design the sampled-data controller, the synchronization error system is represented as a switched system. Based on the derived LMI conditions and average dwell-time method, sufficient conditions for the synchronization of switched error system are derived in terms of LMIs. Finally, numerical example is employed to show the effectiveness of the proposed methods.

  2. Observer-based robust finite time H∞ sliding mode control for Markovian switching systems with mode-dependent time-varying delay and incomplete transition rate.

    PubMed

    Gao, Lijun; Jiang, Xiaoxiao; Wang, Dandan

    2016-03-01

    This paper investigates the problem of robust finite time H∞ sliding mode control for a class of Markovian switching systems. The system is subjected to the mode-dependent time-varying delay, partly unknown transition rate and unmeasurable state. The main difficulty is that, a sliding mode surface cannot be designed based on the unknown transition rate and unmeasurable state directly. To overcome this obstacle, the set of modes is firstly divided into two subsets standing for known transition rate subset and unknown one, based on which a state observer is established. A component robust finite-time sliding mode controller is also designed to cope with the effect of partially unknown transition rate. It is illustrated that the reachability, finite-time stability, finite-time boundedness, finite-time H∞ state feedback stabilization of sliding mode dynamics can be ensured despite the unknown transition rate. Finally, the simulation results verify the effectiveness of robust finite time control problem.

  3. Global O(t(-α)) stability and global asymptotical periodicity for a non-autonomous fractional-order neural networks with time-varying delays.

    PubMed

    Chen, Boshan; Chen, Jiejie

    2016-01-01

    The present paper studies global O(t(-α)) stability and global asymptotical periodicity for a non-autonomous fractional-order neural networks with time-varying delays (FDNN). Firstly, some sufficient conditions are established to ensure that a non-autonomous FDNN is global O(t(-α)) stable based on a new Lyapunov function method and Leibniz rule for fractional differentiation. Next it is shown that the periodic or autonomous FDNN cannot generate exactly nonconstant periodic solution under any circumstances. Finally, we show that all solutions converge to a same periodic function for a periodic FDNN by using a fractional-order differential inequality technique. Our issues, methods and results are all new.

  4. Coexistence and local μ-stability of multiple equilibrium points for memristive neural networks with nonmonotonic piecewise linear activation functions and unbounded time-varying delays.

    PubMed

    Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

    2016-12-01

    In this paper, the coexistence and dynamical behaviors of multiple equilibrium points are discussed for a class of memristive neural networks (MNNs) with unbounded time-varying delays and nonmonotonic piecewise linear activation functions. By means of the fixed point theorem, nonsmooth analysis theory and rigorous mathematical analysis, it is proven that under some conditions, such n-neuron MNNs can have 5(n) equilibrium points located in ℜ(n), and 3(n) of them are locally μ-stable. As a direct application, some criteria are also obtained on the multiple exponential stability, multiple power stability, multiple log-stability and multiple log-log-stability. All these results reveal that the addressed neural networks with activation functions introduced in this paper can generate greater storage capacity than the ones with Mexican-hat-type activation function. Numerical simulations are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays.

    PubMed

    Liu, Peng; Zeng, Zhigang; Wang, Jun

    2016-07-01

    This paper addresses the multistability for a general class of recurrent neural networks with time-varying delays. Without assuming the linearity or monotonicity of the activation functions, several new sufficient conditions are obtained to ensure the existence of (2K+1)(n) equilibrium points and the exponential stability of (K+1)(n) equilibrium points among them for n-neuron neural networks, where K is a positive integer and determined by the type of activation functions and the parameters of neural network jointly. The obtained results generalize and improve the earlier publications. Furthermore, the attraction basins of these exponentially stable equilibrium points are estimated. It is revealed that the attraction basins of these exponentially stable equilibrium points can be larger than their originally partitioned subsets. Finally, three illustrative numerical examples show the effectiveness of theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Adaptive fuzzy decentralized control for large-scale nonlinear systems with time-varying delays and unknown high-frequency gain sign.

    PubMed

    Tong, Shaocheng; Liu, Changliang; Li, Yongming; Zhang, Huaguang

    2011-04-01

    In this paper, an adaptive fuzzy decentralized robust output feedback control approach is proposed for a class of large-scale strict-feedback nonlinear systems without the measurements of the states. The nonlinear systems in this paper are assumed to possess unstructured uncertainties, time-varying delays, and unknown high-frequency gain sign. Fuzzy logic systems are used to approximate the unstructured uncertainties, K-filters are designed to estimate the unmeasured states, and a special Nussbaum gain function is introduced to solve the problem of unknown high-frequency gain sign. Combining the backstepping technique with adaptive fuzzy control theory, an adaptive fuzzy decentralized robust output feedback control scheme is developed. In order to obtain the stability of the closed-loop system, a new lemma is given and proved. Based on this lemma and Lyapunov-Krasovskii functions, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded and that the tracking errors can converge to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated from simulation results.

  7. Multistability of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays.

    PubMed

    Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

    2015-11-01

    The problem of coexistence and dynamical behaviors of multiple equilibrium points is addressed for a class of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays. By virtue of the fixed point theorem, nonsmooth analysis theory and other analytical tools, some sufficient conditions are established to guarantee that such n-dimensional memristive Cohen-Grossberg neural networks can have 5(n) equilibrium points, among which 3(n) equilibrium points are locally exponentially stable. It is shown that greater storage capacity can be achieved by neural networks with the non-monotonic activation functions introduced herein than the ones with Mexican-hat-type activation function. In addition, unlike most existing multistability results of neural networks with monotonic activation functions, those obtained 3(n) locally stable equilibrium points are located both in saturated regions and unsaturated regions. The theoretical findings are verified by an illustrative example with computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Theoretical Delay Time Distributions

    NASA Astrophysics Data System (ADS)

    Nelemans, Gijs; Toonen, Silvia; Bours, Madelon

    2013-01-01

    We briefly discuss the method of population synthesis to calculate theoretical delay time distributions of Type Ia supernova progenitors. We also compare the results of different research groups and conclude that, although one of the main differences in the results for single degenerate progenitors is the retention efficiency with which accreted hydrogen is added to the white dwarf core, this alone cannot explain all the differences.

  9. Distributed finite-time consensus tracking for nonlinear multi-agent systems with a time-varying reference state

    NASA Astrophysics Data System (ADS)

    Wen, Guoguang; Yu, Yongguang; Peng, Zhaoxia; Rahmani, Ahmed

    2016-06-01

    This paper investigates the consensus tracking problem for nonlinear multi-agent systems with a time-varying reference state. The consensus reference is taken as a virtual leader, whose output is only its position information that is available to only a subset of a group of followers. The dynamics of each follower consists of two terms: nonlinear inherent dynamics and a simple communication protocol relying only on the position of its neighbours. In this paper, the consensus tracking problem is respectively considered under fixed and switching communication topologies. Some corresponding sufficient conditions are obtained to guarantee the states of followers can converge to the state of the virtual leader in finite time. Rigorous proofs are given by using graph theory, matrix theory, and Lyapunov theory. Simulations are presented to illustrate the theoretical analysis.

  10. Time Varying Feature Data

    NASA Astrophysics Data System (ADS)

    Echterhoff, J.; Simonis, I.; Atkinson, R.

    2012-04-01

    The infrastructure to gather, store and access information about our environment is improving and growing rapidly. The increasing amount of information allows us to get a better understanding of the current state of our environment, historical processes and to simulate and predict the future state of the environment. Finer grained spatial and temporal data and more reliable communications make it easier to model dynamic states and ephemeral features. The exchange of information within and across geospatial domains is facilitated through the use of harmonized information models. The Observations & Measurements (O&M) developed through OGC and standardised by ISO is an example of such a cross-domain information model. It is used in many domains, including meteorology, hydrology as well as the emergency management. O&M enables harmonized representation of common metadata that belong to the act of determining the state of a feature property, whether by sensors, simulations or humans. In addition to the resulting feature property value, information such as the result quality but especially the time that the result applies to the feature property can be represented. Temporal metadata is critical to modelling past and future states of a feature. The features, and the semantics of each property, are defined in domain specific Application Schema using the General Feature Model (GFM) from ISO 19109 and usually encoded following ISO 19136. However, at the moment these standards provide only limited support for the representation and handling of time varying feature data. Features like rivers, wildfires or gas plumes have a defined state - for example geographic extent - at any given point in time. To keep track of changes, a more complex model for example using time-series coverages is required. Furthermore, the representation and management of feature property value changes via the service interfaces defined by OGC and ISO - namely: WFS and WCS - would be rather complex

  11. Distributed fault-tolerant time-varying formation control for high-order linear multi-agent systems with actuator failures.

    PubMed

    Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

    2017-06-29

    This paper investigates the fault-tolerant time-varying formation control problems for high-order linear multi-agent systems in the presence of actuator failures. Firstly, a fully distributed formation control protocol is presented to compensate for the influences of both bias fault and loss of effectiveness fault. Using the adaptive online updating strategies, no global knowledge about the communication topology is required and the bounds of actuator failures can be unknown. Then an algorithm is proposed to determine the control parameters of the fault-tolerant formation protocol, where the time-varying formation feasible conditions and an approach to expand the feasible formation set are given. Furthermore, the stability of the proposed algorithm is proven based on the Lyapunov-like theory. Finally, two simulation examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Leader-following exponential consensus of fractional order nonlinear multi-agents system with hybrid time-varying delay: A heterogeneous impulsive method

    NASA Astrophysics Data System (ADS)

    Wang, Fei; Yang, Yongqing

    2017-09-01

    In this paper, we study the leader-following exponential consensus of multi-agent system. Each agent in the system is described by nonlinear fractional order differential equation. Both the internal delay and coupling delay are taken into consideration. The heterogeneous impulsive control is used for ensuring the consensus of all agents. Based on Lyapunov function method and matrix analysis, some sufficient conditions for exponential consensus are obtained. Finally, some illustrative examples are given to show the effectiveness of the obtained results.

  13. Existence and uniqueness of pseudo almost-periodic solutions of recurrent neural networks with time-varying coefficients and mixed delays.

    PubMed

    Ammar, Boudour; Chérif, Farouk; Alimi, Adel M

    2012-01-01

    This paper is concerned with the existence and uniqueness of pseudo almost-periodic solutions to recurrent delayed neural networks. Several conditions guaranteeing the existence and uniqueness of such solutions are obtained in a suitable convex domain. Furthermore, several methods are applied to establish sufficient criteria for the globally exponential stability of this system. The approaches are based on constructing suitable Lyapunov functionals and the well-known Banach contraction mapping principle. Moreover, the attractivity and exponential stability of the pseudo almost-periodic solution are also considered for the system. A numerical example is given to illustrate the effectiveness of our results.

  14. Ultrasonic generator and detector using an optical mask having a grating for launching a plurality of spatially distributed, time varying strain pulses in a sample

    DOEpatents

    Maris, Humphrey J.

    2003-01-01

    A method and a system are disclosed for determining at least one characteristic of a sample that contains a substrate and at least one film disposed on or over a surface of the substrate. The method includes a first step of placing a mask over a free surface of the at least one film, where the mask has a top surface and a bottom surface that is placed adjacent to the free surface of the film. The bottom surface of the mask has formed therein or thereon a plurality of features for forming at least one grating. A next step directs optical pump pulses through the mask to the free surface of the film, where individual ones of the pump pulses are followed by at least one optical probe pulse. The pump pulses are spatially distributed by the grating for launching a plurality of spatially distributed, time varying strain pulses within the film, which cause a detectable change in optical constants of the film. A next step detects a reflected or a transmitted portion of the probe pulses, which are also spatially distributed by the grating. A next step measures a change in at least one characteristic of at least one of reflected or transmitted probe pulses due to the change in optical constants, and a further step determines the at least one characteristic of the sample from the measured change in the at least one characteristic of the probe pulses. An optical mask is also disclosed herein, and forms a part of these teachings.

  15. Ultrasonic generator and detector using an optical mask having a grating for launching a plurality of spatially distributed, time varying strain pulses in a sample

    DOEpatents

    Maris, Humphrey J.

    2002-01-01

    A method and a system are disclosed for determining at least one characteristic of a sample that contains a substrate and at least one film disposed on or over a surface of the substrate. The method includes a first step of placing a mask over a free surface of the at least one film, where the mask has a top surface and a bottom surface that is placed adjacent to the free surface of the film. The bottom surface of the mask has formed therein or thereon a plurality of features for forming at least one grating. A next step directs optical pump pulses through the mask to the free surface of the film, where individual ones of the pump pulses are followed by at least one optical probe pulse. The pump pulses are spatially distributed by the grating for launching a plurality of spatially distributed, time varying strain pulses within the film, which cause a detectable change in optical constants of the film. A next step detects a reflected or a transmitted portion of the probe pulses, which are also spatially distributed by the grating. A next step measures a change in at least one characteristic of at least one of reflected or transmitted probe pulses due to the change in optical constants, and a further step determines the at least one characteristic of the sample from the measured change in the at least one characteristic of the probe pulses. An optical mask is also disclosed herein, and forms a part of these teachings.

  16. Event-based distributed set-membership filtering for a class of time-varying non-linear systems over sensor networks with saturation effects

    NASA Astrophysics Data System (ADS)

    Wei, Guoliang; Liu, Shuai; Wang, Licheng; Wang, Yongxiong

    2016-07-01

    In this paper, based on the event-triggered mechanism, the problem of distributed set-membership filtering is concerned for a class of time-varying non-linear systems over sensor networks subject to saturation effects. Different from the traditional periodic sample-data approach, the filter is updated only when the predefined event is satisfied, which the event is defined according to the measurement output. For each node, the proposed novel event-triggered mechanism can reduce the unnecessary information transmission between sensors and filters. The purpose of the addressed problem is to design a series of distributed set-membership filters, for all the admissible unknown but bounded noises, non-linearities and sensor saturation, such that the set of all possible states can be determined. The desired filter parameters are obtained by solving a recursive linear matrix inequality that can be computed recursively using the available MATLAB toolbox. Finally, a simulation example is exploited to show the effectiveness of the proposed design approach in this paper.

  17. Can distributed delays perfectly stabilize dynamical networks?

    NASA Astrophysics Data System (ADS)

    Omi, Takahiro; Shinomoto, Shigeru

    2008-04-01

    Signal transmission delays tend to destabilize dynamical networks leading to oscillation, but their dispersion contributes oppositely toward stabilization. We analyze an integrodifferential equation that describes the collective dynamics of a neural network with distributed signal delays. With the Γ distributed delays less dispersed than exponential distribution, the system exhibits reentrant phenomena, in which the stability is once lost but then recovered as the mean delay is increased. With delays dispersed more highly than exponential, the system never destabilizes.

  18. Exponential stability and robust H∞ control of a class of discrete-time switched non-linear systems with time-varying delays via T-S fuzzy model

    NASA Astrophysics Data System (ADS)

    Mao, Yanbing; Zhang, Hongbin

    2014-05-01

    This paper deals with stability and robust H∞ control of discrete-time switched non-linear systems with time-varying delays. The T-S fuzzy models are utilised to represent each sub-non-linear system. Thus, with two level functions, namely, crisp switching functions and local fuzzy weighting functions, we introduce a discrete-time switched fuzzy systems, which inherently contain the features of the switched hybrid systems and T-S fuzzy systems. Piecewise fuzzy weighting-dependent Lyapunov-Krasovskii functionals (PFLKFs) and average dwell-time approach are utilised in this paper for the exponentially stability analysis and controller design, and with free fuzzy weighting matrix scheme, switching control laws are obtained such that H∞ performance is satisfied. The conditions of stability and the control laws are given in the form of linear matrix inequalities (LMIs) that are numerically feasible. The state decay estimate is explicitly given. A numerical example and the control of delayed single link robot arm with uncertain part are given to demonstrate the efficiency of the proposed method.

  19. Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay.

    PubMed

    Bao, Haibo; Cao, Jinde

    2011-01-01

    This paper is concerned with the state estimation problem for a class of discrete-time stochastic neural networks (DSNNs) with random delays. The effect of both variation range and distribution probability of the time delay are taken into account in the proposed approach. The stochastic disturbances are described in terms of a Brownian motion and the time-varying delay is characterized by introducing a Bernoulli stochastic variable. By employing a Lyapunov-Krasovskii functional, sufficient delay-distribution-dependent conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimator which can be checked readily by the Matlab toolbox. The main feature of the results obtained in this paper is that they are dependent on not only the bound but also the distribution probability of the time delay, and we obtain a larger allowance variation range of the delay, hence our results are less conservative than the traditional delay-independent ones. One example is given to illustrate the effectiveness of the proposed result. Copyright © 2010 Elsevier Ltd. All rights reserved.

  20. Distributed Load Shedding over Directed Communication Networks with Time Delays

    SciTech Connect

    Yang, Tao; Wu, Di

    2016-07-25

    When generation is insufficient to support all loads under emergencies, effective and efficient load shedding needs to be deployed in order to maintain the supply-demand balance. This paper presents a distributed load shedding algorithm, which makes efficient decision based on the discovered global information. In the global information discovery process, each load only communicates with its neighboring load via directed communication links possibly with arbitrarily large but bounded time varying communication delays. We propose a novel distributed information discovery algorithm based on ratio consensus. Simulation results are used to validate the proposed method.

  1. Time-varying cosmological term

    NASA Astrophysics Data System (ADS)

    Socorro, J.; D'oleire, M.; Pimentel, Luis O.

    2015-11-01

    We present the case of time-varying cosmological term using the Lagrangian formalism characterized by a scalar field ϕ with standard kinetic energy and arbitrary potential V(ϕ). This model is applied to Friedmann-Robertson-Walker (FRW)cosmology. Exact solutions of the field equations are obtained by a special ansats to solve the Einstein-Klein-Gordon equation and a particular potential for the scalar field and barotropic perfect fluid. We present the evolution on this cosmological term with different scenarios.

  2. Control and Identification of Time Varying Systems.

    DTIC Science & Technology

    1986-10-03

    distributed delay systems described by 0 0 i(t) = d o(O)x ( +O) + fJd P(c)u (t +’t) (5) where a(-) and P() are matrix valued functions of bounded ... variation and the integrals are of the Stieltjes type. This includes multiple point delayed systems akin to the system (1) as special cases. Some results

  3. Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

    PubMed

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

    2017-06-27

    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.

  4. Transit time distributions and StorAge Selection functions in a sloping soil lysimeter with time-varying flow paths: Direct observation of internal and external transport variability

    NASA Astrophysics Data System (ADS)

    Kim, Minseok; Pangle, Luke A.; Cardoso, Charléne; Lora, Marco; Volkmann, Till H. M.; Wang, Yadi; Harman, Ciaran J.; Troch, Peter A.

    2016-09-01

    Transit times through hydrologic systems vary in time, but the nature of that variability is not well understood. Transit times variability was investigated in a 1 m3 sloping lysimeter, representing a simplified model of a hillslope receiving periodic rainfall events for 28 days. Tracer tests were conducted using an experimental protocol that allows time-variable transit time distributions (TTDs) to be calculated from data. Observed TTDs varied with the storage state of the system, and the history of inflows and outflows. We propose that the observed time variability of the TTDs can be decomposed into two parts: "internal" variability associated with changes in the arrangement of, and partitioning between, flow pathways; and "external" variability driven by fluctuations in the flow rate along all flow pathways. These concepts can be defined quantitatively in terms of rank StorAge Selection (rSAS) functions, which is a theory describing lumped transport dynamics. Internal variability is associated with temporal variability in the rSAS function, while external is not. The rSAS function variability was characterized by an "inverse storage effect," whereby younger water is released in greater proportion under wetter conditions than drier. We hypothesize that this effect is caused by the rapid mobilization of water in the unsaturated zone by the rising water table. Common approximations used to model transport dynamics that neglect internal variability were unable to reproduce the observed breakthrough curves accurately. This suggests that internal variability can play an important role in hydrologic transport dynamics, with implications for field data interpretation and modeling.

  5. Fractal analysis of time varying data

    DOEpatents

    Vo-Dinh, Tuan; Sadana, Ajit

    2002-01-01

    Characteristics of time varying data, such as an electrical signal, are analyzed by converting the data from a temporal domain into a spatial domain pattern. Fractal analysis is performed on the spatial domain pattern, thereby producing a fractal dimension D.sub.F. The fractal dimension indicates the regularity of the time varying data.

  6. TIME-VARYING DYNAMICAL STAR FORMATION RATE

    SciTech Connect

    Lee, Eve J.; Chang, Philip; Murray, Norman

    2015-02-10

    We present numerical evidence of dynamic star formation in which the accreted stellar mass grows superlinearly with time, roughly as t {sup 2}. We perform simulations of star formation in self-gravitating hydrodynamic and magnetohydrodynamic turbulence that is continuously driven. By turning the self-gravity of the gas in the simulations on or off, we demonstrate that self-gravity is the dominant physical effect setting the mass accretion rate at early times before feedback effects take over, contrary to theories of turbulence-regulated star formation. We find that gravitational collapse steepens the density profile around stars, generating the power-law tail on what is otherwise a lognormal density probability distribution function. Furthermore, we find turbulent velocity profiles to flatten inside collapsing regions, altering the size-line width relation. This local flattening reflects enhancements of turbulent velocity on small scales, as verified by changes to the velocity power spectra. Our results indicate that gas self-gravity dynamically alters both density and velocity structures in clouds, giving rise to a time-varying star formation rate. We find that a substantial fraction of the gas that forms stars arrives via low-density flows, as opposed to accreting through high-density filaments.

  7. Mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks.

    PubMed

    Muralisankar, S; Manivannan, A; Balasubramaniam, P

    2015-09-01

    The aim of this manuscript is to investigate the mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks with time-delays. The time-delays are assumed to be interval time-varying and randomly occurring. Based on the new Lyapunov-Krasovskii functional and stochastic analysis approach, a novel sufficient condition is obtained in the form of linear matrix inequality such that the delayed stochastic neural networks are globally robustly asymptotically stable in the mean-square sense for all admissible uncertainties. Finally, the derived theoretical results are validated through numerical examples in which maximum allowable upper bounds are calculated for different lower bounds of time-delay.

  8. Nonstationary Feller process with time-varying coefficients

    NASA Astrophysics Data System (ADS)

    Masoliver, Jaume

    2016-01-01

    We study the nonstationary Feller process with time varying coefficients. We obtain the exact probability distribution exemplified by its characteristic function and cumulants. In some particular cases we exactly invert the distribution and achieve the probability density function. We show that for sufficiently long times this density approaches a Γ distribution with time-varying shape and scale parameters. Not far from the origin the process obeys a power law with an exponent dependent of time, thereby concluding that accessibility to the origin is not static but dynamic. We finally discuss some possible applications of the process.

  9. Nonstationary Feller process with time-varying coefficients.

    PubMed

    Masoliver, Jaume

    2016-01-01

    We study the nonstationary Feller process with time varying coefficients. We obtain the exact probability distribution exemplified by its characteristic function and cumulants. In some particular cases we exactly invert the distribution and achieve the probability density function. We show that for sufficiently long times this density approaches a Γ distribution with time-varying shape and scale parameters. Not far from the origin the process obeys a power law with an exponent dependent of time, thereby concluding that accessibility to the origin is not static but dynamic. We finally discuss some possible applications of the process.

  10. Distributed Time Delay Goodwin's Models of the Business Cycle

    NASA Astrophysics Data System (ADS)

    Antonova, A. O.; Reznik, S. N.; Todorov, M. D.

    2011-11-01

    We consider continuously distributed time delay Goodwin's model of the business cycle. We show that the delay induced sawtooth oscillations, similar to those detected by R. H. Strotz, J. C. McAnulty, J. B. Naines, Econometrica, 21, 390-411 (1953) for Goodwin's model with fixed investment time lag, exist only for very narrow delay distribution when the variance of the delay distribution much less than the average delay.

  11. Convergence of Asymptotic Systems of Non-autonomous Neural Network Models with Infinite Distributed Delays

    NASA Astrophysics Data System (ADS)

    Oliveira, José J.

    2017-02-01

    In this paper, we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between the non-autonomous systems and their asymptotic systems to ensure the boundedness and global convergence of the solutions of the studied models. Our results are new and complement known results in the literature. The theoretical analysis is illustrated with some examples and numerical simulations.

  12. Components in time-varying graphs.

    PubMed

    Nicosia, Vincenzo; Tang, John; Musolesi, Mirco; Russo, Giovanni; Mascolo, Cecilia; Latora, Vito

    2012-06-01

    Real complex systems are inherently time-varying. Thanks to new communication systems and novel technologies, today it is possible to produce and analyze social and biological networks with detailed information on the time of occurrence and duration of each link. However, standard graph metrics introduced so far in complex network theory are mainly suited for static graphs, i.e., graphs in which the links do not change over time, or graphs built from time-varying systems by aggregating all the links as if they were concurrent in time. In this paper, we extend the notion of connectedness, and the definitions of node and graph components, to the case of time-varying graphs, which are represented as time-ordered sequences of graphs defined over a fixed set of nodes. We show that the problem of finding strongly connected components in a time-varying graph can be mapped into the problem of discovering the maximal-cliques in an opportunely constructed static graph, which we name the affine graph. It is, therefore, an NP-complete problem. As a practical example, we have performed a temporal component analysis of time-varying graphs constructed from three data sets of human interactions. The results show that taking time into account in the definition of graph components allows to capture important features of real systems. In particular, we observe a large variability in the size of node temporal in- and out-components. This is due to intrinsic fluctuations in the activity patterns of individuals, which cannot be detected by static graph analysis.

  13. Learning Time-Varying Coverage Functions

    PubMed Central

    Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

    2015-01-01

    Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. PMID:25960624

  14. Synchronization in time-varying networks.

    PubMed

    Kohar, Vivek; Ji, Peng; Choudhary, Anshul; Sinha, Sudeshna; Kurths, Jüergen

    2014-08-01

    We study the stability of the synchronized state in time-varying complex networks using the concept of basin stability, which is a nonlocal and nonlinear measure of stability that can be easily applied to high-dimensional systems [P. J. Menck, J. Heitzig, N. Marwan, and J. Kurths, Nature Phys. 9, 89 (2013)]. The time-varying character is included by stochastically rewiring each link with the average frequency f. We find that the time taken to reach synchronization is lowered and the stability range of the synchronized state increases considerably in dynamic networks. Further we uncover that small-world networks are much more sensitive to link changes than random ones, with the time-varying character of the network having a significant effect at much lower rewiring frequencies. At very high rewiring frequencies, random networks perform better than small-world networks and the synchronized state is stable over a much wider window of coupling strengths. Lastly we show that the stability range of the synchronized state may be quite different for small and large perturbations, and so the linear stability analysis and the basin stability criterion provide complementary indicators of stability.

  15. Synchronization in time-varying networks

    NASA Astrophysics Data System (ADS)

    Kohar, Vivek; Ji, Peng; Choudhary, Anshul; Sinha, Sudeshna; Kurths, Jüergen

    2014-08-01

    We study the stability of the synchronized state in time-varying complex networks using the concept of basin stability, which is a nonlocal and nonlinear measure of stability that can be easily applied to high-dimensional systems [P. J. Menck, J. Heitzig, N. Marwan, and J. Kurths, Nature Phys. 9, 89 (2013), 10.1038/nphys2516]. The time-varying character is included by stochastically rewiring each link with the average frequency f. We find that the time taken to reach synchronization is lowered and the stability range of the synchronized state increases considerably in dynamic networks. Further we uncover that small-world networks are much more sensitive to link changes than random ones, with the time-varying character of the network having a significant effect at much lower rewiring frequencies. At very high rewiring frequencies, random networks perform better than small-world networks and the synchronized state is stable over a much wider window of coupling strengths. Lastly we show that the stability range of the synchronized state may be quite different for small and large perturbations, and so the linear stability analysis and the basin stability criterion provide complementary indicators of stability.

  16. Modeling distributed axonal delays in mean-field brain dynamics

    NASA Astrophysics Data System (ADS)

    Roberts, J. A.; Robinson, P. A.

    2008-11-01

    The range of conduction delays between connected neuronal populations is often modeled as a single discrete delay, assumed to be an effective value averaging over all fiber velocities. This paper shows the effects of distributed delays on signal propagation. A distribution acts as a linear filter, imposing an upper frequency cutoff that is inversely proportional to the delay width. Distributed thalamocortical and corticothalamic delays are incorporated into a physiologically based mean-field model of the cortex and thalamus to illustrate their effects on the electroencephalogram (EEG). The power spectrum is acutely sensitive to the width of the thalamocortical delay distribution, and more so than the corticothalamic distribution, because all input signals must travel along the thalamocortical pathway. This imposes a cutoff frequency above which the spectrum is overly damped. The positions of spectral peaks in the resting EEG depend primarily on the distribution mean, with only weak dependences on distribution width. Increasing distribution width increases the stability of fixed point solutions. A single discrete delay successfully approximates a distribution for frequencies below a cutoff that is inversely proportional to the delay width, provided that other model parameters are moderately adjusted. A pair of discrete delays together having the same mean, variance, and skewness as the distribution approximates the distribution over the same frequency range without needing parameter adjustment. Delay distributions with large fractional widths are well approximated by low-order differential equations.

  17. Control of nonlinear time-varying systems

    NASA Technical Reports Server (NTRS)

    Hunt, L. R.; Su, R.

    1981-01-01

    Necessary and sufficient conditions are given for a time-varying nonlinear system of specific form to be transformed into a time-invariant controllable linear system. Since the present work will be in a neighborhood of the origin, it is unnecessary to name specific sets and it is assumed that all assumptions, conditions and results hold in an open set in the appropriate Euclidean space that contains the origin. This theory can be combined with the global inverse function theorems to produce global results.

  18. A time-varying magnetic flux concentrator

    NASA Astrophysics Data System (ADS)

    Kibret, B.; Premaratne, M.; Lewis, P. M.; Thomson, R.; Fitzgerald, P. B.

    2016-08-01

    It is known that diverse technological applications require the use of focused magnetic fields. This has driven the quest for controlling the magnetic field. Recently, the principles in transformation optics and metamaterials have allowed the realization of practical static magnetic flux concentrators. Extending such progress, here, we propose a time-varying magnetic flux concentrator cylindrical shell that uses electric conductors and ferromagnetic materials to guide magnetic flux to its center. Its performance is discussed based on finite-element simulation results. Our proposed design has potential applications in magnetic sensors, medical devices, wireless power transfer, and near-field wireless communications.

  19. Distributed Nonlocal Feedback Delays May Destabilize Fronts in Neural Fields, Distributed Transmission Delays Do Not

    PubMed Central

    2013-01-01

    The spread of activity in neural populations is a well-known phenomenon. To understand the propagation speed and the stability of stationary fronts in neural populations, the present work considers a neural field model that involves intracortical and cortico-cortical synaptic interactions. This includes distributions of axonal transmission speeds and nonlocal feedback delays as well as general classes of synaptic interactions. The work proves the spectral stability of standing and traveling fronts subject to general transmission speeds for large classes of spatial interactions and derives conditions for the front instabilities subjected to nonlocal feedback delays. Moreover, it turns out that the uniqueness of the stationary traveling fronts guarantees its exponential stability for vanishing feedback delay. Numerical simulations complement the analytical findings. PMID:23899051

  20. Stability analysis of linear fractional differential system with distributed delays

    NASA Astrophysics Data System (ADS)

    Veselinova, Magdalena; Kiskinov, Hristo; Zahariev, Andrey

    2015-11-01

    In the present work we study the Cauchy problem for linear incommensurate fractional differential system with distributed delays. For the autonomous case with distributed delays with derivatives in Riemann-Liouville or Caputo sense, we establish sufficient conditions under which the zero solution is globally asymptotic stable. The established conditions coincide with the conditions which guaranty the same result in the particular case of system with constant delays and for the case of system without delays in the commensurate case too.

  1. Time varying, multivariate volume data reduction

    SciTech Connect

    Ahrens, James P; Fout, Nathaniel; Ma, Kwan - Liu

    2010-01-01

    Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

  2. Efficient Encoding and Rendering of Time-Varying Volume Data

    NASA Technical Reports Server (NTRS)

    Ma, Kwan-Liu; Smith, Diann; Shih, Ming-Yun; Shen, Han-Wei

    1998-01-01

    Visualization of time-varying volumetric data sets, which may be obtained from numerical simulations or sensing instruments, provides scientists insights into the detailed dynamics of the phenomenon under study. This paper describes a coherent solution based on quantization, coupled with octree and difference encoding for visualizing time-varying volumetric data. Quantization is used to attain voxel-level compression and may have a significant influence on the performance of the subsequent encoding and visualization steps. Octree encoding is used for spatial domain compression, and difference encoding for temporal domain compression. In essence, neighboring voxels may be fused into macro voxels if they have similar values, and subtrees at consecutive time steps may be merged if they are identical. The software rendering process is tailored according to the tree structures and the volume visualization process. With the tree representation, selective rendering may be performed very efficiently. Additionally, the I/O costs are reduced. With these combined savings, a higher level of user interactivity is achieved. We have studied a variety of time-varying volume datasets, performed encoding based on data statistics, and optimized the rendering calculations wherever possible. Preliminary tests on workstations have shown in many cases tremendous reduction by as high as 90% in both storage space and inter-frame delay.

  3. Vesicle biomechanics in a time-varying magnetic field.

    PubMed

    Ye, Hui; Curcuru, Austen

    2015-01-01

    Cells exhibit distortion when exposed to a strong electric field, suggesting that the field imposes control over cellular biomechanics. Closed pure lipid bilayer membranes (vesicles) have been widely used for the experimental and theoretical studies of cellular biomechanics under this electrodeformation. An alternative method used to generate an electric field is by electromagnetic induction with a time-varying magnetic field. References reporting the magnetic control of cellular mechanics have recently emerged. However, theoretical analysis of the cellular mechanics under a time-varying magnetic field is inadequate. We developed an analytical theory to investigate the biomechanics of a modeled vesicle under a time-varying magnetic field. Following previous publications and to simplify the calculation, this model treated the inner and suspending media as lossy dielectrics, the membrane thickness set at zero, and the electric resistance of the membrane assumed to be negligible. This work provided the first analytical solutions for the surface charges, electric field, radial pressure, overall translational forces, and rotational torques introduced on a vesicle by the time-varying magnetic field. Frequency responses of these measures were analyzed, particularly the frequency used clinically by transcranial magnetic stimulation (TMS). The induced surface charges interacted with the electric field to produce a biomechanical impact upon the vesicle. The distribution of the induced surface charges depended on the orientation of the coil and field frequency. The densities of these charges were trivial at low frequency ranges, but significant at high frequency ranges. The direction of the radial force on the vesicle was dependent on the conductivity ratio between the vesicle and the medium. At relatively low frequencies (<200 KHz), including the frequency used in TMS, the computed radial pressure and translational forces on the vesicle were both negligible. This work

  4. Modelling tourists arrival using time varying parameter

    NASA Astrophysics Data System (ADS)

    Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.

    2017-06-01

    The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.

  5. Audibility of time-varying signals in time-varying backgrounds: Model and data

    NASA Astrophysics Data System (ADS)

    Moore, Brian C. J.; Glasberg, Brian R.

    2004-05-01

    We have described a model for calculating the partial loudness of a steady signal in the presence of a steady background sound [Moore et al., J. Audio Eng. Soc. 45, 224-240 (1997)]. We have also described a model for calculating the loudness of time-varying signals [B. R. Glasberg and B. C. J. Moore, J. Audio Eng. Soc. 50, 331-342 (2002)]. These two models have been combined to allow calculation of the partial loudness of a time-varying signal in the presence of a time-varying background. To evaluate the model, psychometric functions for the detection of a variety of time-varying signals (e.g., telephone ring tones) have been measured in a variety of background sounds sampled from everyday listening situations, using a two-alternative forced-choice task. The different signals and backgrounds were interleaved, to create stimulus uncertainty, as would occur in everyday life. The data are used to relate the detectability index, d', to the calculated partial loudness. In this way, the model can be used to predict the detectability of any signal, based on its calculated partial loudness. [Work supported by MRC (UK) and by Nokia.

  6. Synchronization of networks of oscillators with distributed delay coupling

    NASA Astrophysics Data System (ADS)

    Kyrychko, Y. N.; Blyuss, K. B.; Schöll, E.

    2014-12-01

    This paper studies the stability of synchronized states in networks, where couplings between nodes are characterized by some distributed time delay, and develops a generalized master stability function approach. Using a generic example of Stuart-Landau oscillators, it is shown how the stability of synchronized solutions in networks with distributed delay coupling can be determined through a semi-analytic computation of Floquet exponents. The analysis of stability of fully synchronized and of cluster or splay states is illustrated for several practically important choices of delay distributions and network topologies.

  7. Reaching a consensus: a discrete nonlinear time-varying case

    NASA Astrophysics Data System (ADS)

    Saburov, M.; Saburov, K.

    2016-07-01

    In this paper, we have considered a nonlinear protocol for a structured time-varying and synchronous multi-agent system. By means of cubic triple stochastic matrices, we present an opinion sharing dynamics of the multi-agent system as a trajectory of a non-homogeneous system of cubic triple stochastic matrices. We show that the multi-agent system eventually reaches to a consensus if either of the following two conditions is satisfied: (1) every member of the group people has a positive subjective distribution on the given task after some revision steps or (2) all entries of some cubic triple stochastic matrix are positive.

  8. Compensation of distributed delays in integrated communication and control systems

    NASA Technical Reports Server (NTRS)

    Ray, Asok; Luck, Rogelio

    1991-01-01

    The concept, analysis, implementation, and verification of a method for compensating delays that are distributed between the sensors, controller, and actuators within a control loop are discussed. With the objective of mitigating the detrimental effects of these network induced delays, a predictor-controller algorithm was formulated and analyzed. Robustness of the delay compensation algorithm was investigated relative to parametric uncertainties in plant modeling. The delay compensator was experimentally verified on an IEEE 802.4 network testbed for velocity control of a DC servomotor.

  9. Time-Varying Effects of Breast Cancer Adjuvant Systemic Therapy

    PubMed Central

    Bandos, Hanna; Jeong, Jong-Hyeon; Anderson, William F.; Romond, Edward H.; Mamounas, Eleftherios P.; Wolmark, Norman

    2016-01-01

    Background: The benefits of breast cancer adjuvant systemic treatments are generally assumed to be proportional (or constant) over time, but limited data suggest that some treatment effects may vary with time. We therefore systematically assessed the proportional hazards assumption across all 19 breast cancer adjuvant systemic therapy trials in the National Surgical Adjuvant Breast and Bowel Project (NSABP) database. Methods: The NSABP breast cancer trials were tested for the proportionality of hazard rates between randomized treatment groups for five endpoints: overall survival, disease-free survival and recurrence, local-regional recurrence, or distant recurrence as first events. When the proportional hazards assumption did not hold, a “change point for the relative risk” technique was used to identify the temporal breakdown of the treatment effect. Results: Time-varying treatment effects were observed in nearly half of the trials (nine of 19). In six (B-05, B-11, B-12, B-14, B-16, and B-20), novel treatment benefits diminished statistically significantly at specific time points following surgery. In B-09 and B-31, novel treatment benefits were delayed and emerged more than one year after surgery (1.57 and 1.32 years correspondingly), but the benefit in B-09 reversed after the third year of follow-up. In one trial (B-23), the initial advantage and subsequent disadvantage of one of the regimens was evident. Conclusions: Breast cancer adjuvant systemic therapy can have statistically significant time-varying effects, which should be considered in the design, analysis, reporting, and translation of clinical trials. These time-dependent effects will have greater relevance as the number of long-term breast cancer survivors increases. PMID:26518884

  10. Time-Varying Effects of Breast Cancer Adjuvant Systemic Therapy.

    PubMed

    Jatoi, Ismail; Bandos, Hanna; Jeong, Jong-Hyeon; Anderson, William F; Romond, Edward H; Mamounas, Eleftherios P; Wolmark, Norman

    2016-01-01

    The benefits of breast cancer adjuvant systemic treatments are generally assumed to be proportional (or constant) over time, but limited data suggest that some treatment effects may vary with time. We therefore systematically assessed the proportional hazards assumption across all 19 breast cancer adjuvant systemic therapy trials in the National Surgical Adjuvant Breast and Bowel Project (NSABP) database. The NSABP breast cancer trials were tested for the proportionality of hazard rates between randomized treatment groups for five endpoints: overall survival, disease-free survival and recurrence, local-regional recurrence, or distant recurrence as first events. When the proportional hazards assumption did not hold, a "change point for the relative risk" technique was used to identify the temporal breakdown of the treatment effect. Time-varying treatment effects were observed in nearly half of the trials (nine of 19). In six (B-05, B-11, B-12, B-14, B-16, and B-20), novel treatment benefits diminished statistically significantly at specific time points following surgery. In B-09 and B-31, novel treatment benefits were delayed and emerged more than one year after surgery (1.57 and 1.32 years correspondingly), but the benefit in B-09 reversed after the third year of follow-up. In one trial (B-23), the initial advantage and subsequent disadvantage of one of the regimens was evident. Breast cancer adjuvant systemic therapy can have statistically significant time-varying effects, which should be considered in the design, analysis, reporting, and translation of clinical trials. These time-dependent effects will have greater relevance as the number of long-term breast cancer survivors increases. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  11. Time Delay Systems with Distribution Dependent Dynamics

    DTIC Science & Technology

    2006-05-10

    sensitivity function for general nonlinear ordinary differential equations (ODEs) in a Banach space. Here we only show the construction of the abstract...shear: A nonlinear stick-slip formulation. CRSC-TR06-07, February, 2006; Differential Equations and Nonlinear Mechanics. Banks, H.T. and H.K. Nguyen (to...dependent dynamical system (in this case a 6 complicated system of partial differential equations ) for which the distribution PL must be estimated in some

  12. Parallel Rendering of Large Time-Varying Volume Data

    NASA Technical Reports Server (NTRS)

    Garbutt, Alexander E.

    2005-01-01

    Interactive visualization of large time-varying 3D volume datasets has been and still is a great challenge to the modem computational world. It stretches the limits of the memory capacity, the disk space, the network bandwidth and the CPU speed of a conventional computer. In this SURF project, we propose to develop a parallel volume rendering program on SGI's Prism, a cluster computer equipped with state-of-the-art graphic hardware. The proposed program combines both parallel computing and hardware rendering in order to achieve an interactive rendering rate. We use 3D texture mapping and a hardware shader to implement 3D volume rendering on each workstation. We use SGI's VisServer to enable remote rendering using Prism's graphic hardware. And last, we will integrate this new program with ParVox, a parallel distributed visualization system developed at JPL. At the end of the project, we Will demonstrate remote interactive visualization using this new hardware volume renderer on JPL's Prism System using a time-varying dataset from selected JPL applications.

  13. Parallel Rendering of Large Time-Varying Volume Data

    NASA Technical Reports Server (NTRS)

    Garbutt, Alexander E.

    2005-01-01

    Interactive visualization of large time-varying 3D volume datasets has been and still is a great challenge to the modem computational world. It stretches the limits of the memory capacity, the disk space, the network bandwidth and the CPU speed of a conventional computer. In this SURF project, we propose to develop a parallel volume rendering program on SGI's Prism, a cluster computer equipped with state-of-the-art graphic hardware. The proposed program combines both parallel computing and hardware rendering in order to achieve an interactive rendering rate. We use 3D texture mapping and a hardware shader to implement 3D volume rendering on each workstation. We use SGI's VisServer to enable remote rendering using Prism's graphic hardware. And last, we will integrate this new program with ParVox, a parallel distributed visualization system developed at JPL. At the end of the project, we Will demonstrate remote interactive visualization using this new hardware volume renderer on JPL's Prism System using a time-varying dataset from selected JPL applications.

  14. [Measures of occupational exposure to time-varying low frequency magnetic fields of non-uniform spatial distribution in the light of international guidelines and electrodynamic exposure effects in the human body].

    PubMed

    Karpowicz, Jolanta; Zradziński, Patryk; Gryz, Krzysztof

    2012-01-01

    The aim of study was to analyze by computer simulations the electrodynamic effects of magnetic field (MF) on workers, to harmonize the principles of occupational hazards assessment with international guidelines. Simulations involved 50 Hz MF of various spatial distributions, representing workers' exposure in enterprises. Homogeneous models of sigma = 0.2 S/m conductivity and dimensions of body parts - palm, head and trunk - were located at 50 cm ("hand-distance") or 5 cm (adjacent) from the source (circle conductor of 20 cm or 200 cm in diameter). Parameters of magnetic flux density (B(i)) affecting the models were the exposure measures, and the induced electric field strength (E(in)) was the measure of MF exposure effects. The ratio E(in)/B(i) in the analyzed cases ranged from 2.59 to 479 (V/m)/T. The strongest correlation (p < 0.001) between B(i) and E(in) was found for parameters characterizing MF at the surface of body models. Parameters characterizing the averaged value of the field affecting models (measures of non-uniform field exposure following ICNIRP guidelines), were less correlated with exposure effects (p < 0.005). E(in)(trunk)/E(in) (palm) estimated from E(in) calculations was 3.81-4.56 but estimated from parameters representing B(i) measurement accounted for 3.96-9.74. It is justified to accept 3.96-9.74 times higher exposure to limb than that to trunk. This supports the regulation of labor law in Poland, which provides that the ceiling value for limb exposure to MF below 800 kHz is fivefold higher than that of the trunk. High uncertainty in assessing the effects of non-uniform fields exposure, resulting from a strong dependence of the E(in)/B(i) ratio on the conditions of exposure and its applied measures, requires special caution when defining the permissible MF levels and the principles of exposure assessment at workplace.

  15. Hopf bifurcation in Hutchinson's equation with distributed delay

    NASA Astrophysics Data System (ADS)

    Darti, I.

    2014-06-01

    In this article we deal with the Hutchinson's equation with distributed delay dx(t)/dt = rx(t)(1-a1x(t)-a2(t-τ)-a3 ∫ -∞tf(t-s)x(s)ds where r,τ,a1,a2,a3 are positive constants and f is the delay kernel function. By analyzing the associated characteristic equation, the local stability of positive equilibrium and Hopf bifurcation are investigated. The bifurcation here is controlled by the time delay. Some numerical simulations are performed to verify and illustrate the analytical findings.

  16. Holographic cinematography of time-varying reflecting and time-varying phase objects using a Nd:YAG laser

    NASA Technical Reports Server (NTRS)

    Decker, A. J.

    1982-01-01

    The use of a Nd:YAG laser to record holographic motion pictures of time-varying reflecting objects and time-varying phase objects is discussed. Sample frames from both types of holographic motion pictures are presented. The holographic system discussed is intended for three-dimensional flow visualization of the time-varying flows that occur in jet-engine components.

  17. Flexible Demand Management under Time-Varying Prices

    NASA Astrophysics Data System (ADS)

    Liang, Yong

    In this dissertation, the problem of flexible demand management under time-varying prices is studied. This generic problem has many applications, which usually have multiple periods in which decisions on satisfying demand need to be made, and prices in these periods are time-varying. Examples of such applications include multi-period procurement problem, operating room scheduling, and user-end demand scheduling in the Smart Grid, where the last application is used as the main motivating story throughout the dissertation. The current grid is experiencing an upgrade with lots of new designs. What is of particular interest is the idea of passing time-varying prices that reflect electricity market conditions to end users as incentives for load shifting. One key component, consequently, is the demand management system at the user-end. The objective of the system is to find the optimal trade-off between cost saving and discomfort increment resulted from load shifting. In this dissertation, we approach this problem from the following aspects: (1) construct a generic model, solve for Pareto optimal solutions, and analyze the robust solution that optimizes the worst-case payoffs, (2) extend to a distribution-free model for multiple types of demand (appliances), for which an approximate dynamic programming (ADP) approach is developed, and (3) design other efficient algorithms for practical purposes of the flexible demand management system. We first construct a novel multi-objective flexible demand management model, in which there are a finite number of periods with time-varying prices, and demand arrives in each period. In each period, the decision maker chooses to either satisfy or defer outstanding demand to minimize costs and discomfort over a certain number of periods. We consider both the deterministic model, models with stochastic demand or prices, and when only partial information about the stochastic demand or prices is known. We first analyze the stochastic

  18. Visualizing Time-Varying Distribution Data in EOS Application

    NASA Technical Reports Server (NTRS)

    Shen, Han-Wei

    2004-01-01

    In this research, we have developed several novel visualization methods for spatial probability density function data. Our focus has been on 2D spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We developed novel clustering algorithms as a means to reduce the information contained in these datasets; and investigated different ways of interpreting and clustering the data.

  19. Poverty index with time-varying consumption and income distributions.

    PubMed

    Chattopadhyay, Amit K; Kumar, T Krishna; Mallick, Sushanta K

    2017-03-01

    Starting from a stochastic agent-based model to represent market exchange in a developing economy, we study time variations of the probability density function of income with simultaneous variation of the consumption deprivation (CD), where CD represents the shortfall in consumption from the saturation level of an essential commodity, cereal. Together, these two models combine income-expenditure-based market dynamics with time variations in consumption due to income. In this new unified theoretical structure, exchange of trade in assets is only allowed when the income exceeds consumption-deprivation while CD itself is endogenously obtained from a separate kinetic model. Our results reveal that the nature of time variation of the CD function leads to a downward trend in the threshold level of consumption of basic necessities, suggesting a possible dietary transition in terms of lower saturation level of food-grain consumption, possibly through an improvement in the level of living. The new poverty index, defined as CD, is amenable to approximate probabilistic prediction within a short time horizon. A major achievement of this work is the intrinsic independence of the poverty index from an exogenous poverty line, making it more objective for policy formulation as opposed to existing poverty indices in the literature.

  20. Poverty index with time-varying consumption and income distributions

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, Amit K.; Kumar, T. Krishna; Mallick, Sushanta K.

    2017-03-01

    Starting from a stochastic agent-based model to represent market exchange in a developing economy, we study time variations of the probability density function of income with simultaneous variation of the consumption deprivation (CD), where CD represents the shortfall in consumption from the saturation level of an essential commodity, cereal. Together, these two models combine income-expenditure-based market dynamics with time variations in consumption due to income. In this new unified theoretical structure, exchange of trade in assets is only allowed when the income exceeds consumption-deprivation while CD itself is endogenously obtained from a separate kinetic model. Our results reveal that the nature of time variation of the CD function leads to a downward trend in the threshold level of consumption of basic necessities, suggesting a possible dietary transition in terms of lower saturation level of food-grain consumption, possibly through an improvement in the level of living. The new poverty index, defined as CD, is amenable to approximate probabilistic prediction within a short time horizon. A major achievement of this work is the intrinsic independence of the poverty index from an exogenous poverty line, making it more objective for policy formulation as opposed to existing poverty indices in the literature.

  1. Propagation of phase modulation signals in time-varying plasma

    NASA Astrophysics Data System (ADS)

    Yang, Min; Li, Xiaoping; Wang, Di; Liu, Yanming; He, Pan

    2016-05-01

    The effects of time-varying plasma to the propagation of phase modulation signals are investigated in this paper. Through theoretical analysis, the mechanism of the interaction between the time-varying plasma and the phase modulation signal is given. A time-varying plasma generator which could produce arbitrary time-varying plasma is built by adjusting the discharge power. A comparison of results from experiment and simulation prove that the time-varying plasma could cause the special rotation of QPSK (Quadrature Phase Shift Keying) constellation, and the mechanism of constellation point's rotation is analyzed. Additionally, the experimental results of the QPSK signals' EVM (Error Vector Magnitude) after time-varying and time-invariant plasma with different ωp/ω are given. This research could be used to improve the TT&C (Tracking Telemeter and Command) system of re-entry vehicles.

  2. Impacts of Time Delays on Distributed Algorithms for Economic Dispatch

    SciTech Connect

    Yang, Tao; Wu, Di; Sun, Yannan; Lian, Jianming

    2015-07-26

    Economic dispatch problem (EDP) is an important problem in power systems. It can be formulated as an optimization problem with the objective to minimize the total generation cost subject to the power balance constraint and generator capacity limits. Recently, several consensus-based algorithms have been proposed to solve EDP in a distributed manner. However, impacts of communication time delays on these distributed algorithms are not fully understood, especially for the case where the communication network is directed, i.e., the information exchange is unidirectional. This paper investigates communication time delay effects on a distributed algorithm for directed communication networks. The algorithm has been tested by applying time delays to different types of information exchange. Several case studies are carried out to evaluate the effectiveness and performance of the algorithm in the presence of time delays in communication networks. It is found that time delay effects have negative effects on the convergence rate, and can even result in an incorrect converge value or fail the algorithm to converge.

  3. Dynamics of strongly coupled spatially distributed logistic equations with delay

    NASA Astrophysics Data System (ADS)

    Kashchenko, I. S.; Kashchenko, S. A.

    2015-04-01

    The dynamics of a system of two logistic delay equations with spatially distributed coupling is studied. The coupling coefficient is assumed to be sufficiently large. Special nonlinear systems of parabolic equations are constructed such that the behavior of their solutions is determined in the first approximation by the dynamical properties of the original system.

  4. Dynamic Factor Analysis Models with Time-Varying Parameters

    ERIC Educational Resources Information Center

    Chow, Sy-Miin; Zu, Jiyun; Shifren, Kim; Zhang, Guangjian

    2011-01-01

    Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor…

  5. Dynamic Factor Analysis Models with Time-Varying Parameters

    ERIC Educational Resources Information Center

    Chow, Sy-Miin; Zu, Jiyun; Shifren, Kim; Zhang, Guangjian

    2011-01-01

    Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor…

  6. Stable Inversion for Nonlinear Nonminimum-Phase Time Varying Systems

    NASA Technical Reports Server (NTRS)

    Devasia, S.; Paden, B.

    1998-01-01

    In this paper, we extend stable inversion to nonlinear time-varying systems and study computational issues; the technique is applicable to minimum-phase as well as nonminimum-phase systems. The inversion technique is new, even in the linear time-varying case, and relies on partitioning (the dichotomic split of) the linearized system dynamics into time-varying, stable, and unstable, submanifolds. This dichotomic split is used to build time-varying filters which are, in turn, the basis of a contraction used to find a bounded inverse input-state trajectory. Finding the inverse input-state trajectory allows the development or exact-output tracking controllers. The method is local to the time-varying trajectory and requires that the internal dynamics vary slowly; however, the method represents a significant advance relative to presently available tracking controllers. Present techniques are restricted to time-invariant nonlinear systems and, in the general case, track only asymptotically.

  7. Stable Inversion for Nonlinear Nonminimum-Phase Time Varying Systems

    NASA Technical Reports Server (NTRS)

    Devasia, S.; Paden, B.

    1998-01-01

    In this paper, we extend stable inversion to nonlinear time-varying systems and study computational issues; the technique is applicable to minimum-phase as well as nonminimum-phase systems. The inversion technique is new, even in the linear time-varying case, and relies on partitioning (the dichotomic split of) the linearized system dynamics into time-varying, stable, and unstable, submanifolds. This dichotomic split is used to build time-varying filters which are, in turn, the basis of a contraction used to find a bounded inverse input-state trajectory. Finding the inverse input-state trajectory allows the development or exact-output tracking controllers. The method is local to the time-varying trajectory and requires that the internal dynamics vary slowly; however, the method represents a significant advance relative to presently available tracking controllers. Present techniques are restricted to time-invariant nonlinear systems and, in the general case, track only asymptotically.

  8. Time varying voltage combustion control and diagnostics sensor

    DOEpatents

    Chorpening, Benjamin T [Morgantown, WV; Thornton, Jimmy D [Morgantown, WV; Huckaby, E David [Morgantown, WV; Fincham, William [Fairmont, WV

    2011-04-19

    A time-varying voltage is applied to an electrode, or a pair of electrodes, of a sensor installed in a fuel nozzle disposed adjacent the combustion zone of a continuous combustion system, such as of the gas turbine engine type. The time-varying voltage induces a time-varying current in the flame which is measured and used to determine flame capacitance using AC electrical circuit analysis. Flame capacitance is used to accurately determine the position of the flame from the sensor and the fuel/air ratio. The fuel and/or air flow rate (s) is/are then adjusted to provide reduced flame instability problems such as flashback, combustion dynamics and lean blowout, as well as reduced emissions. The time-varying voltage may be an alternating voltage and the time-varying current may be an alternating current.

  9. Randomly Distributed Delayed Communication and Coherent Swarm Patterns

    DTIC Science & Technology

    2012-05-01

    and Luis Mier-y- Teran -Romero and Ira B. Schwartz Abstract— Previously we showed how delay communication between globally coupled self-propelled agents...brandon.lindley.ctr@nrl.navy.mil L. Mier-y- Teran -Romero is an NIH post doctoral fellow at the Naval Research Laboratory. luis.miery@nrl.navy.mil I. B. Schwartz...S. Shinomoto, “Can distributed delays perfectly stabilize dynamical networks?,” Phys. Rev. E, vol. 77, APR 2008. [31] L. Mier-y Teran -Romero, E

  10. Neuronal model with distributed delay: analysis and simulation study for gamma distribution memory kernel.

    PubMed

    Karmeshu; Gupta, Varun; Kadambari, K V

    2011-06-01

    A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.

  11. Etiology of phenotype switching strategy in time varying stochastic environment

    NASA Astrophysics Data System (ADS)

    Horvath, Denis; Brutovsky, Branislav

    2016-11-01

    In the paper, we present the two-state discrete-time Markovian model to study the impact of the two alternative switching strategies on the fitness of the population evolving in time varying environment. The first strategy, referred as the 'responsive switching', enables the cell to make transition into the state conferring to it higher fitness in the instant environment. If the alternative strategy, termed 'random switching' is applied, the cell undergoes transition into the new state not regarding the instant environment. Each strategy comes with the respective cost for its physical realization. Within the framework of evolutionary model, mutations occur as random events which change parameters of the probabilistic models corresponding to the respective switching strategies. Most of the general trends of population averages can be easily understood at the intuitive level, with a few exceptions related to the cases when too low mutation noise hampers population to follow rapid environmental changes. On the other hand, the more detailed study of the parameter distributions reveals much more complex structure than expected. The simulation results may help to understand, at the conceptual level, relation between the population heterogeneity and its environment that could find important implications in various areas, such as cancer therapy or development of risk diversifying strategies.

  12. Global asymptotic stability of Hopfield neural network involving distributed delays.

    PubMed

    Zhao, Hongyong

    2004-01-01

    In the paper, we study dynamical behaviors of Hopfield neural networks system with distributed delays. Some new criteria ensuring the existence and uniqueness, and the global asymptotic stability (GAS) of equilibrium point are derived. In the results, we do not assume that the signal propagation functions satisfy the Lipschitz condition and do not require them to be bounded, differentiable or strictly increasing. Moreover, the symmetry of the connection matrix is not also necessary. Thus, we improve some previous works of other researchers. These conditions are presented in terms of system parameters and have importance leading significance in designs and applications of the GAS for Hopfield neural networks system with distributed delays. Two examples are also worked out to demonstrate the advantages of our results.

  13. Stabilization of linear distributed control systems with unbounded delay

    NASA Astrophysics Data System (ADS)

    Henríquez, Hernán R.; Hernández M., Eduardo

    2005-07-01

    In this paper we study the asymptotic stabilization of linear distributed parameter control systems with unbounded delay. Assuming that the semigroup of operators associated with the uncontrolled and nondelayed equation is compact and that the phase space is a uniform fading memory space, we characterize those systems that can be stabilized using a feedback control. As consequence we conclude that every system of this type is stabilizable with an appropriated finite dimensional control.

  14. Analysis and Design of Time-Varying Filter Banks

    NASA Astrophysics Data System (ADS)

    Sodagar, Iraj

    Analysis-synthesis filter banks have been studied extensively and a wide range of theoretical problems have been subsequently addressed. However, almost all the research activity has been concentrated on time-invariant filter banks whose components are fixed and do not change in time. The objective of this thesis is to develop analysis and design techniques for time-varying FIR analysis-synthesis filter banks that are perfect reconstructing (PR). In such systems, the analysis and/or synthesis filters, the down-up sampling rates, or even the number of bands can change in time. The underlying idea is that by adapting the basis functions of the filter bank transform to the signal properties, one can represent the relevant information of the signal more efficiently. For analysis purposes, we derive the time-varying impulse response of the filter bank in terms of the analysis and synthesis filter coefficients. We are able to represent this impulse response in terms of the product of the analysis and synthesis matrix transforms. Our approach to the PR time-varying filter bank design is to change the analysis -synthesis filter bank among a set of time-invariant filter banks. The analysis filter banks are switched instantaneously. To eliminate the distortion during switching, a new time-varying synthesis section is designed for each transition. Three design techniques are developed for the time-varying filter bank design. The first technique uses the least squares synthesis filters. This method improves the reconstruction quality significantly, but does not usually achieve the perfect reconstruction. Using the second technique, one can design PR time-varying systems by redesigning the analysis filters. The drawback is that this method requires numerical optimizations. The third technique introduces a new structure for exactly reconstructing time-varying filter banks. This structure consists of the conventional filter bank followed by a time-varying post filter. The post

  15. Design of 2D time-varying vector fields.

    PubMed

    Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D; Zhang, Eugene

    2012-10-01

    Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects.

  16. Estimation of Time-Varying Pilot Model Parameters

    NASA Technical Reports Server (NTRS)

    Zaal, Peter M. T.; Sweet, Barbara T.

    2011-01-01

    Human control behavior is rarely completely stationary over time due to fatigue or loss of attention. In addition, there are many control tasks for which human operators need to adapt their control strategy to vehicle dynamics that vary in time. In previous studies on the identification of time-varying pilot control behavior wavelets were used to estimate the time-varying frequency response functions. However, the estimation of time-varying pilot model parameters was not considered. Estimating these parameters can be a valuable tool for the quantification of different aspects of human time-varying manual control. This paper presents two methods for the estimation of time-varying pilot model parameters, a two-step method using wavelets and a windowed maximum likelihood estimation method. The methods are evaluated using simulations of a closed-loop control task with time-varying pilot equalization and vehicle dynamics. Simulations are performed with and without remnant. Both methods give accurate results when no pilot remnant is present. The wavelet transform is very sensitive to measurement noise, resulting in inaccurate parameter estimates when considerable pilot remnant is present. Maximum likelihood estimation is less sensitive to pilot remnant, but cannot detect fast changes in pilot control behavior.

  17. Loudness of time-varying stimuli with electric stimulation.

    PubMed

    Francart, Tom; Innes-Brown, Hamish; McDermott, Hugh J; McKay, Colette M

    2014-06-01

    McKay, Henshall, Farrell, and McDermott [J. Acoust. Soc. Am. 113, 2054-2063 (2003)] developed a practical method to estimate the loudness of periodic electrical signals presented through a cochlear implant. In the present work, this method was extended to time-varying sounds based on two models of time-varying loudness for normal listeners. To fit the model parameters, loudness balancing data was collected with six cochlear implant listeners. The pulse rate of a modulated pulse train was adjusted to equalize its loudness to a reference stimulus. The stimuli were single-electrode time-limited pulse bursts, repeated at a rate of 50 Hz, with on-times varying between 2 and 20 ms. The parameters of two different models of time-varying loudness were fitted to the results. For each model, parameters defining the time windows over which the electrical pulses contribute to instantaneous loudness were optimized. In each case, a good fit was obtained with the loudness balancing results. Therefore, the practical method was successfully extended to time-varying sounds by combining it with existing models of time-varying loudness for acoustic stimulation.

  18. Design of Distributed Engine Control Systems with Uncertain Delay.

    PubMed

    Liu, Xiaofeng; Li, Yanxi; Sun, Xu

    Future gas turbine engine control systems will be based on distributed architecture, in which, the sensors and actuators will be connected to the controllers via a communication network. The performance of the distributed engine control (DEC) is dependent on the network performance. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS). Typical turboshaft engine-distributed controllers are designed based on the NCCS framework with a H∞ output feedback under network-induced time delays and uncertain disturbances. The sufficient conditions for robust stability are derived via the Lyapunov stability theory and linear matrix inequality approach. Both numerical and hardware-in-loop simulations illustrate the effectiveness of the presented method.

  19. Design of Distributed Engine Control Systems with Uncertain Delay

    PubMed Central

    Li, Yanxi; Sun, Xu

    2016-01-01

    Future gas turbine engine control systems will be based on distributed architecture, in which, the sensors and actuators will be connected to the controllers via a communication network. The performance of the distributed engine control (DEC) is dependent on the network performance. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS). Typical turboshaft engine-distributed controllers are designed based on the NCCS framework with a H∞ output feedback under network-induced time delays and uncertain disturbances. The sufficient conditions for robust stability are derived via the Lyapunov stability theory and linear matrix inequality approach. Both numerical and hardware-in-loop simulations illustrate the effectiveness of the presented method. PMID:27669005

  20. Dynamic Factor Analysis Models With Time-Varying Parameters.

    PubMed

    Chow, Sy-Miin; Zu, Jiyun; Shifren, Kim; Zhang, Guangjian

    2011-04-11

    Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor model with vector autoregressive relations and time-varying cross-regression parameters at the factor level. Using techniques drawn from the state-space literature, the model was fitted to a set of daily affect data (over 71 days) from 10 participants who had been diagnosed with Parkinson's disease. Our empirical results lend partial support and some potential refinement to the Dynamic Model of Activation with regard to how the time dependencies between positive and negative affects change over time. A simulation study is conducted to examine the performance of the proposed techniques when (a) changes in the time-varying parameters are represented using the true model of change, (b) supposedly time-invariant parameters are represented as time-varying, and

  1. Visualization of Time-Varying Strain Green Tensors

    NASA Astrophysics Data System (ADS)

    Callaghan, S. A.; Maechling, P.

    2006-12-01

    Geophysical tensor data calculated by earthquake wave propagation simulation codes is used to investigate stresses and strains near the earth's surface. To assist scientists with the interpretation of tensor data sets, we have developed distributed processing and visualization techniques for visualizing time-varying, volumetric tensor data. We have applied our techniques to strain Green tensor data calculated for the SCEC/CME CyberShake project in order to explore basin effects in Southern California. One step in the CyberShake project workflow is to generate strain Green tensors for a volume to allow physics-based seismic hazard analysis. These volumes are typically 400 x 400 x 40 km with grid points every 200 m, with 1800 timesteps, yielding multiple terabytes of tensor data in many small files. To graphically display the six-component tensors, we use ellipsoids with the major axes aligned with the three eigenvectors, scaled according to the normalized eigenvalues, and colored based on the magnitude of the eigenvalues. This allows for visualization of the tensor magnitudes, which span a range of over 105, while keeping the ellipsoids a constant size. This software was implemented using the Mesa implementation of OpenGL using the C language. In order to allow interactive visualization of the data, rendering is performed on a parallel computational cluster and real- time images are sent to the user via network sockets. To enable meaningful investigation of the data, a scale for the ellipsoid colors is included. Additionally, a georeferenced surface image is added to provide a point of reference for the user and allow analysis of tensor behavior with other georeferenced data, enabling validation of the CyberShake software and examination of varying ground motions due to basin effects.

  2. Reduction of chemical reaction networks through delay distributions

    NASA Astrophysics Data System (ADS)

    Barrio, Manuel; Leier, André; Marquez-Lago, Tatiana T.

    2013-03-01

    Accurate modelling and simulation of dynamic cellular events require two main ingredients: an adequate description of key chemical reactions and simulation of such chemical events in reasonable time spans. Quite logically, posing the right model is a crucial step for any endeavour in Computational Biology. However, more often than not, it is the associated computational costs which actually limit our capabilities of representing complex cellular behaviour. In this paper, we propose a methodology aimed at representing chains of chemical reactions by much simpler, reduced models. The abridgement is achieved by generation of model-specific delay distribution functions, consecutively fed to a delay stochastic simulation algorithm. We show how such delay distributions can be analytically described whenever the system is solely composed of consecutive first-order reactions, with or without additional "backward" bypass reactions, yielding an exact reduction. For models including other types of monomolecular reactions (constitutive synthesis, degradation, or "forward" bypass reactions), we discuss why one must adopt a numerical approach for its accurate stochastic representation, and propose two alternatives for this. In these cases, the accuracy depends on the respective numerical sample size. Our model reduction methodology yields significantly lower computational costs while retaining accuracy. Quite naturally, computational costs increase alongside network size and separation of time scales. Thus, we expect our model reduction methodologies to significantly decrease computational costs in these instances. We anticipate the use of delays in model reduction will greatly alleviate some of the current restrictions in simulating large sets of chemical reactions, largely applicable in pharmaceutical and biological research.

  3. Pinning-controlled synchronization of delayed neural networks with distributed-delay coupling via impulsive control.

    PubMed

    He, Wangli; Qian, Feng; Cao, Jinde

    2017-01-01

    This paper investigates pinning synchronization of coupled neural networks with both current-state coupling and distributed-delay coupling via impulsive control. A novel impulse pinning strategy involving pinning ratio is proposed and a general criterion is derived to ensure an array of neural networks with two different topologies synchronizes with the desired trajectory. In order to handle the difficulties of high-dimension criteria, some inequality techniques and matrix decomposition methods through simultaneous diagonalization of two matrices are introduced and low-dimensional criteria are obtained. Finally, an illustrative example is given to show the effectiveness of the proposed method. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Time varying networks and the weakness of strong ties

    NASA Astrophysics Data System (ADS)

    Karsai, Márton; Perra, Nicola; Vespignani, Alessandro

    2014-02-01

    In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.

  5. Statistical analysis of the electrical breakdown time delay distributions in krypton

    SciTech Connect

    Maluckov, Cedomir A.; Karamarkovic, Jugoslav P.; Radovic, Miodrag K.; Pejovic, Momcilo M.

    2006-08-15

    The statistical analysis of the experimentally observed electrical breakdown time delay distributions in the krypton-filled diode tube at 2.6 mbar is presented. The experimental distributions are obtained on the basis of 1000 successive and independent measurements. The theoretical electrical breakdown time delay distribution is evaluated as the convolution of the statistical time delay with exponential, and discharge formative time with Gaussian distribution. The distribution parameters are estimated by the stochastic modelling of the time delay distributions, and by comparing them with the experimental distributions for different relaxation times, voltages, and intensities of UV radiation. The transition of distribution shapes, from Gaussian-type to the exponential-like, is investigated by calculating the corresponding skewness and excess kurtosis parameters. It is shown that the mathematical model based on the convolution of two random variable distributions describes experimentally obtained time delay distributions and the separation of the total breakdown time delay to the statistical and formative time delay.

  6. Applicability of delay tolerant networking to distributed satellite systems

    NASA Astrophysics Data System (ADS)

    Freimann, A.; Tzschichholz, T.; Schmidt, M.; Kleinschrodt, A.; Schilling, K.

    2016-12-01

    Currently, a trend towards distributed small satellite missions is emerging using cooperating satellites to achieve joint mission objectives, e.g. for earth observation. Communication is a key feature when cooperation between satellites is desired. Typically those satellite networks are affected by slow data rates, high packet loss and intermittent connectivity. To address these challenges the store-and-forward approach of the delay tolerant networking (DTN) concept is investigated in this article. Network simulations of typical scenarios were carried out and evaluated to derive statements about the applicability of the DTN approach to networks in low earth orbits.

  7. Time-varying Reeb Graphs: A Topological Framework Supporting the Analysis of Continuous Time-varying Data

    SciTech Connect

    Mascarenhas, Ajith Arthur

    2006-01-01

    I present time-varying Reeb graphs as a topological framework to support the analysis of continuous time-varying data. Such data is captured in many studies, including computational fluid dynamics, oceanography, medical imaging, and climate modeling, by measuring physical processes over time, or by modeling and simulating them on a computer. Analysis tools are applied to these data sets by scientists and engineers who seek to understand the underlying physical processes. A popular tool for analyzing scientific datasets is level sets, which are the points in space with a fixed data value s. Displaying level sets allows the user to study their geometry, their topological features such as connected components, handles, and voids, and to study the evolution of these features for varying s. For static data, the Reeb graph encodes the evolution of topological features and compactly represents topological information of all level sets. The Reeb graph essentially contracts each level set component to a point. It can be computed efficiently, and it has several uses: as a succinct summary of the data, as an interface to select meaningful level sets, as a data structure to accelerate level set extraction, and as a guide to remove noise. I extend these uses of Reeb graphs to time-varying data. I characterize the changes to Reeb graphs over time, and develop an algorithm that can maintain a Reeb graph data structure by tracking these changes over time. I store this sequence of Reeb graphs compactly, and call it a time-varying Reeb graph. I augment the time-varying Reeb graph with information that records the topology of level sets of all level values at all times, that maintains the correspondence of level set components over time, and that accelerates the extraction of level sets for a chosen level value and time. Scientific data sampled in space-time must be extended everywhere in this domain using an interpolant. A poor choice of interpolant can create degeneracies that are

  8. Adaptive stabilization of discrete-time systems using linear periodically time varying controllers

    NASA Technical Reports Server (NTRS)

    Ortega, Romeo; Albertos, Pedro; Lozano, Rogelio

    1988-01-01

    A direct adaptive scheme based on the use of linear time-varying periodic controllers is proposed which estimates online the periodic coefficients of the controller. It is shown that adaptive stabilization is attained for all possibly nonstably invertible plants of known order but unknown delay. Although no appeal is made to persistency of excitation arguments, a provision is needed to avoid the singularity of an estimated matrix, this property being required only for the analysis and not the control calculations.

  9. Adaptive stabilization of discrete-time systems using linear periodically time varying controllers

    NASA Technical Reports Server (NTRS)

    Ortega, Romeo; Albertos, Pedro; Lozano, Rogelio

    1988-01-01

    A direct adaptive scheme based on the use of linear time-varying periodic controllers is proposed which estimates online the periodic coefficients of the controller. It is shown that adaptive stabilization is attained for all possibly nonstably invertible plants of known order but unknown delay. Although no appeal is made to persistency of excitation arguments, a provision is needed to avoid the singularity of an estimated matrix, this property being required only for the analysis and not the control calculations.

  10. Cosmology with a time-varying speed of light

    NASA Astrophysics Data System (ADS)

    Albrecht, Andreas

    1999-07-01

    Cosmic inflation is the only known mechanism with the potential to explain the very special initial conditions which are required at the early stages of the evolution of our universe. This article outlines my work with Joao Magueijo which attempts to construct an alternative mechanism based on a time-varying speed of light.

  11. Decoupling in linear time-varying multivariable systems

    NASA Technical Reports Server (NTRS)

    Sankaran, V.

    1973-01-01

    The necessary and sufficient conditions for the decoupling of an m-input, m-output, linear time varying dynamical system by state variable feedback is described. The class of feedback matrices which decouple the system are illustrated. Systems which do not satisfy these results are described and systems with disturbances are considered. Some examples are illustrated to clarify the results.

  12. Time varying market efficiency of the GCC stock markets

    NASA Astrophysics Data System (ADS)

    Charfeddine, Lanouar; Khediri, Karim Ben

    2016-02-01

    This paper investigates the time-varying levels of weak-form market efficiency for the GCC stock markets over the period spanning from May 2005 to September 2013. We use two empirical approaches: (1) the generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) model with state space time varying parameter (Kalman filter), and (2) a rolling technique sample test of the fractional long memory parameter d. As long memory estimation methods, we use the detrended fluctuation analysis (DFA) technique, the modified R/S statistic, the exact local whittle (ELW) and the feasible Exact Local Whittle (FELW) methods. Moreover, we use the Bai and Perron (1998, 2003) multiple structural breaks technique to test and date the time varying behavior of stock market efficiency. Empirical results show that GCC markets have different degrees of time-varying efficiency, and also have experiencing periods of efficiency improvement. Results also show evidence of structural breaks in all GCC markets. Moreover, we observe that the recent financial shocks such as Arab spring and subprime crises have a significant impact on the time path evolution of market efficiency.

  13. Time-Varying Affective Response for Humanoid Robots

    NASA Astrophysics Data System (ADS)

    Moshkina, Lilia; Arkin, Ronald C.; Lee, Jamee K.; Jung, Hyunryong

    This paper describes the design of a complex time-varying affective architecture. It is an expansion of the TAME architecture (traits, attitudes, moods, and emotions) as applied to humanoid robotics. It particular it is intended to promote effective human-robot interaction by conveying the robot’s affective state to the user in an easy-to-interpret manner.

  14. The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices

    PubMed Central

    Marathe, A R.; Taylor, D M

    2015-01-01

    Objective Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: 1) error magnitude, 2) distribution of different frequency components in the decoding errors, 3) processing delays, and 4) command gain. Approach To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Main results Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. Significance This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also

  15. The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices.

    PubMed

    Marathe, A R; Taylor, D M

    2015-08-01

    Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain. To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also provide an efficient way to compare a

  16. The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices

    NASA Astrophysics Data System (ADS)

    Marathe, A. R.; Taylor, D. M.

    2015-08-01

    Objective. Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain. Approach. To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Main results. Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. Significance. This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported

  17. Design of crusher liner based on time - varying uncertainty theory

    NASA Astrophysics Data System (ADS)

    Tang, J. C.; Shi, B. Q.; Yu, H. J.; Wang, R. J.; Zhang, W. Y.

    2017-09-01

    This article puts forward the time-dependent design method considering the load fluctuation factors for the liner based on the time-varying uncertainty theory. In this method, the time-varying uncertainty design model of liner is constructed by introducing the parameters that affect the wear rate, the volatility and the drift rate. Based on the design example, the timevarying design outline of the moving cone liner is obtained. Based on the theory of minimum wear, the gap curve of wear resistant cavity is designed, and the optimized cavity is obtained by the combination of the thickness of the cone and the cavity gap. Taking the PYGB1821 multi cylinder hydraulic cone crusher as an example, it is proved that the service life of the new liner is improved by more than 14.3%.

  18. Stabilising compensators for linear time-varying differential systems

    NASA Astrophysics Data System (ADS)

    Oberst, Ulrich

    2016-04-01

    In this paper, we describe a constructive test to decide whether a given linear time-varying (LTV) differential system admits a stabilising compensator for the control tasks of tracking, disturbance rejection or model matching and construct and parametrise all of them if at least one exists. In analogy to the linear time-invariant (LTI) case, the ring of stable rational functions, noncommutative in the LTV situation, and the Kučera-Youla parametrisation play prominent parts in the theory. We transfer Blumthaler's thesis from the LTI to the LTV case and sharpen, complete and simplify the corresponding results in the book 'Linear Time-Varying Systems' by Bourlès and Marinescu.

  19. Path Flow Estimation Using Time Varying Coefficient State Space Model

    NASA Astrophysics Data System (ADS)

    Jou, Yow-Jen; Lan, Chien-Lun

    2009-08-01

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

  20. Morphable Word Clouds for Time-Varying Text Data Visualization.

    PubMed

    Chi, Ming-Te; Lin, Shih-Syun; Chen, Shiang-Yi; Lin, Chao-Hung; Lee, Tong-Yee

    2015-12-01

    A word cloud is a visual representation of a collection of text documents that uses various font sizes, colors, and spaces to arrange and depict significant words. The majority of previous studies on time-varying word clouds focuses on layout optimization and temporal trend visualization. However, they do not fully consider the spatial shapes and temporal motions of word clouds, which are important factors for attracting people's attention and are also important cues for human visual systems in capturing information from time-varying text data. This paper presents a novel method that uses rigid body dynamics to arrange multi-temporal word-tags in a specific shape sequence under various constraints. Each word-tag is regarded as a rigid body in dynamics. With the aid of geometric, aesthetic, and temporal coherence constraints, the proposed method can generate a temporally morphable word cloud that not only arranges word-tags in their corresponding shapes but also smoothly transforms the shapes of word clouds over time, thus yielding a pleasing time-varying visualization. Using the proposed frame-by-frame and morphable word clouds, people can observe the overall story of a time-varying text data from the shape transition, and people can also observe the details from the word clouds in frames. Experimental results on various data demonstrate the feasibility and flexibility of the proposed method in morphable word cloud generation. In addition, an application that uses the proposed word clouds in a simulated exhibition demonstrates the usefulness of the proposed method.

  1. Acute Exposure Guideline Levels (AEGLs) for Time Varying Toxic Plumes

    DTIC Science & Technology

    2014-09-12

    chemicals for a general population. Inhalation exposures in the real world, however, vary strongly in space and time and thus do not correspond to the...12-09-2014 Memorandum Report Toxic airborne contaminants Health effects prediction Space and time varying exposures Extension of EPA AEGLs 64-4464...few fixed-duration exposures to a few constant-density conditions are tabulated. The issue of how to treat real toxic plumes, whose agent density

  2. Generating survival times to simulate Cox proportional hazards models with time-varying covariates.

    PubMed

    Austin, Peter C

    2012-12-20

    Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow for an examination of the performance of statistical procedures in settings in which analytic and mathematical derivations may not be feasible. A key element in any statistical simulation is the existence of an appropriate data-generating process: one must be able to simulate data from a specified statistical model. We describe data-generating processes for the Cox proportional hazards model with time-varying covariates when event times follow an exponential, Weibull, or Gompertz distribution. We consider three types of time-varying covariates: first, a dichotomous time-varying covariate that can change at most once from untreated to treated (e.g., organ transplant); second, a continuous time-varying covariate such as cumulative exposure at a constant dose to radiation or to a pharmaceutical agent used for a chronic condition; third, a dichotomous time-varying covariate with a subject being able to move repeatedly between treatment states (e.g., current compliance or use of a medication). In each setting, we derive closed-form expressions that allow one to simulate survival times so that survival times are related to a vector of fixed or time-invariant covariates and to a single time-varying covariate. We illustrate the utility of our closed-form expressions for simulating event times by using Monte Carlo simulations to estimate the statistical power to detect as statistically significant the effect of different types of binary time-varying covariates. This is compared with the statistical power to detect as statistically significant a binary time-invariant covariate.

  3. Social contagions on time-varying community networks.

    PubMed

    Liu, Mian-Xin; Wang, Wei; Liu, Ying; Tang, Ming; Cai, Shi-Min; Zhang, Hai-Feng

    2017-05-01

    Time-varying community structures exist widely in real-world networks. However, previous studies on the dynamics of spreading seldom took this characteristic into account, especially those on social contagions. To study the effects of time-varying community structures on social contagions, we propose a non-Markovian social contagion model on time-varying community networks based on the activity-driven network model. A mean-field theory is developed to analyze the proposed model. Through theoretical analyses and numerical simulations, two hierarchical features of the behavior adoption processes are found. That is, when community strength is relatively large, the behavior can easily spread in one of the communities, while in the other community the spreading only occurs at higher behavioral information transmission rates. Meanwhile, in spatial-temporal evolution processes, hierarchical orders are observed for the behavior adoption. Moreover, under different information transmission rates, three distinctive patterns are demonstrated in the change of the whole network's final adoption proportion along with the growing community strength. Within a suitable range of transmission rate, an optimal community strength can be found that can maximize the final adoption proportion. Finally, compared with the average activity potential, the promoting or inhibiting of social contagions is much more influenced by the number of edges generated by active nodes.

  4. Social contagions on time-varying community networks

    NASA Astrophysics Data System (ADS)

    Liu, Mian-Xin; Wang, Wei; Liu, Ying; Tang, Ming; Cai, Shi-Min; Zhang, Hai-Feng

    2017-05-01

    Time-varying community structures exist widely in real-world networks. However, previous studies on the dynamics of spreading seldom took this characteristic into account, especially those on social contagions. To study the effects of time-varying community structures on social contagions, we propose a non-Markovian social contagion model on time-varying community networks based on the activity-driven network model. A mean-field theory is developed to analyze the proposed model. Through theoretical analyses and numerical simulations, two hierarchical features of the behavior adoption processes are found. That is, when community strength is relatively large, the behavior can easily spread in one of the communities, while in the other community the spreading only occurs at higher behavioral information transmission rates. Meanwhile, in spatial-temporal evolution processes, hierarchical orders are observed for the behavior adoption. Moreover, under different information transmission rates, three distinctive patterns are demonstrated in the change of the whole network's final adoption proportion along with the growing community strength. Within a suitable range of transmission rate, an optimal community strength can be found that can maximize the final adoption proportion. Finally, compared with the average activity potential, the promoting or inhibiting of social contagions is much more influenced by the number of edges generated by active nodes.

  5. Sensor trustworthiness in uncertain time varying stochastic environments

    NASA Astrophysics Data System (ADS)

    Verma, Ajay; Fernandes, Ronald; Vadakkeveedu, Kalyan

    2011-06-01

    Persistent surveillance applications require unattended sensors deployed in remote regions to track and monitor some physical stimulant of interest that can be modeled as output of time varying stochastic process. However, the accuracy or the trustworthiness of the information received through a remote and unattended sensor and sensor network cannot be readily assumed, since sensors may get disabled, corrupted, or even compromised, resulting in unreliable information. The aim of this paper is to develop information theory based metric to determine sensor trustworthiness from the sensor data in an uncertain and time varying stochastic environment. In this paper we show an information theory based determination of sensor data trustworthiness using an adaptive stochastic reference sensor model that tracks the sensor performance for the time varying physical feature, and provides a baseline model that is used to compare and analyze the observed sensor output. We present an approach in which relative entropy is used for reference model adaptation and determination of divergence of the sensor signal from the estimated reference baseline. We show that that KL-divergence is a useful metric that can be successfully used in determination of sensor failures or sensor malice of various types.

  6. Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior

    NASA Technical Reports Server (NTRS)

    Popovici, Alexandru; Zaal, Peter M. T.; Pool, Daan M.

    2017-01-01

    A Dual Extended Kalman Filter was implemented for the identification of time-varying human manual control behavior. Two filters that run concurrently were used, a state filter that estimates the equalization dynamics, and a parameter filter that estimates the neuromuscular parameters and time delay. Time-varying parameters were modeled as a random walk. The filter successfully estimated time-varying human control behavior in both simulated and experimental data. Simple guidelines are proposed for the tuning of the process and measurement covariance matrices and the initial parameter estimates. The tuning was performed on simulation data, and when applied on experimental data, only an increase in measurement process noise power was required in order for the filter to converge and estimate all parameters. A sensitivity analysis to initial parameter estimates showed that the filter is more sensitive to poor initial choices of neuromuscular parameters than equalization parameters, and bad choices for initial parameters can result in divergence, slow convergence, or parameter estimates that do not have a real physical interpretation. The promising results when applied to experimental data, together with its simple tuning and low dimension of the state-space, make the use of the Dual Extended Kalman Filter a viable option for identifying time-varying human control parameters in manual tracking tasks, which could be used in real-time human state monitoring and adaptive human-vehicle haptic interfaces.

  7. Adaptive Leader-Following Consensus for Second-Order Time-Varying Nonlinear Multiagent Systems.

    PubMed

    Hua, Changchun; You, Xiu; Guan, Xinping

    2017-06-01

    The leader-following consensus problem is investigated for second-order time-varying nonlinear multiagent systems with unmodeled dynamics and unknown parameters over directed communication topology. Under the assumption that the unknown nonlinearities satisfy Lipschitz conditions with time-varying gains, a local adaptive law is introduced for the design of consensus protocol that enable all followers' state variables to consensus with that of leader asymptotically. The proposed protocols are independent of system parameters and only require the relative state information of its neighbors, and hence they are fully distributed. Simulation examples are given to illustrate the effectiveness of the theoretical results.

  8. The effect of distributed time-delays on the synchronization of neuronal networks

    NASA Astrophysics Data System (ADS)

    Kachhvah, Ajay Deep

    2017-01-01

    Here we investigate the synchronization of networks of FitzHugh-Nagumo neurons coupled in scale-free, small-world and random topologies, in the presence of distributed time delays in the coupling of neurons. We explore how the synchronization transition is affected when the time delays in the interactions between pairs of interacting neurons are non-uniform. We find that the presence of distributed time-delays does not change the behavior of the synchronization transition significantly, vis-a-vis networks with constant time-delay, where the value of the constant time-delay is the mean of the distributed delays. We also notice that a normal distribution of delays gives rise to a transition at marginally lower coupling strengths, vis-a-vis uniformly distributed delays. These trends hold across classes of networks and for varying standard deviations of the delay distribution, indicating the generality of these results. So we conclude that distributed delays, which may be typically expected in real-world situations, do not have a notable effect on synchronization. This allows results obtained with constant delays to remain relevant even in the case of randomly distributed delays.

  9. Time-varying boundaries for diffusion models of decision making and response time

    PubMed Central

    Zhang, Shunan; Lee, Michael D.; Vandekerckhove, Joachim; Maris, Gunter; Wagenmakers, Eric-Jan

    2014-01-01

    Diffusion models are widely-used and successful accounts of the time course of two-choice decision making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time-varying boundaries. We then develop a computational method for finding time-varying boundaries from empirical data, and apply our new method to two problems. The first problem involves finding the time-varying boundaries that make diffusion models equivalent to the alternative sequential sampling class of accumulator models. The second problem involves finding the time-varying boundaries, at the individual level, that best fit empirical data for perceptual stimuli that provide equal evidence for both decision alternatives. We discuss the theoretical and modeling implications of using time-varying boundaries in diffusion models, as well as the limitations and potential of our approach to their inference. PMID:25538642

  10. Visual exploration of complex time-varying graphs.

    PubMed

    Kumar, Gautam; Garland, Michael

    2006-01-01

    Many graph drawing and visualization algorithms, such as force-directed layout and line-dot rendering, work very well on relatively small and sparse graphs. However, they often produce extremely tangled results and exhibit impractical running times for highly non-planar graphs with large edge density. And very few graph layout algorithms support dynamic time-varying graphs; applying them independently to each frame produces distracting temporally incoherent visualizations. We have developed a new visualization technique based on a novel approach to hierarchically structuring dense graphs via stratification. Using this structure, we formulate a hierarchical force-directed layout algorithm that is both efficient and produces quality graph layouts. The stratification of the graph also allows us to present views of the data that abstract away many small details of its structure. Rather than displaying all edges and nodes at once, resulting in a convoluted rendering, we present an interactive tool that filters edges and nodes using the graph hierarchy and allows users to drill down into the graph for details. Our layout algorithm also accommodates time-varying graphs in a natural way, producing a temporally coherent animation that can be used to analyze and extract trends from dynamic graph data. For example, we demonstrate the use of our method to explore financial correlation data for the U.S. stock market in the period from 1990 to 2005. The user can easily analyze the time-varying correlation graph of the market, uncovering information such as market sector trends, representative stocks for portfolio construction, and the interrelationship of stocks over time.

  11. Synthesis fidelity and time-varying spectral change in vowels

    NASA Astrophysics Data System (ADS)

    Assmann, Peter F.; Katz, William F.

    2005-02-01

    Recent studies have shown that synthesized versions of American English vowels are less accurately identified when the natural time-varying spectral changes are eliminated by holding the formant frequencies constant over the duration of the vowel. A limitation of these experiments has been that vowels produced by formant synthesis are generally less accurately identified than the natural vowels after which they are modeled. To overcome this limitation, a high-quality speech analysis-synthesis system (STRAIGHT) was used to synthesize versions of 12 American English vowels spoken by adults and children. Vowels synthesized with STRAIGHT were identified as accurately as the natural versions, in contrast with previous results from our laboratory showing identification rates 9%-12% lower for the same vowels synthesized using the cascade formant model. Consistent with earlier studies, identification accuracy was not reduced when the fundamental frequency was held constant across the vowel. However, elimination of time-varying changes in the spectral envelope using STRAIGHT led to a greater reduction in accuracy (23%) than was previously found with cascade formant synthesis (11%). A statistical pattern recognition model, applied to acoustic measurements of the natural and synthesized vowels, predicted both the higher identification accuracy for vowels synthesized using STRAIGHT compared to formant synthesis, and the greater effects of holding the formant frequencies constant over time with STRAIGHT synthesis. Taken together, the experiment and modeling results suggest that formant estimation errors and incorrect rendering of spectral and temporal cues by cascade formant synthesis contribute to lower identification accuracy and underestimation of the role of time-varying spectral change in vowels. .

  12. Recursive time-varying filter banks for subband image coding

    NASA Technical Reports Server (NTRS)

    Smith, Mark J. T.; Chung, Wilson C.

    1992-01-01

    Filter banks and wavelet decompositions that employ recursive filters have been considered previously and are recognized for their efficiency in partitioning the frequency spectrum. This paper presents an analysis of a new infinite impulse response (IIR) filter bank in which these computationally efficient filters may be changed adaptively in response to the input. The filter bank is presented and discussed in the context of finite-support signals with the intended application in subband image coding. In the absence of quantization errors, exact reconstruction can be achieved and by the proper choice of an adaptation scheme, it is shown that IIR time-varying filter banks can yield improvement over conventional ones.

  13. Dynamical systems with time-varying or unsteady structure

    NASA Astrophysics Data System (ADS)

    Pfeiffer, Friedrich

    A theoretical approach to dynamical systems with time-variant or unsteady topology is presented. Examples from various fields are used to show that such systems are characterized by constraint and indicator functions generated by the dynamical process and at the same time controlling it. A Lagrangian approach permits the unknown generalized acceleration and constraint forces to be evaluated. Mechanical systems with time-variant or unsteady topologies are characterized either by time-varying numbers of DOFs or by an irregular sequence of switching points or by both properties.

  14. Downdating a time-varying square root information filter

    NASA Technical Reports Server (NTRS)

    Muellerschoen, Ronald J.

    1990-01-01

    A new method to efficiently downdate an estimate and covariance generated by a discrete time Square Root Information Filter (SRIF) is presented. The method combines the QR factor downdating algorithm of Gill and the decentralized SRIF algorithm of Bierman. Efficient removal of either measurements or a priori information is possible without loss of numerical integrity. Moreover, the method includes features for detecting potential numerical degradation. Performance on a 300 parameter system with 5800 data points shows that the method can be used in real time and hence is a promising tool for interactive data analysis. Additionally, updating a time-varying SRIF filter with either additional measurements or a priori information proceeds analogously.

  15. Optimal transport in time-varying small-world networks

    NASA Astrophysics Data System (ADS)

    Chen, Qu; Qian, Jiang-Hai; Zhu, Liang; Han, Ding-Ding

    2016-03-01

    The time-order of interactions, which is regulated by some intrinsic activity, surely plays a crucial role regarding the transport efficiency of transportation systems. Here we study the optimal transport structure by measure of the length of time-respecting paths. Our network is built from a two-dimensional regular lattice, and long-range connections are allocated with probability Pi j˜rij -α , where ri j is the Manhattan distance. By assigning each shortcut an activity rate subjected to its geometric distance τi j˜rij -C , long-range links become active intermittently, leading to the time-varying dynamics. We show that for 0 time-varying transportation networks. Empirical studies on British Airways and Austrian Airlines provide consistent evidence with our conclusion.

  16. Filtering Random Graph Processes Over Random Time-Varying Graphs

    NASA Astrophysics Data System (ADS)

    Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert

    2017-08-01

    Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochastic- ity in both the graph topology as well as the signal itself. To bridge this gap, we examine the statistical behavior of the two key filter types, finite impulse response (FIR) and autoregressive moving average (ARMA) graph filters, when operating on random time- varying graph signals (or random graph processes) over random time-varying graphs. Our analysis shows that (i) in expectation, the filters behave as the same deterministic filters operating on a deterministic graph, being the expected graph, having as input signal a deterministic signal, being the expected signal, and (ii) there are meaningful upper bounds for the variance of the filter output. We conclude the paper by proposing two novel ways of exploiting randomness to improve (joint graph-time) noise cancellation, as well as to reduce the computational complexity of graph filtering. As demonstrated by numerical results, these methods outperform the disjoint average and denoise algorithm, and yield a (up to) four times complexity redution, with very little difference from the optimal solution.

  17. Prediction-Correction Algorithms for Time-Varying Constrained Optimization

    DOE PAGES

    Dall-Anese, Emiliano; Simonetto, Andrea

    2017-07-26

    This paper develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

  18. Integrated Sachs-Wolfe effect in time varying vacuum model

    SciTech Connect

    Wang, Y. T.; Gui, Y. X.; Xu, L. X.; Lu, J. B.

    2010-04-15

    The integrated Sachs-Wolfe (ISW) effect is an important implication for dark energy. In this paper, we have calculated the power spectrum of the ISW effect in the time varying vacuum cosmological model, where the model parameter {beta}=4.407 is obtained by the observational constraint of the growth rate. It is found that the source of the ISW effect is not only affected by the different evolutions of the Hubble function H(a) and the dimensionless matter density {Omega}{sub m}(a), but also by the different growth function D{sub +}(a), all of which are changed due to the presence of a matter production term in the time varying vacuum model. However, the difference of the ISW effect in the {Lambda}(t)CDM model and the {Lambda}CDM model is lessened to a certain extent because of the integration from the time of last scattering to the present. It is implied that the observations of the galaxies with high redshift are required to distinguish the two models.

  19. A Symmetric Time-Varying Cluster Rate of Descent Model

    NASA Technical Reports Server (NTRS)

    Ray, Eric S.

    2015-01-01

    A model of the time-varying rate of descent of the Orion vehicle was developed based on the observed correlation between canopy projected area and drag coefficient. This initial version of the model assumes cluster symmetry and only varies the vertical component of velocity. The cluster fly-out angle is modeled as a series of sine waves based on flight test data. The projected area of each canopy is synchronized with the primary fly-out angle mode. The sudden loss of projected area during canopy collisions is modeled at minimum fly-out angles, leading to brief increases in rate of descent. The cluster geometry is converted to drag coefficient using empirically derived constants. A more complete model is under development, which computes the aerodynamic response of each canopy to its local incidence angle.

  20. Epidemic spreading in time-varying community networks

    SciTech Connect

    Ren, Guangming E-mail: ren-guang-ming@163.com; Wang, Xingyuan E-mail: ren-guang-ming@163.com

    2014-06-15

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q{sub c}. The epidemic will survive when q > q{sub c} and die when q < q{sub c}. These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure.

  1. The interpretation of time-varying data with DIAMON-1.

    PubMed

    Steimann, F

    1996-08-01

    Applying the methods of Artificial Intelligence to clinical monitoring requires some kind of signal-to-symbol conversion as a prior step. Subsequent processing of the derived symbolic information must also be sensitive to history and development, as the failure to address temporal relationships between findings invariably leads to inferior results. DIAMON-1, a framework for the design of diagnostic monitors, provides two methods for the interpretation of time-varying data: one for the detection of trends based on classes of courses, and one for the tracking of disease histories modelled through deterministic automata. Both methods make use of fuzzy set theory taking account of the elasticity of medical categories and allowing discrete disease models to mirror the patient's continuous progression through the stages of illness.

  2. Endogenous time-varying risk aversion and asset returns.

    PubMed

    Berardi, Michele

    2016-01-01

    Stylized facts about statistical properties for short horizon returns in financial markets have been identified in the literature, but a satisfactory understanding for their manifestation is yet to be achieved. In this work, we show that a simple asset pricing model with representative agent is able to generate time series of returns that replicate such stylized facts if the risk aversion coefficient is allowed to change endogenously over time in response to unexpected excess returns under evolutionary forces. The same model, under constant risk aversion, would instead generate returns that are essentially Gaussian. We conclude that an endogenous time-varying risk aversion represents a very parsimonious way to make the model match real data on key statistical properties, and therefore deserves careful consideration from economists and practitioners alike.

  3. Interactive isosurface ray tracing of time-varying tetrahedral volumes.

    PubMed

    Wald, Ingo; Friedrich, Heiko; Knoll, Aaron; Hansen, Charles D

    2007-01-01

    We describe a system for interactively rendering isosurfaces of tetrahedral finite-element scalar fields using coherent ray tracing techniques on the CPU. By employing state-of-the art methods in polygonal ray tracing, namely aggressive packet/frustum traversal of a bounding volume hierarchy, we can accomodate large and time-varying unstructured data. In conjunction with this efficiency structure, we introduce a novel technique for intersecting ray packets with tetrahedral primitives. Ray tracing is flexible, allowing for dynamic changes in isovalue and time step, visualization of multiple isosurfaces, shadows, and depth-peeling transparency effects. The resulting system offers the intuitive simplicity of isosurfacing, guaranteed-correct visual results, and ultimately a scalable, dynamic and consistently interactive solution for visualizing unstructured volumes.

  4. Circular motion analysis of time-varying bioimpedance.

    PubMed

    Sanchez, B; Louarroudi, E; Rutkove, S B; Pintelon, R

    2015-11-01

    This paper presents a step forward towards the analysis of a linear periodically time-varying (PTV) bioimpedance ZPTV(jw, t), which is an important subclass of a linear time-varying (LTV) bioimpedance. Similarly to the Fourier coefficients of a periodic signal, a PTV impedance can be decomposed into frequency dependent impedance phasors, [Formula: see text], that are rotating with an angular speed of wr = 2πr/TZ. The vector length of these impedance phasors corresponds to the amplitude of the rth-order harmonic impedance |Zr( jw)| and the initial phase is given by Φr(w, t0) = [Symbol: see text]Zr( jw) + 2πrt0/TZ, with t0∈[0, T] being a time instant within the measurement time T. The impedance period TZ stands for the cycle length of the bio-system under investigation; for example, the elapsed time between two consecutive R-waves in the electrocardiogram or the breathing periodicity in case of the heart or lungs, respectively. First, it is demonstrated that the harmonic impedance phasor [Formula: see text], at a particular measured frequency k, can be represented by a rotating phasor, leading to the so-called circular motion analysis technique. Next, the two dimensional (2D) representation of the harmonic impedance phasors is then extended to a three-dimensional (3D) coordinate system by taking into account the frequency dependence. Finally, we introduce a new visualizing tool to summarize the frequency response behavior of ZPTV( jw, t) into a single 3D plot using the local Frenet-Serret frame. This novel 3D impedance representation is then compared with the 3D Nyquist representation of a PTV impedance. The concepts are illustrated through real measurements conducted on a PTV RC-circuit.

  5. Consensus of Heterogeneous Linear Multiagent Systems With Communication Time-Delays.

    PubMed

    Xu, Xiang; Liu, Lu; Feng, Gang

    2017-05-23

    This paper studies the consensus problem of heterogeneous linear multiagent systems with arbitrarily large constant, time-varying, or distributed communication delays. Novel distributed dynamic controllers are proposed for such multiagent systems with fixed and switching directed communication topologies, respectively. It is shown that the controlled heterogeneous linear multiagent system can reach consensus for arbitrarily large constant, time-varying, and distributed communication delays under some sufficient conditions. Simulation examples are provided to demonstrate the effectiveness of the proposed controllers.

  6. Estimation of the blood Doppler frequency shift by a time-varying parametric approach.

    PubMed

    Girault, J M; Kouamé, D; Ouahabi, A; Patat, F

    2000-03-01

    Doppler ultrasound is widely used in medical applications to extract the blood Doppler flow velocity in the arteries via spectral analysis. The spectral analysis of non-stationary signals and particularly Doppler signals requires adequate tools that should present both good time and frequency resolutions. It is well-known that the most commonly used time-windowed Fourier transform, which provides a time-frequency representation, is limited by the intrinsic trade-off between time and frequency resolutions. Parametric methods have then been introduced as an alternative to overcome this resolution problem. However, the performance of those methods deteriorates when high non-stationarities are present in the Doppler signal. For the purpose of accurately estimating the Doppler frequency shift, even when the temporal flow velocity is rapid (high non-stationarity), we propose to combine the use of the time-varying autoregressive (AR) method and the (dominant) pole frequency. This proposed method performs well in the context where non-stationarities are very high. A comparative evaluation has been made between classical (FFT based) and AR (both block and recursive) algorithms. Among recursive algorithms we test an adaptive recursive method as well as a time-varying recursive method. Finally, the superiority of the time-varying parametric approach in terms of frequency tracking and delay in the frequency estimate is illustrated for both simulated and in vivo Doppler signals.

  7. Spatiotemporal Distributions of Migratory Birds: Patchy Models with Delay

    NASA Astrophysics Data System (ADS)

    Gourley, Stephen A.; Liu, Rongsong; Wu, Jianhong

    2010-01-01

    We derive and analyze a mathematical model for the spatiotemporal distribution of a migratory bird species. The birds have specific sites for breeding and winter feeding, and usually several stopover sites along the migration route, and therefore a patch model is the natural choice. However, we also model the journeys of the birds along the flyways, and this is achieved using a continuous space model of reaction-advection type. In this way proper account is taken of flight times and in-flight mortalities which may vary from sector to sector, and this information is featured in the ordinary differential equations for the populations on the patches through the values of the time delays and the model coefficients. The seasonality of the phenomenon is accommodated by having periodic migration and birth rates. The central result of the paper is a very general theorem on the threshold dynamics, obtained using recent results on discrete monotone dynamical systems, for birth functions which are subhomogeneous. For such functions, depending on the spectral radius of a certain operator, either there is a globally attracting periodic solution, or the bird population becomes extinct. Evaluation of the spectral radius is difficult, so we also present, for the particular case of just one stopover site on the migration route, a verifiable sufficient condition for extinction or survival in the form of an attractive periodic solution. This threshold is illustrated numerically using data from the U.S. Geological Survey on the bar-headed goose and its migration to India from its main breeding sites around Lake Qinghai and Mongolia.

  8. Time-varying trends of global vegetation activity

    NASA Astrophysics Data System (ADS)

    Pan, N.; Feng, X.; Fu, B.

    2016-12-01

    Vegetation plays an important role in regulating the energy change, water cycle and biochemical cycle in terrestrial ecosystems. Monitoring the dynamics of vegetation activity and understanding their driving factors have been an important issue in global change research. Normalized Difference Vegetation Index (NDVI), an indicator of vegetation activity, has been widely used in investigating vegetation changes at regional and global scales. Most studies utilized linear regression or piecewise linear regression approaches to obtain an averaged changing rate over a certain time span, with an implicit assumption that the trend didn't change over time during that period. However, no evidence shows that this assumption is right for the non-linear and non-stationary NDVI time series. In this study, we adopted the multidimensional ensemble empirical mode decomposition (MEEMD) method to extract the time-varying trends of NDVI from original signals without any a priori assumption of their functional form. Our results show that vegetation trends are spatially and temporally non-uniform during 1982-2013. Most vegetated area exhibited greening trends in the 1980s. Nevertheless, the area with greening trends decreased over time since the early 1990s, and the greening trends have stalled or even reversed in many places. Regions with browning trends were mainly located in southern low latitudes in the 1980s, whose area decreased before the middle 1990s and then increased at an accelerated rate. The greening-to-browning reversals were widespread across all continents except Oceania (43% of the vegetated areas), most of which happened after the middle 1990s. In contrast, the browning-to-greening reversals occurred in smaller area and earlier time. The area with monotonic greening and browning trends accounted for 33% and 5% of the vegetated area, respectively. By performing partial correlation analyses between NDVI and climatic elements (temperature, precipitation and cloud cover

  9. Time-varying Concurrent Risk of Extreme Droughts and Heatwaves in California

    NASA Astrophysics Data System (ADS)

    Sarhadi, A.; Diffenbaugh, N. S.; Ausin, M. C.

    2016-12-01

    Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena such as droughts and heatwaves. The concurrent of these nature-changing climatic extremes may result in intensifying undesirable consequences in terms of human health and destructive effects in water resources. The present study assesses the risk of concurrent extreme droughts and heatwaves under dynamic nonstationary conditions arising from climate change in California. For doing so, a generalized fully Bayesian time-varying multivariate risk framework is proposed evolving through time under dynamic human-induced environment. In this methodology, an extreme, Bayesian, dynamic copula (Gumbel) is developed to model the time-varying dependence structure between the two different climate extremes. The time-varying extreme marginals are previously modeled using a Generalized Extreme Value (GEV) distribution. Bayesian Markov Chain Monte Carlo (MCMC) inference is integrated to estimate parameters of the nonstationary marginals and copula using a Gibbs sampling method. Modelled marginals and copula are then used to develop a fully Bayesian, time-varying joint return period concept for the estimation of concurrent risk. Here we argue that climate change has increased the chance of concurrent droughts and heatwaves over decades in California. It is also demonstrated that a time-varying multivariate perspective should be incorporated to assess realistic concurrent risk of the extremes for water resources planning and management in a changing climate in this area. The proposed generalized methodology can be applied for other stochastic nature-changing compound climate extremes that are under the influence of climate change.

  10. Innovation diffusion on time-varying activity driven networks

    NASA Astrophysics Data System (ADS)

    Rizzo, Alessandro; Porfiri, Maurizio

    2016-01-01

    Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass' model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

  11. Study of selected phenotype switching strategies in time varying environment

    NASA Astrophysics Data System (ADS)

    Horvath, Denis; Brutovsky, Branislav

    2016-03-01

    Population heterogeneity plays an important role across many research, as well as the real-world, problems. The population heterogeneity relates to the ability of a population to cope with an environment change (or uncertainty) preventing its extinction. However, this ability is not always desirable as can be exemplified by an intratumor heterogeneity which positively correlates with the development of resistance to therapy. Causation of population heterogeneity is therefore in biology and medicine an intensively studied topic. In this paper the evolution of a specific strategy of population diversification, the phenotype switching, is studied at a conceptual level. The presented simulation model studies evolution of a large population of asexual organisms in a time-varying environment represented by a stochastic Markov process. Each organism disposes with a stochastic or nonlinear deterministic switching strategy realized by discrete-time models with evolvable parameters. We demonstrate that under rapidly varying exogenous conditions organisms operate in the vicinity of the bet-hedging strategy, while the deterministic patterns become relevant as the environmental variations are less frequent. Statistical characterization of the steady state regimes of the populations is done using the Hellinger and Kullback-Leibler functional distances and the Hamming distance.

  12. Video painting with space-time-varying style parameters.

    PubMed

    Kagaya, Mizuki; Brendel, William; Deng, Qingqing; Kesterson, Todd; Todorovic, Sinisa; Neill, Patrick J; Zhang, Eugene

    2011-01-01

    Artists use different means of stylization to control the focus on different objects in the scene. This allows them to portray complex meaning and achieve certain artistic effects. Most prior work on painterly rendering of videos, however, uses only a single painting style, with fixed global parameters, irrespective of objects and their layout in the images. This often leads to inadequate artistic control. Moreover, brush stroke orientation is typically assumed to follow an everywhere continuous directional field. In this paper, we propose a video painting system that accounts for the spatial support of objects in the images or videos, and uses this information to specify style parameters and stroke orientation for painterly rendering. Since objects occupy distinct image locations and move relatively smoothly from one video frame to another, our object-based painterly rendering approach is characterized by style parameters that coherently vary in space and time. Space-time-varying style parameters enable more artistic freedom, such as emphasis/de-emphasis, increase or decrease of contrast, exaggeration or abstraction of different objects in the scene in a temporally coherent fashion.

  13. Opinion formation with time-varying bounded confidence

    PubMed Central

    Liu, QiPeng; Zhang, SiYing

    2017-01-01

    When individuals in social groups communicate with one another and are under the influence of neighbors’ opinions, they typically revise their own opinions to adapt to such peer opinions. The individual threshold of bounded confidence will thus be affected by both a change in individual confidence and by neighbor influence. Individuals thus update their own opinions with new bounded confidence, while their updated opinions also influence their neighbors’ opinions. Based on this reasoned factual assumption, we propose an opinion dynamics model with time-varying bounded confidence. A directed network is formed by the rule of the individual bounded confidence threshold. The threshold of individual bounded confidence involves both confidence variation and the in/out degree of the individual node. When the confidence variation is greater, an individual’s confidence in persisting in his own opinion in interactions is weaker, and the individual is more likely to adopt neighbors’ opinions. In networks, the in/out degree is determined by individual neighbors. Our main research involves the process of opinion evolution and the basic laws of opinion cluster formation. Group opinions converge exponentially to consensus with stable neighbors. An individual opinion evolution is determined by the average neighbor opinion effect strength. We also explore the conditions involved in forming a stable neighbor relationship and the influence of the confidence variation in the convergence of the threshold of bounded confidence. The results show that the influence on opinion evolution is greater with increased confidence variation. PMID:28264038

  14. Opinion formation with time-varying bounded confidence.

    PubMed

    Zhang, YunHong; Liu, QiPeng; Zhang, SiYing

    2017-01-01

    When individuals in social groups communicate with one another and are under the influence of neighbors' opinions, they typically revise their own opinions to adapt to such peer opinions. The individual threshold of bounded confidence will thus be affected by both a change in individual confidence and by neighbor influence. Individuals thus update their own opinions with new bounded confidence, while their updated opinions also influence their neighbors' opinions. Based on this reasoned factual assumption, we propose an opinion dynamics model with time-varying bounded confidence. A directed network is formed by the rule of the individual bounded confidence threshold. The threshold of individual bounded confidence involves both confidence variation and the in/out degree of the individual node. When the confidence variation is greater, an individual's confidence in persisting in his own opinion in interactions is weaker, and the individual is more likely to adopt neighbors' opinions. In networks, the in/out degree is determined by individual neighbors. Our main research involves the process of opinion evolution and the basic laws of opinion cluster formation. Group opinions converge exponentially to consensus with stable neighbors. An individual opinion evolution is determined by the average neighbor opinion effect strength. We also explore the conditions involved in forming a stable neighbor relationship and the influence of the confidence variation in the convergence of the threshold of bounded confidence. The results show that the influence on opinion evolution is greater with increased confidence variation.

  15. Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

    NASA Astrophysics Data System (ADS)

    Valenza, Gaetano; Citi, Luca; Garcia, Ronald G.; Taylor, Jessica Noggle; Toschi, Nicola; Barbieri, Riccardo

    2017-02-01

    The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson’s Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity.

  16. Automated model formulation for time-varying flexible structures

    NASA Technical Reports Server (NTRS)

    Glass, B. J.; Hanagud, S.

    1989-01-01

    Presented here is an identification technique that uses the sensor information to choose a new model out of a finite set of discrete model space, in order to follow the observed changes to the given time varying flexible structure. Boundary condition sets or other information on model variations are used to organize the set of possible models laterally into a search tree with levels of abstraction used to order the models vertically within branches. An object-oriented programming approach is used to represent the model set in the search tree. A modified A (asterisk) best first search algorithm finds the model where the model response best matches the current observations. Several extensions to this methodology are discussed. Methods of possible integration of rules with the current search algorithm are considered to give weight to interpreted trends that may be found in a series of observations. This capability might lead, for instance, to identifying a model that incorporates a progressive damage rather than with incorrect paramenters such as added mass. Another new direction is to consider the use of noisy time domain sensor feedback rather than frequency domain information in the search algorithm to improve the real-time capability of the developed procedure.

  17. Automated model formulation for time-varying flexible structures

    NASA Technical Reports Server (NTRS)

    Glass, B. J.; Hanagud, S.

    1989-01-01

    Presented here is an identification technique that uses the sensor information to choose a new model out of a finite set of discrete model space, in order to follow the observed changes to the given time varying flexible structure. Boundary condition sets or other information on model variations are used to organize the set of possible models laterally into a search tree with levels of abstraction used to order the models vertically within branches. An object-oriented programming approach is used to represent the model set in the search tree. A modified A (asterisk) best first search algorithm finds the model where the model response best matches the current observations. Several extensions to this methodology are discussed. Methods of possible integration of rules with the current search algorithm are considered to give weight to interpreted trends that may be found in a series of observations. This capability might lead, for instance, to identifying a model that incorporates a progressive damage rather than with incorrect paramenters such as added mass. Another new direction is to consider the use of noisy time domain sensor feedback rather than frequency domain information in the search algorithm to improve the real-time capability of the developed procedure.

  18. Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control.

    PubMed

    Valenza, Gaetano; Citi, Luca; Garcia, Ronald G; Taylor, Jessica Noggle; Toschi, Nicola; Barbieri, Riccardo

    2017-02-20

    The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson's Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity.

  19. EEG correlates of time-varying BOLD functional connectivity

    PubMed Central

    Chang, Catie; Liu, Zhongming; Chen, Michael C.; Liu, Xiao; Duyn, Jeff H.

    2013-01-01

    Recent resting-state fMRI studies have shown that the apparent functional connectivity (FC) between brain regions may undergo changes on time-scales of seconds to minutes, the basis and importance of which are largely unknown. Here, we examine the electrophysiological correlates of within-scan FC variations during a condition of eyes-closed rest. A sliding window analysis of simultaneous EEG-fMRI data was performed to examine whether temporal variations in coupling between three major networks (default mode; DMN, dorsal attention; DAN, and salience network; SN) are associated with temporal variations in mental state, as assessed from the amplitude of alpha and theta oscillations in the EEG. In our dataset, alpha power showed a significant inverse relationship with the strength of connectivity between DMN and DAN. In addition, alpha power covaried with the spatial extent of anticorrelation between DMN and DAN, with higher alpha power associated with larger anticorrelation extent. Results suggest an electrical signature of the time-varying FC between the DAN and DMN, potentially reflecting neural and state-dependent variations. PMID:23376790

  20. Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

    PubMed Central

    Valenza, Gaetano; Citi, Luca; Garcia, Ronald G.; Taylor, Jessica Noggle; Toschi, Nicola; Barbieri, Riccardo

    2017-01-01

    The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson’s Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity. PMID:28218249

  1. Modeling of Time Varying Slag Flow in Coal Gasifiers

    SciTech Connect

    Pilli, Siva Prasad; Johnson, Kenneth I.; Williford, Ralph E.; Sundaram, S. K.; Korolev, Vladimir N.; Crum, Jarrod V.

    2008-08-30

    There is considerable interest within government agencies and the energy industries across the globe to further advance the clean and economical conversion of coal into liquid fuels to reduce our dependency on imported oil. To date, advances in these areas have been largely based on experimental work. Although there are some detailed systems level performance models, little work has been done on numerical modeling of the component level processes. If accurate models are developed, then significant R&D time might be saved, new insights into the process might be gained, and some good predictions of process or performance can be made. One such area is the characterization of slag deposition and flow on the gasifier walls. Understanding slag rheology and slag-refractory interactions is critical to design and operation of gasifiers with extended refractory lifetimes and also to better control of operating parameters so that the overall gasifier performance with extended service life can be optimized. In the present work, the literature on slag flow modeling was reviewed and a model similar to Seggiani’s was developed to simulate the time varying slag accumulation and flow on the walls of a Prenflo coal gasifier. This model was further extended and modified to simulate a refractory wall gasifier including heat transfer through the refractory wall with flowing slag in contact with the refractory. The model was used to simulate temperature dependent slag flow using rheology data from our experimental slag testing program. These modeling results as well as experimental validation are presented.

  2. Time-varying Heliospheric Distance to the Heliopause

    NASA Astrophysics Data System (ADS)

    Washimi, Haruichi; Tanaka, Takashi; Zank, Gary P.

    2017-09-01

    Using a three-dimensional MHD simulation, we examine the time-varying outer heliospheric structure and distance to the heliopause. Voyager 2 (V2) solar-wind observations show that a global merged interaction region (GMIR) with a ram-pressure of the order of several nPa normalized at 1 au enters the distant solar wind at an average rate of about one per year. This series of GMIRs adds an additional perturbative increase to the solar-wind ram-pressure in the inner heliosheath, and it also reduces the surrounding interstellar medium pressure acting on the heliopause; consequently, our simulation results in the distance to the heliopause being ∼14 au larger when compared to the case when a series of GMIRs is not taken into account. In addition, OMNI data show that the solar-wind ram-pressure near the Earth increases from ∼1.3 nPA in 2010 and before to 1.7–2.4 nPa after that until the present time. These variations in the overall ram-pressure of the solar wind are also included in our simulation. The inclusion of the time variable solar-wind ram-pressure and the series of GMIRs allows us to illustrate how the realistic distance to the heliopause varies in response to both long- and short time variability in solar activity. This simulation study also explains the puzzle of why V2 has not yet crossed the heliopause, although it is now almost 5 years since Voyager 1 crossed the heliopause in 2012.

  3. Stability and bifurcation analysis for the Kaldor-Kalecki model with a discrete delay and a distributed delay

    NASA Astrophysics Data System (ADS)

    Yu, Jinchen; Peng, Mingshu

    2016-10-01

    In this paper, a Kaldor-Kalecki model of business cycle with both discrete and distributed delays is considered. With the corresponding characteristic equation analyzed, the local stability of the positive equilibrium is investigated. It is found that there exist Hopf bifurcations when the discrete time delay passes a sequence of critical values. By applying the method of multiple scales, the explicit formulae which determine the direction of Hopf bifurcation and the stability of bifurcating periodic solutions are derived. Finally, numerical simulations are carried out to illustrate our main results.

  4. When timeliness matters: the effect of status on reactions to perceived time delay within distributed collaboration.

    PubMed

    Sheldon, Oliver J; Thomas-Hunt, Melissa C; Proell, Chad A

    2006-11-01

    This research examines the interactive effects of status and perceived time delay on acceptance of partner knowledge contributions within a distributive collaboration work environment. Results across 2 studies suggest that within distributed collaboration, time delays attributed to low-status partners had a significantly more harmful effect on influence acceptance than time delay attributed to high-status partners. This was so, despite the fact that partners' actual behavior was held constant across experimental conditions. In addition, results indicate that judgments of partner competence significantly mediated the interactive effects of perceived time delay and partner status on acceptance of partner influence. (c) 2006 APA, all rights reserved

  5. Stochastic processes with distributed delays: chemical Langevin equation and linear-noise approximation.

    PubMed

    Brett, Tobias; Galla, Tobias

    2013-06-21

    We develop a systematic approach to the linear-noise approximation for stochastic reaction systems with distributed delays. Unlike most existing work our formalism does not rely on a master equation; instead it is based upon a dynamical generating functional describing the probability measure over all possible paths of the dynamics. We derive general expressions for the chemical Langevin equation for a broad class of non-Markovian systems with distributed delay. Exemplars of a model of gene regulation with delayed autoinhibition and a model of epidemic spread with delayed recovery provide evidence of the applicability of our results.

  6. Time-varying priority queuing models for human dynamics

    NASA Astrophysics Data System (ADS)

    Jo, Hang-Hyun; Pan, Raj Kumar; Kaski, Kimmo

    2012-06-01

    Queuing models provide insight into the temporal inhomogeneity of human dynamics, characterized by the broad distribution of waiting times of individuals performing tasks. We theoretically study the queuing model of an agent trying to execute a task of interest, the priority of which may vary with time due to the agent's “state of mind.” However, its execution is disrupted by other tasks of random priorities. By considering the priority of the task of interest either decreasing or increasing algebraically in time, we analytically obtain and numerically confirm the bimodal and unimodal waiting time distributions with power-law decaying tails, respectively. These results are also compared to the updating time distribution of papers in arXiv.org and the processing time distribution of papers in Physical Review journals. Our analysis helps to understand human task execution in a more realistic scenario.

  7. ICASE/LaRC Symposium on Visualizing Time-Varying Data

    NASA Technical Reports Server (NTRS)

    Banks, D. C. (Editor); Crockett, T. W. (Editor); Stacy, K. (Editor)

    1996-01-01

    Time-varying datasets present difficult problems for both analysis and visualization. For example, the data may be terabytes in size, distributed across mass storage systems at several sites, with time scales ranging from femtoseconds to eons. In response to these challenges, ICASE and NASA Langley Research Center, in cooperation with ACM SIGGRAPH, organized the first symposium on visualizing time-varying data. The purpose was to bring the producers of time-varying data together with visualization specialists to assess open issues in the field, present new solutions, and encourage collaborative problem-solving. These proceedings contain the peer-reviewed papers which were presented at the symposium. They cover a broad range of topics, from methods for modeling and compressing data to systems for visualizing CFD simulations and World Wide Web traffic. Because the subject matter is inherently dynamic, a paper proceedings cannot adequately convey all aspects of the work. The accompanying video proceedings provide additional context for several of the papers.

  8. Stability Analysis of Distributed Delay Neural Networks Based on Relaxed Lyapunov-Krasovskii Functionals.

    PubMed

    Zhang, Baoyong; Lam, James; Xu, Shengyuan

    2015-07-01

    This paper revisits the problem of asymptotic stability analysis for neural networks with distributed delays. The distributed delays are assumed to be constant and prescribed. Since a positive-definite quadratic functional does not necessarily require all the involved symmetric matrices to be positive definite, it is important for constructing relaxed Lyapunov-Krasovskii functionals, which generally lead to less conservative stability criteria. Based on this fact and using two kinds of integral inequalities, a new delay-dependent condition is obtained, which ensures that the distributed delay neural network under consideration is globally asymptotically stable. This stability criterion is then improved by applying the delay partitioning technique. Two numerical examples are provided to demonstrate the advantage of the presented stability criteria.

  9. Multi-carrier Communications over Time-varying Acoustic Channels

    NASA Astrophysics Data System (ADS)

    Aval, Yashar M.

    Acoustic communication is an enabling technology for many autonomous undersea systems, such as those used for ocean monitoring, offshore oil and gas industry, aquaculture, or port security. There are three main challenges in achieving reliable high-rate underwater communication: the bandwidth of acoustic channels is extremely limited, the propagation delays are long, and the Doppler distortions are more pronounced than those found in wireless radio channels. In this dissertation we focus on assessing the fundamental limitations of acoustic communication, and designing efficient signal processing methods that cam overcome these limitations. We address the fundamental question of acoustic channel capacity (achievable rate) for single-input-multi-output (SIMO) acoustic channels using a per-path Rician fading model, and focusing on two scenarios: narrowband channels where the channel statistics can be approximated as frequency- independent, and wideband channels where the nominal path loss is frequency-dependent. In each scenario, we compare several candidate power allocation techniques, and show that assigning uniform power across all frequencies for the first scenario, and assigning uniform power across a selected frequency-band for the second scenario, are the best practical choices in most cases, because the long propagation delay renders the feedback information outdated for power allocation based on the estimated channel response. We quantify our results using the channel information extracted form the 2010 Mobile Acoustic Communications Experiment (MACE'10). Next, we focus on achieving reliable high-rate communication over underwater acoustic channels. Specifically, we investigate orthogonal frequency division multiplexing (OFDM) as the state-of-the-art technique for dealing with frequency-selective multipath channels, and propose a class of methods that compensate for the time-variation of the underwater acoustic channel. These methods are based on multiple

  10. Exact and heuristic methods for network completion for time-varying genetic networks.

    PubMed

    Nakajima, Natsu; Akutsu, Tatsuya

    2014-01-01

    Robustness in biological networks can be regarded as an important feature of living systems. A system maintains its functions against internal and external perturbations, leading to topological changes in the network with varying delays. To understand the flexibility of biological networks, we propose a novel approach to analyze time-dependent networks, based on the framework of network completion, which aims to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We have developed a novel network completion method for time-varying networks by extending our previous method for the completion of stationary networks. In particular, we introduce a double dynamic programming technique to identify change time points and required modifications. Although this extended method allows us to guarantee the optimality of the solution, this method has relatively low computational efficiency. In order to resolve this difficulty, we developed a heuristic method for speeding up the calculation of minimum least squares errors. We demonstrate the effectiveness of our proposed methods through computational experiments using synthetic data and real microarray gene expression data. The results indicate that our methods exhibit good performance in terms of completing and inferring gene association networks with time-varying structures.

  11. An observer-based compensator for distributed delays in integrated control systems

    NASA Technical Reports Server (NTRS)

    Luck, Rogelio; Ray, Asok

    1989-01-01

    This paper presents an algorithm for compensation of delays that are distributed within a control loop. The observer-based algorithm is especially suitable for compensating network-induced delays that are likely to occur in integrated control systems of the future generation aircraft. The robustness of the algorithm relative to uncertainties in the plant model have been examined.

  12. The Influence of Publication Delays on the Observed Aging Distribution of Scientific Literature.

    ERIC Educational Resources Information Center

    Egghe, Leo; Rousseau, Ronald

    2000-01-01

    Discusses the influence of publication delays on the aging of scientific literature and explains how the undisturbed aging function and the publication delay combine to give the observed aging function through a mathematical operation called convolution. Shows the convolution of various distributions and considers a paradox between theory and real…

  13. Statistical-Mechanical Analysis of LMS Algorithm for Time-Varying Unknown System

    NASA Astrophysics Data System (ADS)

    Ishibushi, Norihiro; Kajikawa, Yoshinobu; Miyoshi, Seiji

    2017-02-01

    We analyze the behaviors of the least-mean-square algorithm for a time-varying unknown system using a statistical-mechanical method. Cross-correlations between the elements of a primary path and those of an adaptive filter and autocorrelations of the elements of the adaptive filter are treated as macroscopic variables. We obtain simultaneous differential equations that describe the dynamical behaviors of the macroscopic variables under conditions in which the tapped delay line is sufficiently long. We analytically show the existence of an optimal step size. This result is supporting evidence of Widrow et al.'s pioneering work that clarified the trade-off between the noise misadjustment and the lag misadjustment. Furthermore, we obtain the exact solution of the optimal step size in the case of a white reference signal. The derived theory includes the behaviors for a time-constant unknown system as a special case.

  14. Integrated planning problem in supply chains with time-varying delivery

    NASA Astrophysics Data System (ADS)

    Wang, Hai-ying; Liu, Da-cheng; Ding, Hua; Guo, Fu

    2011-10-01

    We consider a serial supply chain consisting of a raw material supplier, a manufacturer, a distribution centre and a retailer in the presence of time-varying delivery between manufacturer facility and the retailer warehouse. Delivery time functions are developed based on practical data analysis and the cost models for both linear and non-linear delivery time functions are derived. Analytic solution for system with linear delivery times is derived and a search algorithm for system with non-linear delivery times is established. Finally, sensitivity analysis is made to help decision makers achieve a lower total cost in practice.

  15. Theory of electromagnetic cyclotron wave growth in a time-varying magnetoplasma

    NASA Technical Reports Server (NTRS)

    Gail, William B.

    1990-01-01

    The effect of a time-dependent perturbation in the magnetoplasma on the wave and particle populations is investigated using the Kennel-Petchek (1966) approach. Perturbations in the cold plasma density, energetic particle distribution, and resonance condition are calculated on the basis of the ideal MHD assumption given an arbitrary compressional magnetic field perturbation. An equation is derived describing the time-dependent growth rate for parallel propagating electromagnetic cyclotron waves in a time-varying magnetoplasma with perturbations superimposed on an equilibrium configuration.

  16. Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns

    NASA Astrophysics Data System (ADS)

    Burioni, Raffaella; Ubaldi, Enrico; Vezzani, Alessandro

    2017-05-01

    The recent availability of large-scale, time-resolved and high quality digital datasets has allowed for a deeper understanding of the structure and properties of many real-world networks. The empirical evidence of a temporal dimension prompted the switch of paradigm from a static representation of networks to a time varying one. In this work we briefly review the framework of time-varying-networks in real world social systems, especially focusing on the activity-driven paradigm. We develop a framework that allows for the encoding of three generative mechanisms that seem to play a central role in the social networks’ evolution: the individual’s propensity to engage in social interactions, its strategy in allocate these interactions among its alters and the burstiness of interactions amongst social actors. The functional forms and probability distributions encoding these mechanisms are typically data driven. A natural question arises if different classes of strategies and burstiness distributions, with different local scale behavior and analogous asymptotics can lead to the same long time and large scale structure of the evolving networks. We consider the problem in its full generality, by investigating and solving the system dynamics in the asymptotic limit, for general classes of ties allocation mechanisms and waiting time probability distributions. We show that the asymptotic network evolution is driven by a few characteristics of these functional forms, that can be extracted from direct measurements on large datasets.

  17. The Type Ia Supernova Rate and Delay-Time Distribution

    NASA Astrophysics Data System (ADS)

    Graur, Or

    2013-11-01

    The nature of the progenitor stellar systems of thermonuclear, or Type Ia, supernovae (SNe Ia) remains unknown. Unlike core-collapse (CC) SNe, which have been successfully linked, at least partially, to various types of massive stars, the progenitors of SNe Ia are to date undetected in pre-explosion images and the nature of these progenitors can only be probed using indirect methods. In this thesis, I present three SN surveys aimed at measuring the rates at which SNe Ia explode at different times throughout the Universe's history and in different types of galaxies. I use these rates to re-construct the SN Ia delay-time distribution (DTD), a function that connects between the star-formation history (SFH) of a specific stellar environment and its SN Ia rate, and I use it to constrain different progenitor models. In Chapter 1, I provide a brief introduction of the field. This is followed, in Chapter 2, by a description of the Subaru Deep Field (SDF) SN Survey. Over a period of three years between 2005-2008, the SDF was observed on four independent epochs with Suprime-Cam on the Subaru 8.2-m telescope, with two nights of exposure per epoch, in the R, i', and z' bands. In this survey, I discover 150 SNe out to redshift z ~ 2, including 27 SNe Ia in the range 1.0 < z < 1.5 and 10 in the range 1.5 < z < 2.0. The SN Ia rate measurements from this sample are consistent with those derived from the Hubble Space Telescope (HST) GOODS sample, but the overall uncertainty of the 1.5 < z < 2.0 measurement is a factor of 2 smaller, of 35-50%. Based on this sample, we find that the SN Ia rate evolution levels off at 1.0 < z < 2.0, but shows no sign of declining. Combining our SN Ia rate measurements and those from the literature, and comparing to a wide range of possible SFHs, the best-fitting DTD is a power law of the form Psi(t) ~ t^beta, with index beta = -1.1 ± 0.1 (statistical) ± 0.17 (systematic). By combining the contribution from CC SNe, based on the wide range of SFHs

  18. Hubble expansion and structure formation in time varying vacuum models

    SciTech Connect

    Basilakos, Spyros; Plionis, Manolis; Sola, Joan

    2009-10-15

    {lambda}{proportional_to}H model) in which clusters form at significantly earlier times (z{>=}4) with respect to all other models (z{approx}2). Finally, we derived the theoretically predicted dark matter halo mass function and the corresponding distribution of cluster-size halos for all the models studied. Their expected redshift distribution indicates that it will be difficult to distinguish the closely resembling models (constant vacuum, quantum field, and power law vacuum), using realistic future x-ray surveys of cluster abundances. However, cluster surveys based on the Sunayev-Zeldovich detection method give some hope to distinguish the closely resembling models at high redshifts.

  19. Spherical collapse model in time varying vacuum cosmologies

    SciTech Connect

    Basilakos, Spyros; Plionis, Manolis; Sola, Joan

    2010-10-15

    We investigate the virialization of cosmic structures in the framework of flat Friedmann-Lemaitre-Robertson-Walker cosmological models, in which the vacuum energy density evolves with time. In particular, our analysis focuses on the study of spherical matter perturbations, as they decouple from the background expansion, 'turn around', and finally collapse. We generalize the spherical collapse model in the case when the vacuum energy is a running function of the Hubble rate, {Lambda}={Lambda}(H). A particularly well-motivated model of this type is the so-called quantum field vacuum, in which {Lambda}(H) is a quadratic function, {Lambda}(H)=n{sub 0}+n{sub 2}H{sup 2}, with n{sub 0{ne}}0. This model was previously studied by our team using the latest high quality cosmological data to constrain its free parameters, as well as the predicted cluster formation rate. It turns out that the corresponding Hubble expansion history resembles that of the traditional {Lambda}CDM cosmology. We use this {Lambda}(t)CDM framework to illustrate the fact that the properties of the spherical collapse model (virial density, collapse factor, etc.) depend on the choice of the considered vacuum energy (homogeneous or clustered). In particular, if the distribution of the vacuum energy is clustered, then, under specific conditions, we can produce more concentrated structures with respect to the homogeneous vacuum energy case.

  20. Hopf Bifurcation Analysis of Distributed Delay Equations with Applications to Neural Networks

    NASA Astrophysics Data System (ADS)

    Gentile, Franco S.; Moiola, Jorge L.

    In this paper, we study how to capture smooth oscillations arising from delay-differential equations with distributed delays. For this purpose, we introduce a modified version of the frequency-domain method based on the Graphical Hopf Bifurcation Theorem. Our approach takes advantage of a simple interpretation of the distributed delay effect by means of some Laplace-transformed properties. Our theoretical results are illustrated through an example of two coupled neurons with distributed delay in their communication channel. For this system, we compute several bifurcation diagrams and approximations of the amplitudes of periodic solutions. In addition, we establish analytical conditions for the appearance of a double zero bifurcation and investigate the unfolding by the proposed methodology.

  1. Dynamics of the logistic delay equation with a large spatially distributed control coefficient

    NASA Astrophysics Data System (ADS)

    Kashchenko, I. S.; Kashchenko, S. A.

    2014-05-01

    The local dynamics of the logistic delay equation with a large spatially distributed control coefficient is asymptotically studied. The basic bifurcation scenarios are analyzed depending on the relations between the parameters of the equation. It is shown that the equilibrium states can lose stability even for asymptotically small values of the delay parameter. The corresponding critical cases can have an infinite dimension. Special nonlinear parabolic equations are constructed whose nonlocal dynamics determine the local behavior of solutions to the original boundary value problem.

  2. Stability analysis of a discrete Hutchinson equation with discrete and distributed delay

    NASA Astrophysics Data System (ADS)

    Suryanto, A.; Yanti, I.; Kusumawinahyu, W. M.

    2014-02-01

    In this paper a Hutchinson equation with discrete and distributed delay is discretized by the Euler method. The dynamics of the obtained discrete system is then investigated. Specifically the stability of the positive fixed point is analyzed. It is found that for sufficiently small time-step of integration, the positive equilibrium undergoes a Neimark-Sacker bifurcation which is controlled by the discrete time delay. The results of analysis are then confirmed by some numerical simulations.

  3. Stability and Hopf bifurcation for a regulated logistic growth model with discrete and distributed delays

    NASA Astrophysics Data System (ADS)

    Fang, Shengle; Jiang, Minghui

    2009-12-01

    In this paper, we investigate the stability and Hopf bifurcation of a new regulated logistic growth with discrete and distributed delays. By choosing the discrete delay τ as a bifurcation parameter, we prove that the system is locally asymptotically stable in a range of the delay and Hopf bifurcation occurs as τ crosses a critical value. Furthermore, explicit algorithm for determining the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions is derived by normal form theorem and center manifold argument. Finally, an illustrative example is also given to support the theoretical results.

  4. Least expected time paths in stochastic, time-varying transportation networks

    SciTech Connect

    Miller-Hooks, E.D.; Mahmassani, H.S.

    1999-06-01

    The authors consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic, time-varying networks. Two procedures are presented. The first procedure determines the a priori least expected time paths from all origins to a single destination for each departure time in the peak period. The second procedure determines lower bounds on the expected times of these a priori least expected time paths. This procedure determines an exact solution for the problem where the driver is permitted to react to revealed travel times on traveled links en route, i.e. in a time-adaptive route choice framework. Modifications to each of these procedures for determining least expected cost (where cost is not necessarily travel time) paths and lower bounds on the expected costs of these paths are given. Extensive numerical tests are conducted to illustrate the algorithms` computational performance as well as the properties of the solution.

  5. A Parallel Pipelined Renderer for the Time-Varying Volume Data

    NASA Technical Reports Server (NTRS)

    Chiueh, Tzi-Cker; Ma, Kwan-Liu

    1997-01-01

    This paper presents a strategy for efficiently rendering time-varying volume data sets on a distributed-memory parallel computer. Time-varying volume data take large storage space and visualizing them requires reading large files continuously or periodically throughout the course of the visualization process. Instead of using all the processors to collectively render one volume at a time, a pipelined rendering process is formed by partitioning processors into groups to render multiple volumes concurrently. In this way, the overall rendering time may be greatly reduced because the pipelined rendering tasks are overlapped with the I/O required to load each volume into a group of processors; moreover, parallelization overhead may be reduced as a result of partitioning the processors. We modify an existing parallel volume renderer to exploit various levels of rendering parallelism and to study how the partitioning of processors may lead to optimal rendering performance. Two factors which are important to the overall execution time are re-source utilization efficiency and pipeline startup latency. The optimal partitioning configuration is the one that balances these two factors. Tests on Intel Paragon computers show that in general optimal partitionings do exist for a given rendering task and result in 40-50% saving in overall rendering time.

  6. Finite-Horizon H∞ Consensus Control of Time-Varying Multiagent Systems With Stochastic Communication Protocol.

    PubMed

    Zou, Lei; Wang, Zidong; Gao, Huijun; Alsaadi, Fuad E

    2017-03-31

    This paper is concerned with the distributed H∞ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system. A sequence of random variables is utilized to capture the scheduling behavior of the SCP. By using the mapping technology combined with the Hadamard product, the closed-loop multiagent system is modeled as a time-varying system with a stochastic parameter matrix. The purpose of the addressed problem is to design a cooperative controller for each agent such that, for all probabilistic scheduling behaviors, the H∞ consensus performance is achieved over a given finite horizon for the closed-loop multiagent system. A necessary and sufficient condition is derived to ensure the H∞ consensus performance based on the completing squares approach and the stochastic analysis technique. Then, the controller parameters are obtained by solving two coupled backward recursive Riccati difference equations. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme.

  7. Stability Analysis of SIR Model with Distributed Delay on Complex Networks

    PubMed Central

    Huang, Chuangxia; Cao, Jie; Wen, Fenghua; Yang, Xiaoguang

    2016-01-01

    In this paper, by taking full consideration of distributed delay, demographics and contact heterogeneity of the individuals, we present a detailed analytical study of the Susceptible-Infected-Removed (SIR) epidemic model on complex population networks. The basic reproduction number R0 of the model is dominated by the topology of the underlying network, the properties of individuals which include birth rate, death rate, removed rate and infected rate, and continuously distributed time delay. By constructing suitable Lyapunov functional and employing Kirchhoff’s matrix tree theorem, we investigate the globally asymptotical stability of the disease-free and endemic equilibrium points. Specifically, the system shows threshold behaviors: if R0≤1, then the disease-free equilibrium is globally asymptotically stable, otherwise the endemic equilibrium is globally asymptotically stable. Furthermore, the obtained results show that SIR models with different types of delays have different converge time in the process of contagion: if R0>1, then the system with distributed time delay stabilizes fastest; while R0≤1, the system with distributed time delay converges most slowly. The validness and effectiveness of these results are demonstrated through numerical simulations. PMID:27490363

  8. Stability Analysis of SIR Model with Distributed Delay on Complex Networks.

    PubMed

    Huang, Chuangxia; Cao, Jie; Wen, Fenghua; Yang, Xiaoguang

    2016-01-01

    In this paper, by taking full consideration of distributed delay, demographics and contact heterogeneity of the individuals, we present a detailed analytical study of the Susceptible-Infected-Removed (SIR) epidemic model on complex population networks. The basic reproduction number [Formula: see text] of the model is dominated by the topology of the underlying network, the properties of individuals which include birth rate, death rate, removed rate and infected rate, and continuously distributed time delay. By constructing suitable Lyapunov functional and employing Kirchhoff's matrix tree theorem, we investigate the globally asymptotical stability of the disease-free and endemic equilibrium points. Specifically, the system shows threshold behaviors: if [Formula: see text], then the disease-free equilibrium is globally asymptotically stable, otherwise the endemic equilibrium is globally asymptotically stable. Furthermore, the obtained results show that SIR models with different types of delays have different converge time in the process of contagion: if [Formula: see text], then the system with distributed time delay stabilizes fastest; while [Formula: see text], the system with distributed time delay converges most slowly. The validness and effectiveness of these results are demonstrated through numerical simulations.

  9. Pressure Distribution and Pumping Delay Time in Short-Spacing Parallel Planes

    NASA Astrophysics Data System (ADS)

    Saito, Yoshio; Sato, Y.; Matuda, Namio

    The two-dimensional pressure distribution and pump-down performance between two parallel planes having a short-distance were simulated by a Monte-Carlo method, assuming the cosine-law for desorbing molecules. The distribution results were compared with those calculated by a diffusion model. The surface hitting number per molecule was also counted, and the delay time of the pumping process was evaluated by multiplying the hitting number and the surface sojourn time. The experimental observation for the delay time was compared with the calculated ones.

  10. Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.

    PubMed

    Chiba, Tomoaki; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru

    2017-01-01

    In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.

  11. Recursive state estimation for discrete time-varying stochastic nonlinear systems with randomly occurring deception attacks

    NASA Astrophysics Data System (ADS)

    Ding, Derui; Shen, Yuxuan; Song, Yan; Wang, Yongxiong

    2016-07-01

    This paper is concerned with the state estimation problem for a class of discrete time-varying stochastic nonlinear systems with randomly occurring deception attacks. The stochastic nonlinearity described by statistical means which covers several classes of well-studied nonlinearities as special cases is taken into discussion. The randomly occurring deception attacks are modelled by a set of random variables obeying Bernoulli distributions with given probabilities. The purpose of the addressed state estimation problem is to design an estimator with hope to minimize the upper bound for estimation error covariance at each sampling instant. Such an upper bound is minimized by properly designing the estimator gain. The proposed estimation scheme in the form of two Riccati-like difference equations is of a recursive form. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed scheme.

  12. Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis

    PubMed Central

    Chiba, Tomoaki; Akaho, Shotaro; Murata, Noboru

    2017-01-01

    In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group’s sales beat GM’s sales, which is a reasonable scenario. PMID:28076383

  13. Global Stability of an HIV-1 Infection Model with General Incidence Rate and Distributed Delays

    PubMed Central

    2014-01-01

    In this work an HIV-1 infection model with nonlinear incidence rate and distributed intracellular delays and with humoral immunity is investigated. The disease transmission function is assumed to be governed by general incidence rate f(T, V)V. The intracellular delays describe the time between viral entry into a target cell and the production of new virus particles and the time between infection of a cell and the emission of viral particle. Lyapunov functionals are constructed and LaSalle invariant principle for delay differential equation is used to establish the global asymptotic stability of the infection-free equilibrium, infected equilibrium without B cells response, and infected equilibrium with B cells response. The results obtained show that the global dynamics of the system depend on both the properties of the general incidence function and the value of certain threshold parameters R 0 and R 1 which depends on the delays. PMID:27355007

  14. Global Stability of an HIV-1 Infection Model with General Incidence Rate and Distributed Delays.

    PubMed

    Ndongo, Abdoul Samba; Talibi Alaoui, Hamad

    2014-01-01

    In this work an HIV-1 infection model with nonlinear incidence rate and distributed intracellular delays and with humoral immunity is investigated. The disease transmission function is assumed to be governed by general incidence rate f(T, V)V. The intracellular delays describe the time between viral entry into a target cell and the production of new virus particles and the time between infection of a cell and the emission of viral particle. Lyapunov functionals are constructed and LaSalle invariant principle for delay differential equation is used to establish the global asymptotic stability of the infection-free equilibrium, infected equilibrium without B cells response, and infected equilibrium with B cells response. The results obtained show that the global dynamics of the system depend on both the properties of the general incidence function and the value of certain threshold parameters R 0 and R 1 which depends on the delays.

  15. Immortal time bias in critical care research: application of time-varying Cox regression for observational cohort studies.

    PubMed

    Shintani, Ayumi K; Girard, Timothy D; Eden, Svetlana K; Arbogast, Patrick G; Moons, Karel G M; Ely, E Wesley

    2009-11-01

    To examine the bias introduced by using time-fixed methodology to analyze the effects of a time-varying exposure incurred in the intensive care unit. Prospective cohort and Monte Carlo simulation studies. Medical and coronary intensive care units in a university hospital. A total of 224 mechanically ventilated patients. Part I was a case study analyzing the association between delirium in the intensive care unit (exposure variable) and outcomes (intensive care unit length of stay and 6-mo mortality) in a prospective cohort study. Part II was a Monte Carlo simulation generating 16,000 data sets wherein the true associations between delirium and outcomes were known before analysis. In both parts, we assessed associations between delirium in the intensive care unit and outcomes (intensive care unit length of stay and mortality), using time-fixed vs. time-varying Cox regression methodology. In the case study, delirium analyzed as a time-fixed variable was associated with a delayed intensive care unit discharge (adjusted hazard ratio = 1.9, 95% confidence interval, 1.3-2.7, p < .001), but no association was noted using a time-varying method (adjusted hazard ratio = 1.1, 95% confidence interval = 0.7-1.6, p = .70). Alternatively, delirium analyzed as a time-fixed variable was not associated with 6-mo mortality (adjusted hazard ratio = 2.9, 95% confidence interval, 0.9-5.0, p = .09), whereas delirium analyzed as a time-varying variable was associated with increased mortality (adjusted hazard ratio = 3.2, 95% confidence interval, 1.4-7.7, p = .008). In the simulation study, time-fixed methods produced erroneous results in 97.1% of the data sets with no true association; time-varying methods produced erroneous results in only 3.7%. Similarly, time-fixed methods produced biased results when a true association was present, whereas time-varying methods produced accurate results. Studies using a time-fixed analytic approach to understand relationships between exposures and

  16. Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels

    PubMed Central

    Al-Samman, A. M.; Azmi, M. H.; Rahman, T. A.; Khan, I.; Hindia, M. N.; Fattouh, A.

    2016-01-01

    This work proposes channel impulse response (CIR) prediction for time-varying ultra-wideband (UWB) channels by exploiting the fast movement of channel taps within delay bins. Considering the sparsity of UWB channels, we introduce a window-based CIR (WB-CIR) to approximate the high temporal resolutions of UWB channels. A recursive least square (RLS) algorithm is adopted to predict the time evolution of the WB-CIR. For predicting the future WB-CIR tap of window wk, three RLS filter coefficients are computed from the observed WB-CIRs of the left wk−1, the current wk and the right wk+1 windows. The filter coefficient with the lowest RLS error is used to predict the future WB-CIR tap. To evaluate our proposed prediction method, UWB CIRs are collected through measurement campaigns in outdoor environments considering line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Under similar computational complexity, our proposed method provides an improvement in prediction errors of approximately 80% for LOS and 63% for NLOS scenarios compared with a conventional method. PMID:27992445

  17. Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

    PubMed Central

    LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W.

    2014-01-01

    Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

  18. The rates and time-delay distribution of multiply imaged supernovae behind lensing clusters

    SciTech Connect

    Li, Xue; Hjorth, Jens; Richard, Johan E-mail: jens@dark-cosmology.dk

    2012-11-01

    Time delays of gravitationally lensed sources can be used to constrain the mass model of a deflector and determine cosmological parameters. We here present an analysis of the time-delay distribution of multiply imaged sources behind 17 strong lensing galaxy clusters with well-calibrated mass models. We find that for time delays less than 1000 days, at z = 3.0, their logarithmic probability distribution functions are well represented by P(log Δt) = 5.3 × 10{sup −4}Δt{sup β-tilde}/M{sub 250}{sup 2β-tilde}, with β-tilde = 0.77, where M{sub 250} is the projected cluster mass inside 250 kpc (in 10{sup 14}M{sub ☉}), and β-tilde is the power-law slope of the distribution. The resultant probability distribution function enables us to estimate the time-delay distribution in a lensing cluster of known mass. For a cluster with M{sub 250} = 2 × 10{sup 14}M{sub ☉}, the fraction of time delays less than 1000 days is approximately 3%. Taking Abell 1689 as an example, its dark halo and brightest galaxies, with central velocity dispersions σ≥500kms{sup −1}, mainly produce large time delays, while galaxy-scale mass clumps are responsible for generating smaller time delays. We estimate the probability of observing multiple images of a supernova in the known images of Abell 1689. A two-component model of estimating the supernova rate is applied in this work. For a magnitude threshold of m{sub AB} = 26.5, the yearly rate of Type Ia (core-collapse) supernovae with time delays less than 1000 days is 0.004±0.002 (0.029±0.001). If the magnitude threshold is lowered to m{sub AB} ∼ 27.0, the rate of core-collapse supernovae suitable for time delay observation is 0.044±0.015 per year.

  19. Time-varying formation control for double-integrator multi-agent systems with jointly connected topologies

    NASA Astrophysics Data System (ADS)

    Dong, Xiwang; Han, Liang; Li, Qingdong; Ren, Zhang

    2016-12-01

    Time-varying formation analysis and design problems for double-integrator multi-agent systems with jointly connected topologies are investigated. Different from the previous work on formation control, in this paper, the formation is specified by time-varying piecewise continuously differentiable vectors and the topology can be disconnected at any time instant. First, a distributed formation control protocol is constructed using local neighbour-to-neighbour information. In the case where the switching topology is jointly connected, necessary and sufficient conditions for double-integrator multi-agent systems to achieve time-varying formations are proposed, where the formation feasibility constraint is also derived. To describe the macroscopic movement of the whole formation, explicit expressions of the formation reference are presented, the motion modes of which can be partially assigned. Moreover, an approach to design the formation control protocol is given, which is fully distributed and requires no global information about the topology. Finally, the obtained theoretical results are applied to deal with the time-varying formation control problems of multi-vehicle systems.

  20. Reliable H∞ filtering for discrete piecewise linear systems with infinite distributed delays

    NASA Astrophysics Data System (ADS)

    Wei, Guoliang; Han, Fei; Wang, Licheng; Song, Yan

    2014-05-01

    This paper is concerned with the reliable H∞ filtering problem for discrete-time piecewise linear systems subject to sensor failures and time delays. The considered sensor failures are depicted by bounded variables taking value on a certain interval. The time delays are assumed to be infinitely distributed in the discrete-time domain. The purpose of the addressed reliable H∞ filtering problem is to design a piecewise linear filter such that, for the admissible sensor failures and possible infinite distributed delays, the augmented dynamics is exponentially stable and the H∞ performance is guaranteed with a prescribed attenuation level γ. With the aid of the convex optimal method, the filter parameters are obtained in terms of the solution to a set of LMIs which can be solved by the Matlab Toolbox. At last, an illustrative simulation is presented to demonstrate the effectiveness and applicability of the proposed algorithms.

  1. Distributed Consensus of Stochastic Delayed Multi-agent Systems Under Asynchronous Switching.

    PubMed

    Wu, Xiaotai; Tang, Yang; Cao, Jinde; Zhang, Wenbing

    2016-08-01

    In this paper, the distributed exponential consensus of stochastic delayed multi-agent systems with nonlinear dynamics is investigated under asynchronous switching. The asynchronous switching considered here is to account for the time of identifying the active modes of multi-agent systems. After receipt of confirmation of mode's switching, the matched controller can be applied, which means that the switching time of the matched controller in each node usually lags behind that of system switching. In order to handle the coexistence of switched signals and stochastic disturbances, a comparison principle of stochastic switched delayed systems is first proved. By means of this extended comparison principle, several easy to verified conditions for the existence of an asynchronously switched distributed controller are derived such that stochastic delayed multi-agent systems with asynchronous switching and nonlinear dynamics can achieve global exponential consensus. Two examples are given to illustrate the effectiveness of the proposed method.

  2. A Design Method for Pole Placement and Observer of Linear Time-Varying Discrete MIMO Systems

    NASA Astrophysics Data System (ADS)

    Mutoh, Yasuhiko; Hara, Tomohiro

    It is well known that, the pole placement controller can be designing for linear time-varying systems using the Frobenius canonical form, as for the time invariant case. This paper presents the new approach to the design of the pole placement controller for linear time-varying discrete multivariable systems. The concept of the relative degrees of multivariable system plays an important role, and the time-varying feedback gain can be simply calculated without transforming the system into any canonical form, which is regarded as a discrete Ackerman's method. This method is applied in order to calculate the observer gain for linear time-varying systems.

  3. Synchronization transmission of spatiotemporal chaotic signal in the uncertain time-varying network

    NASA Astrophysics Data System (ADS)

    Lü, Ling; Chen, Liansong; Han, Changhui; Ge, Lianjun; Gao, Liyu

    2017-02-01

    In this paper, a new method is presented for the synchronization transmission of spatiotemporal chaotic signal in the uncertain time-varying network. By designing a special function to construct the Lyapunov function of the network, it is sure that the uncertain time-varying network can effectively synchronize the spatiotemporal chaotic signal generated by the synchronization target. At the same time, we also design the identification laws of uncertain parameters and the adaptive laws of the time-varying coupling matrix elements. Especially in our work, the nodes of the uncertain time-varying network and the synchronization target are different. Obviously, this research has the reference value for the application fields.

  4. Distributed output regulation for linear multi-agent systems with communication delays

    NASA Astrophysics Data System (ADS)

    Yu, Lu; Wang, Jinzhi

    2015-11-01

    In this paper, a distributed output regulation approach is presented for the cooperative control of linear multi-agent systems in the presence of communication delays. Both dynamic state and output feedback control laws are designed for achieving the property of output regulation. Sufficient conditions for the existence of these control laws are provided in terms of linear matrix inequalities. Simulation results are given to support the efficiency of the proposed distributed output regulation approach.

  5. Propagation of a laser beam in a time-varying waveguide. [plasma heating for controlled fusion

    NASA Technical Reports Server (NTRS)

    Chapman, J. M.; Kevorkian, J.

    1978-01-01

    The propagation of an axisymmetric laser beam in a plasma column having a radially parabolic electron density distribution is reported. For the case of an axially uniform waveguide it is found that the basic characteristics of alternating focusing and defocusing beams are maintained. However, the intensity distribution is changed at the foci and outer-beam regions. The features of paraxial beam propagation are discussed with reference to axially varying waveguides. Laser plasma coupling is considered noting the case where laser heating produces a density distribution radially parabolic near the axis and the energy absorbed over the focal length of the plasma is small. It is found that: (1) beam-propagation stability is governed by the relative magnitude of the density fluctuations existing in the axial variation of the waveguides due to laser heating, and (2) for beam propagation in a time-varying waveguide, the global instability of the propagation is a function of the initial fluctuation growth rate as compared to the initial time rate of change in the radial curvature of the waveguide.

  6. Propagation of a laser beam in a time-varying waveguide. [plasma heating for controlled fusion

    NASA Technical Reports Server (NTRS)

    Chapman, J. M.; Kevorkian, J.

    1978-01-01

    The propagation of an axisymmetric laser beam in a plasma column having a radially parabolic electron density distribution is reported. For the case of an axially uniform waveguide it is found that the basic characteristics of alternating focusing and defocusing beams are maintained. However, the intensity distribution is changed at the foci and outer-beam regions. The features of paraxial beam propagation are discussed with reference to axially varying waveguides. Laser plasma coupling is considered noting the case where laser heating produces a density distribution radially parabolic near the axis and the energy absorbed over the focal length of the plasma is small. It is found that: (1) beam-propagation stability is governed by the relative magnitude of the density fluctuations existing in the axial variation of the waveguides due to laser heating, and (2) for beam propagation in a time-varying waveguide, the global instability of the propagation is a function of the initial fluctuation growth rate as compared to the initial time rate of change in the radial curvature of the waveguide.

  7. A multiscale MDCT image-based breathing lung model with time-varying regional ventilation

    SciTech Connect

    Yin, Youbing; Choi, Jiwoong; Hoffman, Eric A.; Tawhai, Merryn H.; Lin, Ching-Long

    2013-07-01

    A novel algorithm is presented that links local structural variables (regional ventilation and deforming central airways) to global function (total lung volume) in the lung over three imaged lung volumes, to derive a breathing lung model for computational fluid dynamics simulation. The algorithm constitutes the core of an integrative, image-based computational framework for subject-specific simulation of the breathing lung. For the first time, the algorithm is applied to three multi-detector row computed tomography (MDCT) volumetric lung images of the same individual. A key technique in linking global and local variables over multiple images is an in-house mass-preserving image registration method. Throughout breathing cycles, cubic interpolation is employed to ensure C{sub 1} continuity in constructing time-varying regional ventilation at the whole lung level, flow rate fractions exiting the terminal airways, and airway deformation. The imaged exit airway flow rate fractions are derived from regional ventilation with the aid of a three-dimensional (3D) and one-dimensional (1D) coupled airway tree that connects the airways to the alveolar tissue. An in-house parallel large-eddy simulation (LES) technique is adopted to capture turbulent-transitional-laminar flows in both normal and deep breathing conditions. The results obtained by the proposed algorithm when using three lung volume images are compared with those using only one or two volume images. The three-volume-based lung model produces physiologically-consistent time-varying pressure and ventilation distribution. The one-volume-based lung model under-predicts pressure drop and yields un-physiological lobar ventilation. The two-volume-based model can account for airway deformation and non-uniform regional ventilation to some extent, but does not capture the non-linear features of the lung.

  8. A multiscale MDCT image-based breathing lung model with time-varying regional ventilation

    NASA Astrophysics Data System (ADS)

    Yin, Youbing; Choi, Jiwoong; Hoffman, Eric A.; Tawhai, Merryn H.; Lin, Ching-Long

    2013-07-01

    A novel algorithm is presented that links local structural variables (regional ventilation and deforming central airways) to global function (total lung volume) in the lung over three imaged lung volumes, to derive a breathing lung model for computational fluid dynamics simulation. The algorithm constitutes the core of an integrative, image-based computational framework for subject-specific simulation of the breathing lung. For the first time, the algorithm is applied to three multi-detector row computed tomography (MDCT) volumetric lung images of the same individual. A key technique in linking global and local variables over multiple images is an in-house mass-preserving image registration method. Throughout breathing cycles, cubic interpolation is employed to ensure C1 continuity in constructing time-varying regional ventilation at the whole lung level, flow rate fractions exiting the terminal airways, and airway deformation. The imaged exit airway flow rate fractions are derived from regional ventilation with the aid of a three-dimensional (3D) and one-dimensional (1D) coupled airway tree that connects the airways to the alveolar tissue. An in-house parallel large-eddy simulation (LES) technique is adopted to capture turbulent-transitional-laminar flows in both normal and deep breathing conditions. The results obtained by the proposed algorithm when using three lung volume images are compared with those using only one or two volume images. The three-volume-based lung model produces physiologically-consistent time-varying pressure and ventilation distribution. The one-volume-based lung model under-predicts pressure drop and yields un-physiological lobar ventilation. The two-volume-based model can account for airway deformation and non-uniform regional ventilation to some extent, but does not capture the non-linear features of the lung.

  9. Exponential Stability of Almost Periodic Solutions for Memristor-Based Neural Networks with Distributed Leakage Delays.

    PubMed

    Xu, Changjin; Li, Peiluan; Pang, Yicheng

    2016-12-01

    In this letter, we deal with a class of memristor-based neural networks with distributed leakage delays. By applying a new Lyapunov function method, we obtain some sufficient conditions that ensure the existence, uniqueness, and global exponential stability of almost periodic solutions of neural networks. We apply the results of this solution to prove the existence and stability of periodic solutions for this delayed neural network with periodic coefficients. We then provide an example to illustrate the effectiveness of the theoretical results. Our results are completely new and complement the previous studies Chen, Zeng, and Jiang ( 2014 ) and Jiang, Zeng, and Chen ( 2015 ).

  10. Mathematical model describing the thyroids-pituitary axis with distributed time delays in hormone transportation

    NASA Astrophysics Data System (ADS)

    Neamţu, Mihaela; Stoian, Dana; Navolan, Dan Bogdan

    2014-12-01

    In the present paper we provide a mathematical model that describe the hypothalamus-pituitary-thyroid axis in autoimmune (Hashimoto's) thyroiditis. Since there is a spatial separation between thyroid and pituitary gland in the body, time is needed for transportation of thyrotropin and thyroxine between the glands. Thus, the distributed time delays are considered as both weak and Dirac kernels. The delayed model is analyzed regarding the stability and bifurcation behavior. The last part contains some numerical simulations to illustrate the effectiveness of our results and conclusions.

  11. Length distributions of nanowires: Effects of surface diffusion versus nucleation delay

    NASA Astrophysics Data System (ADS)

    Dubrovskii, Vladimir G.

    2017-04-01

    It is often thought that the ensembles of semiconductor nanowires are uniform in length due to the initial organization of the growth seeds such as lithographically defined droplets or holes in the substrate. However, several recent works have already demonstrated that most nanowire length distributions are broader than Poissonian. Herein, we consider theoretically the length distributions of non-interacting nanowires that grow by the material collection from the entire length of their sidewalls and with a delay of nucleation of the very first nanowire monolayer. The obtained analytic length distribution is controlled by two parameters that describe the strength of surface diffusion and the nanowire nucleation rate. We show how the distribution changes from the symmetrical Polya shape without the nucleation delay to a much broader and asymmetrical one for longer delays. In the continuum limit (for tall enough nanowires), the length distribution is given by a power law times an incomplete gamma-function. We discuss interesting scaling properties of this solution and give a recipe for analyzing and tailoring the experimental length histograms of nanowires which should work for a wide range of material systems and growth conditions.

  12. Delay-time distribution in the scattering of time-narrow wave packets. (I)

    NASA Astrophysics Data System (ADS)

    Smilansky, Uzy

    2017-05-01

    This is the first of two subsequent publications where the probability distribution of delay-times in scattering of wave packets is discussed. The probability distribution is expressed in terms of the on-shell scattering matrix, the dispersion relation of the scattered beam and the wave packet envelope. In the monochromatic limit (poor time resolution) the mean delay-time coincides with the expression derived by Eisenbud and Wigner and generalized by Smith more than half a century ago. In the opposite limit, and within the semi-classical approximation, the resulting distribution coincides with the result obtained using classical mechanics or geometrical optics. The general expression interpolates smoothly between the two extremes. An application for the scattering of electromagnetic waves in networks of RF transmission lines will be discussed in the next paper to illustrate the method in an experimentally relevant context.

  13. Identification of Time-Varying Pilot Control Behavior in Multi-Axis Control Tasks

    NASA Technical Reports Server (NTRS)

    Zaal, Peter M. T.; Sweet, Barbara T.

    2012-01-01

    Recent developments in fly-by-wire control architectures for rotorcraft have introduced new interest in the identification of time-varying pilot control behavior in multi-axis control tasks. In this paper a maximum likelihood estimation method is used to estimate the parameters of a pilot model with time-dependent sigmoid functions to characterize time-varying human control behavior. An experiment was performed by 9 general aviation pilots who had to perform a simultaneous roll and pitch control task with time-varying aircraft dynamics. In 8 different conditions, the axis containing the time-varying dynamics and the growth factor of the dynamics were varied, allowing for an analysis of the performance of the estimation method when estimating time-dependent parameter functions. In addition, a detailed analysis of pilots adaptation to the time-varying aircraft dynamics in both the roll and pitch axes could be performed. Pilot control behavior in both axes was significantly affected by the time-varying aircraft dynamics in roll and pitch, and by the growth factor. The main effect was found in the axis that contained the time-varying dynamics. However, pilot control behavior also changed over time in the axis not containing the time-varying aircraft dynamics. This indicates that some cross coupling exists in the perception and control processes between the roll and pitch axes.

  14. Time-varying linear systems and the theory of non-linear waves

    NASA Technical Reports Server (NTRS)

    Hermann, R.

    1979-01-01

    The isospectral deformation of a Sturm-Liouville equation is extended to general linear time-varying systems and a method is described for determining the resulting nonlinear partial differential equations. Consideration is given to (1) isospectral deformation of I/O systems with boundary value conditions and (2) the spectral vector bundles attached to linear time-varying systems.

  15. Effects of Paleoclimate and Time-Varying Canopy Structures on Paleo-Water Fluxes

    NASA Astrophysics Data System (ADS)

    Yin, J.; Young, M. H.; Yu, Z.

    2007-12-01

    We combined a long-term (18,000 years) climatological record and time-varying vegetation conditions to evaluate the role that climate change and vegetation might play in paleo-water fluxes in arid settings. The HYDRUS-1D model, which solves Richards Equation for variably saturated flow, the convection-dispersion equation for chloride transport and the heat flow equation, was used to simulate water flux and chloride (Cl) transport. Six distinct case studies were compared, for different boundary conditions and root distributions. A Mojave Desert- type canopy including evergreens, drought deciduous shrubs, annuals, grasses, and succulents was used as representative vegetation to transpire soil water, and was modeled using ground cover percentage and leaf area index (LAI) as the bases for partitioning evapotranspiration (ET). The results showed that, under water limited conditions, realistic root zone distributions and climate sequences (including extreme events) were both needed to simulate the accumulation of Cl in Mojave Desert soils. Results also showed that increasing precipitation intensity affected paleo-water fluxes. However, contrary to the results of other researchers, we found that simulated chloride bulges were located at depths of around 20-30 cm, rather than at the base of the root zone, if current and normal climate conditions were applied. Moreover, the climatic shift beginning in the late Pleistocene was not the major reason for the chloride accumulation.

  16. Effects of paleoclimate and time-varying canopy structures on paleowater fluxes

    NASA Astrophysics Data System (ADS)

    Yin, Jun; Young, Michael H.; Yu, Zhongbo

    2008-03-01

    We combined a long-term (18,000 years) climatological record and time-varying vegetation conditions to evaluate the role that climate change and vegetation might play in paleowater fluxes in arid settings. The HYDRUS-1D model, which solves Richards Equation for variably saturated flow, the convection-dispersion equation for chloride transport and the heat flow equation, was used to simulate water flux and chloride (Cl) transport. Six distinct case studies were compared for different boundary conditions and root distributions. A Mojave Desert-type canopy including evergreens, drought deciduous shrubs, annuals, grasses, and succulents was used as representative vegetation to transpire soil water and was modeled using ground cover percentage and leaf area index (LAI) as the bases for partitioning evapotranspiration (ET). The results showed that under water limited conditions, realistic root zone distributions and climate sequences (including extreme events) were both needed to simulate the accumulation of Cl in Mojave Desert soils. Results also showed that increasing precipitation intensity affected paleowater fluxes. However, contrary to the results of other researchers, we found that simulated chloride bulges were located at depths of around 20-30 cm, rather than at the base of the root zone, if current and normal climate conditions were applied. Moreover, the climatic shift beginning in the late Pleistocene was not the major reason for the chloride accumulation.

  17. Time-Varying Network Measures in Resting and Task States Using Graph Theoretical Analysis.

    PubMed

    Yang, Chia-Yen; Lin, Ching-Po

    2015-07-01

    Recent studies have shown the importance of graph theory in analyzing characteristic features of functional networks of the human brain. However, many of these explorations have focused on static patterns of a representative graph that describe the relatively long-term brain activity. Therefore, this study established and characterized functional networks based on the synchronization likelihood and graph theory. Quasidynamic graphs were constructed simply by dividing a long-term static graph into a sequence of subgraphs that each had a timescale of 1 s. Irregular changes were then used to investigate differences in human brain networks between resting and math-operation states using magnetoencephalography, which may provide insights into the functional substrates underlying logical reasoning. We found that graph properties could differ from brain frequency rhythms, with a higher frequency indicating a lower small-worldness, while changes in human brain state altered the functional networks into more-centralized and segregated distributions according to the task requirements. Time-varying connectivity maps could provide detailed information about the structure distribution. The frontal theta activity represents the essential foundation and may subsequently interact with high-frequency activity in cognitive processing.

  18. Combined risk assessment of nonstationary monthly water quality based on Markov chain and time-varying copula.

    PubMed

    Shi, Wei; Xia, Jun

    2017-02-01

    Water quality risk management is a global hot research linkage with the sustainable water resource development. Ammonium nitrogen (NH3-N) and permanganate index (CODMn) as the focus indicators in Huai River Basin, are selected to reveal their joint transition laws based on Markov theory. The time-varying moments model with either time or land cover index as explanatory variables is applied to build the time-varying marginal distributions of water quality time series. Time-varying copula model, which takes the non-stationarity in the marginal distribution and/or the time variation in dependence structure between water quality series into consideration, is constructed to describe a bivariate frequency analysis for NH3-N and CODMn series at the same monitoring gauge. The larger first-order Markov joint transition probability indicates water quality state Class Vw, Class IV and Class III will occur easily in the water body of Bengbu Sluice. Both marginal distribution and copula models are nonstationary, and the explanatory variable time yields better performance than land cover index in describing the non-stationarities in the marginal distributions. In modelling the dependence structure changes, time-varying copula has a better fitting performance than the copula with the constant or the time-trend dependence parameter. The largest synchronous encounter risk probability of NH3-N and CODMn simultaneously reaching Class V is 50.61%, while the asynchronous encounter risk probability is largest when NH3-N and CODMn is inferior to class V and class IV water quality standards, respectively.

  19. Distributed containment control of heterogeneous fractional-order multi-agent systems with communication delays

    NASA Astrophysics Data System (ADS)

    Yang, Hongyong; Han, Fujun; Zhao, Mei; Zhang, Shuning; Yue, Jun

    2017-08-01

    Because many networked systems can only be characterized with fractional-order dynamics in complex environments, fractional-order calculus has been studied deeply recently. When diverse individual features are shown in different agents of networked systems, heterogeneous fractional-order dynamics will be used to describe the complex systems. Based on the distinguishing properties of agents, heterogeneous fractional-order multi-agent systems (FOMAS) are presented. With the supposition of multiple leader agents in FOMAS, distributed containment control of FOMAS is studied in directed weighted topologies. By applying Laplace transformation and frequency domain theory of the fractional-order operator, an upper bound of delays is obtained to ensure containment consensus of delayed heterogenous FOMAS. Consensus results of delayed FOMAS in this paper can be extended to systems with integer-order models. Finally, numerical examples are used to verify our results.

  20. A monolithic constant-fraction discriminator using distributed R-C delay-line shaping

    SciTech Connect

    Simpson, M.L.; Young, G.R.; Xu, M.

    1995-06-01

    A monolithic, CMOS, constant-fraction discriminator (CFD) was fabricated in the Orbit Semiconductor, 1.2 {mu} N-well process. This circuit uses an on-chip, distributed, R-C delay-line to realize the constant-fraction shaping. The delay-line is constructed from a narrow, 500-{mu} serpentine layer of polysilicon above a wide, grounded, second layer of polysilicon. This R-C delay-line generates about 1.1 ns of delay for 5 ns risetime signals with a slope degradation of only {approx_equal} 15% and an amplitude reduction of about 6.1%. The CFD also features an automatic walk adjustment. The entire circuit, including the delay line, has a 200 {mu} pitch and is 950 {mu} long. The walk for a 5 ns risetime signal was measured as {plus_minus} 100 ps over the 100:1 dynamic range from {minus}15 mV to {minus}1.5 mV. to {minus}1.5 V. The CFD consumes 15 mW.

  1. Decelerated invasion and waning-moon patterns in public goods games with delayed distribution.

    PubMed

    Szolnoki, Attila; Perc, Matjaž

    2013-05-01

    We study the evolution of cooperation in the spatial public goods game, focusing on the effects that are brought about by the delayed distribution of goods that accumulate in groups due to the continuous investments of cooperators. We find that intermediate delays enhance network reciprocity because of a decelerated invasion of defectors, who are unable to reap the same high short-term benefits as they do in the absence of delayed distribution. Long delays, however, introduce a risk because the large accumulated wealth might fall into the wrong hands. Indeed, as soon as the curvature of a cooperative cluster turns negative, the engulfed defectors can collect the heritage of many generations of cooperators and by doing so start a waning-moon pattern that nullifies the benefits of decelerated invasion. Accidental meeting points of growing cooperative clusters may also act as triggers for the waning-moon effect, thus linking the success of cooperators with their propensity to fail in a rather bizarre way. Our results highlight that "investing in the future" is a good idea only if that future is sufficiently near and not likely to be burdened by inflation.

  2. Decelerated invasion and waning-moon patterns in public goods games with delayed distribution

    NASA Astrophysics Data System (ADS)

    Szolnoki, Attila; Perc, Matjaž

    2013-05-01

    We study the evolution of cooperation in the spatial public goods game, focusing on the effects that are brought about by the delayed distribution of goods that accumulate in groups due to the continuous investments of cooperators. We find that intermediate delays enhance network reciprocity because of a decelerated invasion of defectors, who are unable to reap the same high short-term benefits as they do in the absence of delayed distribution. Long delays, however, introduce a risk because the large accumulated wealth might fall into the wrong hands. Indeed, as soon as the curvature of a cooperative cluster turns negative, the engulfed defectors can collect the heritage of many generations of cooperators and by doing so start a waning-moon pattern that nullifies the benefits of decelerated invasion. Accidental meeting points of growing cooperative clusters may also act as triggers for the waning-moon effect, thus linking the success of cooperators with their propensity to fail in a rather bizarre way. Our results highlight that “investing in the future” is a good idea only if that future is sufficiently near and not likely to be burdened by inflation.

  3. Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Chen, Xiaowang; Wang, Tianyang

    2017-07-01

    Rolling bearings often work under variable speed conditions, resulting in nonstationary vibrations. How to effectively extract the time-varying fault frequency from nonstationary vibration signals is a key issue in rolling bearing fault diagnosis. To address this issue, a quality time-frequency analysis of excellent time-frequency readability and robust to noise is necessary. To this end, the concentration of frequency and time (ConceFT) method is exploited. Based on this time-frequency analysis method, and considering the modulation feature of rolling bearing vibrations, we propose joint time-varying amplitude and frequency demodulated spectra to reveal the time-varying fault characteristic frequency. Firstly, the optimal frequency band sensitive to rolling bearing fault is selected by spectral kurtosis. Then, both the amplitude envelope and instantaneous frequency of the sensitive signal component within the selected optimal frequency band are calculated. Next, the ConceFT method is applied to the amplitude envelope and instantaneous frequency to generate the time-varying amplitude and frequency demodulated spectra. Finally, rolling bearing fault can be diagnosed by analysis of the time-varying frequency revealed by the time-varying demodulated spectra. This method is free from complex time-varying sidebands, and is robust to noise interference. It is illustrated by numerical simulated signal analysis, and is further validated via lab experimental rolling bearing vibration signal analyses. The localized defects on both inner and outer race are successfully diagnosed.

  4. Robust Estimation for Neural Networks With Randomly Occurring Distributed Delays and Markovian Jump Coupling.

    PubMed

    Xu, Yong; Lu, Renquan; Shi, Peng; Tao, Jie; Xie, Shengli

    2017-01-24

    This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the l₂-l∞ performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.

  5. Synchronization of uncertain time-varying network based on sliding mode control technique

    NASA Astrophysics Data System (ADS)

    Lü, Ling; Li, Chengren; Bai, Suyuan; Li, Gang; Rong, Tingting; Gao, Yan; Yan, Zhe

    2017-09-01

    We research synchronization of uncertain time-varying network based on sliding mode control technique. The sliding mode control technique is first modified so that it can be applied to network synchronization. Further, by choosing the appropriate sliding surface, the identification law of uncertain parameter, the adaptive law of the time-varying coupling matrix element and the control input of network are designed, it is sure that the uncertain time-varying network can synchronize effectively the synchronization target. At last, we perform some numerical simulations to demonstrate the effectiveness of the proposed results.

  6. Exact reconstruction analysis/synthesis filter banks with time-varying filters

    NASA Technical Reports Server (NTRS)

    Arrowood, J. L., Jr.; Smith, M. J. T.

    1993-01-01

    This paper examines some of the analysis/synthesis issues associated with FIR time-varying filter banks where the filter bank coefficients are allowed to change in response to the input signal. Several issues are identified as being important in order to realize performance gains from time-varying filter banks in image coding applications. These issues relate to the behavior of the filters as transition from one set of filter banks to another occurs. Lattice structure formulations for the time varying filter bank problem are introduced and discussed in terms of their properties and transition characteristics.

  7. Dealing with the time-varying parameter problem of robot manipulators performing path tracking tasks

    NASA Technical Reports Server (NTRS)

    Song, Y. D.; Middleton, R. H.

    1992-01-01

    Many robotic applications involve time-varying payloads during the operation of the robot. It is therefore of interest to consider control schemes that deal with time-varying parameters. Using the properties of the element by element (or Hadarmad) product of matrices, we obtain the robot dynamics in parameter-isolated form, from which a new control scheme is developed. The controller proposed yields zero asymptotic tracking errors when applied to robotic systems with time-varying parameters by using a switching type control law. The results obtained are global in the initial state of the robot, and can be applied to rapidly varying systems.

  8. Non-exponential Stabilization of Linear Time-invariant Systems by Time-varying Controllers

    NASA Astrophysics Data System (ADS)

    Inoue, Masaki; Wada, Teruyo; Ikeda, Masao

    This paper proposes non-exponential stabilization of linear time-invariant systems by linear time-varying controllers. We consider state feedback and dynamic output feedback to make the states of the closed-loop systems decay non-exponentially. We first introduce a non-exponential stability concept that the state of a time-varying system converges to the origin with a bound provided by a desired function. Then, we give non-exponential stabilizability conditions and time-varying controllers to achieve the desired behavior of the closed-loop systems. By the proposed methods, we can realize various non-exponential behaviors, which may improve control performance.

  9. Fractional order stochastic dynamical systems with distributed delayed control and Poisson jumps

    NASA Astrophysics Data System (ADS)

    Sathiyaraj, T.; Balasubramaniam, P.

    2016-02-01

    In this paper, we study the controllability results for nonlinear fractional order stochastic dynamical systems with distributed delayed control and Poisson jumps in finite dimensional space. New set of sufficient conditions are derived based on Schauder's fixed point theorem and the controllability Grammian matrix is defined by Mittag-Leffer matrix function. Finally, a numerical example has been given to validate the efficiency of the proposed theoretical results.

  10. A time-varying biased random walk approach to human growth.

    PubMed

    Suki, Béla; Frey, Urs

    2017-08-10

    Growth and development are dominated by gene-environment interactions. Many approaches have been proposed to model growth, but most are either descriptive or describe population level phenomena. We present a random walk-based growth model capable of predicting individual height, in which the growth increments are taken from time varying distributions mimicking the bursting behaviour of observed saltatory growth. We derive analytic equations and also develop a computational model of such growth that takes into account gene-environment interactions. Using an independent prospective birth cohort study of 190 infants, we predict height at 6 years of age. In a subset of 27 subjects, we adaptively train the model to account for growth between birth and 1 year of age using a Bayesian approach. The 5-year predicted heights compare well with actual data (measured height = 0.838*predicted height + 18.3; R(2) = 0.51) with an average error of 3.3%. In one patient, we also exemplify how our growth prediction model can be used for the early detection of growth deficiency and the evaluation of the effectiveness of growth hormone therapy.

  11. "Tools For Analysis and Visualization of Large Time- Varying CFD Data Sets"

    NASA Technical Reports Server (NTRS)

    Wilhelms, Jane; vanGelder, Allen

    1999-01-01

    During the four years of this grant (including the one year extension), we have explored many aspects of the visualization of large CFD (Computational Fluid Dynamics) datasets. These have included new direct volume rendering approaches, hierarchical methods, volume decimation, error metrics, parallelization, hardware texture mapping, and methods for analyzing and comparing images. First, we implemented an extremely general direct volume rendering approach that can be used to render rectilinear, curvilinear, or tetrahedral grids, including overlapping multiple zone grids, and time-varying grids. Next, we developed techniques for associating the sample data with a k-d tree, a simple hierarchial data model to approximate samples in the regions covered by each node of the tree, and an error metric for the accuracy of the model. We also explored a new method for determining the accuracy of approximate models based on the light field method described at ACM SIGGRAPH (Association for Computing Machinery Special Interest Group on Computer Graphics) '96. In our initial implementation, we automatically image the volume from 32 approximately evenly distributed positions on the surface of an enclosing tessellated sphere. We then calculate differences between these images under different conditions of volume approximation or decimation.

  12. TIME-VARYING COEFFICIENT MODELS FOR JOINT MODELING BINARY AND CONTINUOUS OUTCOMES IN LONGITUDINAL DATA.

    PubMed

    Kürüm, Esra; Li, Runze; Shiffman, Saul; Yao, Weixin

    2016-07-01

    Motivated by an empirical analysis of ecological momentary assessment data (EMA) collected in a smoking cessation study, we propose a joint modeling technique for estimating the time-varying association between two intensively measured longitudinal responses: a continuous one and a binary one. A major challenge in joint modeling these responses is the lack of a multivariate distribution. We suggest introducing a normal latent variable underlying the binary response and factorizing the model into two components: a marginal model for the continuous response, and a conditional model for the binary response given the continuous response. We develop a two-stage estimation procedure and establish the asymptotic normality of the resulting estimators. We also derived the standard error formulas for estimated coefficients. We conduct a Monte Carlo simulation study to assess the finite sample performance of our procedure. The proposed method is illustrated by an empirical analysis of smoking cessation data, in which the question of interest is to investigate the association between urge to smoke, continuous response, and the status of alcohol use, the binary response, and how this association varies over time.

  13. In the interests of time: improving HIV allocative efficiency modelling via optimal time-varying allocations

    PubMed Central

    Shattock, Andrew J; Kerr, Cliff C; Stuart, Robyn M; Masaki, Emiko; Fraser, Nicole; Benedikt, Clemens; Gorgens, Marelize; Wilson, David P; Gray, Richard T

    2016-01-01

    Introduction International investment in the response to HIV and AIDS has plateaued and its future level is uncertain. With many countries committed to ending the epidemic, it is essential to allocate available resources efficiently over different response periods to maximize impact. The objective of this study is to propose a technique to determine the optimal allocation of funds over time across a set of HIV programmes to achieve desirable health outcomes. Methods We developed a technique to determine the optimal time-varying allocation of funds (1) when the future annual HIV budget is pre-defined and (2) when the total budget over a period is pre-defined, but the year-on-year budget is to be optimally determined. We use this methodology with Optima, an HIV transmission model that uses non-linear relationships between programme spending and associated programmatic outcomes to quantify the expected epidemiological impact of spending. We apply these methods to data collected from Zambia to determine the optimal distribution of resources to fund the right programmes, for the right people, at the right time. Results and discussion Considering realistic implementation and ethical constraints, we estimate that the optimal time-varying redistribution of the 2014 Zambian HIV budget between 2015 and 2025 will lead to a 7.6% (7.3% to 7.8%) decrease in cumulative new HIV infections compared with a baseline scenario where programme allocations remain at 2014 levels. This compares to a 5.1% (4.6% to 5.6%) reduction in new infections using an optimal allocation with constant programme spending that recommends unrealistic programmatic changes. Contrasting priorities for programme funding arise when assessing outcomes for a five-year funding period over 5-, 10- and 20-year time horizons. Conclusions Countries increasingly face the need to do more with the resources available. The methodology presented here can aid decision-makers in planning as to when to expand or contract

  14. Necessity of Time-Varying Electromagnetic Force Consideration in MHD Calculation for Electromagnetic Stirring

    NASA Astrophysics Data System (ADS)

    Sato, Shoji; Fujisaki, Keisuke; Furukawa, Tatsuya

    In continuous casting process, MHD calculation is utilized to make clear and design the process. Previous MHD calculation takes no account of time-varying electromagnetic force which is enough smaller than mass inertia, because previous purpose is estimation of time average major flow in interior of mold. However, to improve surface quality of slab more, the dynamic behavior of free surface where the solidification begins must be made clear in detail. In this paper, we evaluated influence that time-varying electromagnetic force exerts on the flow in MHD calculation. As the result, the flow of free surface is disordered intensely by the taking into account of time-varying electromagnetic force. Therefore, time-varying electromagnetic force must be used, to make clear the dynamic behavior of free surface. However, previous MHD calculation with small computation cost suits to evaluate major flow in interior of mold, because it is same approximately in both methods.

  15. Synthesis procedure for linear time-varying feedback systems with large parameter ignorance

    NASA Technical Reports Server (NTRS)

    Mcdonald, T. E., Jr.

    1972-01-01

    The development of synthesis procedures for linear time-varying feedback systems is considered. It is assumed that the plant can be described by linear differential equations with time-varying coefficients; however, ignorance is associated with the plant in that only the range of the time-variations are known instead of exact functional relationships. As a result of this plant ignorance the use of time-varying compensation is ineffective so that only time-invariant compensation is employed. In addition, there is a noise source at the plant output which feeds noise through the feedback elements to the plant input. Because of this noise source the gain of the feedback elements must be as small as possible. No attempt is made to develop a stability criterion for time-varying systems in this work.

  16. Nonlinear time-varying potential bistable energy harvesting from human motion

    NASA Astrophysics Data System (ADS)

    Cao, Junyi; Wang, Wei; Zhou, Shengxi; Inman, Daniel J.; Lin, Jing

    2015-10-01

    A theoretical and experimental investigation into nonlinear bistable energy harvesting with time-varying potential energy is presented. The motivation for examining time-varying potentials comes from the desire to harvest energy from human motion. Time-varying potential energy function of bistable oscillator with respect to the swing angle are established to derive the governing electromechanical model for harvesting vibration energy from the swaying motion during human walking or running. Numerical simulations show good agreement with the experimental potential energy function under different swing angles. Various motion speed treadmill tests are performed to demonstrate the advantage of time-varying bistable harvesters over linear and monostable ones in harvesting energy from human motion.

  17. JOINT STRUCTURE SELECTION AND ESTIMATION IN THE TIME-VARYING COEFFICIENT COX MODEL

    PubMed Central

    Xiao, Wei; Lu, Wenbin; Zhang, Hao Helen

    2016-01-01

    Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding regression coefficients. Combining the ideas of local polynomial smoothing and group nonnegative garrote, we develop a new penalization approach to achieve such goals. Our method is able to identify the underlying true model structure with probability tending to one and simultaneously estimate the time-varying coefficients consistently. The asymptotic normalities of the resulting estimators are also established. We demonstrate the performance of our method using simulations and an application to the primary biliary cirrhosis data. PMID:27540275

  18. A new approach of analyzing time-varying dynamical equation via an optimal principle

    NASA Astrophysics Data System (ADS)

    Zhao, Hui; Li, Lixiang; Peng, Haipeng; Kurths, Jürgen; Xiao, Jinghua; Yang, Yixian; Li, Ang

    2017-03-01

    In this paper, an innovative design approach is proposed to solve time-varying dynamical equation, including matrix inverse equation and Sylvester equation. Based on the precondition of the existing solution of time-varying dynamical equation, different from previous approach to solve unknown matrix, an optimal design principle is used to solve the unknown variables. A performance index is introduced based on the inherent properties of the time-varying dynamical equation and Euler equation. The solution of time-varying dynamical equation is converted to an optimal problem of performance index. Furthermore, convergence and sensitivity to additive noise are also analyzed, and simulation results confirm that the method is feasible and effective. Especially, in simulations we design a tunable positive parameter in the dynamic optimization model. The tunable parameter is not only helpful to accelerate its convergence but also reduce its sensitivity to additive noise. Meanwhile the comparative simulation results are shown for the convergence accuracy and robustness of this method.

  19. Transformation of binaural response properties in the ascending auditory pathway: influence of time-varying interaural phase disparity.

    PubMed

    Spitzer, M W; Semple, M N

    1998-12-01

    motion, resulting in highly contiguous discharge profiles for overlapping stimuli. This finding indicates that responses of PL-SOC units to time-varying IPD reflect only instantaneous IPD with no additional influence of dynamic stimulus attributes. Thus the neuronal representation of auditory spatial information undergoes a major transformation as interaural delay is initially processed in the SOC and subsequently reprocessed in IC. The finding that motion sensitivity in IC emerges from motion-insensitive input suggests that information about change of position is crucial to spatial processing at higher levels of the auditory system.

  20. Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

    NASA Astrophysics Data System (ADS)

    Ma, Zhisai; Liu, Li; Zhou, Sida; Naets, Frank; Heylen, Ward; Desmet, Wim

    2017-03-01

    The problem of linear time-varying(LTV) system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the "frozen-time" assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stability-preserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system's stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam experimental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides a new way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

  1. Empirical mode decomposition as a time-varying multirate signal processing system

    NASA Astrophysics Data System (ADS)

    Yang, Yanli

    2016-08-01

    Empirical mode decomposition (EMD) can adaptively split composite signals into narrow subbands termed intrinsic mode functions (IMFs). Although an analytical expression of IMFs extracted by EMD from signals is introduced in Yang et al. (2013) [1], it is only used for the case of extrema spaced uniformly. In this paper, the EMD algorithm is analyzed from digital signal processing perspective for the case of extrema spaced nonuniformly. Firstly, the extrema extraction is represented by a time-varying extrema decimator. The nonuniform extrema extraction is analyzed through modeling the time-varying extrema decimation at a fixed time point as a time-invariant decimation. Secondly, by using the impulse/summation approach, spline interpolation for knots spaced nonuniformly is shown as two basic operations, time-varying interpolation and filtering by a time-varying spline filter. Thirdly, envelopes of signals are written as the output of the time-varying spline filter. An expression of envelopes of signals in both time and frequency domain is presented. The EMD algorithm is then described as a time-varying multirate signal processing system. Finally, an equation to model IMFs is derived by using a matrix formulation in time domain for the general case of extrema spaced nonuniformly.

  2. A Multi-Moded RF Delay Line Distribution System (MDLDS) for the Next Linear Collider

    SciTech Connect

    Nantista, Christopher D.

    2002-01-17

    The Delay Line Distribution System (DLDS) is an alternative to conventional pulse compression, which enhances the peak power of rf sources while matching the long pulse of those sources to the shorter filling time of accelerator structures. We present an implementation of this scheme that combines pairs of parallel delay lines of the system into single lines. The power of several sources is combined into a single waveguide delay line using a multi-mode launcher. The output mode of the launcher is determined by the phase coding of the input signals. The combined power is extracted from the delay line using mode-selective extractors, each of which extracts a single mode. Hence, the phase coding of the sources controls the output port of the combined power. The power is then fed to the local accelerator structures. We present a detailed design of such a system, including several implementation methods for the launchers, extractors, and ancillary high power rf components. The system is designed so that it can handle the 600 MW peak power required by the NLC design while maintaining high efficiency.

  3. Applied Time Domain Stability Margin Assessment for Nonlinear Time-Varying Systems

    NASA Technical Reports Server (NTRS)

    Kiefer, J. M.; Johnson, M. D.; Wall, J. H.; Dominguez, A.

    2016-01-01

    The baseline stability margins for NASA's Space Launch System (SLS) launch vehicle were generated via the classical approach of linearizing the system equations of motion and determining the gain and phase margins from the resulting frequency domain model. To improve the fidelity of the classical methods, the linear frequency domain approach can be extended by replacing static, memoryless nonlinearities with describing functions. This technique, however, does not address the time varying nature of the dynamics of a launch vehicle in flight. An alternative technique for the evaluation of the stability of the nonlinear launch vehicle dynamics along its trajectory is to incrementally adjust the gain and/or time delay in the time domain simulation until the system exhibits unstable behavior. This technique has the added benefit of providing a direct comparison between the time domain and frequency domain tools in support of simulation validation. This technique was implemented by using the Stability Aerospace Vehicle Analysis Tool (SAVANT) computer simulation to evaluate the stability of the SLS system with the Adaptive Augmenting Control (AAC) active and inactive along its ascent trajectory. The gains for which the vehicle maintains apparent time-domain stability defines the gain margins, and the time delay similarly defines the phase margin. This method of extracting the control stability margins from the time-domain simulation is relatively straightforward and the resultant margins can be compared to the linearized system results. The sections herein describe the techniques employed to extract the time-domain margins, compare the results between these nonlinear and the linear methods, and provide explanations for observed discrepancies. The SLS ascent trajectory was simulated with SAVANT and the classical linear stability margins were evaluated at one second intervals. The linear analysis was performed with the AAC algorithm disabled to attain baseline stability

  4. Model Predictive Optimal Control of a Time-Delay Distributed-Parameter Systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan

    2006-01-01

    This paper presents an optimal control method for a class of distributed-parameter systems governed by first order, quasilinear hyperbolic partial differential equations that arise in many physical systems. Such systems are characterized by time delays since information is transported from one state to another by wave propagation. A general closed-loop hyperbolic transport model is controlled by a boundary control embedded in a periodic boundary condition. The boundary control is subject to a nonlinear differential equation constraint that models actuator dynamics of the system. The hyperbolic equation is thus coupled with the ordinary differential equation via the boundary condition. Optimality of this coupled system is investigated using variational principles to seek an adjoint formulation of the optimal control problem. The results are then applied to implement a model predictive control design for a wind tunnel to eliminate a transport delay effect that causes a poor Mach number regulation.

  5. Bipartite networks of oscillators with distributed delays: Synchronization branches and multistability

    NASA Astrophysics Data System (ADS)

    Punetha, Nirmal; Ramaswamy, Ramakrishna; Atay, Fatihcan M.

    2015-04-01

    We study synchronization in bipartite networks of phase oscillators with general nonlinear coupling and distributed time delays. Phase-locked solutions are shown to arise, where the oscillators in each partition are perfectly synchronized among themselves but can have a phase difference with the other partition, with the phase difference necessarily being either zero or π radians. Analytical conditions for the stability of both types of solutions are obtained and solution branches are explicitly calculated, revealing that the network can have several coexisting stable solutions. With increasing value of the mean delay, the system exhibits hysteresis, phase flips, final state sensitivity, and an extreme form of multistability where the numbers of stable in-phase and antiphase synchronous solutions with distinct frequencies grow without bound. The theory is applied to networks of Landau-Stuart and Rössler oscillators and shown to accurately predict both in-phase and antiphase synchronous behavior in appropriate parameter ranges.

  6. Investigation of the delay time distribution of high power microwave surface flashover

    NASA Astrophysics Data System (ADS)

    Foster, J.; Krompholz, H.; Neuber, A.

    2011-01-01

    Characterizing and modeling the statistics associated with the initiation of gas breakdown has proven to be difficult due to a variety of rather unexplored phenomena involved. Experimental conditions for high power microwave window breakdown for pressures on the order of 100 to several 100 torr are complex: there are little to no naturally occurring free electrons in the breakdown region. The initial electron generation rate, from an external source, for example, is time dependent and so is the charge carrier amplification in the increasing radio frequency (RF) field amplitude with a rise time of 50 ns, which can be on the same order as the breakdown delay time. The probability of reaching a critical electron density within a given time period is composed of the statistical waiting time for the appearance of initiating electrons in the high-field region and the build-up of an avalanche with an inherent statistical distribution of the electron number. High power microwave breakdown and its delay time is of critical importance, since it limits the transmission through necessary windows, especially for high power, high altitude, low pressure applications. The delay time distribution of pulsed high power microwave surface flashover has been examined for nitrogen and argon as test gases for pressures ranging from 60 to 400 torr, with and without external UV illumination. A model has been developed for predicting the discharge delay time for these conditions. The results provide indications that field induced electron generation, other than standard field emission, plays a dominant role, which might be valid for other gas discharge types as well.

  7. Consensus-based distributed estimation in multi-agent systems with time delay

    NASA Astrophysics Data System (ADS)

    Abdelmawgoud, Ahmed

    During the last years, research in the field of cooperative control of swarm of robots, especially Unmanned Aerial Vehicles (UAV); have been improved due to the increase of UAV applications. The ability to track targets using UAVs has a wide range of applications not only civilian but also military as well. For civilian applications, UAVs can perform tasks including, but not limited to: map an unknown area, weather forecasting, land survey, and search and rescue missions. On the other hand, for military personnel, UAV can track and locate a variety of objects, including the movement of enemy vehicles. Consensus problems arise in a number of applications including coordination of UAVs, information processing in wireless sensor networks, and distributed multi-agent optimization. We consider a widely studied consensus algorithms for processing sensed data by different sensors in wireless sensor networks of dynamic agents. Every agent involved in the network forms a weighted average of its own estimated value of some state with the values received from its neighboring agents. We introduced a novelty of consensus-based distributed estimation algorithms. We propose a new algorithm to reach a consensus given time delay constraints. The proposed algorithm performance was observed in a scenario where a swarm of UAVs measuring the location of a ground maneuvering target. We assume that each UAV computes its state prediction and shares it with its neighbors only. However, the shared information applied to different agents with variant time delays. The entire group of UAVs must reach a consensus on target state. Different scenarios were also simulated to examine the effectiveness and performance in terms of overall estimation error, disagreement between delayed and non-delayed agents, and time to reach a consensus for each parameter contributing on the proposed algorithm.

  8. Investigation of the delay time distribution of high power microwave surface flashover

    SciTech Connect

    Foster, J.; Krompholz, H.; Neuber, A.

    2011-01-15

    Characterizing and modeling the statistics associated with the initiation of gas breakdown has proven to be difficult due to a variety of rather unexplored phenomena involved. Experimental conditions for high power microwave window breakdown for pressures on the order of 100 to several 100 torr are complex: there are little to no naturally occurring free electrons in the breakdown region. The initial electron generation rate, from an external source, for example, is time dependent and so is the charge carrier amplification in the increasing radio frequency (RF) field amplitude with a rise time of 50 ns, which can be on the same order as the breakdown delay time. The probability of reaching a critical electron density within a given time period is composed of the statistical waiting time for the appearance of initiating electrons in the high-field region and the build-up of an avalanche with an inherent statistical distribution of the electron number. High power microwave breakdown and its delay time is of critical importance, since it limits the transmission through necessary windows, especially for high power, high altitude, low pressure applications. The delay time distribution of pulsed high power microwave surface flashover has been examined for nitrogen and argon as test gases for pressures ranging from 60 to 400 torr, with and without external UV illumination. A model has been developed for predicting the discharge delay time for these conditions. The results provide indications that field induced electron generation, other than standard field emission, plays a dominant role, which might be valid for other gas discharge types as well.

  9. Time-varying exposure and the impact of stressful life events on onset of affective disorder.

    PubMed

    Wainwright, Nicholas W J; Surtees, Paul G

    2002-07-30

    Stressful life events are now established as risk factors for the onset of affective disorder but few studies have investigated time-varying exposure effects. Discrete (grouped) time survival methods provide a flexible framework for evaluating multiple time-dependent covariates and time-varying covariate effects. Here, we use these methods to investigate the time-varying influence of life events on the onset of affective disorder. Various straightforward time-varying exposure models are compared, involving one or more (stepped) time-dependent covariates and time-dependent covariates constructed or estimated according to exponential decay. These models are applied to data from two quite different studies. The first, a small scale interviewer-based longitudinal study (n = 180) concerned with affective disorder onset following loss (or threat of loss) event experiences. The second, a questionnaire assessment as part of an ongoing population study (n = 3353), provides a history of marital loss events and of depressive disorder onset. From the first study the initial impact of loss events was found to decay with a half-life of 5 weeks. Psychological coping strategy was found to modify vulnerability to the adverse effects of these events. The second study revealed that while men had a lower immediate risk of disorder onset following loss event experience their risk period was greater than for women. Time-varying exposure effects were well described by the appropriate use of simple time-dependent covariates.

  10. Bayesian Switching Factor Analysis for Estimating Time-varying Functional Connectivity in fMRI.

    PubMed

    Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

    2017-03-03

    There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal

  11. Distributed Interplanetary Delay/Disruption Tolerant Network (DTN) Monitor and Control System

    NASA Technical Reports Server (NTRS)

    Wang, Shin-Ywan

    2012-01-01

    The main purpose of Distributed interplanetary Delay Tolerant Network Monitor and Control System as a DTN system network management implementation in JPL is defined to provide methods and tools that can monitor the DTN operation status, detect and resolve DTN operation failures in some automated style while either space network or some heterogeneous network is infused with DTN capability. In this paper, "DTN Monitor and Control system in Deep Space Network (DSN)" exemplifies a case how DTN Monitor and Control system can be adapted into a space network as it is DTN enabled.

  12. Asymptotic behavior of stochastic multi-group epidemic models with distributed delays

    NASA Astrophysics Data System (ADS)

    Liu, Qun; Jiang, Daqing; Shi, Ningzhong; Hayat, Tasawar; Alsaedi, Ahmed

    2017-02-01

    In this paper, we introduce stochasticity into multi-group epidemic models with distributed delays and general kernel functions. The stochasticity in the model is a standard technique in stochastic population modeling. When the perturbations are small, by using the method of stochastic Lyapunov functions, we carry out a detailed analysis on the asymptotic behavior of the stochastic model regarding of the basic reproduction number R0. If R0 ≤ 1, the solution of the stochastic system oscillates around the disease-free equilibrium E0, while if R0 > 1, the solution of the stochastic model fluctuates around the endemic equilibrium E∗. Moreover, we also establish sufficient conditions of these results.

  13. A stage-structured predator-prey model with distributed maturation delay and harvesting.

    PubMed

    Al-Omari, J F M

    2015-01-01

    A stage-structured predator-prey system with distributed maturation delay and harvesting is investigated. General birth and death functions are used. The local stability of each feasible equilibria is discussed. By using the persistence theory, it is proven that the system is permanent if the coexistence equilibrium exists. By using Lyapunov functional and LaSalle invariant principle, it is shown that the trivial equilibrium is globally stable when the other equilibria are not feasible, and that the boundary equilibrium is globally stable if the coexistence equilibrium does not exist. Finally, sufficient conditions are derived for the global stability of the coexistence equilibrium.

  14. Global stability of a multi-group model with vaccination age, distributed delay and random perturbation.

    PubMed

    Xu, Jinhu; Zhou, Yicang

    2015-10-01

    A multi-group epidemic model with distributed delay and vaccination age has been formulated and studied. Mathematical analysis shows that the global dynamics of the model is determined by the basic reproduction number R0: the disease-free equilibrium is globally asymptotically stable if R0 ≤ 1, and the endemic equilibrium is globally asymptotically stable if R0 > 1. Lyapunov functionals are constructed by the non-negative matrix theory and a novel grouping technique to establish the global stability. The stochastic perturbation of the model is studied and it is proved that the endemic equilibrium of the stochastic model is stochastically asymptotically stable in the large under certain conditions.

  15. Distributed Interplanetary Delay/Disruption Tolerant Network (DTN) Monitor and Control System

    NASA Technical Reports Server (NTRS)

    Wang, Shin-Ywan

    2012-01-01

    The main purpose of Distributed interplanetary Delay Tolerant Network Monitor and Control System as a DTN system network management implementation in JPL is defined to provide methods and tools that can monitor the DTN operation status, detect and resolve DTN operation failures in some automated style while either space network or some heterogeneous network is infused with DTN capability. In this paper, "DTN Monitor and Control system in Deep Space Network (DSN)" exemplifies a case how DTN Monitor and Control system can be adapted into a space network as it is DTN enabled.

  16. The effects of impulsive harvest on a predator-prey system with distributed time delay

    NASA Astrophysics Data System (ADS)

    Guo, Hongjian; Chen, Lansun

    2009-05-01

    A kind of predator-prey system with distributed time delay and impulsive harvest is firstly presented and then the effects of impulsive harvest on the system are discussed by means of chain transform. By using the Floquet's theory and the comparison theorem of impulsive differential equation, the thresholds between permanence and extinction of each species are obtained as functions of model parameters. It is proved that the impulsive period and the proportion of the impulsive harvest will ultimately affect the fate of each species. Finally, the theoretical results obtained in this paper are confirmed by numerical simulations.

  17. On-line Parameter Estimation of Time-varying Systems by Radial Basis Function Networks

    NASA Astrophysics Data System (ADS)

    Kobayashi, Yasuhide; Tanaka, Shinichi; Okita, Tsuyoshi

    This paper proposes a new on-line parameter estimation method with radial basis function networks for time-varying linear discrete-time systems. The time-varying parameters of the system are expressed with the radial basis function networks. These parameters are estimated by the nonlinear optimization technique, and the setting rules of the initial values in the optimization are proposed. The system parameters are usually unknown because they are changed by the circumstance conditions. Then, it is reasonable that the structures of the radial basis function networks are regulated according to the change of parameters. The minimum description length criterion studied in the encoding theory is applied to select the network structures. It is demonstrated in digital simulation that the proposed on-line estimation method succeeded to reduce the computaion time extremely, for time-varying parameters system.

  18. Experimental Demonstration of Frequency Autolocking an Optical Cavity Using a Time-Varying Kalman Filter

    NASA Astrophysics Data System (ADS)

    Schütte, Dirk; Hassen, S. Z. Sayed; Karvinen, Kai S.; Boyson, Toby K.; Kallapur, Abhijit G.; Song, Hongbin; Petersen, Ian R.; Huntington, Elanor H.; Heurs, Michèle

    2016-01-01

    We propose and demonstrate a new autolocking scheme using a three-mirror ring cavity consisting of a linear quadratic regulator and a time-varying Kalman filter. Our technique does not require a frequency scan to acquire resonance. We utilize the singular perturbation method to simplify our system dynamics and to permit the application of linear control techniques. The error signal combined with the transmitted power is used to estimate the cavity detuning. This estimate is used by a linear time-varying Kalman filter which enables the implementation of an optimal controller. The experimental results validate the controller design, and we demonstrate improved robustness to disturbances and a faster locking time than a traditional proportional-integral controller. More important, the time-varying Kalman filtering approach automatically reacquires lock for large detunings, where the error signal leaves its linear capture range, a feat which linear time-invariant controllers cannot achieve.

  19. Parasitic modulation of electromagnetic signals caused by time-varying plasma

    SciTech Connect

    Yang, Min Li, Xiaoping; Xie, Kai; Liu, Yanming

    2015-02-15

    An experiment on the propagation of electromagnetic (EM) signals in continuous time-varying plasma is described. The time-varying characteristics of plasma are considered to cause a parasitic modulation in both amplitude and phase, and the strength of this modulation, which carries the information of the electron density profile, is closely related to the plasma frequency and the incident wave frequency. Through theoretical analysis, we give an explanation and mechanism of the interaction between the continuous time-varying plasma and EM waves, which is verified by a comparative analysis with experiments performed under the same conditions. The effects of this modulation on the EM signals in the plasma sheath cannot be ignored.

  20. Visulization of Time-Varying Multiresolution Date Using Error-Based Temporal-Spatial Resuse

    SciTech Connect

    Nuber, C; LaMar, E; Hamann, B; Joy, K

    2002-04-22

    In this paper, we report results on exploration of two-dimensional (2D) time varying datasets. We extend the notion of multiresolution spatial data approximation of static datasets to spatio-temporal approximation of time-varying datasets. Time-varying datasets typically do not change ''uniformly,'' i.e., some spatial sub-domains can experience only little or no change for extended periods of time. In these sub-domains, we show that approximation error bounds can be met when using sub-domains from other time-steps effectively. We generate a more general approximation scheme where sub-domains may approximate congruent sub-domains from any other time steps. While this incurs an O(T2) overhead, where T is the total number of time-steps, we show significant reduction in data transmission. We also discuss ideas for improvements to reduce overhead.

  1. A multi-moded rf delay line distribution system for the next linear collider

    SciTech Connect

    Tantawi, S.G.; Bowden, G.; Farkas, Z.D.; Irwin, J.; Ko, K.; Kroll, N.; Lavine, T.; Li, Z.; Loewen, R.; Miller, R.; Nantista, C.; Ruth, R.D.; Rifkin, J.; Vlieks, A.E.; Wilson, P.B.; Adolphsen, C.; Wang, J.

    1999-07-01

    The Delay Line Distribution System (DLDS) (1) is an alternative to conventional pulse compression which enhances the peak power of an rf source while matching the long pulse of that source to the shorter filling time of the accelerator structure. We present a variation on that scheme that combines the parallel delay lines of the system into one single line. The power of several sources is combined into a single waveguide delay line using a multi-mode launcher. The output mode of the launcher is determined by the phase coding of the input signals. The combined power is extracted using several mode extractors, each of which extracts only one single mode. Hence, the phase coding of the sources controls the output port of the combined power. The power is then fed to the local accelerator structures. We present a detailed design of such a system, including several implementation methods for the launchers, extractors, and ancillary high power rf components. The system is designed so that it can handle the 600 MW peak power required by the NLC design, while maintaining high efficiency. {copyright} {ital 1999 American Institute of Physics.}

  2. Electrically-Tunable Group Delays Using Quantum Wells in a Distributed Bragg Reflector

    NASA Technical Reports Server (NTRS)

    Nelson, Thomas R., Jr.; Loehr, John P.; Fork, Richard L.; Cole, Spencer; Jones, Darryl K.; Keys, Andrew

    1999-01-01

    There is a growing interest in the fabrication of semiconductor optical group delay lines for the development of phased arrays of Vertical-Cavity Surface-Emitting Lasers (VCSELs). We present a novel structure incorporating In(x)GA(1-x)As quantum wells in the GaAs quarter-wave layers of a GaAs/AlAs distributed Bragg reflector (DBR). Application of an electric field across the quantum wells leads to red shifting and peak broadening of the el-hhl exciton peak via the quantum-confined Stark effect. Resultant changes in the index of refraction thereby provide a means for altering the group delay of an incident laser pulse. We discuss the tradeoffs between the maximum amount of change in group delay versus absorption losses for such a device. We also compare a simple theoretical model to experimental results, and discuss both angle and position tuning of the BDR band edge resonance relative to the exciton absorption peak. The advantages of such monolithically grown devices for phased-array VCSEL applications will be detailed.

  3. Quantum optical electromagnetic field and Rabi oscillation in time-varying medium

    NASA Astrophysics Data System (ADS)

    Jang, Eun Ji; Jung, Min; Cha, Ji Hoon; Lee, Young Kyu; Chung, Won Sang

    2016-09-01

    In this paper, we consider the quantum optical electromagnetic wave in time-varying media, where the electric permittivity ɛ( t) and the magnetic permeability μ( t) depend on time explicitly as ɛ( t) = ɛ 0(1+ qt) and μ( t) = μ 0(1+ qt). For these time-varying parameters, we solve Maxwell's equations. To construct time-dependent coherent states for this light, we adopt the invariant method proposed by Lewis and Riesenfeld and express the fields in terms of the time-dependent step operators for the invariant. We also discuss the deformed Rabi oscillation for both on-resonance and off-resonance.

  4. Model-free adaptive fractional order control of stable linear time-varying systems.

    PubMed

    Yakoub, Z; Amairi, M; Chetoui, M; Saidi, B; Aoun, M

    2017-03-01

    This paper presents a new model-free adaptive fractional order control approach for linear time-varying systems. An online algorithm is proposed to determine some frequency characteristics using a selective filtering and to design a fractional PID controller based on the numerical optimization of the frequency-domain criterion. When the system parameters are time-varying, the controller is updated to keep the same desired performances. The main advantage of the proposed approach is that the controller design depends only on the measured input and output signals of the process. The effectiveness of the proposed method is assessed through a numerical example.

  5. Modeling polar cap F-region patches using time varying convection

    SciTech Connect

    Sojka, J.J.; Bowline, M.D.; Schunk, R.W.; Decker, D.T.; Valladares, C.E.; Sheehan, R.; Anderson, D.N.; Heelis, R.A.

    1993-09-03

    Here the authors present the results of computerized simulations of the polar cap regions which were able to model the formation of polar cap patches. They used the Utah State University Time-Dependent Ionospheric Model (TDIM) and the Phillips Laboratory (PL) F-region models in this work. By allowing a time varying magnetospheric electric field in the models, they were able to generate the patches. This time varying field generates a convection in the ionosphere. This convection is similar to convective changes observed in the ionosphere at times of southward pointing interplanetary magnetic field, due to changes in the B[sub y] component of the IMF.

  6. Time-varying long term memory in the European Union stock markets

    NASA Astrophysics Data System (ADS)

    Sensoy, Ahmet; Tabak, Benjamin M.

    2015-10-01

    This paper proposes a new efficiency index to model time-varying inefficiency in stock markets. We focus on European stock markets and show that they have different degrees of time-varying efficiency. We observe that the 2008 global financial crisis has an adverse effect on almost all EU stock markets. However, the Eurozone sovereign debt crisis has a significant adverse effect only on the markets in France, Spain and Greece. For the late members, joining EU does not have a uniform effect on stock market efficiency. Our results have important implications for policy makers, investors, risk managers and academics.

  7. Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions.

    PubMed

    Xu, Song; Li, Yang; Guo, Qi; Yang, Xiao-Feng; Chan, Rosa H M

    2017-02-15

    Tracking the changes of neural dynamics based on neuronal spiking activities is a critical step to understand the neurobiological basis of learning from behaving animals. These dynamical neurobiological processes associated with learning are also time-varying, which makes the modeling problem challenging. We developed a novel multiwavelet-based time-varying generalized Laguerre-Volterra (TVGLV) modeling framework to study the time-varying neural dynamical systems using natural spike train data. By projecting the time-varying parameters in the TVGLV model onto a finite sequence of multiwavelet basis functions, the time-varying identification problem is converted into a time invariant linear-in-the-parameters one. An effective forward orthogonal regression (FOR) algorithm aided by mutual information (MI) criterion is then applied for the selection of significant model regressors or terms and the refinement of model structure. A generalized linear model fit approach is finally employed for parameter estimation from spike train data. The proposed multiwavelet-based TVGLV approach is used to identify both synthetic input-output spike trains and spontaneous retinal spike train recordings. The proposed method gives excellent the performance of tracking either sharply or slowly changing parameters with high sensitivity and accuracy regardless of the a priori knowledge of spike trains, which these results indicate that the proposed method is shown to deal well with spike train data. The proposed multiwavelet-based TVGLV approach was compared with several state-of-art parametric estimation methods like the steepest descent point process filter (SDPPF) or Chebyshev polynomial expansion method. The conventional SDPPF algorithm, or SDPPF with B-splines wavelet expansion method was shown to have the poor performance of tracking the time-varying system changes with the synthetic spike train data due to the slow convergence of the adaptive filter methods. Although the Chebyshev

  8. Finite time control of a class of time-varying unified chaotic systems.

    PubMed

    Ying, Yang; Guopei, Chen

    2013-09-01

    This paper considers the problem of finite time control for a class of time-varying unified chaotic system. First, based on the finite-time stability theory, a novel adaptive control technique is presented to achieve finite-time stabilization for time-varying unified chaotic system. Comparing with the existing methods, the proposed controller only need to be added on one state variable of systems and it is easy to be implemented. Then, a finite time control technique is provided to realize the tracking of any target function with second-order derivatives. Finally, Simulation results are provided to show the effectiveness of the proposed method.

  9. Investigation on the coloured noise in GPS-derived position with time-varying seasonal signals

    NASA Astrophysics Data System (ADS)

    Gruszczynska, Marta; Klos, Anna; Bos, Machiel Simon; Bogusz, Janusz

    2016-04-01

    The seasonal signals in the GPS-derived time series arise from real geophysical signals related to tidal (residual) or non-tidal (loadings from atmosphere, ocean and continental hydrosphere, thermo elastic strain, etc.) effects and numerical artefacts including aliasing from mismodelling in short periods or repeatability of the GPS satellite constellation with respect to the Sun (draconitics). Singular Spectrum Analysis (SSA) is a method for investigation of nonlinear dynamics, suitable to either stationary or non-stationary data series without prior knowledge about their character. The aim of SSA is to mathematically decompose the original time series into a sum of slowly varying trend, seasonal oscillations and noise. In this presentation we will explore the ability of SSA to subtract the time-varying seasonal signals in GPS-derived North-East-Up topocentric components and show properties of coloured noise from residua. For this purpose we used data from globally distributed IGS (International GNSS Service) permanent stations processed by the JPL (Jet Propulsion Laboratory) in a PPP (Precise Point Positioning) mode. After introducing a threshold of 13 years, 264 stations left with a maximum length reaching 23 years. The data was initially pre-processed for outliers, offsets and gaps. The SSA was applied to pre-processed series to estimate the time-varying seasonal signals. We adopted a 3-years window as the optimal dimension of its size determined with the Akaike's Information Criteria (AIC) values. A Fisher-Snedecor test corrected for the presence of temporal correlation was used to determine the statistical significance of reconstructed components. This procedure showed that first four components describing annual and semi-annual signals, are significant at a 99.7% confidence level, which corresponds to 3-sigma criterion. We compared the non-parametric SSA approach with a commonly chosen parametric Least-Squares Estimation that assumes constant amplitudes and

  10. New Stability Criteria for High-Order Neural Networks with Proportional Delays

    NASA Astrophysics Data System (ADS)

    Xu, Chang-Jin; Li, Pei-Luan

    2017-03-01

    This paper is concerned with high-order neural networks with proportional delays. The proportional delay is a time-varying unbounded delay which is different from the constant delay, bounded time-varying delay and distributed delay. By the nonlinear transformation {y}i(t)={u}i({{{e}}}t){{ }}(i=1,2,\\ldots ,n), we transform a class of high-order neural networks with proportional delays into a class of high-order neural networks with constant delays and time-varying coefficients. With the aid of Brouwer fixed point theorem and constructing the delay differential inequality, we obtain some delay-independent and delay-dependent sufficient conditions to ensure the existence, uniqueness and global exponential stability of equilibrium of the network. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. Supported by National Natural Science Foundation of China under Grant Nos. 61673008 and 11261010, and Project of High-level Innovative Talents of Guizhou Province ([2016]5651)

  11. Analysis on global exponential robust stability of reaction diffusion neural networks with S-type distributed delays

    NASA Astrophysics Data System (ADS)

    Liu, Puchen; Yi, Fengqi; Guo, Qiang; Yang, Jun; Wu, Wei

    2008-04-01

    To avoid the unstable phenomena caused by time delays and perturbations, we investigate the sufficient conditions to ensure the global exponential robust stability with a convergence rate for the reaction-diffusion neural networks with S-type distributed delays. Because S-type distributed delays lead to some difficulty, we also introduce a new generalized Halanay inequality and a novel method-system-approximation method into the qualitative research of neural networks. Moreover, the sufficient criteria provided here, which are rather accessible and feasible, have wider adaptive range.

  12. Delay modeling of high-speed distributed interconnect for the signal integrity prediction

    NASA Astrophysics Data System (ADS)

    Raveloa, B.

    2012-02-01

    A relevant modeling-method of distributed interconnect line for the high-speed signal integrity (SI) application is introduced in this paper. By using the microwave and transmission line (TL) theory, the interconnect lines are assumed as its distributed RLC-model. Then, based on the transfer matrix analysis, the second-order global transfer function of the interconnect network comprised of the TL driven by voltage source including its internal resistance and the impedance load is expressed. Thus, mathematical analysis enabling the physical SI-parameters' extraction was established by using the transient response of the loaded line. To verify the relevance of the developed model, RC- and RLC-lines excited by square-wavepulse with 10-Gbits/s-rate were investigated. So, comparisons with SPICE-computations were performed. As results, transient responses perfectly well correlated to the reference SPICE-models were evidenced. As application of the introduced model, evaluations of rise-/fall-times, propagation delays, signal attenuations and even the settling times were realized for different values of TL-parameters. Compared to other methods, the computation execution time and data memory consumed by the program implementing the proposed delay modeling-method algorithm are much better.

  13. Resting-State Time-Varying Analysis Reveals Aberrant Variations of Functional Connectivity in Autism.

    PubMed

    Yao, Zhijun; Hu, Bin; Xie, Yuanwei; Zheng, Fang; Liu, Guangyao; Chen, Xuejiao; Zheng, Weihao

    2016-01-01

    Recently, studies based on time-varying functional connectivity have unveiled brain states diversity in some neuropsychiatric disorders, such as schizophrenia and major depressive disorder. However, time-varying functional connectivity analysis of resting-state functional Magnetic Resonance Imaging (fMRI) have been rarely performed on the Autism Spectrum Disorder (ASD). Hence, we performed time-varying connectivity analysis on resting-state fMRI data to investigate brain states mutation in ASD children. ASD showed an imbalance of connectivity state and aberrant ratio of connectivity with different strengths in the whole brain network, and decreased connectivity associated precuneus/posterior cingulate gyrus with medial prefrontal gyrus in default mode network. As compared to typical development children, weak relevance condition (the strength of a large number of connectivities in the state was less than means minus standard deviation of all connection strength) was maintained for a longer time between brain areas of ASD children, and ratios of weak connectivity in brain states varied dramatically in the ASD. In the ASD, the abnormal brain state might be related to repetitive behaviors and stereotypical interests, and macroscopically reflect disruption of gamma-aminobutyric acid at the cellular level. The detection of brain states based on time-varying functional connectivity analysis of resting-state fMRI might be conducive for diagnosis and early intervention of ASD before obvious clinical symptoms.

  14. Interactions between time-varying mesh stiffness and clearance non-linearities in a geared system

    NASA Astrophysics Data System (ADS)

    Kahraman, A.; Singh, R.

    1991-04-01

    Frequency response characteristics of a non-linear geared rotor-bearing system with time-varying mesh stiffness k h( overlinet) are examined in this paper. First, the single-degree-of-freedom spur gear pair model with backlash is extended to include sinusoidal or periodic mesh stiffness k h( overlinet) . Second, a three-degree-of-freedom model with k h( overlinet) and clearance non-lineariries associated with gear backlash and rolling element bearings, as excited by the static transmission error overlinee( overlinet) under a mean torque load, is developed. The governing equations are solved using digital simulation technique and only the primary resonances are studied. Resonances of the corresponding linear time-varying system associated with parametric and external excitations are identified using the method of multiple scales and digital simulation. Interactions between the mesh stiffness variation and clearance non-linearities have been investigated; a strong interaction between time-varying mesh stiffness k h( overlinet) and gear backlash is found, whereas the coupling between k h( overlinet) and bearing non-linearities is weak. Finally, our time-varying non-linear formulations yield reasonably good predictions when compared with the benchmark experimental results available in the literature.

  15. The Role of Thermal Properties in Periodic Time-Varying Phenomena

    ERIC Educational Resources Information Center

    Marin, E.

    2007-01-01

    The role played by physical parameters governing the transport of heat in periodical time-varying phenomena within solids is discussed. Starting with a brief look at the conduction heat transport mechanism, the equations governing heat conduction under static, stationary and non-stationary conditions, and the physical parameters involved, are…

  16. Modeling the Time-Varying Nature of Student Exceptionality Classification on Achievement Growth

    ERIC Educational Resources Information Center

    Nese, Joseph F. T.; Stevens, Joseph J.; Schulte, Ann C.; Tindal, Gerald; Elliott, Stephen N.

    2017-01-01

    Our purpose was to examine different approaches to modeling the time-varying nature of exceptionality classification. Using longitudinal data from one state's mathematics achievement test for 28,829 students in Grades 3 to 8, we describe the reclassification rate within special education and between general and special education, and compare four…

  17. Time-Critical Cooperative Path Following of Multiple UAVs over Time-Varying Networks

    DTIC Science & Technology

    2011-01-01

    Contract No. W911NF-06-1-0330. †PhD Candidate, Department of Mechanical Science & Engineering, University of Illinois at Urbana-Champaign; xar- gay ...vehicle is allowed to exchange only its coordination parameter ξi(t) with its neigh- bors Gi, which are defined by the possibly time-varying communications

  18. Correcting for Exposure Misclassification Using Survival Analysis with a Time-varying Exposure

    PubMed Central

    Ahrens, Katherine; Lash, Timothy L.; Louik, Carol; Mitchell, Allen A.; Werler, Martha M.

    2012-01-01

    Purpose Survival analysis is increasingly being used in perinatal epidemiology to assess time-varying risk factors for various pregnancy outcomes. Here we show how quantitative correction for exposure misclassification can be applied to a Cox regression model with a time-varying dichotomous exposure. Methods We evaluated influenza vaccination during pregnancy in relation to preterm birth among 2,267 non-malformed infants whose mothers were interviewed as part of the Slone Birth Defects Study during 2006–2011. The hazard of preterm birth was modeled using a time-varying exposure Cox regression model with gestational age as the time-scale. The effect of exposure misclassification was then modeled using a probabilistic bias analysis that incorporated vaccination date assignment. The parameters for the bias analysis were derived from both internal and external validation data. Results Correction for misclassification of prenatal influenza vaccination resulted in an adjusted hazard ratio (AHR) slightly higher and less precise than the conventional analysis: bias corrected AHR 1.04 [95% simulation interval 0.70, 1.52]; conventional AHR 1.00 [95% confidence interval 0.71, 1.41]. Conclusion Probabilistic bias analysis allows epidemiologists to assess quantitatively the possible confounder-adjusted effect of misclassification of a time-varying exposure, in contrast to a speculative approach to understanding information bias. PMID:23041654

  19. Resting-State Time-Varying Analysis Reveals Aberrant Variations of Functional Connectivity in Autism

    PubMed Central

    Yao, Zhijun; Hu, Bin; Xie, Yuanwei; Zheng, Fang; Liu, Guangyao; Chen, Xuejiao; Zheng, Weihao

    2016-01-01

    Recently, studies based on time-varying functional connectivity have unveiled brain states diversity in some neuropsychiatric disorders, such as schizophrenia and major depressive disorder. However, time-varying functional connectivity analysis of resting-state functional Magnetic Resonance Imaging (fMRI) have been rarely performed on the Autism Spectrum Disorder (ASD). Hence, we performed time-varying connectivity analysis on resting-state fMRI data to investigate brain states mutation in ASD children. ASD showed an imbalance of connectivity state and aberrant ratio of connectivity with different strengths in the whole brain network, and decreased connectivity associated precuneus/posterior cingulate gyrus with medial prefrontal gyrus in default mode network. As compared to typical development children, weak relevance condition (the strength of a large number of connectivities in the state was less than means minus standard deviation of all connection strength) was maintained for a longer time between brain areas of ASD children, and ratios of weak connectivity in brain states varied dramatically in the ASD. In the ASD, the abnormal brain state might be related to repetitive behaviors and stereotypical interests, and macroscopically reflect disruption of gamma-aminobutyric acid at the cellular level. The detection of brain states based on time-varying functional connectivity analysis of resting-state fMRI might be conducive for diagnosis and early intervention of ASD before obvious clinical symptoms. PMID:27695408

  20. The generalized harmonic potential theorem in the presence of a time-varying magnetic field.

    PubMed

    Lai, Meng-Yun; Pan, Xiao-Yin

    2016-10-17

    We investigate the evolution of the many-body wave function of a quantum system with time-varying effective mass, confined by a harmonic potential with time-varying frequency in the presence of a uniform time-varying magnetic field, and perturbed by a time-dependent uniform electric field. It is found that the wave function is comprised of a phase factor times the solution to the unperturbed time-dependent Schrödinger equation with the latter being translated by a time-dependent value that satisfies the classical driven equation of motion. In other words, we generalize the harmonic potential theorem to the case when the effective mass, harmonic potential, and the external uniform magnetic field with arbitrary orientation are all time-varying. The results reduce to various special cases obtained in the literature, particulary to that of the harmonic potential theorem wave function when the effective mass and frequency are both static and the external magnetic field is absent.

  1. Software Development for Automation of Space- and Time- Varying Pressurization on Small Caliber Gun Barrels

    DTIC Science & Technology

    2007-08-01

    pressure; M855 ; small caliber gun; space-varying pressurization; time-varying pressurization 16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE...6 Figure 5. Display of M855 (5.56 mm) bullet...latest version of IBHVG2 executable file, and Mr. Robert Keppinger of ARL for providing the M855 bullet picture. This work was supported in the

  2. The generalized harmonic potential theorem in the presence of a time-varying magnetic field

    PubMed Central

    Lai, Meng-Yun; Pan, Xiao-Yin

    2016-01-01

    We investigate the evolution of the many-body wave function of a quantum system with time-varying effective mass, confined by a harmonic potential with time-varying frequency in the presence of a uniform time-varying magnetic field, and perturbed by a time-dependent uniform electric field. It is found that the wave function is comprised of a phase factor times the solution to the unperturbed time-dependent Schrödinger equation with the latter being translated by a time-dependent value that satisfies the classical driven equation of motion. In other words, we generalize the harmonic potential theorem to the case when the effective mass, harmonic potential, and the external uniform magnetic field with arbitrary orientation are all time-varying. The results reduce to various special cases obtained in the literature, particulary to that of the harmonic potential theorem wave function when the effective mass and frequency are both static and the external magnetic field is absent. PMID:27748461

  3. Right factorization of a class of time-varying nonlinear systems

    NASA Technical Reports Server (NTRS)

    Desoer, C. A.; Kabuli, M. G.

    1988-01-01

    A class of nonlinear continuous-time, time-varying plants with a state-space description which has a uniformly completely controllable linear part is considered. For this class, a right factorization is obtained. For the case in which the state is available for feedback, a normalized right-coprime factorization is realized.

  4. Laser imaging of chemistry-flowfield interactions: Enhanced soot formation in time-varying diffusion flames

    SciTech Connect

    Harrington, J.E.; Shaddix, C.R.; Smyth, K.C.

    1994-12-31

    Models of detailed flame chemistry and soot formation are based upon experimental results obtained in steady, laminar flames. For successful application of these descriptions to turbulent combustion, it is instructive to test predictions against measurements in time-varying flowfields. This paper reports the use of optical methods to examine soot production and oxidation processes in a co-flowing, axisymmetric CH{sub 4}/air diffusion flame in which the fuel flow rate is acoustically forced to create a time-varying flowfield. For a particular forcing condition in which tip clipping occurs (0.75 V loudspeaker excitation), elastic scattering of vertically polarized light from the soot particles increases by nearly an order of magnitude with respect to that observed for a steady flame with the same mean fuel flow rate. The visible flame luminosity and laser-induced fluorescence attributed to polycyclic aromatic hydrocarbons (PAH) are also enhanced. Peak soot volume fractions, as measured by time-resolved laser extinction/tomography at 632.8 and 454.5 nm and calibrated laser-induced incandescence (LII), show a factor of 4--5 enhancement in this flickering flame. The LII method is found to track the soot volume fraction closely and to give better signal-to-noise than the extinction measurements in both the steady and time-varying flowfields. A Mie analysis suggests that most of the enhanced soot production results from the formation of larger particles in the time-varying flowfield.

  5. Scalable Video Streaming Adaptive to Time-Varying IEEE 802.11 MAC Parameters

    NASA Astrophysics Data System (ADS)

    Lee, Kyung-Jun; Suh, Doug-Young; Park, Gwang-Hoon; Huh, Jae-Doo

    This letter proposes a QoS control method for video streaming service over wireless networks. Based on statistical analysis, the time-varying MAC parameters highly related to channel condition are selected to predict available bitrate. Adaptive bitrate control of scalably-encoded video guarantees continuity in streaming service even if the channel condition changes abruptly.

  6. The generalized harmonic potential theorem in the presence of a time-varying magnetic field

    NASA Astrophysics Data System (ADS)

    Lai, Meng-Yun; Pan, Xiao-Yin

    2016-10-01

    We investigate the evolution of the many-body wave function of a quantum system with time-varying effective mass, confined by a harmonic potential with time-varying frequency in the presence of a uniform time-varying magnetic field, and perturbed by a time-dependent uniform electric field. It is found that the wave function is comprised of a phase factor times the solution to the unperturbed time-dependent Schrödinger equation with the latter being translated by a time-dependent value that satisfies the classical driven equation of motion. In other words, we generalize the harmonic potential theorem to the case when the effective mass, harmonic potential, and the external uniform magnetic field with arbitrary orientation are all time-varying. The results reduce to various special cases obtained in the literature, particulary to that of the harmonic potential theorem wave function when the effective mass and frequency are both static and the external magnetic field is absent.

  7. Coherent time-varying graph drawing with multifocus+context interaction.

    PubMed

    Feng, Kun-Chuan; Wang, Chaoli; Shen, Han-Wei; Lee, Tong-Yee

    2012-08-01

    We present a new approach for time-varying graph drawing that achieves both spatiotemporal coherence and multifocus+context visualization in a single framework. Our approach utilizes existing graph layout algorithms to produce the initial graph layout, and formulates the problem of generating coherent time-varying graph visualization with the focus+context capability as a specially tailored deformation optimization problem. We adopt the concept of the super graph to maintain spatiotemporal coherence and further balance the needs for aesthetic quality and dynamic stability when interacting with time-varying graphs through focus+context visualization. Our method is particularly useful for multifocus+context visualization of time-varying graphs where we can preserve the mental map by preventing nodes in the focus from undergoing abrupt changes in size and location in the time sequence. Experiments demonstrate that our method strikes a good balance between maintaining spatiotemporal coherence and accentuating visual foci, thus providing a more engaging viewing experience for the users.

  8. Model reference adaptive control for linear time varying and nonlinear systems

    NASA Technical Reports Server (NTRS)

    Abida, L.; Kaufman, H.

    1982-01-01

    Model reference adaptive control is applied to linear time varying systems and to nonlinear systems amenable to virtual linearization. Asymptotic stability is guaranteed even if the perfect model following conditions do not hold, provided that some sufficient conditions are satisfied. Simulations show the scheme to be capable of effectively controlling certain nonlinear systems.

  9. The Role of Thermal Properties in Periodic Time-Varying Phenomena

    ERIC Educational Resources Information Center

    Marin, E.

    2007-01-01

    The role played by physical parameters governing the transport of heat in periodical time-varying phenomena within solids is discussed. Starting with a brief look at the conduction heat transport mechanism, the equations governing heat conduction under static, stationary and non-stationary conditions, and the physical parameters involved, are…

  10. Time-varying linear and nonlinear parametric model for Granger causality analysis.

    PubMed

    Li, Yang; Wei, Hua-Liang; Billings, Steve A; Liao, Xiao-Feng

    2012-04-01

    Statistical measures such as coherence, mutual information, or correlation are usually applied to evaluate the interactions between two or more signals. However, these methods cannot distinguish directions of flow between two signals. The capability to detect causalities is highly desirable for understanding the cooperative nature of complex systems. The main objective of this work is to present a linear and nonlinear time-varying parametric modeling and identification approach that can be used to detect Granger causality, which may change with time and may not be detected by traditional methods. A numerical example, in which the exact causal influences relationships, is presented to illustrate the performance of the method for time-varying Granger causality detection. The approach is applied to EEG signals to track and detect hidden potential causalities. One advantage of the proposed model, compared with traditional Granger causality, is that the results are easier to interpret and yield additional insights into the transient directed dynamical Granger causality interactions.

  11. Structural Nested Mean Models for Assessing Time-Varying Effect Moderation

    PubMed Central

    Almirall, Daniel; Ten Have, Thomas; Murphy, Susan A.

    2009-01-01

    SUMMARY This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time-varying and so are the covariates said to moderate its effect. Intermediate Causal Effects that describe time-varying causal effects of treatment conditional on past covariate history are introduced and considered as part of Robins’ Structural Nested Mean Model. Two estimators of the intermediate causal effects, and their standard errors, are presented and discussed: The first is a proposed 2-Stage Regression Estimator. The second is Robins’ G-Estimator. The results of a small simulation study that begins to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented. The methodology is illustrated using longitudinal data from a depression study. PMID:19397586

  12. Quantitative Characterization of Super-Resolution Infrared Imaging Based on Time-Varying Focal Plane Coding

    NASA Astrophysics Data System (ADS)

    Wang, X.; Yuan, Y.; Zhang, J.; Chen, Y.; Cheng, Y.

    2014-10-01

    High resolution infrared image has been the goal of an infrared imaging system. In this paper, a super-resolution infrared imaging method using time-varying coded mask is proposed based on focal plane coding and compressed sensing theory. The basic idea of this method is to set a coded mask on the focal plane of the optical system, and the same scene could be sampled many times repeatedly by using time-varying control coding strategy, the super-resolution image is further reconstructed by sparse optimization algorithm. The results of simulation are quantitatively evaluated by introducing the Peak Signal-to-Noise Ratio (PSNR) and Modulation Transfer Function (MTF), which illustrate that the effect of compressed measurement coefficient r and coded mask resolution m on the reconstructed image quality. Research results show that the proposed method will promote infrared imaging quality effectively, which will be helpful for the practical design of new type of high resolution ! infrared imaging systems.

  13. Exponential networked synchronization of master-slave chaotic systems with time-varying communication topologies

    NASA Astrophysics Data System (ADS)

    Yang, Dong-Sheng; Liu, Zhen-Wei; Zhao, Yan; Liu, Zhao-Bing

    2012-04-01

    The networked synchronization problem of a class of master-slave chaotic systems with time-varying communication topologies is investigated in this paper. Based on algebraic graph theory and matrix theory, a simple linear state feedback controller is designed to synchronize the master chaotic system and the slave chaotic systems with a time-varying communication topology connection. The exponential stability of the closed-loop networked synchronization error system is guaranteed by applying Lyapunov stability theory. The derived novel criteria are in the form of linear matrix inequalities (LMIs), which are easy to examine and tremendously reduce the computation burden from the feedback matrices. This paper provides an alternative networked secure communication scheme which can be extended conveniently. An illustrative example is given to demonstrate the effectiveness of the proposed networked synchronization method.

  14. Modeling the interaction of electric current and tissue: importance of accounting for time varying electric properties.

    PubMed

    Evans, Daniel J; Manwaring, Mark L

    2007-01-01

    Time varying computer models of the interaction of electric current and tissue are very valuable in helping to understand the complexity of the human body and biological tissue. The electrical properties of tissue, permittivity and conductivity, are vital to accurately modeling the interaction of the human tissue with electric current. Past models have represented the electric properties of the tissue as constant or temperature dependent. This paper presents time dependent electric properties that change as a result of tissue damage, temperature, blood flow, blood vessels, and tissue property. Six models are compared to emphasize the importance of accounting for these different tissue properties in the computer model. In particular, incorporating the time varying nature of the electric properties of human tissue into the model leads to a significant increase in tissue damage. An important feature of the model is the feedback loop created between the electric properties, tissue damage, and temperature.

  15. Well-posedness of the time-varying linear electromagnetic initial-boundary value problem

    NASA Astrophysics Data System (ADS)

    Xie, Li; Lei, Yin-Zhao

    2007-09-01

    The well-posedness of the initial-boundary value problem of the time-varying linear electromagnetic field in a multi-medium region is investigated. Function spaces are defined, with Faraday's law of electromagnetic induction and the initial-boundary conditions considered as constraints. Gauss's formula applied to a multi-medium region is used to derive the energy-estimating inequality. After converting the initial-boundary conditions into homogeneous ones and analysing the characteristics of an operator introduced according to the total current law, the existence, uniqueness and stability of the weak solution to the initial-boundary value problem of the time-varying linear electromagnetic field are proved.

  16. Applications, dosimetry and biological interactions of static and time-varying magnetic fields

    NASA Astrophysics Data System (ADS)

    Tenforde, T. S.

    1988-08-01

    The primary topics of this presentation include: (1) the applications of magnetic fields in research, industry, and medical technologies; (2) mechanisms of interaction of static and time-varying magnetic fields with living systems; (3) human health effects of exposure to static and time-varying magnetic fields in occupational, medical, and residential settings; and (4) recent advances in the dosimetry of extremely-low-frequency electromagnetic fields. The discussion of these topics is centered about two issues of considerable contemporary interest: (1) potential health effects of the fields used in magnetic resonance imaging and in vivo spectroscopy, and (2) the controversial issue of whether exposure to extremely-low-frequency (ELF) electromagnetic fields in the home and workplace leads to an elevated risk of cancer.

  17. An Integral Method for Determining Induced Voltage in Time-Varying Wire Inductors

    SciTech Connect

    Fasenfest, B; White, D; Rockway, J

    2005-05-27

    This report documents the creation of software tools to model time-varying wire inductors. The class of inductors studied consists of arbitrary wire shapes in nonmagnetic material. When the wire structures are deformed, the inductance changes, and a voltage is induced. This voltage is of interest, for instance when the inductor is used to measure or sense a shockwave. An integral technique, which only requires integrating over the wire segments, is used to find the inductance at each time step, with backwards-difference approximations being used on successive time steps to determine the voltage. This method allows for arbitrary time-varying wire structures. It was tested for several canonical problems and used to model a double helix solenoid compressed by a shockwave.

  18. An improved correlation method for amplitude estimation of gravitational background signal with time-varying frequency.

    PubMed

    Wu, Wei-Huang; Tian, Yuan; Luo, Jie; Shao, Cheng-Gang; Xu, Jia-Hao; Wang, Dian-Hong

    2016-09-01

    In the measurement of the gravitational constant G with angular acceleration method, the accurate estimation of the amplitude of the useful angular acceleration generated by source masses depends on the effective subtraction of the spurious gravitational signal caused by room fixed background masses. The gravitational background signal is of time-varying frequency, and mainly consists of the prominent fundamental frequency and second harmonic components. We propose an improved correlation method to estimate the amplitudes of the prominent components of the gravitational background signal with high precision. The improved correlation method converts a sinusoidal signal with time-varying frequency into a standard sinusoidal signal by means of the stretch processing of time. Based on Gaussian white noise model, the theoretical result shows the uncertainty of the estimated amplitude is proportional to σNT, where σ and N are the standard deviation of noise and the number of the useful signal period T, respectively.

  19. Time-varying extreme rainfall intensity-duration-frequency curves in a changing climate

    NASA Astrophysics Data System (ADS)

    Sarhadi, Ali; Soulis, Eric D.

    2017-03-01

    Anthropogenic climate change influences the nature and probabilistic behavior of extreme climate phenomena over time. Current infrastructure design of water systems, however, is based on intensity-duration-frequency (IDF) curves that assume extreme precipitation will not significantly change. To sustain the reliability of infrastructure designs in a changing environment, time-varying nonstationary-based IDF curves must replace the static stationary-based IDF curves. This study outlines a fully time varying risk framework using Bayesian Markov chain Monte Carlo techniques to incorporate the impact of different complex nonstationary conditions on the occurrence of extreme precipitation in the Great Lakes area. The results demonstrate the underestimation of the extreme precipitation using stationary assumptions and the importance of updating infrastructure design strategies in a changing climate.

  20. Finite-time consensus of time-varying nonlinear multi-agent systems

    NASA Astrophysics Data System (ADS)

    Liu, Qingrong; Liang, Zhishan

    2016-08-01

    This paper investigates the problem of leader-follower finite-time consensus for a class of time-varying nonlinear multi-agent systems. The dynamics of each agent is assumed to be represented by a strict feedback nonlinear system, where nonlinearities satisfy Lipschitz growth conditions with time-varying gains. The main design procedure is outlined as follows. First, it is shown that the leader-follower consensus problem is equivalent to a conventional control problem of multi-variable high-dimension systems. Second, by introducing a state transformation, the control problem is converted into the construction problem of two dynamic equations. Third, based on the Lyapunov stability theorem, the global finite-time stability of the closed-loop control system is proved, and the finite-time consensus of the concerned multi-agent systems is thus guaranteed. An example is given to verify the effectiveness of the proposed consensus protocol algorithm.

  1. Partial-Nodes-Based State Estimation for Complex Networks With Unbounded Distributed Delays.

    PubMed

    Liu, Yurong; Wang, Zidong; Yuan, Yuan; Alsaadi, Fuad E

    2017-09-07

    In this brief, the new problem of partial-nodes-based (PNB) state estimation problem is investigated for a class of complex network with unbounded distributed delays and energy-bounded measurement noises. The main novelty lies in that the states of the complex network are estimated through measurement outputs of a fraction of the network nodes. Such fraction of the nodes is determined by either the practical availability or the computational necessity. The PNB state estimator is designed such that the error dynamics of the network state estimation is exponentially ultimately bounded in the presence of measurement errors. Sufficient conditions are established to ensure the existence of the PNB state estimators and then the explicit expression of the gain matrices of such estimators is characterized. When the network measurements are free of noises, the main results specialize to the case of exponential stability for error dynamics. Numerical examples are presented to verify the theoretical results.

  2. Cluster synchronization of community network with distributed time delays via impulsive control

    NASA Astrophysics Data System (ADS)

    Leng, Hui; Wu, Zhao-Yan

    2016-11-01

    Cluster synchronization is an important dynamical behavior in community networks and deserves further investigations. A community network with distributed time delays is investigated in this paper. For achieving cluster synchronization, an impulsive control scheme is introduced to design proper controllers and an adaptive strategy is adopted to make the impulsive controllers unified for different networks. Through taking advantage of the linear matrix inequality technique and constructing Lyapunov functions, some synchronization criteria with respect to the impulsive gains, instants, and system parameters without adaptive strategy are obtained and generalized to the adaptive case. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results. Project supported by the National Natural Science Foundation of China (Grant No. 61463022), the Natural Science Foundation of Jiangxi Province, China (Grant No. 20161BAB201021), and the Natural Science Foundation of Jiangxi Educational Committee, China (Grant No. GJJ14273).

  3. Analysis of an SIR Epidemic Model with Pulse Vaccination and Distributed Time Delay

    PubMed Central

    Gao, Shujing; Teng, Zhidong; Nieto, Juan J.; Torres, Angela

    2007-01-01

    Pulse vaccination, the repeated application of vaccine over a defined age range, is gaining prominence as an effective strategy for the elimination of infectious diseases. An SIR epidemic model with pulse vaccination and distributed time delay is proposed in this paper. Using the discrete dynamical system determined by the stroboscopic map, we obtain the exact infection-free periodic solution of the impulsive epidemic system and prove that the infection-free periodic solution is globally attractive if the vaccination rate is larger enough. Moreover, we show that the disease is uniformly persistent if the vaccination rate is less than some critical value. The permanence of the model is investigated analytically. Our results indicate that a large pulse vaccination rate is sufficient for the eradication of the disease. PMID:18322563

  4. Piezoceramic devices and artificial intelligence time varying concepts in smart structures

    NASA Technical Reports Server (NTRS)

    Hanagud, S.; Calise, A. J.; Glass, B. J.

    1990-01-01

    The problem of development of smart structures and their vibration control by the use of piezoceramic sensors and actuators have been discussed. In particular, these structures are assumed to have time varying model form and parameters. The model form may change significantly and suddenly. Combined identification of the model from parameters of these structures and model adaptive control of these structures are discussed in this paper.

  5. Simulink-Based Implementation and Performance Analysis of TDS-OFDM in Time-Varying Environments

    DTIC Science & Technology

    2014-09-01

    transmission channel is modeled as a time-varying Rayleigh multipath channel . Two standard transmission channels models are considered: the COST 207...performances are evaluated in terms of bit error ratio (BER) for different signal-to-noise ratio (SNR) levels in these two channel environments for...varying transmission channel and reconstructing the transmitted data. Performances are also compared to those obtained by standard OFDM techniques, such

  6. On the Bohl and general exponents of the discrete time-varying linear system

    NASA Astrophysics Data System (ADS)

    Niezabitowski, Michał

    2014-12-01

    Many properties of dynamical systems may be characterized by certain numbers called characteristic exponents. The most important are: Lyapunov, Bohl and general exponents. In this paper we investigate relations between certain subtypes of the general exponents of discrete time-varying linear systems, namely the senior lower and the junior upper once. The main contribution of the paper is to construct an example of a system with the senior lower exponent strictly smaller than the junior upper general exponents.

  7. Time-Varying Effects of a Text-based Smoking Cessation Intervention for Urban Adolescents

    PubMed Central

    Mason, Michael; Mennis, Jeremy; Way, Thomas; Lanza, Stephanie; Russell, Michael; Zaharakis, Nikola

    2016-01-01

    Introduction Craving to smoke is understood as an important mechanism for continued smoking behavior. Identifying how smoking interventions operate on craving with particular populations is critical for advancing intervention science. This study's objective was to investigate the time-varying effect of a text-delivered smoking cessation intervention. Methods Toward this end, we used ecological momentary assessment (EMA) data collected from a five-day, automated text-messaging smoking cessation randomized clinical trial with 200 urban adolescents. We employed a time-varying effect model (TVEM) to estimate the effects of stress (time-varying covariate) and baseline nicotine dependence level (time-invariant covariate) on craving over six months by treatment condition. The TVEM approach models behavioral change and associations of coefficients expressed dynamically and graphically represented as smooth functions of time. Results Controlling for gender, age, and current smoking, differences in trajectories of craving between intervention and control conditions were apparent over the course of the study. During months 2 to 3, the association between stress and craving was significantly stronger among the control group, suggesting treatment dampens this association during this time period. The intervention also reduced the salience of baseline dependence among treatment adolescents, with craving being reduced steadily over time, while the control group increased craving over time. Conclusions These results provide insight into the time-varying nature of treatment effects for adolescents receiving a text-based smoking cessation intervention. The ability to specify when in the course of an intervention the effect is strongest is important in developing targeted and adaptive interventions that can adjust strategically with time. PMID:26507175

  8. Time-varying effects of a text-based smoking cessation intervention for urban adolescents.

    PubMed

    Mason, Michael; Mennis, Jeremy; Way, Thomas; Lanza, Stephanie; Russell, Michael; Zaharakis, Nikola

    2015-12-01

    Craving to smoke is understood as an important mechanism for continued smoking behavior. Identifying how smoking interventions operate on craving with particular populations is critical for advancing intervention science. This study's objective was to investigate the time-varying effect of a text-delivered smoking cessation intervention. Toward this end, we used ecological momentary assessment (EMA) data collected from a five-day, automated text-messaging smoking cessation randomized clinical trial with 200 urban adolescents. We employed a time-varying effect model (TVEM) to estimate the effects of stress (time-varying covariate) and baseline nicotine dependence level (time-invariant covariate) on craving over six months by treatment condition. The TVEM approach models behavioral change and associations of coefficients expressed dynamically and graphically represented as smooth functions of time. Controlling for gender, age, and current smoking, differences in trajectories of craving between intervention and control conditions were apparent over the course of the study. During months 2 to 3, the association between stress and craving was significantly stronger among the control group, suggesting treatment dampens this association during this time period. The intervention also reduced the salience of baseline dependence among treatment adolescents, with craving being reduced steadily over time, while the control group increased craving over time. These results provide insight into the time-varying nature of treatment effects for adolescents receiving a text-based smoking cessation intervention. The ability to specify when in the course of an intervention the effect is strongest is important in developing targeted and adaptive interventions that can adjust strategically with time. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  9. Piezoceramic devices and artificial intelligence time varying concepts in smart structures

    NASA Technical Reports Server (NTRS)

    Hanagud, S.; Calise, A. J.; Glass, B. J.

    1990-01-01

    The problem of development of smart structures and their vibration control by the use of piezoceramic sensors and actuators have been discussed. In particular, these structures are assumed to have time varying model form and parameters. The model form may change significantly and suddenly. Combined identification of the model from parameters of these structures and model adaptive control of these structures are discussed in this paper.

  10. Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.

    PubMed

    Kyle, Ryan P; Moodie, Erica E M; Klein, Marina B; Abrahamowicz, Michał

    2016-08-01

    Unbiased estimation of causal parameters from marginal structural models (MSMs) requires a fundamental assumption of no unmeasured confounding. Unfortunately, the time-varying covariates used to obtain inverse probability weights are often error-prone. Although substantial measurement error in important confounders is known to undermine control of confounders in conventional unweighted regression models, this issue has received comparatively limited attention in the MSM literature. Here we propose a novel application of the simulation-extrapolation (SIMEX) procedure to address measurement error in time-varying covariates, and we compare 2 approaches. The direct approach to SIMEX-based correction targets outcome model parameters, while the indirect approach corrects the weights estimated using the exposure model. We assess the performance of the proposed methods in simulations under different clinically plausible assumptions. The simulations demonstrate that measurement errors in time-dependent covariates may induce substantial bias in MSM estimators of causal effects of time-varying exposures, and that both proposed SIMEX approaches yield practically unbiased estimates in scenarios featuring low-to-moderate degrees of error. We illustrate the proposed approach in a simple analysis of the relationship between sustained virological response and liver fibrosis progression among persons infected with hepatitis C virus, while accounting for measurement error in γ-glutamyltransferase, using data collected in the Canadian Co-infection Cohort Study from 2003 to 2014.

  11. Smart panel with time-varying shunted piezoelectric patch absorbers for broadband vibration control

    NASA Astrophysics Data System (ADS)

    Casagrande, D.; Gardonio, P.; Zilletti, M.

    2017-07-01

    This paper presents a simulation study concerning the low and mid frequencies control of flexural vibration in a lightly damped thin plate equipped with five time-varying shunted piezoelectric patch absorbers. The panel is excited by a rain-on-the-roof broad frequency band stationary disturbance. The absorbers are composed by piezoelectric patches connected to time-varying RL shunt circuits. Discrete or continuous variations over time of the shunts are implemented in such a way as to either switch, between given values, or sweep, within certain ranges, the natural frequency and damping factor of the electro-mechanical absorbers to control either the resonant response of targeted flexural modes of the plate with natural frequency comprised between 30 Hz and 1 kHz or to control the resonant responses of all flexural modes with natural frequencies comprised between 30 Hz and 1 kHz. The proposed system is firstly presented; then, the vibration control effects produced by a single patch and by the array of five patches implementing the switching and sweeping shunts are investigated. Both time-varying operation modes produce significant vibration control effects, with reductions of the resonance peaks of the target resonances or target frequency band up to 12 dB. The piezoelectric patch absorbers with sweeping shunts offer an interesting practical solution since they are operated blindly, thus they do not require a system identification during installation and effectively work without on line tuning also on systems whose response may vary substantially in time.

  12. Time-varying modal parameters identification of a spacecraft with rotating flexible appendage by recursive algorithm

    NASA Astrophysics Data System (ADS)

    Ni, Zhiyu; Mu, Ruinan; Xun, Guangbin; Wu, Zhigang

    2016-01-01

    The rotation of spacecraft flexible appendage may cause changes in modal parameters. For this time-varying system, the computation cost of the frequently-used singular value decomposition (SVD) identification method is high. Some control problems, such as the self-adaptive control, need the latest modal parameters to update the controller parameters in time. In this paper, the projection approximation subspace tracking (PAST) recursive algorithm is applied as an alternative method to identify the time-varying modal parameters. This method avoids the SVD by signal subspace projection and improves the computational efficiency. To verify the ability of this recursive algorithm in spacecraft modal parameters identification, a spacecraft model with rapid rotational appendage, Soil Moisture Active/Passive (SMAP) satellite, is established, and the time-varying modal parameters of the satellite are identified recursively by designing the input and output signals. The results illustrate that this recursive algorithm can obtain the modal parameters in the high signal noise ratio (SNR) and it has better computational efficiency than the SVD method. Moreover, to improve the identification precision of this recursive algorithm in the low SNR, the wavelet de-noising technology is used to decrease the effect of noises.

  13. Opinion formation in time-varying social networks: The case of the naming game

    NASA Astrophysics Data System (ADS)

    Maity, Suman Kalyan; Manoj, T. Venkat; Mukherjee, Animesh

    2012-09-01

    We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

  14. Multimodal Pilot Behavior in Multi-Axis Tracking Tasks with Time-Varying Motion Cueing Gains

    NASA Technical Reports Server (NTRS)

    Zaal, P. M. T; Pool, D. M.

    2014-01-01

    In a large number of motion-base simulators, adaptive motion filters are utilized to maximize the use of the available motion envelope of the motion system. However, not much is known about how the time-varying characteristics of such adaptive filters affect pilots when performing manual aircraft control. This paper presents the results of a study investigating the effects of time-varying motion filter gains on pilot control behavior and performance. An experiment was performed in a motion-base simulator where participants performed a simultaneous roll and pitch tracking task, while the roll and/or pitch motion filter gains changed over time. Results indicate that performance increases over time with increasing motion gains. This increase is a result of a time-varying adaptation of pilots' equalization dynamics, characterized by increased visual and motion response gains and decreased visual lead time constants. Opposite trends are found for decreasing motion filter gains. Even though the trends in both controlled axes are found to be largely the same, effects are less significant in roll. In addition, results indicate minor cross-coupling effects between pitch and roll, where a cueing variation in one axis affects the behavior adopted in the other axis.

  15. The Time-Varying Networks in P300: A Task-Evoked EEG Study.

    PubMed

    Li, Fali; Chen, Bei; Li, He; Zhang, Tao; Wang, Fei; Jiang, Yi; Li, Peiyang; Ma, Teng; Zhang, Rui; Tian, Yin; Liu, Tiejun; Guo, Daqing; Yao, Dezhong; Xu, Peng

    2016-07-01

    P300 is an important event-related potential that can be elicited by external visual, auditory, and somatosensory stimuli. Various cognition-related brain functions (i.e., attention, intelligence, and working memory) and multiple brain regions (i.e., prefrontal, frontal, and parietal) are reported to be involved in the elicitation of P300. However, these studies do not investigate the instant interactions across the neural cortices from the hierarchy of milliseconds. Importantly, time-varying network analysis among these brain regions can uncover the detailed and dynamic information processing in the corresponding cognition process. In the current study, we utilize the adaptive directed transfer function to construct the time-varying networks of P300 based on scalp electroencephalographs, investigating the time-varying information processing in P300 that can depict the deeper neural mechanism of P300 from the network. Our analysis found that different stages of P300 evoked different brain networks, i.e., the center area performs as the central source during the decision process stage, while the source region is transferred to the right prefrontal cortex (rPFC) in the neuronal response stage. Moreover, during the neuronal response stage, the directed information that flows from the rPFC to the parietal cortex are remarkably important. These findings indicate that the two brain hemispheres exhibit asymmetrical functions in processing related information for different P300 stages, and this work may provide new evidence for our better understanding of the neural mechanism of P300 generation.

  16. Inferring time-varying recharge from inverse analysis of long-term water levels

    USGS Publications Warehouse

    Dickinson, J.E.; Hanson, R.T.; Ferre, T. P. A.; Leake, S.A.

    2004-01-01

    Water levels in aquifers typically vary in response to time-varying rates of recharge, suggesting the possibility of inferring time-varying recharge rates on the basis of long-term water level records. Presumably, in the southwestern United States (Arizona, Nevada, New Mexico, southern California, and southern Utah), rates of mountain front recharge to alluvial aquifers depend on variations in precipitation rates due to known climate cycles such as the El Nin??o-Southern Oscillation index and the Pacific Decadal Oscillation. This investigation examined the inverse application of a one-dimensional analytical model for periodic flow described by Lloyd R. Townley in 1995 to estimate periodic recharge variations on the basis of variations in long-term water level records using southwest aquifers as the case study. Time-varying water level records at various locations along the flow line were obtained by simulation of forward models of synthetic basins with applied sinusoidal recharge of either a single period or composite of multiple periods of length similar to known climate cycles. Periodic water level components, reconstructed using singular spectrum analysis (SSA), were used to calibrate the analytical model to estimate each recharge component. The results demonstrated that periodic recharge estimates were most accurate in basins with nearly uniform transmissivity and the accuracy of the recharge estimates depends on monitoring well location. A case study of the San Pedro Basin, Arizona, is presented as an example of calibrating the analytical model to real data.

  17. Time-Varying Vocal Folds Vibration Detection Using a 24 GHz Portable Auditory Radar

    PubMed Central

    Hong, Hong; Zhao, Heng; Peng, Zhengyu; Li, Hui; Gu, Chen; Li, Changzhi; Zhu, Xiaohua

    2016-01-01

    Time-varying vocal folds vibration information is of crucial importance in speech processing, and the traditional devices to acquire speech signals are easily smeared by the high background noise and voice interference. In this paper, we present a non-acoustic way to capture the human vocal folds vibration using a 24-GHz portable auditory radar. Since the vocal folds vibration only reaches several millimeters, the high operating frequency and the 4 × 4 array antennas are applied to achieve the high sensitivity. The Variational Mode Decomposition (VMD) based algorithm is proposed to decompose the radar-detected auditory signal into a sequence of intrinsic modes firstly, and then, extract the time-varying vocal folds vibration frequency from the corresponding mode. Feasibility demonstration, evaluation, and comparison are conducted with tonal and non-tonal languages, and the low relative errors show a high consistency between the radar-detected auditory time-varying vocal folds vibration and acoustic fundamental frequency, except that the auditory radar significantly improves the frequency-resolving power. PMID:27483261

  18. Model selection and change detection for a time-varying mean in process monitoring

    NASA Astrophysics Data System (ADS)

    Burr, Tom; Hamada, Michael S.; Ticknor, Larry; Weaver, Brian

    2014-07-01

    Process monitoring (PM) for nuclear safeguards sometimes requires estimation of thresholds corresponding to small false alarm rates. Threshold estimation is an old topic; however, because possible new roles for PM are being evaluated in nuclear safeguards, it is timely to consider modern model selection options in the context of alarm threshold estimation. One of the possible new PM roles involves PM residuals, where a residual is defined as residual=data-prediction. This paper briefly reviews alarm threshold estimation, introduces model selection options, and considers several assumptions regarding the data-generating mechanism for PM residuals. Four PM examples from nuclear safeguards are included. One example involves frequent by-batch material balance closures where a dissolution vessel has time-varying efficiency, leading to time-varying material holdup. Another example involves periodic partial cleanout of in-process inventory, leading to challenging structure in the time series of PM residuals. Our main focus is model selection to select a defensible model for normal behavior with a time-varying mean in a PM residual stream. We use approximate Bayesian computation to perform the model selection and parameter estimation for normal behavior. We then describe a simple lag-one-differencing option similar to that used to monitor non-stationary times series to monitor for off-normal behavior.

  19. State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.

    PubMed

    Molenaar, Peter C M; Beltz, Adriene M; Gates, Kathleen M; Wilson, Stephen J

    2016-01-15

    Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. Published by Elsevier Inc.

  20. Time-Varying Vocal Folds Vibration Detection Using a 24 GHz Portable Auditory Radar.

    PubMed

    Hong, Hong; Zhao, Heng; Peng, Zhengyu; Li, Hui; Gu, Chen; Li, Changzhi; Zhu, Xiaohua

    2016-07-28

    Time-varying vocal folds vibration information is of crucial importance in speech processing, and the traditional devices to acquire speech signals are easily smeared by the high background noise and voice interference. In this paper, we present a non-acoustic way to capture the human vocal folds vibration using a 24-GHz portable auditory radar. Since the vocal folds vibration only reaches several millimeters, the high operating frequency and the 4 × 4 array antennas are applied to achieve the high sensitivity. The Variational Mode Decomposition (VMD) based algorithm is proposed to decompose the radar-detected auditory signal into a sequence of intrinsic modes firstly, and then, extract the time-varying vocal folds vibration frequency from the corresponding mode. Feasibility demonstration, evaluation, and comparison are conducted with tonal and non-tonal languages, and the low relative errors show a high consistency between the radar-detected auditory time-varying vocal folds vibration and acoustic fundamental frequency, except that the auditory radar significantly improves the frequency-resolving power.

  1. Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

    SciTech Connect

    Olama, Mohammed M; Djouadi, Seddik M; Charalambous, Prof. Charalambos

    2014-01-01

    Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

  2. Tracking control of time-varying knee exoskeleton disturbed by interaction torque.

    PubMed

    Li, Zhan; Ma, Wenhao; Yin, Ziguang; Guo, Hongliang

    2017-08-16

    Knee exoskeletons have been increasingly applied as assistive devices to help lower-extremity impaired people to make their knee joints move through providing external movement compensation. Tracking control of knee exoskeletons guided by human intentions often encounters time-varying (time-dependent) issues and the disturbance interaction torque, which may dramatically put an influence up on their dynamic behaviors. Inertial and viscous parameters of knee exoskeletons can be estimated to be time-varying due to unexpected mechanical vibrations and contact interactions. Moreover, the interaction torque produced from knee joint of wearers has an evident disturbance effect on regular motions of knee exoskeleton. All of these points can increase difficultly of accurate control of knee exoskeletons to follow desired joint angle trajectories. This paper proposes a novel control strategy for controlling knee exoskeleton with time-varying inertial and viscous coefficients disturbed by interaction torque. Such designed controller is able to make the tracking error of joint angle of knee exoskeletons exponentially converge to zero. Meanwhile, the proposed approach is robust to guarantee the tracking error bounded when the interaction torque exists. Illustrative simulation and experiment results are presented to show efficiency of the proposed controller. Additionally, comparisons with gradient dynamic (GD) approach and other methods are also presented to demonstrate efficiency and superiority of the proposed control strategy for tracking joint angle of knee exoskeleton. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Time-varying effect modeling with longitudinal data truncated by death: conditional models, interpretations, and inference.

    PubMed

    Estes, Jason P; Nguyen, Danh V; Dalrymple, Lorien S; Mu, Yi; Şentürk, Damla

    2016-05-20

    Recent studies found that infection-related hospitalization was associated with increased risk of cardiovascular (CV) events, such as myocardial infarction and stroke in the dialysis population. In this work, we develop time-varying effects modeling tools in order to examine the CV outcome risk trajectories during the time periods before and after an initial infection-related hospitalization. For this, we propose partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs) for modeling time-varying effects in longitudinal data with substantial follow-up truncation by death. Unconditional models that implicitly target an immortal population is not a relevant target of inference in applications involving a population with high mortality, like the dialysis population. A partly conditional model characterizes the outcome trajectory for the dynamic cohort of survivors, where each point in the longitudinal trajectory represents a snapshot of the population relationships among subjects who are alive at that time point. In contrast, a fully conditional approach models the time-varying effects of the population stratified by the actual time of death, where the mean response characterizes individual trends in each cohort stratum. We compare and contrast partly and fully conditional PL-GVCMs in our aforementioned application using hospitalization data from the United States Renal Data System. For inference, we develop generalized likelihood ratio tests. Simulation studies examine the efficacy of estimation and inference procedures.

  4. Estimating time-varying effects for overdispersed recurrent events data with treatment switching

    PubMed Central

    CHEN, QINGXIA; ZENG, DONGLIN; IBRAHIM, JOSEPH G.; AKACHA, MOUNA; SCHMIDLI, HEINZ

    2014-01-01

    Summary In the analysis of multivariate event times, frailty models assuming time-independent regression coefficients are often considered, mainly due to their mathematical convenience. In practice, regression coefficients are often time dependent and the temporal effects are of clinical interest. Motivated by a phase III clinical trial in multiple sclerosis, we develop a semiparametric frailty modelling approach to estimate time-varying effects for overdispersed recurrent events data with treatment switching. The proposed model incorporates the treatment switching time in the time-varying coefficients. Theoretical properties of the proposed model are established and an efficient expectation-maximization algorithm is derived to obtain the maximum likelihood estimates. Simulation studies evaluate the numerical performance of the proposed model under various temporal treatment effect curves. The ideas in this paper can also be used for time-varying coefficient frailty models without treatment switching as well as for alternative models when the proportional hazard assumption is violated. A multiple sclerosis dataset is analysed to illustrate our methodology. PMID:24465031

  5. Consensus Control of Nonlinear Multiagent Systems With Time-Varying State Constraints.

    PubMed

    Meng, Wenchao; Yang, Qinmin; Si, Jennie; Sun, Youxian

    2016-12-01

    In this paper, we present a novel adaptive consensus algorithm for a class of nonlinear multiagent systems with time-varying asymmetric state constraints. As such, our contribution is a step forward beyond the usual consensus stabilization result to show that the states of the agents remain within a user defined, time-varying bound. To prove our new results, the original multiagent system is transformed into a new one. Stabilization and consensus of transformed states are sufficient to ensure the consensus of the original networked agents without violating of the predefined asymmetric time-varying state constraints. A single neural network (NN), whose weights are tuned online, is used in our design to approximate the unknown functions in the agent's dynamics. To account for the NN approximation residual, reconstruction error, and external disturbances, a robust term is introduced into the approximating system equation. Additionally in our design, each agent only exchanges the information with its neighbor agents, and thus the proposed consensus algorithm is decentralized. The theoretical results are proved via Lyapunov synthesis. Finally, simulations are performed on a nonlinear multiagent system to illustrate the performance of our consensus design scheme.

  6. A New Time-varying Concept of Risk in a Changing Climate

    NASA Astrophysics Data System (ADS)

    Sarhadi, Ali; Ausín, María Concepción; Wiper, Michael P.

    2016-10-01

    In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments.

  7. A New Time-varying Concept of Risk in a Changing Climate

    PubMed Central

    Sarhadi, Ali; Ausín, María Concepción; Wiper, Michael P.

    2016-01-01

    In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments. PMID:27762398

  8. State Space Modeling of Time-Varying Contemporaneous and Lagged Relations in Connectivity Maps

    PubMed Central

    Molenaar, Peter C. M.; Beltz, Adriene M.; Gates, Kathleen M.; Wilson, Stephen J.

    2017-01-01

    Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. PMID:26546863

  9. Evaluation of the Time-Varying Effect of Prognostic Factors on Survival in Ovarian Cancer.

    PubMed

    Chang, Chung; Chiang, An Jen; Wang, Hui-Ching; Chen, Wei-An; Chen, Jiabin

    2015-11-01

    To explore the risk factors in ovarian cancer with respects of time-varying effects on recurrence and survival. Two hundred and ninety-eight patients with epithelial ovarian cancer in the Kaohsiung Veterans' General Hospital from January 1995 to the end of 2011 were included in the study. The assumption of the Cox proportional hazard model, i.e., the hazard ratio is a constant with time, was tested for available prognostic factors. An extended Cox model was then applied, and a statistical package was constructed to perform multivariate analysis in presence of both time-varying and time-independent factors. Most prognostic factors met the assumption of the Cox proportional hazard model (p > 0.05) except for cancer-associated antigen (CA) 125 nadir concentration during first-line chemotherapy (p = 0.02). Multivariate analysis, where CA125 nadir was allowed to change with time while other factors remained constant, showed that International Federation of Gynecology and Obstetrics (FIGO) stage, residual tumor, CA125 nadir, and age were independent risk factors for recurrence and death. The effect of CA125 nadir on recurrence and overall survival is not constant over time. It loses predictivity on recurrence and survival after 4.5 years. Awareness of the time-varying effects of the prognostic factors is beneficial to gynecologists in patient consultation and case evaluation.

  10. Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

    NASA Astrophysics Data System (ADS)

    Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

    2016-03-01

    A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

  11. A comparison of DC and time-varying measurement of electrical conductivity in randomly generated two-phase networks.

    NASA Astrophysics Data System (ADS)

    Mandolesi, Eric; Moorkamp, Max; Jones, Alan G.

    2015-04-01

    Most electromagnetic (EM) geophysical methods focus on the electrical properties of rocks and sediments to determine reliable images of the subsurface, images routinely used in a broad range of applications. Often laboratory measurements of the same EM properties return equivocal results that are difficult to reconcile with observations obtained by EM imaging techniques. These inconsistencies lead to major interpretation problems. Different numerical approaches have been investigated in order to understand the consequences of the presence or absence of interconnected networks of fractures and pores on EM field measurements. These networks have a crucial effect on the EM field measurements, given that they can be permeated by conductive fluids that enhance the conductivity measurements of the whole environment. Most of the above-mentioned studies restrict their examination to direct current (DC) sources only. Bearing in mind that the time-varying nature of the natural electromagnetic sources play a major role in field measurements, we numerically model the effects of such EM sources on the conductivity measured on the surface of a randomly generated three-dimensional body buried in a uniform conductivity host by simulating a magnetotelluric (MT) station measurements on the top of the target random host itself. As a second experiment we simulated a DC measurement of the target bulk conductivity. The spatial distribution and shape of the conductor network allows in fact the propagation of time-varying EM fields by induction, leading the two different methods to measure a different numerical value for the bulk of the same physical property. We have compared the results from the simulated measurements obtained considering time-varying and DC sources with electrical conductivity predicted by both Hashin-Shtrikman (HS) bounds and Archie's Law, and we have compared these results with statistical properties of the model themselves. Our results suggest that for time-varying

  12. Dissipative control for state-saturated discrete time-varying systems with randomly occurring nonlinearities and missing measurements

    NASA Astrophysics Data System (ADS)

    Ding, Derui; Wang, Zidong; Hu, Jun; Shu, Huisheng

    2013-04-01

    In this paper, the dissipative control problem is investigated for a class of discrete time-varying systems with simultaneous presence of state saturations, randomly occurring nonlinearities as well as multiple missing measurements. In order to render more practical significance of the system model, some Bernoulli distributed white sequences with known conditional probabilities are adopted to describe the phenomena of the randomly occurring nonlinearities and the multiple missing measurements. The purpose of the addressed problem is to design a time-varying output-feedback controller such that the dissipativity performance index is guaranteed over a given finite-horizon. By introducing a free matrix with its infinity norm less than or equal to 1, the system state is bounded by a convex hull so that some sufficient conditions can be obtained in the form of recursive nonlinear matrix inequalities. A novel controller design algorithm is then developed to deal with the recursive nonlinear matrix inequalities. Furthermore, the obtained results are extended to the case when the state saturation is partial. Two numerical simulation examples are provided to demonstrate the effectiveness and applicability of the proposed controller design approach.

  13. Failure Modes in Capacitors When Tested Under a Time-Varying Stress

    NASA Technical Reports Server (NTRS)

    Liu, David (Donhang)

    2011-01-01

    Power-on failure has been the prevalent failure mechanism for solid tantalum capacitors in decoupling applications. A surge step stress test (SSST) has been previously applied to identify the critical stress level of a capacitor batch to give some predictability to the power-on failure mechanism [1]. But SSST can also be viewed as an electrically destructive test under a time-varying stress (voltage). It consists of rapidly charging the capacitor with incremental voltage increases, through a low resistance in series, until the capacitor under test is electrically shorted. When the reliability of capacitors is evaluated, a highly accelerated life test (HALT) is usually adopted since it is a time-efficient method of determining the failure mechanism; however, a destructive test under a time-varying stress such as SSST is even more time efficient. It usually takes days or weeks to complete a HALT test, but it only takes minutes for a time-varying stress test to produce failures. The advantage of incorporating a specific time-varying stress profile into a statistical model is significant in providing an alternative life test method for quickly revealing the failure mechanism in capacitors. In this paper, a time-varying stress that mimics a typical SSST has been incorporated into the Weibull model to characterize the failure mechanism in different types of capacitors. The SSST circuit and transient conditions for correctly surge testing capacitors are discussed. Finally, the SSST was applied for testing Ta capacitors, polymer aluminum capacitors (PA capacitors), and multi-layer ceramic (MLC) capacitors with both precious metal electrodes (PME) and base metal electrodes (BME). The test results are found to be directly associated with the dielectric layer breakdown in Ta and PA capacitors and are independent of the capacitor values, the way the capacitors were built, and the capacitors manufacturers. The test results also show that MLC capacitors exhibit surge breakdown

  14. Time-varying surface electromyography topography as a prognostic tool for chronic low back pain rehabilitation.

    PubMed

    Hu, Yong; Kwok, Jerry Weilun; Tse, Jessica Yuk-Hang; Luk, Keith Dip-Kei

    2014-06-01

    Nonsurgical rehabilitation therapy is a commonly used strategy to treat chronic low back pain (LBP). The selection of the most appropriate therapeutic options is still a big challenge in clinical practices. Surface electromyography (sEMG) topography has been proposed to be an objective assessment of LBP rehabilitation. The quantitative analysis of dynamic sEMG would provide an objective tool of prognosis for LBP rehabilitation. To evaluate the prognostic value of quantitative sEMG topographic analysis and to verify the accuracy of the performance of proposed time-varying topographic parameters for identifying the patients who have better response toward the rehabilitation program. A retrospective study of consecutive patients. Thirty-eight patients with chronic nonspecific LBP and 43 healthy subjects. The accuracy of the time-varying quantitative sEMG topographic analysis for monitoring LBP rehabilitation progress was determined by calculating the corresponding receiver-operating characteristic (ROC) curves. Physiologic measure was the sEMG during lumbar flexion and extension. Patients who suffered from chronic nonspecific LBP without the history of back surgery and any medical conditions causing acute exacerbation of LBP during the clinical test were enlisted to perform the clinical test during the 12-week physiotherapy (PT) treatment. Low back pain patients were classified into two groups: "responding" and "nonresponding" based on the clinical assessment. The responding group referred to the LBP patients who began to recover after the PT treatment, whereas the nonresponding group referred to some LBP patients who did not recover or got worse after the treatment. The results of the time-varying analysis in the responding group were compared with those in the nonresponding group. In addition, the accuracy of the analysis was analyzed through ROC curves. The time-varying analysis showed discrepancies in the root-mean-square difference (RMSD) parameters between the

  15. Representations of Time-Varying Cochlear Implant Stimulation in Auditory Cortex of Awake Marmosets (Callithrix jacchus).

    PubMed

    Johnson, Luke A; Della Santina, Charles C; Wang, Xiaoqin

    2017-07-19

    Electrical stimulation of the auditory periphery organ by cochlear implant (CI) generates highly synchronized inputs to the auditory system. It has long been thought such inputs would lead to highly synchronized neural firing along the ascending auditory pathway. However, neurophysiological studies with hearing animals have shown that the central auditory system progressively converts temporal representations of time-varying sounds to firing rate-based representations. It is not clear whether this coding principle also applies to highly synchronized CI inputs. Higher-frequency modulations in CI stimulation have been found to evoke largely transient responses with little sustained firing in previous studies of the primary auditory cortex (A1) in anesthetized animals. Here, we show that, in addition to neurons displaying synchronized firing to CI stimuli, a large population of A1 neurons in awake marmosets (Callithrix jacchus) responded to rapid time-varying CI stimulation with discharges that were not synchronized to CI stimuli, yet reflected changing repetition frequency by increased firing rate. Marmosets of both sexes were included in this study. By comparing directly each neuron's responses to time-varying acoustic and CI signals, we found that individual A1 neurons encode both modalities with similar firing patterns (stimulus-synchronized or nonsynchronized). These findings suggest that A1 neurons use the same basic coding schemes to represent time-varying acoustic or CI stimulation and provide new insights into mechanisms underlying how the brain processes natural sounds via a CI device.SIGNIFICANCE STATEMENT In modern cochlear implant (CI) processors, the temporal information in speech or environmental sounds is delivered through modulated electric pulse trains. How the auditory cortex represents temporally modulated CI stimulation across multiple time scales has remained largely unclear. In this study, we compared directly neuronal responses in primary

  16. On stability of cooperative and hereditary systems with a distributed delay

    NASA Astrophysics Data System (ADS)

    Berezansky, Leonid; Braverman, Elena

    2015-06-01

    We consider a system \\frac{dx}{dt}=r_1(t) G_1(x) [ \\inth_1(t)t f_1(y(s))~ds R1 (t,s) - x(t) ] , \\frac{dy}{dt}=r_2(t) G_2(y) [ \\inth_2(t)t f_2(x(s))~ds R2 (t,s) - y(t)] with increasing functions f1 and f2, which has at most one positive equilibrium. Here the values of the functions ri, Gi, fi are positive for positive arguments, the delays in the cooperative term can be distributed and unbounded, both systems with concentrated delays and integro-differential systems are a particular case of the considered system. Analyzing the relation of the functions f1 and f2, we obtain several possible scenarios of the global behaviour. They include the cases when all nontrivial positive solutions tend to the same attractor which can be the positive equilibrium, the origin or infinity. Another possibility is the dependency of asymptotics on the initial conditions: either solutions with large enough initial values tend to the equilibrium, while others tend to zero, or solutions with small enough initial values tend to the equilibrium, while others infinitely grow. In some sense solutions of the equation are intrinsically non-oscillatory: if both initial functions are less/greater than the equilibrium value, so is the solution for any positive time value. The paper continues the study of equations with monotone production functions initiated in Berezansky and Braverman (2013 Nonlinearity 26 2833-49).

  17. Differential-phase-shift quantum-key-distribution protocol with a small number of random delays

    NASA Astrophysics Data System (ADS)

    Hatakeyama, Yuki; Mizutani, Akihiro; Kato, Go; Imoto, Nobuyuki; Tamaki, Kiyoshi

    2017-04-01

    The differential-phase-shift (DPS) quantum-key-distribution (QKD) protocol was proposed aiming at simple implementation, but it can tolerate only a small disturbance in a quantum channel. The round-robin DPS (RRDPS) protocol could be a good solution for this problem, which in fact can tolerate even up to 50 % of a bit error rate. Unfortunately, however, such a high tolerance can be achieved only when we compromise the simplicity, i.e., Bob's measurement must involve a large number of random delays (|R | denotes its number), and in a practical regime of |R | being small, the tolerance is low. In this paper, we propose a DPS protocol to achieve a higher tolerance than the one in the original DPS protocol, in which the measurement setup is less demanding than the one of the RRDPS protocol for the high tolerance regime. We call our protocol the small-number-random DPS (SNRDPS) protocol, and in this protocol, we add only a small amount of randomness to the original DPS protocol, i.e., 2 ≤|R |≤10 . In fact, we found that the performance of the SNRDPS protocol is significantly enhanced over the original DPS protocol only by employing a few additional delays such as |R |=2 . Also, we found that the key generation rate of the SNRDPS protocol outperforms the RRDPS protocol without monitoring the bit error rate when it is less than 5 % and |R |≤10 . Our protocol is an intermediate protocol between the original DPS protocol and the RRDPS protocol, and it increases the variety of the DPS-type protocols with quantified security.

  18. Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

    PubMed

    Li, Shuai; Li, Yangming

    2013-10-28

    The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

  19. Tracking time-varying cerebral autoregulation in response to changes in respiratory PaCO2.

    PubMed

    Liu, Jia; Simpson, M David; Yan, Jingyu; Allen, Robert

    2010-10-01

    Cerebral autoregulation has been studied by linear filter systems, with arterial blood pressure (ABP) as the input and cerebral blood flow velocity (CBFV-from transcranial Doppler Ultrasound) as the output. The current work extends this by using adaptive filters to investigate the dynamics of time-varying cerebral autoregulation during step-wise changes in arterial PaCO(2). Cerebral autoregulation was transiently impaired in 11 normal adult volunteers, by switching inspiratory air to a CO(2)/air mixture (5% CO(2), 30% O(2) and 65% N(2)) for approximately 2 min and then back to the ambient air, causing step-wise changes in end-tidal CO(2) (EtCO(2)). Simultaneously, ABP and CBFV were recorded continuously. Simulated data corresponding to the same protocol were also generated using an established physiological model, in order to refine the signal analysis methods. Autoregulation was quantified by the time-varying phase lead, estimated from the adaptive filter model. The adaptive filter was able to follow rapid changes in autoregulation, as was confirmed in the simulated data. In the recorded signals, there was a slow decrease in autoregulatory function following the step-wise increase in PaCO(2) (but this did not reach a steady state within approximately 2 min of recording), with a more rapid change in autoregulation on return to normocapnia. Adaptive filter modelling was thus able to demonstrate time-varying autoregulation. It was further noted that impairment and recovery of autoregulation during transient increases in EtCO(2) occur in an asymmetric manner, which should be taken into account when designing experimental protocols for the study of autoregulation.

  20. Time-varying associations between confidence and motivation to abstain from marijuana during treatment among adolescents

    PubMed Central

    Chung, Tammy; Maisto, Stephen A.

    2016-01-01

    Introduction An important goal of addictions treatment is to develop a positive association between high levels of confidence and motivation to abstain from substance use. This study modeled the time-varying association between confidence and motivation to abstain from marijuana use among youth in treatment, and the time-varying effect of pre-treatment covariates (marijuana abstinence goal and perceived peer marijuana use) on motivation to abstain. Method 150 adolescents (75% male, 83% White) in community-based intensive outpatient treatment in Pennsylvania completed a pre-treatment assessment of abstinence goal, perceived peer marijuana use, and motivation and confidence to abstain from marijuana. Ratings of motivation and confidence to abstain also were collected after each session. A Time-Varying Effect Model (TVEM) was used to characterize changes in the association between confidence and motivation to abstain (lagged), and included covariates representing pre-treatment abstinence goal and perceived peer marijuana use. Results Confidence and motivation to abstain from marijuana generally increased during treatment. The association between confidence and motivation strengthened across sessions 1-4, and was maintained through later sessions. Pre-treatment abstinence goal had an early time-limited effect (through session 6) on motivation to abstain. Pre-treatment perception of peer marijuana use had a significant effect on motivation to abstain only at session 2. Conclusions Early treatment sessions represent a critical period during which the association between confidence and motivation to abstain generally increased. The time-limited effects of pre-treatment characteristics suggest the importance of early sessions in addressing abstinence goal and peer substance use that may impact motivation to abstain from marijuana. PMID:26894550

  1. Time-varying associations between confidence and motivation to abstain from marijuana during treatment among adolescents.

    PubMed

    Chung, Tammy; Maisto, Stephen A

    2016-06-01

    An important goal of addictions treatment is to develop a positive association between high levels of confidence and motivation to abstain from substance use. This study modeled the time-varying association between confidence and motivation to abstain from marijuana use among youth in treatment, and the time-varying effect of pre-treatment covariates (marijuana abstinence goal and perceived peer marijuana use) on motivation to abstain. 150 adolescents (75% male, 83% White) in community-based intensive outpatient treatment in Pennsylvania completed a pre-treatment assessment of abstinence goal, perceived peer marijuana use, and motivation and confidence to abstain from marijuana. Ratings of motivation and confidence to abstain also were collected after each session. A time-varying effect model (TVEM) was used to characterize changes in the association between confidence and motivation to abstain (lagged), and included covariates representing pre-treatment abstinence goal and perceived peer marijuana use. Confidence and motivation to abstain from marijuana generally increased during treatment. The association between confidence and motivation strengthened across sessions 1-4, and was maintained through later sessions. Pre-treatment abstinence goal had an early time-limited effect (through session 6) on motivation to abstain. Pre-treatment perception of peer marijuana use had a significant effect on motivation to abstain only at session 2. Early treatment sessions represent a critical period during which the association between confidence and motivation to abstain generally increased. The time-limited effects of pre-treatment characteristics suggest the importance of early sessions in addressing abstinence goal and peer substance use that may impact motivation to abstain from marijuana. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Energy harvesting using parametric resonant system due to time-varying damping

    NASA Astrophysics Data System (ADS)

    Scapolan, Matteo; Tehrani, Maryam Ghandchi; Bonisoli, Elvio

    2016-10-01

    In this paper, the problem of energy harvesting is considered using an electromechanical oscillator. The energy harvester is modelled as a spring-mass-damper, in which the dissipated energy in the damper can be stored rather than wasted. Previous research provided the optimum damping parameter, to harvest maximum amount of energy, taking into account the stroke limit of the device. However, the amount of the maximum harvested energy is limited to a single frequency in which the device is tuned. Active and semi-active strategies have been suggested, which increases the performance of the harvester. Recently, nonlinear damping in the form of cubic damping has been proposed to extend the dynamic range of the harvester. In this paper, a periodic time-varying damper is introduced, which results in a parametrically excited system. When the frequency of the periodic time-varying damper is twice the excitation frequency, the system internal energy increases proportionally to the energy already stored in the system. Thus, for certain parametric damping values, the system can become unstable. This phenomenon can be exploited for energy harvesting. The transition curves, which separate the stable and unstable dynamics are derived, both analytically using harmonic balance method, and numerically using time simulations. The design of the harvester is such that its response is close to the transition curves of the Floquet diagram, leading to stable but resonant system. The performance of the parametric harvester is compared with the non-parametric one. It is demonstrated that performances and the frequency bandwidth in which the energy can be harvested can be both increased using time-varying damping.

  3. A Kalman-Filter Based Approach to Identification of Time-Varying Gene Regulatory Networks

    PubMed Central

    Xiong, Jie; Zhou, Tong

    2013-01-01

    Motivation Conventional identification methods for gene regulatory networks (GRNs) have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs. Results It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem. PMID:24116005

  4. Effects of training on time-varying spectral energy and sound pressure level in nine male classical singers.

    PubMed

    Ferguson, Sam; Kenny, Dianna T; Cabrera, Densil

    2010-01-01

    The male classical singing voice is a musical instrument that is very important in western culture. It has many acoustic features which should change and improve over the period in which the singer trains. In this study we compare nine singers in different stages of training, from university level students through to international soloists. Typically, Energy Ratio (ER; a measure of mean spectral slope) and mean sound pressure level (SPL) may be calculated to summarize an entire singing sample. We investigate an alternative approach, by calculating the time-varying ER and SPL. The inspection of the distribution of these descriptors over an aria's time period yields a more detailed picture of the strategies for high-frequency energy production used by singers with different levels of training. Copyright 2010 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

  5. Perturbation analysis of queueing systems with a time-varying arrival rate

    NASA Technical Reports Server (NTRS)

    Cassandras, Christos G.; Pan, Jie

    1991-01-01

    The authors consider an M/G/1 queuing with a time-varying arrival rate. The objective is to obtain infinitesimal perturbation analysis (IPA) gradient estimates for various performance measures of interest with respect to certain system parameters. In particular, the authors consider the mean system time over n arrivals and an arrival rate alternating between two values. By choosing a convenient sample path representation of this system, they derive an unbiased IPA gradient estimator which, however, is not consistent, and investigate the nature of this problem.

  6. Modeling of linear time-varying systems by linear time-invariant systems of lower order.

    NASA Technical Reports Server (NTRS)

    Nosrati, H.; Meadows, H. E.

    1973-01-01

    A method for modeling linear time-varying differential systems by linear time-invariant systems of lower order is proposed, extending the results obtained by Bierman (1972) by resolving such qualities as the model stability, various possible models of differing dimensions, and the uniqueness or nonuniqueness of the model coefficient matrix. In addition to the advantages cited by Heffes and Sarachik (1969) and Bierman, often by modeling a subsystem of a larger system it is possible to analyze the overall system behavior more easily, with resulting savings in computation time.

  7. Time-varying Reeb Graphs for Continuous Space-Time Data

    SciTech Connect

    Edelsbrunner, H; Harer, J; Mascarenhas, A; Pascucci, V; Snoeyink, J

    2008-04-22

    The Reeb graph is a useful tool in visualizing real-valued data obtained from computational simulations of physical processes. We characterize the evolution of the Reeb graph of a time-varying continuous function defined in three-dimensional space. We show how to maintain the Reeb graph over time and compress the entire sequence of Reeb graphs into a single, partially persistent data structure, and augment this data structure with Betti numbers to describe the topology of level sets and with path seeds to assist in the fast extraction of level sets for visualization.

  8. Control of Magnetic Bearings for Rotor Unbalance With Plug-In Time-Varying Resonators.

    PubMed

    Kang, Christopher; Tsao, Tsu-Chin

    2016-01-01

    Rotor unbalance, common phenomenon of rotational systems, manifests itself as a periodic disturbance synchronized with the rotor's angular velocity. In active magnetic bearing (AMB) systems, feedback control is required to stabilize the open-loop unstable electromagnetic levitation. Further, feedback action can be added to suppress the repeatable runout but maintain closed-loop stability. In this paper, a plug-in time-varying resonator is designed by inverting cascaded notch filters. This formulation allows flexibility in designing the internal model for appropriate disturbance rejection. The plug-in structure ensures that stability can be maintained for varying rotor speeds. Experimental results of an AMB-rotor system are presented.

  9. Time-Varying Expression of the Formation Flying along Circular Trajectories

    NASA Technical Reports Server (NTRS)

    Kawaguchi, Jun'ichiro

    2007-01-01

    Usually, the formation flying associated with circular orbits is discussed through the well-known Hill s or C-W equations of motion. This paper dares to present and discuss the coordinates that may contain time-varying coefficients. The discussion presents how the controller s performance is affected by the selection of coordinates, and also looks at the special coordinate suitable for designating a target bin to which each spacecraft in the formation has only to be guided. It is revealed that the latter strategy may incorporate the J2 disturbance automatically.

  10. 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

  11. Control of Magnetic Bearings for Rotor Unbalance With Plug-In Time-Varying Resonators

    PubMed Central

    Kang, Christopher; Tsao, Tsu-Chin

    2016-01-01

    Rotor unbalance, common phenomenon of rotational systems, manifests itself as a periodic disturbance synchronized with the rotor's angular velocity. In active magnetic bearing (AMB) systems, feedback control is required to stabilize the open-loop unstable electromagnetic levitation. Further, feedback action can be added to suppress the repeatable runout but maintain closed-loop stability. In this paper, a plug-in time-varying resonator is designed by inverting cascaded notch filters. This formulation allows flexibility in designing the internal model for appropriate disturbance rejection. The plug-in structure ensures that stability can be maintained for varying rotor speeds. Experimental results of an AMB–rotor system are presented. PMID:27222600

  12. Flow visualization of time-varying structural characteristics of dean vortices in a curved channel

    NASA Astrophysics Data System (ADS)

    Bella, David Wayne

    1988-12-01

    The time varying development and structure of Dean vortices were studied using flow visualization. Observations were made over a range of Dean numbers from 40 to 200 using a transparent channel with mild curvature, 40:1 aspect ratio, and an inner to outer radius ratio of 0.979. Seven flow visualization techniques were tried but only one, a wood burning smoke generator, produced usable results. Different vortex characteristics were observed and documented in sequences of photographs spaced one quarter of a second apart at locations ranging from 85 to 135 degrees from the start of curvature. Evidence is presented that supports the twisting/rocking nature of the flow.

  13. Indirect M-MRAC for Systems with Time Varying Parameters and Bounded Disturbances

    NASA Technical Reports Server (NTRS)

    Stepanyan, Vahram; Krishnakumar, Kalmanje

    2012-01-01

    The paper presents a prediction-identification model based adaptive control method for uncertain systems with time varying parameters in the presence of bounded external disturbances. The method guarantees desired tracking performance for the system s state and input signals. This is achieved by feeding back the state prediction error to the identification model. It is shown that the desired closed-loop properties are obtained with fast adaptation when the error feedback gain is selected proportional to the square root of the adaptation rate. The theoretical findings are confirmed via a simulation example.

  14. A method for time-varying annoyance rating of aircraft noise.

    PubMed

    Dickson, Crispin

    2009-07-01

    The method of continuous judgment by category is used and evaluated to measure time-varying attributes in aircraft flyover sounds. The results are also used to estimate preference between the different experimental sounds. Jurors were asked to rate perceived annoyance on a Borg CR 100 scale continuously during the playback of 11 flyover sequences and the results showed differences in perception in the time segment where the sound had been modified. The method can be used to evaluate maximum perceived annoyance, threshold levels, duration of perceptual presence temporal integration in perception, and perceptual mixtures over time.

  15. Carrier frequency offset estimation for OFDM systems with time-varying DC Offset

    NASA Astrophysics Data System (ADS)

    Liu, Tao; Li, Hanzhang

    2012-12-01

    Orthogonal frequency division multiplexing (OFDM) systems with direct-conversion architecture suffer from both carrier frequency offset (CFO) and dc offset (DCO). In this paper, we study CFO estimation problem for OFDM systems with time-varying DCO (TV-DCO) caused by gain mode switch of low noise amplifier (LNA). Based on linear approximation of TV-DCO, a blind algorithm is proposed for CFO estimation by means of DCO compensation and power leakage minimization. Performance of the proposed algorithm is demonstrated by simulations.

  16. A novel time varying signal processing method for Coriolis mass flowmeter.

    PubMed

    Shen, Ting-Ao; Tu, Ya-Qing; Zhang, Hai-Tao

    2014-06-01

    The precision of frequency tracking method and phase difference calculation method affects the measurement precision of Coriolis Mass Flowmeter directly. To improve the accuracy of the mass flowrate, a novel signal processing method for Coriolis Mass Flowmeter is proposed for this time varying signal, which is comprised of a modified adaptive lattice notch filter and a revised sliding recursive discrete-time Fourier transform algorithm. The method cannot only track the change of frequency continuously, but also ensure the calculation accuracy when measuring phase difference. The computational load of the proposed method is small with higher accuracy. Simulation and experiment results show that the proposed method is effective.

  17. Control of amplitude chimeras by time delay in oscillator networks

    NASA Astrophysics Data System (ADS)

    Gjurchinovski, Aleksandar; Schöll, Eckehard; Zakharova, Anna

    2017-04-01

    We investigate the influence of time-delayed coupling in a ring network of nonlocally coupled Stuart-Landau oscillators upon chimera states, i.e., space-time patterns with coexisting partially coherent and partially incoherent domains. We focus on amplitude chimeras, which exhibit incoherent behavior with respect to the amplitude rather than the phase and are transient patterns, and we show that their lifetime can be significantly enhanced by coupling delay. To characterize their transition to phase-lag synchronization (coherent traveling waves) and other coherent structures, we generalize the Kuramoto order parameter. Contrasting the results for instantaneous coupling with those for constant coupling delay, for time-varying delay, and for distributed-delay coupling, we demonstrate that the lifetime of amplitude chimera states and related partially incoherent states can be controlled, i.e., deliberately reduced or increased, depending upon the type of coupling delay.

  18. The Time-Varying Relationship between Mortality and Business Cycles in the USA.

    PubMed

    Lam, Jean-Paul; Piérard, Emmanuelle

    2017-02-01

    We examine the relationship between total mortality, deaths due to motor vehicle accidents, cardiovascular disease and measures of business cycles for the USA, using a time-varying parameter model for the periods 1961-2010. We first present a theoretical model to outline the transmission mechanism from business cycles to health status, to motivate our empirical framework and to explain why the relationship between mortality and the economy may have changed over time. We find overwhelming evidence of structural breaks in the relationship between mortality and business cycles over the sample period. Overall, the relationship between total mortality, cardiovascular mortality and the economy has become less procyclical over time and even countercyclical in recent times for certain age groups. Deaths due to motor vehicle accidents have remained strongly procyclical. Using drugs and medical patent data and data on hours worked, we argue that important advances in medical technology and changes in the effects that working hours have on health are important reasons for this time-varying relationship. Copyright © 2015 John Wiley & Sons, Ltd.

  19. Identification of time-varying structural dynamic systems - An artificial intelligence approach

    NASA Technical Reports Server (NTRS)

    Glass, B. J.; Hanagud, S.

    1992-01-01

    An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

  20. Time-varying coupling functions: Dynamical inference and cause of synchronization transitions

    NASA Astrophysics Data System (ADS)

    Stankovski, Tomislav

    2017-02-01

    Interactions in nature can be described by their coupling strength, direction of coupling, and coupling function. The coupling strength and directionality are relatively well understood and studied, at least for two interacting systems; however, there can be a complexity in the interactions uniquely dependent on the coupling functions. Such a special case is studied here: synchronization transition occurs only due to the time variability of the coupling functions, while the net coupling strength is constant throughout the observation time. To motivate the investigation, an example is used to present an analysis of cross-frequency coupling functions between delta and alpha brain waves extracted from the electroencephalography recording of a healthy human subject in a free-running resting state. The results indicate that time-varying coupling functions are a reality for biological interactions. A model of phase oscillators is used to demonstrate and detect the synchronization transition caused by the varying coupling functions during an invariant coupling strength. The ability to detect this phenomenon is discussed with the method of dynamical Bayesian inference, which was able to infer the time-varying coupling functions. The form of the coupling function acts as an additional dimension for the interactions, and it should be taken into account when detecting biological or other interactions from data.

  1. Time-varying land subsidence detected by radar altimetry: California, Taiwan and north China

    PubMed Central

    Hwang, Cheinway; Yang, Yuande; Kao, Ricky; Han, Jiancheng; Shum, C. K.; Galloway, Devin L.; Sneed, Michelle; Hung, Wei-Chia; Cheng, Yung-Sheng; Li, Fei

    2016-01-01

    Contemporary applications of radar altimetry include sea-level rise, ocean circulation, marine gravity, and icesheet elevation change. Unlike InSAR and GNSS, which are widely used to map surface deformation, altimetry is neither reliant on highly temporally-correlated ground features nor as limited by the available spatial coverage, and can provide long-term temporal subsidence monitoring capability. Here we use multi-mission radar altimetry with an approximately 23 year data-span to quantify land subsidence in cropland areas. Subsidence rates from TOPEX/POSEIDON, JASON-1, ENVISAT, and JASON-2 during 1992–2015 show time-varying trends with respect to displacement over time in California’s San Joaquin Valley and central Taiwan, possibly related to changes in land use, climatic conditions (drought) and regulatory measures affecting groundwater use. Near Hanford, California, subsidence rates reach 18 cm yr−1 with a cumulative subsidence of 206 cm, which potentially could adversely affect operations of the planned California High-Speed Rail. The maximum subsidence rate in central Taiwan is 8 cm yr−1. Radar altimetry also reveals time-varying subsidence in the North China Plain consistent with the declines of groundwater storage and existing water infrastructure detected by the Gravity Recovery And Climate Experiment (GRACE) satellites, with rates reaching 20 cm yr−1 and cumulative subsidence as much as 155 cm. PMID:27324935

  2. Time-varying coupling functions: Dynamical inference and cause of synchronization transitions.

    PubMed

    Stankovski, Tomislav

    2017-02-01

    Interactions in nature can be described by their coupling strength, direction of coupling, and coupling function. The coupling strength and directionality are relatively well understood and studied, at least for two interacting systems; however, there can be a complexity in the interactions uniquely dependent on the coupling functions. Such a special case is studied here: synchronization transition occurs only due to the time variability of the coupling functions, while the net coupling strength is constant throughout the observation time. To motivate the investigation, an example is used to present an analysis of cross-frequency coupling functions between delta and alpha brain waves extracted from the electroencephalography recording of a healthy human subject in a free-running resting state. The results indicate that time-varying coupling functions are a reality for biological interactions. A model of phase oscillators is used to demonstrate and detect the synchronization transition caused by the varying coupling functions during an invariant coupling strength. The ability to detect this phenomenon is discussed with the method of dynamical Bayesian inference, which was able to infer the time-varying coupling functions. The form of the coupling function acts as an additional dimension for the interactions, and it should be taken into account when detecting biological or other interactions from data.

  3. Physiological and Molecular Genetic Effects of Time-Varying Electromagnetic Fields on Human Neuronal Cells

    NASA Technical Reports Server (NTRS)

    Goodwin, Thomas J.

    2003-01-01

    The present investigation details the development of model systems for growing two- and three-dimensional human neural progenitor cells within a culture medium facilitated by a time-varying electromagnetic field (TVEMF). The cells and culture medium are contained within a two- or three-dimensional culture vessel, and the electromagnetic field is emitted from an electrode or coil. These studies further provide methods to promote neural tissue regeneration by means of culturing the neural cells in either configuration. Grown in two dimensions, neuronal cells extended longitudinally, forming tissue strands extending axially along and within electrodes comprising electrically conductive channels or guides through which a time-varying electrical current was conducted. In the three-dimensional aspect, exposure to TVEMF resulted in the development of three-dimensional aggregates, which emulated organized neural tissues. In both experimental configurations, the proliferation rate of the TVEMF cells was 2.5 to 4.0 times the rate of the non-waveform cells. Each of the experimental embodiments resulted in similar molecular genetic changes regarding the growth potential of the tissues as measured by gene chip analyses, which measured more than 10,000 human genes simultaneously.

  4. Adaptive neural control of nonlinear MIMO systems with time-varying output constraints.

    PubMed

    Meng, Wenchao; Yang, Qinmin; Sun, Youxian

    2015-05-01

    In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is sufficient to solve the output constraint problem. It is shown that output tracking is achieved without violation of the output constraint. More specifically, we can shape the system performance arbitrarily on transient and steady-state stages with the output evolving in predefined time-varying boundaries all the time. A single neural network, whose weights are tuned online, is used in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control coefficient matrix is avoided without assumption on the prior knowledge of control input's bound. All the signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded via Lyapunov synthesis. Finally, the merits of the proposed controller are verified in the simulation environment.

  5. First order coupled dynamic model of flexible space structures with time-varying configurations

    NASA Astrophysics Data System (ADS)

    Wang, Jie; Li, Dongxu; Jiang, Jianping

    2017-03-01

    This paper proposes a first order coupled dynamic modeling method for flexible space structures with time-varying configurations for the purpose of deriving the characteristics of the system. The model considers the first time derivative of the coordinate transformation matrix between the platform's body frame and the appendage's floating frame. As a result it can accurately predict characteristics of the system even if flexible appendages rotate with complex trajectory relative to the rigid part. In general, flexible appendages are fixed on the rigid platform or forced to rotate with a slow angular velocity. So only the zero order of the transformation matrix is considered in conventional models. However, due to neglecting of time-varying terms of the transformation matrix, these models introduce severe error when appendages, like antennas, for example, rotate with a fast speed relative to the platform. The first order coupled dynamic model for flexible space structures proposed in this paper resolve this problem by introducing the first time derivative of the transformation matrix. As a numerical example, a central core with a rotating solar panel is considered and the results are compared with those given by the conventional model. It has been shown that the first order terms are of great importance on the attitude of the rigid body and dynamic response of the flexible appendage.

  6. Time-varying land subsidence detected by radar altimetry: California, Taiwan and north China.

    PubMed

    Hwang, Cheinway; Yang, Yuande; Kao, Ricky; Han, Jiancheng; Shum, C K; Galloway, Devin L; Sneed, Michelle; Hung, Wei-Chia; Cheng, Yung-Sheng; Li, Fei

    2016-06-21

    Contemporary applications of radar altimetry include sea-level rise, ocean circulation, marine gravity, and icesheet elevation change. Unlike InSAR and GNSS, which are widely used to map surface deformation, altimetry is neither reliant on highly temporally-correlated ground features nor as limited by the available spatial coverage, and can provide long-term temporal subsidence monitoring capability. Here we use multi-mission radar altimetry with an approximately 23 year data-span to quantify land subsidence in cropland areas. Subsidence rates from TOPEX/POSEIDON, JASON-1, ENVISAT, and JASON-2 during 1992-2015 show time-varying trends with respect to displacement over time in California's San Joaquin Valley and central Taiwan, possibly related to changes in land use, climatic conditions (drought) and regulatory measures affecting groundwater use. Near Hanford, California, subsidence rates reach 18 cm yr(-1) with a cumulative subsidence of 206 cm, which potentially could adversely affect operations of the planned California High-Speed Rail. The maximum subsidence rate in central Taiwan is 8 cm yr(-1). Radar altimetry also reveals time-varying subsidence in the North China Plain consistent with the declines of groundwater storage and existing water infrastructure detected by the Gravity Recovery And Climate Experiment (GRACE) satellites, with rates reaching 20 cm yr(-1) and cumulative subsidence as much as 155 cm.

  7. Time-varying land subsidence detected by radar altimetry: California, Taiwan and north China

    NASA Astrophysics Data System (ADS)

    Hwang, Cheinway; Yang, Yuande; Kao, Ricky; Han, Jiancheng; Shum, C. K.; Galloway, Devin L.; Sneed, Michelle; Hung, Wei-Chia; Cheng, Yung-Sheng; Li, Fei

    2016-06-01

    Contemporary applications of radar altimetry include sea-level rise, ocean circulation, marine gravity, and icesheet elevation change. Unlike InSAR and GNSS, which are widely used to map surface deformation, altimetry is neither reliant on highly temporally-correlated ground features nor as limited by the available spatial coverage, and can provide long-term temporal subsidence monitoring capability. Here we use multi-mission radar altimetry with an approximately 23 year data-span to quantify land subsidence in cropland areas. Subsidence rates from TOPEX/POSEIDON, JASON-1, ENVISAT, and JASON-2 during 1992–2015 show time-varying trends with respect to displacement over time in California’s San Joaquin Valley and central Taiwan, possibly related to changes in land use, climatic conditions (drought) and regulatory measures affecting groundwater use. Near Hanford, California, subsidence rates reach 18 cm yr‑1 with a cumulative subsidence of 206 cm, which potentially could adversely affect operations of the planned California High-Speed Rail. The maximum subsidence rate in central Taiwan is 8 cm yr‑1. Radar altimetry also reveals time-varying subsidence in the North China Plain consistent with the declines of groundwater storage and existing water infrastructure detected by the Gravity Recovery And Climate Experiment (GRACE) satellites, with rates reaching 20 cm yr‑1 and cumulative subsidence as much as 155 cm.

  8. Study on the Variation of Groundwater Level under Time-varying Recharge

    NASA Astrophysics Data System (ADS)

    Wu, Ming-Chang; Hsieh, Ping-Cheng

    2017-04-01

    The slopes of the suburbs come to important areas by focusing on the work of soil and water conservation in recent years. The water table inside the aquifer is affected by rainfall, geology and topography, which will result in the change of groundwater discharge and water level. Currently, the way to obtain water table information is to set up the observation wells; however, owing to that the cost of equipment and the wells excavated is too expensive, we develop a mathematical model instead, which might help us to simulate the groundwater level variation. In this study, we will discuss the groundwater level change in a sloping unconfined aquifer with impermeable bottom under time-varying rainfall events. Referring to Child (1971), we employ the Boussinesq equation as the governing equation, and apply the General Integral Transforms Method (GITM) to analyzing the groundwater level after linearizing the Boussinesq equation. After comparing the solution with Verhoest & Troch (2000) and Bansal & Das (2010), we get satisfactory results. To sum up, we have presented an alternative approach to solve the linearized Boussinesq equation for the response of groundwater level in a sloping unconfined aquifer. The present analytical results combine the effect of bottom slope and the time-varying recharge pattern on the water table fluctuations. Owing to the limitation and difficulty of measuring the groundwater level directly, we develop such a mathematical model that we can predict or simulate the variation of groundwater level affected by any rainfall events in advance.

  9. Hamiltonian formulation of the spin-orbit model with time-varying non-conservative forces

    NASA Astrophysics Data System (ADS)

    Gkolias, Ioannis; Efthymiopoulos, Christos; Pucacco, Giuseppe; Celletti, Alessandra

    2017-10-01

    In a realistic scenario, the evolution of the rotational dynamics of a celestial or artificial body is subject to dissipative effects. Time-varying non-conservative forces can be due to, for example, a variation of the moments of inertia or to tidal interactions. In this work, we consider a simplified model describing the rotational dynamics, known as the spin-orbit problem, where we assume that the orbital motion is provided by a fixed Keplerian ellipse. We consider different examples in which a non-conservative force acts on the model and we propose an analytical method, which reduces the system to a Hamiltonian framework. In particular, we compute a time parametrisation in a series form, which allows us to transform the original system into a Hamiltonian one. We also provide applications of our method to study the rotational motion of a body with time-varying moments of inertia, e.g. an artificial satellite with flexible components, as well as subject to a tidal torque depending linearly on the velocity.

  10. Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

    2016-09-01

    For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

  11. Time-varying land subsidence detected by radar altimetry: California, Taiwan and north China

    USGS Publications Warehouse

    Hwang, Cheinway; Yang, Yuande; Kao, Ricky; Han, Jiancheng; Shum, C.K.; Galloway, Devin L.; Sneed, Michelle; Hung, Wei-Chia; Cheng, Yung-Sheng; Li, Fei

    2016-01-01

    Contemporary applications of radar altimetry include sea-level rise, ocean circulation, marine gravity, and ice sheet elevation change. Unlike InSAR and GNSS, which are widely used to map surface deformation, altimetry is neither reliant on highly temporally-correlated ground features nor as limited by the available spatial coverage, and can provide long-term temporal subsidence monitoring capability. Here we use multi-mission radar altimetry with an approximately 23 year data-span to quantify land subsidence in cropland areas. Subsidence rates from TOPEX/POSEIDON, JASON-1, ENVISAT, and JASON-2 during 1992–2015 show time-varying trends with respect to displacement over time in California’s San Joaquin Valley and central Taiwan, possibly related to changes in land use, climatic conditions (drought) and regulatory measures affecting groundwater use. Near Hanford, California, subsidence rates reach 18 cm/yr with a cumulative subsidence of 206 cm, which potentially could adversely affect operations of the planned California High-Speed Rail. The maximum subsidence rate in central Taiwan is 8 cm/yr. Radar altimetry also reveals time-varying subsidence in the North China Plain consistent with the declines of groundwater storage and existing water infrastructure detected by the Gravity Recovery And Climate Experiment (GRACE) satellites, with rates reaching 20 cm/yr and cumulative subsidence as much as 155 cm.

  12. Biomechanics of cell membrane under low-frequency time-varying magnetic field: a shell model.

    PubMed

    Ye, Hui; Curcuru, Austen

    2016-12-01

    Cell membrane deforms in the electromagnetic field, suggesting an interesting control of cellular physiology by the field. Previous research has focused on the biomechanical analysis of membrane deformation under electric fields that are generated by electrodes. An alternative, noninvasive method to generate an electric field is the use of electromagnetic induction with a time-varying magnetic field, such as that used for transcranial magnetic stimulation (TMS). Although references reporting the magnetic control of cellular mechanics have recently emerged, theoretical analysis of the membrane biomechanics under a time-varying magnetic field is inadequate. We developed a cell model that included the membrane as a low-conductive, capacitive shell and investigated the electric pressure generated on the membrane by a low-frequency magnetic field (0-200 kHz). Our results show that externally applied magnetic field induced surface charges on both sides of the membrane. The charges interacted with the induced electric field to produce a radial pressure upon the membrane. Under the low-frequency range, the radial pressure pulled the cell membrane along the axis that was defined by the magnetically induced electric field. The radial pressure was a function of the field frequency, the conductivity ratio of the cytoplasm to the medium, and the size of the cell. It is quantitatively insignificant in deforming the membrane at the frequency used in TMS, but could be significant at a relatively higher-frequency range (>100 kHz).

  13. Fast Time-Varying Volume Rendering Using Time-Space Partition (TSP) Tree

    NASA Technical Reports Server (NTRS)

    Shen, Han-Wei; Chiang, Ling-Jen; Ma, Kwan-Liu

    1999-01-01

    We present a new, algorithm for rapid rendering of time-varying volumes. A new hierarchical data structure that is capable of capturing both the temporal and the spatial coherence is proposed. Conventional hierarchical data structures such as octrees are effective in characterizing the homogeneity of the field values existing in the spatial domain. However, when treating time merely as another dimension for a time-varying field, difficulties frequently arise due to the discrepancy between the field's spatial and temporal resolutions. In addition, treating spatial and temporal dimensions equally often prevents the possibility of detecting the coherence that is unique in the temporal domain. Using the proposed data structure, our algorithm can meet the following goals. First, both spatial and temporal coherence are identified and exploited for accelerating the rendering process. Second, our algorithm allows the user to supply the desired error tolerances at run time for the purpose of image-quality/rendering-speed trade-off. Third, the amount of data that are required to be loaded into main memory is reduced, and thus the I/O overhead is minimized. This low I/O overhead makes our algorithm suitable for out-of-core applications.

  14. Application of extremum seeking for time-varying systems to resonance control of RF cavities

    SciTech Connect

    Scheinker, Alexander

    2016-09-13

    A recently developed form of extremum seeking for time-varying systems is implemented in hardware for the resonance control of radio-frequency cavities without phase measurements. Normal conducting RF cavity resonance control is performed via a slug tuner, while superconducting TESLA-type cavity resonance control is performed via piezo actuators. The controller maintains resonance by minimizing reflected power by utilizing model-independent adaptive feedback. Unlike standard phase-measurement-based resonance control, the presented approach is not sensitive to arbitrary phase shifts of the RF signals due to temperature-dependent cable length or phasemeasurement hardware changes. The phase independence of this method removes common slowly varying drifts and required periodic recalibration of phase-based methods. A general overview of the adaptive controller is presented along with the proof of principle experimental results at room temperature. Lastly, this method allows us to both maintain a cavity at a desired resonance frequency and also to dynamically modify its resonance frequency to track the unknown time-varying frequency of an RF source, thereby maintaining maximal cavity field strength, based only on power-level measurements.

  15. Time-instant sampling based encoding of time-varying acoustic spectrum

    NASA Astrophysics Data System (ADS)

    Sharma, Neeraj Kumar

    2015-12-01

    The inner ear has been shown to characterize an acoustic stimuli by transducing fluid motion in the inner ear to mechanical bending of stereocilia on the inner hair cells (IHCs). The excitation motion/energy transferred to an IHC is dependent on the frequency spectrum of the acoustic stimuli, and the spatial location of the IHC along the length of the basilar membrane (BM). Subsequently, the afferent auditory nerve fiber (ANF) bundle samples the encoded waveform in the IHCs by synapsing with them. In this work we focus on sampling of information by afferent ANFs from the IHCs, and show computationally that sampling at specific time instants is sufficient for decoding of time-varying acoustic spectrum embedded in the acoustic stimuli. The approach is based on sampling the signal at its zero-crossings and higher-order derivative zero-crossings. We show results of the approach on time-varying acoustic spectrum estimation from cricket call signal recording. The framework gives a time-domain and non-spatial processing perspective to auditory signal processing. The approach works on the full band signal, and is devoid of modeling any bandpass filtering mimicking the BM action. Instead, we motivate the approach from the perspective of event-triggered sampling by afferent ANFs on the stimuli encoded in the IHCs. Though the approach gives acoustic spectrum estimation but it is shallow on its complete understanding for plausible bio-mechanical replication with current mammalian auditory mechanics insights.

  16. Application of extremum seeking for time-varying systems to resonance control of RF cavities

    DOE PAGES

    Scheinker, Alexander

    2016-09-13

    A recently developed form of extremum seeking for time-varying systems is implemented in hardware for the resonance control of radio-frequency cavities without phase measurements. Normal conducting RF cavity resonance control is performed via a slug tuner, while superconducting TESLA-type cavity resonance control is performed via piezo actuators. The controller maintains resonance by minimizing reflected power by utilizing model-independent adaptive feedback. Unlike standard phase-measurement-based resonance control, the presented approach is not sensitive to arbitrary phase shifts of the RF signals due to temperature-dependent cable length or phasemeasurement hardware changes. The phase independence of this method removes common slowly varying drifts andmore » required periodic recalibration of phase-based methods. A general overview of the adaptive controller is presented along with the proof of principle experimental results at room temperature. Lastly, this method allows us to both maintain a cavity at a desired resonance frequency and also to dynamically modify its resonance frequency to track the unknown time-varying frequency of an RF source, thereby maintaining maximal cavity field strength, based only on power-level measurements.« less

  17. Modeling ventricular function during cardiac assist: does time-varying elastance work?

    PubMed

    Vandenberghe, Stijn; Segers, Patrick; Steendijk, Paul; Meyns, Bart; Dion, Robert A E; Antaki, James F; Verdonck, Pascal

    2006-01-01

    The time-varying elastance theory of Suga et al. is widely used to simulate left ventricular function in mathematical models and in contemporary in vitro models. We investigated the validity of this theory in the presence of a left ventricular assist device. Left ventricular pressure and volume data are presented that demonstrate the heart-device interaction for a positive-displacement pump (Novacor) and a rotary blood pump (Medos). The Novacor was implanted in a calf and used in fixed-rate mode (85 BPM), whereas the Medos was used at several flow levels (0-3 l/min) in seven healthy sheep. The Novacor data display high beat-to-beat variations in the amplitude of the elastance curve, and the normalized curves deviate strongly from the typical bovine curve. The Medos data show how the maximum elastance depends on the pump flow level. We conclude that the original time-varying elastance theory insufficiently models the complex hemodynamic behavior of a left ventricle that is mechanically assisted, and that there is need for an updated ventricular model to simulate the heart-device interaction.

  18. Non-Invasive Neuromodulation Using Time-Varying Caloric Vestibular Stimulation

    PubMed Central

    Rogers, Lesco L.; Ade, Kristen K.; Nicoletto, Heather A.; Adkins, Heather D.; Laskowitz, Daniel T.

    2016-01-01

    Caloric vestibular stimulation (CVS) to elicit the vestibulo-ocular reflex has long been used in clinical settings to aid in the diagnosis of balance disorders and to confirm the absence of brainstem function. While a number of studies have hinted at the potential therapeutic applications of CVS, the limitations of existing devices have frustrated that potential. Current CVS irrigators use water or air during short-duration applications; however, this approach is not tenable for longer duration therapeutic protocols or home use. Here, we describe a solid-state CVS device we developed in order to address these limitations. This device delivers tightly controlled time-varying thermal waveforms, which can be programmed through an external control unit. It contains several safety features, which limit patients to the prescribed waveform and prevent the potential for temperature extremes. In this paper, we provide evidence that CVS treatment with time-varying, but not constant temperature waveforms, elicits changes in cerebral blood flow physiology consistent with the neuromodulation of brainstem centers, and we present results from a small pilot study, which demonstrate that the CVS can safely and feasibly be used longitudinally in the home setting to treat episodic migraine. Together, these results indicate that this solid-state CVS device may be a viable tool for non-invasive neuromodulation. PMID:27777829

  19. Adaptive sliding control of non-autonomous active suspension systems with time-varying loadings

    NASA Astrophysics Data System (ADS)

    Chen, Po-Chang; Huang, An-Chyau

    2005-04-01

    An adaptive sliding controller is proposed in this paper for controlling a non-autonomous quarter-car suspension system with time-varying loadings. The bound of the car-body loading is assumed to be available. Then, the reference coordinate is placed at the static position under the nominal loading so that the system dynamic equation is derived. Due to spring nonlinearities, the system property becomes asymmetric after coordinate transformation. Besides, in practical cases, system parameters are not easy to be obtained precisely for controller design. Therefore, in this paper, system uncertainties are lumped into two unknown time-varying functions. Since the variation bound of one of the unknown functions is not available, conventional adaptive schemes and robust designs are not applicable. To deal with this problem, the function approximation technique is employed to represent the unknown function as a finite combination of basis functions. The Lyapunov direct method can thus be used to find adaptive laws for updating coefficients in the approximating series and to prove stability of the closed-loop system. Since the position and velocity measurements of the unsprung mass are lumped into the unknown function, there is no need to install sensors on the axle and wheel assembly in the actual implementation. Simulation results are presented to show the performance of the proposed strategy.

  20. Application of extremum seeking for time-varying systems to resonance control of RF cavities

    SciTech Connect

    Scheinker, Alexander

    2016-09-13

    A recently developed form of extremum seeking for time-varying systems is implemented in hardware for the resonance control of radio-frequency cavities without phase measurements. Normal conducting RF cavity resonance control is performed via a slug tuner, while superconducting TESLA-type cavity resonance control is performed via piezo actuators. The controller maintains resonance by minimizing reflected power by utilizing model-independent adaptive feedback. Unlike standard phase-measurement-based resonance control, the presented approach is not sensitive to arbitrary phase shifts of the RF signals due to temperature-dependent cable length or phasemeasurement hardware changes. The phase independence of this method removes common slowly varying drifts and required periodic recalibration of phase-based methods. A general overview of the adaptive controller is presented along with the proof of principle experimental results at room temperature. Lastly, this method allows us to both maintain a cavity at a desired resonance frequency and also to dynamically modify its resonance frequency to track the unknown time-varying frequency of an RF source, thereby maintaining maximal cavity field strength, based only on power-level measurements.