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.
Christoffersen, Peter; Feunoua, Bruno; Jeon, Yoontae
We estimate a continuous-time model with stochastic volatility and dynamic crash probability for the S&P 500 index and find that market illiquidity dominates other factors in explaining the stock market crash risk. While the crash probability is time-varying, its dynamic depends only weakly on re...
A time-varying magnetic flux concentrator
Kibret, B; Premaratne, M; Lewis, P M; Thomson, R; Fitzgerald, P B
2016-01-01
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. (paper)
Time-Varying Periodicity in Intraday Volatility
Andersen, Torben Gustav; Thyrsgaard, Martin; Todorov, Viktor
We develop a nonparametric test for deciding whether return volatility exhibits time-varying intraday periodicity using a long time-series of high-frequency data. Our null hypothesis, commonly adopted in work on volatility modeling, is that volatility follows a stationary process combined...... with a constant time-of-day periodic component. We first construct time-of-day volatility estimates and studentize the high-frequency returns with these periodic components. If the intraday volatility periodicity is invariant over time, then the distribution of the studentized returns should be identical across...... with estimating volatility moments through their sample counterparts. Critical values are computed via easy-to-implement simulation. In an empirical application to S&P 500 index returns, we find strong evidence for variation in the intraday volatility pattern driven in part by the current level of volatility...
Flexible time-varying filter banks
Tuncer, Temel E.; Nguyen, Truong Q.
1993-09-01
Linear phase maximally flat FIR Butterworth filter approximations are discussed and a new filter design method is introduced. This variable cutoff filter design method uses the cosine modulated versions of a prototype filter. The design procedure is simple and different variants of this procedure can be used to obtain close to optimum linear phase filters. Using this method, flexible time-varying filter banks with good reconstruction error are introduced. These types of oversampled filter banks have small magnitude error which can be easily controlled by the appropriate choice of modulation frequency. This error can be further decreased by magnitude equalization without increasing the computational complexity considerably. Two dimensional design examples are also given.
Time varying, multivariate volume data reduction
Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS
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
Modelling tourists arrival using time varying parameter
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.
TIME-VARYING DYNAMICAL STAR FORMATION RATE
Lee, Eve J.; Chang, Philip; Murray, Norman, E-mail: evelee@berkeley.edu [Canadian Institute for Theoretical Astrophysics, 60 St. George Street, University of Toronto, Toronto, ON M5S 3H8 (Canada)
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.
Scaling properties in time-varying networks with memory
Kim, Hyewon; Ha, Meesoon; Jeong, Hawoong
2015-12-01
The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.
Newtonian cosmology with a time-varying constant of gravitation
McVittie, G.C.
1978-01-01
Newtonian cosmology is based on the Eulerian equations of fluid mechanics combined with Poisson's equation modified by the introduction of a time-varying G. Spherically symmetric model universes are worked out with instantaneously uniform densities. They are indeterminate unless instantaneous uniformity of the pressure is imposed. When G varies as an inverse power of the time, the models can in some cases be shown to depend on the solution of a second-order differential equation which also occurs in the Friedmann models of general relativity. In Section 3, a method for 'passing through' a singularity of this equation is proposed which entails making four arbitrary mathematical assumptions. When G varies as (time) -1 , models with initially cycloidal motion are possible, each cycle becoming longer as time progresses. Finally, gravitation becomes so weak that the model expands to infinity. Kinetic and potential energies for the whole model are derived from the basic equations; their sum is not constant. (author)
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.
Analysis of time-varying psoriasis lesion image patterns
Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær; Nielsen, Allan Aasbjerg
2004-01-01
The multivariate alteration detection transform is applied to pairs of within and between time varying registered psoriasis image patterns. Color band contribution to the variates explaining maximal change is analyzed.......The multivariate alteration detection transform is applied to pairs of within and between time varying registered psoriasis image patterns. Color band contribution to the variates explaining maximal change is analyzed....
Multivariate time-varying volatility modeling using probabilistic fuzzy systems
Basturk, N.; Almeida, R.J.; Golan, R.; Kaymak, U.
2016-01-01
Methods to accurately analyze financial risk have drawn considerable attention in financial institutions. One difficulty in financial risk analysis is the fact that banks and other financial institutions invest in several assets which show time-varying volatilities and hence time-varying financial
Time varying voltage combustion control and diagnostics sensor
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.
Study of selected phenotype switching strategies in time varying environment
Horvath, Denis, E-mail: horvath.denis@gmail.com [Centre of Interdisciplinary Biosciences, Institute of Physics, Faculty of Science, P.J. Šafárik University in Košice, Jesenná 5, 040 01 Košice (Slovakia); Brutovsky, Branislav, E-mail: branislav.brutovsky@upjs.sk [Department of Biophysics, Institute of Physics, P.J. Šafárik University in Košice, Jesenná 5, 040 01 Košice (Slovakia)
2016-03-22
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. - Highlights: • Relation between phenotype switching and environment is studied. • The Markov chain Monte Carlo based model is developed. • Stochastic and deterministic strategies of phenotype switching are utilized. • Statistical measures of the dynamic heterogeneity reveal universal properties. • The results extend to higher lattice dimensions.
Study of selected phenotype switching strategies in time varying environment
Horvath, Denis; Brutovsky, Branislav
2016-01-01
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. - Highlights: • Relation between phenotype switching and environment is studied. • The Markov chain Monte Carlo based model is developed. • Stochastic and deterministic strategies of phenotype switching are utilized. • Statistical measures of the dynamic heterogeneity reveal universal properties. • The results extend to higher lattice dimensions.
Self-Adaptive Systems for Machine Intelligence
He, Haibo
2011-01-01
This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application
Design of 2D Time-Varying Vector Fields
Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D.; Zhang, Eugene
2012-01-01
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. © 1995-2012 IEEE.
Design of 2D time-varying vector fields.
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.
Design of 2D Time-Varying Vector Fields
Chen, Guoning
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. © 1995-2012 IEEE.
Do Time-Varying Covariances, Volatility Comovement and Spillover Matter?
Lakshmi Balasubramanyan
2005-01-01
Financial markets and their respective assets are so intertwined; analyzing any single market in isolation ignores important information. We investigate whether time varying volatility comovement and spillover impact the true variance-covariance matrix under a time-varying correlation set up. Statistically significant volatility spillover and comovement between US, UK and Japan is found. To demonstrate the importance of modelling volatility comovement and spillover, we look at a simple portfo...
Testing for time-varying loadings in dynamic factor models
Mikkelsen, Jakob Guldbæk
Abstract: In this paper we develop a test for time-varying factor loadings in factor models. The test is simple to compute and is constructed from estimated factors and residuals using the principal components estimator. The hypothesis is tested by regressing the squared residuals on the squared...... there is evidence of time-varying loadings on the risk factors underlying portfolio returns for around 80% of the portfolios....
Pemodelan Markov Switching Dengan Time-varying Transition Probability
Savitri, Anggita Puri; Warsito, Budi; Rahmawati, Rita
2016-01-01
Exchange rate or currency is an economic variable which reflects country's state of economy. It fluctuates over time because of its ability to switch the condition or regime caused by economic and political factors. The changes in the exchange rate are depreciation and appreciation. Therefore, it could be modeled using Markov Switching with Time-Varying Transition Probability which observe the conditional changes and use information variable. From this model, time-varying transition probabili...
Overcoming Spurious Regression Using time-Varying Fourier ...
Non-stationary time series data have been traditionally analyzed in the frequency domain by assuming constant amplitudes regardless of the timelag. A new approach called time-varying amplitude method (TVAM) is presented here. Oscillations are analyzed for changes in the magnitude of Fourier Coefficients which are ...
Electromagnetic radiation in a time-varying background medium
Budko, N.V.
2009-01-01
Analytical solutions are presented for the electromagnetic radiation by an arbitrary pulsed source into a homogeneous time-varying background medium. In the constant-impedance case an explicit radiation formula is obtained for the synchronous permittivity and permeability described by any positive
Mediation analysis with time varying exposures and mediators.
VanderWeele, Tyler J; Tchetgen Tchetgen, Eric J
2017-06-01
In this paper we consider causal mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation, and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are time-varying confounders affected by prior exposure and mediator, natural direct and indirect effects are not identified. However, we define a randomized interventional analogue of natural direct and indirect effects that are identified in this setting. The formula that identifies these effects we refer to as the "mediational g-formula." When there is no mediation, the mediational g-formula reduces to Robins' regular g-formula for longitudinal data. When there are no time-varying confounders affected by prior exposure and mediator values, then the mediational g-formula reduces to a longitudinal version of Pearl's mediation formula. However, the mediational g-formula itself can accommodate both mediation and time-varying confounders and constitutes a general approach to mediation analysis with time-varying exposures and mediators.
Time Varying Market Integration and Expected Rteurns in Emerging Markets
de Jong, F.C.J.M.; de Roon, F.A.
2001-01-01
We use a simple model in which the expected returns in emerging markets depend on their systematic risk as measured by their beta relative to the world portfolio as well as on the level of integration in that market.The level of integration is a time-varying variable that depends on the market value
Scattering of a TEM wave from a time varying surface
Elcrat, Alan R.; Harder, T. Mark; Stonebraker, John T.
1990-03-01
A solution is given for reflection of a plane wave with TEM polarization from a planar surface with time varying properties. These properties are given in terms of the currents on the surface. The solution is obtained by numerically solving a system of differential-delay equations in the time domain.
Time-varying correlation and common structures in volatility
Liu, Yang
2016-01-01
This thesis studies time series properties of the covariance structure of multivariate asset returns. First, the time-varying feature of correlation is investigated at the intraday level with a new correlation model incorporating the intraday correlation dynamics. Second, the thesis develops a
Contact Dynamics of EHL Contacts under Time Varying Conditions
Venner, Cornelis H.; Popovici, G.; Wijnant, Ysbrand H.; Dalmaz, G.; Lubrecht, A.A.; Priest, M
2004-01-01
By means of numerical simulations of two situations with time varying operating conditions it is shown that the dynamic behaviour of Elasto-Hydrodynamically Lubricated contacts in terms of vibrations can be characterized as: Changes in the mutual approach lead to film thickness changes in the inlet
Electricity Futures Prices : Time Varying Sensitivity to Fundamentals
S-E. Fleten (Stein-Erik); R. Huisman (Ronald); M. Kilic (Mehtap); H.P.G. Pennings (Enrico); S. Westgaard (Sjur)
2014-01-01
textabstractThis paper provides insight in the time-varying relation between electricity futures prices and fundamentals in the form of prices of contracts for fossil fuels. As supply curves are not constant and different producers have different marginal costs of production, we argue that the
Visualizing time-varying harmonics using filter banks
Duque, C.A.; Da Silveira, P.M.; Ribeiro, P.F.
2011-01-01
Although it is well known that Fourier analysis is in reality only accurately applicable to steady state waveforms, it is a widely used tool to study and monitor time-varying signals, such as are commonplace in electrical power systems. The disadvantages of Fourier analysis, such as frequency
Time-Varying Value of Energy Efficiency in Michigan
Mims, Natalie; Eckman, Tom; Schwartz, Lisa C.
2018-04-02
Quantifying the time-varying value of energy efficiency is necessary to properly account for all of its benefits and costs and to identify and implement efficiency resources that contribute to a low-cost, reliable electric system. Historically, most quantification of the benefits of efficiency has focused largely on the economic value of annual energy reduction. Due to the lack of statistically representative metered end-use load shape data in Michigan (i.e., the hourly or seasonal timing of electricity savings), the ability to confidently characterize the time-varying value of energy efficiency savings in the state, especially for weather-sensitive measures such as central air conditioning, is limited. Still, electric utilities in Michigan can take advantage of opportunities to incorporate the time-varying value of efficiency into their planning. For example, end-use load research and hourly valuation of efficiency savings can be used for a variety of electricity planning functions, including load forecasting, demand-side management and evaluation, capacity planning, long-term resource planning, renewable energy integration, assessing potential grid modernization investments, establishing rates and pricing, and customer service (KEMA 2012). In addition, accurately calculating the time-varying value of efficiency may help energy efficiency program administrators prioritize existing offerings, set incentive or rebate levels that reflect the full value of efficiency, and design new programs.
Vesicle biomechanics in a time-varying magnetic field.
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 (biomechanics under a time-varying magnetic field. Biological effects of clinical TMS are not likely to occur via alteration of the biomechanics of brain
Self-adapted thermocouple-diagnostic complex
Alekseev, S.V.; Grankovskij, K.Eh.; Olejnikov, P.P.; Prijmak, S.V.; Shikalov, V.F.
2003-01-01
A self-adapted thermocouple-diagnostic complex (STDC) for obtaining the reliable data on the coolant temperature in the reactors of NPP is described. The STDC in based on the thermal pulse monitoring of a thermocouple in the measuring channel of a reactor. Measurement method and STDC composition are substantiated. It is shown that introduction of the developed STDC ensures realization of precise and reliable temperature monitoring in the reactors of all types [ru
The Role of Item Feedback in Self-Adapted Testing.
Roos, Linda L.; And Others
1997-01-01
The importance of item feedback in self-adapted testing was studied by comparing feedback and no feedback conditions for computerized adaptive tests and self-adapted tests taken by 363 college students. Results indicate that item feedback is not necessary to realize score differences between self-adapted and computerized adaptive testing. (SLD)
Modeling polar cap F-region patches using time varying convection
Sojka, J.J.; Bowline, M.D.; Schunk, R.W.; Decker, D.T.; Valladares, C.E.; Sheehan, R.; Anderson, D.N.; Heelis, R.A.
1993-01-01
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 y component of the IMF
Time-varying properties of renal autoregulatory mechanisms
Zou, Rui; Cupples, Will A; Yip, K P
2002-01-01
In order to assess the possible time-varying properties of renal autoregulation, time-frequency and time-scaling methods were applied to renal blood flow under broad-band forced arterial blood pressure fluctuations and single-nephron renal blood flow with spontaneous oscillations obtained from...... normotensive (Sprague-Dawley, Wistar, and Long-Evans) rats, and spontaneously hypertensive rats. Time-frequency analyses of normotensive and hypertensive blood flow data obtained from either the whole kidney or the single-nephron show that indeed both the myogenic and tubuloglomerular feedback (TGF) mechanisms...... have time-varying characteristics. Furthermore, we utilized the Renyi entropy to measure the complexity of blood-flow dynamics in the time-frequency plane in an effort to discern differences between normotensive and hypertensive recordings. We found a clear difference in Renyi entropy between...
Electron dynamics in solid state via time varying wavevectors
Khaneja, Navin
2018-06-01
In this paper, we study electron wavepacket dynamics in electric and magnetic fields. We rigorously derive the semiclassical equations of electron dynamics in electric and magnetic fields. We do it both for free electron and electron in a periodic potential. We do this by introducing time varying wavevectors k(t). In the presence of magnetic field, our wavepacket reproduces the classical cyclotron orbits once the origin of the Schröedinger equation is correctly chosen to be center of cyclotron orbit. In the presence of both electric and magnetic fields, our equations for wavepacket dynamics differ from classical Lorentz force equations. We show that in a periodic potential, on application of electric field, the electron wave function adiabatically follows the wavefunction of a time varying Bloch wavevector k(t), with its energies suitably shifted with time. We derive the effective mass equation and discuss conduction in conductors and insulators.
Time varying determinants of bond flows to emerging markets
Yasemin Erduman
2016-06-01
Full Text Available This paper investigates the time varying nature of the determinants of bond flows with a focus on the global financial crisis period. We estimate a time varying regression model using Bayesian estimation methods, where the posterior distribution is approximated by Gibbs sampling algorithm. Our findings suggest that the interest rate differential is the most significant pull factor of portfolio bond flows, along with the inflation rate, while the growth rate does not play a significant role. Among the push factors, global liquidity is the most important driver of bond flows. It matters the most, when unconventional monetary easing policies were first announced; and its importance as a determinant of portfolio bond flows decreases over time, starting with the Eurozone crisis, and diminishes with the tapering talk. Global risk appetite and the risk perception towards the emerging countries also have relatively small and stable significant effects on bond flows.
Modelling Time-Varying Volatility in Financial Returns
Amado, Cristina; Laakkonen, Helinä
2014-01-01
The “unusually uncertain” phase in the global financial markets has inspired many researchers to study the effects of ambiguity (or “Knightian uncertainty”) on the decisions made by investors and their implications for the capital markets. We contribute to this literature by using a modified...... version of the time-varying GARCH model of Amado and Teräsvirta (2013) to analyze whether the increasing uncertainty has caused excess volatility in the US and European government bond markets. In our model, volatility is multiplicatively decomposed into two time-varying conditional components: the first...... being captured by a stable GARCH(1,1) process and the second driven by the level of uncertainty in the financial market....
Housing Cycles in Switzerland - A Time-Varying Approach
Drechsel, Dirk
2015-01-01
In light of the strong increase of house prices in Switzerland, we analyze the effects of mortgage rate shocks, changes in the interplay between housing demand and supply and GDP growth on house prices for the time period 1981- 2014. We employ Bayesian time-varying coefficients vector autoregressions to allow different monetary and immigration regimes over time. A number of structural changes, such as regulatory changes in the aftermath of the 1990s real estate crisis, the introduction of fre...
Modeling information diffusion in time-varying community networks
Cui, Xuelian; Zhao, Narisa
2017-12-01
Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.
Time-varying linear control for tiltrotor aircraft
Jing ZHANG
2018-04-01
Full Text Available Tiltrotor aircraft have three flight modes: helicopter mode, airplane mode, and transition mode. A tiltrotor has characteristics of highly nonlinear, time-varying flight dynamics and inertial/control couplings in its transition mode. It can transit from the helicopter mode to the airplane mode by tilting its nacelles, and an effective controller is crucial to accomplish tilting transition missions. Longitudinal dynamic characteristics of the tiltrotor are described by a nonlinear Lagrange-form model, which takes into account inertial/control couplings and aerodynamic interferences. Reference commands for airspeed velocity and attitude in the transition mode are calculated dynamically by visiting a command library which is founded in advance by analyzing the flight envelope of the tiltrotor. A Time-Varying Linear (TVL model is obtained using a Taylor-expansion based online linearization technique from the nonlinear model. Subsequently, based on an optimal control concept, an online optimization based control method with input constraints considered is proposed. To validate the proposed control method, three typical tilting transition missions are simulated using the nonlinear model of XV-15 tiltrotor aircraft. Simulation results show that the controller can be used to control the tiltrotor throughout its operating envelop which includes a transition flight, and can also deal with vertical gust disturbances. Keywords: Constrained optimal control, Inertia/control couplings, Tiltrotor aircraft, Time-varying control, Transition mode
M. de la Sen
2010-01-01
Full Text Available This paper investigates the stability properties of a class of dynamic linear systems possessing several linear time-invariant parameterizations (or configurations which conform a linear time-varying polytopic dynamic system with a finite number of time-varying time-differentiable point delays. The parameterizations may be timevarying and with bounded discontinuities and they can be subject to mixed regular plus impulsive controls within a sequence of time instants of zero measure. The polytopic parameterization for the dynamics associated with each delay is specific, so that (q+1 polytopic parameterizations are considered for a system with q delays being also subject to delay-free dynamics. The considered general dynamic system includes, as particular cases, a wide class of switched linear systems whose individual parameterizations are timeinvariant which are governed by a switching rule. However, the dynamic system under consideration is viewed as much more general since it is time-varying with timevarying delays and the bounded discontinuous changes of active parameterizations are generated by impulsive controls in the dynamics and, at the same time, there is not a prescribed set of candidate potential parameterizations.
Self-adapted sliding scale spectroscopy ADC
Xu Qichun; Wang Jingjin
1992-01-01
The traditional sliding scale technique causes a disabled range that is equal to the sliding length, thus reduces the analysis range of a MCA. A method for reduce ADC's DNL, which is called self-adapted sliding scale method, has been designed and tested. With this method, the disabled range caused by a traditional sliding scale method can be eliminated by a random trial scale and there is no need of an additional amplitude discriminator with swing threshold. A special trial-and-correct logic is presented. The tested DNL of the spectroscopy ADC described here is less than 0.5%
Parallel Monitors for Self-adaptive Sessions
Mario Coppo
2016-06-01
Full Text Available The paper presents a data-driven model of self-adaptivity for multiparty sessions. System choreography is prescribed by a global type. Participants are incarnated by processes associated with monitors, which control their behaviour. Each participant can access and modify a set of global data, which are able to trigger adaptations in the presence of critical changes of values. The use of the parallel composition for building global types, monitors and processes enables a significant degree of flexibility: an adaptation step can dynamically reconfigure a set of participants only, without altering the remaining participants, even if the two groups communicate.
Time-varying value of electric energy efficiency
Mims, Natalie A.; Eckman, Tom; Goldman, Charles
2017-06-30
Electric energy efficiency resources save energy and may reduce peak demand. Historically, quantification of energy efficiency benefits has largely focused on the economic value of energy savings during the first year and lifetime of the installed measures. Due in part to the lack of publicly available research on end-use load shapes (i.e., the hourly or seasonal timing of electricity savings) and energy savings shapes, consideration of the impact of energy efficiency on peak demand reduction (i.e., capacity savings) has been more limited. End-use load research and the hourly valuation of efficiency savings are used for a variety of electricity planning functions, including load forecasting, demand-side management and evaluation, capacity and demand response planning, long-term resource planning, renewable energy integration, assessing potential grid modernization investments, establishing rates and pricing, and customer service. This study reviews existing literature on the time-varying value of energy efficiency savings, provides examples in four geographically diverse locations of how consideration of the time-varying value of efficiency savings impacts the calculation of power system benefits, and identifies future research needs to enhance the consideration of the time-varying value of energy efficiency in cost-effectiveness screening analysis. Findings from this study include: -The time-varying value of individual energy efficiency measures varies across the locations studied because of the physical and operational characteristics of the individual utility system (e.g., summer or winter peaking, load factor, reserve margin) as well as the time periods during which savings from measures occur. -Across the four locations studied, some of the largest capacity benefits from energy efficiency are derived from the deferral of transmission and distribution system infrastructure upgrades. However, the deferred cost of such upgrades also exhibited the greatest range
Entropy Rate of Time-Varying Wireless Networks
Cika, Arta; Badiu, Mihai Alin; Coon, Justin P.
2018-01-01
In this paper, we present a detailed framework to analyze the evolution of the random topology of a time-varying wireless network via the information theoretic notion of entropy rate. We consider a propagation channel varying over time with random node positions in a closed space and Rayleigh...... fading affecting the connections between nodes. The existence of an edge between two nodes at given locations is modeled by a Markov chain, enabling memory effects in network dynamics. We then derive a lower and an upper bound on the entropy rate of the spatiotemporal network. The entropy rate measures...
Time Varying Market Integration and Expected Rteurns in Emerging Markets
Jong, F.C.J.M. de; Roon, F.A. de
2001-01-01
We use a simple model in which the expected returns in emerging markets depend on their systematic risk as measured by their beta relative to the world portfolio as well as on the level of integration in that market.The level of integration is a time-varying variable that depends on the market value of the assets that can be held by domestic investors only versus the market value of the assets that can be traded freely.Our empirical analysis for 30 emerging markets shows that there are strong...
One-dimensional radionuclide transport under time-varying conditions
Gelbard, F.; Olague, N.E.; Longsine, D.E.
1990-01-01
This paper discusses new analytical and numerical solutions presented for one-dimensional radionuclide transport under time-varying fluid-flow conditions including radioactive decay. The analytical solution assumes that all radionuclides have identical retardation factors, and is limited to instantaneous releases. The numerical solution does not have these limitations, but is tested against the limiting case given for the analytical solution. Reasonable agreement between the two solutions was found. Examples are given for the transport of a three-member radionuclide chain transported over distances and flow rates comparable to those reported for Yucca Mountain, the proposed disposal site for high-level nuclear waste
Timed arrays wideband and time varying antenna arrays
Haupt, Randy L
2015-01-01
Introduces timed arrays and design approaches to meet the new high performance standards The author concentrates on any aspect of an antenna array that must be viewed from a time perspective. The first chapters briefly introduce antenna arrays and explain the difference between phased and timed arrays. Since timed arrays are designed for realistic time-varying signals and scenarios, the book also reviews wideband signals, baseband and passband RF signals, polarization and signal bandwidth. Other topics covered include time domain, mutual coupling, wideband elements, and dispersion. The auth
Investigating Time-Varying Drivers of Grid Project Emissions Impacts
Barrett, Emily L.; Thayer, Brandon L.; Pal, Seemita; Studarus, Karen E.
2017-11-15
The emissions consequences of smart grid technologies depend heavily on their context and vary not only by geographical location, but by time of year. The same technology operated to meet the same objective may increase the emissions associated with energy generation for part of the year and decrease emissions during other times. The Grid Project Impact Quantification (GridPIQ) tool provides the ability to estimate these seasonal variations and garner insight into the time-varying drivers of grid project emissions impacts. This work leverages GridPIQ to examine the emissions implications across years and seasons of adding energy storage technology to reduce daily peak demand in California and New York.
Epidemic spreading in time-varying community networks.
Ren, Guangming; Wang, Xingyuan
2014-06-01
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 qc. The epidemic will survive when q > qc and die when q epidemic spreading in complex networks with community structure.
Stochastic analysis of epidemics on adaptive time varying networks
Kotnis, Bhushan; Kuri, Joy
2013-06-01
Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.
Flexible Demand Management under Time-Varying Prices
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
Multivariate Option Pricing with Time Varying Volatility and Correlations
Rombouts, Jeroen V.K.; Stentoft, Lars Peter
In recent years multivariate models for asset returns have received much attention, in particular this is the case for models with time varying volatility. In this paper we consider models of this class and examine their potential when it comes to option pricing. Specifically, we derive the risk...... neutral dynamics for a general class of multivariate heteroskedastic models, and we provide a feasible way to price options in this framework. Our framework can be used irrespective of the assumed underlying distribution and dynamics, and it nests several important special cases. We provide an application...... to options on the minimum of two indices. Our results show that not only is correlation important for these options but so is allowing this correlation to be dynamic. Moreover, we show that for the general model exposure to correlation risk carries an important premium, and when this is neglected option...
Time-varying Capital Requirements and Disclosure Rules
Kragh, Jonas; Rangvid, Jesper
, implying that resilience in the banking system is also increased. The increase in capital ratios is partly due to a modest reduction in lending. Using a policy changes, we show that banks react stronger to changes in capital requirements when these are public. Our results further suggest that the impact......Unique and confidential Danish data allow us to identify how changes in disclosure requirements and bank-specific time-varying capital requirements affect banks' lending and capital accumu-lation decisions. We find that banks increase their capital ratios after capital requirements are increased...... of capital requirements differ for small and large banks. Large banks raise their capital ratios more, reduce lending less, and accumulate more new capital compared to small banks....
Epidemic spreading in time-varying community networks
Ren, Guangming, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510665 (China); Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China); Wang, Xingyuan, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China)
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.
Time-varying risk aversion. An application to energy hedging
Cotter, John [Centre for Financial Markets, School of Business, University College Dublin, Blackrock, Co. Dublin (Ireland); Hanly, Jim [School of Accounting and Finance, Dublin Institute of Technology, Dublin 2 (Ireland)
2010-03-15
Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison. (author)
Endogenous time-varying risk aversion and asset returns.
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.
Time-varying risk aversion. An application to energy hedging
Cotter, John; Hanly, Jim
2010-01-01
Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison. (author)
Network Coded Cooperation Over Time-Varying Channels
Khamfroush, Hana; Roetter, Daniel Enrique Lucani; Barros, João
2014-01-01
transmissions, e.g., in terms of the rate of packet transmission or the energy consumption. A comprehensive analysis of the MDP solution is carried out under different network conditions to extract optimal rules of packet transmission. Inspired by the extracted rules, we propose two near-optimal heuristics......In this paper, we investigate the optimal design of cooperative network-coded strategies for a three-node wireless network with time-varying, half-duplex erasure channels. To this end, we formulate the problem of minimizing the total cost of transmitting M packets from source to two receivers...... as a Markov Decision Process (MDP). The actions of the MDP model include the source and the type of transmission to be used in a given time slot given perfect knowledge of the system state. The cost of packet transmission is defined such that it can incorporate the difference between broadcast and unicast...
Conditional CAPM: Time-varying Betas in the Brazilian Market
Frances Fischberg Blank
2014-10-01
Full Text Available The conditional CAPM is characterized by time-varying market beta. Based on state-space models approach, beta behavior can be modeled as a stochastic process dependent on conditioning variables related to business cycle and estimated using Kalman filter. This paper studies alternative models for portfolios sorted by size and book-to-market ratio in the Brazilian stock market and compares their adjustment to data. Asset pricing tests based on time-series and cross-sectional approaches are also implemented. A random walk process combined with conditioning variables is the preferred model, reducing pricing errors compared to unconditional CAPM, but the errors are still significant. Cross-sectional test show that book-to-market ratio becomes less relevant, but past returns still capture cross-section variation
Renormalization group theory for percolation in time-varying networks.
Karschau, Jens; Zimmerling, Marco; Friedrich, Benjamin M
2018-05-22
Motivated by multi-hop communication in unreliable wireless networks, we present a percolation theory for time-varying networks. We develop a renormalization group theory for a prototypical network on a regular grid, where individual links switch stochastically between active and inactive states. The question whether a given source node can communicate with a destination node along paths of active links is equivalent to a percolation problem. Our theory maps the temporal existence of multi-hop paths on an effective two-state Markov process. We show analytically how this Markov process converges towards a memoryless Bernoulli process as the hop distance between source and destination node increases. Our work extends classical percolation theory to the dynamic case and elucidates temporal correlations of message losses. Quantification of temporal correlations has implications for the design of wireless communication and control protocols, e.g. in cyber-physical systems such as self-organized swarms of drones or smart traffic networks.
Inferring time-varying network topologies from gene expression data.
Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas
2007-01-01
Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.
Time-varying vector fields and their flows
Jafarpour, Saber
2014-01-01
This short book provides a comprehensive and unified treatment of time-varying vector fields under a variety of regularity hypotheses, namely finitely differentiable, Lipschitz, smooth, holomorphic, and real analytic. The presentation of this material in the real analytic setting is new, as is the manner in which the various hypotheses are unified using functional analysis. Indeed, a major contribution of the book is the coherent development of locally convex topologies for the space of real analytic sections of a vector bundle, and the development of this in a manner that relates easily to classically known topologies in, for example, the finitely differentiable and smooth cases. The tools used in this development will be of use to researchers in the area of geometric functional analysis.
Parametric estimation of time varying baselines in airborne interferometric SAR
Mohr, Johan Jacob; Madsen, Søren Nørvang
1996-01-01
A method for estimation of time varying spatial baselines in airborne interferometric synthetic aperture radar (SAR) is described. The range and azimuth distortions between two images acquired with a non-linear baseline are derived. A parametric model of the baseline is then, in a least square...... sense, estimated from image shifts obtained by cross correlation of numerous small patches throughout the image. The method has been applied to airborne EMISAR imagery from the 1995 campaign over the Storstrommen Glacier in North East Greenland conducted by the Danish Center for Remote Sensing. This has...... reduced the baseline uncertainties from several meters to the centimeter level in a 36 km scene. Though developed for airborne SAR the method can easily be adopted to satellite data...
Epidemic spreading in time-varying community networks
Ren, Guangming; Wang, Xingyuan
2014-01-01
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 c . The epidemic will survive when q > q c and die when q c . These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure
Self-Adaptive Step Firefly Algorithm
Shuhao Yu
2013-01-01
Full Text Available In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.
A Novel Time-Varying Friction Compensation Method for Servomechanism
Bin Feng
2015-01-01
Full Text Available Friction is an inevitable nonlinear phenomenon existing in servomechanisms. Friction errors often affect their motion and contour accuracies during the reverse motion. To reduce friction errors, a novel time-varying friction compensation method is proposed to solve the problem that the traditional friction compensation methods hardly deal with. This problem leads to an unsatisfactory friction compensation performance and the motion and contour accuracies cannot be maintained effectively. In this method, a trapezoidal compensation pulse is adopted to compensate for the friction errors. A generalized regression neural network algorithm is used to generate the optimal pulse amplitude function. The optimal pulse duration function and the pulse amplitude function can be established by the pulse characteristic parameter learning and then the optimal friction compensation pulse can be generated. The feasibility of friction compensation method was verified on a high-precision X-Y worktable. The experimental results indicated that the motion and contour accuracies were improved greatly with reduction of the friction errors, in different working conditions. Moreover, the overall friction compensation performance indicators were decreased by more than 54% and this friction compensation method can be implemented easily on most of servomechanisms in industry.
On the Anonymity Risk of Time-Varying User Profiles
Silvia Puglisi
2017-04-01
Full Text Available Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users’ preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends’ activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.
Innovation diffusion on time-varying activity driven networks
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.
Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control
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.
Monopoly models with time-varying demand function
Cavalli, Fausto; Naimzada, Ahmad
2018-05-01
We study a family of monopoly models for markets characterized by time-varying demand functions, in which a boundedly rational agent chooses output levels on the basis of a gradient adjustment mechanism. After presenting the model for a generic framework, we analytically study the case of cyclically alternating demand functions. We show that both the perturbation size and the agent's reactivity to profitability variation signals can have counterintuitive roles on the resulting period-2 cycles and on their stability. In particular, increasing the perturbation size can have both a destabilizing and a stabilizing effect on the resulting dynamics. Moreover, in contrast with the case of time-constant demand functions, the agent's reactivity is not just destabilizing, but can improve stability, too. This means that a less cautious behavior can provide better performance, both with respect to stability and to achieved profits. We show that, even if the decision mechanism is very simple and is not able to always provide the optimal production decisions, achieved profits are very close to those optimal. Finally, we show that in agreement with the existing empirical literature, the price series obtained simulating the proposed model exhibit a significant deviation from normality and large volatility, in particular when underlying deterministic dynamics become unstable and complex.
Time-varying multiplex network: Intralayer and interlayer synchronization
Rakshit, Sarbendu; Majhi, Soumen; Bera, Bidesh K.; Sinha, Sudeshna; Ghosh, Dibakar
2017-12-01
A large class of engineered and natural systems, ranging from transportation networks to neuronal networks, are best represented by multiplex network architectures, namely a network composed of two or more different layers where the mutual interaction in each layer may differ from other layers. Here we consider a multiplex network where the intralayer coupling interactions are switched stochastically with a characteristic frequency. We explore the intralayer and interlayer synchronization of such a time-varying multiplex network. We find that the analytically derived necessary condition for intralayer and interlayer synchronization, obtained by the master stability function approach, is in excellent agreement with our numerical results. Interestingly, we clearly find that the higher frequency of switching links in the layers enhances both intralayer and interlayer synchrony, yielding larger windows of synchronization. Further, we quantify the resilience of synchronous states against random perturbations, using a global stability measure based on the concept of basin stability, and this reveals that intralayer coupling strength is most crucial for determining both intralayer and interlayer synchrony. Lastly, we investigate the robustness of interlayer synchronization against a progressive demultiplexing of the multiplex structure, and we find that for rapid switching of intralayer links, the interlayer synchronization persists even when a large number of interlayer nodes are disconnected.
Soil erosion under multiple time-varying rainfall events
Heng, B. C. Peter; Barry, D. Andrew; Jomaa, Seifeddine; Sander, Graham C.
2010-05-01
Soil erosion is a function of many factors and process interactions. An erosion event produces changes in surface soil properties such as texture and hydraulic conductivity. These changes in turn alter the erosion response to subsequent events. Laboratory-scale soil erosion studies have typically focused on single independent rainfall events with constant rainfall intensities. This study investigates the effect of multiple time-varying rainfall events on soil erosion using the EPFL erosion flume. The rainfall simulator comprises ten Veejet nozzles mounted on oscillating bars 3 m above a 6 m × 2 m flume. Spray from the nozzles is applied onto the soil surface in sweeps; rainfall intensity is thus controlled by varying the sweeping frequency. Freshly-prepared soil with a uniform slope was subjected to five rainfall events at daily intervals. In each 3-h event, rainfall intensity was ramped up linearly to a maximum of 60 mm/h and then stepped down to zero. Runoff samples were collected and analysed for particle size distribution (PSD) as well as total sediment concentration. We investigate whether there is a hysteretic relationship between sediment concentration and discharge within each event and how this relationship changes from event to event. Trends in the PSD of the eroded sediment are discussed and correlated with changes in sediment concentration. Close-up imagery of the soil surface following each event highlight changes in surface soil structure with time. This study enhances our understanding of erosion processes in the field, with corresponding implications for soil erosion modelling.
Simple Model with Time-Varying Fine-Structure ``Constant''
Berman, M. S.
2009-10-01
Extending the original version written in colaboration with L.A. Trevisan, we study the generalisation of Dirac's LNH, so that time-variation of the fine-structure constant, due to varying electrical and magnetic permittivities is included along with other variations (cosmological and gravitational ``constants''), etc. We consider the present Universe, and also an inflationary scenario. Rotation of the Universe is a given possibility in this model.
Zhang Yunong; Li Zhan
2009-01-01
In this Letter, by following Zhang et al.'s method, a recurrent neural network (termed as Zhang neural network, ZNN) is developed and analyzed for solving online the time-varying convex quadratic-programming problem subject to time-varying linear-equality constraints. Different from conventional gradient-based neural networks (GNN), such a ZNN model makes full use of the time-derivative information of time-varying coefficient. The resultant ZNN model is theoretically proved to have global exponential convergence to the time-varying theoretical optimal solution of the investigated time-varying convex quadratic program. Computer-simulation results further substantiate the effectiveness, efficiency and novelty of such ZNN model and method.
SAGE - MULTIDIMENSIONAL SELF-ADAPTIVE GRID CODE
Davies, C. B.
1994-01-01
SAGE, Self Adaptive Grid codE, is a flexible tool for adapting and restructuring both 2D and 3D grids. Solution-adaptive grid methods are useful tools for efficient and accurate flow predictions. In supersonic and hypersonic flows, strong gradient regions such as shocks, contact discontinuities, shear layers, etc., require careful distribution of grid points to minimize grid error and produce accurate flow-field predictions. SAGE helps the user obtain more accurate solutions by intelligently redistributing (i.e. adapting) the original grid points based on an initial or interim flow-field solution. The user then computes a new solution using the adapted grid as input to the flow solver. The adaptive-grid methodology poses the problem in an algebraic, unidirectional manner for multi-dimensional adaptations. The procedure is analogous to applying tension and torsion spring forces proportional to the local flow gradient at every grid point and finding the equilibrium position of the resulting system of grid points. The multi-dimensional problem of grid adaption is split into a series of one-dimensional problems along the computational coordinate lines. The reduced one dimensional problem then requires a tridiagonal solver to find the location of grid points along a coordinate line. Multi-directional adaption is achieved by the sequential application of the method in each coordinate direction. The tension forces direct the redistribution of points to the strong gradient region. To maintain smoothness and a measure of orthogonality of grid lines, torsional forces are introduced that relate information between the family of lines adjacent to one another. The smoothness and orthogonality constraints are direction-dependent, since they relate only the coordinate lines that are being adapted to the neighboring lines that have already been adapted. Therefore the solutions are non-unique and depend on the order and direction of adaption. Non-uniqueness of the adapted grid is
DySOA : Making service systems self-adaptive
Siljee, J; Bosloper, [No Value; Nijhuis, J; Hammer, D; Benatallah, B; Casati, F; Traverso, P
2005-01-01
Service-centric systems exist in a very dynamic environment. This requires these systems to adapt at runtime in order to keep fulfilling their QoS. In order to create self-adaptive service systems, developers should not only design the service architecture, but also need to design the
From dynamical systems with time-varying delay to circle maps and Koopman operators
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.
Stabilization of the Wave Equation with Boundary Time-Varying Delay
Hao Li
2014-01-01
Full Text Available We study the stabilization of the wave equation with variable coefficients in a bounded domain and a time-varying delay term in the time-varying, weakly nonlinear boundary feedbacks. By the Riemannian geometry methods and a suitable assumption of nonlinearity, we obtain the uniform decay of the energy of the closed loop system.
Identification of Time-Varying Pilot Control Behavior in Multi-Axis Control Tasks
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.
Experimental evidence for amplitude death induced by a time-varying interaction
Suresh, K. [Centre for Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu (India); Shrimali, M.D. [Department of Physics, Central University of Rajasthan, NH-8, Bandar Sindri, Ajmer 305 801 (India); Prasad, Awadhesh [Department of Physics and Astrophysics, University of Delhi, Delhi 110 007 (India); Thamilmaran, K., E-mail: maran.cnld@gmail.com [Centre for Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu (India)
2014-08-01
In this paper, we study the time-varying interaction in coupled oscillatory systems. For this purpose, we have designed a novel time-varying resistive network using an analog switch and inverter circuits. We have applied this time-varying resistive network to mutually coupled identical Chua's oscillators. When the resistances are varied in time, we find that amplitude death arises in coupled identical oscillators. This has been observed numerically as well as verified through hardware experiments. - Highlights: • We have implemented the time-varying interaction in coupled oscillatory systems. • We have designed a novel time-varying resistive network using an analog switch and inverter circuits. • When the resistances are varied in time, we find that amplitude death arises in coupled identical oscillators.
A Novel Self-Adaptive Harmony Search Algorithm
Kaiping Luo
2013-01-01
Full Text Available The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS, improved harmony search (IHS, and other self-adaptive variants for large-scale optimization problems and constrained problems.
Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism
Xu Wang
2013-01-01
Full Text Available A differential evolution (DE algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.
Shi, Lei; Yao, Bo; Zhao, Lei; Liu, Xiaotong; Yang, Min; Liu, Yanming
2018-01-01
The plasma sheath-surrounded hypersonic vehicle is a dynamic and time-varying medium and it is almost impossible to calculate time-varying physical parameters directly. The in-fight detection of the time-varying degree is important to understand the dynamic nature of the physical parameters and their effect on re-entry communication. In this paper, a constant envelope zero autocorrelation (CAZAC) sequence based on time-varying frequency detection and channel sounding method is proposed to detect the plasma sheath electronic density time-varying property and wireless channel characteristic. The proposed method utilizes the CAZAC sequence, which has excellent autocorrelation and spread gain characteristics, to realize dynamic time-varying detection/channel sounding under low signal-to-noise ratio in the plasma sheath environment. Theoretical simulation under a typical time-varying radio channel shows that the proposed method is capable of detecting time-variation frequency up to 200 kHz and can trace the channel amplitude and phase in the time domain well under -10 dB. Experimental results conducted in the RF modulation discharge plasma device verified the time variation detection ability in practical dynamic plasma sheath. Meanwhile, nonlinear phenomenon of dynamic plasma sheath on communication signal is observed thorough channel sounding result.
Synchronization of uncertain time-varying network based on sliding mode control technique
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.
Mingzhu Song
2016-01-01
Full Text Available We address the problem of globally asymptotic stability for a class of stochastic nonlinear systems with time-varying delays. By the backstepping method and Lyapunov theory, we design a linear output feedback controller recursively based on the observable linearization for a class of stochastic nonlinear systems with time-varying delays to guarantee that the closed-loop system is globally asymptotically stable in probability. In particular, we extend the deterministic nonlinear system to stochastic nonlinear systems with time-varying delays. Finally, an example and its simulations are given to illustrate the theoretical results.
Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays
Chunmei Wu
2015-01-01
Full Text Available We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.
On the synchronization of neural networks containing time-varying delays and sector nonlinearity
Yan, J.-J.; Lin, J.-S.; Hung, M.-L.; Liao, T.-L.
2007-01-01
We present a systematic design procedure for synchronization of neural networks subject to time-varying delays and sector nonlinearity in the control input. Based on the drive-response concept and the Lyapunov stability theorem, a memoryless decentralized control law is proposed which guarantees exponential synchronization even when input nonlinearity is present. The supplementary requirement that the time-derivative of time-varying delays must be smaller than one is released for the proposed control scheme. A four-dimensional Hopfield neural network with time-varying delays is presented as the illustrative example to demonstrate the effectiveness of the proposed synchronization scheme
Positive Almost Periodic Solutions for a Time-Varying Fishing Model with Delay
Xia Li
2011-01-01
Full Text Available This paper is concerned with a time-varying fishing model with delay. By means of the continuation theorem of coincidence degree theory, we prove that it has at least one positive almost periodic solution.
Long memory of financial time series and hidden Markov models with time-varying parameters
Nystrup, Peter; Madsen, Henrik; Lindström, Erik
Hidden Markov models are often used to capture stylized facts of daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior for the ability to reproduce the stylized...... facts have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time-varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared...... daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step predictions....
Identification of time-varying nonlinear systems using differential evolution algorithm
Perisic, Nevena; Green, Peter L; Worden, Keith
2013-01-01
(DE) algorithm for the identification of time-varying systems. DE is an evolutionary optimisation method developed to perform direct search in a continuous space without requiring any derivative estimation. DE is modified so that the objective function changes with time to account for the continuing......, thus identification of time-varying systems with nonlinearities can be a very challenging task. In order to avoid conventional least squares and gradient identification methods which require uni-modal and double differentiable objective functions, this work proposes a modified differential evolution...... inclusion of new data within an error metric. This paper presents results of identification of a time-varying SDOF system with Coulomb friction using simulated noise-free and noisy data for the case of time-varying friction coefficient, stiffness and damping. The obtained results are promising and the focus...
Forecast Accuracy and Economic Gains from Bayesian Model Averaging using Time Varying Weights
L.F. Hoogerheide (Lennart); R.H. Kleijn (Richard); H.K. van Dijk (Herman); M.J.C.M. Verbeek (Marno)
2009-01-01
textabstractSeveral Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time
An Explicit MOT-TD-VIE Solver for Time Varying Media
Sayed, Sadeed Bin; Ulku, Huseyin Arda; Bagci, Hakan
2016-01-01
An explicit marching on-in-time (MOT) scheme for solving the time domain electric field integral equation enforced on volumes with time varying dielectric permittivity is proposed. Unknowns of the integral equation and the constitutive relation, i
Wavelet ridge diagnosis of time-varying elliptical signals with application to an oceanic eddy
Lilly , J. M.; Gascard , Jean-Claude
2006-01-01
International audience; A method for diagnosing the physical properties of a time-varying ellipse is presented. This essentially involves extending the notion of instantaneous frequency to the bivariate case. New complications, and possibilities, arise from the fact that there are several meaningful forms in which a time-varying ellipse may be represented. A perturbation analysis valid for the near-circular case clarifies these issues. Diagnosis of the ellipse properties may then be performed...
Global stabilization of linear continuous time-varying systems with bounded controls
Phat, V.N.
2004-08-01
This paper deals with the problem of global stabilization of a class of linear continuous time-varying systems with bounded controls. Based on the controllability of the nominal system, a sufficient condition for the global stabilizability is proposed without solving any Riccati differential equation. Moreover, we give sufficient conditions for the robust stabilizability of perturbation/uncertain linear time-varying systems with bounded controls. (author)
Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays
Syed Ali, M.; Balasubramaniam, P.
2009-01-01
In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Bidirectional Associative Memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by LMI optimization algorithms to guarantee the exponential stability of uncertain BAM neural networks with time-varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.
Shu-Min Lu
2017-01-01
Full Text Available An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.
Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data.
Cinquemani, Eugenio; Laroute, Valérie; Cocaign-Bousquet, Muriel; de Jong, Hidde; Ropers, Delphine
2017-07-15
Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuties in the profiles due to sudden changes in metabolic regime. We present a method for precisely estimating time-varying uptake and excretion rates from time-series measurements of extracellular metabolite concentrations, specifically addressing all of the above issues. The estimation problem is formulated in a regularized Bayesian framework and solved by a combination of extended Kalman filtering and smoothing. The method is shown to improve upon methods based on spline smoothing of the data. Moreover, when applied to two actual datasets, the method recovers known features of overflow metabolism in Escherichia coli and Lactococcus lactis , and provides evidence for acetate uptake by L. lactis after glucose exhaustion. The results raise interesting perspectives for further work on rate estimation from measurements of intracellular metabolites. The Matlab code for the estimation method is available for download at https://team.inria.fr/ibis/rate-estimation-software/ , together with the datasets. eugenio.cinquemani@inria.fr. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Vector-field statistics for the analysis of time varying clinical gait data.
Donnelly, C J; Alexander, C; Pataky, T C; Stannage, K; Reid, S; Robinson, M A
2017-01-01
In clinical settings, the time varying analysis of gait data relies heavily on the experience of the individual(s) assessing these biological signals. Though three dimensional kinematics are recognised as time varying waveforms (1D), exploratory statistical analysis of these data are commonly carried out with multiple discrete or 0D dependent variables. In the absence of an a priori 0D hypothesis, clinicians are at risk of making type I and II errors in their analyis of time varying gait signatures in the event statistics are used in concert with prefered subjective clinical assesment methods. The aim of this communication was to determine if vector field waveform statistics were capable of providing quantitative corroboration to practically significant differences in time varying gait signatures as determined by two clinically trained gait experts. The case study was a left hemiplegic Cerebral Palsy (GMFCS I) gait patient following a botulinum toxin (BoNT-A) injection to their left gastrocnemius muscle. When comparing subjective clinical gait assessments between two testers, they were in agreement with each other for 61% of the joint degrees of freedom and phases of motion analysed. For tester 1 and tester 2, they were in agreement with the vector-field analysis for 78% and 53% of the kinematic variables analysed. When the subjective analyses of tester 1 and tester 2 were pooled together and then compared to the vector-field analysis, they were in agreement for 83% of the time varying kinematic variables analysed. These outcomes demonstrate that in principle, vector-field statistics corroborates with what a team of clinical gait experts would classify as practically meaningful pre- versus post time varying kinematic differences. The potential for vector-field statistics to be used as a useful clinical tool for the objective analysis of time varying clinical gait data is established. Future research is recommended to assess the usefulness of vector-field analyses
On Self-Adaptive Method for General Mixed Variational Inequalities
Abdellah Bnouhachem
2008-01-01
Full Text Available We suggest and analyze a new self-adaptive method for solving general mixed variational inequalities, which can be viewed as an improvement of the method of (Noor 2003. Global convergence of the new method is proved under the same assumptions as Noor's method. Some preliminary computational results are given to illustrate the efficiency of the proposed method. Since the general mixed variational inequalities include general variational inequalities, quasivariational inequalities, and nonlinear (implicit complementarity problems as special cases, results proved in this paper continue to hold for these problems.
Robustness analysis of the Zhang neural network for online time-varying quadratic optimization
Zhang Yunong; Ruan Gongqin; Li Kene; Yang Yiwen
2010-01-01
A general type of recurrent neural network (termed as Zhang neural network, ZNN) has recently been proposed by Zhang et al for the online solution of time-varying quadratic-minimization (QM) and quadratic-programming (QP) problems. Global exponential convergence of the ZNN could be achieved theoretically in an ideal error-free situation. In this paper, with the normal differentiation and dynamics-implementation errors considered, the robustness properties of the ZNN model are investigated for solving these time-varying problems. In addition, linear activation functions and power-sigmoid activation functions could be applied to such a perturbed ZNN model. Both theoretical-analysis and computer-simulation results demonstrate the good ZNN robustness and superior performance for online time-varying QM and QP problem solving, especially when using power-sigmoid activation functions.
Jia, Xingyu; Liu, Zhigang; Tao, Long; Deng, Zhongwen
2017-10-16
Frequency scanning interferometry (FSI) with a single external cavity diode laser (ECDL) and time-invariant Kalman filtering is an effective technique for measuring the distance of a dynamic target. However, due to the hysteresis of the piezoelectric ceramic transducer (PZT) actuator in the ECDL, the optical frequency sweeps of the ECDL exhibit different behaviors, depending on whether the frequency is increasing or decreasing. Consequently, the model parameters of Kalman filter appear time varying in each iteration, which produces state estimation errors with time-invariant filtering. To address this, in this paper, a time-varying Kalman filter is proposed to model the instantaneous movement of a target relative to the different optical frequency tuning durations of the ECDL. The combination of the FSI method with the time-varying Kalman filter was theoretically analyzed, and the simulation and experimental results show the proposed method greatly improves the performance of dynamic FSI measurements.
Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach
Jeyhun I. Mikayilov
2017-11-01
Full Text Available Recent literature has shown that electricity demand elasticities may not be constant over time and this has investigated using time-varying estimation methods. As accurate modeling of electricity demand is very important in Azerbaijan, which is a transitional country facing significant change in its economic outlook, we analyze whether the response of electricity demand to income and price is varying over time in this economy. We employed the Time-Varying Coefficient cointegration approach, a cutting-edge time-varying estimation method. We find evidence that income elasticity demonstrates sizeable variation for the period of investigation ranging from 0.48% to 0.56%. The study has some useful policy implications related to the income and price aspects of the electricity consumption in Azerbaijan.
Finite-time stability of neutral-type neural networks with random time-varying delays
Ali, M. Syed; Saravanan, S.; Zhu, Quanxin
2017-11-01
This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov-Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.
Some properties of zero power neutron noise in a time-varying medium with delayed neutrons
Kitamura, Y.; Pal, L.; Pazsit, I.; Yamamoto, A.; Yamane, Y.
2008-01-01
The temporal evolution of the distribution of the number of neutrons in a time-varying multiplying system, producing only prompt neutrons, was treated recently with the master equation technique by some of the present authors. Such a treatment gives account of both the so-called zero power reactor noise and the power reactor noise simultaneously. In particular, the first two moments of the neutron number, as well as the concept of criticality for time-varying systems, were investigated and discussed. The present paper extends these investigations to the case when delayed neutrons are also taken into account. Due to the complexity of the description, only the expectation of the neutron number is calculated. The concept of criticality of a time-varying system is also generalized to systems with delayed neutrons. The temporal behaviour of the expectation of the number of neutrons and its asymptotic properties are displayed and discussed
Robust stability analysis of uncertain stochastic neural networks with interval time-varying delay
Feng Wei; Yang, Simon X.; Fu Wei; Wu Haixia
2009-01-01
This paper addresses the stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. The parameter uncertainties are assumed to be norm bounded, and the delay factor is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. A sufficient condition is derived such that for all admissible uncertainties, the considered neural network is robustly, globally, asymptotically stable in the mean square. Some stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Finally, numerical examples are provided to demonstrate the usefulness of the proposed criteria.
New results on global exponential stability of recurrent neural networks with time-varying delays
Xu Shengyuan; Chu Yuming; Lu Junwei
2006-01-01
This Letter provides new sufficient conditions for the existence, uniqueness and global exponential stability of the equilibrium point of recurrent neural networks with time-varying delays by employing Lyapunov functions and using the Halanay inequality. The time-varying delays are not necessarily differentiable. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. The derived stability criteria are expressed in terms of linear matrix inequalities (LMIs), which can be checked easily by resorting to recently developed algorithms solving LMIs. Furthermore, the proposed stability results are less conservative than some previous ones in the literature, which is demonstrated via some numerical examples
H ∞ synchronization of the coronary artery system with input time-varying delay
Li Xiao-Meng; Zhao Zhan-Shan; Sun Lian-Kun; Zhang Jing
2016-01-01
This paper investigates the H ∞ synchronization of the coronary artery system with input delay and disturbance. We focus on reducing the conservatism of existing synchronization strategies. Base on the triple integral forms of the Lyapunov–Krasovskii functional (LKF), we utilize single and double integral forms of Wirtinger-based inequality to guarantee that the synchronization feedback controller has good performance against time-varying delay and external disturbance. The effectiveness of our strategy can be exhibited by simulations under the different time-varying delays and different disturbances. (paper)
Generalized Projective Synchronization between Two Complex Networks with Time-Varying Coupling Delay
Mei, Sun; Chang-Yan, Zeng; Li-Xin, Tian
2009-01-01
Generalized projective synchronization (GPS) between two complex networks with time-varying coupling delay is investigated. Based on the Lyapunov stability theory, a nonlinear controller and adaptive updated laws are designed. Feasibility of the proposed scheme is proven in theory. Moreover, two numerical examples are presented, using the energy resource system and Lü's system [Physica A 382 (2007) 672] as the nodes of the networks. GPS between two energy resource complex networks with time-varying coupling delay is achieved. This study can widen the application range of the generalized synchronization methods and will be instructive for the demand–supply of energy resource in some regions of China
Time-varying long term memory in the European Union stock markets
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.
Exponential stability of fuzzy cellular neural networks with constant and time-varying delays
Liu Yanqing; Tang Wansheng
2004-01-01
In this Letter, the global stability of delayed fuzzy cellular neural networks (FCNN) with either constant delays or time varying delays is proposed. Firstly, we give the existence and uniqueness of the equilibrium point by using the theory of topological degree and the properties of nonsingular M-matrix and the sufficient conditions for ascertaining the global exponential stability by constructing a suitable Lyapunov functional. Secondly, the criteria for guaranteeing the global exponential stability of FCNN with time varying delays are given and the estimation of exponential convergence rate with regard to speed of vary of delays is presented by constructing a suitable Lyapunov functional
Zhang Qun-Jiao; Zhao Jun-Chan
2012-01-01
This paper mainly investigates the exponential synchronization of an inner time-varying complex network with coupling delay. Firstly, the synchronization of complex networks is decoupled into the stability of the corresponding dynamical systems. Based on the Lyapunov function theory, some sufficient conditions to guarantee its stability with any given convergence rate are derived, thus the synchronization of the networks is achieved. Finally, the results are illustrated by a simple time-varying network model with a coupling delay. All involved numerical simulations verify the correctness of the theoretical analysis. (general)
New results on global exponential stability of recurrent neural networks with time-varying delays
Xu Shengyuan [Department of Automation, Nanjing University of Science and Technology, Nanjing 210094 (China)]. E-mail: syxu02@yahoo.com.cn; Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou, Zhejiang 313000 (China); Lu Junwei [School of Electrical and Automation Engineering, Nanjing Normal University, 78 Bancang Street, Nanjing, 210042 (China)
2006-04-03
This Letter provides new sufficient conditions for the existence, uniqueness and global exponential stability of the equilibrium point of recurrent neural networks with time-varying delays by employing Lyapunov functions and using the Halanay inequality. The time-varying delays are not necessarily differentiable. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. The derived stability criteria are expressed in terms of linear matrix inequalities (LMIs), which can be checked easily by resorting to recently developed algorithms solving LMIs. Furthermore, the proposed stability results are less conservative than some previous ones in the literature, which is demonstrated via some numerical examples.
Generalized Projective Synchronization between Two Complex Networks with Time-Varying Coupling Delay
Sun, Mei; Zeng, Chang-Yan; Tian, Li-Xin
2009-01-01
Generalized projective synchronization (GPS) between two complex networks with time-varying coupling delay is investigated. Based on the Lyapunov stability theory, a nonlinear controller and adaptive updated laws are designed. Feasibility of the proposed scheme is proven in theory. Moreover, two numerical examples are presented, using the energy resource system and Lü's system [Physica A 382 (2007) 672] as the nodes of the networks. GPS between two energy resource complex networks with time-varying coupling delay is achieved. This study can widen the application range of the generalized synchronization methods and will be instructive for the demand-supply of energy resource in some regions of China.
Modal Vibration Control in Periodic Time-Varying Structures with Focus on Rotor Blade Systems
Christensen, Rene Hardam; Santos, Ilmar
2004-01-01
of active modal controllers. The main aim is to reduce vibrations in periodic time-varying structures. Special emphasis is given to vibration control of coupled bladed rotor systems. A state feedback modal control law is developed based on modal analysis in periodic time-varying structures. The first step...... in the procedure is a transformation of the model into a time-invariant modal form by applying the modal matrices, which are also periodic time-variant. Due to coupled rotor and blade motions complex vibration modes occur in the modal transformed state space model. This implies that the modal transformed model...
Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters
Nystrup, Peter; Madsen, Henrik; Lindström, Erik
2016-01-01
Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior have not been thoroughly examined. This paper presents an adaptive...... to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations....
A Method of Time-Varying Rayleigh Channel Tracking in MIMO Radio System
GONG Yan-fei; HE Zi-shu; HAN Chun-lin
2005-01-01
A method of MIMO channel tracking based on Kalman filter and MMSE-DFE is proposed. The Kalman filter tracks the time-varying channel by using the MMSE-DFE decision and the MMSE-DFE conducts the next decision by using the channel estimates produced by the Kalman filter. Polynomial fitting is used to bridge the gap between the channel estimates produced by the Kalman filter and those needed for the DFE decision. Computer simulation demonstrates that this method can track the MIMO time-varying channel effectively.
Mean Square Exponential Stability of Stochastic Switched System with Interval Time-Varying Delays
Manlika Rajchakit
2012-01-01
Full Text Available This paper is concerned with mean square exponential stability of switched stochastic system with interval time-varying delays. The time delay is any continuous function belonging to a given interval, but not necessary to be differentiable. By constructing a suitable augmented Lyapunov-Krasovskii functional combined with Leibniz-Newton’s formula, a switching rule for the mean square exponential stability of switched stochastic system with interval time-varying delays and new delay-dependent sufficient conditions for the mean square exponential stability of the switched stochastic system are first established in terms of LMIs. Numerical example is given to show the effectiveness of the obtained result.
Time-Varying Biased Proportional Guidance with Seeker’s Field-of-View Limit
Yang, Zhe; Wang, Hui; Lin, Defu
2016-01-01
Traditional guidance laws with range-to-go information or time-to-go estimation may not be implemented in passive homing missiles since passive seekers cannot measure relative range directly. A time-varying biased proportional guidance law, which only uses line-of-sight (LOS) rate and look angle information, is proposed to satisfy both impact angle constraint and seeker’s field-of-view (FOV) limit. In the proposed guidance law, two time-varying bias terms are applied to divide the trajectory ...
Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays
Syed Ali, M.; Balasubramaniam, P.
2008-07-01
In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.
Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays
Syed Ali, M.; Balasubramaniam, P.
2008-01-01
In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB
Optimal critic learning for robot control in time-varying environments.
Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng
2015-10-01
In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.
Beatriz Vaz de Melo Mendes
2005-12-01
Full Text Available It is now widespread the use of Value-at-Risk (VaR as a canonical measure at risk. Most accurate VaR measures make use of some volatility model such as GARCH-type models. However, the pattern of volatility dynamic of a portfolio follows from the (univariate behavior of the risk assets, as well as from the type and strength of the associations among them. Moreover, the dependence structure among the components may change conditionally t past observations. Some papers have attempted to model this characteristic by assuming a multivariate GARCH model, or by considering the conditional correlation coefficient, or by incorporating some possibility for switches in regimes. In this paper we address this problem using time-varying copulas. Our modeling strategy allows for the margins to follow some FIGARCH type model while the copula dependence structure changes over time.
Liu Bingwen
2008-01-01
In this Letter, we consider a class of delayed cellular neural networks with time-varying coefficients. By applying Lyapunov functional method and differential inequality techniques, we establish new results to ensure that all solutions of the networks converge exponentially to zero point
Frequency variations of gravity waves interacting with a time-varying tide
Huang, C.M.; Zhang, S.D.; Yi, F.; Huang, K.M.; Gan, Q.; Gong, Y. [Wuhan Univ., Hubei (China). School of Electronic Information; Ministry of Education, Wuhan, Hubei (China). Key Lab. of Geospace Environment and Geodesy; State Observatory for Atmospheric Remote Sensing, Wuhan, Hubei (China); Zhang, Y.H. [Nanjing Univ. of Information Science and Technology (China). College of Hydrometeorolgy
2013-11-01
Using a nonlinear, 2-D time-dependent numerical model, we simulate the propagation of gravity waves (GWs) in a time-varying tide. Our simulations show that when aGW packet propagates in a time-varying tidal-wind environment, not only its intrinsic frequency but also its ground-based frequency would change significantly. The tidal horizontal-wind acceleration dominates the GW frequency variation. Positive (negative) accelerations induce frequency increases (decreases) with time. More interestingly, tidal-wind acceleration near the critical layers always causes the GW frequency to increase, which may partially explain the observations that high-frequency GW components are more dominant in the middle and upper atmosphere than in the lower atmosphere. The combination of the increased ground-based frequency of propagating GWs in a time-varying tidal-wind field and the transient nature of the critical layer induced by a time-varying tidal zonal wind creates favorable conditions for GWs to penetrate their originally expected critical layers. Consequently, GWs have an impact on the background atmosphere at much higher altitudes than expected, which indicates that the dynamical effects of tidal-GW interactions are more complicated than usually taken into account by GW parameterizations in global models.
A comparison of time-varying covariates in two smoking cessation interventions for cardiac patients
Prenger, Hendrikje Cornelia; Pieterse, Marcel E.; Braakman-Jansen, Louise Marie Antoinette; Bolman, Catherine; Ruitenbeek-Wiggers, L.; de Vries, H.
2013-01-01
The aim of the study was to explore the time-varying contribution of social cognitive determinants of smoking cessation following an intervention on cessation. Secondary analyses were performed on data from two comparable randomized controlled trials on brief smoking cessation interventions for
Chen, S.-F.
2009-01-01
The asymptotic stability problem for discrete-time systems with time-varying delay subject to saturation nonlinearities is addressed in this paper. In terms of linear matrix inequalities (LMIs), a delay-dependent sufficient condition is derived to ensure the asymptotic stability. A numerical example is given to demonstrate the theoretical results.
Delay-Dependent Guaranteed Cost Control of an Interval System with Interval Time-Varying Delay
Xiao Min
2009-01-01
Full Text Available This paper concerns the problem of the delay-dependent robust stability and guaranteed cost control for an interval system with time-varying delay. The interval system with matrix factorization is provided and leads to less conservative conclusions than solving a square root. The time-varying delay is assumed to belong to an interval and the derivative of the interval time-varying delay is not a restriction, which allows a fast time-varying delay; also its applicability is broad. Based on the Lyapunov-Ktasovskii approach, a delay-dependent criterion for the existence of a state feedback controller, which guarantees the closed-loop system stability, the upper bound of cost function, and disturbance attenuation lever for all admissible uncertainties as well as out perturbation, is proposed in terms of linear matrix inequalities (LMIs. The criterion is derived by free weighting matrices that can reduce the conservatism. The effectiveness has been verified in a number example and the compute results are presented to validate the proposed design method.
Time-varying coefficient estimation in SURE models. Application to portfolio management
Casas, Isabel; Ferreira, Eva; Orbe, Susan
This paper provides a detailed analysis of the asymptotic properties of a kernel estimator for a Seemingly Unrelated Regression Equations model with time-varying coefficients (tv-SURE) under very general conditions. Theoretical results together with a simulation study differentiates the cases...
Time-varying market integration and expected returns in emerging mrkets
de Jong, F.C.J.M.; de Roon, F.
2001-01-01
We use a simple model in which the expected returns in emerging markets depend on their systematicrisk as measured by their beta relative to the world portfolio as well as on the level ofintegration in that market. The level of integration is a time-varying variable that depends on themarket value
Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone.
Zhang, Luyan; Liang, Yi; Li, Fali; Sun, Hongbin; Peng, Wenjing; Du, Peishan; Si, Yajing; Song, Limeng; Yu, Liang; Xu, Peng
2017-01-01
The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.
Bank loan components and the time-varying effects of monetary policy shocks
den Haan, W.J.; Sumner, S.W.; Yamashiro, G.M.
2011-01-01
The impulse response function (IRF) of an aggregate variable is time-varying if the IRFs of its components are different from each other and the relative magnitudes of the components are not constant—two conditions likely to be true in practice. We model the behaviour of loan components and document
Rate Control for Network-Coded Multipath Relaying with Time-Varying Connectivity
2010-12-10
Armen Babikyan, Nathaniel M. Jones, Thomas H. Shake, and Andrew P. Worthen MIT Lincoln Laboratory 244 Wood Street Lexington, MA 02420 DDRE, 1777...delay U U U U SAR 11 Zach Sweet 781-981-5997 1 Rate Control for Network-Coded Multipath Relaying with Time-Varying Connectivity Brooke Shrader, Armen
Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior
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.
Delay-dependent exponential stability of cellular neural networks with time-varying delays
Zhang Qiang; Wei Xiaopeng; Xu Jin
2005-01-01
The global exponential stability of cellular neural networks (CNNs) with time-varying delays is analyzed. Two new sufficient conditions ensuring global exponential stability for delayed CNNs are obtained. The conditions presented here are related to the size of delay. The stability results improve the earlier publications. Two examples are given to demonstrate the effectiveness of the obtained results
Exponential stability of switched linear systems with time-varying delay
Satiracoo Pairote
2007-11-01
Full Text Available We use a Lyapunov-Krasovskii functional approach to establish the exponential stability of linear systems with time-varying delay. Our delay-dependent condition allows to compute simultaneously the two bounds that characterize the exponential stability rate of the solution. A simple procedure for constructing switching rule is also presented.
The time-varying shortest path problem with fuzzy transit costs and speedup
Rezapour Hassan
2016-08-01
Full Text Available In this paper, we focus on the time-varying shortest path problem, where the transit costs are fuzzy numbers. Moreover, we consider this problem in which the transit time can be shortened at a fuzzy speedup cost. Speedup may also be a better decision to find the shortest path from a source vertex to a specified vertex.
Andersen, P.; Skjærbæk, P. S.; Kirkegaard, Poul Henning
with the smoothed quanties which have been obtained from SARCOF. The results show the usefulness of the technique for identification of a time varying civil engineering structure. It is found that all the techniques give reliable estiates of the frequencies of the two lowest modes and the first mode shape. Only...
Lyapunov Functions to Caputo Fractional Neural Networks with Time-Varying Delays
Ravi Agarwal
2018-05-01
Full Text Available One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability and often the direct Lyapunov method is used to study stability properties (usually these Lyapunov functions do not depend on the time variable. In connection with the Lyapunov fractional method we present a brief overview of the most popular fractional order derivatives of Lyapunov functions among Caputo fractional delay differential equations. These derivatives are applied to various types of neural networks with variable coefficients and time-varying delays. We show that quadratic Lyapunov functions and their Caputo fractional derivatives are not applicable in some cases when one studies stability properties. Some sufficient conditions for stability of equilibrium of nonlinear Caputo fractional neural networks with time dependent transmission delays, time varying self-regulating parameters of all units and time varying functions of the connection between two neurons in the network are obtained. The cases of time varying Lipschitz coefficients as well as nonLipschitz activation functions are studied. We illustrate our theory on particular nonlinear Caputo fractional neural networks.
Callot, Laurent; Kristensen, Johannes Tang
the monetary policy response to inflation and business cycle fluctuations in the US by estimating a parsimoniously time varying parameter Taylor rule.We document substantial changes in the policy response of the Fed in the 1970s and 1980s, and since 2007, but also document the stability of this response...
The necessity for a time local dimension in systems with time-varying attractors
Særmark, Knud H; Ashkenazy, Y; Levitan, J
1997-01-01
We show that a simple non-linear system for ordinary differential equations may possess a time-varying attractor dimension. This indicates that it is infeasible to characterize EEG and MEG time series with a single time global dimension. We suggest another measure for the description of non...
Analysis of nonlinear systems with time varying inputs and its application to gain scheduling
J.-T. Lim
1996-01-01
Full Text Available An analytical framework for analysis of a class of nonlinear systems with time varying inputs is presented. It is shown that the trajectories of the transformed nonlinear systems are uniformly bounded with an ultimate bound under certain conditions shown in this paper. The result obtained is useful for applications, in particular, analysis and design of gain scheduling.
Pittalà, Fabio; Msallem, Majdi; Hauske, Fabian N.
2012-01-01
We propose a non-weighted feed-forward equalization method with filter update by averaging channel estimations based on short CAZAC sequences. Three averaging methods are presented and tested by simulations in a time-varying 2×2 MIMO optical system....
Global exponential stability of BAM neural networks with time-varying delays and diffusion terms
Wan Li; Zhou Qinghua
2007-01-01
The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established
Global exponential stability of BAM neural networks with time-varying delays and diffusion terms
Wan, Li; Zhou, Qinghua
2007-11-01
The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established.
Etienne, Xiaoli L.; Trujillo-Barrera, Andrés; Hoffman, Linwood A.
2017-01-01
We find distiller's dried grains with solubles (DDGS) prices to be positively correlated with both corn and soybean meal prices in the long run. However, neither corn nor soybean meal prices respond to deviations from this long-run relationship. We also identify strong time-varying dynamic
Scalable Video Streaming Adaptive to Time-Varying IEEE 802.11 MAC Parameters
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.
Overlapping quadratic optimal control of linear time-varying commutative systems
Bakule, Lubomír; Rodellar, J.; Rossell, J. M.
2002-01-01
Roč. 40, č. 5 (2002), s. 1611-1627 ISSN 0363-0129 R&D Projects: GA AV ČR IAA2075802 Institutional research plan: CEZ:AV0Z1075907 Keywords : overlapping * optimal control * linear time-varying systems Subject RIV: BC - Control Systems Theory Impact factor: 1.441, year: 2002
A new time-varying harmonic decomposition structure based on recursive hanning window
Martins, C.H.; Silva, L.R.M.; Duque, C.A.; Cerqueira, A.S.; Teixeira, E.C.; Ribeiro, P.F.
2012-01-01
Analysis of power quality phenomena under time-varying conditions has become an important subject as the complexity of the grid increases. As a consequence, several methods have been developed/applied also to study power quality parameters during transient conditions such as time-frequency methods.
Perfect fluid Bianchi Type-I cosmological models with time varying G ...
Abstract. Bianchi Type-I cosmological models containing perfect fluid with time vary- ing G and Λ have been presented. The solutions obtained represent an expansion scalar θ bearing a constant ratio to the anisotropy in the direction of space-like unit vector λi. Of the two models obtained, one has negative vacuum energy ...
Time-varying Concurrent Risk of Extreme Droughts and Heatwaves in California
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.
A Self Adaptive Differential Evolution Algorithm for Global Optimization
Kumar, Pravesh; Pant, Millie
This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.
Acoustic levitation with self-adaptive flexible reflectors.
Hong, Z Y; Xie, W J; Wei, B
2011-07-01
Two kinds of flexible reflectors are proposed and examined in this paper to improve the stability of single-axis acoustic levitator, especially in the case of levitating high-density and high-temperature samples. One kind is those with a deformable reflecting surface, and the other kind is those with an elastic support, both of which are self-adaptive to the change of acoustic radiation pressure. High-density materials such as iridium (density 22.6 gcm(-3)) are stably levitated at room temperature with a soft reflector made of colloid as well as a rigid reflector supported by a spring. In addition, the containerless melting and solidification of binary In-Bi eutectic alloy (melting point 345.8 K) and ternary Ag-Cu-Ge eutectic alloy (melting point 812 K) are successfully achieved by applying the elastically supported reflector with the assistance of a laser beam.
An estimation of U.S. gasoline demand. A smooth time-varying cointegration approach
Park, Sung Y.; Zhao, Guochang
2010-01-01
In this paper the U.S. gasoline demand from 1976 to 2008 is estimated using a time-varying cointegrating regression. We find that price elasticity increased rapidly during the late 1970s and then decreased until 1987. After a relatively small-scaled 'increase-decrease' cycle from 1987 to 2000, the price elasticity rose again after 2000. The time-varying change of the elasticities may be explained by the proportion of gasoline consumption to income and fluctuation of the degree of necessity. The result of the error correction model shows that a deviation from a long-run equilibrium is corrected quickly, and the welfare analysis illustrates there may be a gain by shifting the tax scheme from income tax to gasoline tax. (author)
H∞ Control for a Networked Control Model of Systems with Two Additive Time-Varying Delays
Hanyong Shao
2014-01-01
Full Text Available This paper is concerned with H∞ control for a networked control model of systems with two additive time-varying delays. A new Lyapunov functional is constructed to make full use of the information of the delays, and for the derivative of the Lyapunov functional a novel technique is employed to compute a tighter upper bound, which is dependent on the two time-varying delays instead of the upper bounds of them. Then the convex polyhedron method is proposed to check the upper bound of the derivative of the Lyapunov functional. The resulting stability criteria have fewer matrix variables but less conservatism than some existing ones. The stability criteria are applied to designing a state feedback controller, which guarantees that the closed-loop system is asymptotically stable with a prescribed H∞ disturbance attenuation level. Finally examples are given to show the advantages of the stability criteria and the effectiveness of the proposed control method.
Robust stabilisation of time-varying delay systems with probabilistic uncertainties
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.
Achieving Synchronization in Arrays of Coupled Differential Systems with Time-Varying Couplings
Xinlei Yi
2013-01-01
Full Text Available We study complete synchronization of the complex dynamical networks described by linearly coupled ordinary differential equation systems (LCODEs. Here, the coupling is timevarying in both network structure and reaction dynamics. Inspired by our previous paper (Lu et al. (2007-2008, the extended Hajnal diameter is introduced and used to measure the synchronization in a general differential system. Then we find that the Hajnal diameter of the linear system induced by the time-varying coupling matrix and the largest Lyapunov exponent of the synchronized system play the key roles in synchronization analysis of LCODEs with identity inner coupling matrix. As an application, we obtain a general sufficient condition guaranteeing directed time-varying graph to reach consensus. Example with numerical simulation is provided to show the effectiveness of the theoretical results.
Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure
Amado, Christina; Teräsvirta, Timo
multiplier type misspecification tests. Finite-sample properties of these procedures and tests are examined by simulation. An empirical application to daily stock returns and another one to daily exchange rate returns illustrate the functioning and properties of our modelling strategy in practice......In this paper, we propose two parametric alternatives to the standard GARCH model. They allow the conditional variance to have a smooth time-varying structure of either ad- ditive or multiplicative type. The suggested parameterizations describe both nonlinearity and structural change...... in the conditional and unconditional variances where the transition between regimes over time is smooth. A modelling strategy for these new time-varying parameter GARCH models is developed. It relies on a sequence of Lagrange multiplier tests, and the adequacy of the estimated models is investigated by Lagrange...
Estimation of time-varying reactivity by the H∞ optimal linear filter
Suzuki, Katsuo; Shimazaki, Junya; Watanabe, Koiti
1995-01-01
The problem of estimating the time-varying net reactivity from flux measurements is solved for a point reactor kinetics model using a linear filtering technique in an H ∞ settings. In order to sue this technique, an appropriate dynamical model of the reactivity is constructed that can be embedded into the reactor model as one of its variables. A filter, which minimizes the H ∞ norm of the estimation error power spectrum, operates on neutron density measurements corrupted by noise and provides an estimate of the dynamic net reactivity. Computer simulations are performed to reveal the basic characteristics of the H ∞ optimal filter. The results of the simulation indicate that the filter can be used to determine the time-varying reactivity from neutron density measurements that have been corrupted by noise
Da Sun
2016-01-01
Full Text Available A novel control algorithm based on the modified wave-variable controllers is proposed to achieve accurate position synchronization and reasonable force tracking of the nonlinear single-master-multiple-slave teleoperation system and simultaneously guarantee overall system’s stability in the presence of large time-varying delays. The system stability in different scenarios of human and environment situations has been analyzed. The proposed method is validated through experimental work based on the 3-DOF trilateral teleoperation system consisting of three different manipulators. The experimental results clearly demonstrate the feasibility of the proposed algorithm to achieve high transparency and robust stability in nonlinear single-master-multiple-slave teleoperation system in the presence of time-varying delays.
Structural nested mean models for assessing time-varying effect moderation.
Almirall, Daniel; Ten Have, Thomas; Murphy, Susan A
2010-03-01
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 two-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.
Compensating Unknown Time-Varying Delay in Opto-Electronic Platform Tracking Servo System
Ruihong Xie
2017-05-01
Full Text Available This paper investigates the problem of compensating miss-distance delay in opto-electronic platform tracking servo system. According to the characteristic of LOS (light-of-sight motion, we setup the Markovian process model and compensate this unknown time-varying delay by feed-forward forecasting controller based on robust H∞ control. Finally, simulation based on double closed-loop PI (Proportion Integration control system indicates that the proposed method is effective for compensating unknown time-varying delay. Tracking experiments on the opto-electronic platform indicate that RMS (root-mean-square error is 1.253 mrad when tracking 10° 0.2 Hz signal.
Yang Dong-Sheng; Liu Zhen-Wei; Liu Zhao-Bing; Zhao Yan
2012-01-01
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. (general)
Cheng Liu
2010-01-01
Full Text Available Time-varying coherence is a powerful tool for revealing functional dynamics between different regions in the brain. In this paper, we address ways of estimating evolutionary spectrum and coherence using the general Cohen's class distributions. We show that the intimate connection between the Cohen's class-based spectra and the evolutionary spectra defined on the locally stationary time series can be linked by the kernel functions of the Cohen's class distributions. The time-varying spectra and coherence are further generalized with the Stockwell transform, a multiscale time-frequency representation. The Stockwell measures can be studied in the framework of the Cohen's class distributions with a generalized frequency-dependent kernel function. A magnetoencephalography study using the Stockwell coherence reveals an interesting temporal interaction between contralateral and ipsilateral motor cortices under the multisource interference task.
An estimation of U.S. gasoline demand. A smooth time-varying cointegration approach
Park, Sung Y. [Department of Economics, University of Illinois, Urbana, IL 61801 (United States); The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005 (China); Zhao, Guochang [Research School of Economics, College of Business and Economics, The Australian National University, Canberra, ACT 2601 (Australia)
2010-01-15
In this paper the U.S. gasoline demand from 1976 to 2008 is estimated using a time-varying cointegrating regression. We find that price elasticity increased rapidly during the late 1970s and then decreased until 1987. After a relatively small-scaled 'increase-decrease' cycle from 1987 to 2000, the price elasticity rose again after 2000. The time-varying change of the elasticities may be explained by the proportion of gasoline consumption to income and fluctuation of the degree of necessity. The result of the error correction model shows that a deviation from a long-run equilibrium is corrected quickly, and the welfare analysis illustrates there may be a gain by shifting the tax scheme from income tax to gasoline tax. (author)
Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.
2013-09-01
This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.
Cao, Jinde; Song, Qiankun
2006-07-01
In this paper, the exponential stability problem is investigated for a class of Cohen-Grossberg-type bidirectional associative memory neural networks with time-varying delays. By using the analysis method, inequality technique and the properties of an M-matrix, several novel sufficient conditions ensuring the existence, uniqueness and global exponential stability of the equilibrium point are derived. Moreover, the exponential convergence rate is estimated. The obtained results are less restrictive than those given in the earlier literature, and the boundedness and differentiability of the activation functions and differentiability of the time-varying delays are removed. Two examples with their simulations are given to show the effectiveness of the obtained results.
Vadivel, P; Sakthivel, R; Mathiyalagan, K; Arunkumar, A
2013-01-01
This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov–Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results. (paper)
Applications, dosimetry and biological interactions of static and time-varying magnetic fields
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. 11 refs
Estimation and Properties of a Time-Varying GQARCH(1,1-M Model
Sofia Anyfantaki
2011-01-01
analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only ( computational operations, where is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.
Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling
Jian-Rui, Chen; Li-Cheng, Jiao; Jian-She, Wu; Xiao-Hua, Wang
2009-01-01
A new general network model for two complex networks with time-varying delay coupling is presented. Then we investigate its synchronization phenomena. The two complex networks of the model differ in dynamic nodes, the number of nodes and the coupling connections. By using adaptive controllers, a synchronization criterion is derived. Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion. This study may widen the application range of synchronization, such as in chaotic secure communication. (general)
Testing for Change in Mean of Independent Multivariate Observations with Time Varying Covariance
Mohamed Boutahar
2012-01-01
Full Text Available We consider a nonparametric CUSUM test for change in the mean of multivariate time series with time varying covariance. We prove that under the null, the test statistic has a Kolmogorov limiting distribution. The asymptotic consistency of the test against a large class of alternatives which contains abrupt, smooth and continuous changes is established. We also perform a simulation study to analyze the size distortion and the power of the proposed test.
Unbiasedness and time varying risk premia in the crude oil futures market
Moosa, I.A.; Al-Loughani, N.E.
1994-01-01
This paper presents some empirical evidence on market efficiency and unbiasedness in the crude oil futures market and some related issues. On the basis of monthly observations on spot and futures prices of the West Texas Intermediate (WTI) crude oil, several tests are carried out on the relevant hypotheses. The evidence suggests that futures prices are neither unbiased nor efficient forecasters of spot prices. Furthermore, a GARCH-M(1,1) model reveals the existence of a time varying risk premium. (author)
Robust convergence of Cohen-Grossberg neural networks with time-varying delays
Xiong Wenjun; Ma Deyi; Liang Jinling
2009-01-01
In this paper, robust convergence is studied for the Cohen-Grossberg neural networks (CGNNs) with time-varying delays. By applying the differential inequality and the Lyapunov method, some delay-independent conditions are derived ensuring the robust CGNNs to converge, globally, uniformly and exponentially, to a ball in the state space with a pre-specified convergence rate. Finally, the effectiveness of our results are verified by an illustrative example.
Tu Fenghua; Liao Xiaofeng
2005-01-01
We study the problem of estimating the exponential convergence rate and exponential stability for neural networks with time-varying delay. Some criteria for exponential stability are derived by using the linear matrix inequality (LMI) approach. They are less conservative than the existing ones. Some analytical methods are employed to investigate the bounds on the interconnection matrix and activation functions so that the systems are exponentially stable
Improving Delay-Range-Dependent Stability Condition for Systems with Interval Time-Varying Delay
Wei Qian
2013-01-01
Full Text Available This paper discusses the delay-range-dependent stability for systems with interval time-varying delay. Through defining the new Lyapunov-Krasovskii functional and estimating the derivative of the LKF by introducing new vectors, using free matrices and reciprocally convex approach, the new delay-range-dependent stability conditions are obtained. Two well-known examples are given to illustrate the less conservatism of the proposed theoretical results.
Delay-Dependent Asymptotic Stability of Cohen-Grossberg Models with Multiple Time-Varying Delays
Xiaofeng Liao
2007-01-01
Full Text Available Dynamical behavior of a class of Cohen-Grossberg models with multiple time-varying delays is studied in detail. Sufficient delay-dependent criteria to ensure local and global asymptotic stabilities of the equilibrium of this network are derived by constructing suitable Lyapunov functionals. The obtained conditions are shown to be less conservative and restrictive than those reported in the known literature. Some numerical examples are included to demonstrate our results.
On global exponential stability of high-order neural networks with time-varying delays
Zhang Baoyong; Xu Shengyuan; Li Yongmin; Chu Yuming
2007-01-01
This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria
On global exponential stability of high-order neural networks with time-varying delays
Zhang Baoyong [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China)]. E-mail: baoyongzhang@yahoo.com.cn; Xu Shengyuan [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China)]. E-mail: syxu02@yahoo.com.cn; Li Yongmin [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China) and Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)]. E-mail: ymlwww@163.com; Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)
2007-06-18
This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria.
Magnetohydrodynamic flow of a rarefied gas near a time-varying accelerated plate
Mishra, S.P.; Mohapatra, Priti
1975-01-01
The flow of an electrically conducting rarefied gas due to the time-varying motion of an infinite flat plate has been studied in the presence of a uniform magnetic field. The magnetic lines of force are taken to be fixed relative to the fluid. General expressions of the velocity and the skin friction have been compared by means of some qraphs and tables. (author)
Time-varying parameter models for catchments with land use change: the importance of model structure
Pathiraja, Sahani; Anghileri, Daniela; Burlando, Paolo; Sharma, Ashish; Marshall, Lucy; Moradkhani, Hamid
2018-05-01
Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.
Time-varying parameter models for catchments with land use change: the importance of model structure
S. Pathiraja
2018-05-01
Full Text Available Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2 in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.
A note on "Multicriteria adaptive paths in stochastic, time-varying networks"
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan
In a recent paper, Opasanon and Miller-Hooks study multicriteria adaptive paths in stochastic time-varying networks. They propose a label correcting algorithm for finding the full set of efficient strategies. In this note we show that their algorithm is not correct, since it is based on a property...... that does not hold in general. Opasanon and Miller-Hooks also propose an algorithm for solving a parametric problem. We give a simplified algorithm which is linear in the input size....
Liang Jinling; Cao Jinde
2003-01-01
Employing general Halanay inequality, we analyze the global exponential stability of a class of reaction-diffusion recurrent neural networks with time-varying delays. Several new sufficient conditions are obtained to ensure existence, uniqueness and global exponential stability of the equilibrium point of delayed reaction-diffusion recurrent neural networks. The results extend and improve the earlier publications. In addition, an example is given to show the effectiveness of the obtained result
Uwate, Y; Nishio, Y; Stoop, R
2009-01-01
We explore the synchronization and switching behavior of a system of two identical van der Pol oscillators coupled by a stochastically timevarying resistor. Triggered by the time-varying resistor, the system of oscillators switches between synchronized and anti-synchronized behavior. We find that the preference of the synchronized/antisynchronized state is determined by the ratio of the probabilities of the two resistor states. The length of the phases of maintained resistor states, however, ...
Reliable Memory Feedback Design for a Class of Nonlinear Fuzzy Systems with Time-varying Delay
You-Qing Wang; Dong-Hua Zhou; Li-Heng Liu
2007-01-01
This paper is concerned with the robust reliable memory controller design for a class of fuzzy uncertain systems with time-varying delay. The system under consideration is more general than those in other existent works. The controller, which is dependent on the magnitudes and derivative of the delay, is proposed in terms of linear matrix inequality (LMI). The closed-loop system is asymptotically stable for all admissible uncertainties as well as actuator faults. A numerical example is presented for illustration.
Park, Ju H.; Lee, S.M.; Kwon, O.M.
2009-01-01
For bidirectional associate memory neural networks with time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the Lyapunov functional method and linear matrix inequality (LMI) technique. A novel criterion for the stability, which give information on the delay-dependent property, is derived. A numerical example is given to demonstrate the effectiveness of the obtained results.
Global exponential stability of fuzzy BAM neural networks with time-varying delays
Zhang Qianhong; Luo Wei
2009-01-01
In this paper, a class of fuzzy bidirectional associated memory (BAM) neural networks with time-varying delays are studied. Employing fixed point theorem, matrix theory and inequality analysis, some sufficient conditions are established for the existence, uniqueness and global exponential stability of equilibrium point. The sufficient conditions are easy to verify at pattern recognition and automatic control. Finally, an example is given to show feasibility and effectiveness of our results.
Passivity of memristive BAM neural networks with leakage and additive time-varying delays
Wang, Weiping; Wang, Meiqi; Luo, Xiong; Li, Lixiang; Zhao, Wenbing; Liu, Linlin; Ping, Yuan
2018-02-01
This paper investigates the passivity of memristive bidirectional associate memory neural networks (MBAMNNs) with leakage and additive time-varying delays. Based on some useful inequalities and appropriate Lyapunov-Krasovskii functionals (LKFs), several delay-dependent conditions for passivity performance are obtained in linear matrix inequalities (LMIs). Moreover, the leakage delays as well as additive delays are considered separately. Finally, numerical simulations are provided to demonstrate the feasibility of the theoretical results.
Time-Varying Dynamic Properties of Offshore Wind Turbines Evaluated by Modal Testing
Damgaard, Mads; Andersen, J. K. F.; Ibsen, Lars Bo
2014-01-01
resonance of the wind turbine structure. In this paper, free vibration tests and a numerical Winkler type approach are used to evaluate the dynamic properties of a total of 30 offshore wind turbines located in the North Sea. Analyses indicate time-varying eigenfrequencies and damping ratios of the lowest...... structural eigenmode. Isolating the oscillation oil damper performance, moveable seabed conditions may lead to the observed time dependency....
Lou, X.; Cui, B.
2008-01-01
In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)
Specification and testing of Multiplicative Time-Varying GARCH models with applications
Amado, Cristina; Teräsvirta, Timo
2017-01-01
In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smooth...... is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns....
Almirall, Daniel; Griffin, Beth Ann; McCaffrey, Daniel F.; Ramchand, Rajeev; Yuen, Robert A.; Murphy, Susan A.
2014-01-01
This article considers the problem of examining time-varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the time-varying moderators are also the sole time-varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time-varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. We illustrate the methodology in a case study to assess if time-varying substance use moderates treatment effects on future substance use. PMID:23873437
Time-varying economic dominance in financial markets: A bistable dynamics approach
He, Xue-Zhong; Li, Kai; Wang, Chuncheng
2018-05-01
By developing a continuous-time heterogeneous agent financial market model of multi-assets traded by fundamental and momentum investors, we provide a potential mechanism for generating time-varying dominance between fundamental and non-fundamental in financial markets. We show that investment constraints lead to the coexistence of a locally stable fundamental steady state and a locally stable limit cycle around the fundamental, characterized by a Bautin bifurcation. This provides a mechanism for market prices to switch stochastically between the two persistent but very different market states, leading to the coexistence and time-varying dominance of seemingly controversial efficient market and price momentum over different time periods. The model also generates other financial market stylized facts, such as spillover effects in both momentum and volatility, market booms, crashes, and correlation reduction due to cross-sectional momentum trading. Empirical evidence based on the U.S. market supports the main findings. The mechanism developed in this paper can be used to characterize time-varying economic dominance in economics and finance in general.
Yoon, Heonjun; Kim, Miso; Park, Choon-Su; Youn, Byeng D.
2018-01-01
Piezoelectric vibration energy harvesting (PVEH) has received much attention as a potential solution that could ultimately realize self-powered wireless sensor networks. Since most ambient vibrations in nature are inherently random and nonstationary, the output performances of PVEH devices also randomly change with time. However, little attention has been paid to investigating the randomly time-varying electroelastic behaviors of PVEH systems both analytically and experimentally. The objective of this study is thus to make a step forward towards a deep understanding of the time-varying performances of PVEH devices under nonstationary random vibrations. Two typical cases of nonstationary random vibration signals are considered: (1) randomly-varying amplitude (amplitude modulation; AM) and (2) randomly-varying amplitude with randomly-varying instantaneous frequency (amplitude and frequency modulation; AM-FM). In both cases, this study pursues well-balanced correlations of analytical predictions and experimental observations to deduce the relationships between the time-varying output performances of the PVEH device and two primary input parameters, such as a central frequency and an external electrical resistance. We introduce three correlation metrics to quantitatively compare analytical prediction and experimental observation, including the normalized root mean square error, the correlation coefficient, and the weighted integrated factor. Analytical predictions are in an excellent agreement with experimental observations both mechanically and electrically. This study provides insightful guidelines for designing PVEH devices to reliably generate electric power under nonstationary random vibrations.
Multimodal Pilot Behavior in Multi-Axis Tracking Tasks with Time-Varying Motion Cueing Gains
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.
State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.
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.
A Tentative Application Of Morphological Filters To Time-Varying Images
Billard, D.; Poquillon, B.
1989-03-01
In this paper, morphological filters, which are commonly used to process either 2D or multidimensional static images, are generalized to the analysis of time-varying image sequence. The introduction of the time dimension induces then interesting prop-erties when designing such spatio-temporal morphological filters. In particular, the specification of spatio-temporal structuring ele-ments (equivalent to time-varying spatial structuring elements) can be adjusted according to the temporal variations of the image sequences to be processed : this allows to derive specific morphological transforms to perform noise filtering or moving objects discrimination on dynamic images viewed by a non-stationary sensor. First, a brief introduction to the basic principles underlying morphological filters will be given. Then, a straightforward gener-alization of these principles to time-varying images will be pro-posed. This will lead us to define spatio-temporal opening and closing and to introduce some of their possible applications to process dynamic images. At last, preliminary results obtained us-ing a natural forward looking infrared (FUR) image sequence are presented.
Lin, Chang Sheng; Tseng, Tse Chuan
2014-01-01
Modal Identification from response data only is studied for structural systems under nonstationary ambient vibration. The topic of this paper is the estimation of modal parameters from nonstationary ambient vibration data by applying the random decrement algorithm with time-varying threshold level. In the conventional random decrement algorithm, the threshold level for evaluating random dec signatures is defined as the standard deviation value of response data of the reference channel. The distortion of random dec signatures may be, however, induced by the error involved in noise from the original response data in practice. To improve the accuracy of identification, a modification of the sampling procedure in random decrement algorithm is proposed for modal-parameter identification from the nonstationary ambient response data. The time-varying threshold level is presented for the acquisition of available sample time history to perform averaging analysis, and defined as the temporal root-mean-square function of structural response, which can appropriately describe a wide variety of nonstationary behaviors in reality, such as the time-varying amplitude (variance) of a nonstationary process in a seismic record. Numerical simulations confirm the validity and robustness of the proposed modal-identification method from nonstationary ambient response data under noisy conditions.
Tracking control of time-varying knee exoskeleton disturbed by interaction torque.
Li, Zhan; Ma, Wenhao; Yin, Ziguang; Guo, Hongliang
2017-11-01
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.
A New Time-varying Concept of Risk in a Changing Climate.
Sarhadi, Ali; Ausín, María Concepción; Wiper, Michael P
2016-10-20
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.
A New Time-varying Concept of Risk in a Changing Climate
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.
Two-dimensional phononic crystals with time-varying properties: a multiple scattering analysis
Wright, D W; Cobbold, R S C
2010-01-01
Multiple scattering theory is a versatile two- and three-dimensional method for characterizing the acoustic wave transmission through many scatterers. It provides analytical solutions to wave propagation in scattering structures, and its computational complexity grows logarithmically with the number of scatterers. In this paper we show how the 2D method can be adapted to include the effects of time-varying material parameters. Specifically, a new T-matrix is defined to include the effects of frequency modulation that occurs in time-varying phononic crystals. Solutions were verified against finite difference time domain (FDTD) simulations and showed excellent agreement. This new method enables fast characterization of time-varying phononic crystals without the need to resort to lengthy FDTD simulations. Also, the method of combining T-matrices to form the T-supermatrix remains unchanged provided that the new matrix definitions are used. The method is quite compatible with existing implementations of multiple scattering theory and could be readily extended to three-dimensional multiple scattering theory
A behavioral asset pricing model with a time-varying second moment
Chiarella, Carl; He Xuezhong; Wang, Duo
2006-01-01
We develop a simple behavioral asset pricing model with fundamentalists and chartists in order to study price behavior in financial markets when chartists estimate both conditional mean and variance by using a weighted averaging process. Through a stability, bifurcation, and normal form analysis, the market impact of the weighting process and time-varying second moment are examined. It is found that the fundamental price becomes stable (unstable) when the activities from both types of traders are balanced (unbalanced). When the fundamental price becomes unstable, the weighting process leads to different price dynamics, depending on whether the chartists act as either trend followers or contrarians. It is also found that a time-varying second moment of the chartists does not change the stability of the fundamental price, but it does influence the stability of the bifurcations. The bifurcation becomes stable (unstable) when the chartists are more (less) concerned about the market risk characterized by the time-varying second moment. Different routes to complicated price dynamics are also observed. The analysis provides an analytical foundation for the statistical analysis of the corresponding stochastic version of this type of behavioral model
The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
Yin Tian
2018-04-01
Full Text Available Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG references, namely, AR (average of all recording channels and reference electrode standardization technique (REST, were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.
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
Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.
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.
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.
Emergence of synchronization and regularity in firing patterns in time-varying neural hypernetworks
Rakshit, Sarbendu; Bera, Bidesh K.; Ghosh, Dibakar; Sinha, Sudeshna
2018-05-01
We study synchronization of dynamical systems coupled in time-varying network architectures, composed of two or more network topologies, corresponding to different interaction schemes. As a representative example of this class of time-varying hypernetworks, we consider coupled Hindmarsh-Rose neurons, involving two distinct types of networks, mimicking interactions that occur through the electrical gap junctions and the chemical synapses. Specifically, we consider the connections corresponding to the electrical gap junctions to form a small-world network, while the chemical synaptic interactions form a unidirectional random network. Further, all the connections in the hypernetwork are allowed to change in time, modeling a more realistic neurobiological scenario. We model this time variation by rewiring the links stochastically with a characteristic rewiring frequency f . We find that the coupling strength necessary to achieve complete neuronal synchrony is lower when the links are switched rapidly. Further, the average time required to reach the synchronized state decreases as synaptic coupling strength and/or rewiring frequency increases. To quantify the local stability of complete synchronous state we use the Master Stability Function approach, and for global stability we employ the concept of basin stability. The analytically derived necessary condition for synchrony is in excellent agreement with numerical results. Further we investigate the resilience of the synchronous states with respect to increasing network size, and we find that synchrony can be maintained up to larger network sizes by increasing either synaptic strength or rewiring frequency. Last, we find that time-varying links not only promote complete synchronization, but also have the capacity to change the local dynamics of each single neuron. Specifically, in a window of rewiring frequency and synaptic coupling strength, we observe that the spiking behavior becomes more regular.
Tracking time-varying cerebral autoregulation in response to changes in respiratory PaCO2
Liu, Jia; Simpson, M David; Allen, Robert; Yan, Jingyu
2010-01-01
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
A Kalman-filter based approach to identification of time-varying gene regulatory networks.
Jie Xiong
Full Text Available 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.
The relationship between global oil price shocks and China's output: A time-varying analysis
Cross, Jamie; Nguyen, Bao H.
2017-01-01
We employ a class of time-varying Bayesian vector autoregressive (VAR) models on new standard dataset of China's GDP constructed by to examine the relationship between China's economic growth and global oil market fluctuations between 1992Q1 and 2015Q3. We find that: (1) the time varying parameter VAR with stochastic volatility provides a better fit as compared to it's constant counterparts; (2) the impacts of intertemporal global oil price shocks on China's output are often small and temporary in nature; (3) oil supply and specific oil demand shocks generally produce negative movements in China's GDP growth whilst oil demand shocks tend to have positive effects; (4) domestic output shocks have no significant impact on price or quantity movements within the global oil market. The results are generally robust to three commonly employed indicators of global economic activity: Kilian's global real economic activity index, the metal price index and the global industrial production index, and two alternative oil price metrics: the US refiners' acquisition cost for imported crude oil and the West Texas Intermediate price of crude oil. - Highlights: • A class of time-varying BVARs is used to examine the relationship between China's economic growth and global oil market fluctuations. • The impacts of intertemporal global oil price shocks on China's output are often small and temporary in nature. • Oil supply and specific oil demand shocks generally produce negative movements in China's GDP growth while oil demand shocks tend to have positive effects. • Domestic output shocks have no significant impact on price or quantity movements within the global oil market.
H∞ Consensus for Multiagent Systems with Heterogeneous Time-Varying Delays
Beibei Wang
2013-01-01
Full Text Available We apply the linear matrix inequality method to consensus and H∞ consensus problems of the single integrator multiagent system with heterogeneous delays in directed networks. To overcome the difficulty caused by heterogeneous time-varying delays, we rewrite the multiagent system into a partially reduced-order system and an integral system. As a result, a particular Lyapunov function is constructed to derive sufficient conditions for consensus of multiagent systems with fixed (switched topologies. We also apply this method to the H∞ consensus of multiagent systems with disturbances and heterogeneous delays. Numerical examples are given to illustrate the theoretical results.
Stagewise pseudo-value regression for time-varying effects on the cumulative incidence
Zöller, Daniela; Schmidtmann, Irene; Weinmann, Arndt
2016-01-01
In a competing risks setting, the cumulative incidence of an event of interest describes the absolute risk for this event as a function of time. For regression analysis, one can either choose to model all competing events by separate cause-specific hazard models or directly model the association...... for time-varying effects. This is implemented by coupling variable selection between the grid times, but determining estimates separately. The effect estimates are regularized to also allow for model fitting with a low to moderate number of observations. This technique is illustrated in an application...
Robust Stabilization of Discrete-Time Systems with Time-Varying Delay: An LMI Approach
Valter J. S. Leite
2008-01-01
Full Text Available Sufficient linear matrix inequality (LMI conditions to verify the robust stability and to design robust state feedback gains for the class of linear discrete-time systems with time-varying delay and polytopic uncertainties are presented. The conditions are obtained through parameter-dependent Lyapunov-Krasovskii functionals and use some extra variables, which yield less conservative LMI conditions. Both problems, robust stability analysis and robust synthesis, are formulated as convex problems where all system matrices can be affected by uncertainty. Some numerical examples are presented to illustrate the advantages of the proposed LMI conditions.
Projective synchronization of time-varying delayed neural network with adaptive scaling factors
Ghosh, Dibakar; Banerjee, Santo
2013-01-01
Highlights: • Projective synchronization in coupled delayed neural chaotic systems with modulated delay time is introduced. • An adaptive rule for the scaling factors is introduced. • This scheme is highly applicable in secure communication. -- Abstract: In this work, the projective synchronization between two continuous time delayed neural systems with time varying delay is investigated. A sufficient condition for synchronization for the coupled systems with modulated delay is presented analytically with the help of the Krasovskii–Lyapunov approach. The effect of adaptive scaling factors on synchronization are also studied in details. Numerical simulations verify the effectiveness of the analytic results
Ponderomotive force of a uniform electromagnetic wave in a time varying dielectric medium
Mori, W.B.; Katsouleas, T.
1992-01-01
A ponderomotive force associated with a uniform electromagnetic wave propagating in a medium with time varying dielectric properties [e.g., ε=ε(x-v 0 t)] is identified. In particular, when a laser ionizes a gas through which it propagates, a force is exerted on the medium at the ionization front that is proportional to (∇ε)E 2 rather than the usual (ε-1)∇E 2 . This force excites a wake in the plasma medium behind the ionization front. The ponderomotive force and wake amplitude are derived and tested with 1D particle-in-cell simulations
Lun Zhai
2014-01-01
Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.
Gold as an Infl ation Hedge in a Time-Varying Coefficient Framework
Beckmann, Joscha; Czudaj, Robert
2012-01-01
This study analyzes the question whether gold provides the ability of hedging against inflation from a new perspective. Using data for four major economies, namely the USA, the UK, the Euro Area, and Japan, we allow for nonlinearity and discriminate between long-run and time-varying short-run dynamics. Thus, we conduct a Markov-switching vector error correction model (MS-VECM) approach for a sample period ranging from January 1970 to December 2011. Our main findings are threefold: First, we s...
Gold as an Infl ation Hedge in a Time-Varying Coeffi cient Framework
Joscha Beckmann; Robert Czudaj
2012-01-01
This study analyzes the question whether gold provides the ability of hedging against inflation from a new perspective. Using data for four major economies, namely the USA, the UK, the Euro Area, and Japan, we allow for nonlinearity and discriminate between long-run and time-varying short-run dynamics. Thus, we conduct a Markov-switching vector error correction model (MS-VECM) approach for a sample period ranging from January 1970 to December 2011. Our main findings are threefold: First, we s...
Distributed Event-Triggered Control of Multiagent Systems with Time-Varying Topology
Jingwei Ma
2014-01-01
Full Text Available This paper studies the consensus of first-order discrete-time multiagent systems, where the interaction topology is time-varying. The event-triggered control is used to update the control input of each agent, and the event-triggering condition is designed based on the combination of the relative states of each agent to its neighbors. By applying the common Lyapunov function method, a sufficient condition for consensus, which is expressed as a group of linear matrix inequalities, is obtained and the feasibility of these linear matrix inequalities is further analyzed. Simulation examples are provided to explain the effectiveness of the theoretical results.
Time-Varying Market Integration and Expected Returns in Emerging Markets
de Jong, Frank; de Roon, Frans
2001-01-01
We use a simple model in which the expected returns in emerging markets depend on their systematic risk as measured by their beta relative to the world portfolio as well as on the level of integration in that market. The level of integration is a time-varying variable that depends on the market value of the assets that can be held by domestic investors only versus the market value of the assets that can be traded freely. Our empirical analysis for 30 emerging markets shows that there are stro...
Combined time-varying forecast based on the proper scoring approach for wind power generation
Chen, Xingying; Jiang, Yu; Yu, Kun
2017-01-01
Compared with traditional point forecasts, combined forecast have been proposed as an effective method to provide more accurate forecasts than individual model. However, the literature and research focus on wind-power combined forecasts are relatively limited. Here, based on forecasting error...... distribution, a proper scoring approach is applied to combine plausible models to form an overall time-varying model for the next day forecasts, rather than weights-based combination. To validate the effectiveness of the proposed method, real data of 3 years were used for testing. Simulation results...... demonstrate that the proposed method improves the accuracy of overall forecasts, even compared with a numerical weather prediction....
Time-varying exchange rate pass-through: experiences of some industrial countries
Toshitaka Sekine
2006-01-01
This paper estimates exchange rate pass-through of six major industrial countries using a time-varying parameter with stochastic volatility model. Exchange rate pass-through is divided into impacts of exchange rate fluctuations to import prices (first-stage pass-through) and those of import price movements to consumer prices (second-stage pass-through). The paper finds that both stages of pass-through have declined over time for all the sample countries. The decline in second-stage pass-throu...
Scalar Aharonov–Bohm Phase in Ramsey Atom Interferometry under Time-Varying Potential
Atsuo Morinaga
2016-08-01
Full Text Available In a Ramsey atom interferometer excited by two electromagnetic fields, if atoms are under a time-varying scalar potential during the interrogation time, the phase of the Ramsey fringes shifts owing to the scalar Aharonov–Bohm effect. The phase shift was precisely examined using a Ramsey atom interferometer with a two-photon Raman transition under the second-order Zeeman potential, and a formula for the phase shift was derived. Using the derived formula, the frequency shift due to the scalar Aharonov–Bohm effect in the frequency standards utilizing the Ramsey atom interferometer was discussed.
Multi-disciplinary techniques for understanding time-varying space-based imagery
Casasent, D.; Sanderson, A.; Kanade, T.
1984-06-01
A multidisciplinary program for space-based image processing is reported. This project combines optical and digital processing techniques and pattern recognition, image understanding and artificial intelligence methodologies. Time change image processing was recognized as the key issue to be addressed. Three time change scenarios were defined based on the frame rate of the data change. This report details the recent research on: various statistical and deterministic image features, recognition of sub-pixel targets in time varying imagery, and 3-D object modeling and recognition.
Estimating time-varying RSA to examine psychophysiological linkage of marital dyads.
Gates, Kathleen M; Gatzke-Kopp, Lisa M; Sandsten, Maria; Blandon, Alysia Y
2015-08-01
One of the primary tenets of polyvagal theory dictates that parasympathetic influence on heart rate, often estimated by respiratory sinus arrhythmia (RSA), shifts rapidly in response to changing environmental demands. The current standard analytic approach of aggregating RSA estimates across time to arrive at one value fails to capture this dynamic property within individuals. By utilizing recent methodological developments that enable precise RSA estimates at smaller time intervals, we demonstrate the utility of computing time-varying RSA for assessing psychophysiological linkage (or synchrony) in husband-wife dyads using time-locked data collected in a naturalistic setting. © 2015 Society for Psychophysiological Research.
Analysis on Passivity for Uncertain Neural Networks with Time-Varying Delays
O. M. Kwon
2014-01-01
Full Text Available The problem of passivity analysis for neural networks with time-varying delays and parameter uncertainties is considered. By the consideration of newly constructed Lyapunov-Krasovskii functionals, improved sufficient conditions to guarantee the passivity of the concerned networks are proposed with the framework of linear matrix inequalities (LMIs, which can be solved easily by various efficient convex optimization algorithms. The enhancement of the feasible region of the proposed criteria is shown via two numerical examples by the comparison of maximum allowable delay bounds.
Liang Jinling; Cao Jinde
2003-01-01
In this Letter, the problems of boundedness and stability for a general class of non-autonomous recurrent neural networks with variable coefficients and time-varying delays are analyzed via employing Young inequality technique and Lyapunov method. Some simple sufficient conditions are given for boundedness and stability of the solutions for the recurrent neural networks. These results generalize and improve the previous works, and they are easy to check and apply in practice. Two illustrative examples and their numerical simulations are also given to demonstrate the effectiveness of the proposed results
Zhu Xunlin; Wang Youyi
2009-01-01
This Letter studies the exponential stability for a class of neural networks (NNs) with both discrete and distributed time-varying delays. Under weaker assumptions on the activation functions, by defining a more general type of Lyapunov functionals and developing a new convex combination technique, new less conservative and less complex stability criteria are established to guarantee the global exponential stability of the discussed NNs. The obtained conditions are dependent on both discrete and distributed delays, are expressed in terms of linear matrix inequalities (LMIs), and contain fewer decision variables. Numerical examples are given to illustrate the effectiveness and the less conservatism of the proposed conditions.
Gong, Shuqing; Yang, Shaofu; Guo, Zhenyuan; Huang, Tingwen
2018-06-01
The paper is concerned with the synchronization problem of inertial memristive neural networks with time-varying delay. First, by choosing a proper variable substitution, inertial memristive neural networks described by second-order differential equations can be transformed into first-order differential equations. Then, a novel controller with a linear diffusive term and discontinuous sign term is designed. By using the controller, the sufficient conditions for assuring the global exponential synchronization of the derive and response neural networks are derived based on Lyapunov stability theory and some inequality techniques. Finally, several numerical simulations are provided to substantiate the effectiveness of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
End-of-the-year economic growth and time-varying expected returns
Møller, Stig V.; Rangvid, Jesper
2015-01-01
We show that macroeconomic growth at the end of the year (fourth quarter or December) strongly influences expected returns on risky financial assets, whereas economic growth during the rest of the year does not. We find this pattern for many different asset classes, across different time periods......, and for US and international data. We also show that movements in the surplus consumption ratio of Campbell and Cochrane (1999) , a theoretically well-founded measure of time-varying risk aversion linked to macroeconomic growth, influence expected returns stronger during the fourth quarter than the other...
Computing and visualizing time-varying merge trees for high-dimensional data
Oesterling, Patrick [Univ. of Leipzig (Germany); Heine, Christian [Univ. of Kaiserslautern (Germany); Weber, Gunther H. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Morozov, Dmitry [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Scheuermann, Gerik [Univ. of Leipzig (Germany)
2017-06-03
We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.
Yueyang Li
2014-01-01
Full Text Available This paper investigates the H∞ fixed-lag fault estimator design for linear discrete time-varying (LDTV systems with intermittent measurements, which is described by a Bernoulli distributed random variable. Through constructing a novel partially equivalent dynamic system, the fault estimator design is converted into a deterministic quadratic minimization problem. By applying the innovation reorganization technique and the projection formula in Krein space, a necessary and sufficient condition is obtained for the existence of the estimator. The parameter matrices of the estimator are derived by recursively solving two standard Riccati equations. An illustrative example is provided to show the effectiveness and applicability of the proposed algorithm.
Adaptive control of chaotic systems with stochastic time varying unknown parameters
Salarieh, Hassan [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu
2008-10-15
In this paper based on the Lyapunov stability theorem, an adaptive control scheme is proposed for stabilizing the unstable periodic orbits (UPO) of chaotic systems. It is assumed that the chaotic system has some linearly dependent unknown parameters which are stochastically time varying. The stochastic parameters are modeled through the Weiner process derivative. To demonstrate the effectiveness of the proposed technique it has been applied to the Lorenz, Chen and Rossler dynamical systems, as some case studies. Simulation results indicate that the proposed adaptive controller has a high performance in stabilizing the UPO of chaotic systems in noisy environment.
Online Estimation of Time-Varying Volatility Using a Continuous-Discrete LMS Algorithm
Jacques Oksman
2008-09-01
Full Text Available The following paper addresses a problem of inference in financial engineering, namely, online time-varying volatility estimation. The proposed method is based on an adaptive predictor for the stock price, built from an implicit integration formula. An estimate for the current volatility value which minimizes the mean square prediction error is calculated recursively using an LMS algorithm. The method is then validated on several synthetic examples as well as on real data. Throughout the illustration, the proposed method is compared with both UKF and offline volatility estimation.
Finite-Time H∞ Filtering for Linear Continuous Time-Varying Systems with Uncertain Observations
Huihong Zhao
2012-01-01
Full Text Available This paper is concerned with the finite-time H∞ filtering problem for linear continuous time-varying systems with uncertain observations and ℒ2-norm bounded noise. The design of finite-time H∞ filter is equivalent to the problem that a certain indefinite quadratic form has a minimum and the filter is such that the minimum is positive. The quadratic form is related to a Krein state-space model according to the Krein space linear estimation theory. By using the projection theory in Krein space, the finite-time H∞ filtering problem is solved. A numerical example is given to illustrate the performance of the H∞ filter.
H∞ state estimation of generalised neural networks with interval time-varying delays
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.
Observation of time-varying photoconductivity and persistent photoconductivity in porous silicon
Frello, T.; Veje, E.; Leistiko, Otto
1996-01-01
We have observed time-varying photoconductivity and persistent photoconductivity in porous silicon, both with time-evolution scales of the order of several minutes or hours. The time evolutions depend on the wavelength and the intensity of the illuminating light. The data indicate the presence...... of at least two competing mechanisms, one is tentatively related to photoinduced creation of charge carriers in the silicon substrate followed by diffusion into the porous silicon layer, and the other is tentatively related to desorption of hydrogen from the porous silicon. ©1996 American Institute of Physics....
Globally exponential stability condition of a class of neural networks with time-varying delays
Liao, T.-L.; Yan, J.-J.; Cheng, C.-J.; Hwang, C.-C.
2005-01-01
In this Letter, the globally exponential stability for a class of neural networks including Hopfield neural networks and cellular neural networks with time-varying delays is investigated. Based on the Lyapunov stability method, a novel and less conservative exponential stability condition is derived. The condition is delay-dependent and easily applied only by checking the Hamiltonian matrix with no eigenvalues on the imaginary axis instead of directly solving an algebraic Riccati equation. Furthermore, the exponential stability degree is more easily assigned than those reported in the literature. Some examples are given to demonstrate validity and excellence of the presented stability condition herein
Resonant e+e- production by time-varying electromagnetic field
Farakos, K.; Koutsoumbas, G.; Tiktopoulos, G.
1990-01-01
As pointed out by Cornwall and Tiktopoulos (CT) strong, time-varying electric fields may produce e + e - pairs in a resonant fashion. This effect could be related to the sharp peaks in the e + e - spectrum observed in the GSI heavy-ion collision experiments. We attempt to go beyond the case of spatially uniform fields discussed by CT. We find that resonant e + e - production indeed takes place for electric fields derived from four-potentials of the form A 1 =A 2 =A 0 =0, A 3 =δ(t)b(x 3 ) provided by b(x) has discontinuities with a jump at least equal to π. (orig.)
Towards Self-adaptation for Dependable Service-Oriented Systems
Cardellini, Valeria; Casalicchio, Emiliano; Grassi, Vincenzo; Lo Presti, Francesco; Mirandola, Raffaela
Increasingly complex information systems operating in dynamic environments ask for management policies able to deal intelligently and autonomously with problems and tasks. An attempt to deal with these aspects can be found in the Service-Oriented Architecture (SOA) paradigm that foresees the creation of business applications from independently developed services, where services and applications build up complex dependencies. Therefore the dependability of SOA systems strongly depends on their ability to self-manage and adapt themselves to cope with changes in the operating conditions and to meet the required dependability with a minimum of resources. In this paper we propose a model-based approach to the realization of self-adaptable SOA systems, aimed at the fulfillment of dependability requirements. Specifically, we provide a methodology driving the system adaptation and we discuss the architectural issues related to its implementation. To bring this approach to fruition, we developed a prototype tool and we show the results that can be achieved with a simple example.
Self-adaptive calibration for staring infrared sensors
Kendall, William B.; Stocker, Alan D.
1993-10-01
This paper presents a new, self-adaptive technique for the correlation of non-uniformities (fixed-pattern noise) in high-density infrared focal-plane detector arrays. We have developed a new approach to non-uniformity correction in which we use multiple image frames of the scene itself, and take advantage of the aim-point wander caused by jitter, residual tracking errors, or deliberately induced motion. Such wander causes each detector in the array to view multiple scene elements, and each scene element to be viewed by multiple detectors. It is therefore possible to formulate (and solve) a set of simultaneous equations from which correction parameters can be computed for the detectors. We have tested our approach with actual images collected by the ARPA-sponsored MUSIC infrared sensor. For these tests we employed a 60-frame (0.75-second) sequence of terrain images for which an out-of-date calibration was deliberately used. The sensor was aimed at a point on the ground via an operator-assisted tracking system having a maximum aim point wander on the order of ten pixels. With these data, we were able to improve the calibration accuracy by a factor of approximately 100.
Relational Database Extension Oriented, Self-adaptive Imagery Pyramid Model
HU Zhenghua
2015-06-01
Full Text Available With the development of remote sensing technology, especially the improvement of sensor resolution, the amount of image data is increasing. This puts forward higher requirements to manage huge amount of data efficiently and intelligently. And how to access massive remote sensing data with efficiency and smartness becomes an increasingly popular topic. In this paper, against current development status of Spatial Data Management System, we proposed a self-adaptive strategy for image blocking and a method for LoD(level of detailmodel construction that adapts, with the combination of database storage, network transmission and the hardware of the client. Confirmed by experiments, this imagery management mechanism can achieve intelligent and efficient storage and access in a variety of different conditions of database, network and client. This study provides a feasible idea and method for efficient image data management, contributing to the efficient access and management for remote sensing image data which are based on database technology under network environment of C/S architecture.
A simple analytical model for dynamics of time-varying target leverage ratios
Lo, C. F.; Hui, C. H.
2012-03-01
In this paper we have formulated a simple theoretical model for the dynamics of the time-varying target leverage ratio of a firm under some assumptions based upon empirical observations. In our theoretical model the time evolution of the target leverage ratio of a firm can be derived self-consistently from a set of coupled Ito's stochastic differential equations governing the leverage ratios of an ensemble of firms by the nonlinear Fokker-Planck equation approach. The theoretically derived time paths of the target leverage ratio bear great resemblance to those used in the time-dependent stationary-leverage (TDSL) model [Hui et al., Int. Rev. Financ. Analy. 15, 220 (2006)]. Thus, our simple model is able to provide a theoretical foundation for the selected time paths of the target leverage ratio in the TDSL model. We also examine how the pace of the adjustment of a firm's target ratio, the volatility of the leverage ratio and the current leverage ratio affect the dynamics of the time-varying target leverage ratio. Hence, with the proposed dynamics of the time-dependent target leverage ratio, the TDSL model can be readily applied to generate the default probabilities of individual firms and to assess the default risk of the firms.
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.
Nonlinear systems time-varying parameter estimation: Application to induction motors
Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, IUT FOTSO Victor, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Ahmed-Ali, Tarek [Ecole Nationale Superieure des Ingenieurs des Etudes et Techniques d' Armement (ENSIETA), 2 Rue Francois Verny, 29806 Brest Cedex 9 (France); Lamnabhi-Lagarrigue, F. [Laboratoire des Signaux et Systemes (L2S), C.N.R.S-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)
2008-11-15
In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines. (author)
Dynamic IQC-Based Control of Uncertain LFT Systems With Time-Varying State Delay.
Yuan, Chengzhi; Wu, Fen
2016-12-01
This paper presents a new exact-memory delay control scheme for a class of uncertain systems with time-varying state delay under the integral quadratic constraint (IQC) framework. The uncertain system is described as a linear fractional transformation model including a state-delayed linear time-invariant (LTI) system and time-varying structured uncertainties. The proposed exact-memory delay controller consists of a linear state-feedback control law and an additional term that captures the delay behavior of the plant. We first explore the delay stability and the L 2 -gain performance using dynamic IQCs incorporated with quadratic Lyapunov functions. Then, the design of exact-memory controllers that guarantee desired L 2 -gain performance is examined. The resulting delay control synthesis conditions are formulated in terms of linear matrix inequalities, which are convex on all design variables including the scaling matrices associated with the IQC multipliers. The IQC-based exact-memory control scheme provides a novel approach for delay control designs via convex optimization, and advances existing control methods in two important ways: 1) better controlled performance and 2) simplified design procedure with less computational cost. The effectiveness and advantages of the proposed approach have been demonstrated through numerical studies.
PCA-based detection of damage in time-varying systems
Bellino, A.; Fasana, A.; Garibaldi, L.; Marchesiello, S.
2010-10-01
When performing Structural Health Monitoring, it is well known that the natural frequencies do not depend only on the damage but also on environmental conditions, such as temperature and humidity. The Principal Component Analysis is used to take this problem into account, because it allows eliminating the effect of external factors. The purpose of the present work is to show that this technique can be successfully used not only for time-invariant systems, but also for time-varying ones. Referring to the latter, one of the most studied systems which shows these characteristics is the bridge with crossing loads, such as the case of the railway bridge studied in present paper; in this case, the mass and the velocity of the train can be considered as "environmental" factors.This paper, after a brief description of the PCA method and one example of its application on time-invariant systems, presents the great potentialities of the methodology when applied to time-varying systems. The results show that this method is able to better detect the presence of damage and also to properly distinguish among different levels of crack depths.
Joint optimization of green vehicle scheduling and routing problem with time-varying speeds
Zhang, Dezhi; Wang, Xin; Ni, Nan; Zhang, Zhuo
2018-01-01
Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions. PMID:29466370
The estimation of time-varying risks in asset pricing modelling using B-Spline method
Nurjannah; Solimun; Rinaldo, Adji
2017-12-01
Asset pricing modelling has been extensively studied in the past few decades to explore the risk-return relationship. The asset pricing literature typically assumed a static risk-return relationship. However, several studies found few anomalies in the asset pricing modelling which captured the presence of the risk instability. The dynamic model is proposed to offer a better model. The main problem highlighted in the dynamic model literature is that the set of conditioning information is unobservable and therefore some assumptions have to be made. Hence, the estimation requires additional assumptions about the dynamics of risk. To overcome this problem, the nonparametric estimators can also be used as an alternative for estimating risk. The flexibility of the nonparametric setting avoids the problem of misspecification derived from selecting a functional form. This paper investigates the estimation of time-varying asset pricing model using B-Spline, as one of nonparametric approach. The advantages of spline method is its computational speed and simplicity, as well as the clarity of controlling curvature directly. The three popular asset pricing models will be investigated namely CAPM (Capital Asset Pricing Model), Fama-French 3-factors model and Carhart 4-factors model. The results suggest that the estimated risks are time-varying and not stable overtime which confirms the risk instability anomaly. The results is more pronounced in Carhart’s 4-factors model.
Bit-level plane image encryption based on coupled map lattice with time-varying delay
Lv, Xiupin; Liao, Xiaofeng; Yang, Bo
2018-04-01
Most of the existing image encryption algorithms had two basic properties: confusion and diffusion in a pixel-level plane based on various chaotic systems. Actually, permutation in a pixel-level plane could not change the statistical characteristics of an image, and many of the existing color image encryption schemes utilized the same method to encrypt R, G and B components, which means that the three color components of a color image are processed three times independently. Additionally, dynamical performance of a single chaotic system degrades greatly with finite precisions in computer simulations. In this paper, a novel coupled map lattice with time-varying delay therefore is applied in color images bit-level plane encryption to solve the above issues. Spatiotemporal chaotic system with both much longer period in digitalization and much excellent performances in cryptography is recommended. Time-varying delay embedded in coupled map lattice enhances dynamical behaviors of the system. Bit-level plane image encryption algorithm has greatly reduced the statistical characteristics of an image through the scrambling processing. The R, G and B components cross and mix with one another, which reduces the correlation among the three components. Finally, simulations are carried out and all the experimental results illustrate that the proposed image encryption algorithm is highly secure, and at the same time, also demonstrates superior performance.
Time-varying mixed logit model for vehicle merging behavior in work zone merging areas.
Weng, Jinxian; Du, Gang; Li, Dan; Yu, Yao
2018-08-01
This study aims to develop a time-varying mixed logit model for the vehicle merging behavior in work zone merging areas during the merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. From the safety perspective, vehicle crash probability and severity between the merging vehicle and its surrounding vehicles are regarded as major factors influencing vehicle merging decisions. Model results show that the model with the use of vehicle crash risk probability and severity could provide higher prediction accuracy than previous models with the use of vehicle speeds and gap sizes. It is found that lead vehicle type, through lead vehicle type, through lag vehicle type, crash probability of the merging vehicle with respect to the through lag vehicle, crash severities of the merging vehicle with respect to the through lead and lag vehicles could exhibit time-varying effects on the merging behavior. One important finding is that the merging vehicle could become more and more aggressive in order to complete the merging maneuver as quickly as possible over the elapsed time, even if it has high vehicle crash risk with respect to the through lead and lag vehicles. Copyright © 2018 Elsevier Ltd. All rights reserved.
Relay selection in cooperative communication systems over continuous time-varying fading channel
Ke Geng
2017-02-01
Full Text Available In this paper, we study relay selection under outdated channel state information (CSI in a decode-and-forward (DF cooperative system. Unlike previous researches on cooperative communication under outdated CSI, we consider that the channel varies continuously over time, i.e., the channel not only changes between relay selection and data transmission but also changes during data transmission. Thus the level of accuracy of the CSI used in relay selection degrades with data transmission. We first evaluate the packet error rate (PER of the cooperative system under continuous time-varying fading channel, and find that the PER performance deteriorates more seriously under continuous time-varying fading channel than when the channel is assumed to be constant during data transmission. Then, we propose a repeated relay selection (RRS strategy to improve the PER performance, in which the forwarded data is divided into multiple segments and relay is reselected before the transmission of each segment based on the updated CSI. Finally, we propose a combined relay selection (CRS strategy which takes advantage of three different relay selection strategies to further mitigate the impact of outdated CSI.
Testing and estimating time-varying elasticities of Swiss gasoline demand
Neto, David
2012-01-01
This paper is intended to test and estimate time-varying elasticities for gasoline demand in Switzerland. For this purpose, a smooth time-varying cointegrating parameters model is investigated in order to describe smooth mutations of the Swiss gasoline demand. The methodology, based on Chebyshev polynomials, is rigorously outlined. Our empirical finding states that the time-invariance assumption does not hold for long-run price and income elasticities. Furthermore they highlight that gasoline demand passed through some periods of sensitivity and non sensitivity with respect to the price. Our empirical statements are of great importance to assess the performance of a gasoline tax as an instrument for CO 2 reduction policy. Indeed, such an instrument can contribute to reduce emissions of greenhouse gases only if the demand is not fully inelastic with respect to the price. Our results suggest that such a carbon-tax would not be always suitable since the price elasticity is found not stable over time and not always significant.
Identification of time-varying structural dynamic systems - An artificial intelligence approach
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.
Adaptive sliding control of non-autonomous active suspension systems with time-varying loadings
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.
Uniform stability for time-varying infinite-dimensional discrete linear systems
Kubrusly, C.S.
1988-09-01
Stability for time-varying discrete linear systems in a Banach space is investigated. On the one hand, it established a fairly complete collection of necessary and sufficient conditions for uniform asymptotic equistability for input-free systems. This includes uniform and strong power equistability, and uniform and strong l p -equistability, among other technical conditions which also play essential role in stability theory. On other hand, it is shown that uniform asymptotic equistability for input-free systems is equivalent to each of the following concepts of uniform stability for forced systems: l p -input l p -state, c o -input c o -state, bounded-input bounded-state, l p>1 -input bounded-state, c sub (o)-input bounded-state, and convergent-input bounded-state; which are also equivalent to their nonuniform counterparts. For time-varying convergent systems, the above is also equivalent to convergent-input convergent-state stability. The proofs presented here are all ''elementary'' in the sense that they are based essentially only on the Banach-Steinhaus theorem. (autor) [pt
Quantum theory for magnons and phonons interactions under time-varying magnetic fields
Guerreiro, S.C.
1971-01-01
The magnon-fonon interaction in a ferromagnetic material submited to a time-varying magnetic field is studied by quantum methods. This problem has already been solved by semi-classical methods, and one of its results is that under certain conditions a state of lattice vibrations may be completely converted into spin oscillations. The main proporties of magnetoelastic waves in static magnetic fields and extend the quantum treatment for the time varying magnetic field case is revised. Field operators whose equations of motion are analogous to the classical ones are introduced. Their equations, which appear as a linear system of first order coupled equations, are converted into equations for complex functions by an expansion of the field operators in a time t as linear combinations of the same operators in a time t 0 prior to the variation of the magnetic field. The quantity g vector obtained from the classical solution is quantized and shown to be the linear momentum density of the magnetoelastic system, the quantum field spin density operator is deduced for the two interacting fields, and finally the results are used to study the magnetization and lattice displacement vector fields in the case of a system described by a coherent state of one of its normal modes
Modeling the time--varying subjective quality of HTTP video streams with rate adaptations.
Chen, Chao; Choi, Lark Kwon; de Veciana, Gustavo; Caramanis, Constantine; Heath, Robert W; Bovik, Alan C
2014-05-01
Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming.
Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns
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.
Finite-Time Reentry Attitude Control Using Time-Varying Sliding Mode and Disturbance Observer
Xuzhong Wu
2015-01-01
Full Text Available This paper presents the finite-time attitude control problem for reentry vehicle with redundant actuators in consideration of planet uncertainties and external disturbances. Firstly, feedback linearization technique is used to cancel the nonlinearities of equations of motion to construct a basic mode for attitude controller. Secondly, two kinds of time-varying sliding mode control methods with disturbance observer are integrated with the basic mode in order to enhance the control performance and system robustness. One method is designed based on boundary layer technique and the other is a novel second-order sliding model control method. The finite-time stability analyses of both resultant closed-loop systems are carried out. Furthermore, after attitude controller produces the torque commands, an optimization control allocation approach is introduced to allocate them into aerodynamic surface deflections and on-off reaction control system thrusts. Finally, the numerical simulation results demonstrate that both of the time-varying sliding mode control methods are robust to uncertainties and disturbances without chattering phenomenon. Moreover, the proposed second-order sliding mode control method possesses better control accuracy.
Study on the Variation of Groundwater Level under Time-varying Recharge
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.
Time-varying metamaterials based on graphene-wrapped microwires: Modeling and potential applications
Salary, Mohammad Mahdi; Jafar-Zanjani, Samad; Mosallaei, Hossein
2018-03-01
The successful realization of metamaterials and metasurfaces requires the judicious choice of constituent elements. In this paper, we demonstrate the implementation of time-varying metamaterials in the terahertz frequency regime by utilizing graphene-wrapped microwires as building blocks and modulation of graphene conductivity through exterior electrical gating. These elements enable enhancement of light-graphene interaction by utilizing optical resonances associated with Mie scattering, yielding a large tunability and modulation depth. We develop a semianalytical framework based on transition-matrix formulation for modeling and analysis of periodic and aperiodic arrays of such time-varying building blocks. The proposed method is validated against full-wave numerical results obtained using the finite-difference time-domain method. It provides an ideal tool for mathematical synthesis and analysis of space-time gradient metamaterials, eliminating the need for computationally expensive numerical models. Moreover, it allows for a wider exploration of exotic space-time scattering phenomena in time-modulated metamaterials. We apply the method to explore the role of modulation parameters in the generation of frequency harmonics and their emerging wavefronts. Several potential applications of such platforms are demonstrated, including frequency conversion, holographic generation of frequency harmonics, and spatiotemporal manipulation of light. The presented results provide key physical insights to design time-modulated functional metadevices using various building blocks and open up new directions in the emerging paradigm of time-modulated metamaterials.
Fredman, Lisa; Lyons, Jennifer G; Cauley, Jane A; Hochberg, Marc; Applebaum, Katie M
2015-09-01
Previous studies have shown inconsistent associations between caregiving and mortality. This may be due to analyzing caregiver status at baseline only, and that better health is probably related to taking on caregiving responsibilities and continuing in that role. The latter is termed The Healthy Caregiver Hypothesis, similar to the Healthy Worker Effect in occupational epidemiology. We applied common approaches from occupational epidemiology to evaluate the association between caregiving and mortality, including treating caregiving as time-varying and lagging exposure up to 5 years. Caregiving status among 1,068 women (baseline mean age = 81.0 years; 35% caregivers) participating in the Caregiver-Study of Osteoporotic Fractures study was assessed at five interviews conducted between 1999 and 2009. Mortality was determined through January 2012. Cox proportional hazards models were used to estimate adjusted hazard ratios and 95% confidence intervals adjusted for sociodemographics, perceived stress, and functional limitations. A total of 483 participants died during follow-up (38.8% and 48.7% of baseline caregivers and noncaregivers, respectively). Using baseline caregiving status, the association with mortality was 0.77, 0.62-0.95. Models of time-varying caregiving status showed a more pronounced reduction in mortality in current caregivers (hazard ratios = 0.54, 0.38-0.75), which diminished with longer lag periods (3-year lag hazard ratio = 0.68, 0.52-0.88, 5-year lag hazard ratios = 0.76, 0.60-0.95). Overall, caregivers had lower mortality rates than noncaregivers in all analyses. These associations were sensitive to the lagged period, indicating that the timing of leaving caregiving does influence this relationship and should be considered in future investigations. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
A self-adaptive feedforward rf control system for linacs
Zhang Renshan; Ben-Zvi, I.; Xie Jialin
1993-01-01
The design and performance of a self-adaptive feedforward rf control system are reported. The system was built for the linac of the Accelerator Test Facility (ATF) at Brookhaven National Laboratory. Variables of time along the linac macropulse, such as field or phase are discretized and represented as vectors. Upon turn-on or after a large change in the operating-point, the control system acquires the response of the system to test signal vectors and generates a linearized system response matrix. During operation an error vector is generated by comparing the linac variable vectors and a target vector. The error vector is multiplied by the inverse of the system's matrix to generate a correction vector is added to an operating point vector. This control system can be used to control a klystron to produce flat rf amplitude and phase pulses, to control a rf cavity to reduce the rf field fluctuation, and to compensate the energy spread among bunches in a rf linac. Beam loading effects can be corrected and a programmed ramp can be produced. The performance of the control system has been evaluated on the control of a klystron's output as well as an rf cavity. Both amplitude and phase have been regulated simultaneously. In initial tests, the rf output from a klystron has been regulated to an amplitude fluctuation of less than ±0.3% and phase variation of less than ±0.6deg. The rf field of the ATF's photo-cathode microwave gun cavity has been regulated to ±5% in amplitude and simultaneously to ±1deg in phase. Regulating just the rf field amplitude in the rf gun cavity, we have achieved amplitude fluctuation of less than ±2%. (orig.)
A hydraulic hybrid propulsion method for automobiles with self-adaptive system
Wu, Wei; Hu, Jibin; Yuan, Shihua; Di, Chongfeng
2016-01-01
A hydraulic hybrid vehicle with the self-adaptive system is proposed. The mode-switching between the driving mode and the hydraulic regenerative braking mode is realised by the pressure cross-feedback control. Extensive simulated and tested results are presented. The control parameters are reduced and the energy efficiency can be increased by the self-adaptive system. The mode-switching response is fast. The response time can be adjusted by changing the controlling spool diameter of the hydraulic operated check valve in the self-adaptive system. The closing of the valve becomes faster with a smaller controlling spool diameter. The hydraulic regenerative braking mode can be achieved by changing the hydraulic transformer controlled angle. Compared with the convention electric-hydraulic system, the self-adaptive system for the hydraulic hybrid vehicle mode-switching has a higher reliability and a lower cost. The efficiency of the hydraulic regenerative braking is also increased. - Highlights: • A new hybrid system with a self-adaptive system for automobiles is presented. • The mode-switching is realised by the pressure cross-feedback control. • The energy efficiency can be increased with the self-adaptive system. • The control parameters are reduced with the self-adaptive system.
Alleviating Border Effects in Wavelet Transforms for Nonlinear Time-varying Signal Analysis
SU, H.
2011-08-01
Full Text Available Border effects are very common in many finite signals analysis and processing approaches using convolution operation. Alleviating the border effects that can occur in the processing of finite-length signals using wavelet transform is considered in this paper. Traditional methods for alleviating the border effects are suitable to compression or coding applications. We propose an algorithm based on Fourier series which is proved to be appropriate to the application of time-frequency analysis of nonlinear signals. Fourier series extension method preserves the time-varying characteristics of the signals. A modified signal duration expression for measuring the extent of border effects region is presented. The proposed algorithm is confirmed to be efficient to alleviate the border effects in comparison to the current methods through the numerical examples.
Huang He; Qu Yuzhong; Li Hanxiong
2005-01-01
With the development of intelligent control, switched systems have been widely studied. Here we try to introduce some ideas of the switched systems into the field of neural networks. In this Letter, a class of switched Hopfield neural networks with time-varying delay is investigated. The parametric uncertainty is considered and assumed to be norm bounded. Firstly, the mathematical model of the switched Hopfield neural networks is established in which a set of Hopfield neural networks are used as the individual subsystems and an arbitrary switching rule is assumed; Secondly, robust stability analysis for such switched Hopfield neural networks is addressed based on the Lyapunov-Krasovskii approach. Some criteria are given to guarantee the switched Hopfield neural networks to be globally exponentially stable for all admissible parametric uncertainties. These conditions are expressed in terms of some strict linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate our results
Gill R. Tsouri
2009-01-01
Full Text Available A method of overloading subcarriers by multiple transmitters to secure OFDM in wireless time-varying channels is proposed and analyzed. The method is based on reverse piloting, superposition modulation, and joint decoding. It makes use of channel randomness, reciprocity, and fast decorrelation in space to secure OFDM with low overheads on encryption, decryption, and key distribution. These properties make it a good alternative to traditional software-based information security algorithms in systems where the costs associated with such algorithms are an implementation obstacle. A necessary and sufficient condition for achieving information theoretic security in accordance with channel and system parameters is derived. Security by complexity is assessed for cases where the condition for information theoretic security is not satisfied. In addition, practical means for implementing the method are derived including generating robust joint constellations, decoding data with low complexity, and mitigating the effects of imperfections due to mobility, power control errors, and synchronization errors.
A Time-Varied Probabilistic ON/OFF Switching Algorithm for Cellular Networks
Rached, Nadhir B.; Ghazzai, Hakim; Kadri, Abdullah; Alouini, Mohamed-Slim
2018-01-01
In this letter, we develop a time-varied probabilistic on/off switching planning method for cellular networks to reduce their energy consumption. It consists in a risk-aware optimization approach that takes into consideration the randomness of the user profile associated with each base station (BS). The proposed approach jointly determines (i) the instants of time at which the current active BS configuration must be updated due to an increase or decrease of the network traffic load, and (ii) the set of minimum BSs to be activated to serve the networks’ subscribers. Probabilistic metrics modeling the traffic profile variation are developed to trigger this dynamic on/off switching operation. Selected simulation results are then performed to validate the proposed algorithm for different system parameters.
Invariant operator theory for the single-photon energy in time-varying media
Jeong-Ryeol, Choi
2010-01-01
After the birth of quantum mechanics, the notion in physics that the frequency of light is the only factor that determines the energy of a single photon has played a fundamental role. However, under the assumption that the theory of Lewis–Riesenfeld invariants is applicable in quantum optics, it is shown in the present work that this widely accepted notion is valid only for light described by a time-independent Hamiltonian, i.e., for light in media satisfying the conditions, ε(i) = ε(0), μ(t) = μ(0), and σ(t) = 0 simultaneously. The use of the Lewis–Riesenfeld invariant operator method in quantum optics leads to a marvelous result: the energy of a single photon propagating through time-varying linear media exhibits nontrivial time dependence without a change of frequency. (general)
Cao Jinde
2011-01-01
Full Text Available Abstract In this paper, an integral sliding mode control approach is presented to investigate synchronization of nonidentical chaotic neural networks with discrete and distributed time-varying delays as well as leakage delay. By considering a proper sliding surface and constructing Lyapunov-Krasovskii functional, as well as employing a combination of the free-weighting matrix method, Newton-Leibniz formulation and inequality technique, a sliding mode controller is designed to achieve the asymptotical synchronization of the addressed nonidentical neural networks. Moreover, a sliding mode control law is also synthesized to guarantee the reachability of the specified sliding surface. The provided conditions are expressed in terms of linear matrix inequalities, and are dependent on the discrete and distributed time delays as well as leakage delay. A simulation example is given to verify the theoretical results.
Decentralized H∞ Control of Interconnected Systems with Time-varying Delays
Amal Zouhri
2017-01-01
Full Text Available This paper focuses on the problem of delay dependent stability/stabilization of interconnected systems with time-varying delays. The approach is based on a new Lyapunov-Krasovskii functional. A decentralized delay-dependent stability analysis is performed to characterize linear matrix inequalities (LMIs based on the conditions under which every local subsystem of the linear interconnected delay system is asymptotically stable. Then we design a decentralized state-feedback stabilization scheme such that the family of closedloop feedback subsystems enjoys the delay-dependent asymptotic stability for each subsystem. The decentralized feedback gains are determined by convex optimization over LMIs. All the developed results are tested on a representative example and compared with some recent previous ones.
Zhang, Langwen; Xie, Wei; Wang, Jingcheng
2017-11-01
In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.
A First-order Prediction-Correction Algorithm for Time-varying (Constrained) Optimization: Preprint
Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Simonetto, Andrea [Universite catholique de Louvain
2017-07-25
This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are established to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.
Almost Periodic Solution for Memristive Neural Networks with Time-Varying Delays
Huaiqin Wu
2013-01-01
Full Text Available This paper is concerned with the dynamical stability analysis for almost periodic solution of memristive neural networks with time-varying delays. Under the framework of Filippov solutions, by applying the inequality analysis techniques, the existence and asymptotically almost periodic behavior of solutions are discussed. Based on the differential inclusions theory and Lyapunov functional approach, the stability issues of almost periodic solution are investigated, and a sufficient condition for the existence, uniqueness, and global exponential stability of the almost periodic solution is established. Moreover, as a special case, the condition which ensures the global exponential stability of a unique periodic solution is also presented for the considered memristive neural networks. Two examples are given to illustrate the validity of the theoretical results.
On-line statistical processing of radiation detector pulse trains with time-varying count rates
Apostolopoulos, G.
2008-01-01
Statistical analysis is of primary importance for the correct interpretation of nuclear measurements, due to the inherent random nature of radioactive decay processes. This paper discusses the application of statistical signal processing techniques to the random pulse trains generated by radiation detectors. The aims of the presented algorithms are: (i) continuous, on-line estimation of the underlying time-varying count rate θ(t) and its first-order derivative dθ/dt; (ii) detection of abrupt changes in both of these quantities and estimation of their new value after the change point. Maximum-likelihood techniques, based on the Poisson probability distribution, are employed for the on-line estimation of θ and dθ/dt. Detection of abrupt changes is achieved on the basis of the generalized likelihood ratio statistical test. The properties of the proposed algorithms are evaluated by extensive simulations and possible applications for on-line radiation monitoring are discussed
The co-movement of monetary policy and its time-varying nature: A DCCA approach
Rohit, Abhishek; Mitra, Subrata Kumar
2018-02-01
Employing a novel methodology of DCCA cross-correlation coefficient (ρDCCA), this study attempts to provide fresh evidences for the co-movement of monetary policies of the advanced (AEs) as well as the emerging economies (EMEs) vis-à-vis the United States. A higher degree of monetary co-movement as measured by ρDCCA values, is identified for the AEs as compared to the EMEs. Lower co-movement of monetary policy is especially noticeable in the short run for EMEs. We further investigate the time-varying nature of such co-movements for the AEs by splitting the period (1980-2014) into four sub periods and also by performing a rolling window estimation for the entire period to reveal smoother dynamics. Significant evidence of higher monetary coordination is revealed for sub-periods with stronger trade and financial linkages.
Time-varying predictability in crude-oil markets: the case of GCC countries
El Hedi Arouri, Mohamed; Thanh Huong Dinh; Duc Khuong Nguyen
2010-01-01
This paper uses a time-varying parameter model with generalized autoregressive conditional heteroscedasticity effects to examine the dynamic behavior of crude-oil prices for the period February 7, 1997-January 8, 2010. Using data from four countries of the Gulf Cooperation Council, we find evidence of short-term predictability in oil-price changes over time, except for several short sub-periods. However, the hypothesis of convergence towards weak-form informational efficiency is rejected for all markets. In addition, we explore the possibility of structural breaks in the time-paths of the estimated predictability indices and detect only one breakpoint, for the oil markets in Qatar and the United Arab Emirates. Our empirical results therefore call for new empirical research to further gauge the predictability characteristics and the determinants of oil-price changes.
Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick
2015-08-01
Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.
A Comparison of Evolutionary Algorithms for Tracking Time-Varying Recursive Systems
White Michael S
2003-01-01
Full Text Available A comparison is made of the behaviour of some evolutionary algorithms in time-varying adaptive recursive filter systems. Simulations show that an algorithm including random immigrants outperforms a more conventional algorithm using the breeder genetic algorithm as the mutation operator when the time variation is discontinuous, but neither algorithm performs well when the time variation is rapid but smooth. To meet this deficit, a new hybrid algorithm which uses a hill climber as an additional genetic operator, applied for several steps at each generation, is introduced. A comparison is made of the effect of applying the hill climbing operator a few times to all members of the population or a larger number of times solely to the best individual; it is found that applying to the whole population yields the better results, substantially improved compared with those obtained using earlier methods.
Measurement of speech levels in the presence of time varying background noise
Pearsons, K. S.; Horonjeff, R.
1982-01-01
Short-term speech level measurements which could be used to note changes in vocal effort in a time varying noise environment were studied. Knowing the changes in speech level would in turn allow prediction of intelligibility in the presence of aircraft flyover noise. Tests indicated that it is possible to use two second samples of speech to estimate long term root mean square speech levels. Other tests were also performed in which people read out loud during aircraft flyover noise. Results of these tests indicate that people do indeed raise their voice during flyovers at a rate of about 3-1/2 dB for each 10 dB increase in background level. This finding is in agreement with other tests of speech levels in the presence of steady state background noise.
Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.
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.
Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.
Tomoaki Chiba
Full Text Available 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.
The time-varying correlation between policy uncertainty and stock returns: Evidence from China
Xiong, Xiong; Bian, Yuxiang; Shen, Dehua
2018-06-01
In this paper, we use a new policy uncertainty index to investigate the time-varying correlation between economic policy uncertainty (EPU) and Chinese stock market returns. The correlation is examined in the period from January 1995 to December 2016. We show that absolute changes in EPU have a significant impact on stock market returns. Specifically, empirical results based on the DCC-GARCH model reveal that the correlation between EPU and stock returns has large fluctuations, especially during a financial crisis; in addition, the impact of EPU on the Shanghai stock market is greater than on the Shenzhen stock market. Robustness results reveal that the impact of EPU on state-owned enterprises is larger than on non-state enterprises. All of these results highlight the important role of EPU in the Chinese stock market, and shed light on such issues for future research.
The time-varying role of the family in student time use and achievement
Marie C. Hull
2017-10-01
Full Text Available Abstract In this paper, I use a unique dataset linking administrative school data with birth records to quantify the importance of time-varying family factors for child achievement and time use. Specifically, I take a model of academic achievement commonly used in the test score literature, and I augment it to include a family-year effect. Identification comes from the large number of sibling pairs observed in the same year. While prior literature has focused on specific shocks, such as job loss, I capture the full set of innovations that are shared across siblings in a given year. The distributions of fixed effects reveal that annual family innovations, relative to what was expected based on the previous year, are more important than teacher assignment for student achievement and also play a substantial role in the time students spend on homework, free reading, and television. JEL Classification I21, J13, J24
Off-Line Robust Constrained MPC for Linear Time-Varying Systems with Persistent Disturbances
P. Bumroongsri
2014-01-01
Full Text Available An off-line robust constrained model predictive control (MPC algorithm for linear time-varying (LTV systems is developed. A novel feature is the fact that both model uncertainty and bounded additive disturbance are explicitly taken into account in the off-line formulation of MPC. In order to reduce the on-line computational burdens, a sequence of explicit control laws corresponding to a sequence of positively invariant sets is computed off-line. At each sampling time, the smallest positively invariant set containing the measured state is determined and the corresponding control law is implemented in the process. The proposed MPC algorithm can guarantee robust stability while ensuring the satisfaction of input and output constraints. The effectiveness of the proposed MPC algorithm is illustrated by two examples.
H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.
Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir
2018-03-01
This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
Multistability and instability analysis of recurrent neural networks with time-varying delays.
Zhang, Fanghai; Zeng, Zhigang
2018-01-01
This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly [Formula: see text] equilibria, [Formula: see text] of which are locally exponentially stable and the others are unstable, where k 0 is a nonnegative integer such that k 0 ≤n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties. This method improves and extends the existing results in the literature. Finally, one numerical example is provided to show the superiority and effectiveness of the presented results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Pinning synchronization of memristor-based neural networks with time-varying delays.
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.
Passivity analysis of memristor-based impulsive inertial neural networks with time-varying delays.
Wan, Peng; Jian, Jigui
2018-03-01
This paper focuses on delay-dependent passivity analysis for a class of memristive impulsive inertial neural networks with time-varying delays. By choosing proper variable transformation, the memristive inertial neural networks can be rewritten as first-order differential equations. The memristive model presented here is regarded as a switching system rather than employing the theory of differential inclusion and set-value map. Based on matrix inequality and Lyapunov-Krasovskii functional method, several delay-dependent passivity conditions are obtained to ascertain the passivity of the addressed networks. In addition, the results obtained here contain those on the passivity for the addressed networks without impulse effects as special cases and can also be generalized to other neural networks with more complex pulse interference. Finally, one numerical example is presented to show the validity of the obtained results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Gu, Huaying; Liu, Zhixue; Weng, Yingliang
2017-04-01
The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.
Estimating time-varying conditional correlations between stock and foreign exchange markets
Tastan, Hüseyin
2006-02-01
This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.
Parameter Estimation of a Closed Loop Coupled Tank Time Varying System using Recursive Methods
Basir, Siti Nora; Yussof, Hanafiah; Shamsuddin, Syamimi; Selamat, Hazlina; Zahari, Nur Ismarrubie
2013-01-01
This project investigates the direct identification of closed loop plant using discrete-time approach. The uses of Recursive Least Squares (RLS), Recursive Instrumental Variable (RIV) and Recursive Instrumental Variable with Centre-Of-Triangle (RIV + COT) in the parameter estimation of closed loop time varying system have been considered. The algorithms were applied in a coupled tank system that employs covariance resetting technique where the time of parameter changes occur is unknown. The performances of all the parameter estimation methods, RLS, RIV and RIV + COT were compared. The estimation of the system whose output was corrupted with white and coloured noises were investigated. Covariance resetting technique successfully executed when the parameters change. RIV + COT gives better estimates than RLS and RIV in terms of convergence and maximum overshoot
Time-varying Entry Heating Profile Replication with a Rotating Arc Jet Test Article
Grinstead, Jay Henderson; Venkatapathy, Ethiraj; Noyes, Eric A.; Mach, Jeffrey J.; Empey, Daniel M.; White, Todd R.
2014-01-01
A new approach for arc jet testing of thermal protection materials at conditions approximating the time-varying conditions of atmospheric entry was developed and demonstrated. The approach relies upon the spatial variation of heat flux and pressure over a cylindrical test model. By slowly rotating a cylindrical arc jet test model during exposure to an arc jet stream, each point on the test model will experience constantly changing applied heat flux. The predicted temporal profile of heat flux at a point on a vehicle can be replicated by rotating the cylinder at a prescribed speed and direction. An electromechanical test model mechanism was designed, built, and operated during an arc jet test to demonstrate the technique.
Horner, Andrew B; Beauchamp, James W; So, Richard H Y
2009-01-01
Gradated spectral interpolations between musical instrument tone pairs were used to investigate discrimination as a function of time-averaged spectral difference. All possible nonidentical pairs taken from a collection of eight musical instrument sounds consisting of bassoon, clarinet, flute, horn, oboe, saxophone, trumpet, and violin were tested. For each pair, several tones were generated with different balances between the primary and secondary instruments, where the balance was fixed across the duration of each tone. Among primary instruments it was found that changes to horn and bassoon [corrected] were most easily discriminable, while changes to saxophone and trumpet timbres were least discriminable. Among secondary instruments, the clarinet had the strongest effect on discrimination, whereas the bassoon had the least effect. For primary instruments, strong negative correlations were found between discrimination and their spectral incoherences, suggesting that the presence of dynamic spectral variations tends to increase the difficulty of detecting time-varying alterations such as spectral interpolation.
Fault Detection for Non-Gaussian Stochastic Systems with Time-Varying Delay
Tao Li
2013-01-01
Full Text Available Fault detection (FD for non-Gaussian stochastic systems with time-varying delay is studied. The available information for the addressed problem is the input and the measured output probability density functions (PDFs of the system. In this framework, firstly, by constructing an augmented Lyapunov functional, which involves some slack variables and a tuning parameter, a delay-dependent condition for the existence of FD observer is derived in terms of linear matrix inequality (LMI and the fault can be detected through a threshold. Secondly, in order to improve the detection sensitivity performance, the optimal algorithm is applied to minimize the threshold value. Finally, paper-making process example is given to demonstrate the applicability of the proposed approach.
A Time-Varied Probabilistic ON/OFF Switching Algorithm for Cellular Networks
Rached, Nadhir B.
2018-01-11
In this letter, we develop a time-varied probabilistic on/off switching planning method for cellular networks to reduce their energy consumption. It consists in a risk-aware optimization approach that takes into consideration the randomness of the user profile associated with each base station (BS). The proposed approach jointly determines (i) the instants of time at which the current active BS configuration must be updated due to an increase or decrease of the network traffic load, and (ii) the set of minimum BSs to be activated to serve the networks’ subscribers. Probabilistic metrics modeling the traffic profile variation are developed to trigger this dynamic on/off switching operation. Selected simulation results are then performed to validate the proposed algorithm for different system parameters.
A topological approach to migration and visualization of time-varying volume data
Fujishiro, Issei; Otsuka, Rieko; Hamaoka, Aya; Takeshima, Yuriko; Takahashi, Shigeo
2004-01-01
Rapid advance in high performance computing and measurement technologies has recently made it possible to produce a stupendous amount of time-varying volume datasets in various disciplines. However, there exist a few known visual exploration tools which allow us to investigate the core of their complex behavior effectively. In this article, our previous approach to topological volume skeletonization is extended to capture the topological skeleton of a 4D volumetric field in terms of critical timing. A cyclic information drilldown scheme, termed T-map, is presented, where a wide choice of information visualization techniques are deployed so that the users are allowed to repeatedly squeeze partial spatiotemporal domains of interest until the size gets fitted into an available computing storage space, prior to topologically-accentuated visualization of the pinpointed volumetric domains. A case study with datasets from atomic collision research is performed to illustrate the feasibility of the present method. (author)
Efficiency or speculation? A time-varying analysis of European sovereign debt
Ferreira, Paulo
2018-01-01
The outbreak of the Greek debt crisis caused turmoil in European markets and drew attention to the problem of public debt and its consequences. The increase in the return rates of sovereign debts was one of these consequences. However, like any other asset, sovereign debt returns are expected to have a memoryless behaviour. Analysing a total of 15 European countries (Eurozone and non-Eurozone), and applying a time-varying analysis of the Hurst exponent, we found evidence of long-range memory in sovereign bonds. When analysing the spreads between each bond and the German one, it is possible to conclude that Eurozone countries' spreads show more evidence of long-range dependence. Considering the Eurozone countries most affected by the Eurozone crisis, that long-range dependence is more evident, but started before the crisis, which could be interpreted as possible speculation by investors.
The optimal replenishment policy for time-varying stochastic demand under vendor managed inventory
Govindan, Kannan
2015-01-01
A Vendor Managed Inventory (VMI) partnership places the responsibility on the vendor (rather than on buyers) to schedule purchase orders for inventory replenishment in the supply chain system. In this research, the supply chain network considers the Silver-Meal heuristic with an augmentation...... quantity replenishment policy between both traditional and VMI systems. We consider time-varying stochastic demand in two-echelon (one vendor, multiple retailers) supply chains. This paper seeks to find the supply chain that minimizes system cost through comparing performance between traditional and VMI...... systems. A mathematical model is developed, and total supply chain cost is used as the measure of comparison. The models are applied in both traditional and VMI supply chains based on pharmaceutical industry data, and we focus on total cost difference compared through the use of Adjusted Silver-Meal (ASM...
Linear response approach to active Brownian particles in time-varying activity fields
Merlitz, Holger; Vuijk, Hidde D.; Brader, Joseph; Sharma, Abhinav; Sommer, Jens-Uwe
2018-05-01
In a theoretical and simulation study, active Brownian particles (ABPs) in three-dimensional bulk systems are exposed to time-varying sinusoidal activity waves that are running through the system. A linear response (Green-Kubo) formalism is applied to derive fully analytical expressions for the torque-free polarization profiles of non-interacting particles. The activity waves induce fluxes that strongly depend on the particle size and may be employed to de-mix mixtures of ABPs or to drive the particles into selected areas of the system. Three-dimensional Langevin dynamics simulations are carried out to verify the accuracy of the linear response formalism, which is shown to work best when the particles are small (i.e., highly Brownian) or operating at low activity levels.
Cai, Shuiming; Hao, Junjun; Liu, Zengrong
2011-06-01
This paper studies the synchronization of coupled chaotic systems with time-varying delays in the presence of parameter mismatches by means of periodically intermittent control. Some novel and useful quasisynchronization criteria are obtained by using the methods which are different from the techniques employed in the existing works, and the derived results are less conservative. Especially, a strong constraint on the control width that the control width should be larger than the time delay imposed by the current references is released in this paper. Moreover, our results show that the synchronization criteria depend on the ratio of control width to control period, but not the control width or the control period. Finally, some numerical simulations are given to show the effectiveness of the theoretical results.
Weighted H∞ Filtering for a Class of Switched Linear Systems with Additive Time-Varying Delays
Li-li Li
2015-01-01
Full Text Available This paper is concerned with the problem of weighted H∞ filtering for a class of switched linear systems with two additive time-varying delays, which represent a general class of switched time-delay systems with strong practical background. Combining average dwell time (ADT technique with piecewise Lyapunov functionals, sufficient conditions are established to guarantee the exponential stability and weighted H∞ performance for the filtering error systems. The parameters of the designed switched filters are obtained by solving linear matrix inequalities (LMIs. A modification of Jensen integral inequality is exploited to derive results with less theoretical conservatism and computational complexity. Finally, two examples are given to demonstrate the effectiveness of the proposed method.
Synchronization criterion for Lur'e type complex dynamical networks with time-varying delay
Ji, D.H.; Park, Ju H.; Yoo, W.J.; Won, S.C.; Lee, S.M.
2010-01-01
In this Letter, the synchronization problem for a class of complex dynamical networks in which every identical node is a Lur'e system with time-varying delay is considered. A delay-dependent synchronization criterion is derived for the synchronization of complex dynamical network that represented by Lur'e system with sector restricted nonlinearities. The derived criterion is a sufficient condition for absolute stability of error dynamics between the each nodes and the isolated node. Using a convex representation of the nonlinearity for error dynamics, the stability condition based on the discretized Lyapunov-Krasovskii functional is obtained via LMI formulation. The proposed delay-dependent synchronization criterion is less conservative than the existing ones. The effectiveness of our work is verified through numerical examples.
I PUTU GEDE DIAN GERRY SUWEDAYANA
2016-08-01
Full Text Available The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE is equal to dan root mean square percentage error (RMSPE is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016.
Xu Shengyuan; Lam, James; Ho, Daniel W.C.
2005-01-01
This Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method
Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis
Liu, Yurong; Wang, Zidong; Serrano, Alan; Liu, Xiaohui
2007-01-01
This Letter is concerned with the analysis problem of exponential stability for a class of discrete-time recurrent neural networks (DRNNs) with time delays. The delay is of the time-varying nature, and the activation functions are assumed to be neither differentiable nor strict monotonic. Furthermore, the description of the activation functions is more general than the recently commonly used Lipschitz conditions. Under such mild conditions, we first prove the existence of the equilibrium point. Then, by employing a Lyapunov-Krasovskii functional, a unified linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the DRNNs to be globally exponentially stable. It is shown that the delayed DRNNs are globally exponentially stable if a certain LMI is solvable, where the feasibility of such an LMI can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition
An Explicit MOT-TD-VIE Solver for Time Varying Media
Sayed, Sadeed Bin
2016-03-15
An explicit marching on-in-time (MOT) scheme for solving the time domain electric field integral equation enforced on volumes with time varying dielectric permittivity is proposed. Unknowns of the integral equation and the constitutive relation, i.e., flux density and field intensity, are discretized using full and half Schaubert-Wilton-Glisson functions in space. Temporal interpolation is carried out using band limited approximate prolate spherical wave functions. The discretized coupled system of integral equation and constitutive relation is integrated in time using a PE(CE)m type linear multistep scheme. Unlike the existing MOT methods, the resulting explicit MOT scheme allows for straightforward incorporation of the time variation in the dielectric permittivity.
Periodic solution for state-dependent impulsive shunting inhibitory CNNs with time-varying delays.
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. Copyright © 2015 Elsevier Ltd. All rights reserved.
Sahoo, P. K.; Sahoo, Parbati; Bishi, Binaya K.; Aygün, Sezgin
2018-04-01
In this paper, we have studied homogeneous and anisotropic locally rotationally symmetric (LRS) Bianchi type-I model with magnetized strange quark matter (MSQM) distribution and cosmological constant Λ in f(R, T) gravity where R is the Ricci scalar and T the trace of matter source. The exact solutions of the field equations are obtained under bilinear and special form of time varying deceleration parameter (DP). Firstly, we have considered two specific forms of bilinear DP with a single parameter of the form: q = α(1-t)/1+t and q = -αt/1+t, which leads to the constant or linear nature of the function based on the constant α. Second one is the special form of the DP as q = - 1 + β/1+aβ. From the results obtained here, one can observe that in the early universe magnetic flux has more effects and it reduces gradually in the later stage. For t → ∞, we get p → -Bc and ρ → Bc. The behaviour of strange quark matter along with magnetic epoch gives an idea of accelerated expansion of the universe as per the observations of the type Ia Supernovae.
A multiscale MDCT image-based breathing lung model with time-varying regional ventilation
Yin, Youbing, E-mail: youbing-yin@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Radiology, The University of Iowa, Iowa City, IA 52242 (United States); Choi, Jiwoong, E-mail: jiwoong-choi@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Hoffman, Eric A., E-mail: eric-hoffman@uiowa.edu [Department of Radiology, The University of Iowa, Iowa City, IA 52242 (United States); Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Internal Medicine, The University of Iowa, Iowa City, IA 52242 (United States); Tawhai, Merryn H., E-mail: m.tawhai@auckland.ac.nz [Auckland Bioengineering Institute, The University of Auckland, Auckland (New Zealand); Lin, Ching-Long, E-mail: ching-long-lin@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States)
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.
Evolution of colloidal dispersions in novel time-varying optical potentials
Koss, Brian Alan
Optical traps use forces exerted by a tightly focused light beam to trap objects from tens of nanometers to tens of micrometers in size. Since their introduction in 1986, optical tweezers have become very useful to biology, chemistry, and soft condensed-matter physics. Work presented here, promises to advance optical tweezers not only in fundamental scientific research, but also in applications outside of the laboratory and into the mainstream of miniaturized manufacturing and diagnostics. By providing unprecedented access to the mesoscopic world, a new generation of optical traps, called Dynamic Holographic Optical Tweezers (HOTs) offers revolutionary new opportunities for fundamental and applied research. To demonstrate this technique, HOTs will be used to pump particles via a new method of transport called Optical Peristalsis (OP). OP is efficient method for transporting mesoscopic objects in three dimensions using short repetitive sequences of holographic optical trapping patterns. Transport in this process is analogous to peristaltic pumping, with the configurations of optical traps mimicking states of a peristaltic pump. While not limited to the deterministic particle transport, OP, can also be a platform to investigate the stochastic limit of particle transport. Advances in recent years have demonstrated that a variety of time-varying perturbations can induce drift in a diffusive system without exerting an overall force. Among these, are thermal ratchet models in which the system is subjected to time-varying energy landscapes that break spatiotemporal symmetry and thereby induce drift. Typically, the potential energy landscape is chosen to be the sawtooth potential. This work describes an alternate class of symmetric thermal ratchet models, that are not sawtooth, and demonstrates their efficacy in biasing the diffusion of colloidal spheres in both the stochastic and deterministic limits. Unlike previous models, each state in this thermal ratchet consists of
A multiscale MDCT image-based breathing lung model with time-varying regional ventilation
Yin, Youbing; Choi, Jiwoong; Hoffman, Eric A.; Tawhai, Merryn H.; Lin, Ching-Long
2012-01-01
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. PMID:23794749
A hepatitis C virus infection model with time-varying drug effectiveness: solution and analysis.
Jessica M Conway
2014-08-01
Full Text Available Simple models of therapy for viral diseases such as hepatitis C virus (HCV or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time.
Toward the integration of European natural gas markets:A time-varying approach
Renou-Maissant, Patricia
2012-01-01
Over the past fifteen years, European gas markets have radically changed. In order to build a single European gas market, a new regulatory framework has been established through three European Gas Directives. The purpose of this article is to investigate the impact of the reforms in the natural gas industry on consumer prices, with a specific focus on gas prices for industrial use. The strength of the relationship between the industrial gas prices of six western European countries is studied by testing the Law of One Price for the period 1991–2009. Estimations were carried out using both cointegration analysis and time-varying parameter models. Results highlight an emerging and on-going process of convergence between the industrial gas prices in western Europe since 2001 for the six EU member states. The strength and the level of convergence differ widely between countries. Strong integration of gas markets in continental Europe, except for the Belgian market, has been established. It appears that the convergence process between continental countries and the UK is not completed. Thus, the integration of European gas markets remains an open issue and the question of how far integration will proceed will still be widely discussed in the coming years. - Highlights: ► We investigate the integration of European natural gas markets. ► We use both cointegration analysis and time-varying parameter models. ► We show the failure of cointegration techniques to take account of evolving processes. ► An emerging and on-going process of convergence between the industrial gas prices is at work. ► Strong integration of gas markets in continental Europe has been established.
Time-varying wing-twist improves aerodynamic efficiency of forward flight in butterflies.
Zheng, Lingxiao; Hedrick, Tyson L; Mittal, Rajat
2013-01-01
Insect wings can undergo significant chordwise (camber) as well as spanwise (twist) deformation during flapping flight but the effect of these deformations is not well understood. The shape and size of butterfly wings leads to particularly large wing deformations, making them an ideal test case for investigation of these effects. Here we use computational models derived from experiments on free-flying butterflies to understand the effect of time-varying twist and camber on the aerodynamic performance of these insects. High-speed videogrammetry is used to capture the wing kinematics, including deformation, of a Painted Lady butterfly (Vanessa cardui) in untethered, forward flight. These experimental results are then analyzed computationally using a high-fidelity, three-dimensional, unsteady Navier-Stokes flow solver. For comparison to this case, a set of non-deforming, flat-plate wing (FPW) models of wing motion are synthesized and subjected to the same analysis along with a wing model that matches the time-varying wing-twist observed for the butterfly, but has no deformation in camber. The simulations show that the observed butterfly wing (OBW) outperforms all the flat-plate wings in terms of usable force production as well as the ratio of lift to power by at least 29% and 46%, respectively. This increase in efficiency of lift production is at least three-fold greater than reported for other insects. Interestingly, we also find that the twist-only-wing (TOW) model recovers much of the performance of the OBW, demonstrating that wing-twist, and not camber is key to forward flight in these insects. The implications of this on the design of flapping wing micro-aerial vehicles are discussed.
Time-varying wing-twist improves aerodynamic efficiency of forward flight in butterflies.
Lingxiao Zheng
Full Text Available Insect wings can undergo significant chordwise (camber as well as spanwise (twist deformation during flapping flight but the effect of these deformations is not well understood. The shape and size of butterfly wings leads to particularly large wing deformations, making them an ideal test case for investigation of these effects. Here we use computational models derived from experiments on free-flying butterflies to understand the effect of time-varying twist and camber on the aerodynamic performance of these insects. High-speed videogrammetry is used to capture the wing kinematics, including deformation, of a Painted Lady butterfly (Vanessa cardui in untethered, forward flight. These experimental results are then analyzed computationally using a high-fidelity, three-dimensional, unsteady Navier-Stokes flow solver. For comparison to this case, a set of non-deforming, flat-plate wing (FPW models of wing motion are synthesized and subjected to the same analysis along with a wing model that matches the time-varying wing-twist observed for the butterfly, but has no deformation in camber. The simulations show that the observed butterfly wing (OBW outperforms all the flat-plate wings in terms of usable force production as well as the ratio of lift to power by at least 29% and 46%, respectively. This increase in efficiency of lift production is at least three-fold greater than reported for other insects. Interestingly, we also find that the twist-only-wing (TOW model recovers much of the performance of the OBW, demonstrating that wing-twist, and not camber is key to forward flight in these insects. The implications of this on the design of flapping wing micro-aerial vehicles are discussed.
Stability of Nonlinear Systems with Unknown Time-varying Feedback Delay
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.
Time-Varying Distortions of Binaural Information by Bilateral Hearing Aids
Rodriguez, Francisco A.; Portnuff, Cory D. F.; Goupell, Matthew J.; Tollin, Daniel J.
2016-01-01
In patients with bilateral hearing loss, the use of two hearing aids (HAs) offers the potential to restore the benefits of binaural hearing, including sound source localization and segregation. However, existing evidence suggests that bilateral HA users’ access to binaural information, namely interaural time and level differences (ITDs and ILDs), can be compromised by device processing. Our objective was to characterize the nature and magnitude of binaural distortions caused by modern digital behind-the-ear HAs using a variety of stimuli and HA program settings. Of particular interest was a common frequency-lowering algorithm known as nonlinear frequency compression, which has not previously been assessed for its effects on binaural information. A binaural beamforming algorithm was also assessed. Wide dynamic range compression was enabled in all programs. HAs were placed on a binaural manikin, and stimuli were presented from an arc of loudspeakers inside an anechoic chamber. Stimuli were broadband noise bursts, 10-Hz sinusoidally amplitude-modulated noise bursts, or consonant–vowel–consonant speech tokens. Binaural information was analyzed in terms of ITDs, ILDs, and interaural coherence, both for whole stimuli and in a time-varying sense (i.e., within a running temporal window) across four different frequency bands (1, 2, 4, and 6 kHz). Key findings were: (a) Nonlinear frequency compression caused distortions of high-frequency envelope ITDs and significantly reduced interaural coherence. (b) For modulated stimuli, all programs caused time-varying distortion of ILDs. (c) HAs altered the relationship between ITDs and ILDs, introducing large ITD–ILD conflicts in some cases. Potential perceptual consequences of measured distortions are discussed. PMID:27698258
Charbonneau, Jeremy
As the perceived quality of a product is becoming more important in the manufacturing industry, more emphasis is being placed on accurately predicting the sound quality of everyday objects. This study was undertaken to improve upon current prediction techniques with regard to the psychoacoustic descriptor of loudness and an improved binaural summation technique. The feasibility of this project was first investigated through a loudness matching experiment involving thirty-one subjects and pure tones of constant sound pressure level. A dependence of binaural summation on frequency was observed which had previously not been a subject of investigation in the reviewed literature. A follow-up investigation was carried out with forty-eight volunteers and pure tones of constant sensation level. Contrary to existing theories in literature the resulting loudness matches revealed an amplitude versus frequency relationship which confirmed the perceived increase in loudness when a signal was presented to both ears simultaneously as opposed to one ear alone. The resulting trend strongly indicated that the higher the frequency of the presented signal, the greater the increase in observed binaural summation. The results from each investigation were summarized into a single binaural summation algorithm and inserted into an improved time-varying loudness model. Using experimental techniques, it was demonstrated that the updated binaural summation algorithm was a considerable improvement over the state of the art approach for predicting the perceived binaural loudness. The improved function retained the ease of use from the original model while additionally providing accurate estimates of diotic listening conditions from monaural WAV files. It was clearly demonstrated using a validation jury test that the revised time-varying loudness model was a significant improvement over the previously standardized approach.
Local inertial oscillations in the surface ocean generated by time-varying winds
Chen, Shengli; Polton, Jeff A.; Hu, Jianyu; Xing, Jiuxing
2015-12-01
A new relationship is presented to give a review study on the evolution of inertial oscillations in the surface ocean locally generated by time-varying wind stress. The inertial oscillation is expressed as the superposition of a previous oscillation and a newly generated oscillation, which depends upon the time-varying wind stress. This relationship is employed to investigate some idealized wind change events. For a wind series varying temporally with different rates, the induced inertial oscillation is dominated by the wind with the greatest variation. The resonant wind, which rotates anti-cyclonically at the local inertial frequency with time, produces maximal amplitude of inertial oscillations, which grows monotonically. For the wind rotating at non-inertial frequencies, the responses vary periodically, with wind injecting inertial energy when it is in phase with the currents, but removing inertial energy when it is out of phase. The wind rotating anti-cyclonically with time is much more favorable to generate inertial oscillations than the cyclonic rotating wind. The wind with a frequency closer to the inertial frequency generates stronger inertial oscillations. For a diurnal wind, the induced inertial oscillation is dependent on latitude and is most significant at 30 °. This relationship is also applied to examine idealized moving cyclones. The inertial oscillation is much stronger on the right-hand side of the cyclone path than on the left-hand side (in the northern hemisphere). This is due to the wind being anti-cyclonic with time on the right-hand side, but cyclonic on the other side. The inertial oscillation varies with the cyclone translation speed. The optimal translation speed generating the greatest inertial oscillations is 2 m/s at the latitude of 10 ° and gradually increases to 6 m/s at the latitude of 30 °.
Fluctuating interaction network and time-varying stability of a natural fish community
Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio
2018-02-01
Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.
Islam S.M. Khalil
2016-06-01
Full Text Available Targeted therapy using magnetic microparticles and nanoparticles has the potential to mitigate the negative side-effects associated with conventional medical treatment. Major technological challenges still need to be addressed in order to translate these particles into in vivo applications. For example, magnetic particles need to be navigated controllably in vessels against flowing streams of body fluid. This paper describes the motion control of paramagnetic microparticles in the flowing streams of fluidic channels with time-varying flow rates (maximum flow is 35 ml.hr−1. This control is designed using a magnetic-based proportional-derivative (PD control system to compensate for the time-varying flow inside the channels (with width and depth of 2 mm and 1.5 mm, respectively. First, we achieve point-to-point motion control against and along flow rates of 4 ml.hr−1, 6 ml.hr−1, 17 ml.hr−1, and 35 ml.hr−1. The average speeds of single microparticle (with average diameter of 100 μm against flow rates of 6 ml.hr−1 and 30 ml.hr−1 are calculated to be 45 μm.s−1 and 15 μm.s−1, respectively. Second, we implement PD control with disturbance estimation and compensation. This control decreases the steady-state error by 50%, 70%, 73%, and 78% at flow rates of 4 ml.hr−1, 6 ml.hr−1, 17 ml.hr−1, and 35 ml.hr−1, respectively. Finally, we consider the problem of finding the optimal path (minimal kinetic energy between two points using calculus of variation, against the mentioned flow rates. Not only do we find that an optimal path between two collinear points with the direction of maximum flow (middle of the fluidic channel decreases the rise time of the microparticles, but we also decrease the input current that is supplied to the electromagnetic coils by minimizing the kinetic energy of the microparticles, compared to a PD control with disturbance compensation.
Self-adaptive phosphor coating technology for wafer-level scale chip packaging
Zhou Linsong; Rao Haibo; Wang Wei; Wan Xianlong; Liao Junyuan; Wang Xuemei; Zhou Da; Lei Qiaolin
2013-01-01
A new self-adaptive phosphor coating technology has been successfully developed, which adopted a slurry method combined with a self-exposure process. A phosphor suspension in the water-soluble photoresist was applied and exposed to LED blue light itself and developed to form a conformal phosphor coating with self-adaptability to the angular distribution of intensity of blue light and better-performing spatial color uniformity. The self-adaptive phosphor coating technology had been successfully adopted in the wafer surface to realize a wafer-level scale phosphor conformal coating. The first-stage experiments show satisfying results and give an adequate demonstration of the flexibility of self-adaptive coating technology on application of WLSCP. (semiconductor devices)
Lenahan, Jack; Nash, Michael P; Charles, Phil
2008-01-01
.... We present the following hypothesis: predictive deliberation management using self-adapting and self-modeling software will be required to provide mission planning adjustments after the start of a mission...
Finite element method for solving Kohn-Sham equations based on self-adaptive tetrahedral mesh
Zhang Dier; Shen Lihua; Zhou Aihui; Gong Xingao
2008-01-01
A finite element (FE) method with self-adaptive mesh-refinement technique is developed for solving the density functional Kohn-Sham equations. The FE method adopts local piecewise polynomials basis functions, which produces sparsely structured matrices of Hamiltonian. The method is well suitable for parallel implementation without using Fourier transform. In addition, the self-adaptive mesh-refinement technique can control the computational accuracy and efficiency with optimal mesh density in different regions
Mass Redistribution in the Core and Time-varying Gravity at the Earth's Surface
Kuang, Wei-Jia; Chao, Benjamin F.; Fang, Ming
2003-01-01
The Earth's liquid outer core is in convection, as suggested by the existence of the geomagnetic field in much of the Earth's history. One consequence of the convection is the redistribution of mass resulting from relative motion among fluid parcels with slightly different densities. This time dependent mass redistribution inside the core produces a small perturbation on the gravity field of the Earth. With our numerical dynamo solutions, we find that the mass redistribution (and the resultant gravity field) symmetric about the equator is much stronger than that anti-symmetric about the equator. In particular, J(sub 2) component is the strongest. In addition, the gravity field variation increases with the Rayleigh number that measures the driving force for the geodynamo in the core. With reasonable scaling from the current dynamo solutions, we could expect that at the surface of the Earth, the J(sub 2) variation from the core is on the order of l0(exp -16)/year relative to the mean (i.e. spherically symmetric) gravity field of the Earth. The possible shielding effect due to core-mantle boundary pressure variation loading is likely much smaller and is therefore negligible. Our results suggest that time-varying gravity field perturbation due to core mass redistribution may be measured with modem space geodetic observations, which will result a new means of detecting dynamical processes in the Earth's deep interior.
Replicability of time-varying connectivity patterns in large resting state fMRI samples.
Abrol, Anees; Damaraju, Eswar; Miller, Robyn L; Stephen, Julia M; Claus, Eric D; Mayer, Andrew R; Calhoun, Vince D
2017-12-01
The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Solution to the monoenergetic time-dependent neutron transport equation with a time-varying source
Ganapol, B.D.
1986-01-01
Even though fundamental time-dependent neutron transport problems have existed since the inception of neutron transport theory, it has only been recently that a reliable numerical solution to one of the basic problems has been obtained. Experience in generating numerical solutions to time-dependent transport equations has indicated that the multiple collision formulation is the most versatile numerical technique for model problems. The formulation coupled with a moment reconstruction of each collided flux component has led to benchmark-quality (four- to five-digit accuracy) numerical evaluation of the neutron flux in plane infinite geometry for any degree of scattering anisotropy and for both pulsed isotropic and beam sources. As will be shown in this presentation, this solution can serve as a Green's function, thus extending the previous results to more complicated source situations. Here we will be concerned with a time-varying source at the center of an infinite medium. If accurate, such solutions have both pedagogical and practical uses as benchmarks against which other more approximate solutions designed for a wider class of problems can be compared
Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields
Wang, B.
2013-06-01
Analyzing critical points and their temporal evolutions plays a crucial role in understanding the behavior of vector fields. A key challenge is to quantify the stability of critical points: more stable points may represent more important phenomena or vice versa. The topological notion of robustness is a tool which allows us to quantify rigorously the stability of each critical point. Intuitively, the robustness of a critical point is the minimum amount of perturbation necessary to cancel it within a local neighborhood, measured under an appropriate metric. In this paper, we introduce a new analysis and visualization framework which enables interactive exploration of robustness of critical points for both stationary and time-varying 2D vector fields. This framework allows the end-users, for the first time, to investigate how the stability of a critical point evolves over time. We show that this depends heavily on the global properties of the vector field and that structural changes can correspond to interesting behavior. We demonstrate the practicality of our theories and techniques on several datasets involving combustion and oceanic eddy simulations and obtain some key insights regarding their stable and unstable features. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Connelly, Blair C.
In order to reduce the emission of pollutants such as soot and NO x from combustion systems, a detailed understanding of pollutant formation is required. In addition to environmental concerns, this is important for a fundamental understanding of flame behavior as significant quantities of soot lower local flame temperatures, increase overall flame length and affect the formation of such temperature-dependent species as NOx. This problem is investigated by carrying out coupled computational and experimental studies of steady and time-varying sooting, coflow diffusion flames. Optical diagnostic techniques are a powerful tool for characterizing combustion systems, as they provide a noninvasive method of probing the environment. Laser diagnostic techniques have added advantages, as systems can be probed with high spectral, temporal and spatial resolution, and with species selectivity. Experimental soot volume fractions were determined by using two-dimensional laser-induced incandescence (LII), calibrated with an on-line extinction measurement, and soot pyrometry. Measurements of soot particle size distributions are made using time-resolved LII (TR-LII). Laser-induced fluorescence measurements are made of NO and formaldehyde. These experimental measurements, and others, are compared with computational results in an effort to understand and model soot formation and to examine the coupled relationship of soot and NO x formation.
Optimal routing of hazardous substances in time-varying, stochastic transportation networks
Woods, A.L.; Miller-Hooks, E.; Mahmassani, H.S.
1998-07-01
This report is concerned with the selection of routes in a network along which to transport hazardous substances, taking into consideration several key factors pertaining to the cost of transport and the risk of population exposure in the event of an accident. Furthermore, the fact that travel time and the risk measures are not constant over time is explicitly recognized in the routing decisions. Existing approaches typically assume static conditions, possibly resulting in inefficient route selection and unnecessary risk exposure. The report described the application of recent advances in network analysis methodologies to the problem of routing hazardous substances. Several specific problem formulations are presented, reflecting different degrees of risk aversion on the part of the decision-maker, as well as different possible operational scenarios. All procedures explicitly consider travel times and travel costs (including risk measures) to be stochastic time-varying quantities. The procedures include both exact algorithms, which may require extensive computational effort in some situations, as well as more efficient heuristics that may not guarantee a Pareto-optimal solution. All procedures are systematically illustrated for an example application using the Texas highway network, for both normal and incident condition scenarios. The application illustrates the trade-offs between the information obtained in the solution and computational efficiency, and highlights the benefits of incorporating these procedures in a decision-support system for hazardous substance shipment routing decisions
Surafel Luleseged Tilahun
2017-01-01
Full Text Available Traffic congestion is one of the main issues in the study of transportation planning and management. It creates different problems including environmental pollution and health problem and incurs a cost which is increasing through years. One-third of this congestion is created by cars searching for parking places. Drivers may be aware that parking places are fully occupied but will drive around hoping that a parking place may become vacant. Opportunistic services, involving learning, predicting, and exploiting Internet of Things scenarios, are able to adapt to dynamic unforeseen situations and have the potential to ease parking search issues. Hence, in this paper, a cooperative dynamic prediction mechanism between multiple agents for parking space availability in the neighborhood, integrating foreseen and unforeseen events and adapting for long-term changes, is proposed. An agent in each parking place will use a dynamic and time varying Markov chain to predict the parking availability and these agents will communicate to produce the parking availability prediction in the whole neighborhood. Furthermore, a learning approach is proposed where the system can adapt to different changes in the parking demand including long-term changes. Simulation results, using synthesized data based on an actual parking lot data from a shopping mall in Geneva, show that the proposed model is promising based on the learning accuracy with service adaptation and performance in different cases.
St-Onge, Guillaume; Young, Jean-Gabriel; Laurence, Edward; Murphy, Charles; Dubé, Louis J.
2018-02-01
We present a degree-based theoretical framework to study the susceptible-infected-susceptible (SIS) dynamics on time-varying (rewired) configuration model networks. Using this framework on a given degree distribution, we provide a detailed analysis of the stationary state using the rewiring rate to explore the whole range of the time variation of the structure relative to that of the SIS process. This analysis is suitable for the characterization of the phase transition and leads to three main contributions: (1) We obtain a self-consistent expression for the absorbing-state threshold, able to capture both collective and hub activation. (2) We recover the predictions of a number of existing approaches as limiting cases of our analysis, providing thereby a unifying point of view for the SIS dynamics on random networks. (3) We obtain bounds for the critical exponents of a number of quantities in the stationary state. This allows us to reinterpret the concept of hub-dominated phase transition. Within our framework, it appears as a heterogeneous critical phenomenon: observables for different degree classes have a different scaling with the infection rate. This phenomenon is followed by the successive activation of the degree classes beyond the epidemic threshold.
On the link between oil price and exchange rate: A time-varying VAR parameter approach
Bremond, Vincent; Razafindrabe, Tovonony; Hache, Emmanuel
2015-07-01
The aim of this paper is to study the relationship between the effective exchange rate of the dollar and the oil price dynamics from 1976 to 2013. In this context, we propose to explore the economic literature dedicated to financial channels factors (exchange rate, monetary policy, and international liquidity) that could affect the oil price dynamics. In addition to oil prices and the effective exchange rate of the dollar, we use the dry cargo index as a proxy for the real economic activity and prices for precious and industrial raw materials. Using a Bayesian time-varying parameter vector auto-regressive estimation, our main results show that the US Dollar effective exchange rate elasticity of the crude oil prices is not constant across the time and remains negative from 1989. It then highlights that a depreciation of the effective exchange rate of the dollar leads to an increase of the crude oil prices. Our paper also demonstrates the growing influence of financial and commodities markets development upon the global economy. (authors)
Time-varying span efficiency through the wingbeat of desert locusts.
Henningsson, Per; Bomphrey, Richard J
2012-06-07
The flight performance of animals depends greatly on the efficacy with which they generate aerodynamic forces. Accordingly, maximum range, load-lifting capacity and peak accelerations during manoeuvres are all constrained by the efficiency of momentum transfer to the wake. Here, we use high-speed particle image velocimetry (1 kHz) to record flow velocities in the near wake of desert locusts (Schistocerca gregaria, Forskål). We use the measured flow fields to calculate time-varying span efficiency throughout the wing stroke cycle. The locusts are found to operate at a maximum span efficiency of 79 per cent, typically at a plateau of about 60 per cent for the majority of the downstroke, but at lower values during the upstroke. Moreover, the calculated span efficiencies are highest when the largest lift forces are being generated (90% of the total lift is generated during the plateau of span efficiency) suggesting that the combination of wing kinematics and morphology in locust flight perform most efficiently when doing the most work.
Optimum Control for Nonlinear Dynamic Radial Deformation of Turbine Casing with Time-Varying LSSVM
Cheng-Wei Fei
2015-01-01
Full Text Available With the development of the high performance and high reliability of aeroengine, the blade-tip radial running clearance (BTRRC of high pressure turbine seriously influences the reliability and performance of aeroengine, wherein the radial deformation control of turbine casing has to be concerned in BTRRC design. To improve BTRRC design, the optimum control-based probabilistic optimization of turbine casing radial deformation was implemented using time-varying least square support vector machine (T-LSSVM by considering nonlinear material properties and dynamic thermal load. First the T-LSSVM method was proposed and its mathematical model was established. And then the nonlinear dynamic optimal control model of casing radial deformation was constructed with T-LSSVM. Thirdly, through the numerical experiments, the T-LSSVM method is demonstrated to be a promising approach in reducing additional design samples and improving computational efficiency with acceptable computational precision. Through the optimum control-based probabilistic optimization for nonlinear dynamic radial turbine casing deformation, the optimum radial deformation is 7.865 × 10−4 m with acceptable reliability degree 0.995 6, which is reduced by 7.86 × 10−5 m relative to that before optimization. These results validate the effectiveness and feasibility of the proposed T-LSSVM method, which provides a useful insight into casing radial deformation, BTRRC control, and the development of gas turbine with high performance and high reliability.
Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays
Emma Delgado
2016-04-01
Full Text Available 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.
Interactive exploration of large-scale time-varying data using dynamic tracking graphs
Widanagamaachchi, W.
2012-10-01
Exploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the "tracks" of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science. © 2012 IEEE.
Design and implementation of multi-signal and time-varying neural reconstructions.
Nanda, Sumit; Chen, Hanbo; Das, Ravi; Bhattacharjee, Shatabdi; Cuntz, Hermann; Torben-Nielsen, Benjamin; Peng, Hanchuan; Cox, Daniel N; De Schutter, Erik; Ascoli, Giorgio A
2018-01-23
Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.
Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays.
Delgado, Emma; Barreiro, Antonio; Falcón, Pablo; Díaz-Cacho, Miguel
2016-04-26
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.
Wang, Zuo-Cai; Ren, Wei-Xin; Chen, Gen-Da
2012-01-01
This paper presents a recursive Hilbert transform method for the time-varying property identification of large-scale shear-type buildings with limited sensor deployments. An observer technique is introduced to estimate the building responses from limited available measurements. For an n-story shear-type building with l measurements (l ≤ n), the responses of other stories without measurements can be estimated based on the first r mode shapes (r ≤ l) as-built conditions and l measurements. Both the measured responses and evaluated responses and their Hilbert transforms are then used to track any variation of structural parameters of a multi-story building over time. Given floor masses, both the stiffness and damping coefficients of the building are identified one-by-one from the top to the bottom story. When variations of parameters are detected, a new developed branch-and-bound technique can be used to update the first r mode shapes with the identified parameters. A 60-story shear building with abruptly varying stiffness at different floors is simulated as an example. The numerical results indicate that the proposed method can detect variations of the parameters of large-scale shear-type buildings with limited sensor deployments at appropriate locations. (paper)
Kumar, V.; Mukherjee, S.
1977-01-01
In the present paper a general time-dependent inelastic analysis procedure for three-dimensional bodies subjected to arbitrary time varying mechanical and thermal loads using these state variable theories is presented. For the purpose of illustrations, the problems of hollow spheres, cylinders and solid circular shafts subjected to various combinations of internal and external pressures, axial force (or constraint) and torque are analyzed using the proposed solution procedure. Various cyclic thermal and mechanical loading histories with rectangular or sawtooth type waves with or without hold-time are considered. Numerical results for these geometrical shapes for various such loading histories are presented using Hart's theory (Journal of Engineering Materials and Technology 1976). The calculations are performed for nickel in the temperature range of 25 0 C to 400 0 C. For integrating forward in time, a method of solving a stiff system of ordinary differential equations is employed which corrects the step size and order of the method automatically. The limit loads for hollow spheres and cylinders are calculated using the proposed method and Hart's theory, and comparisons are made against the known theoretical results. The numerical results for other loading histories are discussed in the context of Hart's state variable type constitutive relations. The significance of phenomena such as strain rate sensitivity, Bauschinger's effect, crep recovery, history dependence and material softening with regard to these multiaxial problems are discussed in the context of Hart's theory
Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields
Wang, B.; Rosen, P.; Skraba, P.; Bhatia, H.; Pascucci, V.
2013-01-01
Analyzing critical points and their temporal evolutions plays a crucial role in understanding the behavior of vector fields. A key challenge is to quantify the stability of critical points: more stable points may represent more important phenomena or vice versa. The topological notion of robustness is a tool which allows us to quantify rigorously the stability of each critical point. Intuitively, the robustness of a critical point is the minimum amount of perturbation necessary to cancel it within a local neighborhood, measured under an appropriate metric. In this paper, we introduce a new analysis and visualization framework which enables interactive exploration of robustness of critical points for both stationary and time-varying 2D vector fields. This framework allows the end-users, for the first time, to investigate how the stability of a critical point evolves over time. We show that this depends heavily on the global properties of the vector field and that structural changes can correspond to interesting behavior. We demonstrate the practicality of our theories and techniques on several datasets involving combustion and oceanic eddy simulations and obtain some key insights regarding their stable and unstable features. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Noise level estimation in weakly nonlinear slowly time-varying systems
Aerts, J R M; Dirckx, J J J; Lataire, J; Pintelon, R
2008-01-01
Recently, a method using multisine excitation was proposed for estimating the frequency response, the nonlinear distortions and the disturbing noise of weakly nonlinear time-invariant systems. This method has been demonstrated on the measurement of nonlinear distortions in the vibration of acoustically driven systems such as a latex membrane, which is a good example of a time-invariant system [1]. However, not all systems are perfectly time invariant, e.g. biomechanical systems. This time variation can be misinterpreted as an elevated noise floor, and the classical noise estimation method gives a wrong result. Two improved methods to retrieve the correct noise information from the measurements are presented. Both of them make use of multisine excitations. First, it is demonstrated that the improved methods give the same result as the classical noise estimation method when applied to a time-invariant system (high-quality microphone membrane). Next, it is demonstrated that the new methods clearly give an improved estimate of the noise level on time-varying systems. As an application example results for the vibration response of an eardrum are shown
Alexander S. Rattner; Donna Post Guillen; Alark Joshi
2012-12-01
Photo- and physically-realistic techniques are often insufficient for visualization of simulation results, especially for 3D and time-varying datasets. Substantial research efforts have been dedicated to the development of non-photorealistic and illustration-inspired visualization techniques for compact and intuitive presentation of such complex datasets. While these efforts have yielded valuable visualization results, a great deal of work has been reproduced in studies as individual research groups often develop purpose-built platforms. Additionally, interoperability between illustrative visualization software is limited due to specialized processing and rendering architectures employed in different studies. In this investigation, a generalized framework for illustrative visualization is proposed, and implemented in marmotViz, a ParaView plugin, enabling its use on variety of computing platforms with various data file formats and mesh geometries. Detailed descriptions of the region-of-interest identification and feature-tracking algorithms incorporated into this tool are provided. Additionally, implementations of multiple illustrative effect algorithms are presented to demonstrate the use and flexibility of this framework. By providing a framework and useful underlying functionality, the marmotViz tool can act as a springboard for future research in the field of illustrative visualization.
Cao, Jiguo; Huang, Jianhua Z.; Wu, Hulin
2012-01-01
Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.
Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.
Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis
2018-01-01
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.
Coded throughput performance simulations for the time-varying satellite channel. M.S. Thesis
Han, LI
1995-01-01
The design of a reliable satellite communication link involving the data transfer from a small, low-orbit satellite to a ground station, but through a geostationary satellite, was examined. In such a scenario, the received signal power to noise density ratio increases as the transmitting low-orbit satellite comes into view, and then decreases as it then departs, resulting in a short-duration, time-varying communication link. The optimal values of the small satellite antenna beamwidth, signaling rate, modulation scheme and the theoretical link throughput (in bits per day) have been determined. The goal of this thesis is to choose a practical coding scheme which maximizes the daily link throughput while satisfying a prescribed probability of error requirement. We examine the throughput of both fixed rate and variable rate concatenated forward error correction (FEC) coding schemes for the additive white Gaussian noise (AWGN) channel, and then examine the effect of radio frequency interference (RFI) on the best coding scheme among them. Interleaving is used to mitigate degradation due to RFI. It was found that the variable rate concatenated coding scheme could achieve 74 percent of the theoretical throughput, equivalent to 1.11 Gbits/day based on the cutoff rate R(sub 0). For comparison, 87 percent is achievable for AWGN-only case.
Reusable Launch Vehicle Attitude Control Using a Time-Varying Sliding Mode Control Technique
Shtessel, Yuri B.; Zhu, J. Jim; Daniels, Dan; Jackson, Scott (Technical Monitor)
2002-01-01
In this paper we present a time-varying sliding mode control (TVSMC) technique for reusable launch vehicle (RLV) attitude control in ascent and entry flight phases. In ascent flight the guidance commands Euler roll, pitch and yaw angles, and in entry flight it commands the aerodynamic angles of bank, attack and sideslip. The controller employs a body rate inner loop and the attitude outer loop, which are separated in time-scale by the singular perturbation principle. The novelty of the TVSMC is that both the sliding surface and the boundary layer dynamics can be varied in real time using the PD-eigenvalue assignment technique. This salient feature is used to cope with control command saturation and integrator windup in the presence of severe disturbance or control effector failure, which enhances the robustness and fault tolerance of the controller. The TV-SMC ascent and descent designs are currently being tested with high fidelity, 6-DOF dispersion simulations. The test results will be presented in the final version of this paper.
Dziak, John J; Li, Runze; Tan, Xianming; Shiffman, Saul; Shiyko, Mariya P
2015-12-01
Behavioral scientists increasingly collect intensive longitudinal data (ILD), in which phenomena are measured at high frequency and in real time. In many such studies, it is of interest to describe the pattern of change over time in important variables as well as the changing nature of the relationship between variables. Individuals' trajectories on variables of interest may be far from linear, and the predictive relationship between variables of interest and related covariates may also change over time in a nonlinear way. Time-varying effect models (TVEMs; see Tan, Shiyko, Li, Li, & Dierker, 2012) address these needs by allowing regression coefficients to be smooth, nonlinear functions of time rather than constants. However, it is possible that not only observed covariates but also unknown, latent variables may be related to the outcome. That is, regression coefficients may change over time and also vary for different kinds of individuals. Therefore, we describe a finite mixture version of TVEM for situations in which the population is heterogeneous and in which a single trajectory would conceal important, interindividual differences. This extended approach, MixTVEM, combines finite mixture modeling with non- or semiparametric regression modeling, to describe a complex pattern of change over time for distinct latent classes of individuals. The usefulness of the method is demonstrated in an empirical example from a smoking cessation study. We provide a versatile SAS macro and R function for fitting MixTVEMs. (c) 2015 APA, all rights reserved).
The Fast Simulation of Scattering Characteristics from a Simplified Time Varying Sea Surface
Yiwen Wei
2015-01-01
Full Text Available This paper aims at applying a simplified sea surface model into the physical optics (PO method to accelerate the scattering calculation from 1D time varying sea surface. To reduce the number of the segments and make further improvement on the efficiency of PO method, a simplified sea surface is proposed. In this simplified sea surface, the geometry of long waves is locally approximated by tilted facets that are much longer than the electromagnetic wavelength. The capillary waves are considered to be sinusoidal line superimposing on the long waves. The wavenumber of the sinusoidal waves is supposed to satisfy the resonant condition of Bragg waves which is dominant in all the scattered short wave components. Since the capillary wave is periodical within one facet, an analytical integration of the PO term can be performed. The backscattering coefficient obtained from a simplified sea surface model agrees well with that obtained from a realistic sea surface. The Doppler shifts and width also agree well with the realistic model since the capillary waves are taken into consideration. The good agreements indicate that the simplified model is reasonable and valid in predicting both the scattering coefficients and the Doppler spectra.
Soni, V.; Hadjadj, A.; Roussel, O.
2017-12-01
In this paper, a fully adaptive multiresolution (MR) finite difference scheme with a time-varying tolerance is developed to study compressible fluid flows containing shock waves in interaction with solid obstacles. To ensure adequate resolution near rigid bodies, the MR algorithm is combined with an immersed boundary method based on a direct-forcing approach in which the solid object is represented by a continuous solid-volume fraction. The resulting algorithm forms an efficient tool capable of solving linear and nonlinear waves on arbitrary geometries. Through a one-dimensional scalar wave equation, the accuracy of the MR computation is, as expected, seen to decrease in time when using a constant MR tolerance considering the accumulation of error. To overcome this problem, a variable tolerance formulation is proposed, which is assessed through a new quality criterion, to ensure a time-convergence solution for a suitable quality resolution. The newly developed algorithm coupled with high-resolution spatial and temporal approximations is successfully applied to shock-bluff body and shock-diffraction problems solving Euler and Navier-Stokes equations. Results show excellent agreement with the available numerical and experimental data, thereby demonstrating the efficiency and the performance of the proposed method.
Do Tick Attachment Times Vary between Different Tick-Pathogen Systems?
Stephanie L. Richards
2017-05-01
Full Text Available Improvements to risk assessments are needed to enhance our understanding of tick-borne disease epidemiology. We review tick vectors and duration of tick attachment required for pathogen transmission for the following pathogens/toxins and diseases: (1 Anaplasma phagocytophilum (anaplasmosis; (2 Babesia microti (babesiosis; (3 Borrelia burgdorferi (Lyme disease; (4 Southern tick-associated rash illness; (5 Borrelia hermsii (tick-borne relapsing fever; (6 Borrelia parkeri (tick-borne relapsing fever; (7 Borrelia turicatae (tick-borne relapsing fever; (8 Borrelia mayonii; (9 Borrelia miyamotoi; (10 Coxiella burnetii (Query fever; (11 Ehrlichia chaffeensis (ehrlichiosis; (12 Ehrlichia ewingii (ehrlichiosis; (13 Ehrlichia muris; (14 Francisella tularensis (tularemia; (15 Rickettsia 364D; (16 Rickettsia montanensis; (17 Rickettsia parkeri (American boutonneuse fever, American tick bite fever; (18 Rickettsia ricketsii (Rocky Mountain spotted fever; (19 Colorado tick fever virus (Colorado tick fever; (20 Heartland virus; (21 Powassan virus (Powassan disease; (22 tick paralysis neurotoxin; and (23 Galactose-α-1,3-galactose (Mammalian Meat Allergy-alpha-gal syndrome. Published studies for 12 of the 23 pathogens/diseases showed tick attachment times. Reported tick attachment times varied (<1 h to seven days between pathogen/toxin type and tick vector. Not all studies were designed to detect the duration of attachment required for transmission. Knowledge of this important aspect of vector competence is lacking and impairs risk assessment for some tick-borne pathogens.
A Class of Prediction-Correction Methods for Time-Varying Convex Optimization
Simonetto, Andrea; Mokhtari, Aryan; Koppel, Alec; Leus, Geert; Ribeiro, Alejandro
2016-09-01
This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O(h^2)$, and in some cases as $O(h^4)$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.
Detection of random alterations to time-varying musical instrument spectra.
Horner, Andrew; Beauchamp, James; So, Richard
2004-09-01
The time-varying spectra of eight musical instrument sounds were randomly altered by a time-invariant process to determine how detection of spectral alteration varies with degree of alteration, instrument, musical experience, and spectral variation. Sounds were resynthesized with centroids equalized to the original sounds, with frequencies harmonically flattened, and with average spectral error levels of 8%, 16%, 24%, 32%, and 48%. Listeners were asked to discriminate the randomly altered sounds from reference sounds resynthesized from the original data. For all eight instruments, discrimination was very good for the 32% and 48% error levels, moderate for the 16% and 24% error levels, and poor for the 8% error levels. When the error levels were 16%, 24%, and 32%, the scores of musically experienced listeners were found to be significantly better than the scores of listeners with no musical experience. Also, in this same error level range, discrimination was significantly affected by the instrument tested. For error levels of 16% and 24%, discrimination scores were significantly, but negatively correlated with measures of spectral incoherence and normalized centroid deviation on unaltered instrument spectra, suggesting that the presence of dynamic spectral variations tends to increase the difficulty of detecting spectral alterations. Correlation between discrimination and a measure of spectral irregularity was comparatively low.
Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert
2018-05-01
Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.
Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.
Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W
2014-11-26
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. Copyright © 2014 the authors 0270-6474/14/3416046-12$15.00/0.
Zhang, Hongjie; Hou, Yanyan; Yang, Tao; Zhang, Qian; Zhao, Jian
2018-05-01
In the spot welding process, a high alternating current is applied, resulting in a time-varying electromagnetic field surrounding the welder. When measuring the welding voltage signal, the impedance of the measuring circuit consists of two parts: dynamic resistance relating to weld nugget nucleation event and inductive reactance caused by mutual inductance. The aim of this study is to develop a method to acquire the dynamic reactance signal and to discuss the possibility of using this signal to evaluate the weld quality. For this purpose, a series of experiments were carried out. The reactance signals under different welding conditions were compared and the results showed that the morphological feature of the reactance signal was closely related to the welding current and it was also significantly influenced by some abnormal welding conditions. Some features were extracted from the reactance signal and combined to construct weld nugget strength and diameter prediction models based on the radial basis function (RBF) neural network. In addition, several features were also used to monitor the expulsion in the welding process by using Fisher linear discriminant analysis. The results indicated that using the dynamic reactance signal to evaluate weld quality is possible and feasible.
Lan Liu
2017-01-01
Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q
Propagation of a laser beam in a time-varying waveguide. [plasma heating for controlled fusion
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.
Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks
Charalambous Charalambos D
2006-01-01
Full Text Available A new time-varying (TV long-term fading (LTF channel model which captures both the space and time variations of wireless systems is developed. The proposed TV LTF model is based on a stochastic differential equation driven by Brownian motion. This model is more realistic than the static models usually encountered in the literature. It allows viewing the wireless channel as a dynamical system, thus enabling well-developed tools of adaptive and nonadaptive estimation and identification techniques to be applied to this class of problems. In contrast with the traditional models, the statistics of the proposed model are shown to be TV, but converge in steady state to their static counterparts. Moreover, optimal power control algorithms (PCAs based on the new model are proposed. A centralized PCA is shown to reduce to a simple linear programming problem if predictable power control strategies (PPCS are used. In addition, an iterative distributed stochastic PCA is used to solve for the optimization problem using stochastic approximations. The latter solely requires each mobile to know its received signal-to-interference ratio. Generalizations of the power control problem based on convex optimization techniques are provided if PPCS are not assumed. Numerical results show that there are potentially large gains to be achieved by using TV stochastic models, and the distributed stochastic PCA provides better power stability and consumption than the distributed deterministic PCA.
Control of the tokamak safety factor profile with time-varying constraints using MPC
Maljaars, E.; Felici, F.; De Baar, M.R.; Geelen, P.J.M.; Steinbuch, M.; Van Dongen, J.; Hogeweij, G.M.D.
2015-01-01
A controller is designed for the tokamak safety factor profile that takes real-time-varying operational and physics limits into account. This so-called model predictive controller (MPC) employs a prediction model in order to compute optimal control inputs that satisfy the given limits. The use of linearized models around a reference trajectory results in a quadratic programming problem that can easily be solved online. The performance of the controller is analysed in a set of ITER L-mode scenarios simulated with the non-linear plasma transport code RAPTOR. It is shown that the controller can reduce the tracking error due to an overestimation or underestimation of the modelled transport, while making a trade-off between residual error and amount of controller action. It is also shown that the controller can account for a sudden decrease in the available actuator power, while providing warnings ahead of time about expected violations of operational and physics limits. This controller can be extended and implemented in existing tokamaks in the near future. (paper)
Johann A. Briffa
2014-06-01
Full Text Available In this study, the authors consider time-varying block (TVB codes, which generalise a number of previous synchronisation error-correcting codes. They also consider various practical issues related to maximum a posteriori (MAP decoding of these codes. Specifically, they give an expression for the expected distribution of drift between transmitter and receiver because of synchronisation errors. They determine an appropriate choice for state space limits based on the drift probability distribution. In turn, they obtain an expression for the decoder complexity under given channel conditions in terms of the state space limits used. For a given state space, they also give a number of optimisations that reduce the algorithm complexity with no further loss of decoder performance. They also show how the MAP decoder can be used in the absence of known frame boundaries, and demonstrate that an appropriate choice of decoder parameters allows the decoder to approach the performance when frame boundaries are known, at the expense of some increase in complexity. Finally, they express some existing constructions as TVB codes, comparing performance with published results and showing that improved performance is possible by taking advantage of the flexibility of TVB codes.
Knowledge diffusion in complex networks by considering time-varying information channels
Zhu, He; Ma, Jing
2018-03-01
In this article, based on a model of epidemic spreading, we explore the knowledge diffusion process with an innovative mechanism for complex networks by considering time-varying information channels. To cover the knowledge diffusion process in homogeneous and heterogeneous networks, two types of networks (the BA network and the ER network) are investigated. The mean-field theory is used to theoretically draw the knowledge diffusion threshold. Numerical simulation demonstrates that the knowledge diffusion threshold is almost linearly correlated with the mean of the activity rate. In addition, under the influence of the activity rate and distinct from the classic Susceptible-Infected-Susceptible (SIS) model, the density of knowers almost linearly grows with the spreading rate. Finally, in consideration of the ubiquitous mechanism of innovation, we further study the evolution of knowledge in our proposed model. The results suggest that compared with the effect of the spreading rate, the average knowledge version of the population is affected more by the innovation parameter and the mean of the activity rate. Furthermore, in the BA network, the average knowledge version of individuals with higher degree is always newer than those with lower degree.
Higham, Timothy E; Russell, Anthony P
2012-02-01
Autotomy (voluntary loss of an appendage) is common among diverse groups of vertebrates and invertebrates, and much attention has been given to ecological and developmental aspects of tail autotomy in lizards. Although most studies have focused on the ramifications for the lizard (behavior, biomechanics, energetics, etc.), the tail itself can exhibit interesting behaviors once segregated from the body. For example, recent work highlighted the ability of leopard gecko tails to jump and flip, in addition to being able to swing back and forth. Little is known, however, about the control mechanisms underlying these movements. Using electromyography, we examined the time-varying in vivo motor patterns at four sites (two proximal and two distal) in the tail of the leopard gecko, Eublepharis macularius, following autotomy. Using these data we tested the hypothesis that the disparity in movements results simply from overlapping pattern generators within the tail. We found that burst duration, but not cycle duration, of the rhythmic swings reached a plateau at approximately 150 s following autotomy. This is likely because of physiological changes related to muscle fatigue and ischemia. For flips and jumps, burst and cycle duration exhibited no regular pattern. The coefficient of variation in motor patterns was significantly greater for jumps and flips than for rhythmic swings. This supports the conclusion that the different tail behaviors do not stem from overlapping pattern generators, but that they rely upon independent neural circuits. The signal controlling jumps and flips may be modified by sensory information from the environment. Finally, we found that jumps and flips are initiated using relatively synchronous activity between the two sides of the tail. In contrast, alternating activation of the right and left sides of the tail result in rhythmic swings. The mechanism underlying this change in tail behavior is comparable to locomotor gait changes in vertebrates.
Neuromuscular mechanisms and neural strategies in the control of time-varying muscle contractions.
Erimaki, Sophia; Agapaki, Orsalia M; Christakos, Constantinos N
2013-09-01
The organization of the neural input to motoneurons that underlies time-varying muscle force is assumed to depend on muscle transfer characteristics and neural strategies or control modes utilizing sensory signals. We jointly addressed these interlinked, but previously studied individually and partially, issues for sinusoidal (range 0.5-5.0 Hz) force-tracking contractions of a human finger muscle. Using spectral and correlation analyses of target signal, force signal, and motor unit (MU) discharges, we studied 1) patterns of such discharges, allowing inferences on the motoneuronal input; 2) transformation of MU population activity (EMG) into quasi-sinusoidal force; and 3) relation of force oscillation to target, carrying information on the input's organization. A broad view of force control mechanisms and strategies emerged. Specifically, synchronized MU and EMG modulations, reflecting a frequency-modulated motoneuronal input, accompanied the force variations. Gain and delay drops between EMG modulation and force oscillation, critical for the appropriate organization of this input, occurred with increasing target frequency. According to our analyses, gain compensation was achieved primarily through rhythmical activation/deactivation of higher-threshold MUs and secondarily through the adaptation of the input's strength expected during tracking tasks. However, the input's timing was not adapted to delay behaviors and seemed to depend on the control modes employed. Thus, for low-frequency targets, the force oscillation was highly coherent with, but led, a target, this timing error being compatible with predictive feedforward control partly based on the target's derivatives. In contrast, the force oscillation was weakly coherent, but in phase, with high-frequency targets, suggesting control mainly based on a target's rhythm.
Stefanou, G.D.
1978-01-01
The work described herein relates to the prediction of stresses in materials which exhibit time varying strains with particular reference to the ligaments of perforated circular concrete slabs, subjected to long-term radial prestress and uniform elevated temperature. The perforations are reinforced with steel liners and arranged in a square central lattice symmetrical about two orthogonal axes. Special reference is made to the distribution of stress in the standpipe region of prestressed concrete cylindrical pressure or containment vessels for gas cooled reactors. In order to assess the stress distribution around the perforated zone of a circular slab, a method of analysis was developed by the author, based on the ''Equivalent Elastic Modulus'' of the perforated zone and the ''Effective Modulus Method'', utilizing experimental data obtained from tests performed on model specimens. The object of this paper is to extend the above method of analysis into the perforated region, and assess the long-term stresses in the ligaments. The proposed method is accomplished by an application of the Finite Element Method for the elastic plane stress case. Comparisons of experimental results and theoretical predictions by the proposed method, and other analytical methods are made for a series of perforated concrete slabs subjected to radial in-plane loading: 10,342 kN/m 2 (1,5000 psi), and uniform elevated temperature of 80 0 C. The investigation, though in general terms, could be applied to the perforated region of cylindrical pressure vessels for nuclear reactors. Finally the paper describes briefly in Appendix 3 a direct solution procedure for calculating time dependent stresses in concrete structures based on the principles of variational calculus. Analytical predictions obtained by the proposed method which is a step-by-step analysis, are compared with the variational principle method. (author)
Stability analysis and backward whirl investigation of cracked rotors with time-varying stiffness
AL-Shudeifat, Mohammad A.
2015-07-01
The dynamic stability of dynamical systems with time-periodic stiffness is addressed here. Cracked rotor systems with time-periodic stiffness are well-known examples of such systems. Time-varying area moments of inertia at the cracked element cross-section of a cracked rotor have been used to formulate the time-periodic finite element stiffness matrix. The semi-infinite coefficient matrix obtained by applying the harmonic balance (HB) solution to the finite element (FE) equations of motion is employed here to study the dynamic stability of the system. Consequently, the sign of the determinant of a scaled version of a sub-matrix of this semi-infinite coefficient matrix at a finite number of harmonics in the HB solution is found to be sufficient for identifying the major unstable zones of the system in the parameter plane. Specifically, it is found that the negative determinant always corresponds to unstable zones in all of the systems considered. This approach is applied to a parametrically excited Mathieu's equation, a two degree-of-freedom linear time-periodic dynamical system, a cracked Jeffcott rotor and a finite element model of the cracked rotor system. Compared to the corresponding results obtained by Floquet's theory, the sign of the determinant of the scaled sub-matrix is found to be an efficient tool for identifying the major unstable zones of the linear time-periodic parametrically excited systems, especially large-scale FE systems. Moreover, it is found that the unstable zones for a FE cracked rotor with an open transverse crack model only appear at the backward whirl. The theoretical and experimental results have been found to agree well for verifying that the open crack model excites the backward whirl amplitudes at the critical backward whirling rotational speeds.
The Assessment of Left Ventricular Time-Varying Radius Using Tissue Doppler Imaging
Fardin Mirbolouk
2012-03-01
Full Text Available Background: Left ventricular twist/torsion is believed to be a sensitive indicator of systolic and diastolic performance. To obtain circumferential rotation using tissue Doppler imaging, we need to estimate the time-varying radius of the left ventricle throughout the cardiac cycle to convert the tangential velocity into angular velocity. Objectives: The aim of this study was to investigate accuracy of measured LV radius using tissue Doppler imaging throughout the cardiac cycle compared to two-dimensional (2D imaging. Methods: A total of 35 subjects (47±12 years old underwent transthoracic echocardiographic standard examinations. Left ventricular radius during complete cardiac cycle measured using tissue Doppler and 2D-imaging at basal and apical short axis levels. For this reason, the 2D-images and velocity-time data derived and transferred to a personal computer for off-line analysis. 2D image frames analyzed via a program written in the MATLAB software. Velocity-time data from anteroseptal at basal level (or anterior wall at apical level and posterior walls transferred to a spreadsheet Excel program for the radius calculations. Linear correlation and Bland-Altman analysis were calculated to assess the relationships and agreements between the tissue Doppler and 2D-measured radii throughout the cardiac cycle. Results: There was significant correlation between tissue Doppler and 2D-measured radii and the Pearson correlation coefficients were 0.84 to 0.97 (P<0.05. Bland-Altman analysis by constructing the 95% limits of agreement showed that the good agreements existed between the two methods. Conclusion: It can be concluded from our experience that the tissue Doppler imaging can reasonably estimate radius of the left ventricle throughout the cardiac cycle.
Caisheng Wei
2017-03-01
Full Text Available A novel low-complexity adaptive control method, capable of guaranteeing the transient and steady-state tracking performance in the presence of unknown nonlinearities and actuator saturation, is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. In order to attenuate the negative effects of classical predefined performance function for unknown initial tracking errors, a modified predefined performance function with time-varying design parameters is presented. Under the newly developed predefined performance function, two novel adaptive controllers with low-complexity computation are proposed for velocity and altitude subsystems of the hypersonic flight vehicle, respectively. Wherein, different from neural network-based approximation, a least square support vector machine with only two design parameters is utilized to approximate the unknown hypersonic dynamics. And the relevant ideal weights are obtained by solving a linear system without resorting to specialized optimization algorithms. Based on the approximation by least square support vector machine, only two adaptive scalars are required to be updated online in the parameter projection method. Besides, a new finite-time-convergent differentiator, with a quite simple structure, is proposed to estimate the unknown generated state variables in the newly established normal output-feedback formulation of altitude subsystem. Moreover, it is also employed to obtain accurate estimations for the derivatives of virtual controllers in a recursive design. This avoids the inherent drawback of backstepping — “explosion of terms” and makes the proposed control method achievable for the hypersonic flight vehicle. Further, the compensation design is employed when the saturations of the actuator occur. Finally, the numerical simulations validate the efficiency of the proposed finite-time-convergent differentiator and control method.
The time-varying association between perceived stress and hunger within and between days.
Huh, Jimi; Shiyko, Mariya; Keller, Stefan; Dunton, Genevieve; Schembre, Susan M
2015-06-01
Examine the association between perceived stress and hunger continuously over a week in free-living individuals. Forty five young adults (70% women, 30% overweight/obese) ages 18 to 24 years (Mean = 20.7, SD = 1.5), with BMI between 17.4 and 36.3 kg/m(2) (Mean = 23.6, SD = 4.0) provided between 513 and 577 concurrent ratings of perceived stress and hunger for 7 days via hourly, text messaging assessments and real-time eating records. Time-varying effect modeling was used to explore whether the within-day fluctuations in stress are related to perceived hunger assessed on a momentary basis. A generally positive stress-hunger relationship was confirmed, but we found that the strength of the relationship was not linear. Rather, the magnitude of the association between perceived stress and hunger changed throughout the day such that only during specific time intervals were stress and hunger significantly related. Specifically, the strength of the positive association peaked during late afternoon hours on weekdays (β = 0.31, p hunger associations that peak in the afternoon or evening hours. While we are unable to infer causality from these analyses, our findings provide empirical evidence for a potentially high-risk time of day for stress-induced eating. Replication of these findings in larger, more diverse samples will aid with the design and implementation of real-time intervention studies aimed at reducing stress-eating. Copyright © 2015 Elsevier Ltd. All rights reserved.
Time-varying bispectral analysis of visually evoked multi-channel EEG
Chandran, Vinod
2012-12-01
Theoretical foundations of higher order spectral analysis are revisited to examine the use of time-varying bicoherence on non-stationary signals using a classical short-time Fourier approach. A methodology is developed to apply this to evoked EEG responses where a stimulus-locked time reference is available. Short-time windowed ensembles of the response at the same offset from the reference are considered as ergodic cyclostationary processes within a non-stationary random process. Bicoherence can be estimated reliably with known levels at which it is significantly different from zero and can be tracked as a function of offset from the stimulus. When this methodology is applied to multi-channel EEG, it is possible to obtain information about phase synchronization at different regions of the brain as the neural response develops. The methodology is applied to analyze evoked EEG response to flash visual stimulii to the left and right eye separately. The EEG electrode array is segmented based on bicoherence evolution with time using the mean absolute difference as a measure of dissimilarity. Segment maps confirm the importance of the occipital region in visual processing and demonstrate a link between the frontal and occipital regions during the response. Maps are constructed using bicoherence at bifrequencies that include the alpha band frequency of 8Hz as well as 4 and 20Hz. Differences are observed between responses from the left eye and the right eye, and also between subjects. The methodology shows potential as a neurological functional imaging technique that can be further developed for diagnosis and monitoring using scalp EEG which is less invasive and less expensive than magnetic resonance imaging.
Identifiability of Additive, Time-Varying Actuator and Sensor Faults by State Augmentation
Upchurch, Jason M.; Gonzalez, Oscar R.; Joshi, Suresh M.
2014-01-01
Recent work has provided a set of necessary and sucient conditions for identifiability of additive step faults (e.g., lock-in-place actuator faults, constant bias in the sensors) using state augmentation. This paper extends these results to an important class of faults which may affect linear, time-invariant systems. In particular, the faults under consideration are those which vary with time and affect the system dynamics additively. Such faults may manifest themselves in aircraft as, for example, control surface oscillations, control surface runaway, and sensor drift. The set of necessary and sucient conditions presented in this paper are general, and apply when a class of time-varying faults affects arbitrary combinations of actuators and sensors. The results in the main theorems are illustrated by two case studies, which provide some insight into how the conditions may be used to check the theoretical identifiability of fault configurations of interest for a given system. It is shown that while state augmentation can be used to identify certain fault configurations, other fault configurations are theoretically impossible to identify using state augmentation, giving practitioners valuable insight into such situations. That is, the limitations of state augmentation for a given system and configuration of faults are made explicit. Another limitation of model-based methods is that there can be large numbers of fault configurations, thus making identification of all possible configurations impractical. However, the theoretical identifiability of known, credible fault configurations can be tested using the theorems presented in this paper, which can then assist the efforts of fault identification practitioners.
Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.
Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod
2017-07-15
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
Propagation of a laser beam in a time-varying waveguide
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 examined. First, an extended paraxial procedure is developed for the case of an axially uniform waveguide. It is shown that the essential feature of an alternate focusing and defocusing beam is retained, but that the intensity distribution is cumulatively modified at the foci and at the outer portions of the beam as compared to that of the paraxial case. Second, some general features of paraxial beam propagation are examined for the case of axially varying waveguides. Finally, laser plasma coupling is examined for the case when laser heating generates a density distribution that is radially parabolic near the axis and when the energy absorbed over a focal length of a plasma lens is small. It is shown that stable or unstable beam propagation depends upon the relative magnitude of the density fluctuations which exist in the axial variation of the waveguides as a result of laser heating. When the fluctuations are small, the propagation is stable, and a simple algebraic expression is obtained which relates the beam diameter to the axially slow averaged variation in the waveguide. When the fluctuations are large, the propagation stability can be determined only by consistently combining plasma dynamics and beam propagation to interrelate the axial variation of the beam to that of the waveguide. In this case of beam propagation in a time-varying waveguide, it is shown that the global stability of the propagation depends upon the initial fluctuation growth rate compared to the initial time rate of change in the radial curvature of the waveguide
On the impact of topography and building mask on time varying gravity due to local hydrology
Deville, S.; Jacob, T.; Chéry, J.; Champollion, C.
2013-01-01
We use 3 yr of surface absolute gravity measurements at three sites on the Larzac plateau (France) to quantify the changes induced by topography and the building on gravity time-series, with respect to an idealized infinite slab approximation. Indeed, local topography and buildings housing ground-based gravity measurement have an effect on the distribution of water storage changes, therefore affecting the associated gravity signal. We first calculate the effects of surrounding topography and building dimensions on the gravity attraction for a uniform layer of water. We show that a gravimetric interpretation of water storage change using an infinite slab, the so-called Bouguer approximation, is generally not suitable. We propose to split the time varying gravity signal in two parts (1) a surface component including topographic and building effects (2) a deep component associated to underground water transfer. A reservoir modelling scheme is herein presented to remove the local site effects and to invert for the effective hydrological properties of the unsaturated zone. We show that effective time constants associated to water transfer vary greatly from site to site. We propose that our modelling scheme can be used to correct for the local site effects on gravity at any site presenting a departure from a flat topography. Depending on sites, the corrected signal can exceed measured values by 5-15 μGal, corresponding to 120-380 mm of water using the Bouguer slab formula. Our approach only requires the knowledge of daily precipitation corrected for evapotranspiration. Therefore, it can be a useful tool to correct any kind of gravimetric time-series data.
Time-Varying Uncertainty in Shock and Vibration Applications Using the Impulse Response
J.B. Weathers
2012-01-01
Full Text Available Design of mechanical systems often necessitates the use of dynamic simulations to calculate the displacements (and their derivatives of the bodies in a system as a function of time in response to dynamic inputs. These types of simulations are especially prevalent in the shock and vibration community where simulations associated with models having complex inputs are routine. If the forcing functions as well as the parameters used in these simulations are subject to uncertainties, then these uncertainties will propagate through the models resulting in uncertainties in the outputs of interest. The uncertainty analysis procedure for these kinds of time-varying problems can be challenging, and in many instances, explicit data reduction equations (DRE's, i.e., analytical formulas, are not available because the outputs of interest are obtained from complex simulation software, e.g. FEA programs. Moreover, uncertainty propagation in systems modeled using nonlinear differential equations can prove to be difficult to analyze. However, if (1 the uncertainties propagate through the models in a linear manner, obeying the principle of superposition, then the complexity of the problem can be significantly simplified. If in addition, (2 the uncertainty in the model parameters do not change during the simulation and the manner in which the outputs of interest respond to small perturbations in the external input forces is not dependent on when the perturbations are applied, then the number of calculations required can be greatly reduced. Conditions (1 and (2 characterize a Linear Time Invariant (LTI uncertainty model. This paper seeks to explain one possible approach to obtain the uncertainty results based on these assumptions.
An estimation of crude oil import demand in Turkey: Evidence from time-varying parameters approach
Ozturk, Ilhan; Arisoy, Ibrahim
2016-01-01
The aim of this study is to model crude oil import demand and estimate the price and income elasticities of imported crude oil in Turkey based on a time-varying parameters (TVP) approach with the aim of obtaining accurate and more robust estimates of price and income elasticities. This study employs annual time series data of domestic oil consumption, real GDP, and oil price for the period 1966–2012. The empirical results indicate that both the income and price elasticities are in line with the theoretical expectations. However, the income elasticity is statistically significant while the price elasticity is statistically insignificant. The relatively high value of income elasticity (1.182) from this study suggests that crude oil import in Turkey is more responsive to changes in income level. This result indicates that imported crude oil is a normal good and rising income levels will foster higher consumption of oil based equipments, vehicles and services by economic agents. The estimated income elasticity of 1.182 suggests that imported crude oil consumption grows at a higher rate than income. This in turn reduces oil intensity over time. Therefore, crude oil import during the estimation period is substantially driven by income. - Highlights: • We estimated the price and income elasticities of imported crude oil in Turkey. • Income elasticity is statistically significant and it is 1.182. • The price elasticity is statistically insignificant. • Crude oil import in Turkey is more responsive to changes in income level. • Crude oil import during the estimation period is substantially driven by income.
Effect of a time varying power level in EBR-II on mixed-oxide fuel burnup
Stone, I.Z.; Jost, J.W.; Baker, R.B.
1979-01-01
A refined prediction of burnup of mixed-oxide fuel in EBR-2 is compared with measured data. The calculation utilizes a time-varying power factor and results in a general improvement to previous calculations
Huiguo Chen
2017-01-01
Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.
Zhang, Chuan; Wang, Xingyuan; Luo, Chao; Li, Junqiu; Wang, Chunpeng
2018-03-01
In this paper, we focus on the robust outer synchronization problem between two nonlinear complex networks with parametric disturbances and mixed time-varying delays. Firstly, a general complex network model is proposed. Besides the nonlinear couplings, the network model in this paper can possess parametric disturbances, internal time-varying delay, discrete time-varying delay and distributed time-varying delay. Then, according to the robust control strategy, linear matrix inequality and Lyapunov stability theory, several outer synchronization protocols are strictly derived. Simple linear matrix controllers are designed to driver the response network synchronize to the drive network. Additionally, our results can be applied on the complex networks without parametric disturbances. Finally, by utilizing the delayed Lorenz chaotic system as the dynamics of all nodes, simulation examples are given to demonstrate the effectiveness of our theoretical results.
Multi-pulse chaotic motions of a rotor-active magnetic bearing system with time-varying stiffness
Zhang, W.; Yao, M.H.; Zhan, X.P.
2006-01-01
In this paper, we investigate the Shilnikov type multi-pulse chaotic dynamics for a rotor-active magnetic bearings (AMB) system with 8-pole legs and the time-varying stiffness. The stiffness in the AMB is considered as the time-varying in a periodic form. The dimensionless equation of motion for the rotor-AMB system with the time-varying stiffness in the horizontal and vertical directions is a two-degree-of-freedom nonlinear system with quadratic and cubic nonlinearities and parametric excitation. The asymptotic perturbation method is used to obtain the averaged equations in the case of primary parametric resonance and 1/2 subharmonic resonance. It is found from the numerical results that there are the phenomena of the Shilnikov type multi-pulse chaotic motions for the rotor-AMB system. A new jumping phenomenon is discovered in the rotor-AMB system with the time-varying stiffness
Kai Chang
2013-01-01
Full Text Available Under departures from the cost-of-carry theory, traded spot prices and conditional volatility disturbed from futures market have significant impacts on futures price of emissions allowances, and then we propose time-varying hedge ratios and hedging effectiveness estimation using ECM-GARCH model. Our empirical results show that conditional variance, conditional covariance, and their correlation between between spot and futures prices exhibit time-varying trends. Conditional volatility of spot prices, conditional volatility disturbed from futures market, and conditional correlation of market noises implied from spot and futures markets have significant effects on time-varying hedge ratios and hedging effectiveness. In the immature emissions allowances market, market participants optimize portfolio sizes between spot and futures assets using historical market information and then achieve higher risk reduction of assets portfolio revenues; accordingly, we can obtain better hedging effectiveness through time-varying hedge ratios with departures from the cost-of-carry theory.
Krishnan, M.; Bhowmik, B.; Hazra, B.; Pakrashi, V.
2018-02-01
In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving
Paciello, Rossana; Coviello, Irina; Filizzola, Carolina; Genzano, Nicola; Lisi, Mariano; Mazzeo, Giuseppe; Pergola, Nicola; Sileo, Giancanio; Tramutoli, Valerio
2014-05-01
In environmental studies the integration of heterogeneous and time-varying data, is a very common requirement for investigating and possibly visualize correlations among physical parameters underlying the dynamics of complex phenomena. Datasets used in such kind of applications has often different spatial and temporal resolutions. In some case superimposition of asynchronous layers is required. Traditionally the platforms used to perform spatio-temporal visual data analyses allow to overlay spatial data, managing the time using 'snapshot' data model, each stack of layers being labeled with different time. But this kind of architecture does not incorporate the temporal indexing neither the third spatial dimension which is usually given as an independent additional layer. Conversely, the full representation of a generic environmental parameter P(x,y,z,t) in the 4D space-time domain could allow to handle asynchronous datasets as well as less traditional data-products (e.g. vertical sections, punctual time-series, etc.) . In this paper we present the 4 Dimensions Environmental Observation Platform (4-DEOS), a system based on a web services architecture Client-Broker-Server. This platform is a new open source solution for both a timely access and an easy integration and visualization of heterogeneous (maps, vertical profiles or sections, punctual time series, etc.) asynchronous, geospatial products. The innovative aspect of the 4-DEOS system is that users can analyze data/products individually moving through time, having also the possibility to stop the display of some data/products and focus on other parameters for better studying their temporal evolution. This platform gives the opportunity to choose between two distinct display modes for time interval or for single instant. Users can choose to visualize data/products in two ways: i) showing each parameter in a dedicated window or ii) visualize all parameters overlapped in a single window. A sliding time bar, allows
Chang, Ying-Pin
2010-01-01
A particle-swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in determining the tilt angle of photovoltaic (PV) modules in Taiwan. The objective is to maximize the output electrical energy of the modules. In this study, seven Taiwanese cities were selected for analysis. First, the sun's position at any time and location was predicted by the mathematical procedure of Julian dating, and then the solar irradiation was obtained at each site under a clear sky. By combining the temperature effect, the PSO-NTVE method is adopted to calculate the optimal tilt angles for fixed south-facing PV modules. In this method, the parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments have an effect that approximates the full factorial experiments. Statistical error analysis was performed to compare the results between the four PSO methods and experimental results. Hengchun city in which the minimum total error value of 6.12% the reasons for the weather more stability and less building shade. A comparison of the measurement results in electrical energy between the four PSO methods and the PV modules set a six tilt angles. Obviously four types of PSO methods simulation of electrical energy value from 231.12 kWh/m 2 for Taipei to 233.81 kWh/m 2 for Hengchun greater than the measurement values from 224.71 kWh/m 2 for Taichung to 228.47 kWh/m 2 for Hengchun by PV module which is due to instability caused by climate change. Finally, the results show that the annual optimal angle for the Taipei area is 18.16 o ; for Taichung, 17.3 o ; for Tainan, 16.15 o ; for Kaosiung, 15.79 o ; for Hengchung, 15.17 o ; for Hualian, 17.16 o ; and for Taitung, 15.94 o . It is evident that the authorized Industrial Technology Research Institute (ITRI) recommends that tilt angle of 23.5 o was not an appropriate use of Taiwan's seven cities. PV modules with the installation of the tilt angle should be
Tzeremes, Panayiotis
2018-02-01
This study is the first attempt to investigate the relationship between CO 2 emissions, energy consumption, and economic growth at a state level, for the 50 US states, through a time-varying causality approach using annual data over the periods 1960-2010. The time-varying causality test facilitates the better understanding of the causal relationship between the covariates owing to the fact that it might identify causalities when the time-constant hypothesis is rejected. Our findings indicate the existence of a time-varying causality at the state level. Specifically, the results probe eight bidirectional time-varying causalities between energy consumption and CO 2 emission, six cases of two-way time-varying causalities between economic growth and energy consumption, and five bidirectional time-varying causalities between economic growth and CO 2 emission. Moreover, we examine the traditional environmental Kuznets curve hypothesis for the states. Notably, our results do not endorse the validity of the EKC, albeit the majority of states support an inverted N-shaped relationship. Lastly, we can identify multiple policy implications based on the empirical results.
Perfect fluid Bianchi Type-I cosmological models with time varying G ...
Home; Journals; Pramana – Journal of Physics; Volume 70; Issue 4 ... Research Articles Volume 70 Issue 4 April 2008 pp 565-574 ... Department of Mathematical Sciences, A.P.S. University, Rewa 486 003, India; Department of Mathematics, Govt. ... Proceedings of the International Workshop/Conference on Computational ...
Handling Interfaces and Time-varying Properties in Radionuclide Transport Models
Robinson, Peter; Watson, Claire (Quintessa Ltd., Henley-on-Thames (United Kingdom))
2010-12-15
This report documents studies undertaken by Quintessa during 2010 in preparation for the SR-Site review that will be initiated by SSM in 2011. The studies relate to consequence analysis calculations, that is to the calculation of radionuclide release and transport if a canister is breached. A sister report documents modelling work undertaken to investigate the coupled processes relevant to copper corrosion and buffer erosion. The Q{sub eq} concept is an important part of SKB's current methodology for radionuclide transport using one-dimensional transport modelling; it is used in particular to model transport at the buffer/fracture interface. Quintessa's QPAC code has been used to investigate the Q{sub eq} approach and to explore the importance of heterogeneity in the fracture and spalling on the deposition hole surface. The key conclusions are that: - The basic approach to calculating Q{sub eq} values is sound and can be reproduced in QPAC. - The fracture resistance dominates over the diffusive resistance in the buffer except for the highest velocity cases. - Heterogeneity in the fracture, in terms of uncorrelated random variations in the fracture aperture, tends to reduce releases, so the use of a constant average aperture approach is conservative. - Narrow channels could lead to the same release as larger fractures with the same pore velocity, so a channel enhancement factor of sq root10 should be considered. - A spalling zone that increases the area of contact between flowing water and the buffer has the potential to increase the release significantly and changes the functional dependence of Q{sub eq}frac on the flowing velocity. Quintessa's AMBER software has previously been used to reproduce SKB's one-dimensional transport calculations and AMBER allows the use of time varying properties. This capability has been used to investigate the effects of glacial episodes on radionuclide transport. The main parameters that could be affected are
Handling Interfaces and Time-varying Properties in Radionuclide Transport Models
Robinson, Peter; Watson, Claire
2010-12-01
This report documents studies undertaken by Quintessa during 2010 in preparation for the SR-Site review that will be initiated by SSM in 2011. The studies relate to consequence analysis calculations, that is to the calculation of radionuclide release and transport if a canister is breached. A sister report documents modelling work undertaken to investigate the coupled processes relevant to copper corrosion and buffer erosion. The Q eq concept is an important part of SKB's current methodology for radionuclide transport using one-dimensional transport modelling; it is used in particular to model transport at the buffer/fracture interface. Quintessa's QPAC code has been used to investigate the Q eq approach and to explore the importance of heterogeneity in the fracture and spalling on the deposition hole surface. The key conclusions are that: - The basic approach to calculating Q eq values is sound and can be reproduced in QPAC. - The fracture resistance dominates over the diffusive resistance in the buffer except for the highest velocity cases. - Heterogeneity in the fracture, in terms of uncorrelated random variations in the fracture aperture, tends to reduce releases, so the use of a constant average aperture approach is conservative. - Narrow channels could lead to the same release as larger fractures with the same pore velocity, so a channel enhancement factor of √10 should be considered. - A spalling zone that increases the area of contact between flowing water and the buffer has the potential to increase the release significantly and changes the functional dependence of Q eq frac on the flowing velocity. Quintessa's AMBER software has previously been used to reproduce SKB's one-dimensional transport calculations and AMBER allows the use of time varying properties. This capability has been used to investigate the effects of glacial episodes on radionuclide transport. The main parameters that could be affected are sorption coefficients and flow rates. For both
Locally Rotationally Symmetric Bianchi Type-I Model with Time Varying Λ Term
Tiwari, R. K.; Jha, Navin Kumar
2009-01-01
We investigate the locally rotationally symmetric (LRS) Bianchi type-I cosmological model for stiff matter and a vacuum solution with a cosmological term proportional to R −m (R is the scale factor and m is a positive constant). The cosmological term decreases with time. We obtain that for both the cases the present universe is accelerating with a large fraction of cosmological density in the form of a cosmological term
Keogh, Ruth H; Mangtani, Punam; Rodrigues, Laura; Nguipdop Djomo, Patrick
2016-01-05
Traditional analyses of standard case-control studies using logistic regression do not allow estimation of time-varying associations between exposures and the outcome. We present two approaches which allow this. The motivation is a study of vaccine efficacy as a function of time since vaccination. Our first approach is to estimate time-varying exposure-outcome associations by fitting a series of logistic regressions within successive time periods, reusing controls across periods. Our second approach treats the case-control sample as a case-cohort study, with the controls forming the subcohort. In the case-cohort analysis, controls contribute information at all times they are at risk. Extensions allow left truncation, frequency matching and, using the case-cohort analysis, time-varying exposures. Simulations are used to investigate the methods. The simulation results show that both methods give correct estimates of time-varying effects of exposures using standard case-control data. Using the logistic approach there are efficiency gains by reusing controls over time and care should be taken over the definition of controls within time periods. However, using the case-cohort analysis there is no ambiguity over the definition of controls. The performance of the two analyses is very similar when controls are used most efficiently under the logistic approach. Using our methods, case-control studies can be used to estimate time-varying exposure-outcome associations where they may not previously have been considered. The case-cohort analysis has several advantages, including that it allows estimation of time-varying associations as a continuous function of time, while the logistic regression approach is restricted to assuming a step function form for the time-varying association.
Ruth H. Keogh
2016-01-01
Full Text Available Abstract Background Traditional analyses of standard case-control studies using logistic regression do not allow estimation of time-varying associations between exposures and the outcome. We present two approaches which allow this. The motivation is a study of vaccine efficacy as a function of time since vaccination. Methods Our first approach is to estimate time-varying exposure-outcome associations by fitting a series of logistic regressions within successive time periods, reusing controls across periods. Our second approach treats the case-control sample as a case-cohort study, with the controls forming the subcohort. In the case-cohort analysis, controls contribute information at all times they are at risk. Extensions allow left truncation, frequency matching and, using the case-cohort analysis, time-varying exposures. Simulations are used to investigate the methods. Results The simulation results show that both methods give correct estimates of time-varying effects of exposures using standard case-control data. Using the logistic approach there are efficiency gains by reusing controls over time and care should be taken over the definition of controls within time periods. However, using the case-cohort analysis there is no ambiguity over the definition of controls. The performance of the two analyses is very similar when controls are used most efficiently under the logistic approach. Conclusions Using our methods, case-control studies can be used to estimate time-varying exposure-outcome associations where they may not previously have been considered. The case-cohort analysis has several advantages, including that it allows estimation of time-varying associations as a continuous function of time, while the logistic regression approach is restricted to assuming a step function form for the time-varying association.
Adabi, Farzin; Karami, Kayoomars; Felegary, Fereshte; Azarmi, Zohre
2012-01-01
We study the entropy-corrected version of the holographic dark energy (HDE) model in the framework of modified Friedmann-Robertson-Walker cosmology. We consider a non-flat universe filled with an interacting viscous entropy-corrected HDE (ECHDE) with dark matter. Also included in our model is the case of the variable gravitational constant G. We obtain the equation of state and the deceleration parameters of the interacting viscous ECHDE. Moreover, we reconstruct the potential and the dynamics of the quintessence, tachyon, K-essence and dilaton scalar field models according to the evolutionary behavior of the interacting viscous ECHDE model with time-varying G. (research papers)
Ma, Zhi-Sai; Liu, Li; Zhou, Si-Da; Yu, Lei; Naets, Frank; Heylen, Ward; Desmet, Wim
2018-01-01
The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time-varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach.
Control of beam halo-chaos using neural network self-adaptation method
Fang Jinqing; Huang Guoxian; Luo Xiaoshu
2004-11-01
Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)
Song Qiankun
2008-01-01
In this paper, the global exponential periodicity and stability of recurrent neural networks with time-varying delays are investigated by applying the idea of vector Lyapunov function, M-matrix theory and inequality technique. We assume neither the global Lipschitz conditions on these activation functions nor the differentiability on these time-varying delays, which were needed in other papers. Several novel criteria are found to ascertain the existence, uniqueness and global exponential stability of periodic solution for recurrent neural network with time-varying delays. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. Some previous results are improved and generalized, and an example is given to show the effectiveness of our method
Lingyun Li
2013-01-01
Full Text Available We provide a new gossip algorithm to investigate the problem of opinion consensus with the time-varying influence factors and weakly connected graph among multiple agents. What is more, we discuss not only the effect of the time-varying factors and the randomized topological structure but also the spread of misinformation and communication constrains described by probabilistic quantized communication in the social network. Under the underlying weakly connected graph, we first denote that all opinion states converge to a stochastic consensus almost surely; that is, our algorithm indeed achieves the consensus with probability one. Furthermore, our results show that the mean of all the opinion states converges to the average of the initial states when time-varying influence factors satisfy some conditions. Finally, we give a result about the square mean error between the dynamic opinion states and the benchmark without quantized communication.
Fengxia Xu
2014-01-01
Full Text Available U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.
Cao, Ying; Rajan, Suja S; Wei, Peng
2016-12-01
A Mendelian randomization (MR) analysis is performed to analyze the causal effect of an exposure variable on a disease outcome in observational studies, by using genetic variants that affect the disease outcome only through the exposure variable. This method has recently gained popularity among epidemiologists given the success of genetic association studies. Many exposure variables of interest in epidemiological studies are time varying, for example, body mass index (BMI). Although longitudinal data have been collected in many cohort studies, current MR studies only use one measurement of a time-varying exposure variable, which cannot adequately capture the long-term time-varying information. We propose using the functional principal component analysis method to recover the underlying individual trajectory of the time-varying exposure from the sparsely and irregularly observed longitudinal data, and then conduct MR analysis using the recovered curves. We further propose two MR analysis methods. The first assumes a cumulative effect of the time-varying exposure variable on the disease risk, while the second assumes a time-varying genetic effect and employs functional regression models. We focus on statistical testing for a causal effect. Our simulation studies mimicking the real data show that the proposed functional data analysis based methods incorporating longitudinal data have substantial power gains compared to standard MR analysis using only one measurement. We used the Framingham Heart Study data to demonstrate the promising performance of the new methods as well as inconsistent results produced by the standard MR analysis that relies on a single measurement of the exposure at some arbitrary time point. © 2016 WILEY PERIODICALS, INC.
Musa, Sarah; Supadi, Siti Suzlin; Omar, Mohd
2014-07-01
Rework is one of the solutions to some of the main issues in reverse logistic and green supply chain as it reduces production cost and environmental problem. Many researchers focus on developing rework model, but to the knowledge of the author, none of them has developed a model for time-varying demand rate. In this paper, we extend previous works and develop multiple batch production system for time-varying demand rate with rework. In this model, the rework is done within the same production cycle.
Rakkiyappan, R.; Sivasamy, R.; Lakshmanan, S.
2014-01-01
In this paper, we study the exponential synchronization of chaotic Lur'e systems with time-varying delays via sampled-data control by using sector nonlinearties. In order to make full use of information about sampling intervals and interval time-varying delays, new Lyapunov—Krasovskii functionals with triple integral terms are introduced. Based on the convex combination technique, two kinds of synchronization criteria are derived in terms of linear matrix inequalities, which can be efficiently solved via standard numerical software. Finally, three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results
Sheng Li; Yang Huizhong
2009-01-01
This paper considers the robust stability of a class of uncertain Markovian jumping Cohen-Grossberg neural networks (UMJCGNNs) with mixed time-varying delays. The parameter uncertainties are norm-bounded and the mixed time-varying delays comprise discrete and distributed time delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. An example is given to show the effectiveness of the proposed results.
Maode Yan
2008-01-01
Full Text Available This paper considers the problem of robust discrete-time sliding-mode control (DT-SMC design for a class of uncertain linear systems with time-varying delays. By applying a descriptor model transformation and Moon's inequality for bounding cross terms, a delay-dependent sufficient condition for the existence of stable sliding surface is given in terms of linear matrix inequalities (LMIs. Based on this existence condition, the synthesized sliding mode controller can guarantee the sliding-mode reaching condition of the specified discrete-time sliding surface for all admissible uncertainties and time-varying delays. An illustrative example verifies the effectiveness of the proposed method.
Cheong, Chin Wen
2008-02-01
This article investigated the influences of structural breaks on the fractionally integrated time-varying volatility model in the Malaysian stock markets which included the Kuala Lumpur composite index and four major sectoral indices. A fractionally integrated time-varying volatility model combined with sudden changes is developed to study the possibility of structural change in the empirical data sets. Our empirical results showed substantial reduction in fractional differencing parameters after the inclusion of structural change during the Asian financial and currency crises. Moreover, the fractionally integrated model with sudden change in volatility performed better in the estimation and specification evaluations.
Ali, M. Syed
2011-01-01
In this paper, the global stability of Takagi—Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature. (general)
Finite-Time Stability of Large-Scale Systems with Interval Time-Varying Delay in Interconnection
T. La-inchua
2017-01-01
Full Text Available We investigate finite-time stability of a class of nonlinear large-scale systems with interval time-varying delays in interconnection. Time-delay functions are continuous but not necessarily differentiable. Based on Lyapunov stability theory and new integral bounding technique, finite-time stability of large-scale systems with interval time-varying delays in interconnection is derived. The finite-time stability criteria are delays-dependent and are given in terms of linear matrix inequalities which can be solved by various available algorithms. Numerical examples are given to illustrate effectiveness of the proposed method.
Li, Kelin
2010-02-01
In this article, a class of impulsive bidirectional associative memory (BAM) fuzzy cellular neural networks (FCNNs) with time-varying delays is formulated and investigated. By employing delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM FCNNs with time-varying delays are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM FCNNs. An example is given to show the effectiveness of the results obtained here.
Time varying G and \\varLambda cosmology in f(R,T) gravity theory
Tiwari, R. K.; Beesham, A.; Singh, Rameshwar; Tiwari, L. K.
2017-08-01
We have studied the time dependence of the gravitational constant G and cosmological constant Λ by taking into account an anisotropic and homogeneous Bianchi type-I space-time in the framework of the modified f(R,T) theory of gravity proposed by Harko et al. (Phys. Rev. D 84:024020, 2011). For a specific choice of f(R,T)=R+2f(T) where f(T)=-λ T, two solutions of the modified gravity field equations have been generated with the help of a variation law between the expansion anisotropy ({σ}/{θ}) and the scale factor (S), together with a general non-linear equation of state. The solution for m≠3 corresponds to singular model of the universe whereas the solution for m=3 represents a non-singular model. We infer that the models entail a constant value of the deceleration parameter. A careful analysis of all the physical parameters of the models has also been carried out.
A parallel direct solver for the self-adaptive hp Finite Element Method
Paszyński, Maciej R.; Pardo, David; Torres-Verdí n, Carlos; Demkowicz, Leszek F.; Calo, Victor M.
2010-01-01
measurement simulations problems. We measure the execution time and memory usage of the solver over a large regular mesh with 1.5 million degrees of freedom as well as on the highly non-regular mesh, generated by the self-adaptive h p-FEM, with finite elements
A Self-adaptive Scope Allocation Scheme for Labeling Dynamic XML Documents
Shen, Y.; Feng, L.; Shen, T.; Wang, B.
This paper proposes a self-adaptive scope allocation scheme for labeling dynamic XML documents. It is general, light-weight and can be built upon existing data retrieval mechanisms. Bayesian inference is used to compute the actual scope allocated for labeling a certain node based on both the prior
Extending and implementing the Self-adaptive Virtual Processor for distributed memory architectures
van Tol, M.W.; Koivisto, J.
2011-01-01
Many-core architectures of the future are likely to have distributed memory organizations and need fine grained concurrency management to be used effectively. The Self-adaptive Virtual Processor (SVP) is an abstract concurrent programming model which can provide this, but the model and its current
Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm
Rao, R.V.; More, K.C.
2017-01-01
Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.
Suweken, G.; van Horssen, W.T.
2002-01-01
In this paper the weakly nonlinear, transversal vibrations of a conveyor belt will be considered. The belt is assumed to move with a low and time-varying speed. Using Kirchhoff's approach a single equation of motion will be derived from a coupled system of partial differential equations describing
Hong Zhang
2014-01-01
Full Text Available This paper is concerned with a nonautonomous fishing model with a time-varying delay. Under proper conditions, we employ a novel argument to establish a criterion on the global exponential stability of positive almost periodic solutions of the model with almost periodic coefficients and delays. Moreover, an example and its numerical simulation are given to illustrate the main results.
Baxa, Jaromír; Horváth, R.; Vašíček, B.
2013-01-01
Roč. 9, č. 1 (2013), s. 117-138 ISSN 1572-3089 Institutional support: RVO:67985556 Keywords : Financial stress * Time-varying parameter model * Endogenous regressors Subject RIV: AH - Economics Impact factor: 2.932, year: 2013 http://library.utia.cas.cz/separaty/2013/E/baxa-0395375.pdf
Koopman, S.J.; Mallee, M.I.P.; van der Wel, M.
2010-01-01
In this article we introduce time-varying parameters in the dynamic Nelson-Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson-Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive
Jacobs, William R; Dodd, Tony J; Anderson, Sean R; Wilson, Emma D; Porrill, John; Assaf, Tareq; Rossiter, Jonathan
2015-01-01
Current models of dielectric elastomer actuators (DEAs) are mostly constrained to first principal descriptions that are not well suited to the application of control design due to their computational complexity. In this work we describe an integrated framework for the identification of control focused, data driven and time-varying DEA models that allow advanced analysis of nonlinear system dynamics in the frequency-domain. Experimentally generated input–output data (voltage-displacement) was used to identify control-focused, nonlinear and time-varying dynamic models of a set of film-type DEAs. The model description used was the nonlinear autoregressive with exogenous input structure. Frequency response analysis of the DEA dynamics was performed using generalized frequency response functions, providing insight and a comparison into the time-varying dynamics across a set of DEA actuators. The results demonstrated that models identified within the presented framework provide a compact and accurate description of the system dynamics. The frequency response analysis revealed variation in the time-varying dynamic behaviour of DEAs fabricated to the same specifications. These results suggest that the modelling and analysis framework presented here is a potentially useful tool for future work in guiding DEA actuator design and fabrication for application domains such as soft robotics. (paper)
Liu Bingwen
2008-01-01
In this Letter the convergence behavior of Cohen-Grossberg neural networks with delays and time-varying coefficients are considered. Some sufficient conditions are established to ensure that the solutions of the networks converge locally exponentially to zero point, which are new and complement of previously known results
Delay-Dependent Guaranteed Cost H∞ Control of an Interval System with Interval Time-Varying Delay
Zhongke Shi
2009-01-01
Full Text Available This paper concerns the problem of the delay-dependent robust stability and guaranteed cost H∞ control for an interval system with time-varying delay. The interval system with matrix factorization is provided and leads to less conservative conclusions than solving a square root. The time-varying delay is assumed to belong to an interval and the derivative of the interval time-varying delay is not a restriction, which allows a fast time-varying delay; also its applicability is broad. Based on the Lyapunov-Ktasovskii approach, a delay-dependent criterion for the existence of a state feedback controller, which guarantees the closed-loop system stability, the upper bound of cost function, and disturbance attenuation lever for all admissible uncertainties as well as out perturbation, is proposed in terms of linear matrix inequalities (LMIs. The criterion is derived by free weighting matrices that can reduce the conservatism. The effectiveness has been verified in a number example and the compute results are presented to validate the proposed design method.
Ophem, S. van; Berkhoff, A.P.
2012-01-01
Tracking behavior and the rate of convergence are critical properties in active noise control applications with time-varying disturbance spectra. As compared to the standard filtered-reference Least Mean Square (LMS) algorithm, improved convergence can be obtained with schemes based on
Farahani, H.H.
2013-01-01
The main focus of the thesis is modelling the static and time-varying parts of the Earth's gravity field at the global scale based on data acquired by the Gravity Recovery And Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Explorer (GOCE). In addition, a new
Abdul Gaffar, MY
2007-10-01
Full Text Available , contributes to ISAR image blurring. Quaternion algebra is used to aid the characterisation of a time-varying Doppler generating axis of rotation on the migration through cross-range cells. Real motion data of a sailing yacht is used to examine the effects of 3...
Perperoglou, Aris
2016-12-10
Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Active control of time-varying broadband noise and vibrations using a sliding-window Kalman filter
van Ophem, S.; Berkhoff, Arthur P.; Sas, P.; Moens, D.; Denayer, H.
2014-01-01
Recently, a multiple-input/multiple-output Kalman filter technique was presented to control time-varying broadband noise and vibrations. By describing the feed-forward broadband active noise control problem in terms of a state estimation problem it was possible to achieve a faster rate of
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.
Yu, Wenwu; Chen, Guanrong; Cao, Ming
Using tools from algebraic graph theory and nonsmooth analysis in combination with ideas of collective potential functions, velocity consensus and navigation feedback, a distributed leader-follower flocking algorithm for multi-agent dynamical systems with time-varying velocities is developed where
Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing
In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.
Time-varying cycle average and daily variation in ambient air pollution and fecundability.
Nobles, Carrie J; Schisterman, Enrique F; Ha, Sandie; Buck Louis, Germaine M; Sherman, Seth; Mendola, Pauline
2018-01-01
fecundability, warranting further investigation. This work was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Longitudinal Investigation of Fertility and the Environment study contract nos. #N01-HD-3-3355, NO1-HD-#-3356, N01-HD-3-3358 and the Air Quality and Reproductive Health Study Contract No. HHSN275200800002I, Task Order No. HHSN27500008). We declare no conflict of interest. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.
Teskey, G.C.; Prato, F.S.; Ossenkopp, K.P.; Kavaliers, M.
1988-01-01
The effects of exposure to clinical magnetic resonance imaging (MRI) on analgesia induced by the mu opiate agonist, fentanyl, was examined in mice. During the dark period, adult male mice were exposed for 23.2 min to the time-varying (0.6 T/sec) magnetic field (TVMF) component of the MRI procedure. Following this exposure, the analgesic potency of fentanyl citrate (0.1 mg/kg) was determined at 5, 10, 15, and 30 min post-injection, using a thermal test stimulus (hot-plate 50 degrees C). Exposure to the magnetic-field gradients attenuated the fentanyl-induced analgesia in a manner comparable to that previously observed with morphine. These results indicate that the time-varying magnetic fields associated with MRI have significant inhibitory effects on the analgesic effects of specific mu-opiate-directed ligands.
Meng, Su; Chen, Jie; Sun, Jian
2017-10-01
This paper investigates the problem of observer-based output feedback control for networked control systems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.
Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.
2018-02-01
This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.
Huaiqin Wu
2012-01-01
Full Text Available By combing the theories of the switched systems and the interval neural networks, the mathematics model of the switched interval neural networks with discrete and distributed time-varying delays of neural type is presented. A set of the interval parameter uncertainty neural networks with discrete and distributed time-varying delays of neural type are used as the individual subsystem, and an arbitrary switching rule is assumed to coordinate the switching between these networks. By applying the augmented Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI techniques, a delay-dependent criterion is achieved to ensure to such switched interval neural networks to be globally asymptotically robustly stable in terms of LMIs. The unknown gain matrix is determined by solving this delay-dependent LMIs. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.
Ohno, Nobuaki; Ohtani, Hiroaki; Horiuchi, Ritoku; Matsuoka, Daisuke
2012-01-01
The particle kinetic effects play an important role in breaking the frozen-in condition and exciting collisionless magnetic reconnection in high temperature plasmas. Because this effect is originating from a complex thermal motion near reconnection point, it is very important to examine particle trajectories using scientific visualization technique, especially in the presence of plasma instability. We developed interactive visualization environment for the particle trajectories in time-varying electromagnetic fields in the CAVE-type virtual reality system based on VFIVE, which is interactive visualization software for the CAVE system. From the analysis of ion trajectories using the particle simulation data, it was found that time-varying electromagnetic fields around the reconnection region accelerate ions toward the downstream region. (author)
Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Thangaraj, P.
2013-02-01
This paper addresses the problem of passivity analysis issue for a class of fuzzy bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time varying delays. A set of sufficient conditions for the passiveness of the considered fuzzy BAM neural network model is derived in terms of linear matrix inequalities by using the delay fractioning technique together with the Lyapunov function approach. In addition, the uncertainties are inevitable in neural networks because of the existence of modeling errors and external disturbance. Further, this result is extended to study the robust passivity criteria for uncertain fuzzy BAM neural networks with time varying delays and uncertainties. These criteria are expressed in the form of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. Two numerical examples are provided to demonstrate the effectiveness of the obtained results.
Changjin Xu
2014-01-01
Full Text Available This paper deals with a kind of nonlinear Duffing equation with a deviating argument and time-varying delay. By using differential inequality techniques, some very verifiable criteria on the existence and exponential stability of antiperiodic solutions for the equation are obtained. Our results are new and complementary to previously known results. An example is given to illustrate the feasibility and effectiveness of our main results.
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 (NH 3 -N) and permanganate index (COD Mn ) 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 NH 3 -N and COD Mn series at the same monitoring gauge. The larger first-order Markov joint transition probability indicates water quality state Class V w , 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 NH 3 -N and COD Mn simultaneously reaching Class V is 50.61%, while the asynchronous encounter risk probability is largest when NH 3 -N and COD Mn is inferior to class V and class IV water quality standards, respectively.
The impact of monetary policy and exchange rate shocks in Poland: evidence from a time-varying VAR
Arratibel, Olga; Michaelis, Henrike
2014-01-01
This paper follows the Bayesian time-varying VAR approach with stochastic volatility developed by Primiceri (2005), to analyse whether the reaction of output and prices to interest rate and exchange rate shocks has changed across time (1996-2012) in the Polish economy. The empirical findings show that: (1) output appears more responsive to an interest rate shock at the beginning of our sample. Since 2000, absorbing this shock has become less costly in terms of output, notwithstanding some rev...
Xing Yin
2011-01-01
uncertain periodic switched recurrent neural networks with time-varying delays. When uncertain discrete-time recurrent neural network is a periodic system, it is expressed as switched neural network for the finite switching state. Based on the switched quadratic Lyapunov functional approach (SQLF and free-weighting matrix approach (FWM, some linear matrix inequality criteria are found to guarantee the delay-dependent asymptotical stability of these systems. Two examples illustrate the exactness of the proposed criteria.
2017-12-08
STATISTICAL LINEAR TIME-VARYING SYSTEM MODEL OF HIGH GRAZING ANGLE SEA CLUTTER FOR COMPUTING INTERFERENCE POWER 1. INTRODUCTION Statistical linear time...beam. We can approximate one of the sinc factors using the Dirichlet kernel to facilitate computation of the integral in (6) as follows: ∣∣∣∣sinc(WB...plotted in Figure 4. The resultant autocorrelation can then be found by substituting (18) into (28). The Python code used to generate Figures 1-4 is found
Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with Time-Varying Delays
Yonggang Chen
2008-01-01
Full Text Available This paper considers the delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays. By constructing the new Lyapunov functional, the improved delay-dependent exponential stability criterion is derived in terms of linear matrix inequality (LMI. Moreover, in order to reduce the conservativeness, some slack matrices are introduced in this paper. Two numerical examples are presented to show the effectiveness and less conservativeness of the proposed method.
Wu, Wei; Cui, Bao-Tong
2007-07-01
In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
Global exponential stability of BAM neural networks with time-varying delays: The discrete-time case
Raja, R.; Marshal Anthoni, S.
2011-02-01
This paper deals with the problem of stability analysis for a class of discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient conditions is proposed for the global exponential stability of discrete-time BAM neural networks. The proposed LMI based results can be easily checked by LMI control toolbox. Moreover, an example is also provided to demonstrate the effectiveness of the proposed method.
Wei Feng; Simon X. Yang; Haixia Wu
2014-01-01
The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported ...
Park, Ju H.; Kwon, O. M.
In the letter, the global asymptotic stability of bidirectional associative memory (BAM) neural networks with delays is investigated. The delay is assumed to be time-varying and belongs to a given interval. A novel stability criterion for the stability is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. Two numerical examples are illustrated to show the effectiveness of our new result.
Suto, Hirofumi; Kanao, Taro; Nagasawa, Tazumi; Mizushima, Koichi; Sato, Rie
2018-05-01
Microwave-assisted magnetization switching (MAS) is attracting attention as a method for reversing nanomagnets with a high magnetic anisotropy by using a small-amplitude magnetic field. We experimentally study MAS of a perpendicularly magnetized nanomagnet by applying a microwave magnetic field with a time-varying frequency. Because the microwave field frequency can follow the nonlinear decrease of the resonance frequency, larger magnetization excitation than that in a constant-frequency microwave field is induced, which enhances the MAS effect. The switching field decreases almost linearly as the start value of the time-varying microwave field frequency increases, and it becomes smaller than the minimum switching field in a constant-frequency microwave field. To obtain this enhancement of the MAS effect, the end value of the time-varying microwave field frequency needs to be almost the same as or lower than the critical frequency for MAS in a constant-frequency microwave field. In addition, the frequency change typically needs to take 1 ns or longer to make the rate of change slow enough for the magnetization to follow the frequency change. This switching behavior is qualitatively explained by the theory based on the macrospin model.
Lu, Fengbin, E-mail: fblu@amss.ac.cn [Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Qiao, Han, E-mail: qiaohan@ucas.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Wang, Shouyang, E-mail: sywang@amss.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Lai, Kin Keung, E-mail: mskklai@cityu.edu.hk [Department of Management Sciences, City University of Hong Kong (Hong Kong); Li, Yuze, E-mail: richardyz.li@mail.utoronto.ca [Department of Industrial Engineering, University of Toronto (Canada)
2017-01-15
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
Balasubramaniam, P.; Lakshmanan, S.; Manivannan, A.
2012-01-01
Highlights: ► Robust stability analysis for Markovian jumping interval neural networks is considered. ► Both linear fractional and interval uncertainties are considered. ► A new LKF is constructed with triple integral terms. ► MATLAB LMI control toolbox is used to validate theoretical results. ► Numerical examples are given to illustrate the effectiveness of the proposed method. - Abstract: This paper investigates robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. The delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional (LKF), some inequality techniques and stochastic stability theory, new delay-dependent stability criteria have been obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results.
Zhang Zhaoyun; Gao Yang; Zhao Xinghai; Zhao Xiang
2011-01-01
Laser's optical output power and frequency are modulated when the optical beam is back-scattered into the active cavity of the laser. By signal processing, the Doppler frequency can be acquired, and the target's velocity can be calculated. Based on these properties, an interferometry velocity sensor can be designed. When target move in time-varying velocity mode, it is difficult to extract the target's velocity. Time-varying velocity measurement by self-mixing laser diode is explored. A mathematics model was proposed for the time-varying velocity (invariable acceleration) measurement by self-mixing laser diode. Based on this model, a Discrete Chirp-Fourier Transform (DCFT) method was applied, DCFT is analogous to DFT. We show that when the signal length N is prime, the magnitudes of all the side lobes are 1, whereas the magnitudes of the main lobe is √N, And the coordinates of the main lobe shows the target's velocity and acceleration information. The simulation results prove the validity of the algorithm even in the situation of low SNR when N is prime.
Cole, David A; Martin, Joan M; Jacquez, Farrah M; Tram, Jane M; Zelkowitz, Rachel; Nick, Elizabeth A; Rights, Jason D
2017-07-01
The longitudinal structure of depression in children and adolescents was examined by applying a Trait-State-Occasion structural equation model to 4 waves of self, teacher, peer, and parent reports in 2 age groups (9 to 13 and 13 to 16 years old). Analyses revealed that the depression latent variable consisted of 2 longitudinal factors: a time-invariant dimension that was completely stable over time and a time-varying dimension that was not perfectly stable over time. Different sources of information were differentially sensitive to these 2 dimensions. Among adolescents, self- and parent reports better reflected the time-invariant aspects. For children and adolescents, peer and teacher reports better reflected the time-varying aspects. Relatively high cross-informant agreement emerged for the time-invariant dimension in both children and adolescents. Cross-informant agreement for the time-varying dimension was high for adolescents but very low for children. Implications emerge for theoretical models of depression and for its measurement, especially when attempting to predict changes in depression in the context of longitudinal studies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Claessens, T E; Georgakopoulos, D; Afanasyeva, M; Vermeersch, S J; Millar, H D; Stergiopulos, N; Westerhof, N; Verdonck, P R; Segers, P
2006-04-01
The linear time-varying elastance theory is frequently used to describe the change in ventricular stiffness during the cardiac cycle. The concept assumes that all isochrones (i.e., curves that connect pressure-volume data occurring at the same time) are linear and have a common volume intercept. Of specific interest is the steepest isochrone, the end-systolic pressure-volume relationship (ESPVR), of which the slope serves as an index for cardiac contractile function. Pressure-volume measurements, achieved with a combined pressure-conductance catheter in the left ventricle of 13 open-chest anesthetized mice, showed a marked curvilinearity of the isochrones. We therefore analyzed the shape of the isochrones by using six regression algorithms (two linear, two quadratic, and two logarithmic, each with a fixed or time-varying intercept) and discussed the consequences for the elastance concept. Our main observations were 1) the volume intercept varies considerably with time; 2) isochrones are equally well described by using quadratic or logarithmic regression; 3) linear regression with a fixed intercept shows poor correlation (R(2) volume intercept of the ESPVR. In conclusion, the linear time-varying elastance fails to provide a sufficiently robust model to account for changes in pressure and volume during the cardiac cycle in the mouse ventricle. A new framework accounting for the nonlinear shape of the isochrones needs to be developed.
Xiao, Lin; Liao, Bolin; Li, Shuai; Chen, Ke
2018-02-01
In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks. Copyright © 2017 Elsevier Ltd. All rights reserved.
Park, Myeongjin; Lee, Seung-Hoon; Kwon, Oh-Min; Seuret, Alexandre
2017-09-06
This paper investigates synchronization in complex dynamical networks (CDNs) with interval time-varying delays. The CDNs are representative of systems composed of a large number of interconnected dynamical units, and for the purpose of the mathematical analysis, the leading work is to model them as graphs whose nodes represent the dynamical units. At this time, we take note of the importance of each node in networks. One way, in this paper, is that the closeness-centrality mentioned in the field of social science is grafted onto the CDNs. By constructing a suitable Lyapunov-Krasovskii functional, and utilizing some mathematical techniques, the sufficient and closeness-centrality-based conditions for synchronization stability of the networks are established in terms of linear matrix inequalities. Ultimately, the use of the closeness-centrality can be weighted with regard to not only the interconnection relation among the nodes, which was utilized in the existing works but also more information about nodes. Here, the centrality will be added as the concerned information. Moreover, to avoid the computational burden causing the nonconvex term including the square of the time-varying delay, how to deal with it is applied by estimating it to the convex term including time-varying delay. Finally, two illustrative examples are given to show the advantage of the closeness-centrality in point of the robustness on time-delay.
Linking the fractional derivative and the Lomnitz creep law to non-Newtonian time-varying viscosity
Pandey, Vikash; Holm, Sverre
2016-09-01
Many of the most interesting complex media are non-Newtonian and exhibit time-dependent behavior of thixotropy and rheopecty. They may also have temporal responses described by power laws. The material behavior is represented by the relaxation modulus and the creep compliance. On the one hand, it is shown that in the special case of a Maxwell model characterized by a linearly time-varying viscosity, the medium's relaxation modulus is a power law which is similar to that of a fractional derivative element often called a springpot. On the other hand, the creep compliance of the time-varying Maxwell model is identified as Lomnitz's logarithmic creep law, making this possibly its first direct derivation. In this way both fractional derivatives and Lomnitz's creep law are linked to time-varying viscosity. A mechanism which yields fractional viscoelasticity and logarithmic creep behavior has therefore been found. Further, as a result of this linking, the curve-fitting parameters involved in the fractional viscoelastic modeling, and the Lomnitz law gain physical interpretation.
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Spitzer, M W; Semple, M N
1998-12-01
Transformation of binaural response properties in the ascending auditory pathway: influence of time-varying interaural phase disparity. J. Neurophysiol. 80: 3062-3076, 1998. Previous studies demonstrated that tuning of inferior colliculus (IC) neurons to interaural phase disparity (IPD) is often profoundly influenced by temporal variation of IPD, which simulates the binaural cue produced by a moving sound source. To determine whether sensitivity to simulated motion arises in IC or at an earlier stage of binaural processing we compared responses in IC with those of two major IPD-sensitive neuronal classes in the superior olivary complex (SOC), neurons whose discharges were phase locked (PL) to tonal stimuli and those that were nonphase locked (NPL). Time-varying IPD stimuli consisted of binaural beats, generated by presenting tones of slightly different frequencies to the two ears, and interaural phase modulation (IPM), generated by presenting a pure tone to one ear and a phase modulated tone to the other. IC neurons and NPL-SOC neurons were more sharply tuned to time-varying than to static IPD, whereas PL-SOC neurons were essentially uninfluenced by the mode of stimulus presentation. Preferred IPD was generally similar in responses to static and time-varying IPD for all unit populations. A few IC neurons were highly influenced by the direction and rate of simulated motion, but the major effect for most IC neurons and all SOC neurons was a linear shift of preferred IPD at high rates-attributable to response latency. Most IC and NPL-SOC neurons were strongly influenced by IPM stimuli simulating motion through restricted ranges of azimuth; simulated motion through partially overlapping azimuthal ranges elicited discharge profiles that were highly discontiguous, indicating that the response associated with a particular IPD is dependent on preceding portions of the stimulus. In contrast, PL-SOC responses tracked instantaneous IPD throughout the trajectory of simulated
Self-adaptive demodulation for polarization extinction ratio in distributed polarization coupling.
Zhang, Hongxia; Ren, Yaguang; Liu, Tiegen; Jia, Dagong; Zhang, Yimo
2013-06-20
A self-adaptive method for distributed polarization extinction ratio (PER) demodulation is demonstrated. It is characterized by dynamic PER threshold coupling intensity (TCI) and nonuniform PER iteration step length (ISL). Based on the preset PER calculation accuracy and original distribution coupling intensity, TCI and ISL can be made self-adaptive to determine contributing coupling points inside the polarizing devices. Distributed PER is calculated by accumulating those coupling points automatically and selectively. Two different kinds of polarization-maintaining fibers are tested, and PERs are obtained after merely 3-5 iterations using the proposed method. Comparison experiments with Thorlabs commercial instrument are also conducted, and results show high consistency. In addition, the optimum preset PER calculation accuracy of 0.05 dB is obtained through many repeated experiments.
Smart Electrochemical Energy Storage Devices with Self-Protection and Self-Adaptation Abilities.
Yang, Yun; Yu, Dandan; Wang, Hua; Guo, Lin
2017-12-01
Currently, with booming development and worldwide usage of rechargeable electrochemical energy storage devices, their safety issues, operation stability, service life, and user experience are garnering special attention. Smart and intelligent energy storage devices with self-protection and self-adaptation abilities aiming to address these challenges are being developed with great urgency. In this Progress Report, we highlight recent achievements in the field of smart energy storage systems that could early-detect incoming internal short circuits and self-protect against thermal runaway. Moreover, intelligent devices that are able to take actions and self-adapt in response to external mechanical disruption or deformation, i.e., exhibiting self-healing or shape-memory behaviors, are discussed. Finally, insights into the future development of smart rechargeable energy storage devices are provided. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs
Li Liu
2015-01-01
we propose a modified version of hidden Markov model (HMM classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.
Hui Liu
2015-01-01
Full Text Available The key problem of computer-aided diagnosis (CAD of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO pulmonary nodules than other typical algorithms.
Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation
Hong SeungHo
2011-01-01
Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.
A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks
Baoguo Yu
2016-01-01
Full Text Available In the wireless sensor network (WSN localization methods based on Received Signal Strength Indicator (RSSI, it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.
Control of suspended low-gravity simulation system based on self-adaptive fuzzy PID
Chen, Zhigang; Qu, Jiangang
2017-09-01
In this paper, an active suspended low-gravity simulation system is proposed to follow the vertical motion of the spacecraft. Firstly, working principle and mathematical model of the low-gravity simulation system are shown. In order to establish the balance process and suppress the strong position interference of the system, the idea of self-adaptive fuzzy PID control strategy is proposed. It combines the PID controller with a fuzzy controll strategy, the control system can be automatically adjusted by changing the proportional parameter, integral parameter and differential parameter of the controller in real-time. At last, we use the Simulink tools to verify the performance of the controller. The results show that the system can reach balanced state quickly without overshoot and oscillation by the method of the self-adaptive fuzzy PID, and follow the speed of 3m/s, while simulation degree of accuracy of system can reach to 95.9% or more.
QoS-aware self-adaptation of communication protocols in a pervasive service middleware
Zhang, Weishan; Hansen, Klaus Marius; Fernandes, João
2010-01-01
Pervasive computing is characterized by heterogeneous devices that usually have scarce resources requiring optimized usage. These devices may use different communication protocols which can be switched at runtime. As different communication protocols have different quality of service (Qo......S) properties, this motivates optimized self-adaption of protocols for devices, e.g., considering power consumption and other QoS requirements, e.g. round trip time (RTT) for service invocations, throughput, and reliability. In this paper, we present an extensible approach for self-adaptation of communication...... protocols for pervasive web services, where protocols are designed as reusable connectors and our middleware infrastructure can hide the complexity of using different communication protocols to upper layers. We also propose to use Genetic Algorithms (GAs) to find optimized configurations at runtime...
Study on the pressure self-adaptive water-tight junction box in underwater vehicle
Haocai Huang
2012-09-01
Full Text Available Underwater vehicles play a very important role in underwater engineering. Water-tight junction box (WJB is one of the key components in underwater vehicle. This paper puts forward a pressure self-adaptive water-tight junction box (PSAWJB which improves the reliability of the WJB significantly by solving the sealing and pressure problems in conventional WJB design. By redundancy design method, the pressure self-adaptive equalizer (PSAE is designed in such a way that it consists of a piston pressure-adaptive compensator (PPAC and a titanium film pressure-adaptive compensator (TFPAC. According to hydro-mechanical simulations, the operating volume of the PSAE is more than or equal to 11.6 % of the volume of WJB liquid system. Furthermore, the required operating volume of the PSAE also increases as the gas content of oil, hydrostatic pressure or temperature difference increases. The reliability of the PSAWJB is proved by hyperbaric chamber tests.
Self-adapting metal-ceramic coating for biomass and waste incineration plants
Faulstich, Martin [Technische Univ. Muenchen (Germany); Fehr, Karl Thomas; Ye, Ya-Ping [Ludwig-Maximilians-Univ., Muenchen (Germany); Loeh, Ingrid; Mocker, Mario; Wolf, Gerhard [ATZ Entwicklungszentrum, Sulzbach-Rosenberg (Germany)
2010-07-01
Thermally sprayed coatings might become a reasonable alternative to cost-intensive cladding of heat exchangers in biomass and waste incineration. Shortcomings of these coatings might be overcome by a double-layer system, consisting of Alloy 625 covered with yttria-stabilized zirconia. Under appropriate conditions, re-crystallized zirconium oxide and chromium oxide form a dense, self-adapting and self-healing barrier against further infiltration of gaseous species. (orig.)
Han Liangxiu
2009-01-01
Grid computing aims to enable 'resource sharing and coordinated problem solving in dynamic, multi-institutional virtual organizations (VOs)'. However, due to the nature of heterogeneous and dynamic resources, dynamic failures in the distributed grid environment usually occur more than in traditional computation platforms, which cause failed VO formations. In this paper, we develop a novel self-adaptive mechanism to dynamic failures during VO formations. Such a self-adaptive scheme allows an individual and member of VOs to automatically find other available or replaceable one once a failure happens and therefore makes systems automatically recover from dynamic failures. We define dynamic failure situations of a system by using two standard indicators: mean time between failures (MTBF) and mean time to recover (MTTR). We model both MTBF and MTTR as Poisson distributions. We investigate and analyze the efficiency of the proposed self-adaptation mechanism to dynamic failures by comparing the success probability of VO formations before and after adopting it in three different cases: (1) different failure situations; (2) different organizational structures and scales; (3) different task complexities. The experimental results show that the proposed scheme can automatically adapt to dynamic failures and effectively improve the dynamic VO formation performance in the event of node failures, which provide a valuable addition to the field.
Lun-Hui Xu
2013-01-01
Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.
Andrew Worthington; Helen Higgs
2005-01-01
This paper examines market risk in four demutualised and self-listed stock exchanges: the Australian Stock Exchange, the Deutsche Börse, the London Stock Exchange and the Singapore Stock Exchange. Daily company and MSCI index returns provide the respective asset and market portfolio data. A bivariate MA-GARCH model is used to estimate time-varying betas for each exchange from listing until 7 June 2005. While the results indicate significant beta volatility, unit root tests show the betas to b...
Yi-You Hou
2014-01-01
Full Text Available This paper considers the problem of the robust stability for the nonlinear system with time-varying delay and parameters uncertainties. Based on the H∞ theorem, Lyapunov-Krasovskii theory, and linear matrix inequality (LMI optimization technique, the H∞ quasi-sliding mode controller and switching function are developed such that the nonlinear system is asymptotically stable in the quasi-sliding mode and satisfies the disturbance attenuation (H∞-norm performance. The effectiveness and accuracy of the proposed methods are shown in numerical simulations.
Cao Jinde; Jiang Qiuhao
2004-01-01
In this Letter, several sufficient conditions are derived for the existence and uniqueness of periodic oscillatory solution for bi-directional associative memory (BAM) networks with time-varying delays by employing a new Lyapunov functional and an elementary inequality, and all other solutions of the BAM networks converge exponentially to the unique periodic solution. These criteria are presented in terms of system parameters and have important leading significance in the design and applications of periodic neural circuits for delayed BAM. As an illustration, two numerical examples are worked out using the results obtained
Jia, Heping; Jin, Wende; Ding, Yi; Song, Yonghua; Yu, Dezhao
2017-01-01
With the expanding proportion of renewable energy generation and development of smart grid technologies, flexible demand resources (FDRs) have been utilized as an approach to accommodating renewable energies. However, multiple uncertainties of FDRs may influence reliable and secure operation of smart grid. Multi-state reliability models for a single FDR and aggregating FDRs have been proposed in this paper with regard to responsive abilities for FDRs and random failures for both FDR devices and information system. The proposed reliability evaluation technique is based on Lz transform method which can formulate time-varying reliability indices. A modified IEEE-RTS has been utilized as an illustration of the proposed technique.
Léchappé, V.; Moulay, E.; Plestan, F.
2018-06-01
The stability of a prediction-based controller for linear time-invariant (LTI) systems is studied in the presence of time-varying input and output delays. The uncertain delay case is treated as well as the partial state knowledge case. The reduction method is used in order to prove the convergence of the closed-loop system including the state observer, the predictor and the plant. Explicit conditions that guarantee the closed-loop stability are given, thanks to a Lyapunov-Razumikhin analysis. Simulations illustrate the theoretical results.
Adaptive estimation of a time-varying phase with a power-law spectrum via continuous squeezed states
Dinani, Hossein T.; Berry, Dominic W.
2016-01-01
When measuring a time-varying phase, the standard quantum limit and Heisenberg limit as usually defined, for a constant phase, do not apply. If the phase has Gaussian statistics and a power-law spectrum $1/|\\omega|^p$ with $p>1$, then the generalized standard quantum limit and Heisenberg limit have recently been found to have scalings of $1/{\\cal N}^{(p-1)/p}$ and $1/{\\cal N}^{2(p-1)/(p+1)}$, respectively, where ${\\cal N}$ is the mean photon flux. We show that this Heisenberg scaling can be a...
O. M. Kwon
2012-01-01
Full Text Available The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delay-dependent criteria for the stability of the networks are derived in terms of linear matrix inequalities (LMIs by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method.
Yingwei Li
2013-01-01
Full Text Available The global exponential stability issues are considered for almost periodic solution of the neural networks with mixed time-varying delays and discontinuous neuron activations. Some sufficient conditions for the existence, uniqueness, and global exponential stability of almost periodic solution are achieved in terms of certain linear matrix inequalities (LMIs, by applying differential inclusions theory, matrix inequality analysis technique, and generalized Lyapunov functional approach. In addition, the existence and asymptotically almost periodic behavior of the solution of the neural networks are also investigated under the framework of the solution in the sense of Filippov. Two simulation examples are given to illustrate the validity of the theoretical results.
Ali, M. Syed
2014-01-01
In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen—Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples
Lu, Wenlian; Zheng, Ren; Chen, Tianping
2016-03-01
In this paper, we discuss outer-synchronization of the asymmetrically connected recurrent time-varying neural networks. By using both centralized and decentralized discretization data sampling principles, we derive several sufficient conditions based on three vector norms to guarantee that the difference of any two trajectories starting from different initial values of the neural network converges to zero. The lower bounds of the common time intervals between data samples in centralized and decentralized principles are proved to be positive, which guarantees exclusion of Zeno behavior. A numerical example is provided to illustrate the efficiency of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Chen Qin
2013-01-01
Full Text Available This paper considers the problems of the robust stability and robust H∞ controller design for time-varying delay switched systems using delta operator approach. Based on the average dwell time approach and delta operator theory, a sufficient condition of the robust exponential stability is presented by choosing an appropriate Lyapunov-Krasovskii functional candidate. Then, a state feedback controller is designed such that the resulting closed-loop system is exponentially stable with a guaranteed H∞ performance. The obtained results are formulated in the form of linear matrix inequalities (LMIs. Finally, a numerical example is provided to explicitly illustrate the feasibility and effectiveness of the proposed method.
Zhang, Guodong; Zeng, Zhigang; Hu, Junhao
2018-01-01
This paper is concerned with the global exponential dissipativity of memristive inertial neural networks with discrete and distributed time-varying delays. By constructing appropriate Lyapunov-Krasovskii functionals, some new sufficient conditions ensuring global exponential dissipativity of memristive inertial neural networks are derived. Moreover, the globally exponential attractive sets and positive invariant sets are also presented here. In addition, the new proposed results here complement and extend the earlier publications on conventional or memristive neural network dynamical systems. Finally, numerical simulations are given to illustrate the effectiveness of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Qiankun Song
2007-06-01
Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.
Cao Jinde
2007-01-01
Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.
Wei Feng
2014-01-01
Full Text Available The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.
Linking the fractional derivative and the Lomnitz creep law to non-Newtonian time-varying viscosity
Pandey, Vikash; Holm, Sverre
2016-01-01
Many of the most interesting complex media are non-Newtonian and exhibit time-dependent behavior of thixotropy and rheopecty. They may also have temporal responses described by power laws. The material behavior is represented by the relaxation modulus and the creep compliance. On the one hand, it is shown that in the special case of a Maxwell model characterized by a linearly time-varying viscosity, the medium's relaxation modulus is a power law which is similar to that of a fractional deriva...
New delay-dependent absolute stability criteria for Lur'e systems with time-varying delay
Chen, Yonggang; Bi, Weiping; Li, Wenlin
2011-07-01
In this article, the absolute stability problem is investigated for Lur'e systems with time-varying delay and sector-bounded nonlinearity. By employing the delay fractioning idea, the new augmented Lyapunov functional is first constructed. Then, by introducing some slack matrices and by reserving the useful term when estimating the upper bound of the derivative of Lyapunov functional, the new delay-dependent absolute stability criteria are derived in terms of linear matrix inequalities. Several numerical examples are presented to show the effectiveness and the less conservativeness of the proposed method.
Li, Jiarong; Jiang, Haijun; Hu, Cheng; Yu, Zhiyong
2018-03-01
This paper is devoted to the exponential synchronization, finite time synchronization, and fixed-time synchronization of Cohen-Grossberg neural networks (CGNNs) with discontinuous activations and time-varying delays. Discontinuous feedback controller and Novel adaptive feedback controller are designed to realize global exponential synchronization, finite time synchronization and fixed-time synchronization by adjusting the values of the parameters ω in the controller. Furthermore, the settling time of the fixed-time synchronization derived in this paper is less conservative and more accurate. Finally, some numerical examples are provided to show the effectiveness and flexibility of the results derived in this paper. Copyright © 2018 Elsevier Ltd. All rights reserved.
Wei, Ruoyu; Cao, Jinde; Alsaedi, Ahmed
2018-02-01
This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results.
Shahbaz, Muhammad; Mahalik, Mantu Kumar; Shah, Syed Hasanat; Sato, João Ricardo
2016-01-01
This paper detects the direction of causality among carbon dioxide (CO_2) emissions, energy consumption, and economic growth in Next 11 countries for the period 1972–2013. Changes in economic, energy, and environmental policies as well as regulatory and technological advancement over time, cause changes in the relationship among the variables. We use a novel approach i.e. time-varying Granger causality and find that economic growth is the cause of CO_2 emissions in Bangladesh and Egypt. Economic growth causes energy consumption in the Philippines, Turkey, and Vietnam but the feedback effect exists between energy consumption and economic growth in South Korea. In the cases of Indonesia and Turkey, we find the unidirectional time-varying Granger causality running from economic growth to CO_2 emissions thus validates the existence of the Environmental Kuznets Curve hypothesis, which indicates that economic growth is achievable at the minimal cost of environment. The paper gives new insights for policy makers to attain sustainable economic growth while maintaining long-run environmental quality.
Li Yang
2017-01-01
Full Text Available Paste-like tailings slurry (PTLS is always simplified as a Bingham plastic fluid, leading to excessive computational errors in the calculation of the hydraulic gradient. In the case of paste-like tailings in long-distance pipeline transportation, to explore a high-precision and reliable hydraulic gradient formula, the rheological behavior of paste-like tailings slurry was analyzed, a time-varying hydraulic gradient model was constructed, and a series of laboratory shear tests were conducted. The results indicate that the PTLS shows noticeable shear-thinning characteristics in constant shear tests; the calculated hydraulic gradient declined by about 56%, from 4.44 MPa·km−1 to 1.95 MPa·km−1 within 253 s, and remained constant for the next four hours during the pipeline transportation. Comparing with the balance hydraulic gradient obtained in a semi-industrial loop test, the computational errors of those calculated by using the time-varying hydraulic gradient model, Jinchuan formula, and Shanxi formula are 15%, 78%, and 130%, respectively. Therefore, our model is a feasible and high-precision solution for the calculation of the hydraulic gradient of paste-like tailings in long-distance pipeline transportation.
Chen, Po-Wei; Chen, Bor-Sen
2011-08-01
Naturally, a cellular network consisted of a large amount of interacting cells is complex. These cells have to be synchronized in order to emerge their phenomena for some biological purposes. However, the inherently stochastic intra and intercellular interactions are noisy and delayed from biochemical processes. In this study, a robust synchronization scheme is proposed for a nonlinear stochastic time-delay coupled cellular network (TdCCN) in spite of the time-varying process delay and intracellular parameter perturbations. Furthermore, a nonlinear stochastic noise filtering ability is also investigated for this synchronized TdCCN against stochastic intercellular and environmental disturbances. Since it is very difficult to solve a robust synchronization problem with the Hamilton-Jacobi inequality (HJI) matrix, a linear matrix inequality (LMI) is employed to solve this problem via the help of a global linearization method. Through this robust synchronization analysis, we can gain a more systemic insight into not only the robust synchronizability but also the noise filtering ability of TdCCN under time-varying process delays, intracellular perturbations and intercellular disturbances. The measures of robustness and noise filtering ability of a synchronized TdCCN have potential application to the designs of neuron transmitters, on-time mass production of biochemical molecules, and synthetic biology. Finally, a benchmark of robust synchronization design in Escherichia coli repressilators is given to confirm the effectiveness of the proposed methods. Copyright © 2011 Elsevier Inc. All rights reserved.
Guidelines for limiting exposure to time-varying electric and magnetic fields (1 Hz to 100 kHz)
Anon.
2011-01-01
This paper is the French translation of an article from the International Commission on Non-Ionizing Radiation Protection (ICNIRP) Guidelines, entitled 'Guidelines for Limiting Exposure to Time-Varying Electric and Magnetic Fields (1 Hz To 100 kHz)'. In This document, guidelines are established for the protection of humans exposed to electric and magnetic fields in the low-frequency range of the electromagnetic spectrum. The general principles for the development of ICNIRP guidelines are published elsewhere (ICNIRP 2002). For the purpose of this document, the low-frequency range extends from 1 Hz to 100 kHz. Above 100 kHz, effects such as heating need to be considered, which are covered by other ICNIRP guidelines. However, in the frequency range from 100 kHz up to approximately 10 MHz protection from both, low frequency effects on the nervous system as well as high frequency effects need to be considered depending on exposure conditions. Therefore, some guidance in this document is extended to 10 MHz to cover the nervous system effects in this frequency range. Guidelines for static magnetic fields have been issued in a separate document (ICNIRP 2009). Guidelines applicable to movement-induced electric fields or time-varying magnetic fields up to 1 Hz will be published separately. This publication replaces the low-frequency part of the 1998 guidelines (ICNIRP 1998). ICNIRP is currently revising the guidelines for the high-frequency portion of the spectrum (above 100 kHz). (authors)
Li Qi
2016-06-01
Full Text Available Dynamic time-varying operational conditions pose great challenge to the estimation of system remaining useful life (RUL for the deteriorating systems. This paper presents a method based on probabilistic and stochastic approaches to estimate system RUL for periodically monitored degradation processes with dynamic time-varying operational conditions and condition-specific failure zones. The method assumes that the degradation rate is influenced by specific operational condition and moreover, the transition between different operational conditions plays the most important role in affecting the degradation process. These operational conditions are assumed to evolve as a discrete-time Markov chain (DTMC. The failure thresholds are also determined by specific operational conditions and described as different failure zones. The 2008 PHM Conference Challenge Data is utilized to illustrate our method, which contains mass sensory signals related to the degradation process of a commercial turbofan engine. The RUL estimation method using the sensor measurements of a single sensor was first developed, and then multiple vital sensors were selected through a particular optimization procedure in order to increase the prediction accuracy. The effectiveness and advantages of the proposed method are presented in a comparison with existing methods for the same dataset.