Memory effects on stochastic resonance
Neiman, Alexander; Sung, Wokyung
1996-02-01
We study the phenomenon of stochastic resonance (SR) in a bistable system with internal colored noise. In this situation the system possesses time-dependent memory friction connected with noise via the fluctuation-dissipation theorem, so that in the absence of periodic driving the system approaches the thermodynamic equilibrium state. For this non-Markovian case we find that memory usually suppresses stochastic resonance. However, for a large memory time SR can be enhanced by the memory.
Dynamics of the stochastic Lorenz chaotic system with long memory effects
Energy Technology Data Exchange (ETDEWEB)
Zeng, Caibin, E-mail: zeng.cb@mail.scut.edu.cn; Yang, Qigui, E-mail: qgyang@scut.edu.cn [School of Mathematics, South China University of Technology, Guangzhou 510640 (China)
2015-12-15
Little seems to be known about the ergodic dynamics of stochastic systems with fractional noise. This paper is devoted to discern such long time dynamics through the stochastic Lorenz chaotic system (SLCS) with long memory effects. By a truncation technique, the SLCS is proved to generate a continuous stochastic dynamical system Λ. Based on the Krylov-Bogoliubov criterion, the required Lyapunov function is further established to ensure the existence of the invariant measure of Λ. Meanwhile, the uniqueness of the invariant measure of Λ is proved by examining the strong Feller property, together with an irreducibility argument. Therefore, the SLCS has exactly one adapted stationary solution.
Distribution of return point memory states for systems with stochastic inputs
International Nuclear Information System (INIS)
Amann, A; Brokate, M; Rachinskii, D; Temnov, G
2011-01-01
We consider the long term effect of stochastic inputs on the state of an open loop system which exhibits the so-called return point memory. An example of such a system is the Preisach model; more generally, systems with the Preisach type input-state relationship, such as in spin-interaction models, are considered. We focus on the characterisation of the expected memory configuration after the system has been effected by the input for sufficiently long period of time. In the case where the input is given by a discrete time random walk process, or the Wiener process, simple closed form expressions for the probability density of the vector of the main input extrema recorded by the memory state, and scaling laws for the dimension of this vector, are derived. If the input is given by a general continuous Markov process, we show that the distribution of previous memory elements can be obtained from a Markov chain scheme which is derived from the solution of an associated one-dimensional escape type problem. Formulas for transition probabilities defining this Markov chain scheme are presented. Moreover, explicit formulas for the conditional probability densities of previous main extrema are obtained for the Ornstein-Uhlenbeck input process. The analytical results are confirmed by numerical experiments.
Stochastic memory: getting memory out of noise
Stotland, Alexander; di Ventra, Massimiliano
2011-03-01
Memory circuit elements, namely memristors, memcapacitors and meminductors, can store information without the need of a power source. These systems are generally defined in terms of deterministic equations of motion for the state variables that are responsible for memory. However, in real systems noise sources can never be eliminated completely. One would then expect noise to be detrimental for memory. Here, we show that under specific conditions on the noise intensity memory can actually be enhanced. We illustrate this phenomenon using a physical model of a memristor in which the addition of white noise into the state variable equation improves the memory and helps the operation of the system. We discuss under which conditions this effect can be realized experimentally, discuss its implications on existing memory systems discussed in the literature, and also analyze the effects of colored noise. Work supported in part by NSF.
Stability of discrete memory states to stochastic fluctuations in neuronal systems
Miller, Paul; Wang, Xiao-Jing
2014-01-01
Noise can degrade memories by causing transitions from one memory state to another. For any biological memory system to be useful, the time scale of such noise-induced transitions must be much longer than the required duration for memory retention. Using biophysically-realistic modeling, we consider two types of memory in the brain: short-term memories maintained by reverberating neuronal activity for a few seconds, and long-term memories maintained by a molecular switch for years. Both systems require persistence of (neuronal or molecular) activity self-sustained by an autocatalytic process and, we argue, that both have limited memory lifetimes because of significant fluctuations. We will first discuss a strongly recurrent cortical network model endowed with feedback loops, for short-term memory. Fluctuations are due to highly irregular spike firing, a salient characteristic of cortical neurons. Then, we will analyze a model for long-term memory, based on an autophosphorylation mechanism of calcium/calmodulin-dependent protein kinase II (CaMKII) molecules. There, fluctuations arise from the fact that there are only a small number of CaMKII molecules at each postsynaptic density (putative synaptic memory unit). Our results are twofold. First, we demonstrate analytically and computationally the exponential dependence of stability on the number of neurons in a self-excitatory network, and on the number of CaMKII proteins in a molecular switch. Second, for each of the two systems, we implement graded memory consisting of a group of bistable switches. For the neuronal network we report interesting ramping temporal dynamics as a result of sequentially switching an increasing number of discrete, bistable, units. The general observation of an exponential increase in memory stability with the system size leads to a trade-off between the robustness of memories (which increases with the size of each bistable unit) and the total amount of information storage (which decreases
Stochastic memory: Memory enhancement due to noise
Stotland, Alexander; di Ventra, Massimiliano
2012-01-01
There are certain classes of resistors, capacitors, and inductors that, when subject to a periodic input of appropriate frequency, develop hysteresis loops in their characteristic response. Here we show that the hysteresis of such memory elements can also be induced by white noise of appropriate intensity even at very low frequencies of the external driving field. We illustrate this phenomenon using a physical model of memory resistor realized by TiO2 thin films sandwiched between metallic electrodes and discuss under which conditions this effect can be observed experimentally. We also discuss its implications on existing memory systems described in the literature and the role of colored noise.
Dynamics of stochastic systems
Klyatskin, Valery I
2005-01-01
Fluctuating parameters appear in a variety of physical systems and phenomena. They typically come either as random forces/sources, or advecting velocities, or media (material) parameters, like refraction index, conductivity, diffusivity, etc. The well known example of Brownian particle suspended in fluid and subjected to random molecular bombardment laid the foundation for modern stochastic calculus and statistical physics. Other important examples include turbulent transport and diffusion of particle-tracers (pollutants), or continuous densities (''''oil slicks''''), wave propagation and scattering in randomly inhomogeneous media, for instance light or sound propagating in the turbulent atmosphere.Such models naturally render to statistical description, where the input parameters and solutions are expressed by random processes and fields.The fundamental problem of stochastic dynamics is to identify the essential characteristics of system (its state and evolution), and relate those to the input parameters of ...
Stochastic goal-oriented error estimation with memory
Ackmann, Jan; Marotzke, Jochem; Korn, Peter
2017-11-01
We propose a stochastic dual-weighted error estimator for the viscous shallow-water equation with boundaries. For this purpose, previous work on memory-less stochastic dual-weighted error estimation is extended by incorporating memory effects. The memory is introduced by describing the local truncation error as a sum of time-correlated random variables. The random variables itself represent the temporal fluctuations in local truncation errors and are estimated from high-resolution information at near-initial times. The resulting error estimator is evaluated experimentally in two classical ocean-type experiments, the Munk gyre and the flow around an island. In these experiments, the stochastic process is adapted locally to the respective dynamical flow regime. Our stochastic dual-weighted error estimator is shown to provide meaningful error bounds for a range of physically relevant goals. We prove, as well as show numerically, that our approach can be interpreted as a linearized stochastic-physics ensemble.
Provably unbounded memory advantage in stochastic simulation using quantum mechanics
Garner, Andrew J. P.; Liu, Qing; Thompson, Jayne; Vedral, Vlatko; Gu, mile
2017-10-01
Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off between the precision to which these quantities are approximated, and the memory required to store them. The statistical accuracy of the simulation is thus generally limited by the internal memory available to the simulator. Here, using tools from computational mechanics, we show that quantum processors with a fixed finite memory can simulate stochastic processes of real variables to arbitrarily high precision. This demonstrates a provable, unbounded memory advantage that a quantum simulator can exhibit over its best possible classical counterpart.
Provably unbounded memory advantage in stochastic simulation using quantum mechanics
International Nuclear Information System (INIS)
Garner, Andrew J P; Thompson, Jayne; Vedral, Vlatko; Gu, Mile; Liu, Qing
2017-01-01
Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off between the precision to which these quantities are approximated, and the memory required to store them. The statistical accuracy of the simulation is thus generally limited by the internal memory available to the simulator. Here, using tools from computational mechanics, we show that quantum processors with a fixed finite memory can simulate stochastic processes of real variables to arbitrarily high precision. This demonstrates a provable, unbounded memory advantage that a quantum simulator can exhibit over its best possible classical counterpart. (paper)
Stochastic runaway of dynamical systems
International Nuclear Information System (INIS)
Pfirsch, D.; Graeff, P.
1984-10-01
One-dimensional, stochastic, dynamical systems are well studied with respect to their stability properties. Less is known for the higher dimensional case. This paper derives sufficient and necessary criteria for the asymptotic divergence of the entropy (runaway) and sufficient ones for the moments of n-dimensional, stochastic, dynamical systems. The crucial implication is the incompressibility of their flow defined by the equations of motion in configuration space. Two possible extensions to compressible flow systems are outlined. (orig.)
Stochastic Models of Polymer Systems
2016-01-01
Distribution Unlimited Final Report: Stochastic Models of Polymer Systems The views, opinions and/or findings contained in this report are those of the...ADDRESS. Princeton University PO Box 0036 87 Prospect Avenue - 2nd floor Princeton, NJ 08544 -2020 14-Mar-2014 ABSTRACT Number of Papers published in...peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Stochastic Models of Polymer Systems Report Title
Short-term memories with a stochastic perturbation
International Nuclear Information System (INIS)
Pontes, Jose C.A. de; Batista, Antonio M.; Viana, Ricardo L.; Lopes, Sergio R.
2005-01-01
We investigate short-term memories in linear and weakly nonlinear coupled map lattices with a periodic external input. We use locally coupled maps to present numerical results about short-term memory formation adding a stochastic perturbation in the maps and in the external input
Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models
S. Peiris (Shelton); M. Asai (Manabu); M.J. McAleer (Michael)
2016-01-01
textabstractIn recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility
Bias-reduced estimation of long memory stochastic volatility
DEFF Research Database (Denmark)
Frederiksen, Per; Nielsen, Morten Ørregaard
We propose to use a variant of the local polynomial Whittle estimator to estimate the memory parameter in volatility for long memory stochastic volatility models with potential nonstation- arity in the volatility process. We show that the estimator is asymptotically normal and capable of obtaining...
Functional Abstraction of Stochastic Hybrid Systems
Bujorianu, L.M.; Blom, Henk A.P.; Hermanns, H.
2006-01-01
The verification problem for stochastic hybrid systems is quite difficult. One method to verify these systems is stochastic reachability analysis. Concepts of abstractions for stochastic hybrid systems are needed to ease the stochastic reachability analysis. In this paper, we set up different ways
Stochastic cooling system in COSY
International Nuclear Information System (INIS)
Brittner, P.; Hacker, H.U.; Prasuhn, D.; Schug, G.; Singer, H.; Spiess, W.; Stassen, R.
1994-01-01
The stochastic cooler system in COSY is designed for proton kinetic energies between 0.8 and 2.5 GeV. Fabrication of the mechanical parts of the system is going on. Test results of the prototype measurements as well as data of the active RF-compontens are presented. (orig.)
Stochastic cooling system in COSY
Energy Technology Data Exchange (ETDEWEB)
Brittner, P [Forschungszentrum Juelich GmbH (Germany); Hacker, H U [Forschungszentrum Juelich GmbH (Germany); Prasuhn, D [Forschungszentrum Juelich GmbH (Germany); Schug, G [Forschungszentrum Juelich GmbH (Germany); Singer, H [Forschungszentrum Juelich GmbH (Germany); Spiess, W [Forschungszentrum Juelich GmbH (Germany); Stassen, R [Forschungszentrum Juelich GmbH (Germany)
1994-09-01
The stochastic cooler system in COSY is designed for proton kinetic energies between 0.8 and 2.5 GeV. Fabrication of the mechanical parts of the system is going on. Test results of the prototype measurements as well as data of the active RF-compontens are presented. (orig.)
Stochastic Modelling of Hydrologic Systems
DEFF Research Database (Denmark)
Jonsdottir, Harpa
2007-01-01
In this PhD project several stochastic modelling methods are studied and applied on various subjects in hydrology. The research was prepared at Informatics and Mathematical Modelling at the Technical University of Denmark. The thesis is divided into two parts. The first part contains...... an introduction and an overview of the papers published. Then an introduction to basic concepts in hydrology along with a description of hydrological data is given. Finally an introduction to stochastic modelling is given. The second part contains the research papers. In the research papers the stochastic methods...... are described, as at the time of publication these methods represent new contribution to hydrology. The second part also contains additional description of software used and a brief introduction to stiff systems. The system in one of the papers is stiff....
Stochastic Reachability Analysis of Hybrid Systems
Bujorianu, Luminita Manuela
2012-01-01
Stochastic reachability analysis (SRA) is a method of analyzing the behavior of control systems which mix discrete and continuous dynamics. For probabilistic discrete systems it has been shown to be a practical verification method but for stochastic hybrid systems it can be rather more. As a verification technique SRA can assess the safety and performance of, for example, autonomous systems, robot and aircraft path planning and multi-agent coordination but it can also be used for the adaptive control of such systems. Stochastic Reachability Analysis of Hybrid Systems is a self-contained and accessible introduction to this novel topic in the analysis and development of stochastic hybrid systems. Beginning with the relevant aspects of Markov models and introducing stochastic hybrid systems, the book then moves on to coverage of reachability analysis for stochastic hybrid systems. Following this build up, the core of the text first formally defines the concept of reachability in the stochastic framework and then...
Stochastic analysis of biochemical systems
Anderson, David F
2015-01-01
This book focuses on counting processes and continuous-time Markov chains motivated by examples and applications drawn from chemical networks in systems biology. The book should serve well as a supplement for courses in probability and stochastic processes. While the material is presented in a manner most suitable for students who have studied stochastic processes up to and including martingales in continuous time, much of the necessary background material is summarized in the Appendix. Students and Researchers with a solid understanding of calculus, differential equations, and elementary probability and who are well-motivated by the applications will find this book of interest. David F. Anderson is Associate Professor in the Department of Mathematics at the University of Wisconsin and Thomas G. Kurtz is Emeritus Professor in the Departments of Mathematics and Statistics at that university. Their research is focused on probability and stochastic processes with applications in biology and other ar...
Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models
Directory of Open Access Journals (Sweden)
Shelton Peiris
2017-12-01
Full Text Available This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV components in order to develop the General Long Memory SV (GLMSV model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.
International Nuclear Information System (INIS)
Zhu, Zhiwen; Zhang, Qingxin; Xu, Jia
2014-01-01
Stochastic bifurcation and fractal and chaos control of a giant magnetostrictive film–shape memory alloy (GMF–SMA) composite cantilever plate subjected to in-plane harmonic and stochastic excitation were studied. Van der Pol items were improved to interpret the hysteretic phenomena of both GMF and SMA, and the nonlinear dynamic model of a GMF–SMA composite cantilever plate subjected to in-plane harmonic and stochastic excitation was developed. The probability density function of the dynamic response of the system was obtained, and the conditions of stochastic Hopf bifurcation were analyzed. The conditions of noise-induced chaotic response were obtained in the stochastic Melnikov integral method, and the fractal boundary of the safe basin of the system was provided. Finally, the chaos control strategy was proposed in the stochastic dynamic programming method. Numerical simulation shows that stochastic Hopf bifurcation and chaos appear in the parameter variation process. The boundary of the safe basin of the system has fractal characteristics, and its area decreases when the noise intensifies. The system reliability was improved through stochastic optimal control, and the safe basin area of the system increased
Safety Analysis of Stochastic Dynamical Systems
DEFF Research Database (Denmark)
Sloth, Christoffer; Wisniewski, Rafael
2015-01-01
This paper presents a method for verifying the safety of a stochastic system. In particular, we show how to compute the largest set of initial conditions such that a given stochastic system is safe with probability p. To compute the set of initial conditions we rely on the moment method that via...... that shows how the p-safe initial set is computed numerically....
Stochastic properties of the Friedman dynamical system
International Nuclear Information System (INIS)
Szydlowski, M.; Heller, M.; Golda, Z.
1985-01-01
Some mathematical aspects of the stochastic cosmology are discussed in the corresponding ordinary Friedman world models. In particulare, it is shown that if the strong and Lorentz energy conditions are known, or the potential function is given, or a stochastic measure is suitably defined then the structure of the phase plane of the Friedman dynamical system is determined. 11 refs., 2 figs. (author)
Stochastic Thermodynamics: A Dynamical Systems Approach
Directory of Open Access Journals (Sweden)
Tanmay Rajpurohit
2017-12-01
Full Text Available In this paper, we develop an energy-based, large-scale dynamical system model driven by Markov diffusion processes to present a unified framework for statistical thermodynamics predicated on a stochastic dynamical systems formalism. Specifically, using a stochastic state space formulation, we develop a nonlinear stochastic compartmental dynamical system model characterized by energy conservation laws that is consistent with statistical thermodynamic principles. In particular, we show that the difference between the average supplied system energy and the average stored system energy for our stochastic thermodynamic model is a martingale with respect to the system filtration. In addition, we show that the average stored system energy is equal to the mean energy that can be extracted from the system and the mean energy that can be delivered to the system in order to transfer it from a zero energy level to an arbitrary nonempty subset in the state space over a finite stopping time.
Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing
Rao, Feng; Ding, Keyuan; Zhou, Yuxing; Zheng, Yonghui; Xia, Mengjiao; Lv, Shilong; Song, Zhitang; Feng, Songlin; Ronneberger, Ider; Mazzarello, Riccardo; Zhang, Wei; Ma, Evan
2017-12-01
Operation speed is a key challenge in phase-change random-access memory (PCRAM) technology, especially for achieving subnanosecond high-speed cache memory. Commercialized PCRAM products are limited by the tens of nanoseconds writing speed, originating from the stochastic crystal nucleation during the crystallization of amorphous germanium antimony telluride (Ge2Sb2Te5). Here, we demonstrate an alloying strategy to speed up the crystallization kinetics. The scandium antimony telluride (Sc0.2Sb2Te3) compound that we designed allows a writing speed of only 700 picoseconds without preprogramming in a large conventional PCRAM device. This ultrafast crystallization stems from the reduced stochasticity of nucleation through geometrically matched and robust scandium telluride (ScTe) chemical bonds that stabilize crystal precursors in the amorphous state. Controlling nucleation through alloy design paves the way for the development of cache-type PCRAM technology to boost the working efficiency of computing systems.
Modeling and analysis of stochastic systems
Kulkarni, Vidyadhar G
2011-01-01
Based on the author's more than 25 years of teaching experience, Modeling and Analysis of Stochastic Systems, Second Edition covers the most important classes of stochastic processes used in the modeling of diverse systems, from supply chains and inventory systems to genetics and biological systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models. Along with reorganizing the material, this edition revises and adds new exercises and examples. New to the second edi
Exact solutions to chaotic and stochastic systems
González, J. A.; Reyes, L. I.; Guerrero, L. E.
2001-03-01
We investigate functions that are exact solutions to chaotic dynamical systems. A generalization of these functions can produce truly random numbers. For the first time, we present solutions to random maps. This allows us to check, analytically, some recent results about the complexity of random dynamical systems. We confirm the result that a negative Lyapunov exponent does not imply predictability in random systems. We test the effectiveness of forecasting methods in distinguishing between chaotic and random time series. Using the explicit random functions, we can give explicit analytical formulas for the output signal in some systems with stochastic resonance. We study the influence of chaos on the stochastic resonance. We show, theoretically, the existence of a new type of solitonic stochastic resonance, where the shape of the kink is crucial. Using our models we can predict specific patterns in the output signal of stochastic resonance systems.
Stochastic Systems Uncertainty Quantification and Propagation
Grigoriu, Mircea
2012-01-01
Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: · A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis · Probabilistic models for random variables an...
Elliott, Thomas J.; Gu, Mile
2018-03-01
Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of information about past behaviour, even for relatively simple models, enforcing limits on precision due to the finite memory of the machine. However, quantum machines can require less information about the past than even their optimal classical counterparts to simulate the future of discrete-time processes, and we demonstrate that this advantage extends to the continuous-time regime. Moreover, we show that this reduction in the memory requirement can be unboundedly large, allowing for arbitrary precision even with a finite quantum memory. We provide a systematic method for finding superior quantum constructions, and a protocol for analogue simulation of continuous-time renewal processes with a quantum machine.
Compositional Modelling of Stochastic Hybrid Systems
Strubbe, S.N.
2005-01-01
In this thesis we present a modelling framework for compositional modelling of stochastic hybrid systems. Hybrid systems consist of a combination of continuous and discrete dynamics. The state space of a hybrid system is hybrid in the sense that it consists of a continuous component and a discrete
Stochastic Modelling Of The Repairable System
Directory of Open Access Journals (Sweden)
Andrzejczak Karol
2015-11-01
Full Text Available All reliability models consisting of random time factors form stochastic processes. In this paper we recall the definitions of the most common point processes which are used for modelling of repairable systems. Particularly this paper presents stochastic processes as examples of reliability systems for the support of the maintenance related decisions. We consider the simplest one-unit system with a negligible repair or replacement time, i.e., the unit is operating and is repaired or replaced at failure, where the time required for repair and replacement is negligible. When the repair or replacement is completed, the unit becomes as good as new and resumes operation. The stochastic modelling of recoverable systems constitutes an excellent method of supporting maintenance related decision-making processes and enables their more rational use.
Dynamic analysis of stochastic bidirectional associative memory neural networks with delays
International Nuclear Information System (INIS)
Zhao Hongyong; Ding Nan
2007-01-01
In this paper, stochastic bidirectional associative memory neural networks model with delays is considered. By constructing Lyapunov functionals, and using stochastic analysis method and inequality technique, we give some sufficient criteria ensuring almost sure exponential stability, pth exponential stability and mean value exponential stability. The obtained criteria can be used as theoretic guidance to stabilize neural networks in practical applications when stochastic noise is taken into consideration
On time-dependent diffusion coefficients arising from stochastic processes with memory
Carpio-Bernido, M. Victoria; Barredo, Wilson I.; Bernido, Christopher C.
2017-08-01
Time-dependent diffusion coefficients arise from anomalous diffusion encountered in many physical systems such as protein transport in cells. We compare these coefficients with those arising from analysis of stochastic processes with memory that go beyond fractional Brownian motion. Facilitated by the Hida white noise functional integral approach, diffusion propagators or probability density functions (pdf) are obtained and shown to be solutions of modified diffusion equations with time-dependent diffusion coefficients. This should be useful in the study of complex transport processes.
Integration of stochastic generation in power systems
Papaefthymiou, G.; Schavemaker, P.H.; Sluis, van der L.; Kling, W.L.; Kurowicka, D.; Cooke, R.M.
2006-01-01
Stochastic generation, i.e., electrical power production by an uncontrolled primary energy source, is expected to play an important role in future power systems. A new power system structure is created due to the large-scale implementation of this small-scale, distributed, non-dispatchable
Stochastic hybrid systems with renewal transitions
Guerreiro Tome Antunes, D.J.; Hespanha, J.P.; Silvestre, C.J.
2010-01-01
We consider Stochastic Hybrid Systems (SHSs) for which the lengths of times that the system stays in each mode are independent random variables with given distributions. We propose an analysis framework based on a set of Volterra renewal-type equations, which allows us to compute any statistical
Rackauckas, Christopher; Nie, Qing
2017-01-01
Adaptive time-stepping with high-order embedded Runge-Kutta pairs and rejection sampling provides efficient approaches for solving differential equations. While many such methods exist for solving deterministic systems, little progress has been made for stochastic variants. One challenge in developing adaptive methods for stochastic differential equations (SDEs) is the construction of embedded schemes with direct error estimates. We present a new class of embedded stochastic Runge-Kutta (SRK) methods with strong order 1.5 which have a natural embedding of strong order 1.0 methods. This allows for the derivation of an error estimate which requires no additional function evaluations. Next we derive a general method to reject the time steps without losing information about the future Brownian path termed Rejection Sampling with Memory (RSwM). This method utilizes a stack data structure to do rejection sampling, costing only a few floating point calculations. We show numerically that the methods generate statistically-correct and tolerance-controlled solutions. Lastly, we show that this form of adaptivity can be applied to systems of equations, and demonstrate that it solves a stiff biological model 12.28x faster than common fixed timestep algorithms. Our approach only requires the solution to a bridging problem and thus lends itself to natural generalizations beyond SDEs.
International Nuclear Information System (INIS)
Zhu, Zhi-Wen; Zhang, Qing-Xin; Xu, Jia
2014-01-01
A kind of shape memory alloy (SMA) hysteretic nonlinear model was developed, and the nonlinear dynamics and bifurcation characteristics of the SMA thin film subjected to in-plane stochastic excitation were investigated. Van der Pol difference item was introduced to describe the hysteretic phenomena of the SMA strain–stress curves, and the nonlinear dynamic model of the SMA thin film subjected to in-plane stochastic excitation was developed. The conditions of global stochastic stability of the system were determined in singular boundary theory, and the probability density function of the system response was obtained. Finally, the conditions of stochastic Hopf bifurcation were analyzed. The results of theoretical analysis and numerical simulation indicate that self-excited vibration is induced by the hysteretic nonlinear characteristics of SMA, and stochastic Hopf bifurcation appears when the bifurcation parameter was changed; there are two limit cycles in the stationary probability density of the dynamic response of the system in some cases, which means that there are two vibration amplitudes whose probabilities are both very high, and jumping phenomena between the two vibration amplitudes appear with the change in conditions. The results obtained in this current paper are helpful for the application of the SMA thin film in stochastic vibration fields. - Highlights: • Hysteretic nonlinear model of shape memory alloy was developed. • Van der Pol item was introduced to interpret hysteretic strain–stress curves. • Nonlinear dynamic characteristics of the shape memory alloy film were analyzed. • Jumping phenomena were observed in the change of the parameters
Wang, Fen; Chen, Yuanlong; Liu, Meichun
2018-02-01
Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Linear System Control Using Stochastic Learning Automata
Ziyad, Nigel; Cox, E. Lucien; Chouikha, Mohamed F.
1998-01-01
This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.
The unsaturated bistable stochastic resonance system.
Zhao, Wenli; Wang, Juan; Wang, Linze
2013-09-01
We investigated the characteristics of the output saturation of the classical continuous bistable system (saturation bistable system) and its impact on stochastic resonance (SR). We further proposed a piecewise bistable SR system (unsaturated bistable system) and developed the expression of signal-to-noise ratio (SNR) using the adiabatic approximation theory. Compared with the saturation bistable system, the SNR is significantly improved in our unsaturated bistable SR system. The numerical simulation showed that the unsaturated bistable system performed better in extracting weak signals from strong background noise than the saturation bistable system.
Stochastic transport processes in discrete biological systems
Frehland, Eckart
1982-01-01
These notes are in part based on a course for advanced students in the applications of stochastic processes held in 1978 at the University of Konstanz. These notes contain the results of re cent studies on the stochastic description of ion transport through biological membranes. In particular, they serve as an introduction to an unified theory of fluctuations in complex biological transport systems. We emphasize that the subject of this volume is not to introduce the mathematics of stochastic processes but to present a field of theoretical biophysics in which stochastic methods are important. In the last years the study of membrane noise has become an important method in biophysics. Valuable information on the ion transport mechanisms in membranes can be obtained from noise analysis. A number of different processes such as the opening and closing of ion channels have been shown to be sources of the measured current or voltage fluctuations. Bio logical 'transport systems can be complex. For example, the tr...
Stochastic cooling with a double rf system
International Nuclear Information System (INIS)
Wei, Jie.
1992-01-01
Stochastic cooling for a bunched beam of hadrons stored in an accelerator with a double rf system of two different frequencies has been investigated. The double rf system broadens the spread in synchrotron-oscillation frequency of the particles when they mostly oscillate near the center of the rf bucket. Compared with the ease of a single rf system, the reduction rates of the bunch dimensions are significantly increased. When the rf voltage is raised, the reduction rate, instead of decreasing linearly, now is independent of the ratio of the bunch area to the bucket area. On the other hand, the spread in synchrotron-oscillation frequency becomes small with the double rf system, if the longitudinal oscillation amplitudes of the particles are comparable to the dimension of the rf bucket. Consequently, stochastic cooling is less effective when the bunch area is close to the bucket area
Stochastic pump effect and geometric phases in dissipative and stochastic systems
Energy Technology Data Exchange (ETDEWEB)
Sinitsyn, Nikolai [Los Alamos National Laboratory
2008-01-01
The success of Berry phases in quantum mechanics stimulated the study of similar phenomena in other areas of physics, including the theory of living cell locomotion and motion of patterns in nonlinear media. More recently, geometric phases have been applied to systems operating in a strongly stochastic environment, such as molecular motors. We discuss such geometric effects in purely classical dissipative stochastic systems and their role in the theory of the stochastic pump effect (SPE).
Stochastic Mesocortical Dynamics and Robustness of Working Memory during Delay-Period.
Directory of Open Access Journals (Sweden)
Melissa Reneaux
Full Text Available The role of prefronto-mesoprefrontal system in the dopaminergic modulation of working memory during delayed response tasks is well-known. Recently, a dynamical model of the closed-loop mesocortical circuit has been proposed which employs a deterministic framework to elucidate the system's behavior in a qualitative manner. Under natural conditions, noise emanating from various sources affects the circuit's functioning to a great extent. Accordingly in the present study, we reformulate the model into a stochastic framework and investigate its steady state properties in the presence of constant background noise during delay-period. From the steady state distribution, global potential landscape and signal-to-noise ratio are obtained which help in defining robustness of the circuit dynamics. This provides insight into the robustness of working memory during delay-period against its disruption due to background noise. The findings reveal that the global profile of circuit's robustness is predominantly governed by the level of D1 receptor activity and high D1 receptor stimulation favors the working memory-associated sustained-firing state over the spontaneous-activity state of the system. Moreover, the circuit's robustness is further fine-tuned by the levels of excitatory and inhibitory activities in a way such that the robustness of sustained-firing state exhibits an inverted-U shaped profile with respect to D1 receptor stimulation. It is predicted that the most robust working memory is formed possibly at a subtle ratio of the excitatory and inhibitory activities achieved at a critical level of D1 receptor stimulation. The study also paves a way to understand various cognitive deficits observed in old-age, acute stress and schizophrenia and suggests possible mechanistic routes to the working memory impairments based on the circuit's robustness profile.
Stochastic Mesocortical Dynamics and Robustness of Working Memory during Delay-Period.
Reneaux, Melissa; Gupta, Rahul; Karmeshu
2015-01-01
The role of prefronto-mesoprefrontal system in the dopaminergic modulation of working memory during delayed response tasks is well-known. Recently, a dynamical model of the closed-loop mesocortical circuit has been proposed which employs a deterministic framework to elucidate the system's behavior in a qualitative manner. Under natural conditions, noise emanating from various sources affects the circuit's functioning to a great extent. Accordingly in the present study, we reformulate the model into a stochastic framework and investigate its steady state properties in the presence of constant background noise during delay-period. From the steady state distribution, global potential landscape and signal-to-noise ratio are obtained which help in defining robustness of the circuit dynamics. This provides insight into the robustness of working memory during delay-period against its disruption due to background noise. The findings reveal that the global profile of circuit's robustness is predominantly governed by the level of D1 receptor activity and high D1 receptor stimulation favors the working memory-associated sustained-firing state over the spontaneous-activity state of the system. Moreover, the circuit's robustness is further fine-tuned by the levels of excitatory and inhibitory activities in a way such that the robustness of sustained-firing state exhibits an inverted-U shaped profile with respect to D1 receptor stimulation. It is predicted that the most robust working memory is formed possibly at a subtle ratio of the excitatory and inhibitory activities achieved at a critical level of D1 receptor stimulation. The study also paves a way to understand various cognitive deficits observed in old-age, acute stress and schizophrenia and suggests possible mechanistic routes to the working memory impairments based on the circuit's robustness profile.
Studies in the Control of Stochastic Systems
2017-10-31
control of continuous time stochastic systems with noise that is Brownian motions or fractional Brownian motions, the control of discrete time...in both continuous and discrete time. All of the above types of problems have been studied with the support of this grant. The achievement of these...scientists and engineers. 2. Math Awareness Months (MAM) (Every April for the past twenty-three years) Agenda: workshops each year for fifth
Stochastic Modelling of Energy Systems
DEFF Research Database (Denmark)
Andersen, Klaus Kaae
2001-01-01
is that the model structure has to be adequate for practical applications, such as system simulation, fault detection and diagnosis, and design of control strategies. This also reflects on the methods used for identification of the component models. The main result from this research is the identification......In this thesis dynamic models of typical components in Danish heating systems are considered. Emphasis is made on describing and evaluating mathematical methods for identification of such models, and on presentation of component models for practical applications. The thesis consists of seven...... research papers (case studies) together with a summary report. Each case study takes it's starting point in typical heating system components and both, the applied mathematical modelling methods and the application aspects, are considered. The summary report gives an introduction to the scope...
Mathematical models of information and stochastic systems
Kornreich, Philipp
2008-01-01
From ancient soothsayers and astrologists to today's pollsters and economists, probability theory has long been used to predict the future on the basis of past and present knowledge. Mathematical Models of Information and Stochastic Systems shows that the amount of knowledge about a system plays an important role in the mathematical models used to foretell the future of the system. It explains how this known quantity of information is used to derive a system's probabilistic properties. After an introduction, the book presents several basic principles that are employed in the remainder of the t
Lectures on Dynamics of Stochastic Systems
Klyatskin, Valery I
2010-01-01
Fluctuating parameters appear in a variety of physical systems and phenomena. They typically come either as random forces/sources, or advecting velocities, or media (material) parameters, like refraction index, conductivity, diffusivity, etc. Models naturally render to statistical description, where random processes and fields express the input parameters and solutions. The fundamental problem of stochastic dynamics is to identify the essential characteristics of system (its state and evolution), and relate those to the input parameters of the system and initial data. This book is a revised a
Information management in DNA replication modeled by directional, stochastic chains with memory
Arias-Gonzalez, J. Ricardo
2016-11-01
Stochastic chains represent a key variety of phenomena in many branches of science within the context of information theory and thermodynamics. They are typically approached by a sequence of independent events or by a memoryless Markov process. Stochastic chains are of special significance to molecular biology, where genes are conveyed by linear polymers made up of molecular subunits and transferred from DNA to proteins by specialized molecular motors in the presence of errors. Here, we demonstrate that when memory is introduced, the statistics of the chain depends on the mechanism by which objects or symbols are assembled, even in the slow dynamics limit wherein friction can be neglected. To analyze these systems, we introduce a sequence-dependent partition function, investigate its properties, and compare it to the standard normalization defined by the statistical physics of ensembles. We then apply this theory to characterize the enzyme-mediated information transfer involved in DNA replication under the real, non-equilibrium conditions, reproducing measured error rates and explaining the typical 100-fold increase in fidelity that is experimentally found when proofreading and edition take place. Our model further predicts that approximately 1 kT has to be consumed to elevate fidelity in one order of magnitude. We anticipate that our results are necessary to interpret configurational order and information management in many molecular systems within biophysics, materials science, communication, and engineering.
The intrinsic stochasticity of near-integrable Hamiltonian systems
Energy Technology Data Exchange (ETDEWEB)
Krlin, L [Ceskoslovenska Akademie Ved, Prague (Czechoslovakia). Ustav Fyziky Plazmatu
1989-09-01
Under certain conditions, the dynamics of near-integrable Hamiltonian systems appears to be stochastic. This stochasticity (intrinsic stochasticity, or deterministic chaos) is closely related to the Kolmogorov-Arnold-Moser (KAM) theorem of the stability of near-integrable multiperiodic Hamiltonian systems. The effect of the intrinsic stochasticity attracts still growing attention both in theory and in various applications in contemporary physics. The paper discusses the relation of the intrinsic stochasticity to the modern ergodic theory and to the KAM theorem, and describes some numerical experiments on related astrophysical and high-temperature plasma problems. Some open questions are mentioned in conclusion. (author).
Assessing predictability of a hydrological stochastic-dynamical system
Gelfan, Alexander
2014-05-01
The water cycle includes the processes with different memory that creates potential for predictability of hydrological system based on separating its long and short memory components and conditioning long-term prediction on slower evolving components (similar to approaches in climate prediction). In the face of the Panta Rhei IAHS Decade questions, it is important to find a conceptual approach to classify hydrological system components with respect to their predictability, define predictable/unpredictable patterns, extend lead-time and improve reliability of hydrological predictions based on the predictable patterns. Representation of hydrological systems as the dynamical systems subjected to the effect of noise (stochastic-dynamical systems) provides possible tool for such conceptualization. A method has been proposed for assessing predictability of hydrological system caused by its sensitivity to both initial and boundary conditions. The predictability is defined through a procedure of convergence of pre-assigned probabilistic measure (e.g. variance) of the system state to stable value. The time interval of the convergence, that is the time interval during which the system losses memory about its initial state, defines limit of the system predictability. The proposed method was applied to assess predictability of soil moisture dynamics in the Nizhnedevitskaya experimental station (51.516N; 38.383E) located in the agricultural zone of the central European Russia. A stochastic-dynamical model combining a deterministic one-dimensional model of hydrothermal regime of soil with a stochastic model of meteorological inputs was developed. The deterministic model describes processes of coupled heat and moisture transfer through unfrozen/frozen soil and accounts for the influence of phase changes on water flow. The stochastic model produces time series of daily meteorological variables (precipitation, air temperature and humidity), whose statistical properties are similar
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
Richtarik, Peter; Taká č, Martin
2017-01-01
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several equivalent interpretations, allowing for researchers from various communities to leverage their domain specific insights. In particular, our reformulation can be equivalently seen as a stochastic optimization problem, stochastic linear system, stochastic fixed point problem and a probabilistic intersection problem. We prove sufficient, and necessary and sufficient conditions for the reformulation to be exact. Further, we propose and analyze three stochastic algorithms for solving the reformulated problem---basic, parallel and accelerated methods---with global linear convergence rates. The rates can be interpreted as condition numbers of a matrix which depends on the system matrix and on the reformulation parameters. This gives rise to a new phenomenon which we call stochastic preconditioning, and which refers to the problem of finding parameters (matrix and distribution) leading to a sufficiently small condition number. Our basic method can be equivalently interpreted as stochastic gradient descent, stochastic Newton method, stochastic proximal point method, stochastic fixed point method, and stochastic projection method, with fixed stepsize (relaxation parameter), applied to the reformulations.
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
Richtarik, Peter
2017-06-04
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several equivalent interpretations, allowing for researchers from various communities to leverage their domain specific insights. In particular, our reformulation can be equivalently seen as a stochastic optimization problem, stochastic linear system, stochastic fixed point problem and a probabilistic intersection problem. We prove sufficient, and necessary and sufficient conditions for the reformulation to be exact. Further, we propose and analyze three stochastic algorithms for solving the reformulated problem---basic, parallel and accelerated methods---with global linear convergence rates. The rates can be interpreted as condition numbers of a matrix which depends on the system matrix and on the reformulation parameters. This gives rise to a new phenomenon which we call stochastic preconditioning, and which refers to the problem of finding parameters (matrix and distribution) leading to a sufficiently small condition number. Our basic method can be equivalently interpreted as stochastic gradient descent, stochastic Newton method, stochastic proximal point method, stochastic fixed point method, and stochastic projection method, with fixed stepsize (relaxation parameter), applied to the reformulations.
Dynamics of non-holonomic systems with stochastic transport
Holm, D. D.; Putkaradze, V.
2018-01-01
This paper formulates a variational approach for treating observational uncertainty and/or computational model errors as stochastic transport in dynamical systems governed by action principles under non-holonomic constraints. For this purpose, we derive, analyse and numerically study the example of an unbalanced spherical ball rolling under gravity along a stochastic path. Our approach uses the Hamilton-Pontryagin variational principle, constrained by a stochastic rolling condition, which we show is equivalent to the corresponding stochastic Lagrange-d'Alembert principle. In the example of the rolling ball, the stochasticity represents uncertainty in the observation and/or error in the computational simulation of the angular velocity of rolling. The influence of the stochasticity on the deterministically conserved quantities is investigated both analytically and numerically. Our approach applies to a wide variety of stochastic, non-holonomically constrained systems, because it preserves the mathematical properties inherited from the variational principle.
Wang, Xiao-Tian; Wu, Min; Zhou, Ze-Min; Jing, Wei-Shu
2012-02-01
This paper deals with the problem of discrete time option pricing using the fractional long memory stochastic volatility model with transaction costs. Through the 'anchoring and adjustment' argument in a discrete time setting, a European call option pricing formula is obtained.
International Nuclear Information System (INIS)
Menezes, G.; Svaiter, N.F.
2006-04-01
We use the method of stochastic quantization in a topological field theory defined in an Euclidean space, assuming a Langevin equation with a memory kernel. We show that our procedure for the Abelian Chern-Simons theory converges regardless of the nature of the Chern-Simons coefficient. (author)
International Nuclear Information System (INIS)
Wan Li; Zhou Qinghua
2007-01-01
The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem
Wan, Li; Zhou, Qinghua
2007-10-01
The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.
Stochastic Erosion of Fractal Structure in Nonlinear Dynamical Systems
Agarwal, S.; Wettlaufer, J. S.
2014-12-01
We analyze the effects of stochastic noise on the Lorenz-63 model in the chaotic regime to demonstrate a set of general issues arising in the interpretation of data from nonlinear dynamical systems typical in geophysics. The model is forced using both additive and multiplicative, white and colored noise and it is shown that, through a suitable choice of the noise intensity, both additive and multiplicative noise can produce similar dynamics. We use a recently developed measure, histogram distance, to show the similarity between the dynamics produced by additive and multiplicative forcing. This phenomenon, in a nonlinear fractal structure with chaotic dynamics can be explained by understanding how noise affects the Unstable Periodic Orbits (UPOs) of the system. For delta-correlated noise, the UPOs erode the fractal structure. In the presence of memory in the noise forcing, the time scale of the noise starts to interact with the period of some UPO and, depending on the noise intensity, stochastic resonance may be observed. This also explains the mixing in dissipative dynamical systems in presence of white noise; as the fractal structure is smoothed, the decay of correlations is enhanced, and hence the rate of mixing increases with noise intensity.
System Entropy Measurement of Stochastic Partial Differential Systems
Directory of Open Access Journals (Sweden)
Bor-Sen Chen
2016-03-01
Full Text Available System entropy describes the dispersal of a system’s energy and is an indication of the disorder of a physical system. Several system entropy measurement methods have been developed for dynamic systems. However, most real physical systems are always modeled using stochastic partial differential dynamic equations in the spatio-temporal domain. No efficient method currently exists that can calculate the system entropy of stochastic partial differential systems (SPDSs in consideration of the effects of intrinsic random fluctuation and compartment diffusion. In this study, a novel indirect measurement method is proposed for calculating of system entropy of SPDSs using a Hamilton–Jacobi integral inequality (HJII-constrained optimization method. In other words, we solve a nonlinear HJII-constrained optimization problem for measuring the system entropy of nonlinear stochastic partial differential systems (NSPDSs. To simplify the system entropy measurement of NSPDSs, the global linearization technique and finite difference scheme were employed to approximate the nonlinear stochastic spatial state space system. This allows the nonlinear HJII-constrained optimization problem for the system entropy measurement to be transformed to an equivalent linear matrix inequalities (LMIs-constrained optimization problem, which can be easily solved using the MATLAB LMI-toolbox (MATLAB R2014a, version 8.3. Finally, several examples are presented to illustrate the system entropy measurement of SPDSs.
Large-scale stochasticity in Hamiltonian systems
International Nuclear Information System (INIS)
Escande, D.F.
1982-01-01
Large scale stochasticity (L.S.S.) in Hamiltonian systems is defined on the paradigm Hamiltonian H(v,x,t) =v 2 /2-M cos x-P cos k(x-t) which describes the motion of one particle in two electrostatic waves. A renormalization transformation Tsub(r) is described which acts as a microscope that focusses on a given KAM (Kolmogorov-Arnold-Moser) torus in phase space. Though approximate, Tsub(r) yields the threshold of L.S.S. in H with an error of 5-10%. The universal behaviour of KAM tori is predicted: for instance the scale invariance of KAM tori and the critical exponent of the Lyapunov exponent of Cantori. The Fourier expansion of KAM tori is computed and several conjectures by L. Kadanoff and S. Shenker are proved. Chirikov's standard mapping for stochastic layers is derived in a simpler way and the width of the layers is computed. A simpler renormalization scheme for these layers is defined. A Mathieu equation for describing the stability of a discrete family of cycles is derived. When combined with Tsub(r), it allows to prove the link between KAM tori and nearby cycles, conjectured by J. Greene and, in particular, to compute the mean residue of a torus. The fractal diagrams defined by G. Schmidt are computed. A sketch of a methodology for computing the L.S.S. threshold in any two-degree-of-freedom Hamiltonian system is given. (Auth.)
Filtering and control of stochastic jump hybrid systems
Yao, Xiuming; Zheng, Wei Xing
2016-01-01
This book presents recent research work on stochastic jump hybrid systems. Specifically, the considered stochastic jump hybrid systems include Markovian jump Ito stochastic systems, Markovian jump linear-parameter-varying (LPV) systems, Markovian jump singular systems, Markovian jump two-dimensional (2-D) systems, and Markovian jump repeated scalar nonlinear systems. Some sufficient conditions are first established respectively for the stability and performances of those kinds of stochastic jump hybrid systems in terms of solution of linear matrix inequalities (LMIs). Based on the derived analysis conditions, the filtering and control problems are addressed. The book presents up-to-date research developments and novel methodologies on stochastic jump hybrid systems. The contents can be divided into two parts: the first part is focused on robust filter design problem, while the second part is put the emphasis on robust control problem. These methodologies provide a framework for stability and performance analy...
Time-ordered product expansions for computational stochastic system biology
International Nuclear Information System (INIS)
Mjolsness, Eric
2013-01-01
The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie’s stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems. (paper)
Stochastic resonance in a stochastic bistable system with additive noises and square–wave signal
International Nuclear Information System (INIS)
Feng, Guo; Xiang-Dong, Luo; Shao-Fu, Li; Yu-Rong, Zhou
2010-01-01
This paper considers the stochastic resonance in a stochastic bistable system driven by a periodic square-wave signal and a static force as well as by additive white noise and dichotomous noise from the viewpoint of signal-to-noise ratio. It finds that the signal-to-noise ratio appears as stochastic resonance behaviour when it is plotted as a function of the noise strength of the white noise and dichotomous noise, as a function of the system parameters, or as a function of the static force. Moreover, the influence of the strength of the stochastic potential force and the correlation rate of the dichotomous noise on the signal-to-noise ratio is investigated. (general)
Population density equations for stochastic processes with memory kernels
Lai, Yi Ming; de Kamps, Marc
2017-06-01
We present a method for solving population density equations (PDEs)-a mean-field technique describing homogeneous populations of uncoupled neurons—where the populations can be subject to non-Markov noise for arbitrary distributions of jump sizes. The method combines recent developments in two different disciplines that traditionally have had limited interaction: computational neuroscience and the theory of random networks. The method uses a geometric binning scheme, based on the method of characteristics, to capture the deterministic neurodynamics of the population, separating the deterministic and stochastic process cleanly. We can independently vary the choice of the deterministic model and the model for the stochastic process, leading to a highly modular numerical solution strategy. We demonstrate this by replacing the master equation implicit in many formulations of the PDE formalism by a generalization called the generalized Montroll-Weiss equation—a recent result from random network theory—describing a random walker subject to transitions realized by a non-Markovian process. We demonstrate the method for leaky- and quadratic-integrate and fire neurons subject to spike trains with Poisson and gamma-distributed interspike intervals. We are able to model jump responses for both models accurately to both excitatory and inhibitory input under the assumption that all inputs are generated by one renewal process.
The response analysis of fractional-order stochastic system via generalized cell mapping method.
Wang, Liang; Xue, Lili; Sun, Chunyan; Yue, Xiaole; Xu, Wei
2018-01-01
This paper is concerned with the response of a fractional-order stochastic system. The short memory principle is introduced to ensure that the response of the system is a Markov process. The generalized cell mapping method is applied to display the global dynamics of the noise-free system, such as attractors, basins of attraction, basin boundary, saddle, and invariant manifolds. The stochastic generalized cell mapping method is employed to obtain the evolutionary process of probability density functions of the response. The fractional-order ϕ 6 oscillator and the fractional-order smooth and discontinuous oscillator are taken as examples to give the implementations of our strategies. Studies have shown that the evolutionary direction of the probability density function of the fractional-order stochastic system is consistent with the unstable manifold. The effectiveness of the method is confirmed using Monte Carlo results.
Computational singular perturbation analysis of stochastic chemical systems with stiffness
Wang, Lijin; Han, Xiaoying; Cao, Yanzhao; Najm, Habib N.
2017-04-01
Computational singular perturbation (CSP) is a useful method for analysis, reduction, and time integration of stiff ordinary differential equation systems. It has found dominant utility, in particular, in chemical reaction systems with a large range of time scales at continuum and deterministic level. On the other hand, CSP is not directly applicable to chemical reaction systems at micro or meso-scale, where stochasticity plays an non-negligible role and thus has to be taken into account. In this work we develop a novel stochastic computational singular perturbation (SCSP) analysis and time integration framework, and associated algorithm, that can be used to not only construct accurately and efficiently the numerical solutions to stiff stochastic chemical reaction systems, but also analyze the dynamics of the reduced stochastic reaction systems. The algorithm is illustrated by an application to a benchmark stochastic differential equation model, and numerical experiments are carried out to demonstrate the effectiveness of the construction.
Characterization of memory states of the Preisach operator with stochastic inputs
International Nuclear Information System (INIS)
Amann, A.; Brokate, M.; McCarthy, S.; Rachinskii, D.; Temnov, G.
2012-01-01
The Preisach operator with inputs defined by a Markov process x t is considered. The question we address is: what is the distribution of the random memory state of the Preisach operator at a given time moment t 0 in the limit r→∞ of infinitely long input history x t , t 0 -r≤t≤t 0 ? In order to answer this question, we introduce a Markov chain (called the memory state Markov chain) where the states are pairs (m k ,M k ) of elements from the monotone sequences of the local minimum input values m k and the local maximum input values M k recorded in the memory state and the index k of the elements plays the role of time. We express the transition probabilities of this Markov chain in terms of the transition probabilities of the input stochastic process and show that the memory state Markov chain and the input process generate the same distribution of the memory states. These results are illustrated by several examples of stochastic inputs such as the Wiener and Bernoulli processes and their mixture (we first discuss a discrete version of these processes and then the continuous time and state setting). The memory state Markov chain is then used to find the distribution of the random number of elements in the memory state sequence. We show that this number has the Poisson distribution for the Wiener and Bernoulli processes inputs. In particular, in the discrete setting, the mean value of the number of elements in the memory state scales as lnN, where N is the number of the input states, while the mean time it takes the input to generate this memory state scales as N 2 for the Wiener process and as N for the Bernoulli process. A similar relationship between the dimension of the memory state vector and the number of iterations in the numerical realization of the input is shown for the mixture of the Wiener and Bernoulli processes, thus confirming that the memory state Markov chain is an efficient tool for generating the distribution of the Preisach operator memory
Characterization of memory states of the Preisach operator with stochastic inputs
Energy Technology Data Exchange (ETDEWEB)
Amann, A. [Department of Applied Mathematics, University College Cork (Ireland); Brokate, M. [Zentrum Mathematik, Technische Universitaet Muenchen (Germany); McCarthy, S. [Department of Applied Mathematics, University College Cork (Ireland); Rachinskii, D., E-mail: d.rachinskii@ucc.ie [Department of Applied Mathematics, University College Cork (Ireland); Temnov, G. [Department of Mathematics, University College Cork (Ireland)
2012-05-01
The Preisach operator with inputs defined by a Markov process x{sup t} is considered. The question we address is: what is the distribution of the random memory state of the Preisach operator at a given time moment t{sub 0} in the limit r{yields}{infinity} of infinitely long input history x{sup t}, t{sub 0}-r{<=}t{<=}t{sub 0}? In order to answer this question, we introduce a Markov chain (called the memory state Markov chain) where the states are pairs (m{sub k},M{sub k}) of elements from the monotone sequences of the local minimum input values m{sub k} and the local maximum input values M{sub k} recorded in the memory state and the index k of the elements plays the role of time. We express the transition probabilities of this Markov chain in terms of the transition probabilities of the input stochastic process and show that the memory state Markov chain and the input process generate the same distribution of the memory states. These results are illustrated by several examples of stochastic inputs such as the Wiener and Bernoulli processes and their mixture (we first discuss a discrete version of these processes and then the continuous time and state setting). The memory state Markov chain is then used to find the distribution of the random number of elements in the memory state sequence. We show that this number has the Poisson distribution for the Wiener and Bernoulli processes inputs. In particular, in the discrete setting, the mean value of the number of elements in the memory state scales as lnN, where N is the number of the input states, while the mean time it takes the input to generate this memory state scales as N{sup 2} for the Wiener process and as N for the Bernoulli process. A similar relationship between the dimension of the memory state vector and the number of iterations in the numerical realization of the input is shown for the mixture of the Wiener and Bernoulli processes, thus confirming that the memory state Markov chain is an efficient tool for
Stochastic resonance in bistable systems driven by harmonic noise
International Nuclear Information System (INIS)
Neiman, A.; Schimansky-Geier, L.
1994-01-01
We study stochastic resonance in a bistable system which is excited simultaneously by white and harmonic noise which we understand as the signal. In our case the spectral line of the signal has a finite width as it occurs in many real situations. Using techniques of cumulant analysis as well as computer simulations we find that the effect of stochastic resonance is preserved in the case of harmonic noise excitation. Moreover we show that the width of the spectral line of the signal at the output can be decreased via stochastic resonance. The last could be of importance in the practical using of the stochastic resonance
Stochasticity and transport in Hamiltonian systems
International Nuclear Information System (INIS)
MacKay, R.S.; Meiss, J.D.; Percival, I.C.
1983-08-01
The theory of transport in nonlinear dynamics is developed in terms of leaky barriers which remain when invariant tori are destroyed. We describe the organization of stochastic motion by these barriers and give an explanation of long-time correlations in the stochastic regime
Gottwald, G.A.; Crommelin, D.T.; Franzke, C.L.E.; Franzke, C.L.E.; O'Kane, T.J.
2017-01-01
In this chapter we review stochastic modelling methods in climate science. First we provide a conceptual framework for stochastic modelling of deterministic dynamical systems based on the Mori-Zwanzig formalism. The Mori-Zwanzig equations contain a Markov term, a memory term and a term suggestive of
Stochastic equations for complex systems theoretical and computational topics
Bessaih, Hakima
2015-01-01
Mathematical analyses and computational predictions of the behavior of complex systems are needed to effectively deal with weather and climate predictions, for example, and the optimal design of technical processes. Given the random nature of such systems and the recognized relevance of randomness, the equations used to describe such systems usually need to involve stochastics. The basic goal of this book is to introduce the mathematics and application of stochastic equations used for the modeling of complex systems. A first focus is on the introduction to different topics in mathematical analysis. A second focus is on the application of mathematical tools to the analysis of stochastic equations. A third focus is on the development and application of stochastic methods to simulate turbulent flows as seen in reality. This book is primarily oriented towards mathematics and engineering PhD students, young and experienced researchers, and professionals working in the area of stochastic differential equations ...
Stochastic stability of four-wheel-steering system
International Nuclear Information System (INIS)
Huang Dongwei; Wang Hongli; Zhu Zhiwen; Feng Zhang
2007-01-01
A four-wheel-steering system subjected to white noise excitations was reduced to a two-degree-of-freedom quasi-non-integrable-Hamiltonian system. Subsequently we obtained an one-dimensional Ito stochastic differential equation for the averaged Hamiltonian of the system by using the stochastic averaging method for quasi-non-integrable-Hamiltonian systems. Thus, the stochastic stability of four-wheel-steering system was analyzed by analyzing the sample behaviors of the averaged Hamiltonian at the boundary H = 0 and calculating its Lyapunov exponent. An example given at the end demonstrated that the conclusion obtained is of considerable significance
Process theory for supervisory control of stochastic systems with data
Markovski, J.
2012-01-01
We propose a process theory for supervisory control of stochastic nondeterministic plants with data-based observations. The Markovian process theory with data relies on the notion of Markovian partial bisimulation to capture controllability of stochastic nondeterministic systems. It presents a
Review of "Stochastic Modelling for Systems Biology" by Darren Wilkinson
Directory of Open Access Journals (Sweden)
Bullinger Eric
2006-12-01
Full Text Available Abstract "Stochastic Modelling for Systems Biology" by Darren Wilkinson introduces the peculiarities of stochastic modelling in biology. This book is particularly suited to as a textbook or for self-study, and for readers with a theoretical background.
Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI
Energy Technology Data Exchange (ETDEWEB)
Chen, Kho Chia; Kane, Ibrahim Lawal; Rahman, Haliza Abd [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310, Johor Bahru (Malaysia); Bahar, Arifah [UTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Universiti Teknologi Malaysia, 81310, Johor Bahru and Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310, Johor Bahru (Malaysia); Ting, Chee-Ming [Center for Biomedical Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru (Malaysia)
2015-02-03
In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parameter estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well.
Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI
Chen, Kho Chia; Bahar, Arifah; Kane, Ibrahim Lawal; Ting, Chee-Ming; Rahman, Haliza Abd
2015-02-01
In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parameter estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well.
Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI
International Nuclear Information System (INIS)
Chen, Kho Chia; Kane, Ibrahim Lawal; Rahman, Haliza Abd; Bahar, Arifah; Ting, Chee-Ming
2015-01-01
In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parameter estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well
Stochastic Wilson–Cowan models of neuronal network dynamics with memory and delay
International Nuclear Information System (INIS)
Goychuk, Igor; Goychuk, Andriy
2015-01-01
We consider a simple Markovian class of the stochastic Wilson–Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around −1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence. (paper)
Numerical analysis of systems of ordinary and stochastic differential equations
Artemiev, S S
1997-01-01
This text deals with numerical analysis of systems of both ordinary and stochastic differential equations. It covers numerical solution problems of the Cauchy problem for stiff ordinary differential equations (ODE) systems by Rosenbrock-type methods (RTMs).
Formal Abstractions for Automated Verification and Synthesis of Stochastic Systems
Esmaeil Zadeh Soudjani, S.
2014-01-01
Stochastic hybrid systems involve the coupling of discrete, continuous, and probabilistic phenomena, in which the composition of continuous and discrete variables captures the behavior of physical systems interacting with digital, computational devices. Because of their versatility and generality,
Generalization of uncertainty relation for quantum and stochastic systems
Koide, T.; Kodama, T.
2018-06-01
The generalized uncertainty relation applicable to quantum and stochastic systems is derived within the stochastic variational method. This relation not only reproduces the well-known inequality in quantum mechanics but also is applicable to the Gross-Pitaevskii equation and the Navier-Stokes-Fourier equation, showing that the finite minimum uncertainty between the position and the momentum is not an inherent property of quantum mechanics but a common feature of stochastic systems. We further discuss the possible implication of the present study in discussing the application of the hydrodynamic picture to microscopic systems, like relativistic heavy-ion collisions.
Kaulakys, B.; Alaburda, M.; Ruseckas, J.
2016-05-01
A well-known fact in the financial markets is the so-called ‘inverse cubic law’ of the cumulative distributions of the long-range memory fluctuations of market indicators such as a number of events of trades, trading volume and the logarithmic price change. We propose the nonlinear stochastic differential equation (SDE) giving both the power-law behavior of the power spectral density and the long-range dependent inverse cubic law of the cumulative distribution. This is achieved using the suggestion that when the market evolves from calm to violent behavior there is a decrease of the delay time of multiplicative feedback of the system in comparison to the driving noise correlation time. This results in a transition from the Itô to the Stratonovich sense of the SDE and yields a long-range memory process.
Distributed Fault Detection for a Class of Nonlinear Stochastic Systems
Directory of Open Access Journals (Sweden)
Bingyong Yan
2014-01-01
Full Text Available A novel distributed fault detection strategy for a class of nonlinear stochastic systems is presented. Different from the existing design procedures for fault detection, a novel fault detection observer, which consists of a nonlinear fault detection filter and a consensus filter, is proposed to detect the nonlinear stochastic systems faults. Firstly, the outputs of the nonlinear stochastic systems act as inputs of a consensus filter. Secondly, a nonlinear fault detection filter is constructed to provide estimation of unmeasurable system states and residual signals using outputs of the consensus filter. Stability analysis of the consensus filter is rigorously investigated. Meanwhile, the design procedures of the nonlinear fault detection filter are given in terms of linear matrix inequalities (LMIs. Taking the influence of the system stochastic noises into consideration, an outstanding feature of the proposed scheme is that false alarms can be reduced dramatically. Finally, simulation results are provided to show the feasibility and effectiveness of the proposed fault detection approach.
Stabilization of memory States by stochastic facilitating synapses.
Miller, Paul
2013-12-06
Bistability within a small neural circuit can arise through an appropriate strength of excitatory recurrent feedback. The stability of a state of neural activity, measured by the mean dwelling time before a noise-induced transition to another state, depends on the neural firing-rate curves, the net strength of excitatory feedback, the statistics of spike times, and increases exponentially with the number of equivalent neurons in the circuit. Here, we show that such stability is greatly enhanced by synaptic facilitation and reduced by synaptic depression. We take into account the alteration in times of synaptic vesicle release, by calculating distributions of inter-release intervals of a synapse, which differ from the distribution of its incoming interspike intervals when the synapse is dynamic. In particular, release intervals produced by a Poisson spike train have a coefficient of variation greater than one when synapses are probabilistic and facilitating, whereas the coefficient of variation is less than one when synapses are depressing. However, in spite of the increased variability in postsynaptic input produced by facilitating synapses, their dominant effect is reduced synaptic efficacy at low input rates compared to high rates, which increases the curvature of neural input-output functions, leading to wider regions of bistability in parameter space and enhanced lifetimes of memory states. Our results are based on analytic methods with approximate formulae and bolstered by simulations of both Poisson processes and of circuits of noisy spiking model neurons.
A decoupled approach to filter design for stochastic systems
Barbata, A.; Zasadzinski, M.; Ali, H. Souley; Messaoud, H.
2016-08-01
This paper presents a new theorem to guarantee the almost sure exponential stability for a class of stochastic triangular systems by studying only the stability of each diagonal subsystems. This result allows to solve the filtering problem of the stochastic systems with multiplicative noises by using the almost sure exponential stability concept. Two kinds of observers are treated: the full-order and reduced-order cases.
Stochastic chemical kinetics theory and (mostly) systems biological applications
Érdi, Péter; Lente, Gabor
2014-01-01
This volume reviews the theory and simulation methods of stochastic kinetics by integrating historical and recent perspectives, presents applications, mostly in the context of systems biology and also in combustion theory. In recent years, due to the development in experimental techniques, such as optical imaging, single cell analysis, and fluorescence spectroscopy, biochemical kinetic data inside single living cells have increasingly been available. The emergence of systems biology brought renaissance in the application of stochastic kinetic methods.
Stochastic systems with cross-correlated Gaussian white noises
International Nuclear Information System (INIS)
Wang Cheng-Yu; Song Yu-Min; Zhou Peng; Yang Hai; Gao Yun
2010-01-01
This paper theoretically investigates three stochastic systems with cross-correlation Gaussian white noises. Both steady state properties of the stochastic nonlinear systems and the nonequilibrium transitions induced by the cross-correlated noises are studied. The stationary solutions of the Fokker—Planck equation for three specific examples are analysed. It is shown explicitly that the cross-correlation of white noises can induce nonequilibrium transitions
Robust stability for stochastic bidirectional associative memory neural networks with time delays
Shu, H. S.; Lv, Z. W.; Wei, G. L.
2008-02-01
In this paper, the asymptotic stability is considered for a class of uncertain stochastic bidirectional associative memory neural networks with time delays and parameter uncertainties. The delays are time-invariant and the uncertainties are norm-bounded that enter into all network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov-Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed criteria.
Stochastic bosonization for a d ≥ 3 Fermi system
International Nuclear Information System (INIS)
Accardi, L.; Lu, Y.G.; Mastropietro, V.
1997-01-01
We consider a system of fermions interacting via an external field and we prove, in d ≥ 3, that a suitable collective operator, bilinear in the fermionic fields, in the stochastic limit becomes a boson quantum brownian motion. The evolution operator after the limit satisfies a quantum stochastic differential equation, in which the imaginary part of the Ito correction is the ground state shift while its real part is the lifetime of the ground state. (orig.)
Stochastic differential equations and a biological system
DEFF Research Database (Denmark)
Wang, Chunyan
1994-01-01
The purpose of this Ph.D. study is to explore the property of a growth process. The study includes solving and simulating of the growth process which is described in terms of stochastic differential equations. The identification of the growth and variability parameters of the process based...... on experimental data is considered. As an example, the growth of bacteria Pseudomonas fluorescens is taken. Due to the specific features of stochastic differential equations, namely that their solutions do not exist in the general sense, two new integrals - the Ito integral and the Stratonovich integral - have...... description. In order to identify the parameters, a Maximum likelihood estimation method is used together with a simplified truncated second order filter. Because of the continuity feature of the predictor equation, two numerical integration methods, called the Odeint and the Discretization method...
Distributed parallel computing in stochastic modeling of groundwater systems.
Dong, Yanhui; Li, Guomin; Xu, Haizhen
2013-03-01
Stochastic modeling is a rapidly evolving, popular approach to the study of the uncertainty and heterogeneity of groundwater systems. However, the use of Monte Carlo-type simulations to solve practical groundwater problems often encounters computational bottlenecks that hinder the acquisition of meaningful results. To improve the computational efficiency, a system that combines stochastic model generation with MODFLOW-related programs and distributed parallel processing is investigated. The distributed computing framework, called the Java Parallel Processing Framework, is integrated into the system to allow the batch processing of stochastic models in distributed and parallel systems. As an example, the system is applied to the stochastic delineation of well capture zones in the Pinggu Basin in Beijing. Through the use of 50 processing threads on a cluster with 10 multicore nodes, the execution times of 500 realizations are reduced to 3% compared with those of a serial execution. Through this application, the system demonstrates its potential in solving difficult computational problems in practical stochastic modeling. © 2012, The Author(s). Groundwater © 2012, National Ground Water Association.
Stochastic Sizing of Energy Storage Systems for Wind Integration
Directory of Open Access Journals (Sweden)
D. D. Le
2018-06-01
Full Text Available In this paper, we present an optimal capacity decision model for energy storage systems (ESSs in combined operation with wind energy in power systems. We use a two-stage stochastic programming approach to take into account both wind and load uncertainties. The planning problem is formulated as an AC optimal power flow (OPF model with the objective of minimizing ESS installation cost and system operation cost. Stochastic wind and load inputs for the model are generated from historical data using clustering technique. The model is tested on the IEEE 39-bus system.
Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.
Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua
2016-11-14
In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.
Probabilistic DHP adaptive critic for nonlinear stochastic control systems.
Herzallah, Randa
2013-06-01
Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Káarnáy, 2011; Kárný, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Káarnáy, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Káarnáy, 2011; Kárný, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained. Copyright © 2013 Elsevier Ltd. All rights reserved.
Hopf Bifurcation of Compound Stochastic van der Pol System
Directory of Open Access Journals (Sweden)
Shaojuan Ma
2016-01-01
Full Text Available Hopf bifurcation analysis for compound stochastic van der Pol system with a bound random parameter and Gaussian white noise is investigated in this paper. By the Karhunen-Loeve (K-L expansion and the orthogonal polynomial approximation, the equivalent deterministic van der Pol system can be deduced. Based on the bifurcation theory of nonlinear deterministic system, the critical value of bifurcation parameter is obtained and the influence of random strength δ and noise intensity σ on stochastic Hopf bifurcation in compound stochastic system is discussed. At last we found that increased δ can relocate the critical value of bifurcation parameter forward while increased σ makes it backward and the influence of δ is more sensitive than σ. The results are verified by numerical simulations.
Optimal Control and Optimization of Stochastic Supply Chain Systems
Song, Dong-Ping
2013-01-01
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject in the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of ...
Nonequilibrium statistical mechanics and stochastic thermodynamics of small systems
International Nuclear Information System (INIS)
Tu Zhanchun
2014-01-01
Thermodynamics is an old subject. The research objects in conventional thermodynamics are macroscopic systems with huge number of particles. In recent 30 years, thermodynamics of small systems is a frontier topic in physics. Here we introduce nonequilibrium statistical mechanics and stochastic thermodynamics of small systems. As a case study, we construct a Canot-like cycle of a stochastic heat engine with a single particle controlled by a time-dependent harmonic potential. We find that the efficiency at maximum power is 1 - √T c /T h , where Tc and Th are the temperatures of cold bath and hot bath, respectively. (author)
Size and stochasticity in irrigated social-ecological systems
Puy, Arnald; Muneepeerakul, Rachata; Balbo, Andrea L.
2017-03-01
This paper presents a systematic study of the relation between the size of irrigation systems and the management of uncertainty. We specifically focus on studying, through a stylized theoretical model, how stochasticity in water availability and taxation interacts with the stochastic behavior of the population within irrigation systems. Our results indicate the existence of two key population thresholds for the sustainability of any irrigation system: or the critical population size required to keep the irrigation system operative, and N* or the population threshold at which the incentive to work inside the irrigation system equals the incentives to work elsewhere. Crossing irretrievably leads to system collapse. N* is the population level with a sub-optimal per capita payoff towards which irrigation systems tend to gravitate. When subjected to strong stochasticity in water availability or taxation, irrigation systems might suffer sharp population drops and irreversibly disintegrate into a system collapse, via a mechanism we dub ‘collapse trap’. Our conceptual study establishes the basis for further work aiming at appraising the dynamics between size and stochasticity in irrigation systems, whose understanding is key for devising mitigation and adaptation measures to ensure their sustainability in the face of increasing and inevitable uncertainty.
Multi-scenario modelling of uncertainty in stochastic chemical systems
International Nuclear Information System (INIS)
Evans, R. David; Ricardez-Sandoval, Luis A.
2014-01-01
Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation. -- Highlights: •A method to model uncertainty on stochastic systems was developed. •The method is based on the Chemical Master Equation. •Uncertainty in an isomerization reaction and a gene regulation network was modelled. •Effects were significant and dependent on the uncertain input and reaction system. •The model was computationally more efficient than Kinetic Monte Carlo
Front propagation and effect of memory in stochastic desertification models with an absorbing state
Herman, Dor; Shnerb, Nadav M.
2017-08-01
Desertification in dryland ecosystems is considered to be a major environmental threat that may lead to devastating consequences. The concern increases when the system admits two alternative steady states and the transition is abrupt and irreversible (catastrophic shift). However, recent studies show that the inherent stochasticity of the birth-death process, when superimposed on the presence of an absorbing state, may lead to a continuous (second order) transition even if the deterministic dynamics supports a catastrophic transition. Following these works we present here a numerical study of a one-dimensional stochastic desertification model, where the deterministic predictions are confronted with the observed dynamics. Our results suggest that a stochastic spatial system allows for a propagating front only when its active phase invades the inactive (desert) one. In the extinction phase one observes transient front propagation followed by a global collapse. In the presence of a seed bank the vegetation state is shown to be more robust against demographic stochasticity, but the transition in that case still belongs to the directed percolation equivalence class.
? filtering for stochastic systems driven by Poisson processes
Song, Bo; Wu, Zheng-Guang; Park, Ju H.; Shi, Guodong; Zhang, Ya
2015-01-01
This paper investigates the ? filtering problem for stochastic systems driven by Poisson processes. By utilising the martingale theory such as the predictable projection operator and the dual predictable projection operator, this paper transforms the expectation of stochastic integral with respect to the Poisson process into the expectation of Lebesgue integral. Then, based on this, this paper designs an ? filter such that the filtering error system is mean-square asymptotically stable and satisfies a prescribed ? performance level. Finally, a simulation example is given to illustrate the effectiveness of the proposed filtering scheme.
Stochastic responses of tumor–immune system with periodic treatment
International Nuclear Information System (INIS)
Li Dong-Xi; Li Ying
2017-01-01
We investigate the stochastic responses of a tumor–immune system competition model with environmental noise and periodic treatment. Firstly, a mathematical model describing the interaction between tumor cells and immune system under external fluctuations and periodic treatment is established based on the stochastic differential equation. Then, sufficient conditions for extinction and persistence of the tumor cells are derived by constructing Lyapunov functions and Ito’s formula. Finally, numerical simulations are introduced to illustrate and verify the results. The results of this work provide the theoretical basis for designing more effective and precise therapeutic strategies to eliminate cancer cells, especially for combining the immunotherapy and the traditional tools. (paper)
Ray and wave optics of integrable and stochastic systems
International Nuclear Information System (INIS)
McDonald, S.W.; Kaufman, A.N.
1979-07-01
The generalization of WKB methods to more than one dimension is discussed in terms of the integrability or non-integrability of the geometrical optics (ray Hamiltonian) system derived in the short-wave approximation. In the two-dimensional case the ray trajectories are either regular or stochastic, and the qualitative differences between these types of motion are manifested in the characteristics of the spectra and eigenfunctions. These are examined for a model system which may be integrable or stochastic, depending on a single parameter
Stochastic simulation of off-shore oil terminal systems
International Nuclear Information System (INIS)
Frankel, E.G.; Oberle, J.
1991-01-01
To cope with the problem of uncertainty and conditionality in the planning, design, and operation of offshore oil transshipment terminal systems, a conditional stochastic simulation approach is presented. Examples are shown, using SLAM II, a computer simulation language based on GERT, a conditional stochastic network analysis methodology in which use of resources such as time and money are expressed by the moment generating function of the statistics of the resource requirements. Similarly each activity has an associated conditional probability of being performed and/or of requiring some of the resources. The terminal system is realistically represented by modelling the statistics of arrivals, loading and unloading times, uncertainties in costs and availabilities, etc
Introduction to modeling and analysis of stochastic systems
Kulkarni, V G
2011-01-01
This is an introductory-level text on stochastic modeling. It is suited for undergraduate students in engineering, operations research, statistics, mathematics, actuarial science, business management, computer science, and public policy. It employs a large number of examples to teach the students to use stochastic models of real-life systems to predict their performance, and use this analysis to design better systems. The book is devoted to the study of important classes of stochastic processes: discrete and continuous time Markov processes, Poisson processes, renewal and regenerative processes, semi-Markov processes, queueing models, and diffusion processes. The book systematically studies the short-term and the long-term behavior, cost/reward models, and first passage times. All the material is illustrated with many examples, and case studies. The book provides a concise review of probability in the appendix. The book emphasizes numerical answers to the problems. A collection of MATLAB programs to accompany...
Noradrenergic System and Memory
Zenger, Manuel
2017-07-22
There is ample evidence indicating that noradrenaline plays an important role in memory mechanisms. Noradrenaline is thought to modulate these procsses through activation of adrenergic receptors in neurons. Astrocytes that form essential partners for synaptic function, also express alpha- and beta-adrenergic receptors. In astrocytes, noradrenaline triggers metabolic actions such as the glycogenolysis leading to an increase in l-lactate formation and release. l-Lactate can be used by neurons as a sourc of energy during memory tasks and can also induc transcription of plasticity genes in neurons. Activation of β-adrenergic receptors can also trigger gliotransmitter release resulting of intracllular calcium waves. These gliotransmitters modulate the synaptic activity and thereby can modulate long-term potentiation mechanisms. In summary, recnt evidencs indicate that noradrenaline exerts its memory-promoting effects through different modes of action both on neurons and astrocytes.
Noradrenergic System and Memory
Zenger, Manuel; Burlet-Godinot, Sophie; Petit, Jean-Marie; Magistretti, Pierre J.
2017-01-01
There is ample evidence indicating that noradrenaline plays an important role in memory mechanisms. Noradrenaline is thought to modulate these procsses through activation of adrenergic receptors in neurons. Astrocytes that form essential partners for synaptic function, also express alpha- and beta-adrenergic receptors. In astrocytes, noradrenaline triggers metabolic actions such as the glycogenolysis leading to an increase in l-lactate formation and release. l-Lactate can be used by neurons as a sourc of energy during memory tasks and can also induc transcription of plasticity genes in neurons. Activation of β-adrenergic receptors can also trigger gliotransmitter release resulting of intracllular calcium waves. These gliotransmitters modulate the synaptic activity and thereby can modulate long-term potentiation mechanisms. In summary, recnt evidencs indicate that noradrenaline exerts its memory-promoting effects through different modes of action both on neurons and astrocytes.
Threshold for extinction and survival in stochastic tumor immune system
Li, Dongxi; Cheng, Fangjuan
2017-10-01
This paper mainly investigates the stochastic character of tumor growth and extinction in the presence of immune response of a host organism. Firstly, the mathematical model describing the interaction and competition between the tumor cells and immune system is established based on the Michaelis-Menten enzyme kinetics. Then, the threshold conditions for extinction, weak persistence and stochastic persistence of tumor cells are derived by the rigorous theoretical proofs. Finally, stochastic simulation are taken to substantiate and illustrate the conclusion we have derived. The modeling results will be beneficial to understand to concept of immunoediting, and develop the cancer immunotherapy. Besides, our simple theoretical model can help to obtain new insight into the complexity of tumor growth.
Working Memory Systems in the Rat.
Bratch, Alexander; Kann, Spencer; Cain, Joshua A; Wu, Jie-En; Rivera-Reyes, Nilda; Dalecki, Stefan; Arman, Diana; Dunn, Austin; Cooper, Shiloh; Corbin, Hannah E; Doyle, Amanda R; Pizzo, Matthew J; Smith, Alexandra E; Crystal, Jonathon D
2016-02-08
A fundamental feature of memory in humans is the ability to simultaneously work with multiple types of information using independent memory systems. Working memory is conceptualized as two independent memory systems under executive control [1, 2]. Although there is a long history of using the term "working memory" to describe short-term memory in animals, it is not known whether multiple, independent memory systems exist in nonhumans. Here, we used two established short-term memory approaches to test the hypothesis that spatial and olfactory memory operate as independent working memory resources in the rat. In the olfactory memory task, rats chose a novel odor from a gradually incrementing set of old odors [3]. In the spatial memory task, rats searched for a depleting food source at multiple locations [4]. We presented rats with information to hold in memory in one domain (e.g., olfactory) while adding a memory load in the other domain (e.g., spatial). Control conditions equated the retention interval delay without adding a second memory load. In a further experiment, we used proactive interference [5-7] in the spatial domain to compromise spatial memory and evaluated the impact of adding an olfactory memory load. Olfactory and spatial memory are resistant to interference from the addition of a memory load in the other domain. Our data suggest that olfactory and spatial memory draw on independent working memory systems in the rat. Copyright © 2016 Elsevier Ltd. All rights reserved.
Deterministic Versus Stochastic Interpretation of Continuously Monitored Sewer Systems
DEFF Research Database (Denmark)
Harremoës, Poul; Carstensen, Niels Jacob
1994-01-01
An analysis has been made of the uncertainty of input parameters to deterministic models for sewer systems. The analysis reveals a very significant uncertainty, which can be decreased, but not eliminated and has to be considered for engineering application. Stochastic models have a potential for ...
Optimal adaptive control for a class of stochastic systems
Bagchi, Arunabha; Chen, Han-Fu
1995-01-01
We study linear-quadratic adaptive tracking problems for a special class of stochastic systems expressed in the state-space form. This is a long-standing problem in the control of aircraft flying through atmospheric turbulence. Using an ELS-based algorithm and introducing dither in the control law
Stochastic Predictive Control of Multi-Microgrid Systems
DEFF Research Database (Denmark)
Bazmohammadi, Najmeh; Tahsiri, Ahmadreza; Anvari-Moghaddam, Amjad
2018-01-01
This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty...
Stochastic Robust Mathematical Programming Model for Power System Optimization
Energy Technology Data Exchange (ETDEWEB)
Liu, Cong; Changhyeok, Lee; Haoyong, Chen; Mehrotra, Sanjay
2016-01-01
This paper presents a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with ifferent uncertainty sets. A case study of unit commitment shows the effectiveness of the proposed model and algorithms.
Stochastic modeling of wetland-groundwater systems
Bertassello, Leonardo Enrico; Rao, P. Suresh C.; Park, Jeryang; Jawitz, James W.; Botter, Gianluca
2018-02-01
Modeling and data analyses were used in this study to examine the temporal hydrological variability in geographically isolated wetlands (GIWs), as influenced by hydrologic connectivity to shallow groundwater, wetland bathymetry, and subject to stochastic hydro-climatic forcing. We examined the general case of GIWs coupled to shallow groundwater through exfiltration or infiltration across wetland bottom. We also examined limiting case with the wetland stage as the local expression of the shallow groundwater. We derive analytical expressions for the steady-state probability density functions (pdfs) for wetland water storage and stage using few, scaled, physically-based parameters. In addition, we analyze the hydrologic crossing time properties of wetland stage, and the dependence of the mean hydroperiod on climatic and wetland morphologic attributes. Our analyses show that it is crucial to account for shallow groundwater connectivity to fully understand the hydrologic dynamics in wetlands. The application of the model to two different case studies in Florida, jointly with a detailed sensitivity analysis, allowed us to identify the main drivers of hydrologic dynamics in GIWs under different climate and morphologic conditions.
Development scenarios for organizational memory information systems
Wijnhoven, Alphonsus B.J.M.
1999-01-01
Well-managed organizational memories have been emphasized in the recent management literature as important sources for business success. Organizational memory infonnation systems (OMIS) have been conceptualized as a framework for information technologies to support these organizational memories.
Stochastic Model Predictive Control with Applications in Smart Energy Systems
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Mølbak, Tommy
2012-01-01
to cover more than 50% of the total consumption by 2050. Energy systems based on significant amounts of renewable energy sources are subject to uncertainties. To accommodate the need for model predictive control (MPC) of such systems, the effect of the stochastic effects on the constraints must...... study, we consider a system consisting of fuel-fired thermal power plants, wind farms and electric vehicles....
Stochastic dynamic analysis of marine risers considering Gaussian system uncertainties
Ni, Pinghe; Li, Jun; Hao, Hong; Xia, Yong
2018-03-01
This paper performs the stochastic dynamic response analysis of marine risers with material uncertainties, i.e. in the mass density and elastic modulus, by using Stochastic Finite Element Method (SFEM) and model reduction technique. These uncertainties are assumed having Gaussian distributions. The random mass density and elastic modulus are represented by using the Karhunen-Loève (KL) expansion. The Polynomial Chaos (PC) expansion is adopted to represent the vibration response because the covariance of the output is unknown. Model reduction based on the Iterated Improved Reduced System (IIRS) technique is applied to eliminate the PC coefficients of the slave degrees of freedom to reduce the dimension of the stochastic system. Monte Carlo Simulation (MCS) is conducted to obtain the reference response statistics. Two numerical examples are studied in this paper. The response statistics from the proposed approach are compared with those from MCS. It is noted that the computational time is significantly reduced while the accuracy is kept. The results demonstrate the efficiency of the proposed approach for stochastic dynamic response analysis of marine risers.
Multivariable controller for discrete stochastic amplitude-constrained systems
Directory of Open Access Journals (Sweden)
Hannu T. Toivonen
1983-04-01
Full Text Available A sub-optimal multivariable controller for discrete stochastic amplitude-constrained systems is presented. In the approach the regulator structure is restricted to the class of linear saturated feedback laws. The stationary covariances of the controlled system are evaluated by approximating the stationary probability distribution of the state by a gaussian distribution. An algorithm for minimizing a quadratic loss function is given, and examples are presented to illustrate the performance of the sub-optimal controller.
Adaptively optimizing stochastic resonance in visual system
Yang, Tao
1998-08-01
Recent psychophysics experiment has showed that the noise strength could affect the perceived image quality. This work gives an adaptive process for achieving the optimal perceived image quality in a simple image perception array, which is a simple model of an image sensor. A reference image from memory is used for constructing a cost function and defining the optimal noise strength where the cost function gets its minimum point. The reference image is a binary image, which is used to define the background and the object. Finally, an adaptive algorithm is proposed for searching the optimal noise strength. Computer experimental results show that if the reference image is a thresholded version of the sub-threshold input image then the output of the sensor array gives an optimal output, in which the background and the object have the biggest contrast. If the reference image is different from a thresholded version of the sub-threshold input image then the output usually gives a sub-optimal contrast between the object and the background.
Improved Stochastic Subspace System Identification for Structural Health Monitoring
Chang, Chia-Ming; Loh, Chin-Hsiung
2015-07-01
Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.
STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB
Klingbeil, G.; Erban, R.; Giles, M.; Maini, P. K.
2011-01-01
Motivation: The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
Directory of Open Access Journals (Sweden)
Daigle Bernie J
2012-05-01
Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods
A Stochastic Operational Planning Model for Smart Power Systems
Directory of Open Access Journals (Sweden)
Sh. Jadid
2014-12-01
Full Text Available Smart Grids are result of utilizing novel technologies such as distributed energy resources, and communication technologies in power system to compensate some of its defects. Various power resources provide some benefits for operation domain however, power system operator should use a powerful methodology to manage them. Renewable resources and load add uncertainty to the problem. So, independent system operator should use a stochastic method to manage them. A Stochastic unit commitment is presented in this paper to schedule various power resources such as distributed generation units, conventional thermal generation units, wind and PV farms, and demand response resources. Demand response resources, interruptible loads, distributed generation units, and conventional thermal generation units are used to provide required reserve for compensating stochastic nature of various resources and loads. In the presented model, resources connected to distribution network can participate in wholesale market through aggregators. Moreover, a novel three-program model which can be used by aggregators is presented in this article. Loads and distributed generation can contract with aggregators by these programs. A three-bus test system and the IEEE RTS are used to illustrate usefulness of the presented model. The results show that ISO can manage the system effectively by using this model
Directory of Open Access Journals (Sweden)
Manman Yuan
2018-01-01
Full Text Available The paper addresses the issue of synchronization of memristive bidirectional associative memory neural networks (MBAMNNs with mixed time-varying delays and stochastic perturbation via a sampled-data controller. First, we propose a new model of MBAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying distributed delays and discrete delays. Second, we design a new method of sampled-data control for the stochastic MBAMNNs. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the methods are carefully designed to confirm the synchronization processes are suitable for the feather of the memristor. Third, sufficient criteria guaranteeing the synchronization of the systems are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.
Estimation of Parameters in Mean-Reverting Stochastic Systems
Directory of Open Access Journals (Sweden)
Tianhai Tian
2014-01-01
Full Text Available Stochastic differential equation (SDE is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.
Stochastic Resonance in a System of Coupled Chaotic Oscillators
International Nuclear Information System (INIS)
Krawiecki, A.
1999-01-01
Noise-free stochastic resonance is investigated numerically in a system of two coupled chaotic Roessler oscillators. Periodic signal is applied either additively or multiplicatively to the coupling term. When the coupling constant is varied the oscillators lose synchronization via attractor bubbling or on-off intermittency. Properly chosen signals are analyzed which reflect the sequence of synchronized (laminar) phases and non-synchronized bursts in the time evolution of the oscillators. Maximum of the signal-to-noise ratio as a function of the coupling constant is observed. Dependence of the signal-to-noise ratio on the frequency of the periodic signal and parameter mismatch between the oscillators is investigated. Possible applications of stochastic resonance in the recovery of signals in secure communication systems based on chaotic synchronization are briefly discussed. (author)
Information theory and stochastics for multiscale nonlinear systems
Majda, Andrew J; Grote, Marcus J
2005-01-01
This book introduces mathematicians to the fascinating emerging mathematical interplay between ideas from stochastics and information theory and important practical issues in studying complex multiscale nonlinear systems. It emphasizes the serendipity between modern applied mathematics and applications where rigorous analysis, the development of qualitative and/or asymptotic models, and numerical modeling all interact to explain complex phenomena. After a brief introduction to the emerging issues in multiscale modeling, the book has three main chapters. The first chapter is an introduction to information theory with novel applications to statistical mechanics, predictability, and Jupiter's Red Spot for geophysical flows. The second chapter discusses new mathematical issues regarding fluctuation-dissipation theorems for complex nonlinear systems including information flow, various approximations, and illustrates applications to various mathematical models. The third chapter discusses stochastic modeling of com...
Stochastic dynamics of a delayed bistable system with multiplicative noise
Energy Technology Data Exchange (ETDEWEB)
Dung, Nguyen Tien, E-mail: dung-nguyentien10@yahoo.com, E-mail: dungnt@fpt.edu.vn [Department of Mathematics, FPT University, No 8 Ton That Thuyet, My Dinh, Tu Liem, Hanoi (Viet Nam)
2014-05-15
In this paper we investigate the properties of a delayed bistable system under the effect of multiplicative noise. We first prove the existence and uniqueness of the positive solution and show that its moments are uniformly bounded. Then, we study stochastic dynamics of the solution in long time, the lower and upper bounds for the paths and an estimate for the average value are provided.
Frequency-difference-dependent stochastic resonance in neural systems
Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong
2017-08-01
Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.
Perturbation expansions of stochastic wavefunctions for open quantum systems
Ke, Yaling; Zhao, Yi
2017-11-01
Based on the stochastic unravelling of the reduced density operator in the Feynman path integral formalism for an open quantum system in touch with harmonic environments, a new non-Markovian stochastic Schrödinger equation (NMSSE) has been established that allows for the systematic perturbation expansion in the system-bath coupling to arbitrary order. This NMSSE can be transformed in a facile manner into the other two NMSSEs, i.e., non-Markovian quantum state diffusion and time-dependent wavepacket diffusion method. Benchmarked by numerically exact results, we have conducted a comparative study of the proposed method in its lowest order approximation, with perturbative quantum master equations in the symmetric spin-boson model and the realistic Fenna-Matthews-Olson complex. It is found that our method outperforms the second-order time-convolutionless quantum master equation in the whole parameter regime and even far better than the fourth-order in the slow bath and high temperature cases. Besides, the method is applicable on an equal footing for any kind of spectral density function and is expected to be a powerful tool to explore the quantum dynamics of large-scale systems, benefiting from the wavefunction framework and the time-local appearance within a single stochastic trajectory.
Energy-Based Controller Design of Stochastic Magnetic Levitation System
Directory of Open Access Journals (Sweden)
Weiwei Sun
2017-01-01
Full Text Available This paper investigates the control problem of magnetic levitation system, in which velocity feedback signal is influenced by stochastic disturbance. Firstly, single-degree-freedom magnetic levitation is regarded as an energy-transform action device. From the view of energy-balance relation, the magnetic levitation system is transformed into port-controlled Hamiltonian system model. Next, based on the Hamiltonian structure, the control law of magnetic levitation system is designed by applying Lyapunov theory. Finally, the simulation verifies the correctness of the proposed results.
Quality control system response to stochastic growth of amyloid fibrils
DEFF Research Database (Denmark)
Pigolotti, S.; Lizana, L.; Sneppen, K.
2013-01-01
We introduce a stochastic model describing aggregation of misfolded proteins and degradation by the protein quality control system in a single cell. Aggregate growth is contrasted by the cell quality control system, that attacks them at different stages of the growth process, with an efficiency...... that decreases with their size. Model parameters are estimated from experimental data. Two qualitatively different behaviors emerge: a homeostatic state, where the quality control system is stable and aggregates of large sizes are not formed, and an oscillatory state, where the quality control system...
Stochastic Prediction of Ventilation System Performance
DEFF Research Database (Denmark)
Haghighat, F.; Brohus, Henrik; Frier, Christian
The paper briefly reviews the existing techniques for predicting the airflow rate due to the random nature of forcing functions, e.g. wind speed. The effort is to establish the relationship between the statistics of the output of a system and the statistics of the random input variables and param......The paper briefly reviews the existing techniques for predicting the airflow rate due to the random nature of forcing functions, e.g. wind speed. The effort is to establish the relationship between the statistics of the output of a system and the statistics of the random input variables...
Phasing of Debuncher Stochastic Cooling Transverse Systems
International Nuclear Information System (INIS)
Pasquinelli, Ralph
2000-01-01
With the higher frequency of the cooling systems in the Debuncher, a modified method of making transfer functions has been developed for transverse systems. (Measuring of the momentum systems is unchanged.) Speed in making the measurements is critical, as the beam tends to decelerate due to vacuum lifetime. In the 4-8 GHz band, the harmonics in the Debuncher are 6,700 to 13,400 times the revolution frequency. Every Hertz change in revolution frequency is multiplied by this harmonic number and becomes a frequency measurement error, which is an appreciable percent of the momentum width of the beam. It was originally thought that a momentum cooling system would be phased first so that the beam could be kept from drifting in revolution frequency. As it turned out, the momentum cooling was so effective (even with the gain turned down) that the momentum width normalized to fo became less than one Hertz on the Schottky pickup. A beam this narrow requires very precise measurement of tune and revolution frequency. It was difficult to get repeatable results. For initial measuring of the transverse arrays, relative phase and delay is all that is required, so the measurement settings outlined below will suffice. Once all input and output arrays are phased, a more precise measurement of all pickups to all kickers can be done with more points and both upper and lower side bands, as in figure 1. Settings on the network analyzer were adjusted for maximum measurement speed. Data is not analyzed until a complete set of measurements is taken. Start and stop frequencies should be chosen to be just slightly wider than the band being measured. For transverse systems, select betatron USB for the measurement type. This will make the measurement two times faster. Select 101 for the number of points, sweep time of 5 seconds, IF bandwidth 30 Hz, averages = 1. It is important during the phasing to continually measure the revolution frequency and beam width of the beam for transverse systems
Quantum dynamics of classical stochastic systems
Energy Technology Data Exchange (ETDEWEB)
Casati, G
1983-01-01
It is shown that one hand Quantum Mechanics introduces limitations to the manifestations of chaotic motion resulting, for the case of the periodically kicked rotator, in the limitation of energy growth; also, as it is confirmed by numerical experiments, phenomena like the exponential instability of orbits, inherent to strongly chaotic systems, are absent here and therefore Quantum Mechanics appear to be more stable and predictable than Classical Mechanics. On the other hand, we have seen that nonrecurrent behavior may arise in Quantum Systems and it is connected to the presence of singular continuous spectrum. We conjecture that the classical chaotic behavior is reflected, at least partially, in the nature of the spectrum and the singular-continuity of the latter may possess a self-similar structure typical of classical chaos.
Stochastic seismic floor response analysis method for various damping systems
International Nuclear Information System (INIS)
Kitada, Y.; Hattori, K.; Ogata, M.; Kanda, J.
1991-01-01
A study using the stochastic seismic response analysis method which is applicable for the estimation of floor response spectra is carried out. It is pointed out as a shortcoming in this stochastic seismic response analysis method, that the method tends to overestimate floor response spectra for low damping systems, e.g. 1% of the critical damping ratio. An investigation on the cause of the shortcoming is carried out and a number of improvements in this method were also made to the original method by taking correlation of successive peaks in a response time history into account. The application of the improved method to a typical BWR reactor building is carried out. The resultant floor response spectra are compared with those obtained by deterministic time history analysis. Floor response spectra estimated by the improved method consistently cover the response spectra obtained by the time history analysis for various damping ratios. (orig.)
Symplectic Integrators to Stochastic Hamiltonian Dynamical Systems Derived from Composition Methods
Directory of Open Access Journals (Sweden)
Tetsuya Misawa
2010-01-01
Full Text Available “Symplectic” schemes for stochastic Hamiltonian dynamical systems are formulated through “composition methods (or operator splitting methods” proposed by Misawa (2001. In the proposed methods, a symplectic map, which is given by the solution of a stochastic Hamiltonian system, is approximated by composition of the stochastic flows derived from simpler Hamiltonian vector fields. The global error orders of the numerical schemes derived from the stochastic composition methods are provided. To examine the superiority of the new schemes, some illustrative numerical simulations on the basis of the proposed schemes are carried out for a stochastic harmonic oscillator system.
Dynamic Stochastic Superresolution of sparsely observed turbulent systems
International Nuclear Information System (INIS)
Branicki, M.; Majda, A.J.
2013-01-01
Real-time capture of the relevant features of the unresolved turbulent dynamics of complex natural systems from sparse noisy observations and imperfect models is a notoriously difficult problem. The resulting lack of observational resolution and statistical accuracy in estimating the important turbulent processes, which intermittently send significant energy to the large-scale fluctuations, hinders efficient parameterization and real-time prediction using discretized PDE models. This issue is particularly subtle and important when dealing with turbulent geophysical systems with an vast range of interacting spatio-temporal scales and rough energy spectra near the mesh scale of numerical models. Here, we introduce and study a suite of general Dynamic Stochastic Superresolution (DSS) algorithms and show that, by appropriately filtering sparse regular observations with the help of cheap stochastic exactly solvable models, one can derive stochastically ‘superresolved’ velocity fields and gain insight into the important characteristics of the unresolved dynamics, including the detection of the so-called black swans. The DSS algorithms operate in Fourier domain and exploit the fact that the coarse observation network aliases high-wavenumber information into the resolved waveband. It is shown that these cheap algorithms are robust and have significant skill on a test bed of turbulent solutions from realistic nonlinear turbulent spatially extended systems in the presence of a significant model error. In particular, the DSS algorithms are capable of successfully capturing time-localized extreme events in the unresolved modes, and they provide good and robust skill for recovery of the unresolved processes in terms of pattern correlation. Moreover, we show that DSS improves the skill for recovering the primary modes associated with the sparse observation mesh which is equally important in applications. The skill of the various DSS algorithms depends on the energy spectrum
Stochastic fluctuations and distributed control of gene expression impact cellular memory.
Directory of Open Access Journals (Sweden)
Guillaume Corre
Full Text Available Despite the stochastic noise that characterizes all cellular processes the cells are able to maintain and transmit to their daughter cells the stable level of gene expression. In order to better understand this phenomenon, we investigated the temporal dynamics of gene expression variation using a double reporter gene model. We compared cell clones with transgenes coding for highly stable mRNA and fluorescent proteins with clones expressing destabilized mRNA-s and proteins. Both types of clones displayed strong heterogeneity of reporter gene expression levels. However, cells expressing stable gene products produced daughter cells with similar level of reporter proteins, while in cell clones with short mRNA and protein half-lives the epigenetic memory of the gene expression level was completely suppressed. Computer simulations also confirmed the role of mRNA and protein stability in the conservation of constant gene expression levels over several cell generations. These data indicate that the conservation of a stable phenotype in a cellular lineage may largely depend on the slow turnover of mRNA-s and proteins.
Optimal Integration of Intermittent Renewables: A System LCOE Stochastic Approach
Directory of Open Access Journals (Sweden)
Carlo Lucheroni
2018-03-01
Full Text Available We propose a system level approach to value the impact on costs of the integration of intermittent renewable generation in a power system, based on expected breakeven cost and breakeven cost risk. To do this, we carefully reconsider the definition of Levelized Cost of Electricity (LCOE when extended to non-dispatchable generation, by examining extra costs and gains originated by the costly management of random power injections. We are thus lead to define a ‘system LCOE’ as a system dependent LCOE that takes properly into account intermittent generation. In order to include breakeven cost risk we further extend this deterministic approach to a stochastic setting, by introducing a ‘stochastic system LCOE’. This extension allows us to discuss the optimal integration of intermittent renewables from a broad, system level point of view. This paper thus aims to provide power producers and policy makers with a new methodological scheme, still based on the LCOE but which updates this valuation technique to current energy system configurations characterized by a large share of non-dispatchable production. Quantifying and optimizing the impact of intermittent renewables integration on power system costs, risk and CO 2 emissions, the proposed methodology can be used as powerful tool of analysis for assessing environmental and energy policies.
Considering inventory distributions in a stochastic periodic inventory routing system
Yadollahi, Ehsan; Aghezzaf, El-Houssaine
2017-07-01
Dealing with the stochasticity of parameters is one of the critical issues in business and industry nowadays. Supply chain planners have difficulties in forecasting stochastic parameters of a distribution system. Demand rates of customers during their lead time are one of these parameters. In addition, holding a huge level of inventory at the retailers is costly and inefficient. To cover the uncertainty of forecasting demand rates, researchers have proposed the usage of safety stock to avoid stock-out. However, finding the precise level of safety stock depends on forecasting the statistical distribution of demand rates and their variations in different settings among the planning horizon. In this paper the demand rate distributions and its parameters are taken into account for each time period in a stochastic periodic IRP. An analysis of the achieved statistical distribution of the inventory and safety stock level is provided to measure the effects of input parameters on the output indicators. Different values for coefficient of variation are applied to the customers' demand rate in the optimization model. The outcome of the deterministic equivalent model of SPIRP is simulated in form of an illustrative case.
On Stochastic Finite-Time Control of Discrete-Time Fuzzy Systems with Packet Dropout
Directory of Open Access Journals (Sweden)
Yingqi Zhang
2012-01-01
Full Text Available This paper is concerned with the stochastic finite-time stability and stochastic finite-time boundedness problems for one family of fuzzy discrete-time systems over networks with packet dropout, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, we present the dynamic model description studied, in which the discrete-time fuzzy T-S systems with packet loss can be described by one class of fuzzy Markovian jump systems. Then, the concepts of stochastic finite-time stability and stochastic finite-time boundedness and problem formulation are given. Based on Lyapunov function approach, sufficient conditions on stochastic finite-time stability and stochastic finite-time boundedness are established for the resulting closed-loop fuzzy discrete-time system with Markovian jumps, and state-feedback controllers are designed to ensure stochastic finite-time stability and stochastic finite-time boundedness of the class of fuzzy systems. The stochastic finite-time stability and stochastic finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the stochastic stability of the class of fuzzy T-S systems with packet loss. Finally, two illustrative examples are presented to show the validity of the developed methodology.
Memory-guided attention: Control from multiple memory systems
Hutchinson, J. Benjamin; Turk-Browne, Nicholas B.
2012-01-01
Attention is strongly influenced by both external stimuli and internal goals. However, this useful dichotomy does not readily capture the ubiquitous and often automatic contribution of past experience stored in memory. We review recent evidence about how multiple memory systems control attention, consider how such interactions are manifested in the brain, and highlight how this framework for ‘memory-guided attention’ might help systematize previous findings and guide future research.
Stochastic analysis of a novel nonautonomous periodic SIRI epidemic system with random disturbances
Zhang, Weiwei; Meng, Xinzhu
2018-02-01
In this paper, a new stochastic nonautonomous SIRI epidemic model is formulated. Given that the incidence rates of diseases may change with the environment, we propose a novel type of transmission function. The main aim of this paper is to obtain the thresholds of the stochastic SIRI epidemic model. To this end, we investigate the dynamics of the stochastic system and establish the conditions for extinction and persistence in mean of the disease by constructing some suitable Lyapunov functions and using stochastic analysis technique. Furthermore, we show that the stochastic system has at least one nontrivial positive periodic solution. Finally, numerical simulations are introduced to illustrate our results.
Maximum principle for a stochastic delayed system involving terminal state constraints.
Wen, Jiaqiang; Shi, Yufeng
2017-01-01
We investigate a stochastic optimal control problem where the controlled system is depicted as a stochastic differential delayed equation; however, at the terminal time, the state is constrained in a convex set. We firstly introduce an equivalent backward delayed system depicted as a time-delayed backward stochastic differential equation. Then a stochastic maximum principle is obtained by virtue of Ekeland's variational principle. Finally, applications to a state constrained stochastic delayed linear-quadratic control model and a production-consumption choice problem are studied to illustrate the main obtained result.
Stochastic Modelling and Optimization of Complex Infrastructure Systems
DEFF Research Database (Denmark)
Thoft-Christensen, Palle
In this paper it is shown that recent progress in stochastic modelling and optimization in combination with advanced computer systems has now made it possible to improve the design and the maintenance strategies for infrastructure systems. The paper concentrates on highway networks and single large...... bridges. united states has perhaps the largest highway networks in the world with more than 0.5 million highway bridges; see Chase, S.B. 1999. About 40% of these bridges are considered deficient and more than $50 billion is estimated needed to correct the deficiencies; see Roberts, J.E. 2001...
Stochastic analysis of residential micro combined heat and power system
DEFF Research Database (Denmark)
Karami, H.; Sanjari, M. J.; Gooi, H. B.
2017-01-01
In this paper the combined heat and power functionality of a fuel-cell in a residential hybrid energy system, including a battery, is studied. The demand uncertainties are modeled by investigating the stochastic load behavior by applying Monte Carlo simulation. The colonial competitive algorithm...... algorithm. The optimized scheduling of different energy resources is listed in an efficient look-up table for all time intervals. The effects of time of use and the battery efficiency and its size are investigated on the operating cost of the hybrid energy system. The results of this paper are expected...
Stochastic network optimization with application to communication and queueing systems
Neely, Michael
2010-01-01
This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are prov
Optimum gain and phase for stochastic cooling systems
International Nuclear Information System (INIS)
Meer, S. van der.
1984-01-01
A detailed analysis of optimum gain and phase adjustment in stochastic cooling systems reveals that the result is strongly influenced by the beam feedback effect and that for optimum performance the system phase should change appreciably across each Schottky band. It is shown that the performance is not greatly diminished if a constant phase is adopted instead. On the other hand, the effect of mixing between pick-up and kicker (which produces a phase change similar to the optimum one) is shown to be less perturbing than is usually assumed, provided that the absolute value of the gain is not too far from the optimum value. (orig.)
The stochastic-cooling system for COSY-Juelich
International Nuclear Information System (INIS)
Brittner, P.; Danzglock, R.; Hacker, H.U.; Maier, R.; Pfister, U.; Prasuhn, D.; Singer, H.; Spiess, W.; Stockhorst, H.
1991-01-01
The cooling in the Cooler Synchrotron COSY will work in the ranges: Band 1: 1 to 1.8 GHz, Band 2: 1.8 to 3 GHz. The system allows cooling in the energy range of 0.8 to 2.5 GeV. The stochastic-cooling system is under development. Cooling characteristics have been calculated. The tanks are similar to those of the CERN-AC. But the COSY parameters have required changes of the tank design. Active RF components have been developed for COSY. Measured results are presented
A stochastic approach for model reduction and memory function design in hydrogeophysical inversion
Hou, Z.; Kellogg, A.; Terry, N.
2009-12-01
Geophysical (e.g., seismic, electromagnetic, radar) techniques and statistical methods are essential for research related to subsurface characterization, including monitoring subsurface flow and transport processes, oil/gas reservoir identification, etc. For deep subsurface characterization such as reservoir petroleum exploration, seismic methods have been widely used. Recently, electromagnetic (EM) methods have drawn great attention in the area of reservoir characterization. However, considering the enormous computational demand corresponding to seismic and EM forward modeling, it is usually a big problem to have too many unknown parameters in the modeling domain. For shallow subsurface applications, the characterization can be very complicated considering the complexity and nonlinearity of flow and transport processes in the unsaturated zone. It is warranted to reduce the dimension of parameter space to a reasonable level. Another common concern is how to make the best use of time-lapse data with spatial-temporal correlations. This is even more critical when we try to monitor subsurface processes using geophysical data collected at different times. The normal practice is to get the inverse images individually. These images are not necessarily continuous or even reasonably related, because of the non-uniqueness of hydrogeophysical inversion. We propose to use a stochastic framework by integrating minimum-relative-entropy concept, quasi Monto Carlo sampling techniques, and statistical tests. The approach allows efficient and sufficient exploration of all possibilities of model parameters and evaluation of their significances to geophysical responses. The analyses enable us to reduce the parameter space significantly. The approach can be combined with Bayesian updating, allowing us to treat the updated ‘posterior’ pdf as a memory function, which stores all the information up to date about the distributions of soil/field attributes/properties, then consider the
Neural systems for tactual memories.
Bonda, E; Petrides, M; Evans, A
1996-04-01
1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.
Fast Initialization of Bubble-Memory Systems
Looney, K. T.; Nichols, C. D.; Hayes, P. J.
1986-01-01
Improved scheme several orders of magnitude faster than normal initialization scheme. State-of-the-art commercial bubble-memory device used. Hardware interface designed connects controlling microprocessor to bubblememory circuitry. System software written to exercise various functions of bubble-memory system in comparison made between normal and fast techniques. Future implementations of approach utilize E2PROM (electrically-erasable programable read-only memory) to provide greater system flexibility. Fastinitialization technique applicable to all bubble-memory devices.
Stochastic Neural Field Theory and the System-Size Expansion
Bressloff, Paul C.
2010-01-01
We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.
Economic MPC for a linear stochastic system of energy units
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Sokoler, Leo Emil; Standardi, Laura
2016-01-01
This paper summarizes comprehensively the work in four recent PhD theses from the Technical University of Denmark related to Economic MPC of future power systems. Future power systems will consist of a large number of decentralized power producers and a large number of controllable power consumers...... in addition to stochastic power producers such as wind turbines and solar power plants. Control of such large scale systems requires new control algorithms. In this paper, we formulate the control of such a system as an Economic Model Predictive Control (MPC) problem. When the power producers and controllable...... power consumers have linear dynamics, the Economic MPC may be expressed as a linear program. We provide linear models for a number of energy units in an energy system, formulate an Economic MPC for coordination of such a system. We indicate how advances in computational MPC makes the solutions...
Approaching complexity by stochastic methods: From biological systems to turbulence
Energy Technology Data Exchange (ETDEWEB)
Friedrich, Rudolf [Institute for Theoretical Physics, University of Muenster, D-48149 Muenster (Germany); Peinke, Joachim [Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); Sahimi, Muhammad [Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089-1211 (United States); Reza Rahimi Tabar, M., E-mail: mohammed.r.rahimi.tabar@uni-oldenburg.de [Department of Physics, Sharif University of Technology, Tehran 11155-9161 (Iran, Islamic Republic of); Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); Fachbereich Physik, Universitaet Osnabrueck, Barbarastrasse 7, 49076 Osnabrueck (Germany)
2011-09-15
This review addresses a central question in the field of complex systems: given a fluctuating (in time or space), sequentially measured set of experimental data, how should one analyze the data, assess their underlying trends, and discover the characteristics of the fluctuations that generate the experimental traces? In recent years, significant progress has been made in addressing this question for a class of stochastic processes that can be modeled by Langevin equations, including additive as well as multiplicative fluctuations or noise. Important results have emerged from the analysis of temporal data for such diverse fields as neuroscience, cardiology, finance, economy, surface science, turbulence, seismic time series and epileptic brain dynamics, to name but a few. Furthermore, it has been recognized that a similar approach can be applied to the data that depend on a length scale, such as velocity increments in fully developed turbulent flow, or height increments that characterize rough surfaces. A basic ingredient of the approach to the analysis of fluctuating data is the presence of a Markovian property, which can be detected in real systems above a certain time or length scale. This scale is referred to as the Markov-Einstein (ME) scale, and has turned out to be a useful characteristic of complex systems. We provide a review of the operational methods that have been developed for analyzing stochastic data in time and scale. We address in detail the following issues: (i) reconstruction of stochastic evolution equations from data in terms of the Langevin equations or the corresponding Fokker-Planck equations and (ii) intermittency, cascades, and multiscale correlation functions.
Approaching complexity by stochastic methods: From biological systems to turbulence
International Nuclear Information System (INIS)
Friedrich, Rudolf; Peinke, Joachim; Sahimi, Muhammad; Reza Rahimi Tabar, M.
2011-01-01
This review addresses a central question in the field of complex systems: given a fluctuating (in time or space), sequentially measured set of experimental data, how should one analyze the data, assess their underlying trends, and discover the characteristics of the fluctuations that generate the experimental traces? In recent years, significant progress has been made in addressing this question for a class of stochastic processes that can be modeled by Langevin equations, including additive as well as multiplicative fluctuations or noise. Important results have emerged from the analysis of temporal data for such diverse fields as neuroscience, cardiology, finance, economy, surface science, turbulence, seismic time series and epileptic brain dynamics, to name but a few. Furthermore, it has been recognized that a similar approach can be applied to the data that depend on a length scale, such as velocity increments in fully developed turbulent flow, or height increments that characterize rough surfaces. A basic ingredient of the approach to the analysis of fluctuating data is the presence of a Markovian property, which can be detected in real systems above a certain time or length scale. This scale is referred to as the Markov-Einstein (ME) scale, and has turned out to be a useful characteristic of complex systems. We provide a review of the operational methods that have been developed for analyzing stochastic data in time and scale. We address in detail the following issues: (i) reconstruction of stochastic evolution equations from data in terms of the Langevin equations or the corresponding Fokker-Planck equations and (ii) intermittency, cascades, and multiscale correlation functions.
The stochastic system approach for estimating dynamic treatments effect.
Commenges, Daniel; Gégout-Petit, Anne
2015-10-01
The problem of assessing the effect of a treatment on a marker in observational studies raises the difficulty that attribution of the treatment may depend on the observed marker values. As an example, we focus on the analysis of the effect of a HAART on CD4 counts, where attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual/potential response formalism. Another approach to causality is based on dynamical models, and causal influence has been formalized in the framework of the Doob-Meyer decomposition of stochastic processes. Causal inference however needs assumptions that we detail in this paper and we call this approach to causality the "stochastic system" approach. First we treat this problem in discrete time, then in continuous time. This approach allows incorporating biological knowledge naturally. When working in continuous time, the mechanistic approach involves distinguishing the model for the system and the model for the observations. Indeed, biological systems live in continuous time, and mechanisms can be expressed in the form of a system of differential equations, while observations are taken at discrete times. Inference in mechanistic models is challenging, particularly from a numerical point of view, but these models can yield much richer and reliable results.
Stochastic population dynamics in spatially extended predator-prey systems
Dobramysl, Ulrich; Mobilia, Mauro; Pleimling, Michel; Täuber, Uwe C.
2018-02-01
Spatially extended population dynamics models that incorporate demographic noise serve as case studies for the crucial role of fluctuations and correlations in biological systems. Numerical and analytic tools from non-equilibrium statistical physics capture the stochastic kinetics of these complex interacting many-particle systems beyond rate equation approximations. Including spatial structure and stochastic noise in models for predator-prey competition invalidates the neutral Lotka-Volterra population cycles. Stochastic models yield long-lived erratic oscillations stemming from a resonant amplification mechanism. Spatially extended predator-prey systems display noise-stabilized activity fronts that generate persistent correlations. Fluctuation-induced renormalizations of the oscillation parameters can be analyzed perturbatively via a Doi-Peliti field theory mapping of the master equation; related tools allow detailed characterization of extinction pathways. The critical steady-state and non-equilibrium relaxation dynamics at the predator extinction threshold are governed by the directed percolation universality class. Spatial predation rate variability results in more localized clusters, enhancing both competing species’ population densities. Affixing variable interaction rates to individual particles and allowing for trait inheritance subject to mutations induces fast evolutionary dynamics for the rate distributions. Stochastic spatial variants of three-species competition with ‘rock-paper-scissors’ interactions metaphorically describe cyclic dominance. These models illustrate intimate connections between population dynamics and evolutionary game theory, underscore the role of fluctuations to drive populations toward extinction, and demonstrate how space can support species diversity. Two-dimensional cyclic three-species May-Leonard models are characterized by the emergence of spiraling patterns whose properties are elucidated by a mapping onto a complex
A stochastic killing system for biological containment of Escherichia coli
DEFF Research Database (Denmark)
Klemm, P.; Jensen, Lars Bogø; Molin, Søren
1995-01-01
Bacteria with a stochastic conditional lethal containment system have been constructed. The invertible switch promoter located upstream of the fimA gene from Escherichia coli was inserted as expression cassette in front of the Lethal gef gene deleted of its own natural promoter. The resulting...... fusion was placed on a plasmid and transformed to E. coli. The phenotype connected with the presence of such a plasmid was to reduce the population growth rate with increasing significance as the cell growth rate was reduced. In very fast growing cells, there was no measurable effect on growth rate. When...
Hitting probabilities for nonlinear systems of stochastic waves
Dalang, Robert C
2015-01-01
The authors consider a d-dimensional random field u = \\{u(t,x)\\} that solves a non-linear system of stochastic wave equations in spatial dimensions k \\in \\{1,2,3\\}, driven by a spatially homogeneous Gaussian noise that is white in time. They mainly consider the case where the spatial covariance is given by a Riesz kernel with exponent \\beta. Using Malliavin calculus, they establish upper and lower bounds on the probabilities that the random field visits a deterministic subset of \\mathbb{R}^d, in terms, respectively, of Hausdorff measure and Newtonian capacity of this set. The dimension that ap
Modelling biochemical reaction systems by stochastic differential equations with reflection.
Niu, Yuanling; Burrage, Kevin; Chen, Luonan
2016-05-07
In this paper, we gave a new framework for modelling and simulating biochemical reaction systems by stochastic differential equations with reflection not in a heuristic way but in a mathematical way. The model is computationally efficient compared with the discrete-state Markov chain approach, and it ensures that both analytic and numerical solutions remain in a biologically plausible region. Specifically, our model mathematically ensures that species numbers lie in the domain D, which is a physical constraint for biochemical reactions, in contrast to the previous models. The domain D is actually obtained according to the structure of the corresponding chemical Langevin equations, i.e., the boundary is inherent in the biochemical reaction system. A variant of projection method was employed to solve the reflected stochastic differential equation model, and it includes three simple steps, i.e., Euler-Maruyama method was applied to the equations first, and then check whether or not the point lies within the domain D, and if not perform an orthogonal projection. It is found that the projection onto the closure D¯ is the solution to a convex quadratic programming problem. Thus, existing methods for the convex quadratic programming problem can be employed for the orthogonal projection map. Numerical tests on several important problems in biological systems confirmed the efficiency and accuracy of this approach. Copyright © 2016 Elsevier Ltd. All rights reserved.
Discrete changes of current statistics in periodically driven stochastic systems
International Nuclear Information System (INIS)
Chernyak, Vladimir Y; Sinitsyn, N A
2010-01-01
We demonstrate that the counting statistics of currents in periodically driven ergodic stochastic systems can show sharp changes of some of its properties in response to continuous changes of the driving protocol. To describe this effect, we introduce a new topological phase factor in the evolution of the moment generating function which is akin to the topological geometric phase in the evolution of a periodically driven quantum mechanical system with time-reversal symmetry. This phase leads to the prediction of a sign change for the difference of the probabilities to find even and odd numbers of particles transferred in a stochastic system in response to cyclic evolution of control parameters. The driving protocols that lead to this sign change should enclose specific degeneracy points in the space of control parameters. The relation between the topology of the paths in the control parameter space and the sign changes can be described in terms of the first Stiefel–Whitney class of topological invariants. (letter)
Information Dynamics of a Nonlinear Stochastic Nanopore System
Directory of Open Access Journals (Sweden)
Claire Gilpin
2018-03-01
Full Text Available Nanopores have become a subject of interest in the scientific community due to their potential uses in nanometer-scale laboratory and research applications, including infectious disease diagnostics and DNA sequencing. Additionally, they display behavioral similarity to molecular and cellular scale physiological processes. Recent advances in information theory have made it possible to probe the information dynamics of nonlinear stochastic dynamical systems, such as autonomously fluctuating nanopore systems, which has enhanced our understanding of the physical systems they model. We present the results of local (LER and specific entropy rate (SER computations from a simulation study of an autonomously fluctuating nanopore system. We learn that both metrics show increases that correspond to fluctuations in the nanopore current, indicating fundamental changes in information generation surrounding these fluctuations.
What can go wrong in stochastic cooling systems
AUTHOR|(CDS)2108502
2016-01-01
This paper discusses very practical aspects of stochastic cooling systems both during construction, running-in, operation and trouble shooting. Due to the high electronic gain, high sensitivity and large bandwidth of such systems, precautions have to be taken to avoid all sorts of EMI/EMC related problems as well as crosstalk and self-oscillations. Since un-intended beam heating is always much more efficient than the desired cooling the overall performance depends critically on avoiding this heating which often takes places outside the nominal frequency band of operation. Another important aspect is “cross heating”, i.e., unavoidable crosstalk from longitudinal to transverse systems and vice versa. Obviously adequate measurement procedures with beam for gain phase and optimum delay are mandatory and certain caveats and hints are given. The paper concludes with a listing of unusual and unexpected problems found during many years of operation of such systems at CERN.
A condition-based maintenance policy for stochastically deteriorating systems
International Nuclear Information System (INIS)
Grall, A.; Berenguer, C.; Dieulle, L.
2002-01-01
We focus on the analytical modeling of a condition-based inspection/replacement policy for a stochastically and continuously deteriorating single-unit system. We consider both the replacement threshold and the inspection schedule as decision variables for this maintenance problem and we propose to implement the maintenance policy using a multi-level control-limit rule. In order to assess the performance of the proposed maintenance policy and to minimize the long run expected maintenance cost per unit time, a mathematical model for the maintained system cost is derived, supported by the existence of a stationary law for the maintained system state. Numerical experiments illustrate the performance of the proposed policy and confirm that the maintenance cost rate on an infinite horizon can be minimized by a joint optimization of the maintenance structure thresholds, or equivalently by a joint optimization of a system replacement threshold and the aperiodic inspection schedule
Quantization of dynamical systems and stochastic control theory
International Nuclear Information System (INIS)
Guerra, F.; Morato, L.M.
1982-09-01
In the general framework of stochastic control theory we introduce a suitable form of stochastic action associated to the controlled process. Then a variational principle gives all main features of Nelson's stochastic mechanics. In particular we derive the expression of the current velocity field as the gradient of the phase action. Moreover the stochastic corrections to the Hamilton-Jacobi equation are in agreement with the quantum mechanical form of the Madelung fluid (equivalent to the Schroedinger equation). Therefore stochastic control theory can provide a very simple model simulating quantum mechanical behavior
A stochastic perturbation theory for non-autonomous systems
Energy Technology Data Exchange (ETDEWEB)
Moon, W., E-mail: wm275@damtp.cam.ac.uk [Yale University, New Haven, Connecticut 06520-8109 (United States); Wettlaufer, J. S., E-mail: wettlaufer@maths.ox.ac.uk [Yale University, New Haven, Connecticut 06520-8109 (United States); Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom)
2013-12-15
We develop a perturbation theory for a class of first order nonlinear non-autonomous stochastic ordinary differential equations that arise in climate physics. The perturbative procedure produces moments in terms of integral delay equations, whose order by order decay is characterized in a Floquet-like sense. Both additive and multiplicative sources of noise are discussed and the question of how the nature of the noise influences the results is addressed theoretically and numerically. By invoking the Martingale property, we rationalize the transformation of the underlying Stratonovich form of the model to an Ito form, independent of whether the noise is additive or multiplicative. The generality of the analysis is demonstrated by developing it both for a Brownian particle moving in a periodically forced quartic potential, which acts as a simple model of stochastic resonance, as well as for our more complex climate physics model. The validity of the approach is shown by comparison with numerical solutions. The particular climate dynamics problem upon which we focus involves a low-order model for the evolution of Arctic sea ice under the influence of increasing greenhouse gas forcing ΔF{sub 0}. The deterministic model, developed by Eisenman and Wettlaufer [“Nonlinear threshold behavior during the loss of Arctic sea ice,” Proc. Natl. Acad. Sci. U.S.A. 106(1), 28–32 (2009)] exhibits several transitions as ΔF{sub 0} increases and the stochastic analysis is used to understand the manner in which noise influences these transitions and the stability of the system.
Stochastic transport in complex systems from molecules to vehicles
Schadschneider, Andreas; Nishinari, Katsuhiro
2011-01-01
What is common between a motor protein, an ant and a vehicle? Each can be modelled as a"self-propelled particle"whose forward movement can be hindered by another in front of it. Traffic flow of such interacting driven"particles"has become an active area of interdisciplinary research involving physics, civil engineering and computer science. We present a unified pedagogical introduction to the analytical and computational methods which are currently used for studying such complex systems far from equilibrium. We also review a number of applications ranging from intra-cellular molecular motor transport in living systems to ant trails and vehicular traffic. Researchers working on complex systems, in general, and on classical stochastic transport, in particular, will find the pedagogical style, scholarly critical overview and extensive list of references extremely useful.
Erdal, Jørgen Sørgård
2017-01-01
This master thesis develops a stochastic optimisation software for household grid-connected batteries combined with PV-systems. The objective of the optimisation is to operate the battery system in order to minimise the costs of the consumer, and it was implemented in MATLAB using a self-written stochastic dynamic programming algorithm. Load was considered as a stochastic variable and modelled as a Markov Chain. Transition probabilities between time steps were calculated using historic load p...
Stress Effects on Multiple Memory System Interactions
Ness, Deborah; Calabrese, Pasquale
2016-01-01
Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour. PMID:27034845
Stress Effects on Multiple Memory System Interactions
Directory of Open Access Journals (Sweden)
Deborah Ness
2016-01-01
Full Text Available Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour.
Stress Effects on Multiple Memory System Interactions.
Ness, Deborah; Calabrese, Pasquale
2016-01-01
Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour.
Stress Effects on Multiple Memory System Interactions
Ness, Deborah; Calabrese, Pasquale
2016-01-01
Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory syst...
Directory of Open Access Journals (Sweden)
Shaolin Ji
2013-01-01
Full Text Available This paper is devoted to a stochastic differential game (SDG of decoupled functional forward-backward stochastic differential equation (FBSDE. For our SDG, the associated upper and lower value functions of the SDG are defined through the solution of controlled functional backward stochastic differential equations (BSDEs. Applying the Girsanov transformation method introduced by Buckdahn and Li (2008, the upper and the lower value functions are shown to be deterministic. We also generalize the Hamilton-Jacobi-Bellman-Isaacs (HJBI equations to the path-dependent ones. By establishing the dynamic programming principal (DPP, we derive that the upper and the lower value functions are the viscosity solutions of the corresponding upper and the lower path-dependent HJBI equations, respectively.
Nonlinear stochastic systems with incomplete information filtering and control
Shen, Bo; Shu, Huisheng
2013-01-01
Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: · a unified framework for handling filtering and control problems in complex communication networks with limited bandwidth; · new concepts such as random sensor and signal saturations for more realistic modeling; and · demonstration of the use of techniques such...
Sandell, N. R., Jr.; Athans, M.
1975-01-01
The development of the theory of the finite - state, finite - memory (FSFM) stochastic control problem is discussed. The sufficiency of the FSFM minimum principle (which is in general only a necessary condition) was investigated. By introducing the notion of a signaling strategy as defined in the literature on games, conditions under which the FSFM minimum principle is sufficient were determined. This result explicitly interconnects the information structure of the FSFM problem with its optimality conditions. The min-H algorithm for the FSFM problem was studied. It is demonstrated that a version of the algorithm always converges to a particular type of local minimum termed a person - by - person extremal.
Stochastic Control Synthesis of Systems with Structured Uncertainty
Padula, Sharon L. (Technical Monitor); Crespo, Luis G.
2003-01-01
This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.
Crisan, Dan
2011-01-01
"Stochastic Analysis" aims to provide mathematical tools to describe and model high dimensional random systems. Such tools arise in the study of Stochastic Differential Equations and Stochastic Partial Differential Equations, Infinite Dimensional Stochastic Geometry, Random Media and Interacting Particle Systems, Super-processes, Stochastic Filtering, Mathematical Finance, etc. Stochastic Analysis has emerged as a core area of late 20th century Mathematics and is currently undergoing a rapid scientific development. The special volume "Stochastic Analysis 2010" provides a sa
Stochastic linear hybrid systems: Modeling, estimation, and application
Seah, Chze Eng
Hybrid systems are dynamical systems which have interacting continuous state and discrete state (or mode). Accurate modeling and state estimation of hybrid systems are important in many applications. We propose a hybrid system model, known as the Stochastic Linear Hybrid System (SLHS), to describe hybrid systems with stochastic linear system dynamics in each mode and stochastic continuous-state-dependent mode transitions. We then develop a hybrid estimation algorithm, called the State-Dependent-Transition Hybrid Estimation (SDTHE) algorithm, to estimate the continuous state and discrete state of the SLHS from noisy measurements. It is shown that the SDTHE algorithm is more accurate or more computationally efficient than existing hybrid estimation algorithms. Next, we develop a performance analysis algorithm to evaluate the performance of the SDTHE algorithm in a given operating scenario. We also investigate sufficient conditions for the stability of the SDTHE algorithm. The proposed SLHS model and SDTHE algorithm are illustrated to be useful in several applications. In Air Traffic Control (ATC), to facilitate implementations of new efficient operational concepts, accurate modeling and estimation of aircraft trajectories are needed. In ATC, an aircraft's trajectory can be divided into a number of flight modes. Furthermore, as the aircraft is required to follow a given flight plan or clearance, its flight mode transitions are dependent of its continuous state. However, the flight mode transitions are also stochastic due to navigation uncertainties or unknown pilot intents. Thus, we develop an aircraft dynamics model in ATC based on the SLHS. The SDTHE algorithm is then used in aircraft tracking applications to estimate the positions/velocities of aircraft and their flight modes accurately. Next, we develop an aircraft conformance monitoring algorithm to detect any deviations of aircraft trajectories in ATC that might compromise safety. In this application, the SLHS
Homodyne detection of holographic memory systems
Urness, Adam C.; Wilson, William L.; Ayres, Mark R.
2014-09-01
We present a homodyne detection system implemented for a page-wise holographic memory architecture. Homodyne detection by holographic memory systems enables phase quadrature multiplexing (doubling address space), and lower exposure times (increasing read transfer rates). It also enables phase modulation, which improves signal-to-noise ratio (SNR) to further increase data capacity. We believe this is the first experimental demonstration of homodyne detection for a page-wise holographic memory system suitable for a commercial design.
Stochastic analysis of residential micro combined heat and power system
International Nuclear Information System (INIS)
Karami, H.; Sanjari, M.J.; Gooi, H.B.; Gharehpetian, G.B.; Guerrero, J.M.
2017-01-01
Highlights: • Applying colonial competitive algorithm to the problem of optimal dispatching. • Economic modeling of the residential integrated energy system. • Investigating differences of stand-alone and system-connected modes of fuel cell operation. • Considering uncertainty on the electrical load. • The effects of battery capacity and its efficiency on the system is investigated. - Abstract: In this paper the combined heat and power functionality of a fuel-cell in a residential hybrid energy system, including a battery, is studied. The demand uncertainties are modeled by investigating the stochastic load behavior by applying Monte Carlo simulation. The colonial competitive algorithm is adopted to the hybrid energy system scheduling problem and different energy resources are optimally scheduled to have optimal operating cost of hybrid energy system. In order to show the effectiveness of the colonial competitive algorithm, the results are compared with the results of the harmony search algorithm. The optimized scheduling of different energy resources is listed in an efficient look-up table for all time intervals. The effects of time of use and the battery efficiency and its size are investigated on the operating cost of the hybrid energy system. The results of this paper are expected to be used effectively in a real hybrid energy system.
Stochastic model of financial markets reproducing scaling and memory in volatility return intervals
Gontis, V.; Havlin, S.; Kononovicius, A.; Podobnik, B.; Stanley, H. E.
2016-11-01
We investigate the volatility return intervals in the NYSE and FOREX markets. We explain previous empirical findings using a model based on the interacting agent hypothesis instead of the widely-used efficient market hypothesis. We derive macroscopic equations based on the microscopic herding interactions of agents and find that they are able to reproduce various stylized facts of different markets and different assets with the same set of model parameters. We show that the power-law properties and the scaling of return intervals and other financial variables have a similar origin and could be a result of a general class of non-linear stochastic differential equations derived from a master equation of an agent system that is coupled by herding interactions. Specifically, we find that this approach enables us to recover the volatility return interval statistics as well as volatility probability and spectral densities for the NYSE and FOREX markets, for different assets, and for different time-scales. We find also that the historical S&P500 monthly series exhibits the same volatility return interval properties recovered by our proposed model. Our statistical results suggest that human herding is so strong that it persists even when other evolving fluctuations perturbate the financial system.
Classical Solutions of Path-Dependent PDEs and Functional Forward-Backward Stochastic Systems
Directory of Open Access Journals (Sweden)
Shaolin Ji
2013-01-01
Full Text Available In this paper we study the relationship between functional forward-backward stochastic systems and path-dependent PDEs. In the framework of functional Itô calculus, we introduce a path-dependent PDE and prove that its solution is uniquely determined by a functional forward-backward stochastic system.
Setting development goals using stochastic dynamical system models.
Ranganathan, Shyam; Nicolis, Stamatios C; Bali Swain, Ranjula; Sumpter, David J T
2017-01-01
The Millennium Development Goals (MDG) programme was an ambitious attempt to encourage a globalised solution to important but often-overlooked development problems. The programme led to wide-ranging development but it has also been criticised for unrealistic and arbitrary targets. In this paper, we show how country-specific development targets can be set using stochastic, dynamical system models built from historical data. In particular, we show that the MDG target of two-thirds reduction of child mortality from 1990 levels was infeasible for most countries, especially in sub-Saharan Africa. At the same time, the MDG targets were not ambitious enough for fast-developing countries such as Brazil and China. We suggest that model-based setting of country-specific targets is essential for the success of global development programmes such as the Sustainable Development Goals (SDG). This approach should provide clear, quantifiable targets for policymakers.
Online prediction and control in nonlinear stochastic systems
DEFF Research Database (Denmark)
Nielsen, Torben Skov
2002-01-01
speed and the relationship between (primarily) wind speed and wind power (the power curve). In paper G the model parameters are estimated using a RLS algorithm and any systematic time-variation of the model parameters is disregarded. Two di erent parameterizations of the power curve is considered...... are estimated using the algorithm proposed in paper C. The power curve and the diurnal variation of wind speed is estimated separately using the local polynomial regression procedure described in paper A . In paper J the parameters of the prediction model is assumed to be smooth functions of wind direction (and......The present thesis consists of a summary report and ten research papers. The subject of the thesis is on-line prediction and control of non-linear and non-stationary systems based on stochastic modelling. The thesis consists of three parts where the rst part deals with on-line estimation in linear...
Stochastic Petri net analysis of a replicated file system
Bechta Dugan, Joanne; Ciardo, Gianfranco
1989-01-01
A stochastic Petri-net model of a replicated file system is presented for a distributed environment where replicated files reside on different hosts and a voting algorithm is used to maintain consistency. Witnesses, which simply record the status of the file but contain no data, can be used in addition to or in place of files to reduce overhead. A model sufficiently detailed to include file status (current or out-of-date), as well as failure and repair of hosts where copies or witnesses reside, is presented. The number of copies and witnesses is a parameter of the model. Two different majority protocols are examined, one where a majority of all copies and witnesses is necessary to form a quorum, and the other where only a majority of the copies and witnesses on operational hosts is needed. The latter, known as adaptive voting, is shown to increase file availability in most cases.
Distinguishing signatures of determinism and stochasticity in spiking complex systems
Aragoneses, Andrés; Rubido, Nicolás; Tiana-Alsina, Jordi; Torrent, M. C.; Masoller, Cristina
2013-01-01
We describe a method to infer signatures of determinism and stochasticity in the sequence of apparently random intensity dropouts emitted by a semiconductor laser with optical feedback. The method uses ordinal time-series analysis to classify experimental data of inter-dropout-intervals (IDIs) in two categories that display statistically significant different features. Despite the apparent randomness of the dropout events, one IDI category is consistent with waiting times in a resting state until noise triggers a dropout, and the other is consistent with dropouts occurring during the return to the resting state, which have a clear deterministic component. The method we describe can be a powerful tool for inferring signatures of determinism in the dynamics of complex systems in noisy environments, at an event-level description of their dynamics.
Computing the optimal path in stochastic dynamical systems
International Nuclear Information System (INIS)
Bauver, Martha; Forgoston, Eric; Billings, Lora
2016-01-01
In stochastic systems, one is often interested in finding the optimal path that maximizes the probability of escape from a metastable state or of switching between metastable states. Even for simple systems, it may be impossible to find an analytic form of the optimal path, and in high-dimensional systems, this is almost always the case. In this article, we formulate a constructive methodology that is used to compute the optimal path numerically. The method utilizes finite-time Lyapunov exponents, statistical selection criteria, and a Newton-based iterative minimizing scheme. The method is applied to four examples. The first example is a two-dimensional system that describes a single population with internal noise. This model has an analytical solution for the optimal path. The numerical solution found using our computational method agrees well with the analytical result. The second example is a more complicated four-dimensional system where our numerical method must be used to find the optimal path. The third example, although a seemingly simple two-dimensional system, demonstrates the success of our method in finding the optimal path where other numerical methods are known to fail. In the fourth example, the optimal path lies in six-dimensional space and demonstrates the power of our method in computing paths in higher-dimensional spaces.
Systemic risk in dynamical networks with stochastic failure criterion
Podobnik, B.; Horvatic, D.; Bertella, M. A.; Feng, L.; Huang, X.; Li, B.
2014-06-01
Complex non-linear interactions between banks and assets we model by two time-dependent Erdős-Renyi network models where each node, representing a bank, can invest either to a single asset (model I) or multiple assets (model II). We use a dynamical network approach to evaluate the collective financial failure —systemic risk— quantified by the fraction of active nodes. The systemic risk can be calculated over any future time period, divided into sub-periods, where within each sub-period banks may contiguously fail due to links to either i) assets or ii) other banks, controlled by two parameters, probability of internal failure p and threshold Th (“solvency” parameter). The systemic risk decreases with the average network degree faster when all assets are equally distributed across banks than if assets are randomly distributed. The more inactive banks each bank can sustain (smaller Th), the smaller the systemic risk —for some Th values in I we report a discontinuity in systemic risk. When contiguous spreading becomes stochastic ii) controlled by probability p2 —a condition for the bank to be solvent (active) is stochastic— the systemic risk decreases with decreasing p2. We analyse the asset allocation for the U.S. banks.
Modeling and Properties of Nonlinear Stochastic Dynamical System of Continuous Culture
Wang, Lei; Feng, Enmin; Ye, Jianxiong; Xiu, Zhilong
The stochastic counterpart to the deterministic description of continuous fermentation with ordinary differential equation is investigated in the process of glycerol bio-dissimilation to 1,3-propanediol by Klebsiella pneumoniae. We briefly discuss the continuous fermentation process driven by three-dimensional Brownian motion and Lipschitz coefficients, which is suitable for the factual fermentation. Subsequently, we study the existence and uniqueness of solutions for the stochastic system as well as the boundedness of the Two-order Moment and the Markov property of the solution. Finally stochastic simulation is carried out under the Stochastic Euler-Maruyama method.
A stochastic approach for automatic generation of urban drainage systems.
Möderl, M; Butler, D; Rauch, W
2009-01-01
Typically, performance evaluation of new developed methodologies is based on one or more case studies. The investigation of multiple real world case studies is tedious and time consuming. Moreover extrapolating conclusions from individual investigations to a general basis is arguable and sometimes even wrong. In this article a stochastic approach is presented to evaluate new developed methodologies on a broader basis. For the approach the Matlab-tool "Case Study Generator" is developed which generates a variety of different virtual urban drainage systems automatically using boundary conditions e.g. length of urban drainage system, slope of catchment surface, etc. as input. The layout of the sewer system is based on an adapted Galton-Watson branching process. The sub catchments are allocated considering a digital terrain model. Sewer system components are designed according to standard values. In total, 10,000 different virtual case studies of urban drainage system are generated and simulated. Consequently, simulation results are evaluated using a performance indicator for surface flooding. Comparison between results of the virtual and two real world case studies indicates the promise of the method. The novelty of the approach is that it is possible to get more general conclusions in contrast to traditional evaluations with few case studies.
A Survey of Phase Change Memory Systems
Institute of Scientific and Technical Information of China (English)
夏飞; 蒋德钧; 熊劲; 孙凝晖
2015-01-01
As the scaling of applications increases, the demand of main memory capacity increases in order to serve large working set. It is diﬃcult for DRAM (dynamic random access memory) based memory system to satisfy the memory capacity requirement due to its limited scalability and high energy consumption. Compared to DRAM, PCM (phase change memory) has better scalability, lower energy leakage, and non-volatility. PCM memory systems have become a hot topic of academic and industrial research. However, PCM technology has the following three drawbacks: long write latency, limited write endurance, and high write energy, which raises challenges to its adoption in practice. This paper surveys architectural research work to optimize PCM memory systems. First, this paper introduces the background of PCM. Then, it surveys research efforts on PCM memory systems in performance optimization, lifetime improving, and energy saving in detail, respectively. This paper also compares and summarizes these techniques from multiple dimensions. Finally, it concludes these optimization techniques and discusses possible research directions of PCM memory systems in future.
Stochastic Resource Allocation for Energy-Constrained Systems
Directory of Open Access Journals (Sweden)
Sachs DanielGrobe
2009-01-01
Full Text Available Battery-powered wireless systems running media applications have tight constraints on energy, CPU, and network capacity, and therefore require the careful allocation of these limited resources to maximize the system's performance while avoiding resource overruns. Usually, resource-allocation problems are solved using standard knapsack-solving techniques. However, when allocating conservable resources like energy (which unlike CPU and network remain available for later use if they are not used immediately knapsack solutions suffer from excessive computational complexity, leading to the use of suboptimal heuristics. We show that use of Lagrangian optimization provides a fast, elegant, and, for convex problems, optimal solution to the allocation of energy across applications as they enter and leave the system, even if the exact sequence and timing of their entrances and exits is not known. This permits significant increases in achieved utility compared to heuristics in common use. As our framework requires only a stochastic description of future workloads, and not a full schedule, we also significantly expand the scope of systems that can be optimized.
Directory of Open Access Journals (Sweden)
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.
Semi-analytical stochastic analysis of the generalized van der Pol system
Czech Academy of Sciences Publication Activity Database
Náprstek, Jiří; Fischer, Cyril
(2018) ISSN 1802-680X R&D Projects: GA ČR(CZ) GA15-01035S Institutional support: RVO:68378297 Keywords : stochastic stability * generalized van der Pol system * stochastic averaging * limit cycles Subject RIV: JM - Building Engineering OBOR OECD: Construction engineering, Municipal and structural engineering https://www.kme.zcu.cz/acm/acm/article/view/407
STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB
Klingbeil, G.
2011-02-25
Motivation: The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB. Results: The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user\\'s models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. © The Author 2011. Published by Oxford University Press. All rights reserved.
Stochastic thermodynamics and entropy production of chemical reaction systems
Tomé, Tânia; de Oliveira, Mário J.
2018-06-01
We investigate the nonequilibrium stationary states of systems consisting of chemical reactions among molecules of several chemical species. To this end, we introduce and develop a stochastic formulation of nonequilibrium thermodynamics of chemical reaction systems based on a master equation defined on the space of microscopic chemical states and on appropriate definitions of entropy and entropy production. The system is in contact with a heat reservoir and is placed out of equilibrium by the contact with particle reservoirs. In our approach, the fluxes of various types, such as the heat and particle fluxes, play a fundamental role in characterizing the nonequilibrium chemical state. We show that the rate of entropy production in the stationary nonequilibrium state is a bilinear form in the affinities and the fluxes of reaction, which are expressed in terms of rate constants and transition rates, respectively. We also show how the description in terms of microscopic states can be reduced to a description in terms of the numbers of particles of each species, from which follows the chemical master equation. As an example, we calculate the rate of entropy production of the first and second Schlögl reaction models.
A Buildings Module for the Stochastic Energy Deployment System
Energy Technology Data Exchange (ETDEWEB)
Lacommare, Kristina S H; Marnay, Chris; Stadler, Michael; Borgeson, Sam; Coffey, Brian; Komiyama, Ryoichi; Lai, Judy
2008-05-15
The U.S. Department of Energy (USDOE) is building a new long-range (to 2050) forecasting model for use in budgetary and management applications called the Stochastic Energy Deployment System (SEDS), which explicitly incorporates uncertainty through its development within the Analytica(R) platform of Lumina Decision Systems. SEDS is designed to be a fast running (a few minutes), user-friendly model that analysts can readily run and modify in its entirety through a visual programming interface. Lawrence Berkeley National Laboratory is responsible for implementing the SEDS Buildings Module. The initial Lite version of the module is complete and integrated with a shared code library for modeling demand-side technology choice developed by the National Renewable Energy Laboratory (NREL) and Lumina. The module covers both commercial and residential buildings at the U.S. national level using an econometric forecast of floorspace requirement and a model of building stock turnover as the basis for forecasting overall demand for building services. Although the module is fundamentally an engineering-economic model with technology adoption decisions based on cost and energy performance characteristics of competing technologies, it differs from standard energy forecasting models by including considerations of passive building systems, interactions between technologies (such as internal heat gains), and on-site power generation.
OPTIMAL TRAINING POLICY FOR PROMOTION - STOCHASTIC MODELS OF MANPOWER SYSTEMS
Directory of Open Access Journals (Sweden)
V.S.S. Yadavalli
2012-01-01
Full Text Available In this paper, the optimal planning of manpower training programmes in a manpower system with two grades is discussed. The planning of manpower training within a given organization involves a trade-off between training costs and expected return. These planning problems are examined through models that reflect the random nature of manpower movement in two grades. To be specific, the system consists of two grades, grade 1 and grade 2. Any number of persons in grade 2 can be sent for training and after the completion of training, they will stay in grade 2 and will be given promotion as and when vacancies arise in grade 1. Vacancies arise in grade 1 only by wastage. A person in grade 1 can leave the system with probability p. Vacancies are filled with persons in grade 2 who have completed the training. It is assumed that there is a perfect passing rate and that the sizes of both grades are fixed. Assuming that the planning horizon is finite and is T, the underlying stochastic process is identified as a finite state Markov chain and using dynamic programming, a policy is evolved to determine how many persons should be sent for training at any time k so as to minimize the total expected cost for the entire planning period T.
Fast cooling for a system of stochastic oscillators
Energy Technology Data Exchange (ETDEWEB)
Chen, Yongxin, E-mail: chen2468@umn.edu; Georgiou, Tryphon T., E-mail: tryphon@umn.edu [Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, Minnesota 55455 (United States); Pavon, Michele, E-mail: pavon@math.unipd.it [Dipartimento di Matematica, Università di Padova, Via Trieste 63, 35121 Padova (Italy)
2015-11-15
We study feedback control of coupled nonlinear stochastic oscillators in a force field. We first consider the problem of asymptotically driving the system to a desired steady state corresponding to reduced thermal noise. Among the feedback controls achieving the desired asymptotic transfer, we find that the most efficient one from an energy point of view is characterized by time-reversibility. We also extend the theory of Schrödinger bridges to this model, thereby steering the system in finite time and with minimum effort to a target steady-state distribution. The system can then be maintained in this state through the optimal steady-state feedback control. The solution, in the finite-horizon case, involves a space-time harmonic function φ, and −logφ plays the role of an artificial, time-varying potential in which the desired evolution occurs. This framework appears extremely general and flexible and can be viewed as a considerable generalization of existing active control strategies such as macromolecular cooling. In the case of a quadratic potential, the results assume a form particularly attractive from the algorithmic viewpoint as the optimal control can be computed via deterministic matricial differential equations. An example involving inertial particles illustrates both transient and steady state optimal feedback control.
Stochastic and Macroscopic Thermodynamics of Strongly Coupled Systems
Directory of Open Access Journals (Sweden)
Christopher Jarzynski
2017-01-01
Full Text Available We develop a thermodynamic framework that describes a classical system of interest S that is strongly coupled to its thermal environment E. Within this framework, seven key thermodynamic quantities—internal energy, entropy, volume, enthalpy, Gibbs free energy, heat, and work—are defined microscopically. These quantities obey thermodynamic relations including both the first and second law, and they satisfy nonequilibrium fluctuation theorems. We additionally impose a macroscopic consistency condition: When S is large, the quantities defined within our framework scale up to their macroscopic counterparts. By satisfying this condition, we demonstrate that a unifying framework can be developed, which encompasses both stochastic thermodynamics at one end, and macroscopic thermodynamics at the other. A central element in our approach is a thermodynamic definition of the volume of the system of interest, which converges to the usual geometric definition when S is large. We also sketch an alternative framework that satisfies the same consistency conditions. The dynamics of the system and environment are modeled using Hamilton’s equations in the full phase space.
Stochastic Modeling and Analysis of Power System with Renewable Generation
DEFF Research Database (Denmark)
Chen, Peiyuan
Unlike traditional fossil-fuel based power generation, renewable generation such as wind power relies on uncontrollable prime sources such as wind speed. Wind speed varies stochastically, which to a large extent determines the stochastic behavior of power generation from wind farms...... that such a stochastic model can be used to simulate the effect of load management on the load duration curve. As CHP units are turned on and off by regulating power, CHP generation has discrete output and thus can be modeled by a transition matrix based discrete Markov chain. As the CHP generation has a strong diurnal...
Control of stochastic resonance in bistable systems by using periodic signals
International Nuclear Information System (INIS)
Min, Lin; Li-Min, Fang; Yong-Jun, Zheng
2009-01-01
According to the characteristic structure of double wells in bistable systems, this paper analyses stochastic fluctuations in the single potential well and probability transitions between the two potential wells and proposes a method of controlling stochastic resonance by using a periodic signal. Results of theoretical analysis and numerical simulation show that the phenomenon of stochastic resonance happens when the time scales of the periodic signal and the noise-induced probability transitions between the two potential wells achieve stochastic synchronization. By adding a bistable system with a controllable periodic signal, fluctuations in the single potential well can be effectively controlled, thus affecting the probability transitions between the two potential wells. In this way, an effective control can be achieved which allows one to either enhance or realize stochastic resonance
International Nuclear Information System (INIS)
Hong, H.P.; Zhou, W.; Zhang, S.; Ye, W.
2014-01-01
Components in engineered systems are subjected to stochastic deterioration due to the operating environmental conditions, and the uncertainty in material properties. The components need to be inspected and possibly replaced based on preventive or failure replacement criteria to provide the intended and safe operation of the system. In the present study, we investigate the influence of dependent stochastic degradation of multiple components on the optimal maintenance decisions. We use copula to model the dependent stochastic degradation of components, and formulate the optimal decision problem based on the minimum expected cost rule and the stochastic dominance rules. The latter is used to cope with decision maker's risk attitude. We illustrate the developed probabilistic analysis approach and the influence of the dependency of the stochastic degradation on the preferred decisions through numerical examples
Stochastic assessment of investment efficiency in a power system
International Nuclear Information System (INIS)
Davidov, Sreten; Pantoš, Miloš
2017-01-01
The assessment of investment efficiency plays a critical role in investment prioritization in the context of electrical network expansion planning. Hence, this paper proposes new criteria for the cost-efficiency investment applied in the investment ranking process in electrical network planning, based on the assessment of the new investment candidates impact on active-power losses, bus voltages and line loadings in the network. These three general criteria are chosen due to their strong economic influence when the active-power losses and line loadings are considered and due to their significant impact on quality of supply allowed for the voltage profile. Electrical network reliability of supply is not addressed, since, this criterion has already been extensively applied in other solutions regarding investment efficiency assessment. The proposed ranking procedure involves a stochastic approach applying the Monte Carlo method in the scenario preparation. The number of scenarios is further reduced by the K-MEANS procedure in order to speed up the investment efficiency assessment. The proposed ranking procedure is tested using the standard New England test system. The results show that based on the newly involved investment assessment criteria indices, system operators will obtain a prioritized list of investments that will prevent excessive and economically wasteful spending. - Highlights: • Active-Power Loss Investment Efficiency Index LEI. • Voltage Profile Investment Efficiency Index VEI. • Active-Power Flow Loading Mitigation Investment Efficiency Index PEI. • Optimization model for network expansion planning with new indices.
Stochastic theory for classical and quantum mechanical systems
International Nuclear Information System (INIS)
Pena, L. de la; Cetto, A.M.
1975-01-01
From first principles a theory of stochastic processes in configuration space is formulated. The fundamental equations of the theory are an equation of motion which generalizes Newton's second law and an equation which expresses the condition of conservation of matter. Two types of stochastic motion are possible, both described by the same general equations, but leading in one case to classical Brownian motion behavior and in the other to quantum mechanical behavior. The Schroedinger equation, which is derived with no further assumption, is thus shown to describe a specific stochastic process. It is explicitly shown that only in the quantum mechanical process does the superposition of probability amplitudes give rise to interference phenomena; moreover, the presence of dissipative forces in the Brownian motion equations invalidates the superposition principle. At no point are any special assumptions made concerning the physical nature of the underlying stochastic medium, although some suggestions are discussed in the last section
MONTE CARLO SIMULATION OF MULTIFOCAL STOCHASTIC SCANNING SYSTEM
Directory of Open Access Journals (Sweden)
LIXIN LIU
2014-01-01
Full Text Available Multifocal multiphoton microscopy (MMM has greatly improved the utilization of excitation light and imaging speed due to parallel multiphoton excitation of the samples and simultaneous detection of the signals, which allows it to perform three-dimensional fast fluorescence imaging. Stochastic scanning can provide continuous, uniform and high-speed excitation of the sample, which makes it a suitable scanning scheme for MMM. In this paper, the graphical programming language — LabVIEW is used to achieve stochastic scanning of the two-dimensional galvo scanners by using white noise signals to control the x and y mirrors independently. Moreover, the stochastic scanning process is simulated by using Monte Carlo method. Our results show that MMM can avoid oversampling or subsampling in the scanning area and meet the requirements of uniform sampling by stochastically scanning the individual units of the N × N foci array. Therefore, continuous and uniform scanning in the whole field of view is implemented.
Dynamic memory management for embedded systems
Atienza Alonso, David; Poucet, Christophe; Peón-Quirós, Miguel; Bartzas, Alexandros; Catthoor, Francky; Soudris, Dimitrios
2015-01-01
This book provides a systematic and unified methodology, including basic principles and reusable processes, for dynamic memory management (DMM) in embedded systems. The authors describe in detail how to design and optimize the use of dynamic memory in modern, multimedia and network applications, targeting the latest generation of portable embedded systems, such as smartphones. Coverage includes a variety of design and optimization topics in electronic design automation of DMM, from high-level software optimization to microarchitecture-level hardware support. The authors describe the design of multi-layer dynamic data structures for the final memory hierarchy layers of the target portable embedded systems and how to create a low-fragmentation, cost-efficient, dynamic memory management subsystem out of configurable components for the particular memory allocation and de-allocation patterns for each type of application. The design methodology described in this book is based on propagating constraints among de...
Mean Square Exponential Stability of Stochastic Switched System with Interval Time-Varying Delays
Directory of Open Access Journals (Sweden)
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.
Modeling and Analysis of Networked Control Systems Using Stochastic Hybrid Systems
2014-09-03
The stability notions considered can be classified in two broad categories: bounds on the probability that the state of the system “ misbehaves ” or...alternative types of condi- tions: One is focused on making sure that the probability that the stochastic process “ misbehaves ” is very small. Such
Scheduling of Power System Cells Integrating Stochastic Power Generation
International Nuclear Information System (INIS)
Costa, L.M.
2008-12-01
Energy supply and climate change are nowadays two of the most outstanding problems which societies have to cope with under a context of increasing energy needs. Public awareness of these problems is driving political willingness to take actions for tackling them in a swift and efficient manner. Such actions mainly focus in increasing energy efficiency, in decreasing dependence on fossil fuels, and in reducing greenhouse gas emissions. In this context, power systems are undergoing important changes in the way they are planned and managed. On the one hand, vertically integrated structures are being replaced by market structures in which power systems are un-bundled. On the other, power systems that once relied on large power generation facilities are witnessing the end of these facilities' life-cycle and, consequently, their decommissioning. The role of distributed energy resources such as wind and solar power generators is becoming increasingly important in this context. However, the large-scale integration of such type of generation presents many challenges due, for instance, to the uncertainty associated to the variability of their production. Nevertheless, advanced forecasting tools may be combined with more controllable elements such as energy storage devices, gas turbines, and controllable loads to form systems that aim to reduce the impacts that may be caused by these uncertainties. This thesis addresses the management under market conditions of these types of systems that act like independent societies and which are herewith named power system cells. From the available literature, a unified view of power system scheduling problems is also proposed as a first step for managing sets of power system cells in a multi-cell management framework. Then, methodologies for performing the optimal day-ahead scheduling of single power system cells are proposed, discussed and evaluated under both a deterministic and a stochastic framework that directly integrates the
Embedded System Synthesis under Memory Constraints
DEFF Research Database (Denmark)
Madsen, Jan; Bjørn-Jørgensen, Peter
1999-01-01
This paper presents a genetic algorithm to solve the system synthesis problem of mapping a time constrained single-rate system specification onto a given heterogeneous architecture which may contain irregular interconnection structures. The synthesis is performed under memory constraints, that is......, the algorithm takes into account the memory size of processors and the size of interface buffers of communication links, and in particular the complicated interplay of these. The presented algorithm is implemented as part of the LY-COS cosynthesis system....
Distributed Consensus of Stochastic Delayed Multi-agent Systems Under Asynchronous Switching.
Wu, Xiaotai; Tang, Yang; Cao, Jinde; Zhang, Wenbing
2016-08-01
In this paper, the distributed exponential consensus of stochastic delayed multi-agent systems with nonlinear dynamics is investigated under asynchronous switching. The asynchronous switching considered here is to account for the time of identifying the active modes of multi-agent systems. After receipt of confirmation of mode's switching, the matched controller can be applied, which means that the switching time of the matched controller in each node usually lags behind that of system switching. In order to handle the coexistence of switched signals and stochastic disturbances, a comparison principle of stochastic switched delayed systems is first proved. By means of this extended comparison principle, several easy to verified conditions for the existence of an asynchronously switched distributed controller are derived such that stochastic delayed multi-agent systems with asynchronous switching and nonlinear dynamics can achieve global exponential consensus. Two examples are given to illustrate the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
S. Aberkane
2007-01-01
Full Text Available This paper deals with dynamic output feedback control of continuous-time active fault tolerant control systems with Markovian parameters (AFTCSMP and state-dependent noise. The main contribution is to formulate conditions for multiperformance design, related to this class of stochastic hybrid systems, that take into account the problematic resulting from the fact that the controller only depends on the fault detection and isolation (FDI process. The specifications and objectives under consideration include stochastic stability, ℋ2 and ℋ∞ (or more generally, stochastic integral quadratic constraints performances. Results are formulated as matrix inequalities. The theoretical results are illustrated using a classical example from literature.
Stochastic bounded consensus of second-order multi-agent systems in noisy environment
International Nuclear Information System (INIS)
Ren Hong-Wei; Deng Fei-Qi
2017-01-01
This paper investigates the stochastic bounded consensus of leader-following second-order multi-agent systems in a noisy environment. It is assumed that each agent received the information of its neighbors corrupted by noises and time delays. Based on the graph theory, stochastic tools, and the Lyapunov function method, we derive the sufficient conditions under which the systems would reach stochastic bounded consensus in mean square with the protocol we designed. Finally, a numerical simulation is illustrated to check the effectiveness of the proposed algorithms. (paper)
Thermodynamic framework for information in nanoscale systems with memory.
Arias-Gonzalez, J Ricardo
2017-11-28
Information is represented by linear strings of symbols with memory that carry errors as a result of their stochastic nature. Proofreading and edition are assumed to improve certainty although such processes may not be effective. Here, we develop a thermodynamic theory for material chains made up of nanoscopic subunits with symbolic meaning in the presence of memory. This framework is based on the characterization of single sequences of symbols constructed under a protocol and is used to derive the behavior of ensembles of sequences similarly constructed. We then analyze the role of proofreading and edition in the presence of memory finding conditions to make revision an effective process, namely, to decrease the entropy of the chain. Finally, we apply our formalism to DNA replication and RNA transcription finding that Watson and Crick hybridization energies with which nucleotides are branched to the template strand during the copying process are optimal to regulate the fidelity in proofreading. These results are important in applications of information theory to a variety of solid-state physical systems and other biomolecular processes.
Thermodynamic framework for information in nanoscale systems with memory
Arias-Gonzalez, J. Ricardo
2017-11-01
Information is represented by linear strings of symbols with memory that carry errors as a result of their stochastic nature. Proofreading and edition are assumed to improve certainty although such processes may not be effective. Here, we develop a thermodynamic theory for material chains made up of nanoscopic subunits with symbolic meaning in the presence of memory. This framework is based on the characterization of single sequences of symbols constructed under a protocol and is used to derive the behavior of ensembles of sequences similarly constructed. We then analyze the role of proofreading and edition in the presence of memory finding conditions to make revision an effective process, namely, to decrease the entropy of the chain. Finally, we apply our formalism to DNA replication and RNA transcription finding that Watson and Crick hybridization energies with which nucleotides are branched to the template strand during the copying process are optimal to regulate the fidelity in proofreading. These results are important in applications of information theory to a variety of solid-state physical systems and other biomolecular processes.
International Nuclear Information System (INIS)
Kostic, Lj.
2003-01-01
The influence of the stochastically pulsed Poisson source to the statistical properties of the subcritical multiplying system is analyzed in the paper. It is shown a strong dependence on the pulse period and pulse width of the source (author)
A constrained approach to multiscale stochastic simulation of chemically reacting systems
Cotter, Simon L.; Zygalakis, Konstantinos C.; Kevrekidis, Ioannis G.; Erban, Radek
2011-01-01
Stochastic simulation of coupled chemical reactions is often computationally intensive, especially if a chemical system contains reactions occurring on different time scales. In this paper, we introduce a multiscale methodology suitable to address
Witte, L.
2014-06-01
To support landing site assessments for HDA-capable flight systems and to facilitate trade studies between the potential HDA architectures versus the yielded probability of safe landing a stochastic landing dispersion model has been developed.
Dynamics of the stochastic low concentration trimolecular oscillatory chemical system with jumps
Wei, Yongchang; Yang, Qigui
2018-06-01
This paper is devoted to discern long time dynamics through the stochastic low concentration trimolecular oscillatory chemical system with jumps. By Lyapunov technique, this system is proved to have a unique global positive solution, and the asymptotic stability in mean square of such model is further established. Moreover, the existence of random attractor and Lyapunov exponents are obtained for the stochastic homeomorphism flow generated by the corresponding global positive solution. And some numerical simulations are given to illustrate the presented results.
Studies to the stochastic theory of coupled reactorkinetic-thermohydraulic systems Pt. 2
International Nuclear Information System (INIS)
Mesko, L.
1983-06-01
The description is given of the noise phenomena taking place in a multivariable coupled system by a comprehensive model based on the theory of stochastic fluctuations. A comparison is made with models using transfer function formalism for systems characterized by deterministic open and closed loop signal transmission properties. The advantages of the stochastic model are illustrated by simple reactor dynamical examples having diagnostical importance. (author)
Hu, Jun; Gao, Huijun
2014-01-01
This monograph introduces methods for handling filtering and control problems in nonlinear stochastic systems arising from network-induced phenomena consequent on limited communication capacity. Such phenomena include communication delay, packet dropout, signal quantization or saturation, randomly occurring nonlinearities and randomly occurring uncertainties.The text is self-contained, beginning with an introduction to nonlinear stochastic systems, network-induced phenomena and filtering and control, moving through a collection of the latest research results which focuses on the three aspects
Composite system reliability evaluation by stochastic calculation of system operation
Energy Technology Data Exchange (ETDEWEB)
Haubrick, H -J; Hinz, H -J; Landeck, E [Dept. of Power Systems and Power Economics (Germany)
1994-12-31
This report describes a new developed probabilistic approach for steady-state composite system reliability evaluation and its exemplary application to a bulk power test system. The new computer program called PHOENIX takes into consideration transmission limitations, outages of lines and power stations and, as a central element, a highly sophisticated model to the dispatcher performing remedial actions after disturbances. The kernel of the new method is a procedure for optimal power flow calculation that has been specially adapted for the use in reliability evaluations under the above mentioned conditions. (author) 11 refs., 8 figs., 1 tab.
Stochastic programming and market equilibrium analysis of microgrids energy management systems
International Nuclear Information System (INIS)
Hu, Ming-Che; Lu, Su-Ying; Chen, Yen-Haw
2016-01-01
Microgrids facilitate optimum utilization of distributed renewable energy, provides better local energy supply, and reduces transmission loss and greenhouse gas emission. Because the uncertainty in energy demand affects the energy demand and supply system, the aim of this research is to develop a stochastic optimization and its market equilibrium for microgrids in the electricity market. Therefore, a two-stage stochastic programming model for microgrids and the market competition model are derived in this paper. In the stochastic model, energy demand and supply uncertainties are considered. Furthermore, a case study of the stochastic model is conducted to simulate the uncertainties on the INER microgrids in Taiwanese market. The optimal investment of the generators and batteries installation and operating strategies are determined under energy demand and supply uncertainties for the INER microgrids. The results show optimal investment and operating strategies for the current INER microgrids are also determined by the proposed two-stage stochastic model in the market. In addition, trade-off between the battery capacity and microgrids performance is investigated. Battery usage and power trading between the microgrids and main grid systems are the functions of battery capacity. - Highlights: • A two-stage stochastic programming model is developed for microgrids. • Market equilibrium analysis of microgrids is conducted. • A case study of the stochastic model is conducted for INER microgrids.
On a quantum system with memory
International Nuclear Information System (INIS)
Loeffelholz, J.
1989-01-01
We consider the integro-differential equation for the classical trajectory of an oscillator coupled to another one. On the quantum level the elimination of the coordinate A of the 'unvisible' oscillator leads to an effective path integral (Χ, Ξ, μ) for the associated imaginary time stochastic process t is an element of (-∞, ∞) → x(t). We prove reflection positivity of the measure dμ ∼ F · dξ, where dξ governes the free oscillator x and F is the counterpart of Feynman's influence functional. Finally, realizing the Hamiltonian semigroup exp(-tH), t ≥ 0, in the physical Hilbert space H = L 2 (Χ, Γ, μ), where Γ is contained in or Ξ + , we try to understand what is memory. (author)
International Nuclear Information System (INIS)
Song Lina; Zhang Hongqing
2007-01-01
In this work, by means of a generalized method and symbolic computation, we extend the Jacobi elliptic function rational expansion method to uniformly construct a series of stochastic wave solutions for stochastic evolution equations. To illustrate the effectiveness of our method, we take the (2+1)-dimensional stochastic dispersive long wave system as an example. We not only have obtained some known solutions, but also have constructed some new rational formal stochastic Jacobi elliptic function solutions.
Stochastic ℋ∞ Finite-Time Control of Discrete-Time Systems with Packet Loss
Directory of Open Access Journals (Sweden)
Yingqi Zhang
2012-01-01
Full Text Available This paper investigates the stochastic finite-time stabilization and ℋ∞ control problem for one family of linear discrete-time systems over networks with packet loss, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, the dynamic model description studied is given, which, if the packet dropout is assumed to be a discrete-time homogenous Markov process, the class of discrete-time linear systems with packet loss can be regarded as Markovian jump systems. Based on Lyapunov function approach, sufficient conditions are established for the resulting closed-loop discrete-time system with Markovian jumps to be stochastic ℋ∞ finite-time boundedness and then state feedback controllers are designed to guarantee stochastic ℋ∞ finite-time stabilization of the class of stochastic systems. The stochastic ℋ∞ finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the robust stochastic stabilization of the class of linear systems with packet loss. Finally, simulation examples are presented to illustrate the validity of the developed scheme.
Wang, Zhongqiang; Ambrogio, Stefano; Balatti, Simone; Ielmini, Daniele
2014-01-01
Resistive (or memristive) switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses) with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i) the natural variability of set/reset processes in the nanoscale switch, and (ii) the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.
A Brain System for Auditory Working Memory.
Kumar, Sukhbinder; Joseph, Sabine; Gander, Phillip E; Barascud, Nicolas; Halpern, Andrea R; Griffiths, Timothy D
2016-04-20
The brain basis for auditory working memory, the process of actively maintaining sounds in memory over short periods of time, is controversial. Using functional magnetic resonance imaging in human participants, we demonstrate that the maintenance of single tones in memory is associated with activation in auditory cortex. In addition, sustained activation was observed in hippocampus and inferior frontal gyrus. Multivoxel pattern analysis showed that patterns of activity in auditory cortex and left inferior frontal gyrus distinguished the tone that was maintained in memory. Functional connectivity during maintenance was demonstrated between auditory cortex and both the hippocampus and inferior frontal cortex. The data support a system for auditory working memory based on the maintenance of sound-specific representations in auditory cortex by projections from higher-order areas, including the hippocampus and frontal cortex. In this work, we demonstrate a system for maintaining sound in working memory based on activity in auditory cortex, hippocampus, and frontal cortex, and functional connectivity among them. Specifically, our work makes three advances from the previous work. First, we robustly demonstrate hippocampal involvement in all phases of auditory working memory (encoding, maintenance, and retrieval): the role of hippocampus in working memory is controversial. Second, using a pattern classification technique, we show that activity in the auditory cortex and inferior frontal gyrus is specific to the maintained tones in working memory. Third, we show long-range connectivity of auditory cortex to hippocampus and frontal cortex, which may be responsible for keeping such representations active during working memory maintenance. Copyright © 2016 Kumar et al.
Optically levitated nanoparticle as a model system for stochastic bistable dynamics.
Ricci, F; Rica, R A; Spasenović, M; Gieseler, J; Rondin, L; Novotny, L; Quidant, R
2017-05-09
Nano-mechanical resonators have gained an increasing importance in nanotechnology owing to their contributions to both fundamental and applied science. Yet, their small dimensions and mass raises some challenges as their dynamics gets dominated by nonlinearities that degrade their performance, for instance in sensing applications. Here, we report on the precise control of the nonlinear and stochastic bistable dynamics of a levitated nanoparticle in high vacuum. We demonstrate how it can lead to efficient signal amplification schemes, including stochastic resonance. This work contributes to showing the use of levitated nanoparticles as a model system for stochastic bistable dynamics, with applications to a wide variety of fields.
Holographic associative memories in document retrieval systems
International Nuclear Information System (INIS)
Becker, P.J.; Bolle, H.; Keller, A.; Kistner, W.; Riecke, W.D.; Wagner, U.
1979-03-01
The objective of this work was the implementation of a holographic memory with associative readout for a document retrieval system. Taking advantage of the favourable properties of holography - associative readout of the memory, parallel processing in the response store - may give shorter response times than sequentially organized data memories. Such a system may also operate in the interactive mode including chain associations. In order to avoid technological difficulties, the experimental setup made use of commercially available components only. As a result an improved holographic structure is proposed which uses volume holograms in photorefractive crystals as storage device. In two chapters of appendix we give a review of the state of the art of electrooptic devices for coherent optical data processing and of competing technologies (semiconductor associative memories and associative program systems). (orig.) [de
Impulse: Memory System Support for Scientific Applications
Directory of Open Access Journals (Sweden)
John B. Carter
1999-01-01
Full Text Available Impulse is a new memory system architecture that adds two important features to a traditional memory controller. First, Impulse supports application‐specific optimizations through configurable physical address remapping. By remapping physical addresses, applications control how their data is accessed and cached, improving their cache and bus utilization. Second, Impulse supports prefetching at the memory controller, which can hide much of the latency of DRAM accesses. Because it requires no modification to processor, cache, or bus designs, Impulse can be adopted in conventional systems. In this paper we describe the design of the Impulse architecture, and show how an Impulse memory system can improve the performance of memory‐bound scientific applications. For instance, Impulse decreases the running time of the NAS conjugate gradient benchmark by 67%. We expect that Impulse will also benefit regularly strided, memory‐bound applications of commercial importance, such as database and multimedia programs.
Speeding up stochastic analysis of bulk water supply systems using ...
African Journals Online (AJOL)
2013-10-22
Oct 22, 2013 ... It is possible to analyse the reliability of municipal storage tanks through stochastic analysis, in which the user demand, fire water demand and pipe failures are simulated using Monte Carlo analysis. While this technique could in principle be used to find the optimal size of a municipal storage tank, ...
Speeding up stochastic analysis of bulk water supply systems using ...
African Journals Online (AJOL)
It is possible to analyse the reliability of municipal storage tanks through stochastic analysis, in which the user demand, fire water demand and pipe failures are simulated using Monte Carlo analysis. While this technique could in principle be used to find the optimal size of a municipal storage tank, in practice the high ...
Memory effects for a stochastic fractional oscillator in a magnetic field
Mankin, Romi; Laas, Katrin; Laas, Tõnu; Paekivi, Sander
2018-01-01
The problem of random motion of harmonically trapped charged particles in a constant external magnetic field is studied. A generalized three-dimensional Langevin equation with a power-law memory kernel is used to model the interaction of Brownian particles with the complex structure of viscoelastic media (e.g., dusty plasmas). The influence of a fluctuating environment is modeled by an additive fractional Gaussian noise. In the long-time limit the exact expressions of the first-order and second-order moments of the fluctuating position for the Brownian particle subjected to an external periodic force in the plane perpendicular to the magnetic field have been calculated. Also, the particle's angular momentum is found. It is shown that an interplay of external periodic forcing, memory, and colored noise can generate a variety of cooperation effects, such as memory-induced sign reversals of the angular momentum, multiresonance versus Larmor frequency, and memory-induced particle confinement in the absence of an external trapping field. Particularly in the case without external trapping, if the memory exponent is lower than a critical value, we find a resonancelike behavior of the anisotropy in the particle position distribution versus the driving frequency, implying that it can be efficiently excited by an oscillating electric field. Similarities and differences between the behaviors of the models with internal and external noises are also discussed.
MEMORY SYSTEMS AND THE ADDICTED BRAIN
Directory of Open Access Journals (Sweden)
Jarid eGoodman
2016-02-01
Full Text Available The view that anatomically distinct memory systems differentially contribute to the development of drug addiction and relapse has received extensive support. The present brief review revisits this hypothesis as it was originally proposed twenty years ago (White, 1996 and highlights several recent developments. Extensive research employing a variety of animal learning paradigms indicates that dissociable neural systems mediate distinct types of learning and memory. Each memory system potentially contributes unique components to the learned behavior supporting drug addiction and relapse. In particular, the shift from recreational drug use to compulsive drug abuse may reflect a neuroanatomical shift from cognitive control of behavior mediated by the hippocampus/dorsomedial striatum toward habitual control of behavior mediated by the dorsolateral striatum (DLS. In addition, stress/anxiety may constitute a cofactor that facilitates DLS-dependent memory, and this may serve as a neurobehavioral mechanism underlying the increased drug use and relapse in humans following stressful life events. Evidence supporting the multiple systems view of drug addiction comes predominantly from studies of learning and memory that have employed as reinforcers addictive substances often considered within the context of drug addiction research, including cocaine, alcohol, and amphetamines. In addition, recent evidence suggests that the memory systems approach may also be helpful for understanding topical sources of addiction that reflect emerging health concerns, including marijuana use, high-fat diet, and video game playing.
Directory of Open Access Journals (Sweden)
Dongping Wei
2015-01-01
Full Text Available Management of ecological tourism in protected areas faces many challenges, with visitation-related resource degradations and cultural impacts being two of them. To address those issues, several strategies including regulations, site managements, and visitor education programs have been commonly used in China and other countries. This paper presents a multiparameter stochastic differential equation model of an Ecological Tourism System to study how the populations of stakeholders vary in a finite time. The solution of Ordinary Differential Equation of Ecological Tourism System reveals that the system collapses when there is a lack of visitor educational intervention. Hence, the Stochastic Dynamic of Ecological Tourism System is introduced to suppress the explosion of the system. But the simulation results of the Stochastic Dynamic of Ecological Tourism System show that the system is still unstable and chaos in some small time interval. The Multiparameters Stochastic Dynamics of Ecological Tourism System is proposed to improve the performance in this paper. The Multiparameters Stochastic Dynamics of Ecological Tourism System not only suppresses the explosion of the system in a finite time, but also keeps the populations of stakeholders in an acceptable level. In conclusion, the Ecological Tourism System develops steadily and sustainably when land managers employ effective visitor education intervention programs to deal with recreation impacts.
On square-wave-driven stochastic resonance for energy harvesting in a bistable system
Energy Technology Data Exchange (ETDEWEB)
Su, Dongxu, E-mail: sudx@iis.u-tokyo.ac.jp [Graduate School of Engineering, The University of Tokyo, Tokyo 1538505 (Japan); Zheng, Rencheng; Nakano, Kimihiko [Institute of Industrial Science, The University of Tokyo, Tokyo 1538505 (Japan); Cartmell, Matthew P [Department of Mechanical Engineering, University of Sheffield, Sheffield S1 3JD (United Kingdom)
2014-11-15
Stochastic resonance is a physical phenomenon through which the throughput of energy within an oscillator excited by a stochastic source can be boosted by adding a small modulating excitation. This study investigates the feasibility of implementing square-wave-driven stochastic resonance to enhance energy harvesting. The motivating hypothesis was that such stochastic resonance can be efficiently realized in a bistable mechanism. However, the condition for the occurrence of stochastic resonance is conventionally defined by the Kramers rate. This definition is inadequate because of the necessity and difficulty in estimating white noise density. A bistable mechanism has been designed using an explicit analytical model which implies a new approach for achieving stochastic resonance in the paper. Experimental tests confirm that the addition of a small-scale force to the bistable system excited by a random signal apparently leads to a corresponding amplification of the response that we now term square-wave-driven stochastic resonance. The study therefore indicates that this approach may be a promising way to improve the performance of an energy harvester under certain forms of random excitation.
On square-wave-driven stochastic resonance for energy harvesting in a bistable system
International Nuclear Information System (INIS)
Su, Dongxu; Zheng, Rencheng; Nakano, Kimihiko; Cartmell, Matthew P
2014-01-01
Stochastic resonance is a physical phenomenon through which the throughput of energy within an oscillator excited by a stochastic source can be boosted by adding a small modulating excitation. This study investigates the feasibility of implementing square-wave-driven stochastic resonance to enhance energy harvesting. The motivating hypothesis was that such stochastic resonance can be efficiently realized in a bistable mechanism. However, the condition for the occurrence of stochastic resonance is conventionally defined by the Kramers rate. This definition is inadequate because of the necessity and difficulty in estimating white noise density. A bistable mechanism has been designed using an explicit analytical model which implies a new approach for achieving stochastic resonance in the paper. Experimental tests confirm that the addition of a small-scale force to the bistable system excited by a random signal apparently leads to a corresponding amplification of the response that we now term square-wave-driven stochastic resonance. The study therefore indicates that this approach may be a promising way to improve the performance of an energy harvester under certain forms of random excitation
Rovee-Collier, Carolyn; Cuevas, Kimberly
2009-01-01
How the memory of adults evolves from the memory abilities of infants is a central problem in cognitive development. The popular solution holds that the multiple memory systems of adults mature at different rates during infancy. The "early-maturing system" (implicit or nondeclarative memory) functions automatically from birth, whereas the…
Event-Triggered Faults Tolerant Control for Stochastic Systems with Time Delays
Directory of Open Access Journals (Sweden)
Ling Huang
2016-01-01
Full Text Available This paper is concerned with the state-feedback controller design for stochastic networked control systems (NCSs with random actuator failures and transmission delays. Firstly, an event-triggered scheme is introduced to optimize the performance of the stochastic NCSs. Secondly, stochastic NCSs under event-triggered scheme are modeled as stochastic time-delay systems. Thirdly, some less conservative delay-dependent stability criteria in terms of linear matrix inequalities for the codesign of both the controller gain and the trigger parameters are obtained by using delay-decomposition technique and convex combination approach. Finally, a numerical example is provided to show the less sampled data transmission and less conservatism of the proposed theory.
Bidirectional Frontoparietal Oscillatory Systems Support Working Memory.
Johnson, Elizabeth L; Dewar, Callum D; Solbakk, Anne-Kristin; Endestad, Tor; Meling, Torstein R; Knight, Robert T
2017-06-19
The ability to represent and select information in working memory provides the neurobiological infrastructure for human cognition. For 80 years, dominant views of working memory have focused on the key role of prefrontal cortex (PFC) [1-8]. However, more recent work has implicated posterior cortical regions [9-12], suggesting that PFC engagement during working memory is dependent on the degree of executive demand. We provide evidence from neurological patients with discrete PFC damage that challenges the dominant models attributing working memory to PFC-dependent systems. We show that neural oscillations, which provide a mechanism for PFC to communicate with posterior cortical regions [13], independently subserve communications both to and from PFC-uncovering parallel oscillatory mechanisms for working memory. Fourteen PFC patients and 20 healthy, age-matched controls performed a working memory task where they encoded, maintained, and actively processed information about pairs of common shapes. In controls, the electroencephalogram (EEG) exhibited oscillatory activity in the low-theta range over PFC and directional connectivity from PFC to parieto-occipital regions commensurate with executive processing demands. Concurrent alpha-beta oscillations were observed over parieto-occipital regions, with directional connectivity from parieto-occipital regions to PFC, regardless of processing demands. Accuracy, PFC low-theta activity, and PFC → parieto-occipital connectivity were attenuated in patients, revealing a PFC-independent, alpha-beta system. The PFC patients still demonstrated task proficiency, which indicates that the posterior alpha-beta system provides sufficient resources for working memory. Taken together, our findings reveal neurologically dissociable PFC and parieto-occipital systems and suggest that parallel, bidirectional oscillatory systems form the basis of working memory. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
Configurable memory system and method for providing atomic counting operations in a memory device
Bellofatto, Ralph E.; Gara, Alan G.; Giampapa, Mark E.; Ohmacht, Martin
2010-09-14
A memory system and method for providing atomic memory-based counter operations to operating systems and applications that make most efficient use of counter-backing memory and virtual and physical address space, while simplifying operating system memory management, and enabling the counter-backing memory to be used for purposes other than counter-backing storage when desired. The encoding and address decoding enabled by the invention provides all this functionality through a combination of software and hardware.
Stochastic resonance in a periodic potential system under a constant force
International Nuclear Information System (INIS)
Hu Gang.
1992-10-01
An overdamped particle moving in a periodic potential, and subject to a constant force and a stochastic force (i.e., χ = -sin(2πχ) + B + Γ(t),Γ(t) is a white noise) is considered. The mobility of the particle, d /dt, is investigated. The stochastic resonance type of behaviour is revealed. The study of the SR problem can thus be extended to systems with periodic force. (author). 13 refs
Memory systems interaction in the pigeon: working and reference memory.
Roberts, William A; Strang, Caroline; Macpherson, Krista
2015-04-01
Pigeons' performance on a working memory task, symbolic delayed matching-to-sample, was used to examine the interaction between working memory and reference memory. Reference memory was established by training pigeons to discriminate between the comparison cues used in delayed matching as S+ and S- stimuli. Delayed matching retention tests then measured accuracy when working and reference memory were congruent and incongruent. In 4 experiments, it was shown that the interaction between working and reference memory is reciprocal: Strengthening either type of memory leads to a decrease in the influence of the other type of memory. A process dissociation procedure analysis of the data from Experiment 4 showed independence of working and reference memory, and a model of working memory and reference memory interaction was shown to predict the findings reported in the 4 experiments. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
Lyapunov-Based Controller for a Class of Stochastic Chaotic Systems
Directory of Open Access Journals (Sweden)
Hossein Shokouhi-Nejad
2014-01-01
Full Text Available This study presents a general control law based on Lyapunov’s direct method for a group of well-known stochastic chaotic systems. Since real chaotic systems have undesired random-like behaviors which have also been deteriorated by environmental noise, chaotic systems are modeled by exciting a deterministic chaotic system with a white noise obtained from derivative of Wiener process which eventually generates an Ito differential equation. Proposed controller not only can asymptotically stabilize these systems in mean-square sense against their undesired intrinsic properties, but also exhibits good transient response. Simulation results highlight effectiveness and feasibility of proposed controller in outperforming stochastic chaotic systems.
Fluctuations in interacting particle systems with memory
International Nuclear Information System (INIS)
Harris, Rosemary J
2015-01-01
We consider the effects of long-range temporal correlations in many-particle systems, focusing particularly on fluctuations about the typical behaviour. For a specific class of memory dependence we discuss the modification of the large deviation principle describing the probability of rare currents and show how superdiffusive behaviour can emerge. We illustrate the general framework with detailed calculations for a memory-dependent version of the totally asymmetric simple exclusion process as well as indicating connections to other recent work
Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective
Energy Technology Data Exchange (ETDEWEB)
Wang, Hong [Pacific Northwest National Laboratory (PNNL); Aziz, H M Abdul [ORNL; Young, Stan [National Renewable Energy Laboratory (NREL); Patil, Sagar [Pacific Northwest National Laboratory (PNNL)
2017-10-01
Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.
Spectral decomposition of nonlinear systems with memory
Svenkeson, Adam; Glaz, Bryan; Stanton, Samuel; West, Bruce J.
2016-02-01
We present an alternative approach to the analysis of nonlinear systems with long-term memory that is based on the Koopman operator and a Lévy transformation in time. Memory effects are considered to be the result of interactions between a system and its surrounding environment. The analysis leads to the decomposition of a nonlinear system with memory into modes whose temporal behavior is anomalous and lacks a characteristic scale. On average, the time evolution of a mode follows a Mittag-Leffler function, and the system can be described using the fractional calculus. The general theory is demonstrated on the fractional linear harmonic oscillator and the fractional nonlinear logistic equation. When analyzing data from an ill-defined (black-box) system, the spectral decomposition in terms of Mittag-Leffler functions that we propose may uncover inherent memory effects through identification of a small set of dynamically relevant structures that would otherwise be obscured by conventional spectral methods. Consequently, the theoretical concepts we present may be useful for developing more general methods for numerical modeling that are able to determine whether observables of a dynamical system are better represented by memoryless operators, or operators with long-term memory in time, when model details are unknown.
A Stochastic Maximum Principle for General Mean-Field Systems
International Nuclear Information System (INIS)
Buckdahn, Rainer; Li, Juan; Ma, Jin
2016-01-01
In this paper we study the optimal control problem for a class of general mean-field stochastic differential equations, in which the coefficients depend, nonlinearly, on both the state process as well as of its law. In particular, we assume that the control set is a general open set that is not necessary convex, and the coefficients are only continuous on the control variable without any further regularity or convexity. We validate the approach of Peng (SIAM J Control Optim 2(4):966–979, 1990) by considering the second order variational equations and the corresponding second order adjoint process in this setting, and we extend the Stochastic Maximum Principle of Buckdahn et al. (Appl Math Optim 64(2):197–216, 2011) to this general case.
Basic aspects of stochastic reliability analysis for redundancy systems
International Nuclear Information System (INIS)
Doerre, P.
1989-01-01
Much confusion has been created by trying to establish common cause failure (CCF) as an extra phenomenon which has to be treated with extra methods in reliability and data analysis. This paper takes another approach which can be roughly described by the statement that dependent failure is the basic phenomenon, while 'independent failure' refers to a special limiting case, namely the perfectly homogeneous population. This approach is motivated by examples demonstrating that common causes do not lead to dependent failure, so far as physical dependencies like shared components are excluded, and that stochastic dependencies are not related to common causes. The possibility to select more than one failure behaviour from an inhomogeneous population is identified as an additional random process which creates stochastic dependence. However, this source of randomness is usually treated in the deterministic limit, which destroys dependence and hence yields incorrect multiple failure frequencies for redundancy structures, thus creating the need for applying corrective CCF models. (author)
A Stochastic Maximum Principle for General Mean-Field Systems
Energy Technology Data Exchange (ETDEWEB)
Buckdahn, Rainer, E-mail: Rainer.Buckdahn@univ-brest.fr [Université de Bretagne-Occidentale, Département de Mathématiques (France); Li, Juan, E-mail: juanli@sdu.edu.cn [Shandong University, Weihai, School of Mathematics and Statistics (China); Ma, Jin, E-mail: jinma@usc.edu [University of Southern California, Department of Mathematics (United States)
2016-12-15
In this paper we study the optimal control problem for a class of general mean-field stochastic differential equations, in which the coefficients depend, nonlinearly, on both the state process as well as of its law. In particular, we assume that the control set is a general open set that is not necessary convex, and the coefficients are only continuous on the control variable without any further regularity or convexity. We validate the approach of Peng (SIAM J Control Optim 2(4):966–979, 1990) by considering the second order variational equations and the corresponding second order adjoint process in this setting, and we extend the Stochastic Maximum Principle of Buckdahn et al. (Appl Math Optim 64(2):197–216, 2011) to this general case.
Effects of stochastic time-delayed feedback on a dynamical system modeling a chemical oscillator
González Ochoa, Héctor O.; Perales, Gualberto Solís; Epstein, Irving R.; Femat, Ricardo
2018-05-01
We examine how stochastic time-delayed negative feedback affects the dynamical behavior of a model oscillatory reaction. We apply constant and stochastic time-delayed negative feedbacks to a point Field-Körös-Noyes photosensitive oscillator and compare their effects. Negative feedback is applied in the form of simulated inhibitory electromagnetic radiation with an intensity proportional to the concentration of oxidized light-sensitive catalyst in the oscillator. We first characterize the system under nondelayed inhibitory feedback; then we explore and compare the effects of constant (deterministic) versus stochastic time-delayed feedback. We find that the oscillatory amplitude, frequency, and waveform are essentially preserved when low-dispersion stochastic delayed feedback is used, whereas small but measurable changes appear when a large dispersion is applied.
Stochastic resonance in a time-delayed asymmetric bistable system with mixed periodic signal
International Nuclear Information System (INIS)
Yong-Feng, Guo; Wei, Xu; Liang, Wang
2010-01-01
This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker–Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time τ on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears. (general)
Microscopic description of nuclear few-body systems with the stochastic variational method
International Nuclear Information System (INIS)
Suzuki, Yasuyuki
2000-01-01
A simple gambling procedure called the stochastic variational method can be applied, together with appropriate variational trial functions, to solve a few-body system where the correlation between the constituents plays an important role in determining its structure. The usefulness of the method is tested by comparing to other accurate solutions for Coulombic systems. Examples of application shown here include few-nucleon systems interacting with realistic forces and few-cluster systems with the Pauli principle being taken into account properly. These examples confirm the power of the stochastic variational method. There still remain many problems for extending to a system consisting of more particles. (author)
Fock space, symbolic algebra, and analytical solutions for small stochastic systems.
Santos, Fernando A N; Gadêlha, Hermes; Gaffney, Eamonn A
2015-12-01
Randomness is ubiquitous in nature. From single-molecule biochemical reactions to macroscale biological systems, stochasticity permeates individual interactions and often regulates emergent properties of the system. While such systems are regularly studied from a modeling viewpoint using stochastic simulation algorithms, numerous potential analytical tools can be inherited from statistical and quantum physics, replacing randomness due to quantum fluctuations with low-copy-number stochasticity. Nevertheless, classical studies remained limited to the abstract level, demonstrating a more general applicability and equivalence between systems in physics and biology rather than exploiting the physics tools to study biological systems. Here the Fock space representation, used in quantum mechanics, is combined with the symbolic algebra of creation and annihilation operators to consider explicit solutions for the chemical master equations describing small, well-mixed, biochemical, or biological systems. This is illustrated with an exact solution for a Michaelis-Menten single enzyme interacting with limited substrate, including a consideration of very short time scales, which emphasizes when stiffness is present even for small copy numbers. Furthermore, we present a general matrix representation for Michaelis-Menten kinetics with an arbitrary number of enzymes and substrates that, following diagonalization, leads to the solution of this ubiquitous, nonlinear enzyme kinetics problem. For this, a flexible symbolic maple code is provided, demonstrating the prospective advantages of this framework compared to stochastic simulation algorithms. This further highlights the possibilities for analytically based studies of stochastic systems in biology and chemistry using tools from theoretical quantum physics.
Wei, J. Q.; Cong, Y. C.; Xiao, M. Q.
2018-05-01
As renewable energies are increasingly integrated into power systems, there is increasing interest in stochastic analysis of power systems.Better techniques should be developed to account for the uncertainty caused by penetration of renewables and consequently analyse its impacts on stochastic stability of power systems. In this paper, the Stochastic Differential Equations (SDEs) are used to represent the evolutionary behaviour of the power systems. The stationary Probability Density Function (PDF) solution to SDEs modelling power systems excited by Gaussian white noise is analysed. Subjected to such random excitation, the Joint Probability Density Function (JPDF) solution to the phase angle and angular velocity is governed by the generalized Fokker-Planck-Kolmogorov (FPK) equation. To solve this equation, the numerical method is adopted. Special measure is taken such that the generalized FPK equation is satisfied in the average sense of integration with the assumed PDF. Both weak and strong intensities of the stochastic excitations are considered in a single machine infinite bus power system. The numerical analysis has the same result as the one given by the Monte Carlo simulation. Potential studies on stochastic behaviour of multi-machine power systems with random excitations are discussed at the end.
Numerical simulation of stochastic point kinetic equation in the dynamical system of nuclear reactor
International Nuclear Information System (INIS)
Saha Ray, S.
2012-01-01
Highlights: ► In this paper stochastic neutron point kinetic equations have been analyzed. ► Euler–Maruyama method and Strong Taylor 1.5 order method have been discussed. ► These methods are applied for the solution of stochastic point kinetic equations. ► Comparison between the results of these methods and others are presented in tables. ► Graphs for neutron and precursor sample paths are also presented. -- Abstract: In the present paper, the numerical approximation methods, applied to efficiently calculate the solution for stochastic point kinetic equations () in nuclear reactor dynamics, are investigated. A system of Itô stochastic differential equations has been analyzed to model the neutron density and the delayed neutron precursors in a point nuclear reactor. The resulting system of Itô stochastic differential equations are solved over each time-step size. The methods are verified by considering different initial conditions, experimental data and over constant reactivities. The computational results indicate that the methods are simple and suitable for solving stochastic point kinetic equations. In this article, a numerical investigation is made in order to observe the random oscillations in neutron and precursor population dynamics in subcritical and critical reactors.
Directory of Open Access Journals (Sweden)
Jianxu Zhou
2018-03-01
Full Text Available Hydraulic vibration exists in various water conveyance projects and has resulted in different operating problems, but its obvious effects on system’s pressure head and stable operation have not been definitively addressed in the issued codes for engineering design, especially considering the uncertainties of hydraulic vibration. After detailed analysis of the randomness in hydraulic vibration and the commonly used stochastic approaches, in the basic equations for hydraulic vibration analysis, the random parameters and the formed stochastic equations were discussed for further probabilistic characteristic analysis of the random variables. Furthermore, preliminary investigation of the stochastic analysis of hydraulic vibration in pressurized pipelines and possible self-excited vibration in pumped-storage systems was presented for further consideration. The detailed discussion indicates that it is necessary to conduct further and systematic stochastic analysis of hydraulic vibration. Further, with the obtained frequencies and amplitudes in the form of a probability statement, the stochastic characteristics of various hydraulic vibrations can be investigated in detail and these solutions will be more reasonable for practical applications. Eventually, the stochastic analysis of hydraulic vibration will provide a basic premise to introduce its effect into the engineering design of water diversion and hydropower systems.
International Nuclear Information System (INIS)
Mayzelis, Z.A.; Apostolov, S.S.; Melnyk, S.S.; Usatenko, O.V.; Yampol'skii, V.A.
2007-01-01
A theory of symbolic dynamic systems with long-range correlations based on the consideration of the binary N-step Markov chains developed earlier in Phys Rev Lett 2003;90:110601 is generalized to the biased case (non-equal numbers of zeros and unities in the chain). In the model, the conditional probability that the ith symbol in the chain equals zero (or unity) is a linear function of the number of unities (zeros) among the preceding N symbols. The correlation and distribution functions as well as the variance of number of symbols in the words of arbitrary length L are obtained analytically and verified by numerical simulations. A self-similarity of the studied stochastic process is revealed and the similarity group transformation of the chain parameters is presented. The diffusion Fokker-Planck equation governing the distribution function of the L-words is explored. If the persistent correlations are not extremely strong, the distribution function is shown to be the Gaussian with the variance being nonlinearly dependent on L. An equation connecting the memory and correlation function of the additive Markov chain is presented. This equation allows reconstructing a memory function using a correlation function of the system. Effectiveness and robustness of the proposed method is demonstrated by simple model examples. Memory functions of concrete coarse-grained literary texts are found and their universal power-law behavior at long distances is revealed
Energy Technology Data Exchange (ETDEWEB)
Mayzelis, Z.A. [Department of Physics, Kharkov National University, 4 Svoboda Sq., Kharkov 61077 (Ukraine); Apostolov, S.S. [Department of Physics, Kharkov National University, 4 Svoboda Sq., Kharkov 61077 (Ukraine); Melnyk, S.S. [A. Ya. Usikov Institute for Radiophysics and Electronics, Ukrainian Academy of Science, 12 Proskura Street, 61085 Kharkov (Ukraine); Usatenko, O.V. [A. Ya. Usikov Institute for Radiophysics and Electronics, Ukrainian Academy of Science, 12 Proskura Street, 61085 Kharkov (Ukraine)]. E-mail: usatenko@ire.kharkov.ua; Yampol' skii, V.A. [A. Ya. Usikov Institute for Radiophysics and Electronics, Ukrainian Academy of Science, 12 Proskura Street, 61085 Kharkov (Ukraine)
2007-10-15
A theory of symbolic dynamic systems with long-range correlations based on the consideration of the binary N-step Markov chains developed earlier in Phys Rev Lett 2003;90:110601 is generalized to the biased case (non-equal numbers of zeros and unities in the chain). In the model, the conditional probability that the ith symbol in the chain equals zero (or unity) is a linear function of the number of unities (zeros) among the preceding N symbols. The correlation and distribution functions as well as the variance of number of symbols in the words of arbitrary length L are obtained analytically and verified by numerical simulations. A self-similarity of the studied stochastic process is revealed and the similarity group transformation of the chain parameters is presented. The diffusion Fokker-Planck equation governing the distribution function of the L-words is explored. If the persistent correlations are not extremely strong, the distribution function is shown to be the Gaussian with the variance being nonlinearly dependent on L. An equation connecting the memory and correlation function of the additive Markov chain is presented. This equation allows reconstructing a memory function using a correlation function of the system. Effectiveness and robustness of the proposed method is demonstrated by simple model examples. Memory functions of concrete coarse-grained literary texts are found and their universal power-law behavior at long distances is revealed.
Reder, Lynne M.; Park, Heekyeong; Kieffaber, Paul D.
2009-01-01
There is a popular hypothesis that performance on implicit and explicit memory tasks reflects 2 distinct memory systems. Explicit memory is said to store those experiences that can be consciously recollected, and implicit memory is said to store experiences and affect subsequent behavior but to be unavailable to conscious awareness. Although this…
On the Use of Information Quality in Stochastic Networked Control Systems
DEFF Research Database (Denmark)
Olsen, Rasmus Løvenstein; Madsen, Jacob Theilgaard; Rasmussen, Jakob Gulddahl
2017-01-01
Networked control is challenged by stochastic delays that are caused by the communication networks as well as by the approach taken to exchange information about system state and set-points. Combined with stochastic changing information, there is a probability that information at the controller....... This is first analyzed in simulation models for the example system of a wind-farm controller. As simulation analysis is subject to stochastic variability and requires large computational effort, the paper develops a Markov model of a simplified networked control system and uses numerical results from the Markov...... is not matching the true system observation, which we call mismatch probability (mmPr). The hypothesis is that the optimization of certain parameters of networked control systems targeting mmPr is equivalent to the optimization targeting control performance, while the former is practically much easier to conduct...
Disentangling multi-level systems: averaging, correlations and memory
International Nuclear Information System (INIS)
Wouters, Jeroen; Lucarini, Valerio
2012-01-01
We consider two weakly coupled systems and adopt a perturbative approach based on the Ruelle response theory to study their interaction. We propose a systematic way of parameterizing the effect of the coupling as a function of only the variables of a system of interest. Our focus is on describing the impacts of the coupling on the long term statistics rather than on the finite-time behavior. By direct calculation, we find that, at first order, the coupling can be surrogated by adding a deterministic perturbation to the autonomous dynamics of the system of interest. At second order, there are additionally two separate and very different contributions. One is a term taking into account the second-order contributions of the fluctuations in the coupling, which can be parameterized as a stochastic forcing with given spectral properties. The other one is a memory term, coupling the system of interest to its previous history, through the correlations of the second system. If these correlations are known, this effect can be implemented as a perturbation with memory on the single system. In order to treat this case, we present an extension to Ruelle's response theory able to deal with integral operators. We discuss our results in the context of other methods previously proposed for disentangling the dynamics of two coupled systems. We emphasize that our results do not rely on assuming a time scale separation, and, if such a separation exists, can be used equally well to study the statistics of the slow variables and that of the fast variables. By recursively applying the technique proposed here, we can treat the general case of multi-level systems
Discretization of Stationary Solutions of Stochastic Systems Driven by Fractional Brownian Motion
International Nuclear Information System (INIS)
Garrido-Atienza, Maria J.; Kloeden, Peter E.; Neuenkirch, Andreas
2009-01-01
In this article we study the behavior of dissipative systems with additive fractional noise of any Hurst parameter. Under a one-sided dissipative Lipschitz condition on the drift the continuous stochastic system is shown to have a unique stationary solution, which pathwise attracts all other solutions. The same holds for the discretized stochastic system, if the drift-implicit Euler method is used for the discretization. Moreover, the unique stationary solution of the drift-implicit Euler scheme converges to the unique stationary solution of the original system as the stepsize of the discretization decreases
Adaptive control of chaotic systems with stochastic time varying unknown parameters
Energy Technology Data Exchange (ETDEWEB)
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.
A two-stage stochastic programming approach for operating multi-energy systems
DEFF Research Database (Denmark)
Zeng, Qing; Fang, Jiakun; Chen, Zhe
2017-01-01
This paper provides a two-stage stochastic programming approach for joint operating multi-energy systems under uncertainty. Simulation is carried out in a test system to demonstrate the feasibility and efficiency of the proposed approach. The test energy system includes a gas subsystem with a gas...
Global behavior analysis for stochastic system of 1,3-PD continuous fermentation
Zhu, Xi; Kliemann, Wolfgang; Li, Chunfa; Feng, Enmin; Xiu, Zhilong
2017-12-01
Global behavior for stochastic system of continuous fermentation in glycerol bio-dissimilation to 1,3-propanediol by Klebsiella pneumoniae is analyzed in this paper. This bioprocess cannot avoid the stochastic perturbation caused by internal and external disturbance which reflect on the growth rate. These negative factors can limit and degrade the achievable performance of controlled systems. Based on multiplicity phenomena, the equilibriums and bifurcations of the deterministic system are analyzed. Then, a stochastic model is presented by a bounded Markov diffusion process. In order to analyze the global behavior, we compute the control sets for the associated control system. The probability distributions of relative supports are also computed. The simulation results indicate that how the disturbed biosystem tend to stationary behavior globally.
Command vector memory systems: high performance at low cost
Corbal San Adrián, Jesús; Espasa Sans, Roger; Valero Cortés, Mateo
1998-01-01
The focus of this paper is on designing both a low cost and high performance, high bandwidth vector memory system that takes advantage of modern commodity SDRAM memory chips. To successfully extract the full bandwidth from SDRAM parts, we propose a new memory system organization based on sending commands to the memory system as opposed to sending individual addresses. A command specifies, in a few bytes, a request for multiple independent memory words. A command is similar to a burst found in...
A higher-order numerical framework for stochastic simulation of chemical reaction systems.
Székely, Tamás
2012-07-15
BACKGROUND: In this paper, we present a framework for improving the accuracy of fixed-step methods for Monte Carlo simulation of discrete stochastic chemical kinetics. Stochasticity is ubiquitous in many areas of cell biology, for example in gene regulation, biochemical cascades and cell-cell interaction. However most discrete stochastic simulation techniques are slow. We apply Richardson extrapolation to the moments of three fixed-step methods, the Euler, midpoint and θ-trapezoidal τ-leap methods, to demonstrate the power of stochastic extrapolation. The extrapolation framework can increase the order of convergence of any fixed-step discrete stochastic solver and is very easy to implement; the only condition for its use is knowledge of the appropriate terms of the global error expansion of the solver in terms of its stepsize. In practical terms, a higher-order method with a larger stepsize can achieve the same level of accuracy as a lower-order method with a smaller one, potentially reducing the computational time of the system. RESULTS: By obtaining a global error expansion for a general weak first-order method, we prove that extrapolation can increase the weak order of convergence for the moments of the Euler and the midpoint τ-leap methods, from one to two. This is supported by numerical simulations of several chemical systems of biological importance using the Euler, midpoint and θ-trapezoidal τ-leap methods. In almost all cases, extrapolation results in an improvement of accuracy. As in the case of ordinary and stochastic differential equations, extrapolation can be repeated to obtain even higher-order approximations. CONCLUSIONS: Extrapolation is a general framework for increasing the order of accuracy of any fixed-step stochastic solver. This enables the simulation of complicated systems in less time, allowing for more realistic biochemical problems to be solved.
ℋ∞ constant gain state feedback stabilization of stochastic hybrid systems with Wiener process
Directory of Open Access Journals (Sweden)
E. K. Boukas
2004-01-01
Full Text Available This paper considers the stabilization problem of the class of continuous-time linear stochastic hybrid systems with Wiener process. The ℋ∞ state feedback stabilization problem is treated. A state feedback controller with constant gain that does not require access to the system mode is designed. LMI-based conditions are developed to design the state feedback controller with constant gain that stochastically stabilizes the studied class of systems and, at the same time, achieve the disturbance rejection of a desired level. The minimum disturbance rejection is also determined. Numerical examples are given to show the usefulness of the proposed results.
International Nuclear Information System (INIS)
Li Jinghui
2008-01-01
In this paper, an electric system with two dichotomous resistors is investigated. It is shown that this system can display two stochastic resonances, which are the amplitude of the periodic response as the functions of the two dichotomous resistors strengthes respectively. In the limits of Gaussian white noise and shot white noise (i.e., the two noises are both Gaussian white noise or shot white noise), no phenomena of resonance appear. By further study, we find that when the system is with three or more multiplicative telegraphic noises, there are three or more stochastic resonances
A penalty guided stochastic fractal search approach for system reliability optimization
International Nuclear Information System (INIS)
Mellal, Mohamed Arezki; Zio, Enrico
2016-01-01
Modern industry requires components and systems with high reliability levels. In this paper, we address the system reliability optimization problem. A penalty guided stochastic fractal search approach is developed for solving reliability allocation, redundancy allocation, and reliability–redundancy allocation problems. Numerical results of ten case studies are presented as benchmark problems for highlighting the superiority of the proposed approach compared to others from literature. - Highlights: • System reliability optimization is investigated. • A penalty guided stochastic fractal search approach is developed. • Results of ten case studies are compared with previously published methods. • Performance of the approach is demonstrated.
Concurrent performance of two memory tasks: evidence for domain-specific working memory systems.
Cocchini, Gianna; Logie, Robert H; Della Sala, Sergio; MacPherson, Sarah E; Baddeley, Alan D
2002-10-01
Previous studies of dual-task coordination in working memory have shown a lack of dual-task interference when a verbal memory task is combined with concurrent perceptuomotor tracking. Two experiments are reported in which participants were required to perform pairwise combinations of (1) a verbal memory task, a visual memory task, and perceptuomotor tracking (Experiment 1), and (2) pairwise combinations of the two memory tasks and articulatory suppression (Experiment 2). Tracking resulted in no disruption of the verbal memory preload over and above the impact of a delay in recall and showed only minimal disruption of the retention of the visual memory load. Performing an ongoing verbal memory task had virtually no impact on retention of a visual memory preload or vice versa, indicating that performing two demanding memory tasks results in little mutual interference. Experiment 2 also showed minimal disruption when the two memory tasks were combined, although verbal memory (but not visual memory) was clearly disrupted by articulatory suppression interpolated between presentation and recall. These data suggest that a multiple-component working memory model provides a better account for performance in concurrent immediate memory tasks than do theories that assume a single processing and storage system or a limited-capacity attentional system coupled with activated memory traces.
A general model for memory interference in a multiprocessor system with memory hierarchy
Taha, Badie A.; Standley, Hilda M.
1989-01-01
The problem of memory interference in a multiprocessor system with a hierarchy of shared buses and memories is addressed. The behavior of the processors is represented by a sequence of memory requests with each followed by a determined amount of processing time. A statistical queuing network model for determining the extent of memory interference in multiprocessor systems with clusters of memory hierarchies is presented. The performance of the system is measured by the expected number of busy memory clusters. The results of the analytic model are compared with simulation results, and the correlation between them is found to be very high.
Stabilizing simulations of complex stochastic representations for quantum dynamical systems
Energy Technology Data Exchange (ETDEWEB)
Perret, C; Petersen, W P, E-mail: wpp@math.ethz.ch [Seminar for Applied Mathematics, ETH, Zurich (Switzerland)
2011-03-04
Path integral representations of quantum dynamics can often be formulated as stochastic differential equations (SDEs). In a series of papers, Corney and Drummond (2004 Phys. Rev. Lett. 93 260401), Deuar and Drummond (2001 Comput. Phys. Commun. 142 442-5), Drummond and Gardnier (1980 J. Phys. A: Math. Gen. 13 2353-68), Gardiner and Zoller (2004 Quantum Noise: A Handbook of Markovian and Non-Markovian Quantum Stochastic Methods with Applications to Quantum Optics (Springer Series in Synergetics) 3rd edn (Berlin: Springer)) and Gilchrist et al (1997 Phys. Rev. A 55 3014-32) and their collaborators have derived SDEs from coherent states representations for density matrices. Computationally, these SDEs are attractive because they seem simple to simulate. They can be quite unstable, however. In this paper, we consider some of the instabilities and propose a few remedies. Particularly, because the variances of the simulated paths typically grow exponentially, the processes become de-localized in relatively short times. Hence, the issues of boundary conditions and stable integration methods become important. We use the Bose-Einstein Hamiltonian as an example. Our results reveal that it is possible to significantly extend integration times and show the periodic structure of certain functionals.
H∞ Filtering for Networked Markovian Jump Systems with Multiple Stochastic Communication Delays
Directory of Open Access Journals (Sweden)
Hui Dong
2015-01-01
Full Text Available This paper is concerned with the H∞ filtering for a class of networked Markovian jump systems with multiple communication delays. Due to the existence of communication constraints, the measurement signal cannot arrive at the filter completely on time, and the stochastic communication delays are considered in the filter design. Firstly, a set of stochastic variables is introduced to model the occurrence probabilities of the delays. Then based on the stochastic system approach, a sufficient condition is obtained such that the filtering error system is stable in the mean-square sense and with a prescribed H∞ disturbance attenuation level. The optimal filter gain parameters can be determined by solving a convex optimization problem. Finally, a simulation example is given to show the effectiveness of the proposed filter design method.
Directory of Open Access Journals (Sweden)
Wen-Jer Chang
2014-01-01
Full Text Available For nonlinear discrete-time stochastic systems, a fuzzy controller design methodology is developed in this paper subject to state variance constraint and passivity constraint. According to fuzzy model based control technique, the nonlinear discrete-time stochastic systems considered in this paper are represented by the discrete-time Takagi-Sugeno fuzzy models with multiplicative noise. Employing Lyapunov stability theory, upper bound covariance control theory, and passivity theory, some sufficient conditions are derived to find parallel distributed compensation based fuzzy controllers. In order to solve these sufficient conditions, an iterative linear matrix inequality algorithm is applied based on the linear matrix inequality technique. Finally, the fuzzy stabilization problem for nonlinear discrete ship steering stochastic systems is investigated in the numerical example to illustrate the feasibility and validity of proposed fuzzy controller design method.
A constrained approach to multiscale stochastic simulation of chemically reacting systems
Cotter, Simon L.
2011-01-01
Stochastic simulation of coupled chemical reactions is often computationally intensive, especially if a chemical system contains reactions occurring on different time scales. In this paper, we introduce a multiscale methodology suitable to address this problem, assuming that the evolution of the slow species in the system is well approximated by a Langevin process. It is based on the conditional stochastic simulation algorithm (CSSA) which samples from the conditional distribution of the suitably defined fast variables, given values for the slow variables. In the constrained multiscale algorithm (CMA) a single realization of the CSSA is then used for each value of the slow variable to approximate the effective drift and diffusion terms, in a similar manner to the constrained mean-force computations in other applications such as molecular dynamics. We then show how using the ensuing Fokker-Planck equation approximation, we can in turn approximate average switching times in stochastic chemical systems. © 2011 American Institute of Physics.
STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB.
Klingbeil, Guido; Erban, Radek; Giles, Mike; Maini, Philip K
2011-04-15
The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB. The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user's models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. The software is open source under the GPL v3 and available at http://www.maths.ox.ac.uk/cmb/STOCHSIMGPU. The web site also contains supplementary information. klingbeil@maths.ox.ac.uk Supplementary data are available at Bioinformatics online.
International Nuclear Information System (INIS)
Kang-Kang, Wang; Xian-Bin, Liu; Yu, Zhou
2015-01-01
In this paper, the stability and stochastic resonance (SR) phenomenon induced by the multiplicative periodic signal for a metapopulation system driven by the additive Gaussian noise, multiplicative non-Gaussian noise and noise correlation time is investigated. By using the fast descent method, unified colored noise approximation and McNamara and Wiesenfeld’s SR theory, the analytical expressions of the stationary probability distribution function and signal-to-noise ratio (SNR) are derived in the adiabatic limit. Via numerical calculations, each effect of the addictive noise intensity, the multiplicative noise intensity and the correlation time upon the steady state probability distribution function and the SNR is discussed, respectively. It is shown that multiplicative, additive noises and the departure parameter from the Gaussian noise can all destroy the stability of the population system. However, the noise correlation time can consolidate the stability of the system. On the other hand, the correlation time always plays an important role in motivating the SR and enhancing the SNR. Under different parameter conditions of the system, the multiplicative, additive noises and the departure parameter can not only excite SR phenomenon, but also restrain the SR phenomenon, which demonstrates the complexity of different noises upon the nonlinear system. (paper)
Stochastic processes crossing from ballistic to fractional diffusion with memory: exact results
Directory of Open Access Journals (Sweden)
V. Ilyin
2010-01-01
Full Text Available We address the now classical problem of a diffusion process that crosses over from a ballistic behavior at short times to a fractional diffusion (sub- or super-diffusion at longer times. Using the standard non-Markovian diffusion equation we demonstrate how to choose the memory kernel to exactly respect the two different asymptotics of the diffusion process. Having done so we solve for the probability distribution function as a continuous function which evolves inside a ballistically expanding domain. This general solution agrees for long times with the probability distribution function obtained within the continuous random walk approach but it is much superior to this solution at shorter times where the effect of the ballistic regime is crucial.
A stochastic process model for life cycle cost analysis of nuclear power plant systems
Van der Weide, J.A.M.; Pandey, M.D.
2013-01-01
The paper presents a general stochastic model to analyze the life cycle cost of an engineering system that is affected by minor but repairable failures interrupting the operation and a major failure that would require the replacement or renewal of the failed system. It is commonly observed that the
Approximation of itô integrals arising in stochastic time-delayed systems
Bagchi, Arunabha
1984-01-01
Likelihood functional for stochastic linear time-delayed systems involve Itô integrals with respect to the observed data. Since the Wiener process appearing in the standard observation process model for such systems is not realizable and the physically observed process is smooth, one needs to study
Random attractors for stochastic lattice reversible Gray-Scott systems with additive noise
Directory of Open Access Journals (Sweden)
Hongyan Li
2015-10-01
Full Text Available In this article, we prove the existence of a random attractor of the stochastic three-component reversible Gray-Scott system on infinite lattice with additive noise. We use a transformation of addition involved with Ornstein-Uhlenbeck process, for proving the pullback absorbing property and the pullback asymptotic compactness of the reaction diffusion system with cubic nonlinearity.
Zhang, Kemei; Zhao, Cong-Ran; Xie, Xue-Jun
2015-12-01
This paper considers the problem of output feedback stabilisation for stochastic high-order feedforward nonlinear systems with time-varying delay. By using the homogeneous domination theory and solving several troublesome obstacles in the design and analysis, an output feedback controller is constructed to drive the closed-loop system globally asymptotically stable in probability.
A Class of Stochastic Hybrid Systems with State-Dependent Switching Noise
DEFF Research Database (Denmark)
Leth, John-Josef; Rasmussen, Jakob Gulddahl; Schiøler, Henrik
2012-01-01
In this paper, we develop theoretical results based on a proposed method for modeling switching noise for a class of hybrid systems with piecewise linear partitioned state space, and state-depending switching. We devise a stochastic model of such systems, whose global dynamics is governed...
Kiesmüller, G.P.
2003-01-01
This paper addresses the control problem of a stochastic recovery system with two stocking points and different leadtimes for production and remanufacturing. For such systems the optimal control policy for a linear cost model is not known. Therefore, in the literature several heuristic policies are
ON THE ANISOTROPIC NORM OF DISCRETE TIME STOCHASTIC SYSTEMS WITH STATE DEPENDENT NOISE
Directory of Open Access Journals (Sweden)
Isaac Yaesh
2013-01-01
Full Text Available The purpose of this paper is to determine conditions for the bound-edness of the anisotropic norm of discrete-time linear stochastic sys-tems with state dependent noise. It is proved that these conditions canbe expressed in terms of the feasibility of a specific system of matrixinequalities.
Stochastic Modeling of Usage Patterns in a Web-Based Information System.
Chen, Hui-Min; Cooper, Michael D.
2002-01-01
Uses continuous-time stochastic models, mainly based on semi-Markov chains, to derive user state transition patterns, both in rates and in probabilities, in a Web-based information system. Describes search sessions from transaction logs of the University of California's MELVYL library catalog system and discusses sequential dependency. (Author/LRW)
CAM: A Collaborative Object Memory System
Vyas, Dhaval; Nijholt, Antinus; Kröner, Alexander
2010-01-01
Physical design objects such as sketches, drawings, collages, storyboards and models play an important role in supporting communication and coordination in design studios. CAM (Cooperative Artefact Memory) is a mobile-tagging based messaging system that allows designers to collaboratively store
Architectural Techniques to Enable Reliable and Scalable Memory Systems
Nair, Prashant J.
2017-01-01
High capacity and scalable memory systems play a vital role in enabling our desktops, smartphones, and pervasive technologies like Internet of Things (IoT). Unfortunately, memory systems are becoming increasingly prone to faults. This is because we rely on technology scaling to improve memory density, and at small feature sizes, memory cells tend to break easily. Today, memory reliability is seen as the key impediment towards using high-density devices, adopting new technologies, and even bui...
Directory of Open Access Journals (Sweden)
Yajun Li
2015-01-01
Full Text Available This paper deals with the robust H∞ filter design problem for a class of uncertain neutral stochastic systems with Markovian jumping parameters and time delay. Based on the Lyapunov-Krasovskii theory and generalized Finsler Lemma, a delay-dependent stability condition is proposed to ensure not only that the filter error system is robustly stochastically stable but also that a prescribed H∞ performance level is satisfied for all admissible uncertainties. All obtained results are expressed in terms of linear matrix inequalities which can be easily solved by MATLAB LMI toolbox. Numerical examples are given to show that the results obtained are both less conservative and less complicated in computation.
A numerical scheme for optimal transition paths of stochastic chemical kinetic systems
International Nuclear Information System (INIS)
Liu Di
2008-01-01
We present a new framework for finding the optimal transition paths of metastable stochastic chemical kinetic systems with large system size. The optimal transition paths are identified to be the most probable paths according to the Large Deviation Theory of stochastic processes. Dynamical equations for the optimal transition paths are derived using the variational principle. A modified Minimum Action Method (MAM) is proposed as a numerical scheme to solve the optimal transition paths. Applications to Gene Regulatory Networks such as the toggle switch model and the Lactose Operon Model in Escherichia coli are presented as numerical examples
Design and analysis of stochastic DSS query optimizers in a distributed database system
Directory of Open Access Journals (Sweden)
Manik Sharma
2016-07-01
Full Text Available Query optimization is a stimulating task of any database system. A number of heuristics have been applied in recent times, which proposed new algorithms for substantially improving the performance of a query. The hunt for a better solution still continues. The imperishable developments in the field of Decision Support System (DSS databases are presenting data at an exceptional rate. The massive volume of DSS data is consequential only when it is able to access and analyze by distinctive researchers. Here, an innovative stochastic framework of DSS query optimizer is proposed to further optimize the design of existing query optimization genetic approaches. The results of Entropy Based Restricted Stochastic Query Optimizer (ERSQO are compared with the results of Exhaustive Enumeration Query Optimizer (EAQO, Simple Genetic Query Optimizer (SGQO, Novel Genetic Query Optimizer (NGQO and Restricted Stochastic Query Optimizer (RSQO. In terms of Total Costs, EAQO outperforms SGQO, NGQO, RSQO and ERSQO. However, stochastic approaches dominate in terms of runtime. The Total Costs produced by ERSQO is better than SGQO, NGQO and RGQO by 12%, 8% and 5% respectively. Moreover, the effect of replicating data on the Total Costs of DSS query is also examined. In addition, the statistical analysis revealed a 2-tailed significant correlation between the number of join operations and the Total Costs of distributed DSS query. Finally, in regard to the consistency of stochastic query optimizers, the results of SGQO, NGQO, RSQO and ERSQO are 96.2%, 97.2%, 97.45 and 97.8% consistent respectively.
Directory of Open Access Journals (Sweden)
Zengyun Wang
2013-01-01
Full Text Available This paper investigates the problem of synchronization for two different stochastic chaotic systems with unknown parameters and uncertain terms. The main work of this paper consists of the following aspects. Firstly, based on the Lyapunov theory in stochastic differential equations and the theory of sliding mode control, we propose a simple sliding surface and discuss the occurrence of the sliding motion. Secondly, we design an adaptive sliding mode controller to realize the asymptotical synchronization in mean squares. Thirdly, we design an adaptive sliding mode controller to realize the almost surely synchronization. Finally, the designed adaptive sliding mode controllers are used to achieve synchronization between two pairs of different stochastic chaos systems (Lorenz-Chen and Chen-Lu in the presence of the uncertainties and unknown parameters. Numerical simulations are given to demonstrate the robustness and efficiency of the proposed robust adaptive sliding mode controller.
Adaptive Finite Element Method Assisted by Stochastic Simulation of Chemical Systems
Cotter, Simon L.; Vejchodský , Tomá š; Erban, Radek
2013-01-01
Stochastic models of chemical systems are often analyzed by solving the corresponding Fokker-Planck equation, which is a drift-diffusion partial differential equation for the probability distribution function. Efficient numerical solution of the Fokker-Planck equation requires adaptive mesh refinements. In this paper, we present a mesh refinement approach which makes use of a stochastic simulation of the underlying chemical system. By observing the stochastic trajectory for a relatively short amount of time, the areas of the state space with nonnegligible probability density are identified. By refining the finite element mesh in these areas, and coarsening elsewhere, a suitable mesh is constructed and used for the computation of the stationary probability density. Numerical examples demonstrate that the presented method is competitive with existing a posteriori methods. © 2013 Society for Industrial and Applied Mathematics.
Effects of intrinsic stochasticity on delayed reaction-diffusion patterning systems
Woolley, Thomas E.; Baker, Ruth E.; Gaffney, Eamonn A.; Maini, Philip K.; Seirin-Lee, Sungrim
2012-01-01
Cellular gene expression is a complex process involving many steps, including the transcription of DNA and translation of mRNA; hence the synthesis of proteins requires a considerable amount of time, from ten minutes to several hours. Since diffusion-driven instability has been observed to be sensitive to perturbations in kinetic delays, the application of Turing patterning mechanisms to the problem of producing spatially heterogeneous differential gene expression has been questioned. In deterministic systems a small delay in the reactions can cause a large increase in the time it takes a system to pattern. Recently, it has been observed that in undelayed systems intrinsic stochasticity can cause pattern initiation to occur earlier than in the analogous deterministic simulations. Here we are interested in adding both stochasticity and delays to Turing systems in order to assess whether stochasticity can reduce the patterning time scale in delayed Turing systems. As analytical insights to this problem are difficult to attain and often limited in their use, we focus on stochastically simulating delayed systems. We consider four different Turing systems and two different forms of delay. Our results are mixed and lead to the conclusion that, although the sensitivity to delays in the Turing mechanism is not completely removed by the addition of intrinsic noise, the effects of the delays are clearly ameliorated in certain specific cases. © 2012 American Physical Society.
Effects of intrinsic stochasticity on delayed reaction-diffusion patterning systems
Woolley, Thomas E.
2012-05-22
Cellular gene expression is a complex process involving many steps, including the transcription of DNA and translation of mRNA; hence the synthesis of proteins requires a considerable amount of time, from ten minutes to several hours. Since diffusion-driven instability has been observed to be sensitive to perturbations in kinetic delays, the application of Turing patterning mechanisms to the problem of producing spatially heterogeneous differential gene expression has been questioned. In deterministic systems a small delay in the reactions can cause a large increase in the time it takes a system to pattern. Recently, it has been observed that in undelayed systems intrinsic stochasticity can cause pattern initiation to occur earlier than in the analogous deterministic simulations. Here we are interested in adding both stochasticity and delays to Turing systems in order to assess whether stochasticity can reduce the patterning time scale in delayed Turing systems. As analytical insights to this problem are difficult to attain and often limited in their use, we focus on stochastically simulating delayed systems. We consider four different Turing systems and two different forms of delay. Our results are mixed and lead to the conclusion that, although the sensitivity to delays in the Turing mechanism is not completely removed by the addition of intrinsic noise, the effects of the delays are clearly ameliorated in certain specific cases. © 2012 American Physical Society.
Identification of a Discontinuous Parameter in Stochastic Parabolic Systems
International Nuclear Information System (INIS)
Aihara, S. I.
1998-01-01
The purpose of this paper is to study the identification problem for a spatially varying discontinuous parameter in stochastic diffusion equations. The consistency property of the maximum likelihood estimate (M.L.E.) and a generating algorithm for M.L.E. have been explored under the condition that the unknown parameter is in a sufficiently regular space with respect to spatial variables. In order to prove the consistency property of the M.L.E. for a discontinuous diffusion coefficient, we use the method of sieves, i.e., first the admissible class of unknown parameters is projected into a finite-dimensional space and next the convergence of the derived finite-dimensional M.L.E. to the infinite-dimensional M.L.E. is justified under some conditions. An iterative algorithm for generating the M.L.E. is also proposed with two numerical examples
Stochastic control applied to the ISWEC Wave Energy System
International Nuclear Information System (INIS)
Bracco, Giovanni; Casassa, Maria; Giorcelli, Ermanno; Mattiazzo, Giuliana; Passione, Biagio; Raffero, Mattia; Vissio, Giacomo; Martini, Michele
2015-01-01
ISWEC (Inertial Sea Wave Energy Converter) is a floating marine device able to harvest sea waves energy by the interaction between the pitching motion of a floater and a spinning flywheel which can drive an electric PTO. In the ISWEC the hull dynamics is governed and controlled by the gyroscopic torque. The optimal control logic results in tuning the floater dynamics to the incoming waves in order to maximize the power transfer from the waves to the floater. In this paper the control problems of the ISWEC are stated and a control scheme based on the sub-optimal stochastic control logic is presented. The control scheme here presented has been tested using real wave records acquired at the deployment location in Pantelleria Island, which is one of the most energetic sites of the Mediterranean Sea.
Wang, Sheng; Wang, Linshan; Wei, Tengda
2018-04-01
This paper concerns the dynamics of a stochastic predator-prey system with Markovian switching and Lévy noise. First, the existence and uniqueness of global positive solution to the system is proved. Then, by combining stochastic analytical techniques with M-matrix analysis, sufficient conditions of stochastic permanence and extinction are obtained. Furthermore, for the stochastic permanence case, by means of four constants related to the stationary probability distribution of the Markov chain and the parameters of the subsystems, both the superior limit and the inferior limit of the average in time of the sample path of the solution are estimated. Finally, our conclusions are illustrated through an example.
Hopf Bifurcation Analysis for a Stochastic Discrete-Time Hyperchaotic System
Directory of Open Access Journals (Sweden)
Jie Ran
2015-01-01
Full Text Available The dynamics of a discrete-time hyperchaotic system and the amplitude control of Hopf bifurcation for a stochastic discrete-time hyperchaotic system are investigated in this paper. Numerical simulations are presented to exhibit the complex dynamical behaviors in the discrete-time hyperchaotic system. Furthermore, the stochastic discrete-time hyperchaotic system with random parameters is transformed into its equivalent deterministic system with the orthogonal polynomial theory of discrete random function. In addition, the dynamical features of the discrete-time hyperchaotic system with random disturbances are obtained through its equivalent deterministic system. By using the Hopf bifurcation conditions of the deterministic discrete-time system, the specific conditions for the existence of Hopf bifurcation in the equivalent deterministic system are derived. And the amplitude control with random intensity is discussed in detail. Finally, the feasibility of the control method is demonstrated by numerical simulations.
Energy Technology Data Exchange (ETDEWEB)
Guo, Kong-Ming, E-mail: kmguo@xidian.edu.cn [School of Electromechanical Engineering, Xidian University, P.O. Box 187, Xi' an 710071 (China); Jiang, Jun, E-mail: jun.jiang@mail.xjtu.edu.cn [State Key Laboratory for Strength and Vibration, Xi' an Jiaotong University, Xi' an 710049 (China)
2014-07-04
To apply stochastic sensitivity function method, which can estimate the probabilistic distribution of stochastic attractors, to non-autonomous dynamical systems, a 1/N-period stroboscopic map for a periodic motion is constructed in order to discretize the continuous cycle into a discrete one. In this way, the sensitivity analysis of a cycle for discrete map can be utilized and a numerical algorithm for the stochastic sensitivity analysis of periodic solutions of non-autonomous nonlinear dynamical systems under stochastic disturbances is devised. An external excited Duffing oscillator and a parametric excited laser system are studied as examples to show the validity of the proposed method. - Highlights: • A method to analyze sensitivity of stochastic periodic attractors in non-autonomous dynamical systems is proposed. • Probabilistic distribution around periodic attractors in an external excited Φ{sup 6} Duffing system is obtained. • Probabilistic distribution around a periodic attractor in a parametric excited laser system is determined.
A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties
International Nuclear Information System (INIS)
Zhang, X.Y.; Huang, G.H.; Zhu, H.; Li, Y.P.
2017-01-01
In this study, a fuzzy stochastic dynamic fractional programming (FSDFP) method is proposed for supporting sustainable management of electric power system (EPS) under dual uncertainties. As an improvement upon the mixed-integer linear fractional programming, FSDFP can not only tackle multi-objective issues effectively without setting weights, but also can deal with uncertain parameters which have both stochastic and fuzzy characteristics. Thus, the developed method can help provide valuable information for supporting capacity-expansion planning and in-depth policy analysis of EPS management problems. For demonstrating these advantages, FSDFP has been applied to a case study of a typical regional EPS planning, where the decision makers have to deal with conflicts between economic development that maximizes the system profit and environmental protection that minimizes the carbon dioxide emissions. The obtained results can be analyzed to generate several decision alternatives, and can then help decision makers make suitable decisions under different input scenarios. Furthermore, comparisons of the solution from FSDFP method with that from fuzzy stochastic dynamic linear programming, linear fractional programming and dynamic stochastic fractional programming methods are undertaken. The contrastive analysis reveals that FSDFP is a more effective approach that can better characterize the complexities and uncertainties of real EPS management problems. - Highlights: • A fuzzy stochastic dynamic fractional programming (FSDFP) method is proposed. • FSDFP can address multiple conflicting objectives without setting weights. • FSDFP can reflect dual uncertainties with both stochastic and fuzzy characteristics. • Some reasonable solutions for a case of power system sustainable planning are generated. • Comparisons of the solutions from FSDFP with other optimization methods are undertaken.
Understanding Organizational Memory from the Integrated Management Systems (ERP)
Gilberto Perez; Isabel Ramos
2013-01-01
With this research, in the form of a theoretical essay addressing the theme of Organizational Memory and Integrated Management Systems (ERP), we tried to present some evidence of how this type of system can contribute to the consolidation of certain features of Organizational Memory. From a theoretical review of the concepts of Human Memory, extending to the Organizational Memory and Information Systems, with emphasis on Integrated Management Systems (ERP) we tried to draw a parallel between ...
Open system evolution and 'memory dressing'
International Nuclear Information System (INIS)
Knezevic, Irena; Ferry, David K.
2004-01-01
Due to recent advances in quantum information, as well as in mesoscopic and nanoscale physics, the interest in the theory of open systems and decoherence has significantly increased. In this paper, we present an interesting approach to solving a time-convolutionless equation of motion for the open system reduced density matrix beyond the limit of weak coupling with the environment. Our approach is based on identifying an effective, memory-containing interaction in the equations of motion for the representation submatrices of the evolution operator (these submatices are written in a special basis, adapted for the 'partial-trace-free' approach, in the system+environment Liouville space). We then identify the 'memory dressing', a quantity crucial for solving the equation of motion for the reduced density matrix, which separates the effective from the real physical interaction. The memory dressing obeys a self-contained nonlinear equation of motion, which we solve exactly. The solution can be represented in a diagrammatic fashion after introducing an 'information exchange propagator', a quantity that describes the transfer of information to and from the system, so the cumulative effect of the information exchange results in the memory dressing. In the case of weak system-environment coupling, we present the expansion of the reduced density matrix in terms of the physical interaction up to the third order. However, our approach is capable of going beyond the weak-coupling limit, and we show how short-time behavior of an open system can be analyzed for arbitrary coupling strength. We illustrate the approach with a simple numerical example of single-particle level broadening for a two-particle interacting system on short time scales. Furthermore, we point out a way to identify the structure of decoherence-free subspaces using the present approach
Optimal control strategy for an impulsive stochastic competition system with time delays and jumps
Liu, Lidan; Meng, Xinzhu; Zhang, Tonghua
2017-07-01
Driven by both white and jump noises, a stochastic delayed model with two competitive species in a polluted environment is proposed and investigated. By using the comparison theorem of stochastic differential equations and limit superior theory, sufficient conditions for persistence in mean and extinction of two species are established. In addition, we obtain that the system is asymptotically stable in distribution by using ergodic method. Furthermore, the optimal harvesting effort and the maximum of expectation of sustainable yield (ESY) are derived from Hessian matrix method and optimal harvesting theory of differential equations. Finally, some numerical simulations are provided to illustrate the theoretical results.
Optimal Stochastic Control Problem for General Linear Dynamical Systems in Neuroscience
Directory of Open Access Journals (Sweden)
Yan Chen
2017-01-01
Full Text Available This paper considers a d-dimensional stochastic optimization problem in neuroscience. Suppose the arm’s movement trajectory is modeled by high-order linear stochastic differential dynamic system in d-dimensional space, the optimal trajectory, velocity, and variance are explicitly obtained by using stochastic control method, which allows us to analytically establish exact relationships between various quantities. Moreover, the optimal trajectory is almost a straight line for a reaching movement; the optimal velocity bell-shaped and the optimal variance are consistent with the experimental Fitts law; that is, the longer the time of a reaching movement, the higher the accuracy of arriving at the target position, and the results can be directly applied to designing a reaching movement performed by a robotic arm in a more general environment.
Nonlinear Damping Identification in Nonlinear Dynamic System Based on Stochastic Inverse Approach
Directory of Open Access Journals (Sweden)
S. L. Han
2012-01-01
Full Text Available The nonlinear model is crucial to prepare, supervise, and analyze mechanical system. In this paper, a new nonparametric and output-only identification procedure for nonlinear damping is studied. By introducing the concept of the stochastic state space, we formulate a stochastic inverse problem for a nonlinear damping. The solution of the stochastic inverse problem is designed as probabilistic expression via the hierarchical Bayesian formulation by considering various uncertainties such as the information insufficiency in parameter of interests or errors in measurement. The probability space is estimated using Markov chain Monte Carlo (MCMC. The applicability of the proposed method is demonstrated through numerical experiment and particular application to a realistic problem related to ship roll motion.
Liu, Xiangdong; Li, Qingze; Pan, Jianxin
2018-06-01
Modern medical studies show that chemotherapy can help most cancer patients, especially for those diagnosed early, to stabilize their disease conditions from months to years, which means the population of tumor cells remained nearly unchanged in quite a long time after fighting against immune system and drugs. In order to better understand the dynamics of tumor-immune responses under chemotherapy, deterministic and stochastic differential equation models are constructed to characterize the dynamical change of tumor cells and immune cells in this paper. The basic dynamical properties, such as boundedness, existence and stability of equilibrium points, are investigated in the deterministic model. Extended stochastic models include stochastic differential equations (SDEs) model and continuous-time Markov chain (CTMC) model, which accounts for the variability in cellular reproduction, growth and death, interspecific competitions, and immune response to chemotherapy. The CTMC model is harnessed to estimate the extinction probability of tumor cells. Numerical simulations are performed, which confirms the obtained theoretical results.
Effects of error feedback on a nonlinear bistable system with stochastic resonance
International Nuclear Information System (INIS)
Li Jian-Long; Zhou Hui
2012-01-01
In this paper, we discuss the effects of error feedback on the output of a nonlinear bistable system with stochastic resonance. The bit error rate is employed to quantify the performance of the system. The theoretical analysis and the numerical simulation are presented. By investigating the performances of the nonlinear systems with different strengths of error feedback, we argue that the presented system may provide guidance for practical nonlinear signal processing
Darmon, David
2018-03-01
In the absence of mechanistic or phenomenological models of real-world systems, data-driven models become necessary. The discovery of various embedding theorems in the 1980s and 1990s motivated a powerful set of tools for analyzing deterministic dynamical systems via delay-coordinate embeddings of observations of their component states. However, in many branches of science, the condition of operational determinism is not satisfied, and stochastic models must be brought to bear. For such stochastic models, the tool set developed for delay-coordinate embedding is no longer appropriate, and a new toolkit must be developed. We present an information-theoretic criterion, the negative log-predictive likelihood, for selecting the embedding dimension for a predictively optimal data-driven model of a stochastic dynamical system. We develop a nonparametric estimator for the negative log-predictive likelihood and compare its performance to a recently proposed criterion based on active information storage. Finally, we show how the output of the model selection procedure can be used to compare candidate predictors for a stochastic system to an information-theoretic lower bound.
Path integral methods for the dynamics of stochastic and disordered systems
DEFF Research Database (Denmark)
Hertz, John A.; Roudi, Yasser; Sollich, Peter
2017-01-01
We review some of the techniques used to study the dynamics of disordered systems subject to both quenched and fast (thermal) noise. Starting from the Martin–Siggia–Rose/Janssen–De Dominicis–Peliti path integral formalism for a single variable stochastic dynamics, we provide a pedagogical survey...
International Nuclear Information System (INIS)
Frank, T.D.
2006-01-01
First-order approximations of time-dependent solutions are determined for stochastic systems perturbed by time-delayed feedback forces. To this end, the theory of delay Fokker-Planck equations is applied in combination with Bayes' theorem. Applications to a time-delayed Ornstein-Uhlenbeck process and the geometric Brownian walk of financial physics are discussed
Directory of Open Access Journals (Sweden)
C. Parthasarathy
2013-03-01
Full Text Available In this paper, we study the controllability results of first order impulsive stochastic differential and neutral differential systems with state-dependent delay by using semigroup theory. The controllability results are derived by the means of Leray-SchauderAlternative fixed point theorem. An example is provided to illustrate the theory.
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik
2014-01-01
This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP...
Stochastic long term modelling of a drainage system with estimation of return period uncertainty
DEFF Research Database (Denmark)
Thorndahl, Søren
2009-01-01
Long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems are associated with large uncertainties. Especially on rainfall inputs, parameters, and assessment of return periods. This paper proposes a Monte Carlo based methodology for stochastic prediction of...
Factors that influence the relative use of multiple memory systems.
Packard, Mark G; Goodman, Jarid
2013-11-01
Neurobehavioral evidence supports the existence of at least two anatomically distinct "memory systems" in the mammalian brain that mediate dissociable types of learning and memory; a "cognitive" memory system dependent upon the hippocampus and a "stimulus-response/habit" memory system dependent upon the dorsolateral striatum. Several findings indicate that despite their anatomical and functional distinctiveness, hippocampal- and dorsolateral striatal-dependent memory systems may potentially interact and that, depending on the learning situation, this interaction may be cooperative or competitive. One approach to examining the neural mechanisms underlying these interactions is to consider how various factors influence the relative use of multiple memory systems. The present review examines several such factors, including information compatibility, temporal sequence of training, the visual sensory environment, reinforcement parameters, emotional arousal, and memory modulatory systems. Altering these parameters can lead to selective enhancements of either hippocampal-dependent or dorsolateral striatal-dependent memory, and bias animals toward the use of either cognitive or habit memory in dual-solution tasks that may be solved adequately with either memory system. In many learning situations, the influence of such experimental factors on the relative use of memory systems likely reflects a competitive interaction between the systems. Research examining how various factors influence the relative use of multiple memory systems may be a useful method for investigating how these systems interact with one another. Copyright © 2013 Wiley Periodicals, Inc.
Informing augmented memory system design through autobiographical memory theory
Hoven, van den E.A.W.H.; Eggen, J.H.
2008-01-01
Autobiographical memory (AM) is the "memory for the events in one’s life" [1]. Often it is assumed that in order to remember all those events, you just need to record everything and when you replay these recordings you will remember those events. You can compare this with a library metaphor that has
Visual software system for memory interleaving simulation
Directory of Open Access Journals (Sweden)
Milenković Katarina
2017-01-01
Full Text Available This paper describes the visual software system for memory interleaving simulation (VSMIS, implemented for the purpose of the course Computer Architecture and Organization 1, at the School of Electrical Engineering, University of Belgrade. The simulator enables students to expand their knowledge through practical work in the laboratory, as well as through independent work at home. VSMIS gives users the possibility to initialize parts of the system and to control simulation steps. The user has the ability to monitor simulation through graphical representation. It is possible to navigate through the entire hierarchy of the system using simple navigation. During the simulation the user can observe and set the values of the memory location. At any time, the user can reset the simulation of the system and observe it for different memory states; in addition, it is possible to save the current state of the simulation and continue with the execution of the simulation later. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III44009
Haji Ali, Abdul Lateef
2016-01-01
I discuss using single level and multilevel Monte Carlo methods to compute quantities of interests of a stochastic particle system in the mean-field. In this context, the stochastic particles follow a coupled system of Ito stochastic differential equations (SDEs). Moreover, this stochastic particle system converges to a stochastic mean-field limit as the number of particles tends to infinity. I start by recalling the results of applying different versions of Multilevel Monte Carlo (MLMC) for particle systems, both with respect to time steps and the number of particles and using a partitioning estimator. Next, I expand on these results by proposing the use of our recent Multi-index Monte Carlo method to obtain improved convergence rates.
Haji Ali, Abdul Lateef
2016-01-08
I discuss using single level and multilevel Monte Carlo methods to compute quantities of interests of a stochastic particle system in the mean-field. In this context, the stochastic particles follow a coupled system of Ito stochastic differential equations (SDEs). Moreover, this stochastic particle system converges to a stochastic mean-field limit as the number of particles tends to infinity. I start by recalling the results of applying different versions of Multilevel Monte Carlo (MLMC) for particle systems, both with respect to time steps and the number of particles and using a partitioning estimator. Next, I expand on these results by proposing the use of our recent Multi-index Monte Carlo method to obtain improved convergence rates.
A Scalable Unsegmented Multiport Memory for FPGA-Based Systems
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Kevin R. Townsend
2015-01-01
Full Text Available On-chip multiport memory cores are crucial primitives for many modern high-performance reconfigurable architectures and multicore systems. Previous approaches for scaling memory cores come at the cost of operating frequency, communication overhead, and logic resources without increasing the storage capacity of the memory. In this paper, we present two approaches for designing multiport memory cores that are suitable for reconfigurable accelerators with substantial on-chip memory or complex communication. Our design approaches tackle these challenges by banking RAM blocks and utilizing interconnect networks which allows scaling without sacrificing logic resources. With banking, memory congestion is unavoidable and we evaluate our multiport memory cores under different memory access patterns to gain insights about different design trade-offs. We demonstrate our implementation with up to 256 memory ports using a Xilinx Virtex-7 FPGA. Our experimental results report high throughput memories with resource usage that scales with the number of ports.
A Newton-Based Extremum Seeking MPPT Method for Photovoltaic Systems with Stochastic Perturbations
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Heng Li
2014-01-01
Full Text Available Microcontroller based maximum power point tracking (MPPT has been the most popular MPPT approach in photovoltaic systems due to its high flexibility and efficiency in different photovoltaic systems. It is well known that PV systems typically operate under a range of uncertain environmental parameters and disturbances, which implies that MPPT controllers generally suffer from some unknown stochastic perturbations. To address this issue, a novel Newton-based stochastic extremum seeking MPPT method is proposed. Treating stochastic perturbations as excitation signals, the proposed MPPT controller has a good tolerance of stochastic perturbations in nature. Different from conventional gradient-based extremum seeking MPPT algorithm, the convergence rate of the proposed controller can be totally user-assignable rather than determined by unknown power map. The stability and convergence of the proposed controller are rigorously proved. We further discuss the effects of partial shading and PV module ageing on the proposed controller. Numerical simulations and experiments are conducted to show the effectiveness of the proposed MPPT algorithm.
A Stochastic Hybrid Systems framework for analysis of Markov reward models
International Nuclear Information System (INIS)
Dhople, S.V.; DeVille, L.; Domínguez-García, A.D.
2014-01-01
In this paper, we propose a framework to analyze Markov reward models, which are commonly used in system performability analysis. The framework builds on a set of analytical tools developed for a class of stochastic processes referred to as Stochastic Hybrid Systems (SHS). The state space of an SHS is comprised of: (i) a discrete state that describes the possible configurations/modes that a system can adopt, which includes the nominal (non-faulty) operational mode, but also those operational modes that arise due to component faults, and (ii) a continuous state that describes the reward. Discrete state transitions are stochastic, and governed by transition rates that are (in general) a function of time and the value of the continuous state. The evolution of the continuous state is described by a stochastic differential equation and reward measures are defined as functions of the continuous state. Additionally, each transition is associated with a reset map that defines the mapping between the pre- and post-transition values of the discrete and continuous states; these mappings enable the definition of impulses and losses in the reward. The proposed SHS-based framework unifies the analysis of a variety of previously studied reward models. We illustrate the application of the framework to performability analysis via analytical and numerical examples
International Nuclear Information System (INIS)
Qian, Hong
2014-01-01
We propose a mathematical formulation of the zeroth law of thermodynamics and develop a stochastic dynamical theory, with a consistent irreversible thermodynamics, for systems possessing sustained conservative stationary current in phase space while in equilibrium with a heat bath. The theory generalizes underdamped mechanical equilibrium: dx=gdt+{−D∇ϕdt+√(2D)dB(t)}, with ∇⋅g=0 and {⋯} respectively representing phase-volume preserving dynamics and stochastic damping. The zeroth law implies stationary distribution u ss (x)=e −ϕ(x) . We find an orthogonality ∇ϕ⋅g=0 as a hallmark of the system. Stochastic thermodynamics based on time reversal (t,ϕ,g)→(−t,ϕ,−g) is formulated: entropy production e p # (t)=−dF(t)/dt; generalized “heat” h d # (t)=−dU(t)/dt, U(t)=∫ R n ϕ(x)u(x,t)dx being “internal energy”, and “free energy” F(t)=U(t)+∫ R n u(x,t)lnu(x,t)dx never increases. Entropy follows (dS)/(dt) =e p # −h d # . Our formulation is shown to be consistent with an earlier theory of P. Ao. Its contradistinctions to other theories, potential-flux decomposition, stochastic Hamiltonian system with even and odd variables, Klein–Kramers equation, Freidlin–Wentzell's theory, and GENERIC, are discussed.
Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic
2014-01-01
The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid
A Stochastic Model for Improving Information Security in Supply Chain Systems
Ibrahim Al Kattan; Ahmed Al Nunu; Kassem Saleh
2009-01-01
This article presents a probabilistic security model for supply chain management systems (SCM) in which the basic goals of security (including confidentiality, integrity, availability and accountability, CIAA) are modeled and analyzed. Consequently, the weak points in system security are identified. A stochastic model using measurable values to describe the information system security of a SCM is introduced. Information security is a crucial and integral part of the network of supply chains. ...
Stochastic Averaging and Stochastic Extremum Seeking
Liu, Shu-Jun
2012-01-01
Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering and analysis of bacterial convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments. The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon. The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms...
International Nuclear Information System (INIS)
Gershgorin, B.; Harlim, J.; Majda, A.J.
2010-01-01
The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates
Declarative and nondeclarative memory: multiple brain systems supporting learning and memory.
Squire, L R
1992-01-01
Abstract The topic of multiple forms of memory is considered from a biological point of view. Fact-and-event (declarative, explicit) memory is contrasted with a collection of non conscious (non-declarative, implicit) memory abilities including skills and habits, priming, and simple conditioning. Recent evidence is reviewed indicating that declarative and non declarative forms of memory have different operating characteristics and depend on separate brain systems. A brain-systems framework for understanding memory phenomena is developed in light of lesion studies involving rats, monkeys, and humans, as well as recent studies with normal humans using the divided visual field technique, event-related potentials, and positron emission tomography (PET).
Smolen, Paul; Baxter, Douglas A.; Byrne, John H.
2012-01-01
Multiple interlinked positive feedback loops shape the stimulus responses of various biochemical systems, such as the cell cycle or intracellular Ca2+ release. Recent studies with simplified models have identified two advantages of coupling fast and slow feedback loops. This dual-time structure enables a fast response while enhancing resistances of responses and bistability to stimulus noise. We now find that: 1) the dual-time structure similarly confers resistance to internal noise due to mo...
Smolen, Paul; Baxter, Douglas A; Byrne, John H
2009-03-01
Multiple interlinked positive feedback loops shape the stimulus responses of various biochemical systems, such as the cell cycle or intracellular Ca2+ release. Recent studies with simplified models have identified two advantages of coupling fast and slow feedback loops. This dual-time structure enables a fast response while enhancing resistances of responses and bistability to stimulus noise. We now find that (1) the dual-time structure similarly confers resistance to internal noise due to molecule number fluctuations, and (2) model variants with altered coupling, which better represent some specific biochemical systems, share all the above advantages. We also develop a similar bistable model with coupling of a fast autoactivation loop to a slow loop. This model's topology was suggested by positive feedback proposed to play a role in long-term synaptic potentiation (LTP). The advantages of fast response and noise resistance are also present in this autoactivation model. Empirically, LTP develops resistance to reversal over approximately 1h . The model suggests this resistance may result from increased amounts of synaptic kinases involved in positive feedback.
Directory of Open Access Journals (Sweden)
Pengfei Guo
2014-01-01
Full Text Available This paper deals with the fault detection problem for a class of discrete-time wireless networked control systems described by switching topology with uncertainties and disturbances. System states of each individual node are affected not only by its own measurements, but also by other nodes’ measurements according to a certain network topology. As the topology of system can be switched in a stochastic way, we aim to design H∞ fault detection observers for nodes in the dynamic time-delay systems. By using the Lyapunov method and stochastic analysis techniques, sufficient conditions are acquired to guarantee the existence of the filters satisfying the H∞ performance constraint, and observer gains are derived by solving linear matrix inequalities. Finally, an illustrated example is provided to verify the effectiveness of the theoretical results.
Model reduction for slow–fast stochastic systems with metastable behaviour
International Nuclear Information System (INIS)
Bruna, Maria; Chapman, S. Jonathan; Smith, Matthew J.
2014-01-01
The quasi-steady-state approximation (or stochastic averaging principle) is a useful tool in the study of multiscale stochastic systems, giving a practical method by which to reduce the number of degrees of freedom in a model. The method is extended here to slow–fast systems in which the fast variables exhibit metastable behaviour. The key parameter that determines the form of the reduced model is the ratio of the timescale for the switching of the fast variables between metastable states to the timescale for the evolution of the slow variables. The method is illustrated with two examples: one from biochemistry (a fast-species-mediated chemical switch coupled to a slower varying species), and one from ecology (a predator–prey system). Numerical simulations of each model reduction are compared with those of the full system
International Nuclear Information System (INIS)
Olsson, Magnus; Perninge, Magnus; Soeder, Lennart
2010-01-01
The inclusion of wind power into power systems has a significant impact on the demand for real-time balancing power due to the stochastic nature of wind power production. The overall aim of this paper is to present probabilistic models of the impact of large-scale integration of wind power on the continuous demand in MW for real-time balancing power. This is important not only for system operators, but also for producers and consumers since they in most systems through various market solutions provide balancing power. Since there can occur situations where the wind power variations cancel out other types of deviations in the system, models on an hourly basis are not sufficient. Therefore the developed model is in continuous time and is based on stochastic differential equations (SDE). The model can be used within an analytical framework or in Monte Carlo simulations. (author)
Endogenous versus exogenous shocks in systems with memory
Sornette, D.; Helmstetter, A.
2003-02-01
Systems with long-range persistence and memory are shown to exhibit different precursory as well as recovery patterns in response to shocks of exogenous versus endogenous origins. By endogenous, we envision either fluctuations resulting from an underlying chaotic dynamics or from a stochastic forcing origin which may be external or be an effective coarse-grained description of the microscopic fluctuations. In this scenario, endogenous shocks result from a kind of constructive interference of accumulated fluctuations whose impacts survive longer than the large shocks themselves. As a consequence, the recovery after an endogenous shock is in general slower at early times and can be at long times either slower or faster than after an exogenous perturbation. This offers the tantalizing possibility of distinguishing between an endogenous versus exogenous cause of a given shock, even when there is no “smoking gun”. This could help in investigating the exogenous versus self-organized origins in problems such as the causes of major biological extinctions, of change of weather regimes and of the climate, in tracing the source of social upheaval and wars, and so on. Sornette et al., Volatility fingerprints of large stocks: endogenous versus exogenous, cond-mat/0204626 has already shown how this concept can be applied concretely to differentiate the effects on financial markets of the 11 September 2001 attack or of the coup against Gorbachev on 19 August 1991 (exogenous) from financial crashes such as October 1987 (endogenous).
Resonant behavior of the generalized Langevin system with tempered Mittag–Leffler memory kernel
Chen, Yao; Wang, Xudong; Deng, Weihua
2018-05-01
The generalized Langevin equation describes anomalous dynamics. Noise is not only the origin of uncertainty but also plays a positive role in helping to detect signals with information, termed stochastic resonance (SR). This paper analyzes the anomalous resonant behaviors of the generalized Langevin system with a multiplicative dichotomous noise and an internal tempered Mittag–Leffler noise. For a system with a fluctuating harmonic potential, we obtain the exact expressions of several types of SR such as the first moment, the amplitude and autocorrelation function for the output signal as well as the signal–noise ratio. We analyze the influence of the tempering parameter and memory exponent on the bona fide SR and the general SR. Moreover, it is detected that the critical memory exponent changes regularly with the increase of the tempering parameter. Almost all the theoretical results are validated by numerical simulations.
A decision dependent stochastic process model for repairable systems with applications
Directory of Open Access Journals (Sweden)
Paul F. Zantek
2015-12-01
This paper mathematically formalizes the notion of how management actions impact the functioning of a repairable system over time by developing a new stochastic process model for such systems. The proposed model is illustrated using both simulated and real data. The proposed model compares favorably to other models for well-known data on Boeing airplanes. The model is further illustrated and compared to other models on failure time and maintenance data stemming from the South Texas Project nuclear power plant.
PARAMETRIC IDENTIFICATION OF STOCHASTIC SYSTEM BY NON-GRADIENT RANDOM SEARCHING
Directory of Open Access Journals (Sweden)
A. A. Lobaty
2017-01-01
Full Text Available At this moment we know a great variety of identification objects, tasks and methods and its significance is constantly increasing in various fields of science and technology. The identification problem is dependent on a priori information about identification object, besides that the existing approaches and methods of identification are determined by the form of mathematical models (deterministic, stochastic, frequency, temporal, spectral etc.. The paper considers a problem for determination of system parameters (identification object which is assigned by the stochastic mathematical model including random functions of time. It has been shown that while making optimization of the stochastic systems subject to random actions deterministic methods can be applied only for a limited approximate optimization of the system by taking into account average random effects and fixed structure of the system. The paper proposes an algorithm for identification of parameters in a mathematical model of the stochastic system by non-gradient random searching. A specific feature of the algorithm is its applicability practically to mathematic models of any type because the applied algorithm does not depend on linearization and differentiability of functions included in the mathematical model of the system. The proposed algorithm ensures searching of an extremum for the specified quality criteria in terms of external uncertainties and limitations while using random searching of parameters for a mathematical model of the system. The paper presents results of the investigations on operational capability of the considered identification method while using mathematical simulation of hypothetical control system with a priori unknown parameter values of the mathematical model. The presented results of the mathematical simulation obviously demonstrate the operational capability of the proposed identification method.
Yin, Fancheng; Yu, Xiaoyan
2015-01-01
This paper is concerned with the existence of stationary distribution and extinction for multispecies stochastic Lotka-Volterra predator-prey system. The contributions of this paper are as follows. (a) By using Lyapunov methods, the sufficient conditions on existence of stationary distribution and extinction are established. (b) By using the space decomposition technique and the continuity of probability, weaker conditions on extinction of the system are obtained. Finally, a numer...
Moreno, Pablo; García, Marcelo
2016-01-01
The increase in energy consumption, especially in residential consumers, means that the electrical system should grow at pair, in infrastructure and installed capacity, the energy prices vary to meet these needs, so this paper uses the methodology of demand response using stochastic methods such as Markov, to optimize energy consumption of residential users. It is necessary to involve customers in the electrical system because in this way it can be verified the actual amount of electric charg...
SWINE BREEDING SYSTEMS: A STOCHASTIC EVALUATION WITH IMPLICATIONS FOR EMERGING TECHNOLOGY
Massey, Raymond E.; Williams, Joseph E.
1991-01-01
The after-tax net present value for 27 swine breeding systems composed of Duroc, Hampshire, and Yorkshire breeds were simulated and ordered using stochastic dominance analysis. The concept of the value of information was expanded to develop the concept of the willingness to pay to adopt a new technology. For producers not currently using the dominant system, estimates of the allowable present value cost of adoption are reported and used to explain diverse production practices.
International Nuclear Information System (INIS)
Brett, Tobias; Galla, Tobias
2014-01-01
We present a heuristic derivation of Gaussian approximations for stochastic chemical reaction systems with distributed delay. In particular, we derive the corresponding chemical Langevin equation. Due to the non-Markovian character of the underlying dynamics, these equations are integro-differential equations, and the noise in the Gaussian approximation is coloured. Following on from the chemical Langevin equation, a further reduction leads to the linear-noise approximation. We apply the formalism to a delay variant of the celebrated Brusselator model, and show how it can be used to characterise noise-driven quasi-cycles, as well as noise-triggered spiking. We find surprisingly intricate dependence of the typical frequency of quasi-cycles on the delay period
Brett, Tobias; Galla, Tobias
2014-03-28
We present a heuristic derivation of Gaussian approximations for stochastic chemical reaction systems with distributed delay. In particular, we derive the corresponding chemical Langevin equation. Due to the non-Markovian character of the underlying dynamics, these equations are integro-differential equations, and the noise in the Gaussian approximation is coloured. Following on from the chemical Langevin equation, a further reduction leads to the linear-noise approximation. We apply the formalism to a delay variant of the celebrated Brusselator model, and show how it can be used to characterise noise-driven quasi-cycles, as well as noise-triggered spiking. We find surprisingly intricate dependence of the typical frequency of quasi-cycles on the delay period.
Stability Criterion of Linear Stochastic Systems Subject to Mixed H2/Passivity Performance
Directory of Open Access Journals (Sweden)
Cheung-Chieh Ku
2015-01-01
Full Text Available The H2 control scheme and passivity theory are applied to investigate the stability criterion of continuous-time linear stochastic system subject to mixed performance. Based on the stochastic differential equation, the stochastic behaviors can be described as multiplicative noise terms. For the considered system, the H2 control scheme is applied to deal with the problem on minimizing output energy. And the asymptotical stability of the system can be guaranteed under desired initial conditions. Besides, the passivity theory is employed to constrain the effect of external disturbance on the system. Moreover, the Itô formula and Lyapunov function are used to derive the sufficient conditions which are converted into linear matrix inequality (LMI form for applying convex optimization algorithm. Via solving the sufficient conditions, the state feedback controller can be established such that the asymptotical stability and mixed performance of the system are achieved in the mean square. Finally, the synchronous generator system is used to verify the effectiveness and applicability of the proposed design method.
Structure Learning in Stochastic Non-linear Dynamical Systems
Morris, R. D.; Smelyanskiy, V. N.; Luchinsky, D. G.
2005-12-01
A great many systems can be modeled in the non-linear dynamical systems framework, as x˙ = f(x) + ξ(t), where f(x) is the potential function for the system, and ξ(t) is the driving noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We show that using the Bayesian Information Criteria (BIC) to rank models, and the beam search technique, that we can accurately determine the structure of simple non-linear dynamical system models, and the structure of the coupling between non-linear dynamical systems where the individual systems are known. This last case has important ecological applications, for example in predator-prey systems, where the very structure of the coupling between predator-prey pairs can have great ecological significance.
A stochastic model for an urea decomposition system
Directory of Open Access Journals (Sweden)
VSS Yadavalli
2005-12-01
Full Text Available Availability is an important measure in describing the performance of a system. The availability of a decomposition process in an urea production system in the fertilizer industry is considered in this paper. The system contains four subsystems and is supported by a standby unit. An estimation study of the steady state availability of the system is performed and illustrated by means of a numerical example.
Identifying bottlenecks in manufacturing systems using stochastic criticality analysis
Nogueira Bastos, J.P.; van der Sanden, L.J.; Donk, O.; Voeten, J.P.M.; Stuijk, S.; Schiffelers, R.R.H.; Corporaal, H.
2018-01-01
System design is a difficult process with many design-choices for which the impact may be difficult to foresee. Manufacturing system design is no exception to this. Increased use of flexible manufacturing systems which are able to perform different operations/use-cases further raises the design
Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics.
Chen, Minghan; Li, Fei; Wang, Shuo; Cao, Young
2017-03-14
Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have discovered that for the RDME, when discretization size approaches zero, reaction time for bimolecular reactions in high dimensional domains tends to infinity. In this paper, we demonstrate that in the 1D domain, highly nonlinear reaction dynamics given by Hill function may also have dramatic change when discretization size is smaller than a critical value. Moreover, we discuss methods to avoid this problem: smoothing over space, fixed length smoothing over space and a hybrid method. Our analysis reveals that the switch-like Hill dynamics reduces to a linear function of discretization size when the discretization size is small enough. The three proposed methods could correctly (under certain precision) simulate Hill function dynamics in the microscopic RDME system.
Wang, Jianhui; Liu, Zhi; Chen, C L Philip; Zhang, Yun
2017-10-12
Hysteresis exists ubiquitously in physical actuators. Besides, actuator failures/faults may also occur in practice. Both effects would deteriorate the transient tracking performance, and even trigger instability. In this paper, we consider the problem of compensating for actuator failures and input hysteresis by proposing a fuzzy control scheme for stochastic nonlinear systems. Compared with the existing research on stochastic nonlinear uncertain systems, it is found that how to guarantee a prescribed transient tracking performance when taking into account actuator failures and hysteresis simultaneously also remains to be answered. Our proposed control scheme is designed on the basis of the fuzzy logic system and backstepping techniques for this purpose. It is proven that all the signals remain bounded and the tracking error is ensured to be within a preestablished bound with the failures of hysteretic actuator. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.
Sliding mode control-based linear functional observers for discrete-time stochastic systems
Singh, Satnesh; Janardhanan, Sivaramakrishnan
2017-11-01
Sliding mode control (SMC) is one of the most popular techniques to stabilise linear discrete-time stochastic systems. However, application of SMC becomes difficult when the system states are not available for feedback. This paper presents a new approach to design a SMC-based functional observer for discrete-time stochastic systems. The functional observer is based on the Kronecker product approach. Existence conditions and stability analysis of the proposed observer are given. The control input is estimated by a novel linear functional observer. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. Furthermore, the functional observer is designed in such a way that the effect of process and measurement noise is minimised. Simulation example is given to illustrate and validate the proposed design method.
Energy Technology Data Exchange (ETDEWEB)
Ohmacht, Martin
2017-08-15
In a multiprocessor system, a central memory synchronization module coordinates memory synchronization requests responsive to memory access requests in flight, a generation counter, and a reclaim pointer. The central module communicates via point-to-point communication. The module includes a global OR reduce tree for each memory access requesting device, for detecting memory access requests in flight. An interface unit is implemented associated with each processor requesting synchronization. The interface unit includes multiple generation completion detectors. The generation count and reclaim pointer do not pass one another.
Ohmacht, Martin
2014-09-09
In a multiprocessor system, a central memory synchronization module coordinates memory synchronization requests responsive to memory access requests in flight, a generation counter, and a reclaim pointer. The central module communicates via point-to-point communication. The module includes a global OR reduce tree for each memory access requesting device, for detecting memory access requests in flight. An interface unit is implemented associated with each processor requesting synchronization. The interface unit includes multiple generation completion detectors. The generation count and reclaim pointer do not pass one another.
Modeling reliability of power systems substations by using stochastic automata networks
International Nuclear Information System (INIS)
Šnipas, Mindaugas; Radziukynas, Virginijus; Valakevičius, Eimutis
2017-01-01
In this paper, stochastic automata networks (SANs) formalism to model reliability of power systems substations is applied. The proposed strategy allows reducing the size of state space of Markov chain model and simplifying system specification. Two case studies of standard configurations of substations are considered in detail. SAN models with different assumptions were created. SAN approach is compared with exact reliability calculation by using a minimal path set method. Modeling results showed that total independence of automata can be assumed for relatively small power systems substations with reliable equipment. In this case, the implementation of Markov chain model by a using SAN method is a relatively easy task. - Highlights: • We present the methodology to apply stochastic automata network formalism to create Markov chain models of power systems. • The stochastic automata network approach is combined with minimal path sets and structural functions. • Two models of substation configurations with different model assumptions are presented to illustrate the proposed methodology. • Modeling results of system with independent automata and functional transition rates are similar. • The conditions when total independence of automata can be assumed are addressed.
Analytic descriptions of stochastic bistable systems under force ramp.
Friddle, Raymond W
2016-05-01
Solving the two-state master equation with time-dependent rates, the ubiquitous driven bistable system, is a long-standing problem that does not permit a complete solution for all driving rates. Here we show an accurate approximation to this problem by considering the system in the control parameter regime. The results are immediately applicable to a diverse range of bistable systems including single-molecule mechanics.
International Nuclear Information System (INIS)
Hsiang, J.-T.; Hu, B.L.
2015-01-01
The existence and uniqueness of a steady state for nonequilibrium systems (NESS) is a fundamental subject and a main theme of research in statistical mechanics for decades. For Gaussian systems, such as a chain of classical harmonic oscillators connected at each end to a heat bath, and for classical anharmonic oscillators under specified conditions, definitive answers exist in the form of proven theorems. Answering this question for quantum many-body systems poses a challenge for the present. In this work we address this issue by deriving the stochastic equations for the reduced system with self-consistent backaction from the two baths, calculating the energy flow from one bath to the chain to the other bath, and exhibiting a power balance relation in the total (chain + baths) system which testifies to the existence of a NESS in this system at late times. Its insensitivity to the initial conditions of the chain corroborates to its uniqueness. The functional method we adopt here entails the use of the influence functional, the coarse-grained and stochastic effective actions, from which one can derive the stochastic equations and calculate the average values of physical variables in open quantum systems. This involves both taking the expectation values of quantum operators of the system and the distributional averages of stochastic variables stemming from the coarse-grained environment. This method though formal in appearance is compact and complete. It can also easily accommodate perturbative techniques and diagrammatic methods from field theory. Taken all together it provides a solid platform for carrying out systematic investigations into the nonequilibrium dynamics of open quantum systems and quantum thermodynamics. -- Highlights: •Nonequilibrium steady state (NESS) for interacting quantum many-body systems. •Derivation of stochastic equations for quantum oscillator chain with two heat baths. •Explicit calculation of the energy flow from one bath to the
Stochastic Methods Applied to Power System Operations with Renewable Energy: A Review
Energy Technology Data Exchange (ETDEWEB)
Zhou, Z. [Argonne National Lab. (ANL), Argonne, IL (United States); Liu, C. [Argonne National Lab. (ANL), Argonne, IL (United States); Electric Reliability Council of Texas (ERCOT), Austin, TX (United States); Botterud, A. [Argonne National Lab. (ANL), Argonne, IL (United States)
2016-08-01
Renewable energy resources have been rapidly integrated into power systems in many parts of the world, contributing to a cleaner and more sustainable supply of electricity. Wind and solar resources also introduce new challenges for system operations and planning in terms of economics and reliability because of their variability and uncertainty. Operational strategies based on stochastic optimization have been developed recently to address these challenges. In general terms, these stochastic strategies either embed uncertainties into the scheduling formulations (e.g., the unit commitment [UC] problem) in probabilistic forms or develop more appropriate operating reserve strategies to take advantage of advanced forecasting techniques. Other approaches to address uncertainty are also proposed, where operational feasibility is ensured within an uncertainty set of forecasting intervals. In this report, a comprehensive review is conducted to present the state of the art through Spring 2015 in the area of stochastic methods applied to power system operations with high penetration of renewable energy. Chapters 1 and 2 give a brief introduction and overview of power system and electricity market operations, as well as the impact of renewable energy and how this impact is typically considered in modeling tools. Chapter 3 reviews relevant literature on operating reserves and specifically probabilistic methods to estimate the need for system reserve requirements. Chapter 4 looks at stochastic programming formulations of the UC and economic dispatch (ED) problems, highlighting benefits reported in the literature as well as recent industry developments. Chapter 5 briefly introduces alternative formulations of UC under uncertainty, such as robust, chance-constrained, and interval programming. Finally, in Chapter 6, we conclude with the main observations from our review and important directions for future work.
Mass memory formatter subsystem of the adaptive intrusion data system
International Nuclear Information System (INIS)
Corlis, N.E.
1980-09-01
The Mass Memory Formatter was developed as part of the Adaptive Intrusion Data System (AIDS) to control a 2.4-megabit mass memory. The data from a Memory Controlled Processor is formatted before it is stored in the memory and reformatted during the readout mode. The data is then transmitted to a NOVA 2 minicomputer-controlled magnetic tape recorder for storage. Techniques and circuits are described
Energy Technology Data Exchange (ETDEWEB)
Wang, Yishen [Univ. of Washington, Seattle, WA (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Zhou, Zhi [Argonne National Lab. (ANL), Argonne, IL (United States); Liu, Cong [Argonne National Lab. (ANL), Argonne, IL (United States); Electric Reliability Council of Texas (ERCOT), Austin, TX (United States); Botterud, Audun [Argonne National Lab. (ANL), Argonne, IL (United States)
2016-08-01
As more wind power and other renewable resources are being integrated into the electric power grid, the forecast uncertainty brings operational challenges for the power system operators. In this report, different operational strategies for uncertainty management are presented and evaluated. A comprehensive and consistent simulation framework is developed to analyze the performance of different reserve policies and scheduling techniques under uncertainty in wind power. Numerical simulations are conducted on a modified version of the IEEE 118-bus system with a 20% wind penetration level, comparing deterministic, interval, and stochastic unit commitment strategies. The results show that stochastic unit commitment provides a reliable schedule without large increases in operational costs. Moreover, decomposition techniques, such as load shift factor and Benders decomposition, can help in overcoming the computational obstacles to stochastic unit commitment and enable the use of a larger scenario set to represent forecast uncertainty. In contrast, deterministic and interval unit commitment tend to give higher system costs as more reserves are being scheduled to address forecast uncertainty. However, these approaches require a much lower computational effort Choosing a proper lower bound for the forecast uncertainty is important for balancing reliability and system operational cost in deterministic and interval unit commitment. Finally, we find that the introduction of zonal reserve requirements improves reliability, but at the expense of higher operational costs.
Can we observe open loop transfer functions in a stochastic feedback system ?
International Nuclear Information System (INIS)
Kishida, Kuniharu; Suda, Nobuhide.
1991-01-01
There are two kinds of problems concerning open loop and closed loop transfer functions in a feedback system. One is a problem even in the deterministic case, and the other is in the stochastic case. In the deterministic case it is guaranteed under a necessary and sufficient condition that total sum of degrees of sub-transfer functions coincides to the degree of the total system. In the stochastic case a systematic understanding of a physical state model, a theoretical innovation model and a data-oriented innovation model is indispensable for determination of open loop transfer functions from time series data. Undesirable factors appear in determination of open loop transfer functions, since a transfer function matrix from input noises to output variables has a redundancy factor of diagonal matrix. (author)
Risk-sensitive control of stochastic hybrid systems on infinite time horizon
Directory of Open Access Journals (Sweden)
Runolfsson Thordur
1999-01-01
Full Text Available A risk-sensitive optimal control problem is considered for a hybrid system that consists of continuous time diffusion process that depends on a discrete valued mode variable that is modeled as a Markov chain. Optimality conditions are presented and conditions for the existence of optimal controls are derived. It is shown that the optimal risk-sensitive control problem is equivalent to the upper value of an associated stochastic differential game, and insight into the contributions of the noise input and mode variable to the risk sensitivity of the cost functional is given. Furthermore, it is shown that due to the mode variable risk sensitivity, the equivalence relationship that has been observed between risk-sensitive and H ∞ control in the nonhybrid case does not hold for stochastic hybrid systems.
International Nuclear Information System (INIS)
Gomes, I.L.R.; Pousinho, H.M.I.; Melício, R.; Mendes, V.M.F.
2017-01-01
This paper presents an optimal bid submission in a day-ahead electricity market for the problem of joint operation of wind with photovoltaic power systems having an energy storage device. Uncertainty not only due to the electricity market price, but also due to wind and photovoltaic powers is one of the main characteristics of this submission. The problem is formulated as a two-stage stochastic programming problem. The optimal bids and the energy flow in the batteries are the first-stage variables and the energy deviation is the second stage variable of the problem. Energy storage is a way to harness renewable energy conversion, allowing the store and discharge of energy at conveniently market prices. A case study with data from the Iberian day-ahead electricity market is presented and a comparison between joint and disjoint operations is discussed. - • Joint wind and PV systems with energy storage. • Electricity markets. • Stochastic optimization. • Day-ahead market.
Multistage Stochastic Programming and its Applications in Energy Systems Modeling and Optimization
Golari, Mehdi
Electric energy constitutes one of the most crucial elements to almost every aspect of life of people. The modern electric power systems face several challenges such as efficiency, economics, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, weak conditions, unexpected events, hidden failures, human errors, terrorist attacks, and natural disasters. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic programming provides a mathematical framework for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the intrinsic uncertainty into the decision making process. In this dissertation, we focus on application of two-stage and multistage stochastic programming approaches to electric energy systems modeling and optimization. Particularly, we develop models and algorithms addressing the sustainability and reliability issues in power systems. First, we consider how to improve the reliability of power systems under severe failures or contingencies prone to cascading blackouts by so called islanding operations. We present a two-stage stochastic mixed-integer model to find optimal islanding operations as a powerful preventive action against cascading failures in case of extreme contingencies. Further, we study the properties of this problem and propose efficient solution methods to solve this problem for large-scale power systems. We present the numerical results showing the effectiveness of the model and investigate the performance of the solution methods. Next, we address the sustainability issue
Investigation of fast initialization of spacecraft bubble memory systems
Looney, K. T.; Nichols, C. D.; Hayes, P. J.
1984-01-01
Bubble domain technology offers significant improvement in reliability and functionality for spacecraft onboard memory applications. In considering potential memory systems organizations, minimization of power in high capacity bubble memory systems necessitates the activation of only the desired portions of the memory. In power strobing arbitrary memory segments, a capability of fast turn on is required. Bubble device architectures, which provide redundant loop coding in the bubble devices, limit the initialization speed. Alternate initialization techniques are investigated to overcome this design limitation. An initialization technique using a small amount of external storage is demonstrated.
Relationships of dispersive mass transport and stochastic convective flow through hydrologic systems
International Nuclear Information System (INIS)
Simmons, C.S.
1981-01-01
Uncertainty in water flow velocity appears to be a major factor in determining the magnitude of contaminant dispersion expected in a ground water system. This report discusses some concepts and mathematical methods relating dispersive contaminant transport to stochastic aspects of ground water flow. The theory developed should not be construed as absolutely rigorous mathematics, but is presented with the intention of clarifying the physical concepts
Energy Technology Data Exchange (ETDEWEB)
Liu, Yunlong; Wang, Aiping; Guo, Lei; Wang, Hong
2017-07-09
This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.
Joint market clearing in a stochastic framework considering power system security
International Nuclear Information System (INIS)
Aghaei, J.; Shayanfar, H.A.; Amjady, N.
2009-01-01
This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte-Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.
Modeling and simulation of high dimensional stochastic multiscale PDE systems at the exascale
Energy Technology Data Exchange (ETDEWEB)
Zabaras, Nicolas J. [Cornell Univ., Ithaca, NY (United States)
2016-11-08
Predictive Modeling of multiscale and Multiphysics systems requires accurate data driven characterization of the input uncertainties, and understanding of how they propagate across scales and alter the final solution. This project develops a rigorous mathematical framework and scalable uncertainty quantification algorithms to efficiently construct realistic low dimensional input models, and surrogate low complexity systems for the analysis, design, and control of physical systems represented by multiscale stochastic PDEs. The work can be applied to many areas including physical and biological processes, from climate modeling to systems biology.
Improving the performance of power-limited transverse stochastic cooling systems
International Nuclear Information System (INIS)
Goldberg, D.A.; Lambertson, G.R.
1989-08-01
We present the formulas relevant to the behavior of (transverse) stochastic cooling systems which operate under the not uncommon condition that performance is limited by available output power, and contrast the operation of such systems with non-power-limited ones. In particular, we show that for power-limited systems, the two most effective improvements are the use of pickups/kickers which operate in both planes simultaneously and/or plunging of the cooling system electrodes, and present an example where increasing bandwidth is counter-productive. We apply our results to the proposed upgrade of the Fermilab bar p source. 4 refs., 1 fig., 2 tabs
Integral-based event triggering controller design for stochastic LTI systems via convex optimisation
Mousavi, S. H.; Marquez, H. J.
2016-07-01
The presence of measurement noise in the event-based systems can lower system efficiency both in terms of data exchange rate and performance. In this paper, an integral-based event triggering control system is proposed for LTI systems with stochastic measurement noise. We show that the new mechanism is robust against noise and effectively reduces the flow of communication between plant and controller, and also improves output performance. Using a Lyapunov approach, stability in the mean square sense is proved. A simulated example illustrates the properties of our approach.
Steady State Analysis of Stochastic Systems with Multiple Time Delays
Xu, W.; Sun, C. Y.; Zhang, H. Q.
In this paper, attention is focused on the steady state analysis of a class of nonlinear dynamic systems with multi-delayed feedbacks driven by multiplicative correlated Gaussian white noises. The Fokker-Planck equations for delayed variables are at first derived by Novikov's theorem. Then, under small delay assumption, the approximate stationary solutions are obtained by the probability density approach. As a special case, the effects of multidelay feedbacks and the correlated additive and multiplicative Gaussian white noises on the response of a bistable system are considered. It is shown that the obtained analytical results are in good agreement with experimental results in Monte Carlo simulations.
Measurability and Safety Verification for Stochastic Hybrid Systems
DEFF Research Database (Denmark)
Fränzle, Martin; Hahn, Ernst Moritz; Hermanns, Holger
2011-01-01
method that establishes safe upper bounds on reachability probabilities. To arrive there requires us to solve semantic intricacies as well as practical problems. In particular, we show that measurability of a complete system follows from the measurability of its constituent parts. On the practical side......-time behaviour is given by differential equations, as for usual hybrid systems, but the targets of discrete jumps are chosen by probability distributions. These distributions may be general measures on state sets. Also non-determinism is supported, and the latter is exploited in an abstraction and evaluation...
Analysis of dynamic characteristics of stochastic influences in cognitive systems
Directory of Open Access Journals (Sweden)
Alexander A. Solodov
2017-01-01
Full Text Available The aim of the study is to provide an analytical description of the dynamics of the processes to form images in the cognitive system and their subsequent processing by the consciousness, as well as the study of the simplest characteristics of the quality of the cognitive system functioning in the form of the signal/noise ratio.In accordance with the ideas of the cognitive theory, it is believed that images (schemes, categories, Gestalt, systems, archetypes, etc. are firstly generated in the human brain and then processed by the consciousness.These images are formed at random in time and are characterized by a random force of effects and subsequently processed by the consciousness.The images are characterized by random numbers, the common interpretation of which is the amount of information corresponding to the appearance of a certain image. The times of appearance are points on the time axis; their number and position are random as well.The work consists of a logically completed model including the following components:• Justification of a statistical model of the appearance of effects during the operation of the cognitive system in the form of the Poisson point process, characterized by the intensity of occurrence of effects and the random values of those effects.• Development of a mathematical model in the consciousness processing of the random effects in the form of reducing response function, which depends on the current time, the time of occurrence of effects and the magnitudes of these effects. To obtain applied results, exponential response function was applied and the analytical results for the mathematical expectations of the processed and not processed information by the consciousness were received.• Introduction for consideration of the signal/noise ratio, characterizing the performance of cognitive systems in the presence of interference and study of its behavior in the situations with the presence of random background noise
Nonequilibrium Enhances Adaptation Efficiency of Stochastic Biochemical Systems.
Directory of Open Access Journals (Sweden)
Chen Jia
Full Text Available Adaptation is a crucial biological function possessed by many sensory systems. Early work has shown that some influential equilibrium models can achieve accurate adaptation. However, recent studies indicate that there are close relationships between adaptation and nonequilibrium. In this paper, we provide an explanation of these two seemingly contradictory results based on Markov models with relatively simple networks. We show that as the nonequilibrium driving becomes stronger, the system under consideration will undergo a phase transition along a fixed direction: from non-adaptation to simple adaptation then to oscillatory adaptation, while the transition in the opposite direction is forbidden. This indicates that although adaptation may be observed in equilibrium systems, it tends to occur in systems far away from equilibrium. In addition, we find that nonequilibrium will improve the performance of adaptation by enhancing the adaptation efficiency. All these results provide a deeper insight into the connection between adaptation and nonequilibrium. Finally, we use a more complicated network model of bacterial chemotaxis to validate the main results of this paper.
Distributed Fuzzy and Stochastic Observers for Nonlinear Systems
Lendek, Z.
2009-01-01
Many problems in decision making, control, and monitoring require that all variables of interest, usually states and parameters of the system, are known at all times. However, in practical situations, not all variables are measurable or they are not measured due to technical or economical reasons.
Modeling stochastic lead times in multi-echelon systems
Diks, E.B.; Heijden, van der M.C.
1996-01-01
In many multi-echelon inventory systems the lead times are random variables. A common and reasonable assumption in most models is that replenishment orders do not cross, which implies that successive lead times are correlated. However, the process which generates such lead times is usually not
Modeling stochastic lead times in multi-echelon systems
Diks, E.B.; van der Heijden, M.C.
1997-01-01
In many multi-echelon inventory systems, the lead times are random variables. A common and reasonable assumption in most models is that replenishment orders do not cross, which implies that successive lead times are correlated. However, the process that generates such lead times is usually not well
Simiu, Emil
2002-01-01
The classical Melnikov method provides information on the behavior of deterministic planar systems that may exhibit transitions, i.e. escapes from and captures into preferred regions of phase space. This book develops a unified treatment of deterministic and stochastic systems that extends the applicability of the Melnikov method to physically realizable stochastic planar systems with additive, state-dependent, white, colored, or dichotomous noise. The extended Melnikov method yields the novel result that motions with transitions are chaotic regardless of whether the excitation is deterministic or stochastic. It explains the role in the occurrence of transitions of the characteristics of the system and its deterministic or stochastic excitation, and is a powerful modeling and identification tool. The book is designed primarily for readers interested in applications. The level of preparation required corresponds to the equivalent of a first-year graduate course in applied mathematics. No previous exposure to d...
Analysis of a Stochastic Chemical System Close to a SNIPER Bifurcation of Its Mean-Field Model
Erban, Radek; Chapman, S. Jonathan; Kevrekidis, Ioannis G.; Vejchodský , Tomá š
2009-01-01
A framework for the analysis of stochastic models of chemical systems for which the deterministic mean-field description is undergoing a saddle-node infinite period (SNIPER) bifurcation is presented. Such a bifurcation occurs, for example
A termination criterion for parameter estimation in stochastic models in systems biology.
Zimmer, Christoph; Sahle, Sven
2015-11-01
Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model. Copyright © 2015. Published by Elsevier Ireland Ltd.
The Development of Attention Systems and Working Memory in Infancy.
Reynolds, Greg D; Romano, Alexandra C
2016-01-01
In this article, we review research and theory on the development of attention and working memory in infancy using a developmental cognitive neuroscience framework. We begin with a review of studies examining the influence of attention on neural and behavioral correlates of an earlier developing and closely related form of memory (i.e., recognition memory). Findings from studies measuring attention utilizing looking measures, heart rate, and event-related potentials (ERPs) indicate significant developmental change in sustained and selective attention across the infancy period. For example, infants show gains in the magnitude of the attention related response and spend a greater proportion of time engaged in attention with increasing age (Richards and Turner, 2001). Throughout infancy, attention has a significant impact on infant performance on a variety of tasks tapping into recognition memory; however, this approach to examining the influence of infant attention on memory performance has yet to be utilized in research on working memory. In the second half of the article, we review research on working memory in infancy focusing on studies that provide insight into the developmental timing of significant gains in working memory as well as research and theory related to neural systems potentially involved in working memory in early development. We also examine issues related to measuring and distinguishing between working memory and recognition memory in infancy. To conclude, we discuss relations between the development of attention systems and working memory.
Robust Performance And Dissipation of Stochastic Control Systems
DEFF Research Database (Denmark)
Thygesen, Uffe Høgsbro
and topology on the space of supply rates. For instance, we give conditions under which the available storage is a continuous convex function of the supply rate. Dissipation theory in the existing literature applies only to deterministic systems. This is unfortunate since robust control applications typically...... is a prototype of robust adaptive control problems. We show that the optimal (minimax) controller for this problem is finite dimensional but not based on certainty equivalence, and we discuss the heuristic certainty equivalence controller....
Stochastic systems driven by alpha-stable noises
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Ditlevsen, P.
1998-01-01
with observed data. In particular the tailsof the observed response distributions may even for linear systems be more fat than the tails obtained for Gaussianwhite noise input. Also the excitation may show jumps that cannot be modeled by Gaussian white noise. The paper supports the possibility of using...... the larger class of so-calledalpha-stable white noises to provide a better fit. A geophysical application concerning ice age climate variations is described....
Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system
Keane, R. J.; Plant, R. S.; Tennant, W. J.
2015-12-01
The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.
Non-cooperative stochastic differential game theory of generalized Markov jump linear systems
Zhang, Cheng-ke; Zhou, Hai-ying; Bin, Ning
2017-01-01
This book systematically studies the stochastic non-cooperative differential game theory of generalized linear Markov jump systems and its application in the field of finance and insurance. The book is an in-depth research book of the continuous time and discrete time linear quadratic stochastic differential game, in order to establish a relatively complete framework of dynamic non-cooperative differential game theory. It uses the method of dynamic programming principle and Riccati equation, and derives it into all kinds of existence conditions and calculating method of the equilibrium strategies of dynamic non-cooperative differential game. Based on the game theory method, this book studies the corresponding robust control problem, especially the existence condition and design method of the optimal robust control strategy. The book discusses the theoretical results and its applications in the risk control, option pricing, and the optimal investment problem in the field of finance and insurance, enriching the...
Optimising stochastic trajectories in exact quantum jump approaches of interacting systems
International Nuclear Information System (INIS)
Lacroix, D.
2004-11-01
The standard methods used to substitute the quantum dynamics of two interacting systems by a quantum jump approach based on the Stochastic Schroedinger Equation (SSE) are described. It turns out that for a given situation, there exists an infinite number of SSE reformulation. This fact is used to propose general strategies to optimise the stochastic paths in order to reduce the statistical fluctuations. In this procedure, called the 'adaptative noise method', a specific SSE is obtained for which the noise depends explicitly on both the initial state and on the properties of the interaction Hamiltonian. It is also shown that this method can be further improved by the introduction of a mean-field dynamics. The different optimisation procedures are illustrated quantitatively in the case of interacting spins. A significant reduction of the statistical fluctuations is obtained. Consequently, a much smaller number of trajectories is needed to accurately reproduce the exact dynamics as compared to the standard SSE method. (author)
Bifurcation Regulations Governed by Delay Self-Control Feedback in a Stochastic Birhythmic System
Ma, Zhidan; Ning, Lijuan
2017-12-01
We aim to investigate bifurcation behaviors in a stochastic birhythmic van der Pol (BVDP) system subjected to delay self-control feedback. First, the harmonic approximation is adopted to drive the delay self-control feedback to state variables without delay. Then, Fokker-Planck-Kolmogorov (FPK) equation and stationary probability density function (SPDF) for amplitude are obtained by applying stochastic averaging method. Finally, dynamical scenarios of the change of delay self-control feedback as well as noise that markedly influence bifurcation performance are observed. It is found that: the big feedback strength and delay will suppress the large amplitude limit cycle (LC) while the relatively big noise strength facilitates the large amplitude LC, which imply the proposed regulation strategies are feasible. Interestingly enough, the inner LC is never destroyed due to noise. Furthermore, the validity of analytical results was verified by Monte Carlo simulation of the dynamics.
On the interpretations of Langevin stochastic equation in different coordinate systems
International Nuclear Information System (INIS)
Martinez, E.; Lopez-Diaz, L.; Torres, L.; Alejos, O.
2004-01-01
The stochastic Langevin Landau-Lifshitz equation is usually utilized in micromagnetics formalism to account for thermal effects. Commonly, two different interpretations of the stochastic integrals can be made: Ito and Stratonovich. In this work, the Langevin-Landau-Lifshitz (LLL) equation is written in both Cartesian and Spherical coordinates. If Spherical coordinates are employed, the noise is additive, and therefore, Ito and Stratonovich solutions are equal. This is not the case when (LLL) equation is written in Cartesian coordinates. In this case, the Langevin equation must be interpreted in the Stratonovich sense in order to reproduce correct statistical results. Nevertheless, the statistics of the numerical results obtained from Euler-Ito and Euler-Stratonovich schemes are equivalent due to the additional numerical constraint imposed in Cartesian system after each time step, which itself assures that the magnitude of the magnetization is preserved
Neuro-Inspired Computing with Stochastic Electronics
Naous, Rawan
2016-01-06
The extensive scaling and integration within electronic systems have set the standards for what is addressed to as stochastic electronics. The individual components are increasingly diverting away from their reliable behavior and producing un-deterministic outputs. This stochastic operation highly mimics the biological medium within the brain. Hence, building on the inherent variability, particularly within novel non-volatile memory technologies, paves the way for unconventional neuromorphic designs. Neuro-inspired networks with brain-like structures of neurons and synapses allow for computations and levels of learning for diverse recognition tasks and applications.
Decentralized Stackelberg Strategies for Interconnected Stochastic Dynamic Systems
1977-10-01
Solutions" IM, Vol.8, No.6, p.413- 430, 1971. (42) Rhodes, I.B., and Luenberger, D.G., "Differential Games with Imperfect State Information", E Trans...34, Proc. Systems E for Power, ERDA Conf. Henniker, New Hampshire, 1975. [47) Starr, A.W., and Ho, Y.C., "Nonzero-Sum Differential Games ", Jt_., [ Vol.3, p...CONTROLLING OFFICE NAME AND ADDRESS 12. REPORT DATE October, 1977 Joint Services Electronics Program ,3. NUMSEROWPAGES 97 14. MONITORiNG &GENCY NAME 1
Stochastic Resonance and First Arrival Time for Excitable Systems
Duki, Solomon Fekade; Taye, Mesfin Asfaw
2018-06-01
We study the noise induced thermally activated barrier crossing of Brownian particles that hop in a piecewise linear potential. Using the exact analytic solutions and via numerical simulations not only we explore the dependence for the first passage time of a single particle but also we calculate the first arrival time for one particle out of N particles. The first arrival time decreases as the number of particles increases as expected. We then explore the thermally activated barrier crossing rate of the system in the presence of time varying signal. The dependence of signal to noise ratio SNR as well as the power amplification (η ) on model parameters is explored. η and SNR depict a pronounced peak at particular noise strength. In the presence of N particles, η is considerably amplified as N steps up showing the weak periodic signal plays a vital role in controlling the noise induced dynamics of the system. Moreover, for the sake of generality, the viscous friction γ is considered to decrease exponentially when the temperature T of the medium increases (γ =Be^{-A T}) as proposed originally by Reynolds (Philos Trans R Soc Lond 177:157, 1886).
Stochastic perturbations in open chaotic systems: random versus noisy maps.
Bódai, Tamás; Altmann, Eduardo G; Endler, Antonio
2013-04-01
We investigate the effects of random perturbations on fully chaotic open systems. Perturbations can be applied to each trajectory independently (white noise) or simultaneously to all trajectories (random map). We compare these two scenarios by generalizing the theory of open chaotic systems and introducing a time-dependent conditionally-map-invariant measure. For the same perturbation strength we show that the escape rate of the random map is always larger than that of the noisy map. In random maps we show that the escape rate κ and dimensions D of the relevant fractal sets often depend nonmonotonically on the intensity of the random perturbation. We discuss the accuracy (bias) and precision (variance) of finite-size estimators of κ and D, and show that the improvement of the precision of the estimations with the number of trajectories N is extremely slow ([proportionality]1/lnN). We also argue that the finite-size D estimators are typically biased. General theoretical results are combined with analytical calculations and numerical simulations in area-preserving baker maps.
Stochastic Modelling and Self Tuning Control of a Continuous Cement Raw Material Mixing System
Directory of Open Access Journals (Sweden)
Hannu T. Toivonen
1980-01-01
Full Text Available The control of a continuously operating system for cement raw material mixing is studied. The purpose of the mixing system is to maintain a constant composition of the cement raw meal for the kiln despite variations of the raw material compositions. Experimental knowledge of the process dynamics and the characteristics of the various disturbances is used for deriving a stochastic model of the system. The optimal control strategy is then obtained as a minimum variance strategy. The control problem is finally solved using a self-tuning minimum variance regulator, and results from a successful implementation of the regulator are given.
Memristor-based neural networks: Synaptic versus neuronal stochasticity
Naous, Rawan
2016-11-02
In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.
Stochastic Modelling of Linear Programming Application to Brewing Operational Systems
Directory of Open Access Journals (Sweden)
Akanbi O.P.
2014-07-01
Full Text Available System where a large number of interrelated operations exist, technically-based operational mechanism is always required to achieve potential. An intuitive solution, which is common practice in most of the breweries, perhaps may not uncover the optimal solution, as there is hardly any guarantee to satisfy the best policy application. There is always high foreign exchange involved in procurement of imported raw materials and thus increases the cost of production, abandonment and poor utilization of available locally-sourced raw materials. This study focuses on the approaches which highlight the steps and mechanisms involved in optimizing the wort extract by the use of different types of adjuncts and formulating wort production models which are useful in proffering expected solutions. Optimization techniques, the generalized models and an overview of typical brewing processes were considered.
Design issues for block-oriented reflective memory system
Energy Technology Data Exchange (ETDEWEB)
Jovanovic, M; Tomasevic, M; Milutinovic, V
1996-12-31
The block-oriented reflective memory (BORM) system represents a modular bus-based system architecture that belongs to the class of distributed shared memory systems. The results of the evaluation study of the BORM implementation strategies and design decisions in regard to the different values of input parameters are presented. 5 refs.
A two-stage stochastic programming model for the optimal design of distributed energy systems
International Nuclear Information System (INIS)
Zhou, Zhe; Zhang, Jianyun; Liu, Pei; Li, Zheng; Georgiadis, Michael C.; Pistikopoulos, Efstratios N.
2013-01-01
Highlights: ► The optimal design of distributed energy systems under uncertainty is studied. ► A stochastic model is developed using genetic algorithm and Monte Carlo method. ► The proposed system possesses inherent robustness under uncertainty. ► The inherent robustness is due to energy storage facilities and grid connection. -- Abstract: A distributed energy system is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. The optimal design of such a complex system under energy demand and supply uncertainty poses significant challenges in terms of both modelling and corresponding solution strategies. This paper proposes a two-stage stochastic programming model for the optimal design of distributed energy systems. A two-stage decomposition based solution strategy is used to solve the optimization problem with genetic algorithm performing the search on the first stage variables and a Monte Carlo method dealing with uncertainty in the second stage. The model is applied to the planning of a distributed energy system in a hotel. Detailed computational results are presented and compared with those generated by a deterministic model. The impacts of demand and supply uncertainty on the optimal design of distributed energy systems are systematically investigated using proposed modelling framework and solution approach.
Emotional Arousal and Multiple Memory Systems in the Mammalian Brain
Directory of Open Access Journals (Sweden)
Mark G. Packard
2012-03-01
Full Text Available Emotional arousal induced by stress and/or anxiety can exert complex effects on learning and memory processes in mammals. Recent studies have begun to link study of the influence of emotional arousal on memory with earlier research indicating that memory is organized in multiple systems in the brain that differ in terms of the type of memory they mediate. Specifically, these studies have examined whether emotional arousal may have a differential effect on the cognitive and stimulus-response habit memory processes subserved by the hippocampus and dorsal striatum, respectively. Evidence indicates that stress or the peripheral injection of anxiogenic drugs can bias animals and humans towards the use of striatal-dependent habit memory in dual-solution tasks in which both hippocampal and stritatal-based strategies can provide an adequate solution. A bias towards the use of habit memory can also be produced by intra-basolateral amygdala administration of anxiogenic drugs, consistent with the well documented role of efferent projections of this brain region in mediating the modulatory influence of emotional arousal on memory. In some learning situations, the bias towards the use of habit memory produced by emotional arousal appears to result from an impairing effect on hippocampus-dependent cognitive memory. Further research examining the neural mechanisms linking emotion and the relative use of multiple memory systems should prove useful in view of the potential role for maladaptive habitual behaviors in various human psychopathologies.
Design of SMART alarm system using main memory database
International Nuclear Information System (INIS)
Jang, Kue Sook; Seo, Yong Seok; Park, Keun Oak; Lee, Jong Bok; Kim, Dong Hoon
2001-01-01
To achieve design goal of SMART alarm system, first of all we have to decide on how to handle and manage alarm information and how to use database. So this paper analyses concepts and deficiencies of main memory database applied in real time system. And this paper sets up structure and processing principles of main memory database using nonvolatile memory such as flash memory and develops recovery strategy and process board structures using these. Therefore this paper shows design of SMART alarm system is suited functions and requirements
Parametric sensitivity analysis for stochastic molecular systems using information theoretic metrics
Energy Technology Data Exchange (ETDEWEB)
Tsourtis, Anastasios, E-mail: tsourtis@uoc.gr [Department of Mathematics and Applied Mathematics, University of Crete, Crete (Greece); Pantazis, Yannis, E-mail: pantazis@math.umass.edu; Katsoulakis, Markos A., E-mail: markos@math.umass.edu [Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003 (United States); Harmandaris, Vagelis, E-mail: harman@uoc.gr [Department of Mathematics and Applied Mathematics, University of Crete, and Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas (FORTH), GR-70013 Heraklion, Crete (Greece)
2015-07-07
In this paper, we present a parametric sensitivity analysis (SA) methodology for continuous time and continuous space Markov processes represented by stochastic differential equations. Particularly, we focus on stochastic molecular dynamics as described by the Langevin equation. The utilized SA method is based on the computation of the information-theoretic (and thermodynamic) quantity of relative entropy rate (RER) and the associated Fisher information matrix (FIM) between path distributions, and it is an extension of the work proposed by Y. Pantazis and M. A. Katsoulakis [J. Chem. Phys. 138, 054115 (2013)]. A major advantage of the pathwise SA method is that both RER and pathwise FIM depend only on averages of the force field; therefore, they are tractable and computable as ergodic averages from a single run of the molecular dynamics simulation both in equilibrium and in non-equilibrium steady state regimes. We validate the performance of the extended SA method to two different molecular stochastic systems, a standard Lennard-Jones fluid and an all-atom methane liquid, and compare the obtained parameter sensitivities with parameter sensitivities on three popular and well-studied observable functions, namely, the radial distribution function, the mean squared displacement, and the pressure. Results show that the RER-based sensitivities are highly correlated with the observable-based sensitivities.
Optimal design of distributed energy resource systems based on two-stage stochastic programming
International Nuclear Information System (INIS)
Yang, Yun; Zhang, Shijie; Xiao, Yunhan
2017-01-01
Highlights: • A two-stage stochastic programming model is built to design DER systems under uncertainties. • Uncertain energy demands have a significant effect on the optimal design. • Uncertain energy prices and renewable energy intensity have little effect on the optimal design. • The economy is overestimated if the system is designed without considering the uncertainties. • The uncertainty in energy prices has the significant and greatest effect on the economy. - Abstract: Multiple uncertainties exist in the optimal design of distributed energy resource (DER) systems. The expected energy, economic, and environmental benefits may not be achieved and a deficit in energy supply may occur if the uncertainties are not handled properly. This study focuses on the optimal design of DER systems with consideration of the uncertainties. A two-stage stochastic programming model is built in consideration of the discreteness of equipment capacities, equipment partial load operation and output bounds as well as of the influence of ambient temperature on gas turbine performance. The stochastic model is then transformed into its deterministic equivalent and solved. For an illustrative example, the model is applied to a hospital in Lianyungang, China. Comparative studies are performed to evaluate the effect of the uncertainties in load demands, energy prices, and renewable energy intensity separately and simultaneously on the system’s economy and optimal design. Results show that the uncertainties in load demands have a significant effect on the optimal system design, whereas the uncertainties in energy prices and renewable energy intensity have almost no effect. Results regarding economy show that it is obviously overestimated if the system is designed without considering the uncertainties.
PROBABILISTIC FLOW DISTRIBUTION AS A REACTION TO THE STOCHASTICITY OF THE LOAD IN THE POWER SYSTEM
Directory of Open Access Journals (Sweden)
A. M. Hashimov
2016-01-01
Full Text Available For the analysis and control of power systems deterministic approaches that are implemented in the form of well-known methods and models of calculation of steady-state and transient modes are mostly use in current practice. With the use of these methods it is possible to obtain solutions only for fixed circuit parameters of the system scheme and assuming that active and reactive powers as well as generation in nodal points of the network remain the same. In reality the stochastic character of power consumption cause the casual fluctuations of voltages at the nodes and power flows in electric power lines of the power system. Such casual fluctuations of operation can be estimated with the use of probabilistic simulation of the power flows. In the article the results of research of the influence of depth of casual fluctuations of the load power of the system on the probability distribution of voltage at nodes as well as on the flows of active and reactive power in the lines are presented. Probabilistic modeling of flow under stochastic load change is performed for different levels of fluctuations and under loading of the mode of the system up to peak load power. Test study to quantify the effect of stochastic variability of loads on the probabilistic distribution parameters of the modes was carried out on behalf of the electrical network of the real power system. The results of the simulation of the probability flow distribution for these fluctuations of the load, represented in the form of discrete sample values of the active power obtained with the use of the analytical Monte-Carlo method, and real data measurements of their values in the network under examination were compared.
DETECTION OF CHANGES OF THE SYSTEM TECHNICAL STATE USING STOCHASTIC SUBSPACE OBSERVATION METHOD
Directory of Open Access Journals (Sweden)
Andrzej Puchalski
2014-03-01
Full Text Available System diagnostics based on vibroacoustics signals, carried out by means of stochastic subspace methods was undertaken in the hereby paper. Subspace methods are the ones based on numerical linear algebra tools. The considered solutions belong to diagnostic methods according to data, leading to the generation of residuals allowing failure recognition of elements and assemblies in machines and devices. The algorithm of diagnostics according to the subspace observation method was applied – in the paper – for the estimation of the valve system of the spark ignition engine.
Modelling M/G/1 queueing systems with server vacations using stochastic Petri nets
Directory of Open Access Journals (Sweden)
K Ramanath
2006-12-01
Full Text Available The theory of non-Markovian stochastic Petri nets is employed in this paper to derive an alternative method for studying the steady state behaviour of the M/G/1 vacation queueing system with a limited service discipline. Three types of vacation schemes are considered, and sytems with both a finite population and those with an infinite population (but finite capacity are considered. Simple numerical examples are also provided to illustrate the functionality of the methods and some useful performance measures for the system are obtained.
International Nuclear Information System (INIS)
Bisognano, J.; Leemann, C.
1982-03-01
Stochastic cooling is the damping of betatron oscillations and momentum spread of a particle beam by a feedback system. In its simplest form, a pickup electrode detects the transverse positions or momenta of particles in a storage ring, and the signal produced is amplified and applied downstream to a kicker. The time delay of the cable and electronics is designed to match the transit time of particles along the arc of the storage ring between the pickup and kicker so that an individual particle receives the amplified version of the signal it produced at the pick-up. If there were only a single particle in the ring, it is obvious that betatron oscillations and momentum offset could be damped. However, in addition to its own signal, a particle receives signals from other beam particles. In the limit of an infinite number of particles, no damping could be achieved; we have Liouville's theorem with constant density of the phase space fluid. For a finite, albeit large number of particles, there remains a residue of the single particle damping which is of practical use in accumulating low phase space density beams of particles such as antiprotons. It was the realization of this fact that led to the invention of stochastic cooling by S. van der Meer in 1968. Since its conception, stochastic cooling has been the subject of much theoretical and experimental work. The earliest experiments were performed at the ISR in 1974, with the subsequent ICE studies firmly establishing the stochastic cooling technique. This work directly led to the design and construction of the Antiproton Accumulator at CERN and the beginnings of p anti p colliding beam physics at the SPS. Experiments in stochastic cooling have been performed at Fermilab in collaboration with LBL, and a design is currently under development for a anti p accumulator for the Tevatron
Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M
2018-03-01
This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.
Ring interconnection for distributed memory automation and computing system
Energy Technology Data Exchange (ETDEWEB)
Vinogradov, V I [Inst. for Nuclear Research of the Russian Academy of Sciences, Moscow (Russian Federation)
1996-12-31
Problems of development of measurement, acquisition and central systems based on a distributed memory and a ring interface are discussed. It has been found that the RAM LINK-type protocol can be used for ringlet links in non-symmetrical distributed memory architecture multiprocessor system interaction. 5 refs.
Eichhorn, Ralf; Aurell, Erik
2014-04-01
'Stochastic thermodynamics as a conceptual framework combines the stochastic energetics approach introduced a decade ago by Sekimoto [1] with the idea that entropy can consistently be assigned to a single fluctuating trajectory [2]'. This quote, taken from Udo Seifert's [3] 2008 review, nicely summarizes the basic ideas behind stochastic thermodynamics: for small systems, driven by external forces and in contact with a heat bath at a well-defined temperature, stochastic energetics [4] defines the exchanged work and heat along a single fluctuating trajectory and connects them to changes in the internal (system) energy by an energy balance analogous to the first law of thermodynamics. Additionally, providing a consistent definition of trajectory-wise entropy production gives rise to second-law-like relations and forms the basis for a 'stochastic thermodynamics' along individual fluctuating trajectories. In order to construct meaningful concepts of work, heat and entropy production for single trajectories, their definitions are based on the stochastic equations of motion modeling the physical system of interest. Because of this, they are valid even for systems that are prevented from equilibrating with the thermal environment by external driving forces (or other sources of non-equilibrium). In that way, the central notions of equilibrium thermodynamics, such as heat, work and entropy, are consistently extended to the non-equilibrium realm. In the (non-equilibrium) ensemble, the trajectory-wise quantities acquire distributions. General statements derived within stochastic thermodynamics typically refer to properties of these distributions, and are valid in the non-equilibrium regime even beyond the linear response. The extension of statistical mechanics and of exact thermodynamic statements to the non-equilibrium realm has been discussed from the early days of statistical mechanics more than 100 years ago. This debate culminated in the development of linear response
Stress and multiple memory systems: from 'thinking' to 'doing'.
Schwabe, Lars; Wolf, Oliver T
2013-02-01
Although it has been known for decades that stress influences memory performance, it was only recently shown that stress may alter the contribution of multiple, anatomically and functionally distinct memory systems to behavior. Here, we review recent animal and human studies demonstrating that stress promotes a shift from flexible 'cognitive' to rather rigid 'habit' memory systems and discuss, based on recent neuroimaging data in humans, the underlying brain mechanisms. We argue that, despite being generally adaptive, this stress-induced shift towards 'habit' memory may, in vulnerable individuals, be a risk factor for psychopathology. Copyright © 2012 Elsevier Ltd. All rights reserved.
Memory systems, computation, and the second law of thermodynamics
International Nuclear Information System (INIS)
Wolpert, D.H.
1992-01-01
A memory is a physical system for transferring information form one moment in time to another, where that information concerns something external to the system itself. This paper argues on information-theoretic and statistical mechanical grounds that useful memories must be of one of two types, exemplified by memory in abstract computer programs and by memory in photographs. Photograph-type memories work by exploring a collapse of state space flow to an attractor state. (This attractor state is the open-quotes initializedclose quotes state of the memory.) The central assumption of the theory of reversible computation tells us that in any such collapsing, regardless of whether the collapsing must increase in entropy of the system. In concert with the second law, this establishes the logical necessity of the empirical observation that photograph-type memories are temporally asymmetric (they can tell us about the past but not about the future). Under the assumption that human memory is a photograph-type memory, this result also explains why we humans can remember only our past and not our future. In contrast to photo-graph-type memories, computer-type memories do not require any initialization, and therefore are not directly affected by the second law. As a result, computer memories can be of the future as easily as of the past, even if the program running on the computer is logically irreversible. This is entirely in accord with the well-known temporal reversibility of the process of computation. This paper ends by arguing that the asymmetry of the psychological arrow of time is a direct consequence of the asymmetry of human memory. With the rest of this paper, this explains, explicitly and rigorously, why the psychological and thermodynamic arrows of time are correlated with one another. 24 refs
Revisiting Reuse in Main Memory Database Systems
Dursun, Kayhan; Binnig, Carsten; Cetintemel, Ugur; Kraska, Tim
2016-01-01
Reusing intermediates in databases to speed-up analytical query processing has been studied in the past. Existing solutions typically require intermediate results of individual operators to be materialized into temporary tables to be considered for reuse in subsequent queries. However, these approaches are fundamentally ill-suited for use in modern main memory databases. The reason is that modern main memory DBMSs are typically limited by the bandwidth of the memory bus, thus query execution ...
Cache memory modelling method and system
Posadas Cobo, Héctor; Villar Bonet, Eugenio; Díaz Suárez, Luis
2011-01-01
The invention relates to a method for modelling a data cache memory of a destination processor, in order to simulate the behaviour of said data cache memory during the execution of a software code on a platform comprising said destination processor. According to the invention, the simulation is performed on a native platform having a processor different from the destination processor comprising the aforementioned data cache memory to be modelled, said modelling being performed by means of the...
The relationships between memory systems and sleep stages.
Rauchs, Géraldine; Desgranges, Béatrice; Foret, Jean; Eustache, Francis
2005-06-01
Sleep function remains elusive despite our rapidly increasing comprehension of the processes generating and maintaining the different sleep stages. Several lines of evidence support the hypothesis that sleep is involved in the off-line reprocessing of recently-acquired memories. In this review, we summarize the main results obtained in the field of sleep and memory consolidation in both animals and humans, and try to connect sleep stages with the different memory systems. To this end, we have collated data obtained using several methodological approaches, including electrophysiological recordings of neuronal ensembles, post-training modifications of sleep architecture, sleep deprivation and functional neuroimaging studies. Broadly speaking, all the various studies emphasize the fact that the four long-term memory systems (procedural memory, perceptual representation system, semantic and episodic memory, according to Tulving's SPI model; Tulving, 1995) benefit either from non-rapid eye movement (NREM) (not just SWS) or rapid eye movement (REM) sleep, or from both sleep stages. Tulving's classification of memory systems appears more pertinent than the declarative/non-declarative dichotomy when it comes to understanding the role of sleep in memory. Indeed, this model allows us to resolve several contradictions, notably the fact that episodic and semantic memory (the two memory systems encompassed in declarative memory) appear to rely on different sleep stages. Likewise, this model provides an explanation for why the acquisition of various types of skills (perceptual-motor, sensory-perceptual and cognitive skills) and priming effects, subserved by different brain structures but all designated by the generic term of implicit or non-declarative memory, may not benefit from the same sleep stages.
The Stochastic Dynamics for Ecological Tourism System with Visitor Educational Intervention
Directory of Open Access Journals (Sweden)
Dongping Wei
2013-01-01
Full Text Available The ever-increasing visitation in parks and protected areas continues to present a considerable challenge for worldwide land managers with allowing recreational use while preserving natural conditions. In China, the fast expanding visitation in protected areas is quickly damaging the natural resources and precious culture without effective visitor education, while regulation and site management are also gaining very limited efficacy. We propose a differential equation to describe the ecological tourism system. Shown by the theoretical proof and numerical simulation, the ecological tourism system is unstable without any perturbed factors, especially visitor educational intervention, because the solution of the dynamic system explodes in a finite time given any initial value. Supposing that the intrinsic increasing rate of stakeholders in the systems stochastically perturbed by the visitor educational intervention, we discover that the stochastic dynamic model can effectively suppress the explosion of the solution. As such, we demonstrate that the tourism system can develop steadily and safely even under a large amount of visitors in public vacation, when employing continuous visitor education intervention programmes.
Analysis methods of stochastic transient electro–magnetic processes in electric traction system
Directory of Open Access Journals (Sweden)
T. M. Mishchenko
2013-04-01
Full Text Available Purpose. The essence and basic characteristics of calculation methods of transient electromagnetic processes in the elements and devices of non–linear dynamic electric traction systems taking into account the stochastic changes of voltages and currents in traction networks of power supply subsystem and power circuits of electric rolling stock are developed. Methodology. Classical methods and the methods of non–linear electric engineering, as well as probability theory method, especially the methods of stationary ergodic and non–stationary stochastic processes application are used in the research. Findings. Using the above-mentioned methods an equivalent circuit and the system of nonlinear integra–differential equations for electromagnetic condition of the double–track inter-substation zone of alternating current electric traction system are drawn up. Calculations allow obtaining electric traction current distribution in the areas of feeder zones. Originality. First of all the paper is interesting and important from scientific point of view due to the methods, which allow taking into account probabilistic character of change for traction voltages and electric traction system currents. On the second hand the researches develop the most efficient methods of nonlinear circuits’ analysis. Practical value. The practical value of the research is presented in application of the methods to the analysis of electromagnetic and electric energy processes in the traction power supply system in the case of high-speed train traffic.
Stochastic optimized life cycle models for risk mitigation in power system applications
International Nuclear Information System (INIS)
Sageder, A.
1998-01-01
This ork shows the relevance of stochastic optimization in complex power system applications. It was proven that usual deterministic mean value models not only predict inaccurate results but are also most often on the risky side. The change in the market effects all kind of evaluation processes (e.g. fuel type and technology but especially financial engineering evaluations) in the endeavor of a strict risk mitigation comparison. But not only IPPs also traditional Utilities dash for risk/return optimized investment opportunities. In this study I developed a 2-phase model which can support a decision-maker in finding optimal solutions on investment and profitability. It has to be stated, that in this study no objective function will be optimized in an algorithmically way. On the one hand focus is laid on finding optimal solutions out of different choices (highest return at lowest possible risk); on the other hand the endeavor was to provide a decision makers with a better assessment of the likelihood of outcomes on investment considerations. The first (deterministic) phase computes in a Total Cost of Ownership (TCO) approach (Life cycle Calculation; DCF method). Most of the causal relations (day of operation, escalation of personal expanses, inflation, depreciation period, etc.) are defined within this phase. The second (stochastic) phase is a total new way in optimizing risk/return relations. With the some decision theory mathematics an expected value of stochastic solutions can be calculated. Furthermore probability function have to be defined out of historical data. The model not only supports profitability analysis (including regress and sensitivity analysis) but also supports a decision-maker in a decision process. Emphasis was laid on risk-return analysis, which can give the decision-maker first hand informations of the type of risk return problem (risk concave, averse or linear). Five important parameters were chosen which have the characteristics of typical
[Dominating motivation in systemic memory mechanisms].
Sudakov, K V
2005-01-01
The materials provided in the article support the key role of dominating motivation in the systemic processes of fixation and opening of memory mechanisms. The activating mechanisms of dominating motivations in the systemic architectonics of behavioural acts provide the basis for development of a multicomponent acceptor apparatus of an action outcomes broadly represented in various analysing brain sections. As result of enhancement of action outcomes on acceptors structures, molecular behaviour engrammes form within the functional systems. It is these molecular engrammes that are opened by dominating motivations in the same spatial-temporal sequence in which training takes place, and determine deliberate actions of animals. It was demonstrated that dominating motivation opens genetic information with an approximating-exploratory reaction under strong activation of early genes expression, in particular, of c-fos gene protein. Inherent motivation reactions are not blocked by inhibitors of proteins synthesis, by cycloheximide, in particular. In the process of training animals, i.e., satisfaction of the demands which are the basis of dominating motivations, expression of early genes in reduced, while expression of late genes is initiated. In this case, blockators of protein synthesis begin to produce strong inhibiting impact on behaviour of animals.
Stochastic resonance and vibrational resonance in an excitable system: The golden mean barrier
International Nuclear Information System (INIS)
Stan, Cristina; Cristescu, C.P.; Alexandroaei, D.; Agop, M.
2009-01-01
We report on stochastic resonance and vibrational resonance in an electric charge double layer configuration as usually found in electrical discharges, biological cell membranes, chemical systems and nanostructures. The experiment and numerical computation show the existence of a barrier expressible in terms of the golden mean above which the two phenomena do not take place. We consider this as new evidence for the importance of the golden mean criticality in the oscillatory dynamics, in agreement with El Naschie's E-infinity theory. In our experiment, the dynamics of a charge double layer generated in the inter-anode space of a twin electrical discharge is investigated under noise-harmonic and harmonic-harmonic perturbations. In the first case, a Gaussian noise can enhance the response of the system to a weak injected periodic signal, a clear mark of stochastic resonance. In the second case, similar enhancement can appear if the noise is replaced by a harmonic perturbation with a frequency much higher than the frequency of the weak oscillation. The amplitude of the low frequency oscillation shows a maximum versus the amplitude of the high frequency perturbation demonstrating vibrational resonance. In order to model these dynamics, we derived an excitable system by modifying a biased van der Pol oscillator. The computational study considers the behaviour of this system under the same types of perturbation as in the experimental investigations and is found to give consistent results in both situations.
Understanding Organizational Memory from the Integrated Management Systems (ERP
Directory of Open Access Journals (Sweden)
Gilberto Perez
2013-10-01
Full Text Available With this research, in the form of a theoretical essay addressing the theme of Organizational Memory and Integrated Management Systems (ERP, we tried to present some evidence of how this type of system can contribute to the consolidation of certain features of Organizational Memory. From a theoretical review of the concepts of Human Memory, extending to the Organizational Memory and Information Systems, with emphasis on Integrated Management Systems (ERP we tried to draw a parallel between the functions and structures of Organizational Memory and features and characteristics of ERPs. The choice of the ERP system for this study was made due to the complexity and broad scope of this system. It was verified that the ERPs adequately support many functions of the Organizational Memory, highlighting the implementation of logical processes, practices and rules in business. It is hoped that the dialogue presented here can contribute to the advancement of the understanding of organizational memory, since the similarity of Human Memory is a fertile field and there is still much to be researched.
Mélykúti, Bence; Burrage, Kevin; Zygalakis, Konstantinos C.
2010-01-01
The Chemical Langevin Equation (CLE), which is a stochastic differential equation driven by a multidimensional Wiener process, acts as a bridge between the discrete stochastic simulation algorithm and the deterministic reaction rate equation when
Simulating local measurements on a quantum many-body system with stochastic matrix product states
DEFF Research Database (Denmark)
Gammelmark, Søren; Mølmer, Klaus
2010-01-01
We demonstrate how to simulate both discrete and continuous stochastic evolutions of a quantum many-body system subject to measurements using matrix product states. A particular, but generally applicable, measurement model is analyzed and a simple representation in terms of matrix product operators...... is found. The technique is exemplified by numerical simulations of the antiferromagnetic Heisenberg spin-chain model subject to various instances of the measurement model. In particular, we focus on local measurements with small support and nonlocal measurements, which induce long-range correlations....
DEFF Research Database (Denmark)
Konicz, Agnieszka Karolina; Mulvey, John M.
2013-01-01
of such financial decisions, especially in the retirement arena. They present as an example the choice to purchase a life annuity for a middle-aged person. Buyers must choose whether to purchase before retirement or at the date of retirement. The article provides some guidelines on whether or not to purchase......Individuals are often faced with financial decisions that have long-term implications for themselves and their families, but they have few sources of unbiased assistance. The authors suggest that a stochastic financial planning system, properly constructed and calibrated, can be applied to a number...... deferred life annuities, and who might most benefit from such a purchase....
Li, L. L.; Jin, C. L.; Ge, X.
2018-01-01
In this paper, the output regulation problem with dissipative property for a class of switched stochastic delay systems is investigated, based on an error-dependent switching law. Under the assumption that none subsystem is solvable for the problem, a sufficient condition is derived by structuring multiple Lyapunov-Krasovskii functionals with respect to multiple supply rates, via designing error feedback regulators. The condition is also established when dissipative property reduces to passive property. Finally, two numerical examples are given to demonstrate the feasibility and efficiency of the present method.
Chemical memory reactions induced bursting dynamics in gene expression.
Tian, Tianhai
2013-01-01
Memory is a ubiquitous phenomenon in biological systems in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many memorial phenomena are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict to the extant stochastic approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. To tackle the challenge, I proposed a novel theory consisting of the memory chemical master equations and memory stochastic simulation algorithm. A stochastic model for single-gene expression was proposed to illustrate the key function of memory reactions in inducing bursting dynamics of gene expression that has been observed in experiments recently. The importance of memory reactions has been further validated by the stochastic model of the p53-MDM2 core module. Simulations showed that memory reactions is a major mechanism for realizing both sustained oscillations of p53 protein numbers in single cells and damped oscillations over a population of cells. These successful applications of the memory modeling framework suggested that this innovative theory is an effective and powerful tool to study memory process and conditional chemical reactions in a wide range of complex biological systems.
Frank, T D; Beek, P J
2001-08-01
Recently, Küchler and Mensch [Stochastics Stochastics Rep. 40, 23 (1992)] derived exact stationary probability densities for linear stochastic delay differential equations. This paper presents an alternative derivation of these solutions by means of the Fokker-Planck approach introduced by Guillouzic [Phys. Rev. E 59, 3970 (1999); 61, 4906 (2000)]. Applications of this approach, which is argued to have greater generality, are discussed in the context of stochastic models for population growth and tracking movements.
Lin, Yi-Kuei; Huang, Cheng-Fu; Yeh, Cheng-Ta
2016-04-01
In supply chain management, satisfying customer demand is the most concerned for the manager. However, the goods may rot or be spoilt during delivery owing to natural disasters, inclement weather, traffic accidents, collisions, and so on, such that the intact goods may not meet market demand. This paper concentrates on a stochastic-flow distribution network (SFDN), in which a node denotes a supplier, a transfer station, or a market, while a route denotes a carrier providing the delivery service for a pair of nodes. The available capacity of the carrier is stochastic because the capacity may be partially reserved by other customers. The addressed problem is to evaluate the system reliability, the probability that the SFDN can satisfy the market demand with the spoilage rate under the budget constraint from multiple suppliers to the customer. An algorithm is developed in terms of minimal paths to evaluate the system reliability along with a numerical example to illustrate the solution procedure. A practical case of fruit distribution is presented accordingly to emphasise the management implication of the system reliability.
Determination of kinetics parameters using stochastic methods in a 252Cf system
International Nuclear Information System (INIS)
Difilippo, F.C.
1988-01-01
Safety analysis and control system design of nuclear systems require the knowledge of neutron kinetics related parameters like effective delayed neutron fraction, neutron lifetime, time between neutron generations and subcriticality margins. Many methods, deterministic and stochastic, are being used, some since the beginning of nuclear power, to measure these important parameters. The method based on the use of the 252 Cf neutron source has been under intense study at the Oak Ridge National Laboratory, both experimentally and theoretically, during the last years. The increasing demand for this isotope in industrial and medical applications and new designs of advanced high flux reactors to produce it make the isotope available as neutron source (only few micrograms are necessary). A thin layer of 252 Cf is deposited in one of the electrodes of a fission chamber which produces pulses each time the 252 Cf disintegrates via α or spontaneous fission decay; the smaller pulses associated with the α decay can be easily discriminated with the important result that we known the time when v/sub c/ neutrons are injected into the system (number of neutrons per fission of 252 Cf). Thus, a small (few cm 3 ) and nonintrusive device can be used as a random pulsed neutron source with known natural properties that do no depend on biases associated with more complex interrogating devices like accelerators. This paper presents a general formalism that relates the kinetics parameters with stochastic descriptors that naturally appear because of the random nature of the production and transport of neutrons
Stochastic behavior of a cold standby system with maximum repair time
Directory of Open Access Journals (Sweden)
Ashish Kumar
2015-09-01
Full Text Available The main aim of the present paper is to analyze the stochastic behavior of a cold standby system with concept of preventive maintenance, priority and maximum repair time. For this purpose, a stochastic model is developed in which initially one unit is operative and other is kept as cold standby. There is a single server who visits the system immediately as and when required. The server takes the unit under preventive maintenance after a maximum operation time at normal mode if one standby unit is available for operation. If the repair of the failed unit is not possible up to a maximum repair time, failed unit is replaced by new one. The failure time, maximum operation time and maximum repair time distributions of the unit are considered as exponentially distributed while repair and maintenance time distributions are considered as arbitrary. All random variables are statistically independent and repairs are perfect. Various measures of system effectiveness are obtained by using the technique of semi-Markov process and RPT. To highlight the importance of the study numerical results are also obtained for MTSF, availability and profit function.
International Nuclear Information System (INIS)
Lu, Yunfan; Wang, Jun; Niu, Hongli
2015-01-01
An agent-based financial stock price model is developed and investigated by a stochastic interacting epidemic system, which is one of the statistical physics systems and has been used to model the spread of an epidemic or a forest fire. Numerical and statistical analysis are performed on the simulated returns of the proposed financial model. Complexity properties of the financial time series are explored by calculating the correlation dimension and using the modified multiscale entropy method. In order to verify the rationality of the financial model, the real stock market indexes, Shanghai Composite Index and Shenzhen Component Index, are studied in comparison with the simulation data of the proposed model for the different infectiousness parameters. The empirical research reveals that this financial model can reproduce some important features of the real stock markets. - Highlights: • A new agent-based financial price model is developed by stochastic interacting epidemic system. • The structure of the proposed model allows to simulate the financial dynamics. • Correlation dimension and MMSE are applied to complexity analysis of financial time series. • Empirical results show the rationality of the proposed financial model
Energy Technology Data Exchange (ETDEWEB)
Lu, Yunfan, E-mail: yunfanlu@yeah.net; Wang, Jun; Niu, Hongli
2015-06-12
An agent-based financial stock price model is developed and investigated by a stochastic interacting epidemic system, which is one of the statistical physics systems and has been used to model the spread of an epidemic or a forest fire. Numerical and statistical analysis are performed on the simulated returns of the proposed financial model. Complexity properties of the financial time series are explored by calculating the correlation dimension and using the modified multiscale entropy method. In order to verify the rationality of the financial model, the real stock market indexes, Shanghai Composite Index and Shenzhen Component Index, are studied in comparison with the simulation data of the proposed model for the different infectiousness parameters. The empirical research reveals that this financial model can reproduce some important features of the real stock markets. - Highlights: • A new agent-based financial price model is developed by stochastic interacting epidemic system. • The structure of the proposed model allows to simulate the financial dynamics. • Correlation dimension and MMSE are applied to complexity analysis of financial time series. • Empirical results show the rationality of the proposed financial model.
NN-Based Implicit Stochastic Optimization of Multi-Reservoir Systems Management
Directory of Open Access Journals (Sweden)
Matteo Sangiorgio
2018-03-01
Full Text Available Multi-reservoir systems management is complex because of the uncertainty on future events and the variety of purposes, usually conflicting, of the involved actors. An efficient management of these systems can help improving resource allocation, preventing political crisis and reducing the conflicts between the stakeholders. Bellman stochastic dynamic programming (SDP is the most famous among the many proposed approaches to solve this optimal control problem. Unfortunately, SDP is affected by the curse of dimensionality: computational effort increases exponentially with the complexity of the considered system (i.e., number of reservoirs, and the problem rapidly becomes intractable. This paper proposes an implicit stochastic optimization approach for the solution of the reservoir management problem. The core idea is using extremely flexible functions, such as artificial neural networks (NN, for designing release rules which approximate the optimal policies obtained by an open-loop approach. These trained NNs can then be used to take decisions in real time. The approach thus requires a sufficiently long series of historical or synthetic inflows, and the definition of a compromise solution to be approximated. This work analyzes with particular emphasis the importance of the information which represents the input of the control laws, investigating the effects of different degrees of completeness. The methodology is applied to the Nile River basin considering the main management objectives (minimization of the irrigation water deficit and maximization of the hydropower production, but can be easily adopted also in other cases.
International Nuclear Information System (INIS)
Jiang, Yibo; Xu, Jian; Sun, Yuanzhang; Wei, Congying; Wang, Jing; Ke, Deping; Li, Xiong; Yang, Jun; Peng, Xiaotao; Tang, Bowen
2017-01-01
Highlights: • Improving the utilization of wind power by the demand response of residential hybrid energy system. • An optimal scheduling of home energy management system integrating micro-CHP. • The scattered response capability of consumers is aggregated by demand bidding curve. • A stochastic day-ahead economic dispatch model considering demand response and wind power. - Abstract: As the installed capacity of wind power is growing, the stochastic variability of wind power leads to the mismatch of demand and generated power. Employing the regulating capability of demand to improve the utilization of wind power has become a new research direction. Meanwhile, the micro combined heat and power (micro-CHP) allows residential consumers to choose whether generating electricity by themselves or purchasing from the utility company, which forms a residential hybrid energy system. However, the impact of the demand response with hybrid energy system contained micro-CHP on the large-scale wind power utilization has not been analyzed quantitatively. This paper proposes an operation optimization model of the residential hybrid energy system based on price response, integrating micro-CHP and smart appliances intelligently. Moreover, a novel load aggregation method is adopted to centralize scattered response capability of residential load. At the power grid level, a day-ahead stochastic economic dispatch model considering demand response and wind power is constructed. Furthermore, simulation is conducted respectively on the modified 6-bus system and IEEE 118-bus system. The results show that with the method proposed, the wind power curtailment of the system decreases by 78% in 6-bus system. In the meantime, the energy costs of residential consumers and the operating costs of the power system reduced by 10.7% and 11.7% in 118-bus system, respectively.
Assessment of serotonergic system in formation of memory and learning
Directory of Open Access Journals (Sweden)
J. C. da Silva
2017-11-01
Full Text Available Abstract We evaluated the involvement of the serotonergic system on memory formation and learning processes in healthy adults Wistar rats. Fifty-seven rats of 5 groups had one serotonergic nuclei damaged by an electric current. Electrolytic lesion was carried out using a continuous current of 2mA during two seconds by stereotactic surgery. Animals were submitted to learning and memory tests. Rats presented different responses in the memory tests depending on the serotonergic nucleus involved. Both explicit and implicit memory may be affected after lesion although some groups showed significant difference and others did not. A damage in the serotonergic nucleus was able to cause impairment in the memory of Wistar. The formation of implicit and explicit memory is impaired after injury in some serotonergic nuclei.
Thomas, Philipp; Matuschek, Hannes; Grima, Ramon
2012-01-01
The accepted stochastic descriptions of biochemical dynamics under well-mixed conditions are given by the Chemical Master Equation and the Stochastic Simulation Algorithm, which are equivalent. The latter is a Monte-Carlo method, which, despite enjoying broad availability in a large number of existing software packages, is computationally expensive due to the huge amounts of ensemble averaging required for obtaining accurate statistical information. The former is a set of coupled differential-difference equations for the probability of the system being in any one of the possible mesoscopic states; these equations are typically computationally intractable because of the inherently large state space. Here we introduce the software package intrinsic Noise Analyzer (iNA), which allows for systematic analysis of stochastic biochemical kinetics by means of van Kampen's system size expansion of the Chemical Master Equation. iNA is platform independent and supports the popular SBML format natively. The present implementation is the first to adopt a complementary approach that combines state-of-the-art analysis tools using the computer algebra system Ginac with traditional methods of stochastic simulation. iNA integrates two approximation methods based on the system size expansion, the Linear Noise Approximation and effective mesoscopic rate equations, which to-date have not been available to non-expert users, into an easy-to-use graphical user interface. In particular, the present methods allow for quick approximate analysis of time-dependent mean concentrations, variances, covariances and correlations coefficients, which typically outperforms stochastic simulations. These analytical tools are complemented by automated multi-core stochastic simulations with direct statistical evaluation and visualization. We showcase iNA's performance by using it to explore the stochastic properties of cooperative and non-cooperative enzyme kinetics and a gene network associated with
International Nuclear Information System (INIS)
Li Jianlong; Zeng Lingzao
2010-01-01
We discuss in detail the effects of the multi-time-delayed feedback driven by an aperiodic signal on the output of a stochastic resonance (SR) system. The effective potential function and dynamical probability density function (PDF) are derived. To measure the performance of the SR system in the presence of a binary random signal, the bit error rate (BER) defined by the dynamical PDF is employed, as is commonly used in digital communications. We find that the delay time, strength of the feedback, and number of time-delayed terms can change the effective potential function and the effective amplitude of the signal, and then affect the BER of the SR system. The numerical simulations strongly support the theoretical results. The goal of this investigation is to explore the effects of the multi-time-delayed feedback on SR and give a guidance to nonlinear systems in the application of information processing.
Predictive control for stochastic systems based on multi-layer probabilistic sets
Directory of Open Access Journals (Sweden)
Huaqing LIANG
2016-04-01
Full Text Available Aiming at a class of discrete-time stochastic systems with Markov jump features, the state-feedback predictive control problem under probabilistic constraints of input variables is researched. On the basis of the concept and method of the multi-layer probabilistic sets, the predictive controller design algorithm with the soft constraints of different probabilities is presented. Under the control of the multi-step feedback laws, the system state moves to different ellipses with specified probabilities. The stability of the system is guaranteed, the feasible region of the control problem is enlarged, and the system performance is improved. Finally, a simulation example is given to prove the effectiveness of the proposed method.
Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems
Directory of Open Access Journals (Sweden)
Lili Zhang
2014-01-01
Full Text Available A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.
Sulis, William H
2017-10-01
Walter Freeman III pioneered the application of nonlinear dynamical systems theories and methodologies in his work on mesoscopic brain dynamics.Sadly, mainstream psychology and psychiatry still cling to linear correlation based data analysis techniques, which threaten to subvert the process of experimentation and theory building. In order to progress, it is necessary to develop tools capable of managing the stochastic complexity of complex biopsychosocial systems, which includes multilevel feedback relationships, nonlinear interactions, chaotic dynamics and adaptability. In addition, however, these systems exhibit intrinsic randomness, non-Gaussian probability distributions, non-stationarity, contextuality, and non-Kolmogorov probabilities, as well as the absence of mean and/or variance and conditional probabilities. These properties and their implications for statistical analysis are discussed. An alternative approach, the Process Algebra approach, is described. It is a generative model, capable of generating non-Kolmogorov probabilities. It has proven useful in addressing fundamental problems in quantum mechanics and in the modeling of developing psychosocial systems.
Control of deterministic and stochastic systems with several small parameters - A survey
Directory of Open Access Journals (Sweden)
Vasile Dragan
2009-07-01
Full Text Available The past three decades of research on multiparametric singularly perturbed systems are reviewed, including recent results. Particular attention is paid to stability analysis, control, filtering problems and dynamic games. First, a parameter-independent design methodology is summarized, which employs a two-time-scale and descriptor system approach without information on the small parameters. Further, variational computational algorithms are included to avoid ill-conditioned systems : the exact slow-fast decomposition method, the recursive algorithm and Newton's method are considered in particular. Convergence results are presented and the existence and uniqueness of the solutions are discussed. Second, the new results obtained via the stochastic approach are presented. Finally, the results of a simulation of a practical power system are presented to validate the efficiency of the considered design methods.
A selective logging mechanism for hardware transactional memory systems
Lupon Navazo, Marc; Magklis, Grigorios; González Colás, Antonio María
2011-01-01
Log-based Hardware Transactional Memory (HTM) systems offer an elegant solution to handle speculative data that overflow transactional L1 caches. By keeping the pre-transactional values on a software-resident log, speculative values can be safely moved across the memory hierarchy, without requiring expensive searches on L1 misses or commits.
Multiple Systems of Spatial Memory: Evidence from Described Scenes
Avraamides, Marios N.; Kelly, Jonathan W.
2010-01-01
Recent models in spatial cognition posit that distinct memory systems are responsible for maintaining transient and enduring spatial relations. The authors used perspective-taking performance to assess the presence of these enduring and transient spatial memories for locations encoded through verbal descriptions. Across 3 experiments, spatial…
International Nuclear Information System (INIS)
Labadi, Karim; Saggadi, Samira; Amodeo, Lionel
2009-01-01
The dynamic behavior of a discrete event dynamic system can be significantly affected for some uncertain changes in its decision parameters. So, parameter sensitivity analysis would be a useful way in studying the effects of these changes on the system performance. In the past, the sensitivity analysis approaches are frequently based on simulation models. In recent years, formal methods based on stochastic process including Markov process are proposed in the literature. In this paper, we are interested in the parameter sensitivity analysis of discrete event dynamic systems by using stochastic Petri nets models as a tool for modelling and performance evaluation. A sensitivity analysis approach based on stochastic Petri nets, called PSA-SPN method, will be proposed with an application to a production line system.
International Nuclear Information System (INIS)
Albrecht, R.W.; Crowe, R.D.; McGuire, J.J.
1978-01-01
The potential to automatically collect, classify, and report on stochastic data (signals with random, time-varying components) from power plants has long been discussed by utilities, government, industries, national laboratories and universities. It has become clear to all concerned that such signals often contain information about plant conditions which may provide the basis for increased plant availability through early detection and warning of developing malfunctions. Maintenance can then be scheduled at opportune times. Inopportune failures of major and minor power plant components are a major cause of down-time and detracts significantly from availability of the plant. A complete system to realize automatic stochastic data processing has been conceptually designed. Development of the FAST-DATA system has been initiated through a program of periodic measurements performed on the vibration and loose parts monitoring system of the Trojan reactor (1130-MW(e)PWR) operated by Portland General Electric Company. The development plan for the system consists of a six-step procedure. The initial steps depend on a significant level of human involvement. In the course of development of the system, the routine duties of operators and analysts are gradually replaced by computerized automatic data handling procedures. In the final configuration, the operator and analysts are completely freed of routine chores by logical machinery. The results achieved to date from actual application of the proof-of-principle system are discussed. The early developmental phases have concentrated on system organization and examination of a representative data base. Preliminary results from the signature analysis program using Trojan data indicate that the performance specifications predicted for the FAST-DATA system are achievable in practice. (author)
International Nuclear Information System (INIS)
Kaplani, E.; Kaplanis, S.
2012-01-01
Highlights: ► Solar radiation data for European cities follow the Extreme Value or Weibull distribution. ► Simulation model for the sizing of SAPV systems based on energy balance and stochastic analysis. ► Simulation of PV Generator-Loads-Battery Storage System performance for all months. ► Minimum peak power and battery capacity required for reliable SAPV sizing for various European cities. ► Peak power and battery capacity reduced by more than 30% for operation 95% success rate. -- Abstract: The large fluctuations observed in the daily solar radiation profiles affect highly the reliability of the PV system sizing. Increasing the reliability of the PV system requires higher installed peak power (P m ) and larger battery storage capacity (C L ). This leads to increased costs, and makes PV technology less competitive. This research paper presents a new stochastic simulation model for stand-alone PV systems, developed to determine the minimum installed P m and C L for the PV system to be energy independent. The stochastic simulation model developed, makes use of knowledge acquired from an in-depth statistical analysis of the solar radiation data for the site, and simulates the energy delivered, the excess energy burnt, the load profiles and the state of charge of the battery system for the month the sizing is applied, and the PV system performance for the entire year. The simulation model provides the user with values for the autonomy factor d, simulating PV performance in order to determine the minimum P m and C L depending on the requirements of the application, i.e. operation with critical or non-critical loads. The model makes use of NASA’s Surface meteorology and Solar Energy database for the years 1990–2004 for various cities in Europe with a different climate. The results obtained with this new methodology indicate a substantial reduction in installed peak power and battery capacity, both for critical and non-critical operation, when compared to
International Nuclear Information System (INIS)
Yin, George; Wang, Le Yi; Zhang, Hongwei
2014-01-01
Stochastic approximation methods have found extensive and diversified applications. Recent emergence of networked systems and cyber-physical systems has generated renewed interest in advancing stochastic approximation into a general framework to support algorithm development for information processing and decisions in such systems. This paper presents a survey on some recent developments in stochastic approximation methods and their applications. Using connected vehicles in platoon formation and coordination as a platform, we highlight some traditional and new methodologies of stochastic approximation algorithms and explain how they can be used to capture essential features in networked systems. Distinct features of networked systems with randomly switching topologies, dynamically evolving parameters, and unknown delays are presented, and control strategies are provided
Effluent trading in river systems through stochastic decision-making process: a case study.
Zolfagharipoor, Mohammad Amin; Ahmadi, Azadeh
2017-09-01
The objective of this paper is to provide an efficient framework for effluent trading in river systems. The proposed framework consists of two pessimistic and optimistic decision-making models to increase the executability of river water quality trading programs. The models used for this purpose are (1) stochastic fallback bargaining (SFB) to reach an agreement among wastewater dischargers and (2) stochastic multi-criteria decision-making (SMCDM) to determine the optimal treatment strategy. The Monte-Carlo simulation method is used to incorporate the uncertainty into analysis. This uncertainty arises from stochastic nature and the errors in the calculation of wastewater treatment costs. The results of river water quality simulation model are used as the inputs of models. The proposed models are used in a case study on the Zarjoub River in northern Iran to determine the best solution for the pollution load allocation. The best treatment alternatives selected by each model are imported, as the initial pollution discharge permits, into an optimization model developed for trading of pollution discharge permits among pollutant sources. The results show that the SFB-based water pollution trading approach reduces the costs by US$ 14,834 while providing a relative consensus among pollutant sources. Meanwhile, the SMCDM-based water pollution trading approach reduces the costs by US$ 218,852, but it is less acceptable by pollutant sources. Therefore, it appears that giving due attention to stability, or in other words acceptability of pollution trading programs for all pollutant sources, is an essential element of their success.
Alfonso, Lester; Zamora, Jose; Cruz, Pedro
2015-04-01
The stochastic approach to coagulation considers the coalescence process going in a system of a finite number of particles enclosed in a finite volume. Within this approach, the full description of the system can be obtained from the solution of the multivariate master equation, which models the evolution of the probability distribution of the state vector for the number of particles of a given mass. Unfortunately, due to its complexity, only limited results were obtained for certain type of kernels and monodisperse initial conditions. In this work, a novel numerical algorithm for the solution of the multivariate master equation for stochastic coalescence that works for any type of kernels and initial conditions is introduced. The performance of the method was checked by comparing the numerically calculated particle mass spectrum with analytical solutions obtained for the constant and sum kernels, with an excellent correspondence between the analytical and numerical solutions. In order to increase the speedup of the algorithm, software parallelization techniques with OpenMP standard were used, along with an implementation in order to take advantage of new accelerator technologies. Simulations results show an important speedup of the parallelized algorithms. This study was funded by a grant from Consejo Nacional de Ciencia y Tecnologia de Mexico SEP-CONACYT CB-131879. The authors also thanks LUFAC® Computacion SA de CV for CPU time and all the support provided.
An equilibrium for frustrated quantum spin systems in the stochastic state selection method
International Nuclear Information System (INIS)
Munehisa, Tomo; Munehisa, Yasuko
2007-01-01
We develop a new method to calculate eigenvalues in frustrated quantum spin models. It is based on the stochastic state selection (SSS) method, which is an unconventional Monte Carlo technique that we have investigated in recent years. We observe that a kind of equilibrium is realized under some conditions when we repeatedly operate a Hamiltonian and a random choice operator, which is defined by stochastic variables in the SSS method, to a trial state. In this equilibrium, which we call the SSS equilibrium, we can evaluate the lowest eigenvalue of the Hamiltonian using the statistical average of the normalization factor of the generated state. The SSS equilibrium itself has already been observed in unfrustrated models. Our study in this paper shows that we can also see the equilibrium in frustrated models, with some restriction on values of a parameter introduced in the SSS method. As a concrete example, we employ the spin-1/2 frustrated J 1 -J 2 Heisenberg model on the square lattice. We present numerical results on the 20-, 32-, and 36-site systems, which demonstrate that statistical averages of the normalization factors reproduce the known exact eigenvalue to good precision. Finally, we apply the method to the 40-site system. Then we obtain the value of the lowest energy eigenvalue with an error of less than 0.2%
Stochastic resonance in multi-stable coupled systems driven by two driving signals
Xu, Pengfei; Jin, Yanfei
2018-02-01
The stochastic resonance (SR) in multi-stable coupled systems subjected to Gaussian white noises and two different driving signals is investigated in this paper. Using the adiabatic approximation and the perturbation method, the coupled systems with four-well potential are transformed into the master equations and the amplitude of the response is obtained. The signal-to-noise ratio (SNR) is calculated numerically to demonstrate the occurrence of SR. For the case of two driving signals with different amplitudes, the interwell resonance between two wells S1 and S3 emerges for strong coupling. The SR can appear in the subsystem with weaker signal amplitude or even without driving signal with the help of coupling. For the case of two driving signals with different frequencies, the effects of SR in two subsystems driven by high and low frequency signals are both weakened with an increase in coupling strength. The stochastic multi-resonance phenomenon is observed in the subsystem subjected to the low frequency signal. Moreover, an effective scheme for phase suppressing SR is proposed by using a relative phase between two driving signals.
DEFF Research Database (Denmark)
Maurico-Iglesias, Miguel; Castro, Ignacio Montero; Mollerup, Ane Loft
2015-01-01
. Such controller is aimed at keeping the system close to the optimal performance, thanks to an optimal selection of controlled variables. The definition of an optimal performance was carried out by a two-stage optimisation (stochastic and deterministic) to take into account both the overflow during the current......The design of sewer system control is a complex task given the large size of the sewer networks, the transient dynamics of the water flow and the stochastic nature of rainfall. This contribution presents a generic methodology for the design of a self-optimising controller in sewer systems...
Stochastic volatility and stochastic leverage
DEFF Research Database (Denmark)
Veraart, Almut; Veraart, Luitgard A. M.
This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic...... treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by means of a linear transformation of a Jacobi process. Such models are both analytically tractable and allow for a direct economic interpretation. In particular, we propose two new stochastic volatility...... models which allow for a stochastic leverage effect: the generalised Heston model and the generalised Barndorff-Nielsen & Shephard model. We investigate the impact of a stochastic leverage effect in the risk neutral world by focusing on implied volatilities generated by option prices derived from our new...
Dynamics of Shape Memory Alloy Systems, Phase 2
2015-12-22
Nonlinear Dynamics and Chaos in Systems with Discontinuous Support Using a Switch Model”, DINAME 2005 - XI International Conference on Dynamic Problems in...AFRL-AFOSR-CL-TR-2016-0003 Dynamics of Shape Memory Alloy Systems , Phase 2 Marcelo Savi FUNDACAO COORDENACAO DE PROJETOS PESQUISAS E EEUDOS TECNOL...release. 2 AFOSR FINAL REPORT Grant Title: Nonlinear Dynamics of Shape Memory Alloy Systems , Phase 2 Grant #: FA9550-11-1-0284 Reporting Period
Directory of Open Access Journals (Sweden)
Dongyan Chen
2015-01-01
Full Text Available This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE. Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.
Fuzzy Stochastic Optimal Guaranteed Cost Control of Bio-Economic Singular Markovian Jump Systems.
Li, Li; Zhang, Qingling; Zhu, Baoyan
2015-11-01
This paper establishes a bio-economic singular Markovian jump model by considering the price of the commodity as a Markov chain. The controller is designed for this system such that its biomass achieves the specified range with the least cost in a finite-time. Firstly, this system is described by Takagi-Sugeno fuzzy model. Secondly, a new design method of fuzzy state-feedback controllers is presented to ensure not only the regularity, nonimpulse, and stochastic singular finite-time boundedness of this kind of systems, but also an upper bound achieved for the cost function in the form of strict linear matrix inequalities. Finally, two examples including a practical example of eel seedling breeding are given to illustrate the merit and usability of the approach proposed in this paper.
Ding, Bo; Fang, Huajing
2017-05-01
This paper is concerned with the fault prediction for the nonlinear stochastic system with incipient faults. Based on the particle filter and the reasonable assumption about the incipient faults, the modified fault estimation algorithm is proposed, and the system state is estimated simultaneously. According to the modified fault estimation, an intuitive fault detection strategy is introduced. Once each of the incipient fault is detected, the parameters of which are identified by a nonlinear regression method. Then, based on the estimated parameters, the future fault signal can be predicted. Finally, the effectiveness of the proposed method is verified by the simulations of the Three-tank system. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Cortical Thickness and Episodic Memory Impairment in Systemic Lupus Erythematosus.
Bizzo, Bernardo Canedo; Sanchez, Tiago Arruda; Tukamoto, Gustavo; Zimmermann, Nicolle; Netto, Tania Maria; Gasparetto, Emerson Leandro
2017-01-01
The purpose of this study was to investigate differences in brain cortical thickness of systemic lupus erythematosus (SLE) patients with and without episodic memory impairment and healthy controls. We studied 51 patients divided in 2 groups (SLE with episodic memory deficit, n = 17; SLE without episodic memory deficit, n = 34) by the Rey Auditory Verbal Learning Test and 34 healthy controls. Groups were paired based on sex, age, education, Mini-Mental State Examination score, and accumulation of disease burden. Cortical thickness from magnetic resonance imaging scans was determined using the FreeSurfer software package. SLE patients with episodic memory deficits presented reduced cortical thickness in the left supramarginal cortex and superior temporal gyrus when compared to the control group and in the right superior frontal, caudal, and rostral middle frontal and precentral gyri when compared to the SLE group without episodic memory impairment considering time since diagnosis of SLE as covaried. There were no significant differences in the cortical thickness between the SLE without episodic memory and control groups. Different memory-related cortical regions thinning were found in the episodic memory deficit group when individually compared to the groups of patients without memory impairment and healthy controls. Copyright © 2016 by the American Society of Neuroimaging.
Memory systems, processes, and tasks: taxonomic clarification via factor analysis.
Bruss, Peter J; Mitchell, David B
2009-01-01
The nature of various memory systems was examined using factor analysis. We reanalyzed data from 11 memory tasks previously reported in Mitchell and Bruss (2003). Four well-defined factors emerged, closely resembling episodic and semantic memory and conceptual and perceptual implicit memory, in line with both memory systems and transfer-appropriate processing accounts. To explore taxonomic issues, we ran separate analyses on the implicit tasks. Using a cross-format manipulation (pictures vs. words), we identified 3 prototypical tasks. Word fragment completion and picture fragment identification tasks were "factor pure," tapping perceptual processes uniquely. Category exemplar generation revealed its conceptual nature, yielding both cross-format priming and a picture superiority effect. In contrast, word stem completion and picture naming were more complex, revealing attributes of both processes.
Interacting Brain Systems Modulate Memory Consolidation
McIntyre, Christa K.; McGaugh, James L.; Williams, Cedric L.
2011-01-01
Emotional arousal influences the consolidation of long-term memory. This review discusses experimental approaches and relevant findings that provide the foundation for current understanding of coordinated interactions between arousal activated peripheral hormones and the brain processes that modulate memory formation. Rewarding or aversive experiences release the stress hormones epinephrine (adrenalin) and glucocorticoids from the adrenal glands into the bloodstream. The effect of these hormones on memory consolidation depends upon binding of norepinephrine to beta-adrenergic receptors in the basolateral complex of the amygdala (BLA). Much evidence indicates that the stress hormones influence release of norepinephrine in the BLA through peripheral actions on the vagus nerve which stimulates, through polysynaptic connections, cells of the locus coeruleus to release norepinephrine. The BLA influences memory storage by actions on synapses, distributed throughout the brain, that are engaged in sensory and cognitive processing at the time of amygdala activation. The implications of the activation of these stress-activated memory processes are discussed in relation to stress-related memory disorders. PMID:22085800
... it has to decide what is worth remembering. Memory is the process of storing and then remembering this information. There are different types of memory. Short-term memory stores information for a few ...
Directory of Open Access Journals (Sweden)
Weiwei Sun
2015-01-01
Full Text Available This paper presents H∞ excitation control design problem for power systems with input time delay and disturbances by using nonlinear Hamiltonian system theory. The impact of time delays introduced by remote signal transmission and processing in wide-area measurement system (WAMS is well considered. Meanwhile, the systems under investigation are disturbed by random fluctuation. First, under prefeedback technique, the power systems are described as a nonlinear Hamiltonian system. Then the H∞ excitation controller of generators connected to distant power systems with time delay and stochasticity is designed. Based on Lyapunov functional method, some sufficient conditions are proposed to guarantee the rationality and validity of the proposed control law. The closed-loop systems under the control law are asymptotically stable in mean square independent of the time delay. And we through a simulation of a two-machine power system prove the effectiveness of the results proposed in this paper.
Effects of time delay on stochastic resonance of the stock prices in financial system
International Nuclear Information System (INIS)
Li, Jiang-Cheng; Li, Chun; Mei, Dong-Cheng
2014-01-01
The effect of time delay on stochastic resonance of the stock prices in finance system was investigated. The time delay is introduced into the Heston model driven by the extrinsic and intrinsic periodic information for stock price. The signal power amplification (SPA) was calculated by numerical simulation. The results indicate that an optimal critical value of delay time maximally enhances the reverse-resonance in the behaviors of SPA as a function of long-run variance of volatility or cross correlation coefficient between noises for both cases of intrinsic and extrinsic periodic information. Moreover, in both cases, being a critical value in the delay time, when the delay time takes value below the critical value, reverse-resonance increases with the delay time increasing, however, when the delay time takes value above the critical value, the reverse-resonance decrease with the delay time increasing. - Highlights: • The effects of delay time on stochastic resonance of the stock prices was investigated. • There is an optimal critical value of delay time maximally enhances the reverse-resonance • The reverse-resonance increases with the delay time increasing as the delay time takes value below the critical value • The reverse-resonance decrease with the delay time increasing as the delay time takes value above the critical value
Effects of time delay on stochastic resonance of the stock prices in financial system
Energy Technology Data Exchange (ETDEWEB)
Li, Jiang-Cheng [Department of Physics, Yunnan University, Kunming, 650091 (China); Li, Chun [Department of Computer Science, Puer Teachers' College, Puer 665000 (China); Mei, Dong-Cheng, E-mail: meidch@ynu.edu.cn [Department of Physics, Yunnan University, Kunming, 650091 (China)
2014-06-13
The effect of time delay on stochastic resonance of the stock prices in finance system was investigated. The time delay is introduced into the Heston model driven by the extrinsic and intrinsic periodic information for stock price. The signal power amplification (SPA) was calculated by numerical simulation. The results indicate that an optimal critical value of delay time maximally enhances the reverse-resonance in the behaviors of SPA as a function of long-run variance of volatility or cross correlation coefficient between noises for both cases of intrinsic and extrinsic periodic information. Moreover, in both cases, being a critical value in the delay time, when the delay time takes value below the critical value, reverse-resonance increases with the delay time increasing, however, when the delay time takes value above the critical value, the reverse-resonance decrease with the delay time increasing. - Highlights: • The effects of delay time on stochastic resonance of the stock prices was investigated. • There is an optimal critical value of delay time maximally enhances the reverse-resonance • The reverse-resonance increases with the delay time increasing as the delay time takes value below the critical value • The reverse-resonance decrease with the delay time increasing as the delay time takes value above the critical value.
Molecular finite-size effects in stochastic models of equilibrium chemical systems.
Cianci, Claudia; Smith, Stephen; Grima, Ramon
2016-02-28
The reaction-diffusion master equation (RDME) is a standard modelling approach for understanding stochastic and spatial chemical kinetics. An inherent assumption is that molecules are point-like. Here, we introduce the excluded volume reaction-diffusion master equation (vRDME) which takes into account volume exclusion effects on stochastic kinetics due to a finite molecular radius. We obtain an exact closed form solution of the RDME and of the vRDME for a general chemical system in equilibrium conditions. The difference between the two solutions increases with the ratio of molecular diameter to the compartment length scale. We show that an increase in the fraction of excluded space can (i) lead to deviations from the classical inverse square root law for the noise-strength, (ii) flip the skewness of the probability distribution from right to left-skewed, (iii) shift the equilibrium of bimolecular reactions so that more product molecules are formed, and (iv) strongly modulate the Fano factors and coefficients of variation. These volume exclusion effects are found to be particularly pronounced for chemical species not involved in chemical conservation laws. Finally, we show that statistics obtained using the vRDME are in good agreement with those obtained from Brownian dynamics with excluded volume interactions.
The memory systems of children with (central) auditory disorder.
Pires, Mayra Monteiro; Mota, Mailce Borges; Pinheiro, Maria Madalena Canina
2015-01-01
This study aims to investigate working, declarative, and procedural memory in children with (central) auditory processing disorder who showed poor phonological awareness. Thirty 9- and 10-year-old children participated in the study and were distributed into two groups: a control group consisting of 15 children with typical development, and an experimental group consisting of 15 children with (central) auditory processing disorder who were classified according to three behavioral tests and who showed poor phonological awareness in the CONFIAS test battery. The memory systems were assessed through the adapted tests in the program E-PRIME 2.0. The working memory was assessed by the Working Memory Test Battery for Children (WMTB-C), whereas the declarative memory was assessed by a picture-naming test and the procedural memory was assessed by means of a morphosyntactic processing test. The results showed that, when compared to the control group, children with poor phonological awareness scored lower in the working, declarative, and procedural memory tasks. The results of this study suggest that in children with (central) auditory processing disorder, phonological awareness is associated with the analyzed memory systems.
Dissociating response systems: erasing fear from memory.
Soeter, Marieke; Kindt, Merel
2010-07-01
In addition to the extensive evidence in animals, we previously showed that disrupting reconsolidation by noradrenergic blockade produced amnesia for the original fear response in humans. Interestingly, the declarative memory for the fear association remained intact. These results asked for a solid replication. Moreover, given the constructive nature of memories, the intact recollection of the fear association could eventually 'rebuild' the fear memory, resulting in the spontaneous recovery of the fear response. Yet, perseverance of the amnesic effects would have substantial clinical implications, as even the most effective treatments for psychiatric disorders display high percentages of relapse. Using a differential fear conditioning procedure in humans, we replicated our previous findings by showing that administering propranolol (40mg) prior to memory reactivation eliminated the startle fear response 24h later. But most importantly, this effect persisted at one month follow-up. Notably, the propranolol manipulation not only left the declarative memory for the acquired contingency untouched, but also skin conductance discrimination. In addition, a close association between declarative knowledge and skin conductance responses was found. These findings are in line with the supposed double dissociation of fear conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. They support the view that skin conductance conditioning primarily reflects contingency learning, whereas the startle response is a rather specific measure of fear. Furthermore, the results indicate the absence of a causal link between the actual knowledge of a fear association and its fear response, even though they often operate in parallel. Interventions targeting the amygdalar fear memory may be essential in specifically and persistently dampening the emotional impact of fear. From a clinical and ethical perspective, disrupting reconsolidation points to promising
Fayolle, G; Fayolle, Guy; Furtlehner, Cyril
2006-01-01
This report is the foreword of a series of stochastic deformations of curves. Problems are set in terms of exclusion processes, the ultimate goal being to derive hydrodynamic limits for these systems after proper scalings. In this study, solely the basic texts system on the torus is analyzed. The usual sequence of empirical measures, converges in probability to a deterministic measure, which is the unique weak solution of a Cauchy problem. The method presents some new features, letting hope for extensions to higher dimension. It relies on the analysis of a family of parabolic differential operators, involving variational calculus. Namely, the variables are the values of functions at given points, their number being possibly infinite.
Sun, Ying; Ding, Derui; Zhang, Sunjie; Wei, Guoliang; Liu, Hongjian
2018-07-01
In this paper, the non-fragile ?-? control problem is investigated for a class of discrete-time stochastic nonlinear systems under event-triggered communication protocols, which determine whether the measurement output should be transmitted to the controller or not. The main purpose of the addressed problem is to design an event-based output feedback controller subject to gain variations guaranteeing the prescribed disturbance attenuation level described by the ?-? performance index. By utilizing the Lyapunov stability theory combined with S-procedure, a sufficient condition is established to guarantee both the exponential mean-square stability and the ?-? performance for the closed-loop system. In addition, with the help of the orthogonal decomposition, the desired controller parameter is obtained in terms of the solution to certain linear matrix inequalities. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed event-based controller design scheme.
Discrete-time state estimation for stochastic polynomial systems over polynomial observations
Hernandez-Gonzalez, M.; Basin, M.; Stepanov, O.
2018-07-01
This paper presents a solution to the mean-square state estimation problem for stochastic nonlinear polynomial systems over polynomial observations confused with additive white Gaussian noises. The solution is given in two steps: (a) computing the time-update equations and (b) computing the measurement-update equations for the state estimate and error covariance matrix. A closed form of this filter is obtained by expressing conditional expectations of polynomial terms as functions of the state estimate and error covariance. As a particular case, the mean-square filtering equations are derived for a third-degree polynomial system with second-degree polynomial measurements. Numerical simulations show effectiveness of the proposed filter compared to the extended Kalman filter.
Stochastic resonances in a distributed genetic broadcasting system: the NFκB/IκB paradigm.
Wang, Zhipeng; Potoyan, Davit A; Wolynes, Peter G
2018-01-01
Gene regulatory networks must relay information from extracellular signals to downstream genes in an efficient, timely and coherent manner. Many complex functional tasks such as the immune response require system-wide broadcasting of information not to one but to many genes carrying out distinct functions whose dynamical binding and unbinding characteristics are widely distributed. In such broadcasting networks, the intended target sites are also often dwarfed in number by the even more numerous non-functional binding sites. Taking the genetic regulatory network of NF κ B as an exemplary system we explore the impact of having numerous distributed sites on the stochastic dynamics of oscillatory broadcasting genetic networks pointing out how resonances in binding cycles control the network's specificity and performance. We also show that active kinetic regulation of binding and unbinding through molecular stripping of DNA bound transcription factors can lead to a higher coherence of gene-co-expression and synchronous clearance. © 2018 The Author(s).
Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems
Mahdi Alavi, S. M.; Saif, Mehrdad
2013-12-01
This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.
International Nuclear Information System (INIS)
Liu Jian; Zhai Qi-Qing; Wang You-Guo; Liu Jin
2016-01-01
In this paper, we propose a parameter allocation scheme in a parallel array bistable stochastic resonance-based communication system (P-BSR-CS) to improve the performance of weak binary pulse amplitude modulated (BPAM) signal transmissions. The optimal parameter allocation policy of the P-BSR-CS is provided to minimize the bit error rate (BER) and maximize the channel capacity (CC) under the adiabatic approximation condition. On this basis, we further derive the best parameter selection theorem in realistic communication scenarios via variable transformation. Specifically, the P-BSR structure design not only brings the robustness of parameter selection optimization, where the optimal parameter pair is not fixed but variable in quite a wide range, but also produces outstanding system performance. Theoretical analysis and simulation results indicate that in the P-BSR-CS the proposed parameter allocation scheme yields considerable performance improvement, particularly in very low signal-to-noise ratio (SNR) environments. (paper)
International Nuclear Information System (INIS)
Kishida, K.
1996-01-01
Research concerning power reactor noise analysis makes rapid progress in the areas of the system identification, prediction and diagnosis. Keywords in these studies are artificial intelligence, neural network, fuzzy, and chaos. Nonlinear, nonstationary, or non-Gaussian processes as well as linear and steady processes are also studied in fluctuation analysis. However, we have not enough time to study a fundamental theory, since we are urged to obtain results or applications in power reactor fluctuations. Furthermore, we have no systematic approach to handle observed time series data in the linear process, since power reactor noise phenomena are complicated. Hence, it is important to study it from the fundamental viewpoint. It is a main aim of the present review paper to describe a unified formalism for reactor system identification and stochastic diagnosis
Fault Detection for Non-Gaussian Stochastic Systems with Time-Varying Delay
Directory of Open Access Journals (Sweden)
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.
International Nuclear Information System (INIS)
Zhou Bingchang; Xu Wei
2008-01-01
The phenomenon of stochastic resonance (SR) in a bistable system driven by multiplicative colored and additive white noises and a periodic rectangular signal with a constant component is studied by using the unified colored noise approximation and the theory of signal-to-noise (SNR) in the adiabatic limit. The analytic expression of the SNR is obtained for arbitrary signal amplitude without being restricted to small amplitudes. The SNR is a non-monotonic function of intensities of multiplicative colored and additive white noises and correlation time of multiplicative colored noise, so SR exhibits in the bistable system. The effects of potential asymmetry r and correlation time τ of multiplicative colored noise on SNR are opposite. Moreover, It is more sensitive to control SR through adjusting the additive white noise intensity D than adjusting the multiplicative colored noise intensity Q
Control mechanisms for stochastic biochemical systems via computation of reachable sets.
Lakatos, Eszter; Stumpf, Michael P H
2017-08-01
Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently nonlinear. We present an approach to studying the impact of control measures on motifs of molecular interactions that addresses the problems faced in many biological systems: stochasticity, parameter uncertainty and nonlinearity. We show that our reachability analysis formalism can describe the potential behaviour of biological (naturally evolved as well as engineered) systems, and provides a set of bounds on their dynamics at the level of population statistics: for example, we can obtain the possible ranges of means and variances of mRNA and protein expression levels, even in the presence of uncertainty about model parameters.
DEFF Research Database (Denmark)
Köyluoglu, H.U.; Nielsen, Søren R.K.; Cakmak, A.S.
1994-01-01
perturbation method using stochastic differential equations. The joint statistical moments entering the perturbation solution are determined by considering an augmented dynamic system with state variables made up of the displacement and velocity vector and their first and second derivatives with respect......The paper deals with the first and second order statistical moments of the response of linear systems with random parameters subject to random excitation modelled as white-noise multiplied by an envelope function with random parameters. The method of analysis is basically a second order...... to the random parameters of the problem. Equations for partial derivatives are obtained from the partial differentiation of the equations of motion. The zero time-lag joint statistical moment equations for the augmented state vector are derived from the Itô differential formula. General formulation is given...
Three problems of organizational memory information systems development
Wijnhoven, Alphonsus B.J.M.; van Slooten, C.; White, D.
2002-01-01
Organizational memory information systems have a diversity of contents and may need a variety of information technologies. To cope with this diversity, OMIS requires specific development methodological guidelines. First the OMIS's objectives have to be stated in organizational functional
Massively Parallel Polar Decomposition on Distributed-Memory Systems
Ltaief, Hatem; Sukkari, Dalal E.; Esposito, Aniello; Nakatsukasa, Yuji; Keyes, David E.
2018-01-01
We present a high-performance implementation of the Polar Decomposition (PD) on distributed-memory systems. Building upon on the QR-based Dynamically Weighted Halley (QDWH) algorithm, the key idea lies in finding the best rational approximation
Stochastic reactive power dispatch in hybrid power system with intermittent wind power generation
International Nuclear Information System (INIS)
Taghavi, Reza; Seifi, Ali Reza; Samet, Haidar
2015-01-01
Environmental concerns besides fuel costs are the predominant reasons for unprecedented escalating integration of wind turbine on power systems. Operation and planning of power systems are affected by this type of energy due to the intermittent nature of wind speed inputs with high uncertainty in the optimization output variables. Consequently, in order to model this high inherent uncertainty, a PRPO (probabilistic reactive power optimization) framework should be devised. Although MC (Monte-Carlo) techniques can solve the PRPO with high precision, PEMs (point estimate methods) can preserve the accuracy to attain reasonable results when diminishing the computational effort. Also, this paper introduces a methodology for optimally dispatching the reactive power in the transmission system, while minimizing the active power losses. The optimization problem is formulated as a LFP (linear fuzzy programing). The core of the problem lay on generation of 2m + 1 point estimates for solving PRPO, where n is the number of input stochastic variables. The proposed methodology is investigated using the IEEE-14 bus test system equipped with HVDC (high voltage direct current), UPFC (unified power flow controller) and DFIG (doubly fed induction generator) devices. The accuracy of the method is demonstrated in the case study. - Highlights: • This paper uses stochastic loads in optimization process. • AC–DC load flow is modified to use some advantages of DC part in optimization process. • UPFC and DFIG are simulated in a way that could be effective in optimization process. • Fuzzy set has been used as an uncertainty analysis tool in the optimization
Stochastic learning in oxide binary synaptic device for neuromorphic computing.
Yu, Shimeng; Gao, Bin; Fang, Zheng; Yu, Hongyu; Kang, Jinfeng; Wong, H-S Philip
2013-01-01
Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.
International Nuclear Information System (INIS)
Colombino, A.; Mosiello, R.; Norelli, F.; Jorio, V.M.; Pacilio, N.
1975-01-01
A nuclear system kinetics is formulated according to a stochastic approach. The detailed probability balance equations are written for the probability of finding the mixed population of neutrons and detected neutrons, i.e. detectrons, at a given level for a given instant of time. Equations are integrated in search of a probability profile: a series of cases is analyzed through a progressive criterium. It tends to take into account an increasing number of physical processes within the chosen model. The most important contribution is that solutions interpret analytically experimental conditions of equilibrium (moise analysis) and non equilibrium (pulsed neutron measurements, source drop technique, start up procedures)
Ben Daya, Ibrahim; Chen, Albert I H; Shafiee, Mohammad Javad; Wong, Alexander; Yeow, John T W
2017-09-06
The row-column method received a lot of attention for 3-D ultrasound imaging. By reducing the number of connections required to address the 2-D array and therefore reducing the amount of data to handle, this addressing method allows for real time 3-D imaging. Row-column still has its limitations: the issues of sparsity, speckle noise inherent to ultrasound, the spatially varying point spread function, and the ghosting artifacts inherent to the row-column method must all be taken into account when building a reconstruction framework. In this research, we build on a previously published system and propose an edge-guided, compensated row-column ultrasound imaging system that incorporates multilayered edge-guided stochastically fully connected conditional random fields to address the limitations of the row-column method. Tests carried out on simulated and real row-column ultrasound images show the effectiveness of our proposed system over other published systems. Visual assessment show our proposed system's potential at preserving edges and reducing speckle. Quantitative analysis shows that our proposed system outperforms previously published systems when evaluated with metrics such as Peak Signal-to-Noise Ratio, Coefficient of Correlation, and Effective Number of Looks. These results show the potential of our proposed system as an effective tool for enhancing 3-D row-column imaging.
Si, Wenjie; Dong, Xunde; Yang, Feifei
2018-03-01
This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.
Jhang, Wun-Sin; Lo, Shih-Chiang; Yeh, Chen-Chao; Shu, Che-Chi
2017-06-30
The cellular behaviors under the control of genetic circuits are subject to stochastic fluctuations, or noise. The stochasticity in gene regulation, far from a nuisance, has been gradually appreciated for its unusual function in cellular activities. In this work, with Chemical Master Equation (CME), we discovered that the addition of inhibitors altered the stochasticity of regulatory proteins. For a bistable system of a mutually inhibitory network, such a change of noise led to the migration of cells in the bimodal distribution. We proposed that the consumption of regulatory protein caused by the addition of inhibitor is not the only reason for pushing cells to the specific state; the change of the intracellular stochasticity is also the main cause for the redistribution. For the level of the inhibitor capable of driving 99% of cells, if there is no consumption of regulatory protein, 88% of cells were guided to the specific state. It implied that cells were pushed, by the inhibitor, to the specific state due to the change of stochasticity.
Energy Technology Data Exchange (ETDEWEB)
Xiao, Yanwen; Xu, Wei, E-mail: weixu@nwpu.edu.cn; Wang, Liang [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China)
2016-03-15
This paper focuses on the study of the stochastic Van der Pol vibro-impact system with fractional derivative damping under Gaussian white noise excitation. The equations of the original system are simplified by non-smooth transformation. For the simplified equation, the stochastic averaging approach is applied to solve it. Then, the fractional derivative damping term is facilitated by a numerical scheme, therewith the fourth-order Runge-Kutta method is used to obtain the numerical results. And the numerical simulation results fit the analytical solutions. Therefore, the proposed analytical means to study this system are proved to be feasible. In this context, the effects on the response stationary probability density functions (PDFs) caused by noise excitation, restitution condition, and fractional derivative damping are considered, in addition the stochastic P-bifurcation is also explored in this paper through varying the value of the coefficient of fractional derivative damping and the restitution coefficient. These system parameters not only influence the response PDFs of this system but also can cause the stochastic P-bifurcation.
Xiao, Yanwen; Xu, Wei; Wang, Liang
2016-03-01
This paper focuses on the study of the stochastic Van der Pol vibro-impact system with fractional derivative damping under Gaussian white noise excitation. The equations of the original system are simplified by non-smooth transformation. For the simplified equation, the stochastic averaging approach is applied to solve it. Then, the fractional derivative damping term is facilitated by a numerical scheme, therewith the fourth-order Runge-Kutta method is used to obtain the numerical results. And the numerical simulation results fit the analytical solutions. Therefore, the proposed analytical means to study this system are proved to be feasible. In this context, the effects on the response stationary probability density functions (PDFs) caused by noise excitation, restitution condition, and fractional derivative damping are considered, in addition the stochastic P-bifurcation is also explored in this paper through varying the value of the coefficient of fractional derivative damping and the restitution coefficient. These system parameters not only influence the response PDFs of this system but also can cause the stochastic P-bifurcation.
Stochastic modeling of friction force and vibration analysis of a mechanical system using the model
International Nuclear Information System (INIS)
Kang, Won Seok; Choi, Chan Kyu; Yoo, Hong Hee
2015-01-01
The squeal noise generated from a disk brake or chatter occurred in a machine tool primarily results from friction-induced vibration. Since friction-induced vibration is usually accompanied by abrasion and lifespan reduction of mechanical parts, it is necessary to develop a reliable analysis model by which friction-induced vibration phenomena can be accurately analyzed. The original Coulomb's friction model or the modified Coulomb friction model employed in most commercial programs employs deterministic friction coefficients. However, observing friction phenomena between two contact surfaces, one may observe that friction coefficients keep changing due to the unevenness of contact surface, temperature, lubrication and humidity. Therefore, in this study, friction coefficients are modeled as random parameters that keep changing during the motion of a mechanical system undergoing friction force. The integrity of the proposed stochastic friction model was validated by comparing the analysis results obtained by the proposed model with experimental results.
Path integral methods for the dynamics of stochastic and disordered systems
International Nuclear Information System (INIS)
Hertz, John A; Roudi, Yasser; Sollich, Peter
2017-01-01
We review some of the techniques used to study the dynamics of disordered systems subject to both quenched and fast (thermal) noise. Starting from the Martin–Siggia–Rose/Janssen–De Dominicis–Peliti path integral formalism for a single variable stochastic dynamics, we provide a pedagogical survey of the perturbative, i.e. diagrammatic, approach to dynamics and how this formalism can be used for studying soft spin models. We review the supersymmetric formulation of the Langevin dynamics of these models and discuss the physical implications of the supersymmetry. We also describe the key steps involved in studying the disorder-averaged dynamics. Finally, we discuss the path integral approach for the case of hard Ising spins and review some recent developments in the dynamics of such kinetic Ising models. (topical review)
Multilevel coarse graining and nano-pattern discovery in many particle stochastic systems
International Nuclear Information System (INIS)
Kalligiannaki, Evangelia; Katsoulakis, Markos A.; Plecháč, Petr; Vlachos, Dionisios G.
2012-01-01
In this work we propose a hierarchy of Markov chain Monte Carlo methods for sampling equilibrium properties of stochastic lattice systems with competing short and long range interactions. Each Monte Carlo step is composed by two or more sub-steps efficiently coupling coarse and finer state spaces. The method can be designed to sample the exact or controlled-error approximations of the target distribution, providing information on levels of different resolutions, as well as at the microscopic level. In both strategies the method achieves significant reduction of the computational cost compared to conventional Markov chain Monte Carlo methods. Applications in phase transition and pattern formation problems confirm the efficiency of the proposed methods.
Tucker, C. J.; Garratt, M. W.
1977-01-01
A stochastic leaf radiation model based upon physical and physiological properties of dicot leaves has been developed. The model accurately predicts the absorbed, reflected, and transmitted radiation of normal incidence as a function of wavelength resulting from the leaf-irradiance interaction over the spectral interval of 0.40-2.50 micron. The leaf optical system has been represented as Markov process with a unique transition matrix at each 0.01-micron increment between 0.40 micron and 2.50 micron. Probabilities are calculated at every wavelength interval from leaf thickness, structure, pigment composition, and water content. Simulation results indicate that this approach gives accurate estimations of actual measured values for dicot leaf absorption, reflection, and transmission as a function of wavelength.
Limit theorems for Markov chains and stochastic properties of dynamical systems by quasi-compactness
Hervé, Loïc
2001-01-01
This book shows how techniques from the perturbation theory of operators, applied to a quasi-compact positive kernel, may be used to obtain limit theorems for Markov chains or to describe stochastic properties of dynamical systems. A general framework for this method is given and then applied to treat several specific cases. An essential element of this work is the description of the peripheral spectra of a quasi-compact Markov kernel and of its Fourier-Laplace perturbations. This is first done in the ergodic but non-mixing case. This work is extended by the second author to the non-ergodic case. The only prerequisites for this book are a knowledge of the basic techniques of probability theory and of notions of elementary functional analysis.