Stability analysis for stochastic BAM nonlinear neural network with delays
Lv, Z. W.; Shu, H. S.; Wei, G. L.
2008-02-01
In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria.
Path to Stochastic Stability: Comparative Analysis of Stochastic Learning Dynamics in Games
Jaleel, Hassan
2018-04-08
Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a framework for the comparative analysis of stochastic learning dynamics with different update rules but same steady-state behavior. We present the framework in the context of two learning dynamics: Log-Linear Learning (LLL) and Metropolis Learning (ML). Although both of these dynamics have the same stochastically stable states, LLL and ML correspond to different behavioral models for decision making. Moreover, we demonstrate through an example setup of sensor coverage game that for each of these dynamics, the paths to stochastically stable states exhibit distinctive behaviors. Therefore, we propose multiple criteria to analyze and quantify the differences in the short and medium run behavior of stochastic learning dynamics. We derive and compare upper bounds on the expected hitting time to the set of Nash equilibria for both LLL and ML. For the medium to long-run behavior, we identify a set of tools from the theory of perturbed Markov chains that result in a hierarchical decomposition of the state space into collections of states called cycles. We compare LLL and ML based on the proposed criteria and develop invaluable insights into the comparative behavior of the two dynamics.
Electricity Market Stochastic Dynamic Model and Its Mean Stability Analysis
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Zhanhui Lu
2014-01-01
Full Text Available Based on the deterministic dynamic model of electricity market proposed by Alvarado, a stochastic electricity market model, considering the random nature of demand sides, is presented in this paper on the assumption that generator cost function and consumer utility function are quadratic functions. The stochastic electricity market model is a generalization of the deterministic dynamic model. Using the theory of stochastic differential equations, stochastic process theory, and eigenvalue techniques, the determining conditions of the mean stability for this electricity market model under small Gauss type random excitation are provided and testified theoretically. That is, if the demand elasticity of suppliers is nonnegative and the demand elasticity of consumers is negative, then the stochastic electricity market model is mean stable. It implies that the stability can be judged directly by initial data without any computation. Taking deterministic electricity market data combined with small Gauss type random excitation as numerical samples to interpret random phenomena from a statistical perspective, the results indicate the conclusions above are correct, valid, and practical.
Exponential stability analysis for delayed stochastic Cohen-Grossberg neural network
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Guanjun Wang
2010-04-01
Full Text Available In this paper, the exponential stability problems are addressed for a class of delayed Cohen-Grossberg neural networks which are also perturbed by some stochastic noises. By employing the Lyapunov method, stochastic analysis and some inequality techniques, sufficient conditions are acquired for checking the pth(p g 1 and the 1st moment exponential stability of the network. Finally, One example is given to show the effectiveness of the proposed results.
The asymptotic stability analysis in stochastic logistic model with Poisson growth coefficient
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Shaojuan Ma
2014-01-01
Full Text Available The asymptotic stability of a discrete logistic model with random growth coefficient is studied in this paper. Firstly, the discrete logistic model with random growth coefficient is built and reduced into its deterministic equivalent system by orthogonal polynomial approximation. Then, the linear stability theory and the Jury criterion of nonlinear deterministic discrete systems are applied to the equivalent one. At last, by mathematical analysis, we find that the parameter interval for asymptotic stability of nontrivial equilibrium in stochastic logistic system gets smaller as the random intensity or statistical parameters of random variable is increased and the random parameter's influence on asymptotic stability in stochastic logistic system becomes prominent.
Global asymptotic stability analysis for neutral stochastic neural networks with time-varying delays
Su, Weiwei; Chen, Yiming
2009-04-01
In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays and norm-bounded uncertainties. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criteria are derived to ensure the global, robust, asymptotic stability of the addressed system in the mean square for all admissible parameter uncertainties. The criteria can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the feasibility and effectiveness of the results.
Su, Weiwei; Chen, Yiming
2009-05-01
In this paper, the global exponential stability is investigated for a class of stochastic interval neural networks with time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. Based on Lyapunov stable theory and stochastic analysis approaches, the delay-dependent criteria are derived to ensure the global, robust, exponential stability of the addressed system in the mean square. The criteria can be checked easily by the LMI control toolbox in Matlab. A numerical example is given to illustrate the effectiveness and improvement over some existing results.
Chadha, Alka; Bora, Swaroop Nandan
2017-11-01
This paper studies the existence, uniqueness, and exponential stability in mean square for the mild solution of neutral second order stochastic partial differential equations with infinite delay and Poisson jumps. By utilizing the Banach fixed point theorem, first the existence and uniqueness of the mild solution of neutral second order stochastic differential equations is established. Then, the mean square exponential stability for the mild solution of the stochastic system with Poisson jumps is obtained with the help of an established integral inequality.
Phase stability analysis of liquid-liquid equilibrium with stochastic methods
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G. Nagatani
2008-09-01
Full Text Available Minimization of Gibbs free energy using activity coefficient models and nonlinear equation solution techniques is commonly applied to phase stability problems. However, when conventional techniques, such as the Newton-Raphson method, are employed, serious convergence problems may arise. Due to the existence of multiple solutions, several problems can be found in modeling liquid-liquid equilibrium of multicomponent systems, which are highly dependent on the initial guess. In this work phase stability analysis of liquid-liquid equilibrium is investigated using the NRTL model. For this purpose, two distinct stochastic numerical algorithms are employed to minimize the tangent plane distance of Gibbs free energy: a subdivision algorithm that can find all roots of nonlinear equations for liquid-liquid stability analysis and the Simulated Annealing method. Results obtained in this work for the two stochastic algorithms are compared with those of the Interval Newton method from the literature. Several different binary and multicomponent systems from the literature were successfully investigated.
Stability analysis of switched stochastic neural networks with time-varying delays.
Wu, Xiaotai; Tang, Yang; Zhang, Wenbing
2014-03-01
This paper is concerned with the global exponential stability of switched stochastic neural networks with time-varying delays. Firstly, the stability of switched stochastic delayed neural networks with stable subsystems is investigated by utilizing the mathematical induction method, the piecewise Lyapunov function and the average dwell time approach. Secondly, by utilizing the extended comparison principle from impulsive systems, the stability of stochastic switched delayed neural networks with both stable and unstable subsystems is analyzed and several easy to verify conditions are derived to ensure the exponential mean square stability of switched delayed neural networks with stochastic disturbances. The effectiveness of the proposed results is illustrated by two simulation examples. Copyright © 2013 Elsevier Ltd. All rights reserved.
Robust stability analysis of uncertain stochastic neural networks with interval time-varying delay
International Nuclear Information System (INIS)
Feng Wei; Yang, Simon X.; Fu Wei; Wu Haixia
2009-01-01
This paper addresses the stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. The parameter uncertainties are assumed to be norm bounded, and the delay factor is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. A sufficient condition is derived such that for all admissible uncertainties, the considered neural network is robustly, globally, asymptotically stable in the mean square. Some stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Finally, numerical examples are provided to demonstrate the usefulness of the proposed criteria.
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
Improved result on stability analysis of discrete stochastic neural networks with time delay
International Nuclear Information System (INIS)
Wu Zhengguang; Su Hongye; Chu Jian; Zhou Wuneng
2009-01-01
This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.
Stochastic stabilization of cosmological photons
International Nuclear Information System (INIS)
Dettmann, C P; Keating, J P; Prado, S D
2004-01-01
The stability of photon trajectories in models of the universe that have constant spatial curvature is determined by the sign of the curvature: they are exponentially unstable if the curvature is negative and stable if it is positive or zero. We demonstrate that random fluctuations in the curvature provide an additional stabilizing mechanism. This mechanism is analogous to the one responsible for stabilizing the stochastic Kapitsa pendulum. When the mean curvature is negative it is capable of stabilizing the photon trajectories; when the mean curvature is zero or positive it determines the characteristic frequency with which neighbouring trajectories oscillate about each other. In constant negative curvature models of the universe that have compact topology, exponential instability implies chaos (e.g. mixing) in the photon dynamics. We discuss some consequences of stochastic stabilization in this context. (letter to the editor)
Liu, Hongjian; Wang, Zidong; Shen, Bo; Huang, Tingwen; Alsaadi, Fuad E
2018-06-01
This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a sequence of Bernoulli distributed random variables is utilized to determine within which intervals the time-varying delays fall at certain time instant. The sector-bounded activation function is considered in the addressed DSMNN. By taking into account the state-dependent characteristics of the network parameters and choosing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square. The derived conditions are made dependent on both the leakage and the probabilistic delays, and are therefore less conservative than the traditional delay-independent criteria. A simulation example is given to show the effectiveness of the proposed stability criterion. Copyright © 2018 Elsevier Ltd. All rights reserved.
Mean square exponential stability of stochastic delayed Hopfield neural networks
International Nuclear Information System (INIS)
Wan Li; Sun Jianhua
2005-01-01
Stochastic effects to the stability property of Hopfield neural networks (HNN) with discrete and continuously distributed delay are considered. By using the method of variation parameter, inequality technique and stochastic analysis, the sufficient conditions to guarantee the mean square exponential stability of an equilibrium solution are given. Two examples are also given to demonstrate our results
Stochastic stability and bifurcation in a macroeconomic model
International Nuclear Information System (INIS)
Li Wei; Xu Wei; Zhao Junfeng; Jin Yanfei
2007-01-01
On the basis of the work of Goodwin and Puu, a new business cycle model subject to a stochastically parametric excitation is derived in this paper. At first, we reduce the model to a one-dimensional diffusion process by applying the stochastic averaging method of quasi-nonintegrable Hamiltonian system. Secondly, we utilize the methods of Lyapunov exponent and boundary classification associated with diffusion process respectively to analyze the stochastic stability of the trivial solution of system. The numerical results obtained illustrate that the trivial solution of system must be globally stable if it is locally stable in the state space. Thirdly, we explore the stochastic Hopf bifurcation of the business cycle model according to the qualitative changes in stationary probability density of system response. It is concluded that the stochastic Hopf bifurcation occurs at two critical parametric values. Finally, some explanations are given in a simply way on the potential applications of stochastic stability and bifurcation analysis
Gharbieh, Mohammad
2017-05-02
The Internet of Things (IoT) is large-scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, will play fundamental rule in delivering the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the traffic requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.
International Nuclear Information System (INIS)
Ali, M. Syed
2011-01-01
In this paper, the global stability of Takagi—Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature. (general)
Evolutionary stability concepts in a stochastic environment
Zheng, Xiu-Deng; Li, Cong; Lessard, Sabin; Tao, Yi
2017-09-01
Over the past 30 years, evolutionary game theory and the concept of an evolutionarily stable strategy have been not only extensively developed and successfully applied to explain the evolution of animal behaviors, but also widely used in economics and social sciences. Nonetheless, the stochastic dynamical properties of evolutionary games in randomly fluctuating environments are still unclear. In this study, we investigate conditions for stochastic local stability of fixation states and constant interior equilibria in a two-phenotype model with random payoffs following pairwise interactions. Based on this model, we develop the concepts of stochastic evolutionary stability (SES) and stochastic convergence stability (SCS). We show that the condition for a pure strategy to be SES and SCS is more stringent than in a constant environment, while the condition for a constant mixed strategy to be SES is less stringent than the condition to be SCS, which is less stringent than the condition in a constant environment.
International Nuclear Information System (INIS)
Ali, M. Syed
2014-01-01
In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen—Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples
Exponential Stability of Stochastic Nonlinear Dynamical Price System with Delay
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Wenli Zhu
2013-01-01
Full Text Available Based on Lyapunov stability theory, Itô formula, stochastic analysis, and matrix theory, we study the exponential stability of the stochastic nonlinear dynamical price system. Using Taylor's theorem, the stochastic nonlinear system with delay is reduced to an n-dimensional semilinear stochastic differential equation with delay. Some sufficient conditions of exponential stability and corollaries for such price system are established by virtue of Lyapunov function. The time delay upper limit is solved by using our theoretical results when the system is exponentially stable. Our theoretical results show that if the classical price Rayleigh equation is exponentially stable, so is its perturbed system with delay provided that both the time delay and the intensity of perturbations are small enough. Two examples are presented to illustrate our results.
Foundations of stochastic analysis
Rao, M M; Lukacs, E
1981-01-01
Foundations of Stochastic Analysis deals with the foundations of the theory of Kolmogorov and Bochner and its impact on the growth of stochastic analysis. Topics covered range from conditional expectations and probabilities to projective and direct limits, as well as martingales and likelihood ratios. Abstract martingales and their applications are also discussed. Comprised of five chapters, this volume begins with an overview of the basic Kolmogorov-Bochner theorem, followed by a discussion on conditional expectations and probabilities containing several characterizations of operators and mea
Exponential Stability of Stochastic Systems with Delay and Poisson Jumps
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Wenli Zhu
2014-01-01
Full Text Available This paper focuses on the model of a class of nonlinear stochastic delay systems with Poisson jumps based on Lyapunov stability theory, stochastic analysis, and inequality technique. The existence and uniqueness of the adapted solution to such systems are proved by applying the fixed point theorem. By constructing a Lyapunov function and using Doob’s martingale inequality and Borel-Cantelli lemma, sufficient conditions are given to establish the exponential stability in the mean square of such systems, and we prove that the exponentially stable in the mean square of such systems implies the almost surely exponentially stable. The obtained results show that if stochastic systems is exponentially stable and the time delay is sufficiently small, then the corresponding stochastic delay systems with Poisson jumps will remain exponentially stable, and time delay upper limit is solved by using the obtained results when the system is exponentially stable, and they are more easily verified and applied in practice.
Exponential Stability of Stochastic Differential Equation with Mixed Delay
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Wenli Zhu
2014-01-01
Full Text Available This paper focuses on a class of stochastic differential equations with mixed delay based on Lyapunov stability theory, Itô formula, stochastic analysis, and inequality technique. A sufficient condition for existence and uniqueness of the adapted solution to such systems is established by employing fixed point theorem. Some sufficient conditions of exponential stability and corollaries for such systems are obtained by using Lyapunov function. By utilizing Doob’s martingale inequality and Borel-Cantelli lemma, it is shown that the exponentially stable in the mean square of such systems implies the almost surely exponentially stable. In particular, our theoretical results show that if stochastic differential equation is exponentially stable and the time delay is sufficiently small, then the corresponding stochastic differential equation with mixed delay will remain exponentially stable. Moreover, time delay upper limit is solved by using our theoretical results when the system is exponentially stable, and they are more easily verified and applied in practice.
Stochastic stability properties of jump linear systems
Feng, Xiangbo; Loparo, Kenneth A.; Ji, Yuandong; Chizeck, Howard J.
1992-01-01
Jump linear systems are defined as a family of linear systems with randomly jumping parameters (usually governed by a Markov jump process) and are used to model systems subject to failures or changes in structure. The authors study stochastic stability properties in jump linear systems and the relationship among various moment and sample path stability properties. It is shown that all second moment stability properties are equivalent and are sufficient for almost sure sample path stability, and a testable necessary and sufficient condition for second moment stability is derived. The Lyapunov exponent method for the study of almost sure sample stability is discussed, and a theorem which characterizes the Lyapunov exponents of jump linear systems is presented.
International Nuclear Information System (INIS)
Wang Shen-Quan; Feng Jian; Zhao Qing
2012-01-01
In this paper, the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks (CRNNs) with stochastic delay. Different from the common assumptions on time delays, it is assumed that the probability distribution of the delay taking values in some intervals is known a priori. By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique (the reciprocally convex combination method), less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Two numerical examples show that our results are better than the existing ones. (general)
Stochastic stability in three-player games.
Kamiński, Dominik; Miekisz, Jacek; Zaborowski, Marcin
2005-11-01
Animal behavior and evolution can often be described by game-theoretic models. Although in many situations the number of players is very large, their strategic interactions are usually decomposed into a sum of two-player games. Only recently were evolutionarily stable strategies defined for multi-player games and their properties analyzed [Broom, M., Cannings, C., Vickers, G.T., 1997. Multi-player matrix games. Bull. Math. Biol. 59, 931-952]. Here we study the long-run behavior of stochastic dynamics of populations of randomly matched individuals playing symmetric three-player games. We analyze the stochastic stability of equilibria in games with multiple evolutionarily stable strategies. We also show that, in some games, a population may not evolve in the long run to an evolutionarily stable equilibrium.
Stability of stochastic optimization problem - nonmeasurable case
Czech Academy of Sciences Publication Activity Database
Lachout, Petr
2008-01-01
Roč. 44, č. 2 (2008), s. 259-276 ISSN 0023-5954 R&D Projects: GA ČR GA201/08/0539 Grant - others:GA ČR(CZ) GA201/05/2340 Institutional research plan: CEZ:AV0Z10750506 Source of funding: V - iné verejné zdroje Keywords : Stochastic optimization problem * Sensitivity and stability * Measurability * Weak convergence of probability measures Subject RIV: BA - General Mathematics Impact factor: 0.281, year: 2008
Exponential p-stability of impulsive stochastic differential equations with delays
International Nuclear Information System (INIS)
Yang Zhiguo; Xu Daoyi; Xiang Li
2006-01-01
In this Letter, we establish a method to study the exponential p-stability of the zero solution of impulsive stochastic differential equations with delays. By establishing an L-operator inequality and using the properties of M-cone and stochastic analysis technique, we obtain some new conditions ensuring the exponential p-stability of the zero solution of impulsive stochastic differential equations with delays. Two illustrative examples have been provided to show the effectiveness of our results
Lyapunov functionals and stability of stochastic functional differential equations
Shaikhet, Leonid
2013-01-01
Stability conditions for functional differential equations can be obtained using Lyapunov functionals. Lyapunov Functionals and Stability of Stochastic Functional Differential Equations describes the general method of construction of Lyapunov functionals to investigate the stability of differential equations with delays. This work continues and complements the author’s previous book Lyapunov Functionals and Stability of Stochastic Difference Equations, where this method is described for discrete- and continuous-time difference equations. The text begins with a description of the peculiarities of deterministic and stochastic functional differential equations. There follow basic definitions for stability theory of stochastic hereditary systems, and a formal procedure of Lyapunov functionals construction is presented. Stability investigation is conducted for stochastic linear and nonlinear differential equations with constant and distributed delays. The proposed method is used for stability investigation of di...
Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances
Energy Technology Data Exchange (ETDEWEB)
Ju, Ping [Hohai Univ., Nanjing (China); Li, Hongyu [Hohai Univ., Nanjing (China); Gan, Chun [The Univ. of Tennessee, Knoxville, TN (United States); Liu, Yong [The Univ. of Tennessee, Knoxville, TN (United States); Yu, Yiping [Hohai Univ., Nanjing (China); Liu, Yilu [Univ. of Tennessee, Knoxville, TN (United States)
2017-06-28
Here, with the growing integration of renewable power generation, plug-in electric vehicles, and other sources of uncertainty, increasing stochastic continuous disturbances are brought to power systems. The impact of stochastic continuous disturbances on power system transient stability attracts significant attention. To address this problem, this paper proposes an analytical assessment method for transient stability of multi-machine power systems under stochastic continuous disturbances. In the proposed method, a probability measure of transient stability is presented and analytically solved by stochastic averaging. Compared with the conventional method (Monte Carlo simulation), the proposed method is many orders of magnitude faster, which makes it very attractive in practice when many plans for transient stability must be compared or when transient stability must be analyzed quickly. Also, it is found that the evolution of system energy over time is almost a simple diffusion process by the proposed method, which explains the impact mechanism of stochastic continuous disturbances on transient stability in theory.
Stochastic Analysis and Related Topics
Ustunel, Ali
1988-01-01
The Silvri Workshop was divided into a short summer school and a working conference, producing lectures and research papers on recent developments in stochastic analysis on Wiener space. The topics treated in the lectures relate to the Malliavin calculus, the Skorohod integral and nonlinear functionals of white noise. Most of the research papers are applications of these subjects. This volume addresses researchers and graduate students in stochastic processes and theoretical physics.
Almost Surely Exponential Stability of Numerical Solutions for Stochastic Pantograph Equations
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Shaobo Zhou
2014-01-01
Full Text Available Our effort is to develop a criterion on almost surely exponential stability of numerical solution to stochastic pantograph differential equations, with the help of the discrete semimartingale convergence theorem and the technique used in stable analysis of the exact solution. We will prove that the Euler-Maruyama (EM method can preserve almost surely exponential stability of stochastic pantograph differential equations under the linear growth conditions. And the backward EM method can reproduce almost surely exponential stability for highly nonlinear stochastic pantograph differential equations. A highly nonlinear example is provided to illustrate the main theory.
Exponential stability of uncertain stochastic neural networks with mixed time-delays
International Nuclear Information System (INIS)
Wang Zidong; Lauria, Stanislao; Fang Jian'an; Liu Xiaohui
2007-01-01
This paper is concerned with the global exponential stability analysis problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability analysis problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov-Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria
Stochastic and non-stochastic effects - a conceptual analysis
International Nuclear Information System (INIS)
Karhausen, L.R.
1980-01-01
The attempt to divide radiation effects into stochastic and non-stochastic effects is discussed. It is argued that radiation or toxicological effects are contingently related to radiation or chemical exposure. Biological effects in general can be described by general laws but these laws never represent a necessary connection. Actually stochastic effects express contingent, or empirical, connections while non-stochastic effects represent semantic and non-factual connections. These two expressions stem from two different levels of discourse. The consequence of this analysis for radiation biology and radiation protection is discussed. (author)
Stochastic geometry for image analysis
Descombes, Xavier
2013-01-01
This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.
Stochastic and infinite dimensional analysis
Carpio-Bernido, Maria; Grothaus, Martin; Kuna, Tobias; Oliveira, Maria; Silva, José
2016-01-01
This volume presents a collection of papers covering applications from a wide range of systems with infinitely many degrees of freedom studied using techniques from stochastic and infinite dimensional analysis, e.g. Feynman path integrals, the statistical mechanics of polymer chains, complex networks, and quantum field theory. Systems of infinitely many degrees of freedom create their particular mathematical challenges which have been addressed by different mathematical theories, namely in the theories of stochastic processes, Malliavin calculus, and especially white noise analysis. These proceedings are inspired by a conference held on the occasion of Prof. Ludwig Streit’s 75th birthday and celebrate his pioneering and ongoing work in these fields.
Almost sure exponential stability of stochastic fuzzy cellular neural networks with delays
International Nuclear Information System (INIS)
Zhao Hongyong; Ding Nan; Chen Ling
2009-01-01
This paper is concerned with the problem of exponential stability analysis for fuzzy cellular neural network with delays. By constructing suitable Lyapunov functional and using stochastic analysis we present some sufficient conditions ensuring almost sure exponential stability for the network. Moreover, an example is given to demonstrate the advantages of our method.
Stabilization Strategies of Supply Networks with Stochastic Switched Topology
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Shukai Li
2013-01-01
Full Text Available In this paper, a dynamical supply networks model with stochastic switched topology is presented, in which the stochastic switched topology is dependent on a continuous time Markov process. The goal is to design the state-feedback control strategies to stabilize the dynamical supply networks. Based on Lyapunov stability theory, sufficient conditions for the existence of state feedback control strategies are given in terms of matrix inequalities, which ensure the robust stability of the supply networks at the stationary states and a prescribed H∞ disturbance attenuation level with respect to the uncertain demand. A numerical example is given to illustrate the effectiveness of the proposed method.
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
Stochastic modeling of mode interactions via linear parabolized stability equations
Ran, Wei; Zare, Armin; Hack, M. J. Philipp; Jovanovic, Mihailo
2017-11-01
Low-complexity approximations of the Navier-Stokes equations have been widely used in the analysis of wall-bounded shear flows. In particular, the parabolized stability equations (PSE) and Floquet theory have been employed to capture the evolution of primary and secondary instabilities in spatially-evolving flows. We augment linear PSE with Floquet analysis to formally treat modal interactions and the evolution of secondary instabilities in the transitional boundary layer via a linear progression. To this end, we leverage Floquet theory by incorporating the primary instability into the base flow and accounting for different harmonics in the flow state. A stochastic forcing is introduced into the resulting linear dynamics to model the effect of nonlinear interactions on the evolution of modes. We examine the H-type transition scenario to demonstrate how our approach can be used to model nonlinear effects and capture the growth of the fundamental and subharmonic modes observed in direct numerical simulations and experiments.
Foundations of infinitesimal stochastic analysis
Stroyan, KD
2011-01-01
This book gives a complete and elementary account of fundamental results on hyperfinite measures and their application to stochastic processes, including the *-finite Stieltjes sum approximation of martingale integrals. Many detailed examples, not found in the literature, are included. It begins with a brief chapter on tools from logic and infinitesimal (or non-standard) analysis so that the material is accessible to beginning graduate students.
Fourier analysis and stochastic processes
Brémaud, Pierre
2014-01-01
This work is unique as it provides a uniform treatment of the Fourier theories of functions (Fourier transforms and series, z-transforms), finite measures (characteristic functions, convergence in distribution), and stochastic processes (including arma series and point processes). It emphasises the links between these three themes. The chapter on the Fourier theory of point processes and signals structured by point processes is a novel addition to the literature on Fourier analysis of stochastic processes. It also connects the theory with recent lines of research such as biological spike signals and ultrawide-band communications. Although the treatment is mathematically rigorous, the convivial style makes the book accessible to a large audience. In particular, it will be interesting to anyone working in electrical engineering and communications, biology (point process signals) and econometrics (arma models). A careful review of the prerequisites (integration and probability theory in the appendix, Hilbert spa...
pth Moment Exponential Stability of Nonlinear Hybrid Stochastic Heat Equations
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Xuetao Yang
2014-01-01
Full Text Available We are concerned with the exponential stability problem of a class of nonlinear hybrid stochastic heat equations (known as stochastic heat equations with Markovian switching in an infinite state space. The fixed point theory is utilized to discuss the existence, uniqueness, and pth moment exponential stability of the mild solution. Moreover, we also acquire the Lyapunov exponents by combining the fixed point theory and the Gronwall inequality. At last, two examples are provided to verify the effectiveness of our obtained results.
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...
Stability theorems for stochastic differential equations driven by G-Brownian motion
Zhang, Defei
2011-01-01
In this paper, stability theorems for stochastic differential equations and backward stochastic differential equations driven by G-Brownian motion are obtained. We show the existence and uniqueness of solutions to forward-backward stochastic differential equations driven by G-Brownian motion. Stability theorem for forward-backward stochastic differential equations driven by G-Brownian motion is also presented.
Using metrics in stability of stochastic programming problems
Czech Academy of Sciences Publication Activity Database
Houda, Michal
2005-01-01
Roč. 13, č. 1 (2005), s. 128-134 ISSN 0572-3043 R&D Projects: GA ČR(CZ) GA402/04/1294 Institutional research plan: CEZ:AV0Z10750506 Keywords : stochastic programming * quantitative stability * Wasserstein metrics * Kolmogorov metrics * simulation study Subject RIV: BB - Applied Statistics, Operational Research
Stability of Solutions to Semilinear Stochastic Evolution Equations
Czech Academy of Sciences Publication Activity Database
Leha, G.; Maslowski, Bohdan; Ritter, G.
1999-01-01
Roč. 17, č. 6 (1999), s. 1009-1051 ISSN 0736-2994 R&D Projects: GA ČR GA201/95/0629 Institutional research plan: CEZ:AV0Z1019905; CEZ:AV0Z1019905 Keywords : stochastic evolution equations * Lyapunov stability * forward inequality Subject RIV: BA - General Mathematics Impact factor: 0.263, year: 1999
Stochastic Analysis : A Series of Lectures
Dozzi, Marco; Flandoli, Franco; Russo, Francesco
2015-01-01
This book presents in thirteen refereed survey articles an overview of modern activity in stochastic analysis, written by leading international experts. The topics addressed include stochastic fluid dynamics and regularization by noise of deterministic dynamical systems; stochastic partial differential equations driven by Gaussian or Lévy noise, including the relationship between parabolic equations and particle systems, and wave equations in a geometric framework; Malliavin calculus and applications to stochastic numerics; stochastic integration in Banach spaces; porous media-type equations; stochastic deformations of classical mechanics and Feynman integrals and stochastic differential equations with reflection. The articles are based on short courses given at the Centre Interfacultaire Bernoulli of the Ecole Polytechnique Fédérale de Lausanne, Switzerland, from January to June 2012. They offer a valuable resource not only for specialists, but also for other researchers and Ph.D. students in the fields o...
pth moment asymptotic stability of stochastic delayed hybrid systems with Lévy noise
Yang, Jun; Zhou, Wuneng; Yang, Xueqing; Hu, Xiaotao; Xie, Lili
2015-09-01
The problem of pth moment asymptotic stability analysis is considered for stochastic delayed hybrid systems with Lévy noise. By virtue of Itô's formula and M-matrix theories, we propose some sufficient conditions to guarantee the asymptotic stability and exponential stability of the system. The criterion of mean square asymptotic stability is derived as well for delayed neural networks with Lévy noise. A numerical example is provided to show the usefulness of the proposed asymptotic stability criterion.
Stochastic ferromagnetism analysis and numerics
Brzezniak, Zdzislaw; Neklyudov, Mikhail; Prohl, Andreas
2013-01-01
This monograph examines magnetization dynamics at elevated temperatures which can be described by the stochastic Landau-Lifshitz-Gilbert equation (SLLG). Comparative computational studies with the stochastic model are included. Constructive tools such as e.g. finite element methods are used to derive the theoretical results, which are then used for computational studies.
Stochastic Modelling and Analysis of Warehouse Operations
Y. Gong (Yeming)
2009-01-01
textabstractThis thesis has studied stochastic models and analysis of warehouse operations. After an overview of stochastic research in warehouse operations, we explore the following topics. Firstly, we search optimal batch sizes in a parallel-aisle warehouse with online order arrivals. We employ a
Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks
International Nuclear Information System (INIS)
Mathiyalagan, K.; Sakthivel, R.; Marshal Anthoni, S.
2012-01-01
This Letter addresses the stability analysis problem for a class of uncertain discrete-time stochastic fuzzy neural networks (DSFNNs) with time-varying delays. By constructing a new Lyapunov–Krasovskii functional combined with the free weighting matrix technique, a new set of delay-dependent sufficient conditions for the robust exponential stability of the considered DSFNNs is established in terms of Linear Matrix Inequalities (LMIs). Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained theory. -- Highlights: ► Applications of neural networks require the knowledge of dynamic behaviors. ► Exponential stability of discrete-time stochastic fuzzy neural networks is studied. ► Linear matrix inequality optimization approach is used to obtain the result. ► Delay-dependent stability criterion is established in terms of LMIs. ► Examples with simulation are provided to show the effectiveness of the result.
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
Stability and synchronization control of stochastic neural networks
Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing
2016-01-01
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.
Extending Stochastic Network Calculus to Loss Analysis
Directory of Open Access Journals (Sweden)
Chao Luo
2013-01-01
Full Text Available Loss is an important parameter of Quality of Service (QoS. Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.
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...
Control of stochastic sensitivity in a stabilization problem for gas discharge system
Energy Technology Data Exchange (ETDEWEB)
Bashkirtseva, Irina [Ural Federal University, Lenina, 51, Ekaterinburg, 620000 (Russian Federation)
2015-11-30
We consider a nonlinear dynamic stochastic system with control. A problem of stochastic sensitivity synthesis of the equilibrium is studied. A mathematical technique of the solution of this problem is discussed. This technique is applied to the problem of the stabilization of the operating mode for the stochastic gas discharge system. We construct a feedback regulator that reduces the stochastic sensitivity of the equilibrium, suppresses large-amplitude oscillations, and provides a proper operation of this engineering device.
Almost Sure Stability and Stabilization for Hybrid Stochastic Systems with Time-Varying Delays
Directory of Open Access Journals (Sweden)
Hua Yang
2012-01-01
Full Text Available The problems of almost sure (a.s. stability and a.s. stabilization are investigated for hybrid stochastic systems (HSSs with time-varying delays. The different time-varying delays in the drift part and in the diffusion part are considered. Based on nonnegative semimartingale convergence theorem, Hölder’s inequality, Doob’s martingale inequality, and Chebyshev’s inequality, some sufficient conditions are proposed to guarantee that the underlying nonlinear hybrid stochastic delay systems (HSDSs are almost surely (a.s. stable. With these conditions, a.s. stabilization problem for a class of nonlinear HSDSs is addressed through designing linear state feedback controllers, which are obtained in terms of the solutions to a set of linear matrix inequalities (LMIs. Two numerical simulation examples are given to show the usefulness of the results derived.
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.
International Seminar on Stability Problems for Stochastic Models
Zolatarev, Vladimir
1993-01-01
The subject of this book is a new direction in the field of probability theory and mathematical statistics which can be called "stability theory": it deals with evaluating the effects of perturbing initial probabilistic models and embraces quite varied subtopics: limit theorems, queueing models, statistical inference, probability metrics, etc. The contributions are original research articles developing new ideas and methods of stability analysis.
Stochastic analysis for finance with simulations
Choe, Geon Ho
2016-01-01
This book is an introduction to stochastic analysis and quantitative finance; it includes both theoretical and computational methods. Topics covered are stochastic calculus, option pricing, optimal portfolio investment, and interest rate models. Also included are simulations of stochastic phenomena, numerical solutions of the Black–Scholes–Merton equation, Monte Carlo methods, and time series. Basic measure theory is used as a tool to describe probabilistic phenomena. The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoret...
The Stochastic stability of a Logistic model with Poisson white noise
International Nuclear Information System (INIS)
Duan Dong-Hai; Xu Wei; Zhou Bing-Chang; Su Jun
2011-01-01
The stochastic stability of a logistic model subjected to the effect of a random natural environment, modeled as Poisson white noise process, is investigated. The properties of the stochastic response are discussed for calculating the Lyapunov exponent, which had proven to be the most useful diagnostic tool for the stability of dynamical systems. The generalised Itô differentiation formula is used to analyse the stochastic stability of the response. The results indicate that the stability of the response is related to the intensity and amplitude distribution of the environment noise and the growth rate of the species. (general)
The Stochastic stability of a Logistic model with Poisson white noise
Duan, Dong-Hai; Xu, Wei; Su, Jun; Zhou, Bing-Chang
2011-03-01
The stochastic stability of a logistic model subjected to the effect of a random natural environment, modeled as Poisson white noise process, is investigated. The properties of the stochastic response are discussed for calculating the Lyapunov exponent, which had proven to be the most useful diagnostic tool for the stability of dynamical systems. The generalised Itô differentiation formula is used to analyse the stochastic stability of the response. The results indicate that the stability of the response is related to the intensity and amplitude distribution of the environment noise and the growth rate of the species. Project supported by the National Natural Science Foundation of China (Grant Nos. 10872165 and 10932009).
Stochastic flux analysis of chemical reaction networks.
Kahramanoğulları, Ozan; Lynch, James F
2013-12-07
Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.
International Nuclear Information System (INIS)
Hurricane, O.A.
1994-09-01
In this dissertation, a new linear Vlasov kinetic theory is developed for calculating the plasma response to perturbing electromagnetic fields in cases where the particle dynamics are stochastic; for modes with frequencies less than the typical particle bounce frequency. A variational form is arrived at which allows one to properly perform a stability analysis for a stochastic plasma. In the case of stochastic dynamics, the authors demonstrate that the plasma responds to the flux tube volume average of the perturbing potentials as opposed to the usual case of adiabatic dynamics where plasma responds to the bounce average of the perturbed potentials. They show that for the stochastic plasma, the kinetic variational form maps into the Bernstein energy principle if the perturbation frequency is large compared to all drift frequencies, the perpendicular wavelength is large compared to the Larmor radius, and vanishing of the potentials associated with the parallel electric field are all assumed. By explicit minimization of the energy principle, it is established that the stochastic plasma is always less stable than an adiabatic plasma. Lastly, the effect of strictly enforcing the quasi-neutrality (QN) condition upon a gyro-kinetic type stability analysis is explored. From simple mathematical considerations, it is shown that when the QN condition is imposed convective type modes that are equipotentials along magnetic field lines are created that alter the stability properties of the plasma. The pertinent modifications to the Bernstein energy principle are given
Directory of Open Access Journals (Sweden)
Xiaoming Fan
2014-01-01
Full Text Available We discuss multigroup SIRS (susceptible, infectious, and recovered epidemic models with random perturbations. We carry out a detailed analysis on the asymptotic behavior of the stochastic model; when reproduction number ℛ0>1, we deduce the globally asymptotic stability of the endemic equilibrium by measuring the difference between the solution and the endemic equilibrium of the deterministic model in time average. Numerical methods are employed to illustrate the dynamic behavior of the model and simulate the system of equations developed. The effect of the rate of immunity loss on susceptible and recovered individuals is also analyzed in the deterministic model.
Stochastic Non-Parametric Frontier Analysis
Mohammad Rahmani; Gholamreza Jahanshahloo
2014-01-01
In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production eciency. Specically, our approach rst allows for statistical noise, similar to Stochastic frontier analysis , and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods. The methodology is based on the theory of local maximum likelih...
Stabilization and destabilization effects of the electric field on stochastic precipitate pattern
Lagzi, István; Izsak, F.
2004-01-01
Stabilization and destabilization effects of an applied electric field on the Liesegang pattern formation in low concentration gradient were studied with numerical model simulations. In the absence of an electric field pattern formation exhibits increasingly stochastic behaviour as the initial
Second Workshop on Stochastic Analysis and Related Topics
Ustunel, Ali
1990-01-01
The Second Silivri Workshop functioned as a short summer school and a working conference, producing lecture notes and research papers on recent developments of Stochastic Analysis on Wiener space. The topics of the lectures concern short time asymptotic problems and anticipative stochastic differential equations. Research papers are mostly extensions and applications of the techniques of anticipative stochastic calculus.
Semiclassical analysis for diffusions and stochastic processes
Kolokoltsov, Vassili N
2000-01-01
The monograph is devoted mainly to the analytical study of the differential, pseudo-differential and stochastic evolution equations describing the transition probabilities of various Markov processes. These include (i) diffusions (in particular,degenerate diffusions), (ii) more general jump-diffusions, especially stable jump-diffusions driven by stable Lévy processes, (iii) complex stochastic Schrödinger equations which correspond to models of quantum open systems. The main results of the book concern the existence, two-sided estimates, path integral representation, and small time and semiclassical asymptotics for the Green functions (or fundamental solutions) of these equations, which represent the transition probability densities of the corresponding random process. The boundary value problem for Hamiltonian systems and some spectral asymptotics ar also discussed. Readers should have an elementary knowledge of probability, complex and functional analysis, and calculus.
International Nuclear Information System (INIS)
Huang Zaitang; Yang Qigui
2009-01-01
The paper considers the problems of existence of quadratic mean almost periodic and global exponential stability for stochastic cellular neural networks with delays. By employing the Holder's inequality and fixed points principle, we present some new criteria ensuring existence and uniqueness of a quadratic mean almost periodic and global exponential stability. These criteria are important in signal processing and the design of networks. Moreover, these criteria are also applied in others stochastic biological neural systems.
Stochastic analysis of temperature fields in frozen foundation soils
Burkov, Pyotr; Konan, Eme Cesar; Burkov, Vladimir; Burkova, Svetlana; Kolesov, Aleks
2017-01-01
One of the most crucial issues of compressor stations engineering and construction is to provide foundation stability and robustness of such stations in permafrost conditions. To date, one of the most used protection methods for compressor stations in permafrost conditions is thermal stabilization of soil. This paper is focused on calculation of the temperature stabilizing foundation based on the mathematical model of stochastic analysis and the forecast of temperature field impacts. Thermotechnical calculations can be used to provide the best estimate of the standard values of strength and deformation parameters of permafrost soils subjected to shear stress and pile foot pressure. The best estimate will be useful for optimization of engineering solutions in terms of support and foundation structures.
Stochastic simulation algorithms and analysis
Asmussen, Soren
2007-01-01
Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods, whereas the second half discusses model-specific algorithms. Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value.
Symbolic Computing in Probabilistic and Stochastic Analysis
Directory of Open Access Journals (Sweden)
Kamiński Marcin
2015-12-01
Full Text Available The main aim is to present recent developments in applications of symbolic computing in probabilistic and stochastic analysis, and this is done using the example of the well-known MAPLE system. The key theoretical methods discussed are (i analytical derivations, (ii the classical Monte-Carlo simulation approach, (iii the stochastic perturbation technique, as well as (iv some semi-analytical approaches. It is demonstrated in particular how to engage the basic symbolic tools implemented in any system to derive the basic equations for the stochastic perturbation technique and how to make an efficient implementation of the semi-analytical methods using an automatic differentiation and integration provided by the computer algebra program itself. The second important illustration is probabilistic extension of the finite element and finite difference methods coded in MAPLE, showing how to solve boundary value problems with random parameters in the environment of symbolic computing. The response function method belongs to the third group, where interference of classical deterministic software with the non-linear fitting numerical techniques available in various symbolic environments is displayed. We recover in this context the probabilistic structural response in engineering systems and show how to solve partial differential equations including Gaussian randomness in their coefficients.
Stability of Exponential Euler Method for Stochastic Systems under Poisson White Noise Excitations
Li, Longsuo; Zhang, Yu
2014-12-01
The stability of stochastic systems under Poisson white noise excitations which based on the quantum theory is investigated in this paper. In general, the exact solution of the most of the stochastic systems with jumps is not easy to get. So it is very necessary to investigate the numerical solution of equations. On the one hand, exponential Euler method is applied to study stochastic delay differential equations, we can find the sufficient conditions for keeping mean square stability by investigating numerical method of systems. Through the comparison, we get the step-size of this method which is longer than the Euler-Maruyama method. On the other hand, mean square exponential stability of exponential Euler method for semi-linear stochastic delay differential equations under Poisson white noise excitations is confirmed.
Stochastic convex sparse principal component analysis.
Baytas, Inci M; Lin, Kaixiang; Wang, Fei; Jain, Anil K; Zhou, Jiayu
2016-12-01
Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ 1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.
Conference on Stochastic Analysis and Related Topics
Peterson, Jonathon
2017-01-01
The articles in this collection are a sampling of some of the research presented during the conference “Stochastic Analysis and Related Topics”, held in May of 2015 at Purdue University in honor of the 60th birthday of Rodrigo Bañuelos. A wide variety of topics in probability theory is covered in these proceedings, including heat kernel estimates, Malliavin calculus, rough paths differential equations, Lévy processes, Brownian motion on manifolds, and spin glasses, among other topics.
Stochastic analysis of virus transport in aquifers
Campbell Rehmann, Linda L.; Welty, Claire; Harvey, Ronald W.
1999-01-01
A large-scale model of virus transport in aquifers is derived using spectral perturbation analysis. The effects of spatial variability in aquifer hydraulic conductivity and virus transport (attachment, detachment, and inactivation) parameters on large-scale virus transport are evaluated. A stochastic mean model of virus transport is developed by linking a simple system of local-scale free-virus transport and attached-virus conservation equations from the current literature with a random-field representation of aquifer and virus transport properties. The resultant mean equations for free and attached viruses are found to differ considerably from the local-scale equations on which they are based and include effects such as a free-virus effective velocity that is a function of aquifer heterogeneity as well as virus transport parameters. Stochastic mean free-virus breakthrough curves are compared with local model output in order to observe the effects of spatial variability on mean one-dimensional virus transport in three-dimensionally heterogeneous porous media. Significant findings from this theoretical analysis include the following: (1) Stochastic model breakthrough occurs earlier than local model breakthrough, and this effect is most pronounced for the least conductive aquifers studied. (2) A high degree of aquifer heterogeneity can lead to virus breakthrough actually preceding that of a conservative tracer. (3) As the mean hydraulic conductivity is increased, the mean model shows less sensitivity to the variance of the natural-logarithm hydraulic conductivity and mean virus diameter. (4) Incorporation of a heterogeneous colloid filtration term results in higher predicted concentrations than a simple first-order adsorption term for a given mean attachment rate. (5) Incorporation of aquifer heterogeneity leads to a greater range of virus diameters for which significant breakthrough occurs. (6) The mean model is more sensitive to the inactivation rate of viruses
Weak Second Order Explicit Stabilized Methods for Stiff Stochastic Differential Equations
Abdulle, Assyr
2013-01-01
We introduce a new family of explicit integrators for stiff Itô stochastic differential equations (SDEs) of weak order two. These numerical methods belong to the class of one-step stabilized methods with extended stability domains and do not suffer from the step size reduction faced by standard explicit methods. The family is based on the standard second order orthogonal Runge-Kutta-Chebyshev (ROCK2) methods for deterministic problems. The convergence, meansquare, and asymptotic stability properties of the methods are analyzed. Numerical experiments, including applications to nonlinear SDEs and parabolic stochastic partial differential equations are presented and confirm the theoretical results. © 2013 Society for Industrial and Applied Mathematics.
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.
pth Moment stability of impulsive stochastic delay differential systems with Markovian switching
Wu, Xiaotai; Zhang, Wenbing; Tang, Yang
2013-07-01
This paper is concerned with the pth moment stability of impulsive stochastic delay differential systems with Markovian switching. By using the Razumikhin-type method, some stability criteria are obtained, which can loosen the constraints of the existing results and thus reduce the conservativeness. Two examples are presented to demonstrate the usefulness of the proposed results.
Fixed Points and Stability in Neutral Stochastic Differential Equations with Variable Delays
Directory of Open Access Journals (Sweden)
Chang-Wen Zhao
2008-07-01
Full Text Available We consider the mean square asymptotic stability of a generalized linear neutral stochastic differential equation with variable delays by using the fixed point theory. An asymptotic mean square stability theorem with a necessary and sufficient condition is proved, which improves and generalizes some results due to Burton, Zhang and Luo. Two examples are also given to illustrate our results.
Directory of Open Access Journals (Sweden)
Zhanhua Yu
2011-01-01
Full Text Available We study the almost surely asymptotic stability of exact solutions to neutral stochastic pantograph equations (NSPEs, and sufficient conditions are obtained. Based on these sufficient conditions, we show that the backward Euler method (BEM with variable stepsize can preserve the almost surely asymptotic stability. Numerical examples are demonstrated for illustration.
Error analysis of stochastic gradient descent ranking.
Chen, Hong; Tang, Yi; Li, Luoqing; Yuan, Yuan; Li, Xuelong; Tang, Yuanyan
2013-06-01
Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.
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.
Energy Technology Data Exchange (ETDEWEB)
Ryashko, Lev [Ural Federal University, Lenina, 51, Ekaterinburg, 620000 (Russian Federation)
2015-11-30
A stabilization problem of the equilibrium of stochastically forced nonlinear discrete-time system with incomplete information is considered. Our approach uses a regulator which synthesizes the required stochastic sensitivity of the equilibrium. Mathematically, this problem is reduced to the solution of some quadratic matrix equations. A description of attainability sets and algorithms for regulators design is given. The general results are applied to the suppression of unwanted large-amplitude oscillations around the equilibria of the stochastically forced Verhulst model with noisy observations.
Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays
Syed Ali, M.; Balasubramaniam, P.
2008-07-01
In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.
Influences of plasticity on a sheet pile phased stochastic FE analysis
Boer, A. de; Waarts, P.H.
2000-01-01
The paper deals with the stochastic analysis of the stability of a sheet pile soil structure. Most areas in the Netherlands have layered soil conditions. The decisive parameter in the nonlinear FE analysis is the behaviour of the soil. For layered soil conditions, the correct modelling of the
Stochastic Modeling and Analysis of Power System with Renewable Generation
DEFF Research Database (Denmark)
Chen, Peiyuan
. With the increasing number of wind turbines (WTs) connected to distribution systems, network operators are concerned about how such a stochastic generation affects power losses of the network. Furthermore, the operators need to estimate how much and when the stochastic generation can reduce the loading of substation...... be achieved through a probabilistic analysis that takes into account the stochastic behavior of wind power generation (WPG) and load demand. Such a probabilistic analysis may help network operators to cut down the cost associated with system planning. Thus, the objective of this thesis is to develop...... stochastic models of renewable generation and load demand for the optimal operation and planning of modern distribution systems through a probabilistic approach. On the basis of statistical data, stochastic models of WPG, load and combined heat and power (CHP) generation are developed. The stochastic wind...
International Nuclear Information System (INIS)
Doan, N.V.; Martin, G.; Haider, F.; Bellon, P.
1989-01-01
Assessing cascade size effects on compound stability under irradiation requires a safe stochastic description of the order-disorder transition under external forcing. To address multidimensional order parameter structures, we introduce the Kubo Ansatz technique and apply it to the FCC lattice. Irradiation-induced stabilization of unexpected structures is predicted: a diagram for the respective stability of L1 2 , L1 0 and disordered FCC solid solution is established
Elements of stochastic calculus and analysis
Stroock, Daniel W
2018-01-01
This book gives a somewhat unconventional introduction to stochastic analysis. Although most of the material covered here has appeared in other places, this book attempts to explain the core ideas on which that material is based. As a consequence, the presentation is more an extended mathematical essay than a ``definition, lemma, theorem'' text. In addition, it includes several topics that are not usually treated elsewhere. For example, Wiener's theory of homogeneous chaos is discussed, Stratovich integration is given a novel development and applied to derive Wong and Zakai's approximation theorem, and examples are given of the application of Malliavin's calculus to partial differential equations. Each chapter concludes with several exercises, some of which are quite challenging. The book is intended for use by advanced graduate students and research mathematicians who may be familiar with many of the topics but want to broaden their understanding of them.
Stochastic Non-Parametric Frontier Analysis
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Mohammad Rahmani
2014-05-01
Full Text Available In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production eciency. Specically, our approach rst allows for statistical noise, similar to Stochastic frontier analysis , and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods. The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Kumbhakar et al. We will use local-spherical coordinate system to transform multi-input multi-output data to more exible system which we can use in our approach. We also illustrate the performance of our approach with simulated example
Chen, Guiling; Li, Dingshi; Shi, Lin; van Gaans, Onno; Verduyn Lunel, Sjoerd
2018-03-01
We present new conditions for asymptotic stability and exponential stability of a class of stochastic recurrent neural networks with discrete and distributed time varying delays. Our approach is based on the method using fixed point theory, which do not resort to any Liapunov function or Liapunov functional. Our results neither require the boundedness, monotonicity and differentiability of the activation functions nor differentiability of the time varying delays. In particular, a class of neural networks without stochastic perturbations is also considered. Examples are given to illustrate our main results.
International Nuclear Information System (INIS)
Wang Linshan; Zhang Zhe; Wang Yangfan
2008-01-01
Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities
Fu, Xilin; Li, Xiaodi
2011-01-01
In this paper, the global asymptotic stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays is investigated by using Lyapunov-Krasovskii functional method and the linear matrix inequality (LMI) technique. The mixed time delays comprise both the multiple time-varying and continuously distributed delays. Some new sufficient conditions are obtained to guarantee the global asymptotic stability of the addressed model in the stochastic sense using the powerful MATLAB LMI toolbox. The results extend and improve the earlier publications. Two numerical examples are given to illustrate the effectiveness of our results.
Muralisankar, S.; Manivannan, A.; Balasubramaniam, P.
2012-10-01
In this paper, the robust stability for uncertain neutral stochastic system with Takagi-Sugeno (T-S) fuzzy model and Markovian jumping parameters (MJPs) are investigated. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite-state space. Some novel sufficient conditions are derived to guarantee the asymptotic stability of the equilibrium point in the mean square. By utilizing the Lyapunov-Krasovskii functional, stochastic analysis theory, some free weighting matrices and linear matrix inequality (LMI) technique, the upper bound of time-varying delay is obtained by using Matlab® control toolbox. Finally, some numerical examples are given to show the effectiveness of the obtained results.
PC analysis of stochastic differential equations driven by Wiener noise
Le Maitre, Olivier
2015-03-01
A polynomial chaos (PC) analysis with stochastic expansion coefficients is proposed for stochastic differential equations driven by additive or multiplicative Wiener noise. It is shown that for this setting, a Galerkin formalism naturally leads to the definition of a hierarchy of stochastic differential equations governing the evolution of the PC modes. Under the mild assumption that the Wiener and uncertain parameters can be treated as independent random variables, it is also shown that the Galerkin formalism naturally separates parametric uncertainty and stochastic forcing dependences. This enables us to perform an orthogonal decomposition of the process variance, and consequently identify contributions arising from the uncertainty in parameters, the stochastic forcing, and a coupled term. Insight gained from this decomposition is illustrated in light of implementation to simplified linear and non-linear problems; the case of a stochastic bifurcation is also considered.
Pettersson, Per
2013-05-01
The stochastic Galerkin and collocation methods are used to solve an advection-diffusion equation with uncertain and spatially varying viscosity. We investigate well-posedness, monotonicity and stability for the extended system resulting from the Galerkin projection of the advection-diffusion equation onto the stochastic basis functions. High-order summation-by-parts operators and weak imposition of boundary conditions are used to prove stability of the semi-discrete system.It is essential that the eigenvalues of the resulting viscosity matrix of the stochastic Galerkin system are positive and we investigate conditions for this to hold. When the viscosity matrix is diagonalizable, stochastic Galerkin and stochastic collocation are similar in terms of computational cost, and for some cases the accuracy is higher for stochastic Galerkin provided that monotonicity requirements are met. We also investigate the total spatial operator of the semi-discretized system and its impact on the convergence to steady-state. © 2013 Elsevier B.V.
Su, Weiwei; Chen, Yiming
2009-02-01
The paper is concerned with the problem of robust asymptotic stability analysis of stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technology, some sufficient conditions are derived to ensure the global robust convergence of the equilibrium point. The proposed conditions can be checked easily by LMI Control Toolbox in Matlab. Furthermore, all the results are obtained under mild conditions, assuming neither differentiability nor strict monotonicity for activation function. A numerical example is given to demonstrate the effectiveness of our results.
Polynomial asymptotic stability of damped stochastic differential equations
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John Appleby
2004-08-01
Full Text Available The paper studies the polynomial convergence of solutions of a scalar nonlinear It\\^{o} stochastic differential equation\\[dX(t = -f(X(t\\,dt + \\sigma(t\\,dB(t\\] where it is known, {\\it a priori}, that $\\lim_{t\\rightarrow\\infty} X(t=0$, a.s. The intensity of the stochastic perturbation $\\sigma$ is a deterministic, continuous and square integrable function, which tends to zero more quickly than a polynomially decaying function. The function $f$ obeys $\\lim_{x\\rightarrow 0}\\mbox{sgn}(xf(x/|x|^\\beta = a$, for some $\\beta>1$, and $a>0$.We study two asymptotic regimes: when $\\sigma$ tends to zero sufficiently quickly the polynomial decay rate of solutions is the same as for the deterministic equation (when $\\sigma\\equiv0$. When $\\sigma$ decays more slowly, a weaker almost sure polynomial upper bound on the decay rate of solutions is established. Results which establish the necessity for $\\sigma$ to decay polynomially in order to guarantee the almost sure polynomial decay of solutions are also proven.
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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.
International Nuclear Information System (INIS)
Grigoriu, Mircea; Samorodnitsky, Gennady
2004-01-01
Two methods are considered for assessing the asymptotic stability of the trivial solution of linear stochastic differential equations driven by Poisson white noise, interpreted as the formal derivative of a compound Poisson process. The first method attempts to extend a result for diffusion processes satisfying linear stochastic differential equations to the case of linear equations with Poisson white noise. The developments for the method are based on Ito's formula for semimartingales and Lyapunov exponents. The second method is based on a geometric ergodic theorem for Markov chains providing a criterion for the asymptotic stability of the solution of linear stochastic differential equations with Poisson white noise. Two examples are presented to illustrate the use and evaluate the potential of the two methods. The examples demonstrate limitations of the first method and the generality of the second method
On a theory of stability for nonlinear stochastic chemical reaction networks
Smadbeck, Patrick; Kaznessis, Yiannis N.
2015-01-01
We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms. PMID:25978877
11th International Seminar on Stability Problems for Stochastic Models
Zolotarev, Vladimir
1989-01-01
Traditionally the Stability seminar, organized in Moscow but held in different locations, has dealt with a spectrum of topics centering around characterization problems and their stability, limit theorems, probabil- ity metrics and theoretical robustness. This volume likewise focusses on these main topics in a series of original and recent research articles.
Stochastic reliability analysis using Fokker Planck equations
International Nuclear Information System (INIS)
Hari Prasad, M.; Rami Reddy, G.; Srividya, A.; Verma, A.K.
2011-01-01
The Fokker-Planck equation describes the time evolution of the probability density function of the velocity of a particle, and can be generalized to other observables as well. It is also known as the Kolmogorov forward equation (diffusion). Hence, for any process, which evolves with time, the probability density function as a function of time can be represented with Fokker-Planck equation. In stochastic reliability analysis one is more interested in finding out the reliability or failure probability of the components or structures as a function of time rather than instantaneous failure probabilities. In this analysis the variables are represented with random processes instead of random variables. A random processes can be either stationary or non stationary. If the random process is stationary then the failure probability doesn't change with time where as in the case of non stationary processes the failure probability changes with time. In the present paper Fokker Planck equations have been used to find out the probability density function of the non stationary random processes. In this paper a flow chart has been provided which describes step by step process for carrying out stochastic reliability analysis using Fokker-Planck equations. As a first step one has to identify the failure function as a function of random processes. Then one has to solve the Fokker-Planck equation for each random process. In this paper the Fokker-Planck equation has been solved by using Finite difference method. As a result one gets the probability density values of the random process in the sample space as well as time space. Later at each time step appropriate probability distribution has to be identified based on the available probability density values. For checking the better fitness of the data Kolmogorov-Smirnov Goodness of fit test has been performed. In this way one can find out the distribution of the random process at each time step. Once one has the probability distribution
Stochastic Subspace Method for Experimental Modal Analysis
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Liu Dazhi
2016-01-01
Full Text Available The formula of stochastic subspace identification method is deduced in details and the program is written out. The two methods are verified by a vibration test on a 5-floor rigid frame model. In this test the gauss white noise generated from a shaker table to simulate the ambient vibration on the model, and the response signals are measured. Next, the response data of experiment are processed by auto-cross spectrum density method and stochastic subspace identification method respectively, the two methods are verified by comparing with the theory result. and bearing out the superiority of stochastic subspace identification method compared to auto-cross spectrum density method.
Asymptotic analysis for functional stochastic differential equations
Bao, Jianhai; Yuan, Chenggui
2016-01-01
This brief treats dynamical systems that involve delays and random disturbances. The study is motivated by a wide variety of systems in real life in which random noise has to be taken into consideration and the effect of delays cannot be ignored. Concentrating on such systems that are described by functional stochastic differential equations, this work focuses on the study of large time behavior, in particular, ergodicity. This brief is written for probabilists, applied mathematicians, engineers, and scientists who need to use delay systems and functional stochastic differential equations in their work. Selected topics from the brief can also be used in a graduate level topics course in probability and stochastic processes.
Lyapunov functionals and stability of stochastic difference equations
Shaikhet, Leonid
2011-01-01
This book offers a general method of Lyapunov functional construction which lets researchers analyze the degree to which the stability properties of differential equations are preserved in their difference analogues. Includes examples from physical systems.
Dynamic analysis of stochastic transcription cycles.
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Claire V Harper
2011-04-01
Full Text Available In individual mammalian cells the expression of some genes such as prolactin is highly variable over time and has been suggested to occur in stochastic pulses. To investigate the origins of this behavior and to understand its functional relevance, we quantitatively analyzed this variability using new mathematical tools that allowed us to reconstruct dynamic transcription rates of different reporter genes controlled by identical promoters in the same living cell. Quantitative microscopic analysis of two reporter genes, firefly luciferase and destabilized EGFP, was used to analyze the dynamics of prolactin promoter-directed gene expression in living individual clonal and primary pituitary cells over periods of up to 25 h. We quantified the time-dependence and cyclicity of the transcription pulses and estimated the length and variation of active and inactive transcription phases. We showed an average cycle period of approximately 11 h and demonstrated that while the measured time distribution of active phases agreed with commonly accepted models of transcription, the inactive phases were differently distributed and showed strong memory, with a refractory period of transcriptional inactivation close to 3 h. Cycles in transcription occurred at two distinct prolactin-promoter controlled reporter genes in the same individual clonal or primary cells. However, the timing of the cycles was independent and out-of-phase. For the first time, we have analyzed transcription dynamics from two equivalent loci in real-time in single cells. In unstimulated conditions, cells showed independent transcription dynamics at each locus. A key result from these analyses was the evidence for a minimum refractory period in the inactive-phase of transcription. The response to acute signals and the result of manipulation of histone acetylation was consistent with the hypothesis that this refractory period corresponded to a phase of chromatin remodeling which significantly
Real-time analysis for Stochastic errors of MEMS gyro
Miao, Zhiyong; Shi, Hongyang; Zhang, Yi
2017-10-01
Since a good knowledge of MEMS gyro stochastic errors is important and critical to MEMS INS/GPS integration system. Therefore, the stochastic errors of MEMS gyro should be accurately modeled and identified. The Allan variance method is IEEE standard method in the filed of analysis stochastic errors of gyro. This kind of method can fully characterize the random character of stochastic errors. However, it requires a large amount of data to be stored, resulting in large offline computational burden. Moreover, it has a painful procedure of drawing slope lines for estimation. To overcome the barriers, a simple linear state-space model was established for MEMS gyro. Then, a recursive EM algorithm was implemented to estimate the stochastic errors of MEMS gyro in real time. The experimental results of ADIS16405 IMU show that the real-time estimations of proposed approach are well within the error limits of Allan variance method. Moreover, the proposed method effectively avoids the storage of data.
Stochastic time series analysis of hydrology data for water resources
Sathish, S.; Khadar Babu, S. K.
2017-11-01
The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.
A Stability Result for Stochastic Differential Equations Driven by Fractional Brownian Motions
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Bruno Saussereau
2012-01-01
Full Text Available We study the stability of the solutions of stochastic differential equations driven by fractional Brownian motions with Hurst parameter greater than half. We prove that when the initial conditions, the drift, and the diffusion coefficients as well as the fractional Brownian motions converge in a suitable sense, then the sequence of the solutions of the corresponding equations converge in Hölder norm to the solution of a stochastic differential equation. The limit equation is driven by the limit fractional Brownian motion and its coefficients are the limits of the sequence of the coefficients.
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).
Stochastic Wake Modelling Based on POD Analysis
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David Bastine
2018-03-01
Full Text Available In this work, large eddy simulation data is analysed to investigate a new stochastic modeling approach for the wake of a wind turbine. The data is generated by the large eddy simulation (LES model PALM combined with an actuator disk with rotation representing the turbine. After applying a proper orthogonal decomposition (POD, three different stochastic models for the weighting coefficients of the POD modes are deduced resulting in three different wake models. Their performance is investigated mainly on the basis of aeroelastic simulations of a wind turbine in the wake. Three different load cases and their statistical characteristics are compared for the original LES, truncated PODs and the stochastic wake models including different numbers of POD modes. It is shown that approximately six POD modes are enough to capture the load dynamics on large temporal scales. Modeling the weighting coefficients as independent stochastic processes leads to similar load characteristics as in the case of the truncated POD. To complete this simplified wake description, we show evidence that the small-scale dynamics can be captured by adding to our model a homogeneous turbulent field. In this way, we present a procedure to derive stochastic wake models from costly computational fluid dynamics (CFD calculations or elaborated experimental investigations. These numerically efficient models provide the added value of possible long-term studies. Depending on the aspects of interest, different minimalized models may be obtained.
International Conference on Modern Problems of Stochastic Analysis and Statistics
2017-01-01
This book brings together the latest findings in the area of stochastic analysis and statistics. The individual chapters cover a wide range of topics from limit theorems, Markov processes, nonparametric methods, acturial science, population dynamics, and many others. The volume is dedicated to Valentin Konakov, head of the International Laboratory of Stochastic Analysis and its Applications on the occasion of his 70th birthday. Contributions were prepared by the participants of the international conference of the international conference “Modern problems of stochastic analysis and statistics”, held at the Higher School of Economics in Moscow from May 29 - June 2, 2016. It offers a valuable reference resource for researchers and graduate students interested in modern stochastics.
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...... Haviland's theorem allows an infinite dimensional optimization problem on measures to be formulated as a polynomial optimization problem. Subsequently, the moment sequence is truncated (relaxed) to obtain a finite dimensional polynomial optimization problem. Finally, we provide an illustrative example...
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)
Directory of Open Access Journals (Sweden)
Dan Ye
2013-01-01
Full Text Available This paper is concerned with delay-dependent stochastic stability for time-delay Markovian jump systems (MJSs with sector-bounded nonlinearities and more general transition probabilities. Different from the previous results where the transition probability matrix is completely known, a more general transition probability matrix is considered which includes completely known elements, boundary known elements, and completely unknown ones. In order to get less conservative criterion, the state and transition probability information is used as much as possible to construct the Lyapunov-Krasovskii functional and deal with stability analysis. The delay-dependent sufficient conditions are derived in terms of linear matrix inequalities to guarantee the stability of systems. Finally, numerical examples are exploited to demonstrate the effectiveness of the proposed method.
Analysis of dynamic regimes in stochastically forced Kaldor model
International Nuclear Information System (INIS)
Bashkirtseva, Irina; Ryazanova, Tatyana; Ryashko, Lev
2015-01-01
We consider the business cycle Kaldor model forced by random noise. Detailed parametric analysis of deterministic system is carried out and zones of coexisting stable equilibrium and stable limit cycle are found. Noise-induced transitions between these attractors are studied using stochastic sensitivity function technique and confidence domains method. Critical values of noise intensity corresponding to noise-induced transitions “equilibrium → cycle” and “cycle → equilibrium” are estimated. Dominants in combined stochastic regimes are discussed.
Robust Stabilization of T-S Fuzzy Stochastic Descriptor Systems via Integral Sliding Modes.
Li, Jinghao; Zhang, Qingling; Yan, Xing-Gang; Spurgeon, Sarah K
2017-09-19
This paper addresses the robust stabilization problem for T-S fuzzy stochastic descriptor systems using an integral sliding mode control paradigm. A classical integral sliding mode control scheme and a nonparallel distributed compensation (Non-PDC) integral sliding mode control scheme are presented. It is shown that two restrictive assumptions previously adopted developing sliding mode controllers for Takagi-Sugeno (T-S) fuzzy stochastic systems are not required with the proposed framework. A unified framework for sliding mode control of T-S fuzzy systems is formulated. The proposed Non-PDC integral sliding mode control scheme encompasses existing schemes when the previously imposed assumptions hold. Stability of the sliding motion is analyzed and the sliding mode controller is parameterized in terms of the solutions of a set of linear matrix inequalities which facilitates design. The methodology is applied to an inverted pendulum model to validate the effectiveness of the results presented.
Asymptotical stability of stochastic neural networks with multiple time-varying delays
Zhou, Xianghui; Zhou, Wuneng; Dai, Anding; Yang, Jun; Xie, Lili
2015-03-01
The stochastic neural networks can be considered as an expansion of cellular neural networks and Hopfield neural networks. In real world, the neural networks are prone to be instable due to time delay and external disturbance. In this paper, we consider the asymptotic stability for the stochastic neural networks with multiple time-varying delays. By employing a Lyapunov-Krasovskii function, a sufficient condition which guarantees the asymptotic stability of the state trajectory in the mean square is obtained. The criteria proposed can be verified readily by utilising the linear matrix inequality toolbox in Matlab, and no parameters to be tuned. In the end, two numerical examples are provided to demonstrate the effectiveness of the proposed method.
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Xiaolin Zhu
2014-01-01
Full Text Available This paper studies the T-stability of the Heun method and balanced method for solving stochastic differential delay equations (SDDEs. Two T-stable conditions of the Heun method are obtained for two kinds of linear SDDEs. Moreover, two conditions under which the balanced method is T-stable are obtained for two kinds of linear SDDEs. Some numerical examples verify the theoretical results proposed.
Stochastic stability of Cohen-Grossberg neural networks with unbounded distributed delays
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Ping Chen
2010-03-01
Full Text Available In this article, we consider a model that describes the dynamics of Cohen-Grossberg neural networks with unbounded distributed delays, whose state variable are governed by stochastic non-linear integro-differential equations. Without assuming the smoothness, monotonicity and boundedness of the activation functions, by constructing suitable Lyapunov functional, employing the semi-martingale convergence theorem and some inequality, we obtain some sufficient criteria to check the almost exponential stability of networks.
Containment vessel stability analysis
International Nuclear Information System (INIS)
Harstead, G.A.; Morris, N.F.; Unsal, A.I.
1983-01-01
The stability analysis for a steel containment shell is presented herein. The containment is a freestanding shell consisting of a vertical cylinder with a hemispherical dome. It is stiffened by large ring stiffeners and relatively small longitudinal stiffeners. The containment vessel is subjected to both static and dynamic loads which can cause buckling. These loads must be combined prior to their use in a stability analysis. The buckling loads were computed with the aid of the ASME Code case N-284 used in conjunction with general purpose computer codes and in-house programs. The equations contained in the Code case were used to compute the knockdown factors due to shell imperfections. After these knockdown factors were applied to the critical stress states determined by freezing the maximum dynamic stresses and combining them with other static stresses, a linear bifurcation analysis was carried out with the aid of the BOSOR4 program. Since the containment shell contained large penetrations, the Code case had to be supplemented by a local buckling analysis of the shell area surrounding the largest penetration. This analysis was carried out with the aid of the NASTRAN program. Although the factor of safety against buckling obtained in this analysis was satisfactory, it is claimed that the use of the Code case knockdown factors are unduly conservative when applied to the analysis of buckling around penetrations. (orig.)
Stability Criterion of Linear Stochastic Systems Subject to Mixed H2/Passivity Performance
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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.
Nonlinear Stochastic PDEs: Analysis and Approximations
2016-05-23
Approximation to Nonlinear SPDEs with Discrete Random Variables , SIAM J Scientific Computing, (08 2015): 1872. doi: R. Mikulevicius, B. Rozovskii. On...multiplicative discrete random variables , ( ) S. Lototsky, B. Rozovsky. Stochastic Partial Differential Equations, (09 2015) B. Rozovsky, R...B. Rozovsky and G.E. Karniadakis, "Adaptive Wick-Malliavin approximation to nonlinear SPDEs with discrete random variables ," SIAM J. Sci. Comput., 37
Modeling the lake eutrophication stochastic ecosystem and the research of its stability.
Wang, Bo; Qi, Qianqian
2018-03-20
capable of effectively explaining the regime shift theory and agreed with the realistic analyze. These conclusions also confirms the two paths for the system to move from one stable state to another proposed by Beisner et al. (2003), which may help understand the occurrence mechanism related to the lake eutrophication from the view point of the stochastic model and mathematical analysis. Copyright © 2018. Published by Elsevier Inc.
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Roberts Jason A
2006-01-01
Full Text Available We consider the reliability of some numerical methods in preserving the stability properties of the linear stochastic functional differential equation , where α, β, σ, τ ≥ 0 are real constants, and W(t is a standard Wiener process. The areas of the regions of asymptotic stability for the class of methods considered, indicated by the sufficient conditions for the discrete system, are shown to be equal in size to each other and we show that an upper bound can be put on the time-step parameter for the numerical method for which the system is asymptotically mean-square stable. We illustrate our results by means of numerical experiments and various stability diagrams. We examine the extent to which the continuous system can tolerate stochastic perturbations before losing its stability properties and we illustrate how one may accurately choose a numerical method to preserve the stability properties of the original problem in the numerical solution. Our numerical experiments also indicate that the quality of the sufficient conditions is very high.
3-Dimensional Stochastic Seepage Analysis of a Yangtze River Embankment
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Yajun Wang
2015-01-01
Full Text Available Three-dimensional stochastic simulation was performed to investigate the complexity of the seepage field of an embankment. Three-dimensional anisotropic heterogeneous steady state random seepage finite element model was developed. The material input data were derived from a statistical analysis of strata soil characteristics and geological columns. The Kolmogorov-Smirnov test was used to validate the hypothesis that the Gaussian probability distribution is applicable to the random permeability tensors. A stochastic boundary condition, the random variation of upstream and downstream water level, was taken into account in the three-dimensional finite element modelling. Furthermore, the functions of sheet-pile breakwater and catchwater were also incorporated as turbulent sources. This case, together with the variability of soil permeability, has been analyzed to investigate their influence on the hydraulic potential distributed and the random evolution of stochastic seepage field. Results from stochastic analyses have also been compared against those of deterministic analyses. The insights gained in this study suggest it is necessary, feasible, and practical to employ stochastic studies in seepage field problems. The method provides a more comprehensive stochastic algorithm than conventional ones to characterize and analyze three-dimensional random seepage field problems.
Zhang, Ling
2017-01-01
The main purpose of this paper is to investigate the strong convergence and exponential stability in mean square of the exponential Euler method to semi-linear stochastic delay differential equations (SLSDDEs). It is proved that the exponential Euler approximation solution converges to the analytic solution with the strong order [Formula: see text] to SLSDDEs. On the one hand, the classical stability theorem to SLSDDEs is given by the Lyapunov functions. However, in this paper we study the exponential stability in mean square of the exact solution to SLSDDEs by using the definition of logarithmic norm. On the other hand, the implicit Euler scheme to SLSDDEs is known to be exponentially stable in mean square for any step size. However, in this article we propose an explicit method to show that the exponential Euler method to SLSDDEs is proved to share the same stability for any step size by the property of logarithmic norm.
International Nuclear Information System (INIS)
Wang, Tao; Zhou, Guoqing; Wang, Jianzhou; Zhao, Xiaodong
2016-01-01
Highlights: • The influence of stochastic properties and conditions on permafrost foundation was investigated. • A stochastic analysis for the uncertain thermal characteristic of crude oil pipe is presented. • The mean temperature and standard deviation of foundation soils are obtained and analyzed. • Average standard deviation and maximum standard deviation of foundation soils increase with time. - Abstract: For foundation soils surrounding the crude oil pipeline in permafrost regions, the soil properties and the upper boundary conditions are stochastic because of complex geological processes and changeable atmospheric environment. The conventional finite element analysis of thermal characteristics for crude oil pipeline is always deterministic, rather than taking stochastic parameters and conditions into account. This study investigated the stochastic influence of an underground crude oil pipeline on the thermal stability of the permafrost foundation on the basis of a stochastic analysis model and the stochastic finite element method. A stochastic finite element program is compiled by Matrix Laboratory (MATLAB) software, and the random temperature fields of foundation soils surrounding a crude oil pipeline in a permafrost region are obtained and analyzed by Neumann stochastic finite element method (NSFEM). The results provide a new way to predict the thermal effects of the crude oil pipeline in permafrost regions, and it shows that the standard deviations in temperature increase with time when considering the stochastic effect of soil properties and boundary conditions, which imply that the results of conventional deterministic analysis may be far from the true value, even if in different seasons. It can improve our understanding of the random temperature field of foundation soils surrounding the crude oil pipeline and provide a theoretical basis for actual engineering design in permafrost regions.
Analysis of stochastic effects in Kaldor-type business cycle discrete model
Bashkirtseva, Irina; Ryashko, Lev; Sysolyatina, Anna
2016-07-01
We study nonlinear stochastic phenomena in the discrete Kaldor model of business cycles. A numerical parametric analysis of stochastically forced attractors (equilibria, closed invariant curves, discrete cycles) of this model is performed using the stochastic sensitivity functions technique. A spatial arrangement of random states in stochastic attractors is modeled by confidence domains. The phenomenon of noise-induced transitions ;chaos-order; is discussed.
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 ...
Stochastic description of cascade size effects on phase stability under irradiation
International Nuclear Information System (INIS)
Martin, G.; Bellon, P.
1988-01-01
Cascade size may affect phase stability under irradiation because of two distinct contributions: the replacement to displacement cross section ratio depends on the deposited energy density; ballistic jumps which tend to disorder ordere compounds occur by bursts (of size b), while thermal jumps which restored long range order occur one by one. The latter effect cannot be handled by standard rate theory. A stochastic treatment of the problem, based on a Fokker Planck approximation of the relevant master equation is summarized. It is shown that the possible values of the long range order parameter under irradiation are not affected by the size b of the bursts, but that the respective stability of the former is b dependent. As a consequence, the stability diagram of phases under irradiation varies with b. Such a diagram is computed for the Ni 4 Mo system where three structures are competing: the disordered solid solution, D1 a and DO 23 . A broadening by 100K of the stability domain of the short range ordered structure to the expense of the long range ordered one is predicted when increasing b from 1 to 100. The stochastic potentials introduced in the present treatment are by no means free energies of some constrained state. They can however be computed in a mean field type approximation. 23 refs
Conditional stability in determination of initial data for stochastic parabolic equations
International Nuclear Information System (INIS)
Yuan, Ganghua
2017-01-01
In this paper, we solve two kinds of inverse problems in determination of the initial data for stochastic parabolic equations. One is determination of the initial data by lateral boundary observation on arbitrary portion of the boundary, the second one is determination of the initial data by internal observation in a subregion inside the domain. We obtain conditional stability for the two kinds of inverse problems. To prove the results, we estimate the initial data by a terminal observation near the initial time, then we estimate this terminal observation by lateral boundary observation on arbitrary portion of the boundary or internal observation in a subregion inside the domain. To achieve those goals, we derive several new Carleman estimates for stochastic parabolic equations in this paper. (paper)
Conditional stability in determination of initial data for stochastic parabolic equations
Yuan, Ganghua
2017-03-01
In this paper, we solve two kinds of inverse problems in determination of the initial data for stochastic parabolic equations. One is determination of the initial data by lateral boundary observation on arbitrary portion of the boundary, the second one is determination of the initial data by internal observation in a subregion inside the domain. We obtain conditional stability for the two kinds of inverse problems. To prove the results, we estimate the initial data by a terminal observation near the initial time, then we estimate this terminal observation by lateral boundary observation on arbitrary portion of the boundary or internal observation in a subregion inside the domain. To achieve those goals, we derive several new Carleman estimates for stochastic parabolic equations in this paper.
Stochastic analysis in production process and ecology under uncertainty
Bieda, Bogusław
2014-01-01
The monograph addresses a problem of stochastic analysis based on the uncertainty assessment by simulation and application of this method in ecology and steel industry under uncertainty. The first chapter defines the Monte Carlo (MC) method and random variables in stochastic models. Chapter two deals with the contamination transport in porous media. Stochastic approach for Municipal Solid Waste transit time contaminants modeling using MC simulation has been worked out. The third chapter describes the risk analysis of the waste to energy facility proposal for Konin city, including the financial aspects. Environmental impact assessment of the ArcelorMittal Steel Power Plant, in Kraków - in the chapter four - is given. Thus, four scenarios of the energy mix production processes were studied. Chapter five contains examples of using ecological Life Cycle Assessment (LCA) - a relatively new method of environmental impact assessment - which help in preparing pro-ecological strategy, and which can lead to reducing t...
Sensitivity analysis of stochastically forced quasiperiodic self-oscillations
Directory of Open Access Journals (Sweden)
Irina Bashkirtseva
2016-08-01
Full Text Available We study a problem of stochastically forced quasi-periodic self-oscillations of nonlinear dynamic systems, which are modelled by an invariant torus in the phase space. For weak noise, an asymptotic of the stationary distribution of random trajectories is studied using the quasipotential. For the constructive analysis of a probabilistic distribution near a torus, we use a quadratic approximation of the quasipotential. A parametric description of this approximation is based on the stochastic sensitivity functions (SSF technique. Using this technique, we create a new mathematical method for the probabilistic analysis of stochastic flows near the torus. The construction of SSF is reduced to a boundary value problem for a linear differential matrix equation. For the case of the two-torus in the three-dimensional space, a constructive solution of this problem is given. Our theoretical results are illustrated with an example.
Stochastic analysis/synthesis using sinusoidal atoms
DEFF Research Database (Denmark)
Jensen, Kristoffer
2008-01-01
This work proposes a method for re-synthesizing music for use in perceptual experiments regarding structural changes and in music creation. Atoms are estimated from music audio, modelled in a stochastic model, and re-synthesized from the model pa- rameters. The atoms are found by splitting...... sinusoids into short segments, and modelled into amplitude and envelope shape, frequency, time and duration. A simple model for creating envelopes with percussive, sustained or crescendo shape is presented. Single variable and joint probability density functions are created from the atom parameters and used...... to re-create sounds with the same distribution of the atoms parameters. A novel method for visualization music, the musigram, permits a better understanding of the re- synthesized sounds....
Bloch-Salisbury, Elisabeth; Indic, Premananda; Bednarek, Frank
2009-01-01
Breathing patterns in preterm infants consist of highly variable interbreath intervals (IBIs) that might originate from nonlinear properties of the respiratory oscillator and its input-output responses to peripheral and central signals. Here, we explore a property of nonlinear control, the potential for large improvement in the stability of breathing using low-level exogenous stochastic stimulation. Stimulation was administered to 10 preterm infants (postconceptional age: mean 33.3 wk, SD 1.7) using a mattress with embedded actuators that delivered small stochastic displacements (0.021 mm root mean square, 0.090 mm maximum, 30–60 Hz); this stimulus was subthreshold for causing arousal from sleep to wakefulness or other detectable changes in the behavioral state evaluated with polysomnography. We used a test-retest protocol with multiple 10-min intervals of stimulation, each paired with 10-min intervals of no stimulation. Stimulation induced an ∼50% reduction (P = 0.003) in the variance of IBIs and an ∼50% reduction (P = 0.002) in the incidence of IBIs > 5 s. The improved stability of eupneic breathing was associated with an ∼65% reduction (P = 0.04) in the duration of O2 desaturation. Our findings suggest that nonlinear properties of the immature respiratory control system can be harnessed using afferent stimuli to stabilize eupneic breathing, thereby potentially reducing the incidence of apnea and hypoxia. PMID:19608934
Response spectrum analysis of a stochastic seismic model
International Nuclear Information System (INIS)
Kimura, Koji; Sakata, Masaru; Takemoto, Shinichiro.
1990-01-01
The stochastic response spectrum approach is presented for predicting the dynamic behavior of structures to earthquake excitation expressed by a random process, one of whose sample functions can be regarded as a recorded strong-motion earthquake accelerogram. The approach consists of modeling recorded ground motion by a random process and the root-mean-square response (rms) analysis of a single-degree-of-freedom system by using the moment equations method. The stochastic response spectrum is obtained as a plot of the maximum rms response versus the natural period of the system and is compared with the conventional response spectrum. (author)
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...
Stochastic processes analysis in nuclear reactor using ARMA models
International Nuclear Information System (INIS)
Zavaljevski, N.
1990-01-01
The analysis of ARMA model derived from general stochastic state equations of nuclear reactor is given. The dependence of ARMA model parameters on the main physical characteristics of RB nuclear reactor in Vinca is presented. Preliminary identification results are presented, observed discrepancies between theory and experiment are explained and the possibilities of identification improvement are anticipated. (author)
Stochastic sensitivity analysis using HDMR and score function
Indian Academy of Sciences (India)
... in reliability analysis and often crucial towards understanding the physical behaviour underlying failure and modifying the design to mitigate and manage risk. This article presents a new computational approach for calculating stochastic sensitivities of mechanical systems with respect to distribution parameters of random ...
Stochastic sensitivity analysis using HDMR and score function
Indian Academy of Sciences (India)
Section 4 presents a brief overview of HDMR and its applicability to reliability analysis. Section 5 presents approximation of the original ...... above mentioned one or two failure criteria satisfies. For evaluating the failure probability ..... be applied to solve any multi-physics problems. Some of the work, in the field of stochastic.
Institutions and Bank Performance; A Stochastic Frontier Analysis
Lensink, B.W.; Meesters, A.
2014-01-01
This article investigates the impact of institutions on bank efficiency and technology, using a stochastic frontier analysis of a data set of 7,959 banks across 136 countries over 10 years. The results confirm the importance of well-developed institutions for the efficient operation of commercial
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.
Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation.
Cardelli, Luca; Kwiatkowska, Marta; Laurenti, Luca
2016-11-01
Stochastic evolution of Chemical Reactions Networks (CRNs) over time is usually analyzed through solving the Chemical Master Equation (CME) or performing extensive simulations. Analysing stochasticity is often needed, particularly when some molecules occur in low numbers. Unfortunately, both approaches become infeasible if the system is complex and/or it cannot be ensured that initial populations are small. We develop a probabilistic logic for CRNs that enables stochastic analysis of the evolution of populations of molecular species. We present an approximate model checking algorithm based on the Linear Noise Approximation (LNA) of the CME, whose computational complexity is independent of the population size of each species and polynomial in the number of different species. The algorithm requires the solution of first order polynomial differential equations. We prove that our approach is valid for any CRN close enough to the thermodynamical limit. However, we show on four case studies that it can still provide good approximation even for low molecule counts. Our approach enables rigorous analysis of CRNs that are not analyzable by solving the CME, but are far from the deterministic limit. Moreover, it can be used for a fast approximate stochastic characterization of a CRN. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.
Disordered Stabilization of Stochastic Delay Systems: The Disorder-Dependent Approach
Directory of Open Access Journals (Sweden)
Guoliang Wang
2017-01-01
Full Text Available In this paper, a general stabilization problem of stochastic delay systems is realized by a disordered controller and studied by exploiting the disorder-dependent approach. Different from the traditional results, the stabilizing controller here experiences a disorder between control gains and system states. Firstly, the above disorder is described by the robust method, whose probability distribution is embodied by a Markov process with two modes. Based on this description, a kind of disordered controller having special uncertainties and depending on a Markov process is proposed. Then, by exploiting a disorder-dependent Lyapunov functional, two respective conditions for the existence of such a disordered controller are provided with LMIs. Moreover, the presented results are further extended to a general case that the corresponding transition rate matrix of the disordered controller has uncertainties. Finally, a numerical example is exploited to demonstrate the effectiveness and superiority of the proposed methods.
An Excursion-Theoretic Approach to Stability of Discrete-Time Stochastic Hybrid Systems
Energy Technology Data Exchange (ETDEWEB)
Chatterjee, Debasish, E-mail: chatterjee@control.ee.ethz.ch [ETH Zuerich, ETL I19 (Switzerland); Pal, Soumik, E-mail: soumik@math.washington.edu [University of Washington, Department of Mathematics (United States)
2011-04-15
We address stability of a class of Markovian discrete-time stochastic hybrid systems. This class of systems is characterized by the state-space of the system being partitioned into a safe or target set and its exterior, and the dynamics of the system being different in each domain. We give conditions for L{sub 1}-boundedness of Lyapunov functions based on certain negative drift conditions outside the target set, together with some more minor assumptions. We then apply our results to a wide class of randomly switched systems (or iterated function systems), for which we give conditions for global asymptotic stability almost surely and in L{sub 1}. The systems need not be time-homogeneous, and our results apply to certain systems for which functional-analytic or martingale-based estimates are difficult or impossible to get.
Rouz, Omid Farkhondeh; Ahmadian, Davood; Milev, Mariyan
2017-12-01
This paper establishes exponential mean square stability of two classes of theta Milstein methods, namely split-step theta Milstein (SSTM) method and stochastic theta Milstein (STM) method, for stochastic differential delay equations (SDDEs). We consider the SDDEs problem under a coupled monotone condition on drift and diffusion coefficients, as well as a necessary linear growth condition on the last term of theta Milstein method. It is proved that the SSTM method with θ ∈ [0, ½] can recover the exponential mean square stability of the exact solution with some restrictive conditions on stepsize, but for θ ∈ (½, 1], we proved that the stability results hold for any stepsize. Then, based on the stability results of SSTM method, we examine the exponential mean square stability of the STM method and obtain the similar stability results to that of the SSTM method. In the numerical section the figures show thevalidity of our claims.
Subspace dynamic mode decomposition for stochastic Koopman analysis
Takeishi, Naoya; Kawahara, Yoshinobu; Yairi, Takehisa
2017-09-01
The analysis of nonlinear dynamical systems based on the Koopman operator is attracting attention in various applications. Dynamic mode decomposition (DMD) is a data-driven algorithm for Koopman spectral analysis, and several variants with a wide range of applications have been proposed. However, popular implementations of DMD suffer from observation noise on random dynamical systems and generate inaccurate estimation of the spectra of the stochastic Koopman operator. In this paper, we propose subspace DMD as an algorithm for the Koopman analysis of random dynamical systems with observation noise. Subspace DMD first computes the orthogonal projection of future snapshots to the space of past snapshots and then estimates the spectra of a linear model, and its output converges to the spectra of the stochastic Koopman operator under standard assumptions. We investigate the empirical performance of subspace DMD with several dynamical systems and show its utility for the Koopman analysis of random dynamical systems.
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.
Global sensitivity analysis in stochastic simulators of uncertain reaction networks
Navarro, María
2016-12-26
Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
Stochastic stability assessment of a semi-free piston engine generator concept
Kigezi, T. N.; Gonzalez Anaya, J. A.; Dunne, J. F.
2016-09-01
Small engines, as power generators with low-noise and vibration characteristics, are needed in two niche application areas: as electric vehicle range extenders and as domestic micro Combined Heat and Power systems. A recent semi-free piston design known as the AMOCATIC generator fully meets this requirement. The engine potentially allows for high energy conversion efficiencies at resonance derived from having a mass and spring assembly. As with free-piston engines in general, stability and control of piston motion has been cited as the prime challenge limiting the technology's widespread application. Using physical principles, we derive in this paper two important results: an energy balance criterion and a related general stability criterion for a semi-free piston engine. Control is achieved by systematically designing a Proportional Integral (PI) controller using a control-oriented engine model for which a specific stability condition is stated. All results are presented in closed form throughout the paper. Simulation results under stochastic pressure conditions show that the proposed energy balance, stability criterion, and PI controller, operate as predicted to yield stable engine operation at fixed compression ratio.
Stochastic stability assessment of a semi-free piston engine generator concept
International Nuclear Information System (INIS)
Kigezi, T N; Anaya, J A Gonzalez; Dunne, J F
2016-01-01
Small engines, as power generators with low-noise and vibration characteristics, are needed in two niche application areas: as electric vehicle range extenders and as domestic micro Combined Heat and Power systems. A recent semi-free piston design known as the AMOCATIC generator fully meets this requirement. The engine potentially allows for high energy conversion efficiencies at resonance derived from having a mass and spring assembly. As with free-piston engines in general, stability and control of piston motion has been cited as the prime challenge limiting the technology's widespread application. Using physical principles, we derive in this paper two important results: an energy balance criterion and a related general stability criterion for a semi-free piston engine. Control is achieved by systematically designing a Proportional Integral (PI) controller using a control-oriented engine model for which a specific stability condition is stated. All results are presented in closed form throughout the paper. Simulation results under stochastic pressure conditions show that the proposed energy balance, stability criterion, and PI controller, operate as predicted to yield stable engine operation at fixed compression ratio. (paper)
Stochastic back analysis of permeability coefficient using generalized Bayesian method
Directory of Open Access Journals (Sweden)
Zheng Guilan
2008-09-01
Full Text Available Owing to the fact that the conventional deterministic back analysis of the permeability coefficient cannot reflect the uncertainties of parameters, including the hydraulic head at the boundary, the permeability coefficient and measured hydraulic head, a stochastic back analysis taking consideration of uncertainties of parameters was performed using the generalized Bayesian method. Based on the stochastic finite element method (SFEM for a seepage field, the variable metric algorithm and the generalized Bayesian method, formulas for stochastic back analysis of the permeability coefficient were derived. A case study of seepage analysis of a sluice foundation was performed to illustrate the proposed method. The results indicate that, with the generalized Bayesian method that considers the uncertainties of measured hydraulic head, the permeability coefficient and the hydraulic head at the boundary, both the mean and standard deviation of the permeability coefficient can be obtained and the standard deviation is less than that obtained by the conventional Bayesian method. Therefore, the present method is valid and applicable.
An h-adaptive stochastic collocation method for stochastic EMC/EMI analysis
Yücel, Abdulkadir C.
2010-07-01
The analysis of electromagnetic compatibility and interference (EMC/EMI) phenomena is often fraught by randomness in a system\\'s excitation (e.g., the amplitude, phase, and location of internal noise sources) or configuration (e.g., the routing of cables, the placement of electronic systems, component specifications, etc.). To bound the probability of system malfunction, fast and accurate techniques to quantify the uncertainty in system observables (e.g., voltages across mission-critical circuit elements) are called for. Recently proposed stochastic frameworks [1-2] combine deterministic electromagnetic (EM) simulators with stochastic collocation (SC) methods that approximate system observables using generalized polynomial chaos expansion (gPC) [3] (viz. orthogonal polynomials spanning the entire random domain) to estimate their statistical moments and probability density functions (pdfs). When constructing gPC expansions, the EM simulator is used solely to evaluate system observables at collocation points prescribed by the SC-gPC scheme. The frameworks in [1-2] therefore are non-intrusive and straightforward to implement. That said, they become inefficient and inaccurate for system observables that vary rapidly or are discontinuous in the random variables (as their representations may require very high-order polynomials). © 2010 IEEE.
Vulnerability Analysis of CSP Based on Stochastic Game Theory
Directory of Open Access Journals (Sweden)
Jiajun Shen
2016-01-01
Full Text Available With the development of industrial informatization, the industrial control network has gradually become much accessible for attackers. A series of vulnerabilities will therefore be exposed, especially the vulnerability of exclusive industrial communication protocols (ICPs, which has not yet been attached with enough emphasis. In this paper, stochastic game theory is applied on the vulnerability analysis of clock synchronization protocol (CSP, one of the pivotal ICPs. The stochastic game model is built strictly according to the protocol with both Man-in-the-Middle (MIM attack and dependability failures being taken into account. The situation of multiple attack routes is considered for depicting the practical attack scenarios, and the introduction of time aspect characterizes the success probabilities of attackers actions. The vulnerability analysis is then realized through determining the optimal strategies of attacker under different states of system, respectively.
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.
Arampatzis, Georgios; Katsoulakis, Markos A; Pantazis, Yannis
2015-01-01
Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.
Directory of Open Access Journals (Sweden)
Georgios Arampatzis
Full Text Available Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of
Stochastic Consequence Analysis for Waste Leaks
Energy Technology Data Exchange (ETDEWEB)
HEY, B.E.
2000-05-31
This analysis evaluates the radiological consequences of potential Hanford Tank Farm waste transfer leaks. These include ex-tank leaks into structures, underneath the soil, and exposed to the atmosphere. It also includes potential misroutes, tank overflow
Stochastic Consequence Analysis for Waste Leaks
International Nuclear Information System (INIS)
HEY, B.E.
2000-01-01
This analysis evaluates the radiological consequences of potential Hanford Tank Farm waste transfer leaks. These include ex-tank leaks into structures, underneath the soil, and exposed to the atmosphere. It also includes potential misroutes, tank overflow
Coastal zone management with stochastic multi-criteria analysis.
Félix, A; Baquerizo, A; Santiago, J M; Losada, M A
2012-12-15
The methodology for coastal management proposed in this study takes into account the physical processes of the coastal system and the stochastic nature of forcing agents. Simulation techniques are used to assess the uncertainty in the performance of a set of predefined management strategies based on different criteria representing the main concerns of interest groups. This statistical information as well as the distribution function that characterizes the uncertainty regarding the preferences of the decision makers is fed into a stochastic multi-criteria acceptability analysis that provides the probability of alternatives obtaining certain ranks and also calculates the preferences of a typical decision maker who supports an alternative. This methodology was applied as a management solution for Playa Granada in the Guadalfeo River Delta (Granada, Spain), where the construction of a dam in the river basin is causing severe erosion. The analysis of shoreline evolution took into account the coupled action of atmosphere, ocean, and land agents and their intrinsic stochastic character. This study considered five different management strategies. The criteria selected for the analysis were the economic benefits for three interest groups: (i) indirect beneficiaries of tourist activities; (ii) beach homeowners; and (iii) the administration. The strategies were ranked according to their effectiveness, and the relative importance given to each criterion was obtained. Copyright © 2012 Elsevier Ltd. All rights reserved.
Stochastic analysis of complex reaction networks using binomial moment equations.
Barzel, Baruch; Biham, Ofer
2012-09-01
The stochastic analysis of complex reaction networks is a difficult problem because the number of microscopic states in such systems increases exponentially with the number of reactive species. Direct integration of the master equation is thus infeasible and is most often replaced by Monte Carlo simulations. While Monte Carlo simulations are a highly effective tool, equation-based formulations are more amenable to analytical treatment and may provide deeper insight into the dynamics of the network. Here, we present a highly efficient equation-based method for the analysis of stochastic reaction networks. The method is based on the recently introduced binomial moment equations [Barzel and Biham, Phys. Rev. Lett. 106, 150602 (2011)]. The binomial moments are linear combinations of the ordinary moments of the probability distribution function of the population sizes of the interacting species. They capture the essential combinatorics of the reaction processes reflecting their stoichiometric structure. This leads to a simple and transparent form of the equations, and allows a highly efficient and surprisingly simple truncation scheme. Unlike ordinary moment equations, in which the inclusion of high order moments is prohibitively complicated, the binomial moment equations can be easily constructed up to any desired order. The result is a set of equations that enables the stochastic analysis of complex reaction networks under a broad range of conditions. The number of equations is dramatically reduced from the exponential proliferation of the master equation to a polynomial (and often quadratic) dependence on the number of reactive species in the binomial moment equations. The aim of this paper is twofold: to present a complete derivation of the binomial moment equations; to demonstrate the applicability of the moment equations for a representative set of example networks, in which stochastic effects play an important role.
Increment definitions for scale-dependent analysis of stochastic data.
Waechter, Matthias; Kouzmitchev, Alexei; Peinke, Joachim
2004-11-01
It is common for scale-dependent analysis of stochastic data to use the increment Delta(t,r) =xi(t+r)-xi(t) of a data set xi(t) as a stochastic measure, where r denotes the scale. For joint statistics of Delta(t,r) and Delta(t, r') the question of how to nest the increments on different scales r, r' is investigated. Here we show that in some cases spurious correlations between scales can be introduced by the common left-justified definition. The consequences for a Markov process are discussed. These spurious correlations can be avoided by an appropriate nesting of increments. We demonstrate this effect for different data sets and show how it can be detected and quantified. The problem allows to propose a unique method to distinguish between experimental data generated by a noiselike or a Langevin-like random-walk process, respectively.
SBOAT: A Stochastic BPMN Analysis and Optimisation Tool
DEFF Research Database (Denmark)
Herbert, Luke Thomas; Hansen, Zaza Nadja Lee; Jacobsen, Peter
2014-01-01
In this paper we present a description of a tool development framework, called SBOAT, for the quantitative analysis of graph based process modelling languages based upon the Business Process Modelling and Notation (BPMN) language, extended with intention preserving stochastic branching and parame......In this paper we present a description of a tool development framework, called SBOAT, for the quantitative analysis of graph based process modelling languages based upon the Business Process Modelling and Notation (BPMN) language, extended with intention preserving stochastic branching...... and parameterised reward annotations. SBOAT allows the optimisation of these processes by specifying optimisation goals by means of probabilistic control tree logic (PCTL). Optimisation is performed by means of an evolutionary algorithm where stochastic model checking, in the form of the PRISM model checker......, is used to compute the fitness, the performance of a candidate in terms of the specified goals, of variants of a process. Our evolutionary algorithm approach uses a matrix representation of process models to efficiently allow mutation and crossover of a process model to be performed, allowing broad...
Sakthivel, R.; Karthik Raja, U.; Mathiyalagan, K.; Leelamani, A.
2012-03-01
This paper is concerned with the problem of robust stabilization and H∞ control for a class of uncertain stochastic neural networks with time-varying delays and time-varying norm-bounded parameter uncertainties. The delay is of a time-varying nature, and the activation functions are assumed to be neither differentiable nor strictly monotonic. Moreover, the description of the activation functions is more general than the commonly used Lipschitz conditions. By using the Lyapunov function approach together with the linear matrix inequality (LMI) technique, for the robust stabilization we propose a state feedback controller to ensure that the closed loop system is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. For the robust H∞ control problem, a state feedback controller is designed such that in addition to the requirement of robust stability, a prescribed H∞ performance level is to be satisfied. The results obtained are formulated in terms of LMIs which can be easily checked by the MATLAB LMI control toolbox. Numerical examples are presented to illustrate the effectiveness of the obtained method and the improvement over some existing results.
SBAT. A stochastic BPMN analysis tool
DEFF Research Database (Denmark)
Herbert, Luke Thomas; Hansen, Zaza Nadja Lee; Jacobsen, Peter
2014-01-01
This paper presents SBAT, a tool framework for the modelling and analysis of complex business workflows. SBAT is applied to analyse an example from the Danish baked goods industry. Based upon the Business Process Modelling and Notation (BPMN) language for business process modelling, we describe...... and the value of associated rewards in states of interest for a real-world example from a case company in the Danish baked goods industry. The developments are presented in a generalised fashion to make them relevant to the general problem of implementing quantitative probabilistic model checking of graph...
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
TOOMPEA HILL STABILITY ANALYSIS
Pastarus, Jüri-Rivaldo
1997-01-01
On Toompea Hill situated in the center of Tallinn there are several natural, ancient architectural and historical monuments. It has become apparent that the processes in the rock mass caused an unfavorable environmental side effect accompanied by significant subsidence of the Toompea Hill. Identification of the reasons of the Toompea Hill's subsidence and opening the physical substance of these processes is the main aim of the present work. For the feasibility study stability problems were in...
International Nuclear Information System (INIS)
Friedman, A.; Auerbach, S.P.
1988-01-01
In a one-dimensional anharmonic potential well, the period of an orbit is a function of its energy. The true motion in such a well is regular, since energy conservation constrains the velocity at each value of the coordinate. Nontheless, when the orbit is computed numerically, stochastic behavior can result. The phenomenon of numerically induced stochasticity has significance in several contexts. Firstly, a numerical investigation of the regions of phase space accessible to an orbit may lead to erroneous results, if the timestep is too large or the mover inappropriate. Furthermore, conclusions about orbital stability based on numerical integrations may be erroneous, since neighboring chaotic orbits diverge exponentially, even if the chaos is numerically induced. When studying the dynamics of a physical system, one should demonstrate that any chaos observed is not numerically induced. Also, linearized simulations of collective phenomena must avoid numerically induced stochasticity, since the zero-order and perturbed trajectories are 'neighboring'. Finally, trajectory crossings in PIC simulations can lead to enhanced noise and other errors. In addition to these investigations, an analysis is also made of the long-term behavior of numerical trajectories (small ΔT analysis). (Nogami, K.)
Smolyak-Grid-Based Flutter Analysis with the Stochastic Aerodynamic Uncertainty
Directory of Open Access Journals (Sweden)
Yuting Dai
2014-01-01
Full Text Available How to estimate the stochastic aerodynamic parametric uncertainty on aeroelastic stability is studied in this current work. The aerodynamic uncertainty is more complicated than the structural one, and it takes more significant effect on the flutter boundary. First, the nominal unsteady aerodynamic influence coefficients were calculated with the doublet lattice method. Based on this nominal model, the stochastic uncertainty model for unsteady aerodynamic pressure coefficients was constructed with physical meaning. Afterwards, the methodology for flutter uncertainty quantification due to aerodynamic perturbation was developed, based on the nonintrusive polynomial chaos expansion theory. In order to enhance the computational efficiency, the integration algorithm, namely, Smolyak sparse grids, was employed to calculate the coefficients of the stochastic polynomial basis. Finally, the flutter uncertainty analysis methodology was applied to an aircraft's wing model. The influence of uncertainty with uniform distribution for aerodynamic pressure coefficients on flutter boundary was quantified. The numerical results indicate that, the influence of unsteady aerodynamic pressure due to the motion of coupling modes takes significant effect on flutter boundary. It is validated that the flutter uncertainty analysis based on Smolyak sparse grids integration is efficient and accurate for quantifying input uncertainty with high dimensions.
Climate change threatens polar bear populations: A stochastic demographic analysis
Hunter, C.M.; Caswell, H.; Runge, M.C.; Regehr, E.V.; Amstrup, Steven C.; Stirling, I.
2010-01-01
The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in ?? in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log ??s, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log ??s ' - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic
Climate change threatens polar bear populations: a stochastic demographic analysis.
Hunter, Christine M; Caswell, Hal; Runge, Michael C; Regehr, Eric V; Amstrup, Steve C; Stirling, Ian
2010-10-01
The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in lambda in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log lambdas, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log lambdas approximately - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population
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.
Stochastic Analysis of Offshore Steel Structures An Analytical Appraisal
Karadeniz, Halil
2013-01-01
Stochastic Analysis of Offshore Steel Structures provides a clear and detailed guide to advanced analysis methods of fixed offshore steel structures using 3D beam finite elements under random wave and earthquake loadings. Advanced and up-to-date research results are coupled with modern analysis methods and essential theoretical information to consider optimal solutions to structural issues. As these methods require and use knowledge of different subject matters, a general introduction to the key areas is provided. This is followed by in-depth explanations supported by design examples, relevant calculations and supplementary material containing related computer programmers. By combining this theoretical and practical approach Stochastic Analysis of Offshore Steel Structures cover a range of key concepts in detail including: · The basic principles of standard 3D beam finite elements and special connections, · Wave loading - from hydrodynamics to the calculation of wave load...
An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems
Kuwahara, Hiroyuki
2011-01-01
Gene therapy has a great potential to become an effective treatment for a wide variety of diseases. One of the main challenges to make gene therapy practical in clinical settings is the development of efficient and safe mechanisms to deliver foreign DNA molecules into the nucleus of target cells. Several computational and experimental studies have shown that the design process of synthetic gene transfer vectors can be greatly enhanced by computational modeling and simulation. This paper proposes a novel, effective parallelization of the stochastic simulation algorithm (SSA) for pharmacokinetic models that characterize the rate-limiting, multi-step processes of intracellular gene delivery. While efficient parallelizations of the SSA are still an open problem in a general setting, the proposed parallel simulation method is able to substantially accelerate the next reaction selection scheme and the reaction update scheme in the SSA by exploiting and decomposing the structures of stochastic gene delivery models. This, thus, makes computationally intensive analysis such as parameter optimizations and gene dosage control for specific cell types, gene vectors, and transgene expression stability substantially more practical than that could otherwise be with the standard SSA. Here, we translated the nonviral gene delivery model based on mass-action kinetics by Varga et al. [Molecular Therapy, 4(5), 2001] into a more realistic model that captures intracellular fluctuations based on stochastic chemical kinetics, and as a case study we applied our parallel simulation to this stochastic model. Our results show that our simulation method is able to increase the efficiency of statistical analysis by at least 50% in various settings. © 2011 ACM.
Directory of Open Access Journals (Sweden)
Weihua Mao
2012-01-01
Full Text Available This paper discusses the mean-square exponential stability of uncertain neutral linear stochastic systems with interval time-varying delays. A new augmented Lyapunov-Krasovskii functional (LKF has been constructed to derive improved delay-dependent robust mean-square exponential stability criteria, which are forms of linear matrix inequalities (LMIs. By free-weight matrices method, the usual restriction that the stability conditions only bear slow-varying derivative of the delay is removed. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method.
International Nuclear Information System (INIS)
Valtonen, K.
1990-01-01
The objective of this study has been to examine TVO-I oscillation incident, which occured in February 22.1987 and to find out safety implications of oscillations in ATWS incidents. Calculations have been performed with RAMONA-3B and TRAB codes. RAMONA-3B is a BWR transient analysis code with three-dimencional neutron kinetics and nonequilibrium, nonhomogeneous thermal hydraulics. TRAB code is a one-dimencional BWR transient code which uses methods similar to RAMONA-3B. The results have shown that both codes are capable of analyzing of the oscillation incidents. Both out-of-phase and in-phase oscillations are possible. If the reactor scram fails (ATWS) during oscillations the severe fuel failures are always possible and the reactor core may exceed the prompt criticality
Stochastic stabilization of phenotypic States: the genetic bistable switch as a case study.
Weber, Marc; Buceta, Javier
2013-01-01
We study by means of analytical calculation and stochastic simulations how intrinsic noise modifies the bifurcation diagram of gene regulatory processes that can be effectively described by the Langevin formalism. In a general context, our study raises the intriguing question of how biochemical fluctuations redesign the epigenetic landscape in differentiation processes. We have applied our findings to a general class of regulatory processes that includes the simplest case that displays a bistable behavior and hence phenotypic variability: the genetic auto-activating switch. Thus, we explain why and how the noise promotes the stability of the low-state phenotype of the switch and show that the bistable region is extended when increasing the intensity of the fluctuations. This phenomenology is found in a simple one-dimensional model of the genetic switch as well as in a more detailed model that takes into account the binding of the protein to the promoter region. Altogether, we prescribe the analytical means to understand and quantify the noise-induced modifications of the bifurcation points for a general class of regulatory processes where the genetic bistable switch is included.
On the Stability of Classical Orbits of the Hydrogen Ground State in Stochastic Electrodynamics
Directory of Open Access Journals (Sweden)
Theodorus M. Nieuwenhuizen
2016-04-01
Full Text Available De la Peña 1980 and Puthoff 1987 show that circular orbits in the hydrogen problem of Stochastic Electrodynamics connect to a stable situation, where the electron neither collapses onto the nucleus nor gets expelled from the atom. Although the Cole-Zou 2003 simulations support the stability, our recent numerics always lead to self-ionisation. Here the de la Peña-Puthoff argument is extended to elliptic orbits. For very eccentric orbits with energy close to zero and angular momentum below some not-small value, there is on the average a net gain in energy for each revolution, which explains the self-ionisation. Next, an 1 / r 2 potential is added, which could stem from a dipolar deformation of the nuclear charge by the electron at its moving position. This shape retains the analytical solvability. When it is enough repulsive, the ground state of this modified hydrogen problem is predicted to be stable. The same conclusions hold for positronium.
Directory of Open Access Journals (Sweden)
Amabile Alessia
2016-01-01
Full Text Available Flooding is a worldwide phenomenon. Over the last few decades the world has experienced a rising number of devastating flood events and the trend in such natural disasters is increasing. Furthermore, escalations in both the probability and magnitude of flood hazards are expected as a result of climate change. Flood defence embankments are one of the major flood defence measures and reliability assessment for these structures is therefore a very important process. Routine hydro-mechanical models for the stability of flood embankments are based on the assumptions of steady-state through-flow and zero pore-pressures above the phreatic surface, i.e. negative capillary pressure (suction is ignored. Despite common belief, these assumptions may not always lead to conservative design. In addition, hydraulic loading is stochastic in nature and flood embankment stability should therefore be assessed in probabilistic terms. This cannot be accommodated by steady-state flow models. The paper presents an approach for reliability analysis of flood embankment taking into account the transient water through-flow. The factor of safety of the embankment is assessed in probabilistic terms based on a stochastic distribution for the hydraulic loading. Two different probabilistic approaches are tested to compare and validate the results.
Bayesian phylogeny analysis via stochastic approximation Monte Carlo
Cheon, Sooyoung
2009-11-01
Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.
Li, Zhe; Xu, Rui
2012-04-01
In this paper, a class of stochastic reaction-diffusion neural networks with time delays in the leakage terms is investigated. By using the Lyapunov functional method and linear matrix inequality (LMI) approach, sufficient conditions are derived to ensure the global asymptotic stability of an equilibrium point of the networks in the mean square. The results can be easily solved by MATLAB LMI toolbox. Finally, a numerical example is given to demonstrate the effectiveness and conservativeness of our theoretical results.
Stability analysis of nonlinear systems
Lakshmikantham, Vangipuram; Martynyuk, Anatoly A
2015-01-01
The book investigates stability theory in terms of two different measure, exhibiting the advantage of employing families of Lyapunov functions and treats the theory of a variety of inequalities, clearly bringing out the underlying theme. It also demonstrates manifestations of the general Lyapunov method, showing how this technique can be adapted to various apparently diverse nonlinear problems. Furthermore it discusses the application of theoretical results to several different models chosen from real world phenomena, furnishing data that is particularly relevant for practitioners. Stability Analysis of Nonlinear Systems is an invaluable single-sourse reference for industrial and applied mathematicians, statisticians, engineers, researchers in the applied sciences, and graduate students studying differential equations.
Numerical Analysis for Stochastic Partial Differential Delay Equations with Jumps
Li, Yan; Hu, Junhao
2013-01-01
We investigate the convergence rate of Euler-Maruyama method for a class of stochastic partial differential delay equations driven by both Brownian motion and Poisson point processes. We discretize in space by a Galerkin method and in time by using a stochastic exponential integrator. We generalize some results of Bao et al. (2011) and Jacob et al. (2009) in finite dimensions to a class of stochastic partial differential delay equations with jumps in infinite dimensions.
Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays
Directory of Open Access Journals (Sweden)
Chunmei Wu
2015-01-01
Full Text Available We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.
Introduction to stochastic analysis integrals and differential equations
Mackevicius, Vigirdas
2013-01-01
This is an introduction to stochastic integration and stochastic differential equations written in an understandable way for a wide audience, from students of mathematics to practitioners in biology, chemistry, physics, and finances. The presentation is based on the naïve stochastic integration, rather than on abstract theories of measure and stochastic processes. The proofs are rather simple for practitioners and, at the same time, rather rigorous for mathematicians. Detailed application examples in natural sciences and finance are presented. Much attention is paid to simulation diffusion pro
Directory of Open Access Journals (Sweden)
Ruofeng Rao
2013-01-01
Full Text Available The robust exponential stability of delayed fuzzy Markovian-jumping Cohen-Grossberg neural networks (CGNNs with nonlinear p-Laplace diffusion is studied. Fuzzy mathematical model brings a great difficulty in setting up LMI criteria for the stability, and stochastic functional differential equations model with nonlinear diffusion makes it harder. To study the stability of fuzzy CGNNs with diffusion, we have to construct a Lyapunov-Krasovskii functional in non-matrix form. But stochastic mathematical formulae are always described in matrix forms. By way of some variational methods in W1,p(Ω, Itô formula, Dynkin formula, the semi-martingale convergence theorem, Schur Complement Theorem, and LMI technique, the LMI-based criteria on the robust exponential stability and almost sure exponential robust stability are finally obtained, the feasibility of which can efficiently be computed and confirmed by computer MatLab LMI toolbox. It is worth mentioning that even corollaries of the main results of this paper improve some recent related existing results. Moreover, some numerical examples are presented to illustrate the effectiveness and less conservatism of the proposed method due to the significant improvement in the allowable upper bounds of time delays.
Stochastic analysis of an ecosystem of two competing species
Indian Academy of Sciences (India)
gated using a Monte Carlo-type simulation technique. Some key characteristics of the stochastic system are found to be different from the corresponding determinis- tic system without the stochastic variation. A single stable state in the deterministic system is diffused into a region of stable states, and a separatrix dividing the ...
Peccati, Giovanni
2016-01-01
Stochastic geometry is the branch of mathematics that studies geometric structures associated with random configurations, such as random graphs, tilings and mosaics. Due to its close ties with stereology and spatial statistics, the results in this area are relevant for a large number of important applications, e.g. to the mathematical modeling and statistical analysis of telecommunication networks, geostatistics and image analysis. In recent years – due mainly to the impetus of the authors and their collaborators – a powerful connection has been established between stochastic geometry and the Malliavin calculus of variations, which is a collection of probabilistic techniques based on the properties of infinite-dimensional differential operators. This has led in particular to the discovery of a large number of new quantitative limit theorems for high-dimensional geometric objects. This unique book presents an organic collection of authoritative surveys written by the principal actors in this rapidly evolvi...
PATRIMONIAL ANALYSIS OF FINANCIAL STABILITY
Directory of Open Access Journals (Sweden)
GABRIELA CORINA SLUSARIUC
2011-01-01
Full Text Available Patrimonial analysis of financial stability is realized with the help of some indicator determined on the balance: working capital; required working capital and net treasury. These indicators are determined and presented in evolution at two companies with different situations, and there are given conclusions and suggestions concerning achieving and maintaining the financial equilibrium or initiating corrective measures in time, before the imbalance would take irrecoverable forms.
Stochastic stability of mechanical systems under renewal jump process parametric excitation
DEFF Research Database (Denmark)
Iwankiewicz, R.; Nielsen, Søren R.K.; Larsen, Jesper Winther
2005-01-01
independent, negative exponential distributed variables; hence, the arrival process may be termed as a generalized Erlang renewal process. The excitation process is governed by the stochastic equation driven by two independent Poisson processes, with different parameters. If the response in a single mode...... is investigated, the problem is governed in the state space by two stochastic equations, because the stochastic equation for the excitation process is autonomic. However due to the parametric nature of the excitation, the nonlinear term appears at the right-hand sides of the equations. The equations become linear...... of the stochastic equation governing the natural logarithm of the hyperspherical amplitude process and using the modification of the method wherein the time averaging of the pertinent expressions is replaced by ensemble averaging. It is found that the direct simulation is more suitable and that the asymptotic mean...
A stochastic model for EEG microstate sequence analysis.
Gärtner, Matthias; Brodbeck, Verena; Laufs, Helmut; Schneider, Gaby
2015-01-01
The analysis of spontaneous resting state neuronal activity is assumed to give insight into the brain function. One noninvasive technique to study resting state activity is electroencephalography (EEG) with a subsequent microstate analysis. This technique reduces the recorded EEG signal to a sequence of prototypical topographical maps, which is hypothesized to capture important spatio-temporal properties of the signal. In a statistical EEG microstate analysis of healthy subjects in wakefulness and three stages of sleep, we observed a simple structure in the microstate transition matrix. It can be described with a first order Markov chain in which the transition probability from the current state (i.e., map) to a different map does not depend on the current map. The resulting transition matrix shows a high agreement with the observed transition matrix, requiring only about 2% of mass transport (1/2 L1-distance). In the second part, we introduce an extended framework in which the simple Markov chain is used to make inferences on a potential underlying time continuous process. This process cannot be directly observed and is therefore usually estimated from discrete sampling points of the EEG signal given by the local maxima of the global field power. Therefore, we propose a simple stochastic model called sampled marked intervals (SMI) model that relates the observed sequence of microstates to an assumed underlying process of background intervals and thus, complements approaches that focus on the analysis of observable microstate sequences. Copyright © 2014 Elsevier Inc. All rights reserved.
Stochastic life-cycle cost analysis of wind parks
International Nuclear Information System (INIS)
Lagaros, Nikos D.; Karlaftis, Matthew G.; Paida, Maria K.
2015-01-01
We develop a life-cycle cost model for assessing wind parks; implementing the model requires calculation of cost components that are related to wind tower structural performance for multiple wind hazard levels. We compute the structural capacity of the wind towers by means of nonlinear static structural analysis for three wind hazard levels; then, the limit state dependent and life-cycle costs for the wind park are calculated based on the proposed model. The wind load for each wind hazard level is based on actual collected data and is generated probabilistically. Application of the proposed life-cycle cost analysis model is tested for a wind park with known characteristics (number and location of wind towers, wind potential, and so on). - Highlights: • A life-cycle cost model for wind parks based on nonlinear structural analysis. • The wind load for each wind hazard level is considered by means of stochastic fields. • Implementation of the life-cycle cost analysis model to a wind park in Cyprus.
Czech Academy of Sciences Publication Activity Database
Kaňková, Vlasta
2017-01-01
Roč. 53, č. 6 (2017), s. 1026-1046 ISSN 0023-5954 R&D Projects: GA ČR GA15-10331S Institutional support: RVO:67985556 Keywords : stochastic programming * stochastic dominance * empirical estimates * financial applications Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/E/kankova-0485151.pdf
Directory of Open Access Journals (Sweden)
Yang Fang
2016-01-01
Full Text Available The robust exponential stability problem for a class of uncertain impulsive stochastic neural networks of neutral-type with Markovian parameters and mixed time-varying delays is investigated. By constructing a proper exponential-type Lyapunov-Krasovskii functional and employing Jensen integral inequality, free-weight matrix method, some novel delay-dependent stability criteria that ensure the robust exponential stability in mean square of the trivial solution of the considered networks are established in the form of linear matrix inequalities (LMIs. The proposed results do not require the derivatives of discrete and distributed time-varying delays to be 0 or smaller than 1. Moreover, the main contribution of the proposed approach compared with related methods lies in the use of three types of impulses. Finally, two numerical examples are worked out to verify the effectiveness and less conservativeness of our theoretical results over existing literature.
Asymptotic and transient analysis of stochastic core ecosystem models
Directory of Open Access Journals (Sweden)
Thomas C. Gard
2000-07-01
Full Text Available General results on ultimate boundedness and exit probability estimates for stochastic differential equations are applied to investigate asymptotic and transient properties of models of plankton-fish dynamics in uncertain environments
Logics and Models for Stochastic Analysis Beyond Markov Chains
DEFF Research Database (Denmark)
Zeng, Kebin
form of discrete PH distributions as computational vehicle on measuring the performance of concurrent wireless sensor networks. Secondly, choosing stochastic process algebras as a widely accepted formalism, we study the compositionality of continuous PH distributions in order to support modelling...
Analysis of stochastic model for nonlinear volcanic dynamics
Alexandrov, D. V.; Bashkirtseva, I. A.; Ryashko, L. B.
2015-01-01
Motivated by important geophysical applications we consider a dynamic model of the magma-plug system previously derived by Iverson et al.~(2006) under the influence of stochastic forcing. Due to strong nonlinearity of the friction force for a solid plug along its margins, the initial deterministic system exhibits impulsive oscillations. Two types of dynamic behavior of the system under the influence of the parametric stochastic forcing have been found: random trajectories ar...
Analysis of stochastic model for non-linear volcanic dynamics
D. Alexandrov; I. Bashkirtseva; L. Ryashko
2014-01-01
Motivated by important geophysical applications we consider a dynamic model of the magma-plug system previously derived by Iverson et al. (2006) under the influence of stochastic forcing. Due to strong nonlinearity of the friction force for solid plug along its margins, the initial deterministic system exhibits impulsive oscillations. Two types of dynamic behavior of the system under the influence of the parametric stochastic forcing have been found: random ...
Variability analysis of complex networks measures based on stochastic distances
Cabral, Raquel S.; Frery, Alejandro C.; Ramírez, Jaime A.
2014-12-01
Complex networks can model the structure and dynamics of different types of systems. It has been shown that they are characterized by a set of measures. In this work, we evaluate the variability of complex network measures face to perturbations and, for this purpose, we impose controlled perturbations and quantify their effect. We analyze theoretical models (random, small-world and scale-free) and real networks (a collaboration network and a metabolic networks) along with the shortest path length, vertex degree, local cluster coefficient and betweenness centrality measures. In such an analysis, we propose the use of three stochastic quantifiers: the Kullback-Leibler divergence and the Jensen-Shannon and Hellinger distances. The sensitivity of these measures was analyzed with respect to the following perturbations: edge addition, edge removal, edge rewiring and node removal, all of them applied at different intensities. The results reveal that the evaluated measures are influenced by these perturbations. Additionally, hypotheses tests were performed to verify the behavior of the degree distribution to identify the intensity of the perturbations that leads to break this property.
Technical Efficiency of Thai Manufacturing SMEs: A Stochastic Frontier Analysis
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Teerawat Charoenrat
2013-03-01
Full Text Available AbstractA major motivation of this study is to examine the factors that are the most important in contributing to the relatively poor efficiency performance of Thai manufacturing small and medium sized enterprises (SMEs. The results obtained will be significant in devising effective policies aimed at tackling this poor performance.This paper uses data on manufacturing SMEs in the North-eastern region of Thailand in 2007 as a case study, by applying a stochastic frontier analysis (SFA and a technical inefficiency effects model. The empirical results obtained indicate that the mean technical efficiency of all categories of manufacturing SMEs in theNorth-eastern region is 43%, implying that manufacturing SMEs have high levels of technical inefficiency in their production processes.Manufacturing SMEs in the North-eastern region are particularly labour-intensive. The empirical results of the technical inefficiency effects model suggest that skilled labour, the municipal area and ownership characteristics are important firm-specific factors affecting technical efficiency. The paper argues that the government should play a more substantial role in developing manufacturing SMEs in the North-eastern provinces through: providing training programs for employees and employers; encouraging a greater usage of capital and technology in the production process of SMEs; enhancing the efficiency of state-ownedenterprises; encouraging a wide range of ownership forms; and improving information and communications infrastructure.
Rodríguez, Clara Rojas; Fernández Calvo, Gabriel; Ramis-Conde, Ignacio; Belmonte-Beitia, Juan
2017-08-01
Tumor-normal cell interplay defines the course of a neoplastic malignancy. The outcome of this dual relation is the ultimate prevailing of one of the cells and the death or retreat of the other. In this paper we study the mathematical principles that underlay one important scenario: that of slow-progressing cancers. For this, we develop, within a stochastic framework, a mathematical model to account for tumor-normal cell interaction in such a clinically relevant situation and derive a number of deterministic approximations from the stochastic model. We consider in detail the existence and uniqueness of the solutions of the deterministic model and study the stability analysis. We then focus our model to the specific case of low grade gliomas, where we introduce an optimal control problem for different objective functionals under the administration of chemotherapy. We derive the conditions for which singular and bang-bang control exist and calculate the optimal control and states.
Geomechanics-Based Stochastic Analysis of Injection- Induced Seismicity
Energy Technology Data Exchange (ETDEWEB)
Ghassemi, Ahmad [Univ. of Oklahoma, Norman, OK (United States)
2017-08-21
The production of geothermal energy from dry and low permeability reservoirs is achieved by water circulation in natural and/or man-made fractures, and is referred to as enhanced or engineered geothermal systems (EGS). Often, the permeable zones have to be created by stimulation, a process which involves fracture initiation and/or activation of discontinuities such as faults and joints due to pore pressure and the in-situ stress perturbations. The stimulation of a rock mass is often accompanied by multiple microseismic events. Micro-seismic events associated with rock failure in shear, and shear slip on new or pre-existing fracture planes and possibly their propagations. The microseismic signals contain information about the sources of energy that can be used for understanding the hydraulic fracturing process and the created reservoir properties. Detection and interpretation of microseismic events is useful for estimating the stimulated zone, created reservoir permeability and fracture growth, and geometry of the geological structures and the in-situ stress state. The process commonly is referred to as seismicity-based reservoir characterization (SBRC). Although, progress has been made by scientific & geothermal communities for quantitative and qualitative analysis of reservoir stimulation using SBRC several key questions remain unresolved in the analysis of micro-seismicity namely, variation of seismic activity with injection rate, delayed micro-seismicity, and the relation of stimulated zone to the injected volume and its rate, and the resulting reservoir permeability. In addition, the current approach to SBRC does not consider the full range of relevant poroelastic and thermoelastic phenomena and neglects the uncertainty in rock properties and in-situ stress in the data inversion process. The objective of this research and technology developments was to develop a 3D SBRC model that addresses these shortcomings by taking into account hydro
Stability analysis of tokamak plasmas
International Nuclear Information System (INIS)
Bourdelle, C.
2000-10-01
In a tokamak plasma, the energy transport is mainly turbulent. In order to increase the fusion reactions rate, it is needed to improve the energy confinement. The present work is dedicated to the identification of the key parameters leading to plasmas with a better confined energy in order to guide the future experiments. For this purpose, a numerical code has been developed. It calculates the growth rates characterizing the instabilities onset. The stability analysis is completed by the evaluation of the shearing rate of the rotation due to the radial electric field. When this shearing rate is greater than the growth rate the ion turbulence is fully stabilised. The shearing rate and the growth rate are determined from the density, temperature and security factor profiles of a given plasma. Three types of plasmas have been analysed. In the Radiative Improved modes of TEXTOR, high charge number ions seeding lowers the growth rates. In Tore Supra-high density plasmas, a strong magnetic shear and/or a more efficient ion heating linked to a bifurcation of the toroidal rotation direction (which is not understood) trigger the improvement of the confinement. In other Tore Supra plasmas, locally steep electron pressure gradients have been obtained following magnetic shear reversal. This locally negative magnetic shear has a stabilizing effect. In these three families of plasmas, the growth rates decrease, the confinement improves, the density and temperature profiles are steeper. This steepening induces an increase of the rotation shearing rate, which then maintains the confinement high quality. (author)
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.
Nonlinear Stochastic Analysis of Subharmonic Response of a Shallow Cable
DEFF Research Database (Denmark)
Zhou, Q.; Stærdahl, Jesper Winther; Nielsen, Søren R.K.
2007-01-01
The paper deals with the subharmonic response of a shallow cable due to time variations of the chord length of the equilibrium suspension, caused by time varying support point motions. Initially, the capability of a simple nonlinear two-degree-of-freedom model for the prediction of chaotic...... and stochastic subharmonic response is demonstrated upon comparison with a more involved model based on a spatial finite difference discretization of the full nonlinear partial differential equations of the cable. Since the stochastic response quantities are obtained by Monte Carlo simulation, which is extremely...
Stochastic analysis of transport of conservative solutes in caisson experiments
International Nuclear Information System (INIS)
Dagan, G.
1995-01-01
The Los Alamos National Laboratory has conducted in the past a series of experiments of transport of conservative and reactive solutes. The experimental setup and the experimental results are presented in a series of reports. The main aim of the experiments was to validate models of transport of solutes in unsaturated flow at the caisson intermediate scale, which is much larger than the one pertaining to laboratory columns. First attempts to analyze the experimental results were by one-dimensional convective-dispersion models. These models could not explain the observed solute breakthrough curves and particularly the large solute dispersion in the caisson effluent Since there were some question marks about the uniformity of water distribution at the caisson top, the transport experiments were repeated under conditions of saturated flow. In these experiments constant heads were applied at the top and the bottom of the caisson and the number of concentration monitoring stations was quadrupled. The analysis of the measurements by the same one-dimensional model indicated clearly that the fitted dispersivity is much larger than the pore-sole dispersivity and that it grows with the distance in an approximately linear fashion. This led to the conclusion, raised before, that transport in the caisson is dominated by heterogeneity effects, i.e. by spatial variability of the material Such effects cannot be captured by traditional one-dimensional models. In order to account for the effect of heterogeneity, the saturated flow experiments have been analyzed by using stochastic transport modeling. The apparent linear growth of dispersivity with distance suggested that the system behaves like a stratified one. Consequently, the model of Dagan and Bresier has been adopted in order to interpret concentration measurements. In this simple model the caisson is viewed as a bundle of columns of different permeabilities, which are characterized by a p.d.f. (probability denasity function)
Schulte, R.P.O.
2003-01-01
The analysis of the intrinsic properties and processes of ecosystems, which regulate the production stability of mixed grasslands, has been complicated by the environmental noise caused by stochastic weather fluctuations. A mathematical framework is presented to deduct the actual, the extrinsic and
Optimal allosteric stabilization sites using contact stabilization analysis.
Dickson, Alex; Bailey, Christopher T; Karanicolas, John
2017-06-05
Proteins can be destabilized by a number of environmental factors such as temperature, pH, and mutation. The ability to subsequently restore function under these conditions by adding small molecule stabilizers, or by introducing disulfide bonds, would be a very powerful tool, but the physical principles that drive this stabilization are not well understood. The first problem lies is in choosing an appropriate binding site or disulfide bond location to best confer stability to the active site and restore function. Here, we present a general framework for predicting which allosteric binding sites correlate with stability in the active site. Using the Karanicolas-Brooks Gō-like model, we examine the dynamics of the enzyme β-glucuronidase using an Umbrella Sampling method to thoroughly sample the conformational landscape. Each intramolecular contact is assigned a score termed a "stabilization factor" that measures its correlation with structural changes in the active site. We have carried out this analysis for three different scaling strengths for the intramolecular contacts, and we examine how the calculated stabilization factors depend on the ensemble of destabilized conformations. We further examine a locally destabilized mutant of β-glucuronidase that has been characterized experimentally, and show that this brings about local changes in the stabilization factors. We find that the proximity to the active site is not sufficient to determine which contacts can confer active site stability. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Methods of Stochastic Analysis of Complex Regimes in the 3D Hindmarsh-Rose Neuron Model
Bashkirtseva, Irina; Ryashko, Lev; Slepukhina, Evdokia
A problem of the stochastic nonlinear analysis of neuronal activity is studied by the example of the Hindmarsh-Rose (HR) model. For the parametric region of tonic spiking oscillations, it is shown that random noise transforms the spiking dynamic regime into the bursting one. This stochastic phenomenon is specified by qualitative changes in distributions of random trajectories and interspike intervals (ISIs). For a quantitative analysis of the noise-induced bursting, we suggest a constructive semi-analytical approach based on the stochastic sensitivity function (SSF) technique and the method of confidence domains that allows us to describe geometrically a distribution of random states around the deterministic attractors. Using this approach, we develop a new algorithm for estimation of critical values for the noise intensity corresponding to the qualitative changes in stochastic dynamics. We show that the obtained estimations are in good agreement with the numerical results. An interplay between noise-induced bursting and transitions from order to chaos is discussed.
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
The estimation of the technical efficiency comprises a vast literature in the field of applied production economics. There are two predominant approaches: the non-parametric and non-stochastic Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). The DEA...... of specifying an unsuitable functional form and thus, model misspecification and biased parameter estimates. Given these problems of the DEA and the SFA, Fan, Li and Weersink (1996) proposed a semi-parametric stochastic frontier model that estimates the production function (frontier) by non-parametric......), Kumbhakar et al. (2007), and Henningsen and Kumbhakar (2009). The aim of this paper and its main contribution to the existing literature is the estimation semi-parametric stochastic frontier models using a different non-parametric estimation technique: spline regression (Ma et al. 2011). We apply...
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.
Complementary programs for stochastic analysis of radionuclide transport
International Nuclear Information System (INIS)
Gomez Hernandez, J.J.
1993-01-01
The present programs will permit to analyze the risks using parametric and non parametric technic. The programs are presented in two groups: 1) variable estimation through indicator krigeaje and variable estimation by Cokrigeaje 2) variable simulation with multi gassiness stochastic model and non gassiness. This report includes new programs for the non parametric geostatistics
Stochastic sensitivity analysis using HDMR and score function
Indian Academy of Sciences (India)
The method involves high dimensional model representation and score functions associated with probability distribution of a random input. The proposed approach facilitates first-and second-order approximation of stochastic sensitivity measures and statistical simulation. The formulation is general such that any simulation ...
Stochastic Dominance in Portfolio Analysis and Asset Pricing
A.M. Lizyayev (Andrey)
2010-01-01
textabstractStochastic Dominance relation is a probabilistic concept which allows random outcomes such as portfolio returns to be ranked, by utilizing the full information about the distribution of the returns, in contrast to the mean-variance rule or other mean-risk models which only use a single
An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning
National Research Council Canada - National Science Library
Bowling, Michael
2000-01-01
.... In this paper we contribute a comprehensive presentation of the relevant techniques for solving stochastic games from both the game theory community and reinforcement learning communities. We examine the assumptions and limitations of these algorithms, and identify similarities between these algorithms, single agent reinforcement learners, and basic game theory techniques.
A translog stochastic frontier analysis of plot size and cost ...
African Journals Online (AJOL)
There is need for policies aimed at encouraging the youths who are agile and stronger as well as the experienced to increase production. Land re-distribution policies are advocated to make lands available to the small-holder farmers who form the bulk of the farming population. Keywords: Translog stochastic frontier, plot ...
Analysis of Stochastic Gilpin-Ayala Competition System
Directory of Open Access Journals (Sweden)
Lei Liu
2014-01-01
Full Text Available This paper is concerned with the asymptotic behavior for stochastic Gilpin-Ayala competition system. The sufficient conditions for existence of stationary distribution and extinction are established. And a certain asymptotic property of the solution is also obtained. A nontrivial example is provided to illustrate our results.
Stochastic mean payoff games: smoothed analysis and approximation schemes
Boros, Endre; Elbassioni, Khaled; Fouz, Mahmoud; Gurvich, Vladimir; Makino, Kazuhisa; Manthey, Bodo; Aceto, Luca; Henzinger, Monika; Sgall, Jiří
2011-01-01
In this paper, we consider two-player zero-sum stochastic mean payoff games with perfect information modeled by a digraph with black, white, and random vertices. These BWR-games games are polynomially equivalent with the classical Gillette games, which include many well-known subclasses, such as
Stochastic Analysis of Differential GPS Surveys for Earth Dam ...
African Journals Online (AJOL)
In GPS measurement, we try to model not just the deterministic part of the measurement but also try to account for their stochastic behavior using the measurement variance-covariance matrix. The variance-covariance matrices are computed as part of a least squares adjustment. In this study, the results of GPS survey by ...
International Nuclear Information System (INIS)
Karthik Raja, U; Leelamani, A; Raja, R; Samidurai, R
2013-01-01
In this paper, the exponential stability for a class of stochastic neural networks with time-varying delays and impulsive effects is considered. By constructing suitable Lyapunov functionals and by using the linear matrix inequality optimization approach, we obtain sufficient delay-dependent criteria to ensure the exponential stability of stochastic neural networks with time-varying delays and impulses. Two numerical examples with simulation results are provided to illustrate the effectiveness of the obtained results over those already existing in the literature. (paper)
Concordance measures and second order stochastic dominance-portfolio efficiency analysis
Czech Academy of Sciences Publication Activity Database
Kopa, Miloš; Tichý, T.
2012-01-01
Roč. 15, č. 4 (2012), s. 110-120 ISSN 1212-3609 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : dependency * concordance * portfolio selection * second order stochastic dominance Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.633, year: 2012 http://library.utia.cas.cz/separaty/2013/E/kopa-concordance measures and second order stochastic dominance- portfolio efficiency analysis.pdf
Infinite Dimensional Stochastic Analysis : in Honor of Hui-Hsiung Kuo
Sundar, Pushpa
2008-01-01
This volume contains current work at the frontiers of research in infinite dimensional stochastic analysis. It presents a carefully chosen collection of articles by experts to highlight the latest developments in white noise theory, infinite dimensional transforms, quantum probability, stochastic partial differential equations, and applications to mathematical finance. Included in this volume are expository papers which will help increase communication between researchers working in these areas. The tools and techniques presented here will be of great value to research mathematicians, graduate
Stability Analysis of ISS Medications
Wotring, V. E.
2014-01-01
the United States Pharmacopeia (USP) to measure the amount of intact active ingredient, identify degradation products and measure their amounts. Some analyses were conducted by an independent analytical laboratory, but certain (Schedule) medications could not be shipped to their facility and were analyzed at JSC. RESULTS Nine medications were analyzed with respect to active pharmaceutical ingredient (API) and degradant amounts. Results were compared to the USP requirements for API and degradants/impurities content for every FDA-approved medication. One medication met USP requirements at 5 months after its expiration date. Four of the nine (44% of those tested) medications tested met USP requirements up to 8 months post-expiration. Another 3 medications (33% of those tested) met USP guidelines 2-3 months before expiration. One medication, a compound classed by the FDA as a dietary supplement and sometimes used as a sleep aid, failed to meet USP requirements at 11 months post-expiration. CONCLUSION Analysis of each medication at a single time point provides limited information on the stability of a medication stored in particular conditions; it is not possible to predict how long a medication may be safe and effective from these data. Notwithstanding, five of the nine medications tested (56%) met USP requirements for API and degradants/impurities at least 5 months past expiration dates. The single compound that failed to meet USP requirements is not regulated as strictly as prescription medications are during manufacture; it is unknown if this medication would have met the requirements prior to flight. Notably, it was the furthest beyond its expiration date. Only more comprehensive analysis of flight-aged samples compared to appropriate ground controls will permit determination of spaceflight effects on medication stability.
Analysis of future nuclear power plants competitiveness with stochastic methods
International Nuclear Information System (INIS)
Feretic, D.; Tomsic, Z.
2004-01-01
To satisfy the increased demand it is necessary to build new electrical power plants, which could in an optimal way meet, the imposed acceptability criteria. The main criteria are potential to supply the required energy, to supply this energy with minimal (or at least acceptable) costs, to satisfy licensing requirements and be acceptable to public. The main competitors for unlimited electricity production in next few decades are fossil power plants (coal and gas) and nuclear power plants. New renewable power plants (solar, wind, biomass) are also important but due to limited energy supply potential and high costs can be only supplement to the main generating units. Large hydropower plans would be competitive under condition of existence of suitable sites for construction of such plants. The paper describes the application of a stochastic method for comparing economic parameters of future electrical power generating systems including conventional and nuclear power plants. The method is applied to establish competitive specific investment costs of future nuclear power plants when compared with combined cycle gas fired units combined with wind electricity generators using best estimated and optimistic input data. The bases for economic comparison of potential options are plant life time levelized electricity generating costs. The purpose is to assess the uncertainty of several key performance and cost of electricity produced in coal fired power plant, gas fired power plant and nuclear power plant developing probability distribution of levelized price of electricity from different Power Plants, cumulative probability of levelized price of electricity for each technology and probability distribution of cost difference between the technologies. The key parameters evaluated include: levelized electrical energy cost USD/kWh,, discount rate, interest rate for credit repayment, rate of expected increase of fuel cost, plant investment cost , fuel cost , constant annual
Modeling and stochastic analysis of dynamic mechanisms of the perception
Pisarchik, A.; Bashkirtseva, I.; Ryashko, L.
2017-10-01
Modern studies in physiology and cognitive neuroscience consider a noise as an important constructive factor of the brain functionality. Under the adequate noise, the brain can rapidly access different ordered states, and provide decision-making by preventing deadlocks. Bistable dynamic models are often used for the study of the underlying mechanisms of the visual perception. In the present paper, we consider a bistable energy model subject to both additive and parametric noise. Using the catastrophe theory formalism and stochastic sensitivity functions technique, we analyze a response of the equilibria to noise, and study noise-induced transitions between equilibria. We demonstrate and analyse the effect of hysteresis squeezing when the intensity of noise is increased. Stochastic bifurcations connected with the suppression of oscillations by parametric noises are discussed.
Dynamic analysis of a stochastic delayed rumor propagation model
Jia, Fangju; Lv, Guangying; Wang, Shuangfeng; Zou, Guang-an
2018-02-01
The rapid development of the Internet, especially the emergence of the social networks, has led rumor propagation into a new media era. In this paper, we are concerned with a stochastic delayed rumor propagation model. Firstly, we obtain the existence of the global solution. Secondly, sufficient conditions for extinction of the rumor are established. Lastly, the boundedness of solution is proved and some simulations are given to verify our results.
Stochastic Turing Patterns: Analysis of Compartment-Based Approaches
Cao, Yang
2014-11-25
© 2014, Society for Mathematical Biology. Turing patterns can be observed in reaction-diffusion systems where chemical species have different diffusion constants. In recent years, several studies investigated the effects of noise on Turing patterns and showed that the parameter regimes, for which stochastic Turing patterns are observed, can be larger than the parameter regimes predicted by deterministic models, which are written in terms of partial differential equations (PDEs) for species concentrations. A common stochastic reaction-diffusion approach is written in terms of compartment-based (lattice-based) models, where the domain of interest is divided into artificial compartments and the number of molecules in each compartment is simulated. In this paper, the dependence of stochastic Turing patterns on the compartment size is investigated. It has previously been shown (for relatively simpler systems) that a modeler should not choose compartment sizes which are too small or too large, and that the optimal compartment size depends on the diffusion constant. Taking these results into account, we propose and study a compartment-based model of Turing patterns where each chemical species is described using a different set of compartments. It is shown that the parameter regions where spatial patterns form are different from the regions obtained by classical deterministic PDE-based models, but they are also different from the results obtained for the stochastic reaction-diffusion models which use a single set of compartments for all chemical species. In particular, it is argued that some previously reported results on the effect of noise on Turing patterns in biological systems need to be reinterpreted.
Explaining Cost Efficiency of Scottish Farms: A Stochastic Frontier Analysis
Revoredo-Giha, Cesar; Milne, Catherine E.; Leat, Philip M.K.; Cho, Woong Je
2006-01-01
In this paper the cost efficiency of Scottish farms is determined, variables that explain the relative cost efficiency by farm type are identified and implications discussed. A cost efficiency approach was selected as it can deal with farms producing multiple outputs (in contrast to production frontiers), and second because it can accommodate output constraints imposed by the Common Agricultural Policy (CAP). To estimate the stochastic cost frontier, a generalised multi-product translog cost ...
Directory of Open Access Journals (Sweden)
Junhai Ma
2017-01-01
Full Text Available Apart from the price fluctuation, the retailers’ service level becomes another key factor that affects the market demand. This paper depicts a modified price and demand game model based on the stochastic demand and the retailer’s service level which influences the market demand decided by customers’ preference, while the market demand is stochastic in this model. We explore how the price adjustment speed affects the stability of the supply chain system with respect to service level and stochastic demand. The dynamic behavior of the system is researched by simulation and the stability domain and the bifurcation phenomenon are shown clearly. The largest Lyapunov exponent and the chaotic attractor are also given to confirm the chaotic characteristic of the system. The simulation results indicate that relatively small price adjustment speed may maintain the system at stable state. With the price adjustment speed gradually increasing, the price system gets unstable and finally becomes chaotic. This chaotic phenomenon will perturb the product market and this phenomenon should be controlled to keep the system stay in the stable region. So the chaos control is done and the chaos can be controlled completely. The conclusion makes significant contribution to the system referring to the price fluctuation based on the service level and stochastic demand.
Some Topics in Stochastic Control
2010-10-14
network. Using stability estimates of this paper one can establish tightness of these occupation measures and then classical martingale ...problems in image analysis, is studied in this work). Thus, following Kunita’s notation for stochastic integration with respect to semi- martingales
A remark on the analysis of multistage stochastic programs: Markov depedence
Czech Academy of Sciences Publication Activity Database
Kaňková, Vlasta
2002-01-01
Roč. 82, 11/12 (2002), s. 781-793 ISSN 0044-2267 R&D Projects: GA ČR GA402/01/0539; GA ČR GA402/99/1136 Grant - others:Deutsche Foshugsgemeinschaft(DE) 436TSE113/40 Institutional research plan: CEZ:AV0Z1075907 Keywords : multistage stochastic programs * Markov depedence * stability and estimates Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.085, year: 2002
Globally Asymptotic Stability of Stochastic Nonlinear Systems by the Output Feedback
Directory of Open Access Journals (Sweden)
Wenwen Cheng
2015-01-01
the traditional mathematical induction method. Indeed, we develop a new method to study the globally asymptotic stability by introducing a series of specific inequalities. Moreover, an example and its simulations are given to illustrate the theoretical result.
The influences of delay time on the stability of a market model with stochastic volatility
Li, Jiang-Cheng; Mei, Dong-Cheng
2013-02-01
The effects of the delay time on the stability of a market model are investigated, by using a modified Heston model with a cubic nonlinearity and cross-correlated noise sources. These results indicate that: (i) There is an optimal delay time τo which maximally enhances the stability of the stock price under strong demand elasticity of stock price, and maximally reduces the stability of the stock price under weak demand elasticity of stock price; (ii) The cross correlation coefficient of noises and the delay time play an opposite role on the stability for the case of the delay time τo. Moreover, the probability density function of the escape time of stock price returns, the probability density function of the returns and the correlation function of the returns are compared with other literatures.
Dynamic analysis of a stochastic rumor propagation model
Jia, Fangju; Lv, Guangying
2018-01-01
The rapid development of the Internet, especially the emergence of the social networks, leads rumor propagation into a new media era. In this paper, we are concerned with a stochastic rumor propagation model. Sufficient conditions for extinction and persistence in the mean of the rumor are established. The threshold between persistence in the mean and extinction of the rumor is obtained. Compared with the corresponding deterministic model, the threshold affected by the white noise is smaller than the basic reproduction number R0 of the deterministic system.
High beta and second stability region transport and stability analysis
International Nuclear Information System (INIS)
1990-01-01
This document summarizes progress made on the research of high beta and second region transport and stability. In the area second stability region studies we report on an investigation of the possibility of second region access in the center of TFTR ''supershots.'' The instabilities found may coincide with experimental observation. Significant progress has been made on the resistive stability properties of high beta poloidal ''supershot'' discharges. For these studies profiles were taken from the TRANSP transport analysis code which analyzes experimental data. Invoking flattening of the pressure profile on mode rational surfaces causes tearing modes to persist into the experimental range of interest. Further, the experimental observation of the modes seems to be consistent with the predictions of the MHD model. In addition, code development in several areas has proceeded
A stochastic model of AIDS and condom use
Dalal, Nirav; Greenhalgh, David; Mao, Xuerong
2007-01-01
In this paper we introduce stochasticity into a model of AIDS and condom use via the technique of parameter perturbation which is standard in stochastic population modelling. We show that the model established in this paper possesses non-negative solutions as desired in any population dynamics. We also carry out a detailed analysis on asymptotic stability both in probability one and in pth moment. Our results reveal that a certain type of stochastic perturbation may help to stabilise the underlying system.
Jimenez, M. Navarro
2017-04-18
A Galerkin polynomial chaos (PC) method was recently proposed to perform variance decomposition and sensitivity analysis in stochastic differential equations (SDEs), driven by Wiener noise and involving uncertain parameters. The present paper extends the PC method to nonintrusive approaches enabling its application to more complex systems hardly amenable to stochastic Galerkin projection methods. We also discuss parallel implementations and the variance decomposition of the derived quantity of interest within the framework of nonintrusive approaches. In particular, a novel hybrid PC-sampling-based strategy is proposed in the case of nonsmooth quantities of interest (QoIs) but smooth SDE solution. Numerical examples are provided that illustrate the decomposition of the variance of QoIs into contributions arising from the uncertain parameters, the inherent stochastic forcing, and joint effects. The simulations are also used to support a brief analysis of the computational complexity of the method, providing insight on the types of problems that would benefit from the present developments.
Stochastic motion of particles in tandem mirror devices
International Nuclear Information System (INIS)
Ichikawa, Y.H.; Kamimura, T.
1982-01-01
Stochastic motion of particles in tandem mirror devices is examined on basis of a nonlinear mapping of particle positions on the equatorial plane. Local stability analysis provides detailed informations on particle trajectories. The rate of stochastic plasma diffusion is estimated from numerical observations of motions of particles over a large number of time steps. (author)
Collaborative Research: Robust Climate Projections and Stochastic Stability of Dynamical Systems
Energy Technology Data Exchange (ETDEWEB)
Ghil, Michael; McWilliams, James; Neelin, J. David; Zaliapin, Ilya; Chekroun, Mickael; Kondrashov, Dmitri; Simonnet, Eric
2011-10-13
The project was completed along the lines of the original proposal, with additional elements arising as new results were obtained. The originally proposed three thrusts were expanded to include an additional, fourth one. (i) The e ffects of stochastic perturbations on climate models have been examined at the fundamental level by using the theory of deterministic and random dynamical systems, in both nite and in nite dimensions. (ii) The theoretical results have been implemented first on a delay-diff erential equation (DDE) model of the El-Nino/Southern-Oscillation (ENSO) phenomenon. (iii) More detailed, physical aspects of model robustness have been considered, as proposed, within the stripped-down ICTP-AGCM (formerly SPEEDY) climate model. This aspect of the research has been complemented by both observational and intermediate-model aspects of mid-latitude and tropical climate. (iv) An additional thrust of the research relied on new and unexpected results of (i) and involved reduced-modeling strategies and associated prediction aspects have been tested within the team's empirical model reduction (EMR) framework. Finally, more detailed, physical aspects have been considered within the stripped-down SPEEDY climate model. The results of each of these four complementary e fforts are presented in the next four sections, organized by topic and by the team members concentrating on the topic under discussion.
Univariate stability analysis methods for determining genotype ...
African Journals Online (AJOL)
Twenty two different stability statistics were used for analyzing genotype × environment (GE) interaction of durum wheat experimental data (20 genotypes in 15 environments). Combined analysis of variance indicated that GE interaction significantly influenced genotypes yield. According to type I stability concept, genotypes ...
Power system stability modelling, analysis and control
Sallam, Abdelhay A
2015-01-01
This book provides a comprehensive treatment of the subject from both a physical and mathematical perspective and covers a range of topics including modelling, computation of load flow in the transmission grid, stability analysis under both steady-state and disturbed conditions, and appropriate controls to enhance stability.
Directory of Open Access Journals (Sweden)
Zhanhua Yu
2011-01-01
convergence theorem. It is shown that the Euler method and the backward Euler method can reproduce the almost surely asymptotic stability of exact solutions to NSDDEs under additional conditions. Numerical examples are demonstrated to illustrate the effectiveness of our theoretical results.
DEFF Research Database (Denmark)
Schiøler, Henrik; Leth, John-Josef
2011-01-01
Results are given in [Yang et. al. 2009] regarding the overall stability of switched diffusion processes based on stability properties of separate processes combined through stochastic switching. This paper argues two main results to be empty, in that the presented hypotheses are logically...
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...... 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...
CO2 volatility impact on energy portfolio choice: A fully stochastic LCOE theory analysis
International Nuclear Information System (INIS)
Lucheroni, Carlo; Mari, Carlo
2017-01-01
Highlights: • Stochastic LCOE theory is an extension of the levelized cost of electricity analysis. • The fully stochastic analysis include stochastic processes for fossil fuels prices and CO 2 prices. • The nuclear asset is risky through uncertainty about construction times and it is used as a hedge. • Volatility of CO 2 prices has a strong influence on CO 2 emissions reduction. - Abstract: Market based pricing of CO 2 was designed to control CO 2 emissions by means of the price level, since high CO 2 price levels discourage emissions. In this paper, it will be shown that the level of uncertainty on CO 2 market prices, i.e. the volatility of CO 2 prices itself, has a strong influence not only on generation portfolio risk management but also on CO 2 emissions abatement. A reduction of emissions can be obtained when rational power generation capacity investors decide that the capacity expansion cost risk induced jointly by CO 2 volatility and fossil fuels prices volatility can be efficiently hedged adding to otherwise fossil fuel portfolios some nuclear power as a carbon free asset. This intriguing effect will be discussed using a recently introduced economic analysis tool, called stochastic LCOE theory. The stochastic LCOE theory used here was designed to investigate diversification effects on energy portfolios. In previous papers this theory was used to study diversification effects on portfolios composed of carbon risky fossil technologies and a carbon risk-free nuclear technology in a risk-reward trade-off frame. In this paper the stochastic LCOE theory will be extended to include uncertainty about nuclear power plant construction times, i.e. considering nuclear risky as well, this being the main uncertainty source of financial risk in nuclear technology. Two measures of risk will be used, standard deviation and CVaR deviation, to derive efficient frontiers for generation portfolios. Frontier portfolios will be analyzed in their implications on emissions
Conference on Stochastic Analysis and Applications 2014 : in honour of Terry Lyons
Hambly, Ben; Zariphopoulou, Thaleia
2014-01-01
Articles from many of the main contributors to recent progress in stochastic analysis are included in this volume, which provides a snapshot of the current state of the area and its ongoing developments. It constitutes the proceedings of the conference on "Stochastic Analysis and Applications" held at the University of Oxford and the Oxford-Man Institute during 23-27 September, 2013. The conference honored the 60th birthday of Professor Terry Lyons FLSW FRSE FRS, Wallis Professor of Mathematics, University of Oxford. Terry Lyons is one of the leaders in the field of stochastic analysis. His introduction of the notion of rough paths has revolutionized the field, both in theory and in practice. Stochastic Analysis is the branch of mathematics that deals with the analysis of dynamical systems affected by noise. It emerged as a core area of mathematics in the late 20th century and has subsequently developed into an important theory with a wide range of powerful and novel tools, and with impressive applications ...
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.
Stability analysis of rubblemound breakwater using ANN
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Rao, S.; Manjunath, Y.R.; Kim, D.H.
The stability analysis of coastal structure is very important because it involves many design parameters to be considered for the safe and economical design of structure. In the present study neural network technique is adopted to predict...
Stochastic processes and functional analysis a volume of recent advances in honor of M. M. Rao
Krinik, Alan C
2004-01-01
This extraordinary compilation is an expansion of the recent American Mathematical Society Special Session celebrating M. M. Rao's distinguished career and includes most of the presented papers as well as ancillary contributions from session invitees. This book shows the effectiveness of abstract analysis for solving fundamental problems of stochastic theory, specifically the use of functional analytic methods for elucidating stochastic processes, as made manifest in M. M. Rao's prolific research achievements. Featuring a biography of M. M. Rao, a complete bibliography of his published works,
MHD stability analysis of helical system plasmas
International Nuclear Information System (INIS)
Nakamura, Yuji
2000-01-01
Several topics of the MHD stability studies in helical system plasmas are reviewed with respect to the linear and ideal modes mainly. Difference of the method of the MHD stability analysis in helical system plasmas from that in tokamak plasmas is emphasized. Lack of the cyclic (symmetric) coordinate makes an analysis more difficult. Recent topic about TAE modes in a helical system is also described briefly. (author)
Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks
Czech Academy of Sciences Publication Activity Database
Liao, S.; Vejchodský, Tomáš; Erban, R.
2015-01-01
Roč. 12, č. 108 (2015), s. 20150233 ISSN 1742-5689 EU Projects: European Commission(XE) 328008 - STOCHDETBIOMODEL Institutional support: RVO:67985840 Keywords : gene regulatory networks * stochastic modelling * parametric analysis Subject RIV: BA - General Mathematics Impact factor: 3.818, year: 2015 http://rsif.royalsocietypublishing.org/content/12/108/20150233
Stochastic ﬁnite element analysis of long-span bridges with CFRP ...
Indian Academy of Sciences (India)
Stochastic seismic analysis of long-span bridges with Carbon ﬁbre reinforced polymer (CFRP) cables are presented in this study through combination of the advantages ... Gümüşhane University, Department of Civil Engineering, 29000, Gümüşhane, Turkey; Karadeniz Technical University, Department of Civil Engineering, ...
On the necessity of stochastic material descriptions in the computational analysis of soils
Gutierrez, M.A.; Borst, R. de
1999-01-01
The necessity of considering stochastic imperfections in the numerical analysis of localisation phenomena in soils is demonstrated by means of a biaxial compression test on a viscoplastic material. The material strength, the Young's modulus and the softening modulus are considered to be random
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...
Stochastic Drought Risk Analysis and Projection Methods For Thermoelectric Power Systems
Bekera, Behailu Belamo
the systematic approach can be used for better understanding of pertinent vulnerabilities by providing risk-based information to stakeholders in the power sector. Vulnerabilities as well as our understanding of their extent and likelihood change over time. Keeping up with the changes and making informed decisions demands a time-dependent method that incorporates new evidence into risk assessment framework. This study presents a statistical time-dependent risk analysis approach, which allows for life cycle drought risk assessment of thermoelectric power systems. Also, a Bayesian Belief Network (BBN) extension to the proposed framework is developed. The BBN allows for incorporating new evidence, such as observing power curtailments due to extreme heat or lowflow situations, and updating our knowledge and understanding of the pertinent risk. In sum, the proposed approach can help improve adaptive capacity of the electric power infrastructure, thereby enhancing its resilience to events potentially threatening grid reliability and economic stability. The proposed drought characterization methodology is applied on a daily streamflow series obtained from three United States Geological Survey (USGS) water gauges on the Tennessee River basin. The stochastic water supply risk assessment and projection methods are demonstrated for two power plants on the White River, Indiana: Frank E. Ratts and Petersburg, using water temperature and streamflow time series data obtained from a nearby USGS gauge.
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.
Bayesian analysis of deterministic and stochastic prisoner's dilemma games
Directory of Open Access Journals (Sweden)
Howard Kunreuther
2009-08-01
Full Text Available This paper compares the behavior of individuals playing a classic two-person deterministic prisoner's dilemma (PD game with choice data obtained from repeated interdependent security prisoner's dilemma games with varying probabilities of loss and the ability to learn (or not learn about the actions of one's counterpart, an area of recent interest in experimental economics. This novel data set, from a series of controlled laboratory experiments, is analyzed using Bayesian hierarchical methods, the first application of such methods in this research domain. We find that individuals are much more likely to be cooperative when payoffs are deterministic than when the outcomes are probabilistic. A key factor explaining this difference is that subjects in a stochastic PD game respond not just to what their counterparts did but also to whether or not they suffered a loss. These findings are interpreted in the context of behavioral theories of commitment, altruism and reciprocity. The work provides a linkage between Bayesian statistics, experimental economics, and consumer psychology.
International Nuclear Information System (INIS)
Tolis, Athanasios; Tatsiopoulos, Ilias; Doukelis, Aggelos
2010-01-01
A systematic impact assessment of stochastic interest and inflation rates on the analysis of energy investments is presented. A real-options algorithm has been created for this task. Constant interest rates incorporating high risk premium have been extensively used for economic calculations, within the framework of traditional direct cash flow methods, thus favouring immediate, irreversible investments in the expense of, sometimes, insubstantially low anticipated yields. In this article, not only incomes and expenses but also interest and inflation rates are considered stochastically evolving according to specific probabilistic models. The numerical experiments indicated that the stochastic interest rate forecasts fluctuate in such low levels that may signal delayed investment entry in favour of higher expected yields. The implementation of stochastically evolving interest rates in energy investment analysis may have a controversial effect on sustainability. Displacements of inefficient plants may be significantly delayed, thus prolonging high CO 2 emission rates. Under the current CO 2 allowance prices or their medium-term forecasts, this situation may not be improved and flexible policy interventions may be necessitated. (author)
A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis
Pham, Minh Tu; Vernieuwe, Hilde; De Baets, Bernard; Verhoest, Niko E. C.
2018-02-01
A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis.
Stability Analysis of the Embankment Model
Directory of Open Access Journals (Sweden)
G.S. Gopalakrishna
2009-01-01
Full Text Available In analysis of embankment model affected by dynamic force, employment of shaking table is a scientific way in assessment of earthquake behavior. This work focused on saturated loose sandy foundation and enbankment. The results generated through the pore pressure sensors indicated pore water pressure playing main role in creation of liquefaction and stability of the system, and also revealed deformation, settlement, liquefaction intensity and time stability of system in direct correlation with the strength and characteristics of soil. One of the economical methods in stabilization of soil foundation is improvement of some part soil foundation.
Stability analysis of zigzag boron nitride nanoribbons
International Nuclear Information System (INIS)
Rai, Hari Mohan; Late, Ravikiran; Saxena, Shailendra K.; Kumar, Rajesh; Sagdeo, Pankaj R.; Jaiswal, Neeraj K.; Srivastava, Pankaj
2015-01-01
We have explored the structural stability of bare and hydrogenated zigzag boron nitride nanoribbons (ZBNNRs). In order to investigate the structural stability, we calculate the cohesive energy for bare, one-edge and both edges H-terminated ZBNNRs with different widths. It is found that the ZBNNRs with width Nz=8 are energetically more favorable than the lower-width counterparts (Nz<8). Bare ZBNNRs have been found energetically most stable as compared to the edge terminated ribbons. Our analysis reveals that the structural stability is a function of ribbon-width and it is not affected significantly by the type of edge-passivation (one-edge or both-edges)
A Fast Fourier transform stochastic analysis of the contaminant transport problem
Deng, F.W.; Cushman, J.H.; Delleur, J.W.
1993-01-01
A three-dimensional stochastic analysis of the contaminant transport problem is developed in the spirit of Naff (1990). The new derivation is more general and simpler than previous analysis. The fast Fourier transformation is used extensively to obtain numerical estimates of the mean concentration and various spatial moments. Data from both the Borden and Cape Cod experiments are used to test the methodology. Results are comparable to results obtained by other methods, and to the experiments themselves.
Bashkirtseva, Irina; Ryashko, Lev; Ryazanova, Tatyana
2018-01-01
A problem of mathematical modeling of complex stochastic processes in macroeconomics is discussed. For the description of dynamics of income and capital stock, the well-known Kaldor model of business cycles is used as a basic example. The aim of the paper is to give an overview of the variety of stochastic phenomena which occur in Kaldor model forced by additive and parametric random noise. We study a generation of small- and large-amplitude stochastic oscillations, and their mixed-mode intermittency. To analyze these phenomena, we suggest a constructive approach combining the study of the peculiarities of deterministic phase portrait, and stochastic sensitivity of attractors. We show how parametric noise can stabilize the unstable equilibrium and transform dynamics of Kaldor system from order to chaos.
Kala, Zdeněk; Sandovič, GiedrÄ--
2012-09-01
The paper deals with non-linear analysis of ultimate and serviceability limit states of two-span pedestrian steel bridge. The effects of random material and geometrical characteristics on limit states are analyzed. The Monte Carlo method was applied to stochastic analysis. For the serviceability limit state, also influence of fuzzy uncertainty of the limit deflection value on random characteristics of load capacity of variable action was studied. The results prove that, for the type of structure studied, the serviceability limit state is decisive from the point of view of design. The present paper opens a discussion on the use of stochastic analysis to verify the limit deflections given in the standards EUROCODES.
International Nuclear Information System (INIS)
Bauer, J.
1980-01-01
Thesis dealing with the analysis of earthquake response of structures. In order to achieve a reliable risk assessment, the results of the seismic risk analysis have to be seen in an overall view together with the results of stochastic vibrational analyses, and the data on maximum supportable stresses of the structure. Taking into account stochastic seismic focus models and calculation methods is of special significance in this connection. Based upon well-known seismic risk assessment models, the calculation of the annual probability for exceeding the acceleration level is carried out also considering the length of the failure zone, assuming that the energy released during an earthquake is uniformly, distributed over this fracture zone. The strong influence of local parameters on the annual exceeding probability is shown by a sensitivity analysis. (orig./RW) [de
Linear stability analysis of heated parallel channels
International Nuclear Information System (INIS)
Nourbakhsh, H.P.; Isbin, H.S.
1982-01-01
An analyis is presented of thermal hydraulic stability of flow in parallel channels covering the range from inlet subcooling to exit superheat. The model is based on a one-dimensional drift velocity formulation of the two phase flow conservation equations. The system of equations is linearized by assuming small disturbances about the steady state. The dynamic response of the system to an inlet flow perturbation is derived yielding the characteristic equation which predicts the onset of instabilities. A specific application is carried out for homogeneous and regional uniformly heated systems. The particular case of equal characteristic frequencies of two-phase and single phase vapor region is studied in detail. The D-partition method and the Mikhailov stability criterion are used for determining the marginal stability boundary. Stability predictions from the present analysis are compared with the experimental data from the solar test facility. 8 references
Stability analysis of transmission system with high penetration of distributed generation
Energy Technology Data Exchange (ETDEWEB)
Reza, M.
2006-12-21
Nowadays, interest in generating electricity using decentralized generators of relatively small scale ('distributed generation', DG) is increasing. This work deals with the impact of implementing DG on the transmission system transient stability, with the emphasis on a potential transition from a 'vertical power system' to a 'horizontal power system. A problem in power systems is maintaining synchronous operation of all (centralized) synchronous machines. This stability problem associated is called rotor angle stability. In this work, the impact of the DG implementation on this is investigated. The impact of DG levels on the system transient stability when the increasing DG level is followed by a reduction of centralized generators in service resulting in a 'vertical to horizontal' transformation of the power system is also investigated. Furthermore, a stochastic analysis is used to study the transient stability of the power systems. The results show that including the stochastic behavior of DG leads to a more complete and detailed view of the system performance. Finally, the situation when the power system is pushed towards a scenario, where DG penetration reaches a level that covers the total load of the original power system (100% DG level) is investigated. The research performed in this work indicates that from the transmission system stability point of view, if higher DG penetration levels are coming up, sufficient inertia and voltage support must be installed. Furthermore, one should be aware of the fact that the system behaves stochastically, especially with DG. To a certain extent regional balancing of power can be performed by local voltage control.
Using an atmospheric turbulence model for the stochastic model of geodetic VLBI data analysis
Halsig, Sebastian; Artz, Thomas; Iddink, Andreas; Nothnagel, Axel
2016-06-01
Space-geodetic techniques at radio wavelength, such as global navigation satellite systems and very long baseline interferometry (VLBI), suffer from refractivity of the Earth's atmosphere. These highly dynamic processes, particularly refractivity variations in the neutral atmosphere, contribute considerably to the error budget of these space-geodetic techniques. Here, microscale fluctuations in refractivity lead to elevation-dependent uncertainties and induce physical correlations between the observations. However, up to now such correlations are not considered routinely in the stochastic model of space-geodetic observations, which leads to very optimistic standard deviations of the derived target parameters, such as Earth orientation parameters and station positions. In this study, the standard stochastic model of VLBI observations, which only includes, almost exclusively, the uncertainties from the VLBI correlation process, is now augmented by a variance-covariance matrix derived from an atmospheric turbulence model. Thus, atmospheric refractivity fluctuations in space and time can be quantified. One of the main objectives is to realize a suitable stochastic model of VLBI observations in an operational way. In order to validate the new approach, the turbulence model is applied to several VLBI observation campaigns consisting of different network geometries leading the path for the next-generation VLBI campaigns. It is shown that the stochastic model of VLBI observations can be improved by using high-frequency atmospheric variations and, thus, refining the stochastic model leads to far more realistic standard deviations of the target parameters. The baseline length repeatabilities as a general measure of accuracy of baseline length determinations improve for the turbulence-based solution. Further, this method is well suited for routine VLBI data analysis with limited computational costs.
Directory of Open Access Journals (Sweden)
Fei Li
2018-01-01
Full Text Available This paper considers a high-dimensional stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered epidemic model with quarantine-adjusted incidence and the imperfect vaccination. The main aim of this study is to investigate stochastic effects on the SEIQR epidemic model and obtain its thresholds. We first obtain the sufficient condition for extinction of the disease of the stochastic system. Then, by using the theory of Hasminskii and the Lyapunov analysis methods, we show there is a unique stationary distribution of the stochastic system and it has an ergodic property, which means the infectious disease is prevalent. This implies that the stochastic disturbance is conducive to epidemic diseases control. At last, computer numerical simulations are carried out to illustrate our theoretical results.
Error performance analysis in K-tier uplink cellular networks using a stochastic geometric approach
Afify, Laila H.
2015-09-14
In this work, we develop an analytical paradigm to analyze the average symbol error probability (ASEP) performance of uplink traffic in a multi-tier cellular network. The analysis is based on the recently developed Equivalent-in-Distribution approach that utilizes stochastic geometric tools to account for the network geometry in the performance characterization. Different from the other stochastic geometry models adopted in the literature, the developed analysis accounts for important communication system parameters and goes beyond signal-to-interference-plus-noise ratio characterization. That is, the presented model accounts for the modulation scheme, constellation type, and signal recovery techniques to model the ASEP. To this end, we derive single integral expressions for the ASEP for different modulation schemes due to aggregate network interference. Finally, all theoretical findings of the paper are verified via Monte Carlo simulations.
A Stochastic Wavelet Finite Element Method for 1D and 2D Structures Analysis
Xingwu Zhang; Xuefeng Chen; Zhibo Yang; Bing Li; Zhengjia He
2014-01-01
A stochastic finite element method based on B-spline wavelet on the interval (BSWI-SFEM) is presented for static analysis of 1D and 2D structures in this paper. Instead of conventional polynomial interpolation, the scaling functions of BSWI are employed to construct the displacement field. By means of virtual work principle and BSWI, the wavelet finite elements of beam, plate, and plane rigid frame are obtained. Combining the Monte Carlo method and the constructed BSWI elements together, the...
Measuring efficiency of governmental hospitals in Palestine using stochastic frontier analysis
Hamidi, Samer
2016-01-01
Background The Palestinian government has been under increasing pressure to improve provision of health services while seeking to effectively employ its scare resources. Governmental hospitals remain the leading costly units as they consume about 60?% of governmental health budget. A clearer understanding of the technical efficiency of hospitals is crucial to shape future health policy reforms. In this paper, we used stochastic frontier analysis to measure technical efficiency of governmental...
Stochastic Analysis of a Queue Length Model Using a Graphics Processing Unit
Czech Academy of Sciences Publication Activity Database
Přikryl, Jan; Kocijan, J.
2012-01-01
Roč. 5, č. 2 (2012), s. 55-62 ISSN 1802-971X R&D Projects: GA MŠk(CZ) MEB091015 Institutional support: RVO:67985556 Keywords : graphics processing unit * GPU * Monte Carlo simulation * computer simulation * modeling Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2012/AS/prikryl-stochastic analysis of a queue length model using a graphics processing unit.pdf
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...
Pattern theory the stochastic analysis of real-world signals
Mumford, David
2010-01-01
Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis of new signals. This book treats the mathematical tools, the models themselves, and the computational algorithms for applying statistics to analyze six representative classes of signals of increasing complexity. The book covers patterns in text, sound
Stability Analysis of Bulk Viscous Cosmology
Sharif, M.; Mumtaz, Saadia
2018-01-01
In this paper, we study phase space analysis of FRW universe model by taking a power-law model for bulk viscosity coefficient. An autonomous system of equations is developed by defining normalized dimensionless variables. We find corresponding critical points for di.erent values of the parameters to investigate stability of the system. It is found that the presence of power-law model of bulk viscosity appears as an e.ective ingredient to enhance the stability of the respective universe model.
Stochastic filtering of quantitative data from STR DNA analysis
DEFF Research Database (Denmark)
Tvedebrink, Torben; Eriksen, Poul Svante; Mogensen, Helle Smidt
due to the apparatus used for measurements). Pull-up effects (more systematic increase caused by overlap in the spectrum) Stutters (peaks located four basepairs before the true peak). We present filtering techniques for all three technical artifacts based on statistical analysis of data from...
Stochastic Analysis of Rainfall Events in Ilorin, Nigeria | Ogunlela ...
African Journals Online (AJOL)
Five probability distribution namely the normal, lognormal, logPearson typeIII exponential and extreme value type I distributions – were used in this study because of their desirable properties. The analysis was based on 41 years of daily and monthly rainfall data (1955-1995) for Ilorin, with peak values computed for each ...
Stochastic Response and Reliability Analysis of Hysteretic Structures
DEFF Research Database (Denmark)
Mørk, Kim Jørgensen
During the last 30 years response analysis of structures under random excitation has been studied in detail. These studies are motivated by the fact that most of natures excitations, such as earthquakes, wind and wave loads exhibit randomly fluctuating characters. For safety reasons this randomness...
International Nuclear Information System (INIS)
Balakrishnan, Meera; Trivedi, Kishor S.
1996-01-01
In this paper, we present a comparative reliability analysis of an application on a corporate B-ISDN network under various alternate-routing protocols. For simple cases, the reliability problem can be cast into fault-tree models and solved rapidly by means of known methods. For more complex scenarios, state space (Markov) models are required. However, generation of large state space models can get very labor intensive and error prone. We advocate the use of stochastic reward nets (a variant of stochastic Petri nets) for the concise specification, automated generation and solution of alternate-routing protocols in networks. This paper is written in a tutorial style so as to make it accessible to a large audience
Directory of Open Access Journals (Sweden)
Kulikov Vladimir
2016-01-01
Full Text Available We have been elaborating an approach founded on the identification of multimodal laws of the complex structure distribution in medicine, biology, chemistry of ultrapure materials and membrane technology as well as in technical applications. The method is based on the formulation and solution of inverse problems in mathematical physics for the respective probability density functions. The verification of the used algorithmic tools is carried out on model limited-scope samples. For stochastic structures and systems under study the method is supplemented with an original option of a regression analysis taking into account the identified stochastic laws displaying numerical parameters into the binary space. The proposed approach has been tested on clinical material in practical medicine.
A Posteriori Error Analysis of Stochastic Differential Equations Using Polynomial Chaos Expansions
Butler, T.
2011-01-01
We develop computable a posteriori error estimates for linear functionals of a solution to a general nonlinear stochastic differential equation with random model/source parameters. These error estimates are based on a variational analysis applied to stochastic Galerkin methods for forward and adjoint problems. The result is a representation for the error estimate as a polynomial in the random model/source parameter. The advantage of this method is that we use polynomial chaos representations for the forward and adjoint systems to cheaply produce error estimates by simple evaluation of a polynomial. By comparison, the typical method of producing such estimates requires repeated forward/adjoint solves for each new choice of random parameter. We present numerical examples showing that there is excellent agreement between these methods. © 2011 Society for Industrial and Applied Mathematics.
Efficient stochastic EMC/EMI analysis using HDMR-generated surrogate models
Yücel, Abdulkadir C.
2011-08-01
Stochastic methods have been used extensively to quantify effects due to uncertainty in system parameters (e.g. material, geometrical, and electrical constants) and/or excitation on observables pertinent to electromagnetic compatibility and interference (EMC/EMI) analysis (e.g. voltages across mission-critical circuit elements) [1]. In recent years, stochastic collocation (SC) methods, especially those leveraging generalized polynomial chaos (gPC) expansions, have received significant attention [2, 3]. SC-gPC methods probe surrogate models (i.e. compact polynomial input-output representations) to statistically characterize observables. They are nonintrusive, that is they use existing deterministic simulators, and often cost only a fraction of direct Monte-Carlo (MC) methods. Unfortunately, SC-gPC-generated surrogate models often lack accuracy (i) when the number of uncertain/random system variables is large and/or (ii) when the observables exhibit rapid variations. © 2011 IEEE.
A Stochastic Wavelet Finite Element Method for 1D and 2D Structures Analysis
Directory of Open Access Journals (Sweden)
Xingwu Zhang
2014-01-01
Full Text Available A stochastic finite element method based on B-spline wavelet on the interval (BSWI-SFEM is presented for static analysis of 1D and 2D structures in this paper. Instead of conventional polynomial interpolation, the scaling functions of BSWI are employed to construct the displacement field. By means of virtual work principle and BSWI, the wavelet finite elements of beam, plate, and plane rigid frame are obtained. Combining the Monte Carlo method and the constructed BSWI elements together, the BSWI-SFEM is formulated. The constructed BSWI-SFEM can deal with the problems of structural response uncertainty caused by the variability of the material properties, static load amplitudes, and so on. Taking the widely used Timoshenko beam, the Mindlin plate, and the plane rigid frame as examples, numerical results have demonstrated that the proposed method can give a higher accuracy and a better constringency than the conventional stochastic finite element methods.
Directory of Open Access Journals (Sweden)
S. Lalléchère
2012-10-01
Full Text Available This paper deals with the advanced integration of uncertainties in electromagnetic interferences (EMI and electromagnetic compatibility (EMC problems. In this context, the Monte Carlo formalism may provide a reliable reference to proceed to statistical assessments. After all, other less expensive and efﬁcient techniques have been implemented more recently (the unscented transform and stochastic collocation methods for instance and will be illustrated through uncertain EMC problems. Finally, we will present how the use of sensitivity analysis techniques may offer an efﬁcient complement to rough statistical or stochastic studies.
A combined stochastic analysis of mean daily temperature and diurnal temperature range
Sirangelo, B.; Caloiero, T.; Coscarelli, R.; Ferrari, E.
2018-03-01
In this paper, a stochastic model, previously proposed for the maximum daily temperature, has been improved for the combined analysis of mean daily temperature and diurnal temperature range. In particular, the procedure applied to each variable sequentially performs the deseasonalization, by means of truncated Fourier series expansions, and the normalization of the temperature data, with the use of proper transformation functions. Then, a joint stochastic analysis of both the climatic variables has been performed by means of a FARIMA model, taking into account the stochastic dependency between the variables, namely introducing a cross-correlation between the standardized noises. The model has been applied to five daily temperature series of southern Italy. After the application of a Monte Carlo simulation procedure, the return periods of the joint behavior of the mean daily temperature and the diurnal temperature range have been evaluated. Moreover, the annual maxima of the temperature excursions in consecutive days have been analyzed for the synthetic series. The results obtained showed different behaviors probably linked to the distance from the sea and to the latitude of the station.
Directory of Open Access Journals (Sweden)
Muhammad Ali Nasir
2016-12-01
Full Text Available This study derives an optimal macroeconomic policy combination for financial sector stability in the United Kingdom by employing a New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE framework. The empirical results obtained show that disciplined fiscal and accommodative monetary policies stance is optimal for financial sector stability. Furthermore, fiscal indiscipline countered by contractionary monetary stance adversely affects financial sector stability. Financial markets, e.g. stocks and Gilts show a short-term asymmetric response to macroeconomic policy interaction and to each other. The asymmetry is a reflection of portfolio adjustment. However in the long-run, the responses to suggested optimal policy combination had homogenous effects and there was evidence of co-movement in the stock and Gilt markets.
International Nuclear Information System (INIS)
Kincaid, C.T.; Vail, L.W.; Devary, J.L.
1983-07-01
Research was conducted at Pacific Northwest Laboratory to develop a research computational package for the stochastic analysis of ground-water flow. Both unsteady and steady-state analysis were examined, and a steady-state research code was developed for the study of stochastic processes. This report describes the theoretical development of both unsteady and steady analyses, and presents the preliminary studies undertaken to verify and exercise the encoded algorithm. The stochastic analysis of ground-water flow is a promising new method which can supply more comprehensive analyses of the ground-water environment. The work reported herein provided experience in the methodology while producing a research-oriented stochastic analysis capability. Single-layer aquifers of horizontal extent were selected for this effort. Kriging has been employed to describe the uncertainty in field data. The resulting stochastic parameters enter the problem physics through boundary conditions and Darcy's equation. The mean and variance of the piezometric head are estimated by the stochastic analysis
Alvarez-Guerra, Manuel; Canis, Laure; Voulvoulis, Nikolaos; Viguri, Javier R; Linkov, Igor
2010-09-15
Decision-making for sediment management is a complex task that requires the consideration of temporal and spatial impacts of several remedial alternatives as well as the associated economic, social and political impact. Multicriteria decision analysis (MCDA) is becoming increasingly recognized as an important environmental management tool that can be used to support the selection of suitable remediation alternatives and prioritization of management units in space and time. This paper proposes an MCDA framework for prioritizing sediment management alternatives. This framework involves identifying of a set of feasible options, as well as defining and evaluating criteria which integrate relevant technical, economic, social and environmental aspects of remedies. The methodology allows an explicit consideration of uncertainty in criteria scores and weights by assigning probability distributions and analyzing subsequent Monte-Carlo simulations. The consideration of different stakeholder simulated values is used to assess the robustness of alternative rankings and to guide the selection of remediation options. An application of this methodology to a case study in the Bay of Santander, Spain, is presented. An assessment is conducted for the case of unknown preferences as well as for hypothetical preferences profiles for four types of stakeholders: Idealist, Politician, Environmentalist and Balanced. The results are used to visualize stakeholder positions and potential disagreements, allowing for the identification of a group of least preferred alternatives for each stakeholder. Stakeholder involvement has the potential to ease the remedy selection process during all stages of the decision-making process and to eventually remedy implementation. Published by Elsevier B.V.
Energy Technology Data Exchange (ETDEWEB)
Alvarez-Guerra, Manuel [Department of Chemical Engineering and Inorganic Chemistry, ETSIIT, University of Cantabria, Avda. de los Castros s/n 39005, Santander (Spain); Canis, Laure [U.S. Army Engineer Research and Development Center, 696 Virginia Rd, Concord, MA 01742 (United States); Voulvoulis, Nikolaos [Centre for Environmental Policy, Imperial College London, London, SW7 2AZ (United Kingdom); Viguri, Javier R. [Department of Chemical Engineering and Inorganic Chemistry, ETSIIT, University of Cantabria, Avda. de los Castros s/n 39005, Santander (Spain); Linkov, Igor, E-mail: Igor.Linkov@usace.army.mil [U.S. Army Engineer Research and Development Center, 696 Virginia Rd, Concord, MA 01742 (United States)
2010-09-15
Decision-making for sediment management is a complex task that requires the consideration of temporal and spatial impacts of several remedial alternatives as well as the associated economic, social and political impact. Multicriteria decision analysis (MCDA) is becoming increasingly recognized as an important environmental management tool that can be used to support the selection of suitable remediation alternatives and prioritization of management units in space and time. This paper proposes an MCDA framework for prioritizing sediment management alternatives. This framework involves identifying of a set of feasible options, as well as defining and evaluating criteria which integrate relevant technical, economic, social and environmental aspects of remedies. The methodology allows an explicit consideration of uncertainty in criteria scores and weights by assigning probability distributions and analyzing subsequent Monte-Carlo simulations. The consideration of different stakeholder simulated values is used to assess the robustness of alternative rankings and to guide the selection of remediation options. An application of this methodology to a case study in the Bay of Santander, Spain, is presented. An assessment is conducted for the case of unknown preferences as well as for hypothetical preferences profiles for four types of stakeholders: Idealist, Politician, Environmentalist and Balanced. The results are used to visualize stakeholder positions and potential disagreements, allowing for the identification of a group of least preferred alternatives for each stakeholder. Stakeholder involvement has the potential to ease the remedy selection process during all stages of the decision-making process and to eventually remedy implementation.
Lutaif, N A; Palazzo, R; Gontijo, J A R
2014-01-01
Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.
Energy Technology Data Exchange (ETDEWEB)
Lutaif, N.A. [Departamento de Clínica Médica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, SP (Brazil); Palazzo, R. Jr [Departamento de Telemática, Faculdade de Engenharia Elétrica e Computação, Universidade Estadual de Campinas, Campinas, SP (Brazil); Gontijo, J.A.R. [Departamento de Clínica Médica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, SP (Brazil)
2014-01-17
Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.
Wang, Ting; Plecháč, Petr
2017-12-21
Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.
Directory of Open Access Journals (Sweden)
N.A. Lutaif
2014-01-01
Full Text Available Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group. By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.
International Nuclear Information System (INIS)
Lutaif, N.A.; Palazzo, R. Jr; Gontijo, J.A.R.
2014-01-01
Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile
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.
Wang, Ting; Plecháč, Petr
2017-12-01
Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.
Directory of Open Access Journals (Sweden)
Giorgos Minas
2017-07-01
Full Text Available In order to analyse large complex stochastic dynamical models such as those studied in systems biology there is currently a great need for both analytical tools and also algorithms for accurate and fast simulation and estimation. We present a new stochastic approximation of biological oscillators that addresses these needs. Our method, called phase-corrected LNA (pcLNA overcomes the main limitations of the standard Linear Noise Approximation (LNA to remain uniformly accurate for long times, still maintaining the speed and analytically tractability of the LNA. As part of this, we develop analytical expressions for key probability distributions and associated quantities, such as the Fisher Information Matrix and Kullback-Leibler divergence and we introduce a new approach to system-global sensitivity analysis. We also present algorithms for statistical inference and for long-term simulation of oscillating systems that are shown to be as accurate but much faster than leaping algorithms and algorithms for integration of diffusion equations. Stochastic versions of published models of the circadian clock and NF-κB system are used to illustrate our results.
Stability analysis for three-plane wedges
Tharp, Thomas M.
Stability analysis for rock wedges bounded by three planar discontinuities is a time-consuming procedure usually carried out by stereographic projection. An algorithm is presented which identifies the behavior mode for wedges and calculates the factor of safety more accurately than is possible by graphical methods. The upper and lower hemisphere stereographic projections also are plotted. This is the standard presentation format and it allows a visual check of the influence of assumed geometries and friction angles.
Directory of Open Access Journals (Sweden)
S M Ali
Full Text Available In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand and independent variables (weather data for utility load management, generation control, and network expansion.
Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M
2016-01-01
In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.
Nonlinear physical systems spectral analysis, stability and bifurcations
Kirillov, Oleg N
2013-01-01
Bringing together 18 chapters written by leading experts in dynamical systems, operator theory, partial differential equations, and solid and fluid mechanics, this book presents state-of-the-art approaches to a wide spectrum of new and challenging stability problems.Nonlinear Physical Systems: Spectral Analysis, Stability and Bifurcations focuses on problems of spectral analysis, stability and bifurcations arising in the nonlinear partial differential equations of modern physics. Bifurcations and stability of solitary waves, geometrical optics stability analysis in hydro- and magnetohydrodynam
Stability analysis in K-means clustering.
Steinley, Douglas
2008-11-01
This paper develops a new procedure, called stability analysis, for K-means clustering. Instead of ignoring local optima and only considering the best solution found, this procedure takes advantage of additional information from a K-means cluster analysis. The information from the locally optimal solutions is collected in an object by object co-occurrence matrix. The co-occurrence matrix is clustered and subsequently reordered by a steepest ascent quadratic assignment procedure to aid visual interpretation of the multidimensional cluster structure. Subsequently, measures are developed to determine the overall structure of a data set, the number of clusters and the multidimensional relationships between the clusters.
Stability Analysis of Recurrent Neural Networks with Random Delay and Markovian Switching
Directory of Open Access Journals (Sweden)
Enwen Zhu
2010-01-01
Full Text Available In this paper, the exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs with random delay and Markovian switching. The evolution of the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed recurrent neural network with Markovian switching is exponentially stable. The analysis is based on the Lyapunov-Krasovskii functional and stochastic analysis approach, and the conditions are expressed in terms of linear matrix inequalities, which can be readily checked by using some standard numerical packages such as the Matlab LMI Toolbox. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.
Nonlinear stability analysis of the frame structures
Directory of Open Access Journals (Sweden)
Ćorić Stanko
2016-01-01
Full Text Available In this paper the phenomenon of instability of frames in elasto-plastic domain was investigated. Numerical analysis was performed by the finite element method. Stiffness matrices were derived using the trigonometric shape functions related to exact solution of the differential equation of bending according to the second order theory. When the buckling of structure occurs in plastic domain, it is necessary to replace the constant modulus of elasticity E with the tangent modulus Et. Tangent modulus is stress dependent function and takes into account the changes of the member stiffness in the inelastic range. For the purposes of numerical investigation in this analysis, part of the computer program ALIN was created in a way that this program now can be used for elastic and elasto-plastic stability analysis of frame structures. This program is developed in the C++ programming language. Using this program, it is possible to calculate the critical load of frames in the elastic and inelastic domain. In this analysis, the algorithm for the calculation of buckling lengths of compressed columns of the frames was also established. The algorithm is based on the calculation of the global stability analysis of frame structures. Results obtained using this algorithm were compared with the approximate solutions from the European (EC3 and national (JUS standards for the steel structures. By the given procedure in this paper it is possible to follow the behavior of the plane frames in plastic domain and to calculate the real critical load in that domain.
Stability Analysis Method of Parallel Inverter
Directory of Open Access Journals (Sweden)
Jun Li
2017-01-01
Full Text Available In order to further provide theoretical support for the stability of an auxiliary inverter parallel system, a new model which covers most of control parameters needs to be established. However, the ability of the small-signal model established by the traditional method is extremely limited, so this paper proposes a new small-signal modeling method for the parallel system. The new small-signal model not only can analyze the influence of the droop parameters on the system performance, but also can analyze the influence of the output impedance of the inverter, the unbalanced and nonlinear loads, and the power calculation method and cut-off frequency of the low-pass filter on the system performance and stability. Based on this method, this paper carries out a comprehensive analysis on the performance of a parallel inverter system. And the correctness of the modeling method and analysis process of the system performance and stability are verified by the consistency of the simulation and experimental results.
AFM stochastic analysis of surface twisted nanograin chains of iron oxide: a kinetic study
International Nuclear Information System (INIS)
Akhavan, O; Azimirad, R
2009-01-01
We have studied the stochastic parameters of surface iron oxide nanograin chains, 97 nm in diameter and 2.4 μm in length, prepared at different annealing temperatures, using atomic force microscopy (AFM) spectral analysis. In this regard, the roughness of the thin films including self-assembled twisted nanograin chains has been analysed and characterized using the height-height correlation function, the roughness exponent as well as the power spectrum density of the AFM profiles and their gradient, for the different annealing temperatures. The tip convolution effect on the stochastic parameters under study has also been investigated. The kinetics of the formation of nanograins on the film surface has been obtained using the AFM spectral analysis of the profiles and their gradient. The activation energy needed for the formation of surface nanograin chains was found to be 0.55 eV. It has been shown that the tip-surface interaction affects mainly the diffusion parameters obtained by using the surface roughness analysis of the profiles, while use of the surface roughness analysis of the gradient of the profiles results in a nearly independent tip convolution effect on the diffusion parameters. Hence, this work also provides a method for calculating the required activation energy for the formation of self-assembled nanostructures affecting the roughness of a surface.
Modelling and Analysis of Smart Grid: A Stochastic Model Checking Case Study
DEFF Research Database (Denmark)
Yuksel, Ender; Zhu, Huibiao; Nielson, Hanne Riis
2012-01-01
that require novel methods and applications. In this context, an important issue is the verification of certain quantitative properties of the system. In this paper, we consider a specific Chinese Smart Grid implementation as a case study and address the verification problem for performance and energy......Cyber-physical systems integrate information and communication technology functions to the physical elements of a system for monitoring and controlling purposes. The conversion of traditional power grid into a smart grid, a fundamental example of a cyber-physical system, raises a number of issues...... consumption. We employ stochastic model checking approach and present our modelling and analysis study using PRISM model checker....
Quantitative modelling and analysis of a Chinese smart grid: a stochastic model checking case study
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
2014-01-01
that require novel methods and applications. One of the important issues in this context is the verification of certain quantitative properties of the system. In this paper, we consider a specific Chinese smart grid implementation as a case study and address the verification problem for performance and energy......Cyber-physical systems integrate information and communication technology with the physical elements of a system, mainly for monitoring and controlling purposes. The conversion of traditional power grid into a smart grid, a fundamental example of a cyber-physical system, raises a number of issues...... consumption.We employ stochastic model checking approach and present our modelling and analysis study using PRISM model checker....
Gutjahr, Walter J
2012-01-01
For stochastic multi-objective combinatorial optimization (SMOCO) problems, the adaptive Pareto sampling (APS) framework has been proposed, which is based on sampling and on the solution of deterministic multi-objective subproblems. We show that when plugging in the well-known simple evolutionary multi-objective optimizer (SEMO) as a subprocedure into APS, ε-dominance has to be used to achieve fast convergence to the Pareto front. Two general theorems are presented indicating how runtime complexity results for APS can be derived from corresponding results for SEMO. This may be a starting point for the runtime analysis of evolutionary SMOCO algorithms.
Stability Analysis and Stabilization of Miduk Heap Leaching Structure, Iran
Directory of Open Access Journals (Sweden)
Mehdi Amini
2013-06-01
Full Text Available To construct copper heap leaching structures, a stepped heap of ore is placed over an isolated sloping surface and then washed with sulphuric acid. The isolated bed of such a heap consists of some natural and geosynthetic layers. Shear strength parameters between these layers are low, so they form the possible sliding surfaces of the heaps. Economic and environmental considerations call for studying such slides. In this study, firstly, results of the laboratory tests carried on the materials of the heap leaching structures bed are presented. Then, the instability mechanisms of such structures are investigated and proper approaches are summarized for their stabilization. Finally, stability of the Miduk copper heap is evaluated as a case history, and appropriate approaches and their effects are discussed for its stabilization.
Milanović, Jovica V
2017-08-13
Future power systems will be significantly different compared with their present states. They will be characterized by an unprecedented mix of a wide range of electricity generation and transmission technologies, as well as responsive and highly flexible demand and storage devices with significant temporal and spatial uncertainty. The importance of probabilistic approaches towards power system stability analysis, as a subsection of power system studies routinely carried out by power system operators, has been highlighted in previous research. However, it may not be feasible (or even possible) to accurately model all of the uncertainties that exist within a power system. This paper describes for the first time an integral approach to probabilistic stability analysis of power systems, including small and large angular stability and frequency stability. It provides guidance for handling uncertainties in power system stability studies and some illustrative examples of the most recent results of probabilistic stability analysis of uncertain power systems.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).
Ge, Hao; Wu, Pingping; Qian, Hong; Xie, Xiaoliang Sunney
2018-03-01
Within an isogenic population, even in the same extracellular environment, individual cells can exhibit various phenotypic states. The exact role of stochastic gene-state switching regulating the transition among these phenotypic states in a single cell is not fully understood, especially in the presence of positive feedback. Recent high-precision single-cell measurements showed that, at least in bacteria, switching in gene states is slow relative to the typical rates of active transcription and translation. Hence using the lac operon as an archetype, in such a region of operon-state switching, we present a fluctuating-rate model for this classical gene regulation module, incorporating the more realistic operon-state switching mechanism that was recently elucidated. We found that the positive feedback mechanism induces bistability (referred to as deterministic bistability), and that the parameter range for its occurrence is significantly broadened by stochastic operon-state switching. We further show that in the absence of positive feedback, operon-state switching must be extremely slow to trigger bistability by itself. However, in the presence of positive feedback, which stabilizes the induced state, the relatively slow operon-state switching kinetics within the physiological region are sufficient to stabilize the uninduced state, together generating a broadened parameter region of bistability (referred to as stochastic bistability). We illustrate the opposite phenotype-transition rate dependence upon the operon-state switching rates in the two types of bistability, with the aid of a recently proposed rate formula for fluctuating-rate models. The rate formula also predicts a maximal transition rate in the intermediate region of operon-state switching, which is validated by numerical simulations in our model. Overall, our findings suggest a biological function of transcriptional "variations" among genetically identical cells, for the emergence of bistability and
International Nuclear Information System (INIS)
Lee, Kwang Ho; Roh, Myung Sub
2013-01-01
There are so many different factors to consider when constructing a nuclear power plant successfully from planning to decommissioning. According to PMBOK, all projects have nine domains from a holistic project management perspective. They are equally important to all projects, however, this study focuses mostly on the processes required to manage timely completion of the project and conduct risk management. The overall objective of this study is to let you know what the risk analysis derived from scheduling of NPP project is, and understand how to implement the stochastic process modeling through risk management. Building the Nuclear Power Plant is required a great deal of time and fundamental knowledge related to all engineering. That means that integrated project scheduling management with so many activities is necessary and very important. Simulation techniques for scheduling of NPP project using Open Plan program, Crystal Ball program, and Minitab program can be useful tools for designing optimal schedule planning. Thus far, Open Plan and Monte Carlo programs have been used to calculate the critical path for scheduling network analysis. And also, Minitab program has been applied to monitor the scheduling risk. This approach to stochastic modeling through risk analysis of project activities is very useful for optimizing the schedules of activities using Critical Path Method and managing the scheduling control of NPP project. This study has shown new approach to optimal scheduling of NPP project, however, this does not consider the characteristic of activities according to the NPP site conditions. Hence, this study needs more research considering those factors
Energy Technology Data Exchange (ETDEWEB)
Lee, Kwang Ho; Roh, Myung Sub [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)
2013-10-15
There are so many different factors to consider when constructing a nuclear power plant successfully from planning to decommissioning. According to PMBOK, all projects have nine domains from a holistic project management perspective. They are equally important to all projects, however, this study focuses mostly on the processes required to manage timely completion of the project and conduct risk management. The overall objective of this study is to let you know what the risk analysis derived from scheduling of NPP project is, and understand how to implement the stochastic process modeling through risk management. Building the Nuclear Power Plant is required a great deal of time and fundamental knowledge related to all engineering. That means that integrated project scheduling management with so many activities is necessary and very important. Simulation techniques for scheduling of NPP project using Open Plan program, Crystal Ball program, and Minitab program can be useful tools for designing optimal schedule planning. Thus far, Open Plan and Monte Carlo programs have been used to calculate the critical path for scheduling network analysis. And also, Minitab program has been applied to monitor the scheduling risk. This approach to stochastic modeling through risk analysis of project activities is very useful for optimizing the schedules of activities using Critical Path Method and managing the scheduling control of NPP project. This study has shown new approach to optimal scheduling of NPP project, however, this does not consider the characteristic of activities according to the NPP site conditions. Hence, this study needs more research considering those factors.
Airfoil stall interpreted through linear stability analysis
Busquet, Denis; Juniper, Matthew; Richez, Francois; Marquet, Olivier; Sipp, Denis
2017-11-01
Although airfoil stall has been widely investigated, the origin of this phenomenon, which manifests as a sudden drop of lift, is still not clearly understood. In the specific case of static stall, multiple steady solutions have been identified experimentally and numerically around the stall angle. We are interested here in investigating the stability of these steady solutions so as to first model and then control the dynamics. The study is performed on a 2D helicopter blade airfoil OA209 at low Mach number, M 0.2 and high Reynolds number, Re 1.8 ×106 . Steady RANS computation using a Spalart-Allmaras model is coupled with continuation methods (pseudo-arclength and Newton's method) to obtain steady states for several angles of incidence. The results show one upper branch (high lift), one lower branch (low lift) connected by a middle branch, characterizing an hysteresis phenomenon. A linear stability analysis performed around these equilibrium states highlights a mode responsible for stall, which starts with a low frequency oscillation. A bifurcation scenario is deduced from the behaviour of this mode. To shed light on the nonlinear behavior, a low order nonlinear model is created with the same linear stability behavior as that observed for that airfoil.
Stability analysis of intershaft squeeze film dampers
El-Shafei, A.
1991-08-01
Intershaft squeeze film dampers have been investigated for damping of dual rotor aircraft jet engines. Initial investigations indicated that the intershaft dampers would attenuate the amplitude of the engine vibration and decrease the force transmitted through the intershaft bearing, thereby increasing its life. Also it was thought that the intershaft damper would enhance the stability of the rotor-bearing system. Unfortunately, it was determined both theoretically and experimentally that the intershaft squeeze film damper was unstable above the engine's first critical speed. In this paper, a stability analysis of rotors incorporating intershaft squeeze film dampers is performed. A rotor model consisting of two Jeffcott rotors with two intershaft squeeze film dampers is investigated. Examining the system characteristic equation for the conditions at which the roots indicate an ever growing unstable motion results in the stability conditions. The cause of the instability is identified as the rotation of the oil in the damper clearance. The oil rotation adds energy to the forward whirl of the rotor system above the critical speed and thus causes the instability. Below the critical speed the oil film removes energy from the forward rotor whirl. It is also shown that the backward whirl of the rotor system is always stable. Several proposed configurations of intershaft squeeze film dampers are discussed, and it is shown that the intershaft dampers are stable supercritically only with a configuration in which the oil film does not rotate.
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.
Recently, a variant of stochastic dominance called stochastic efficiency with respect to a function (SERF) has been developed and applied. Unlike traditional stochastic dominance approaches, SERF uses the concept of certainty equivalents (CEs) to rank a set of risk-efficient alternatives instead of...
Stability analysis of cylindrical Vlasov equilibria
International Nuclear Information System (INIS)
Short, R.W.
1979-01-01
A general method of stability analysis is described which may be applied to a large class of such problems, namely those which are described dynamically by the Vlasov equation, and geometrically by cylindrical symmetry. The method is presented for the simple case of the Vlasov-Poisson (electrostatic) equations, and the results are applied to a calculation of the lower-hybrid-drift instability in a plasma with a rigid rotor distribution function. The method is extended to the full Vlasov-Maxwell (electromagnetic) equations. These results are applied to a calculation of the instability of the extraordinary electromagnetic mode in a relativistic E-layer interacting with a background plasma
Stability analysis of cylindrical Vlasov equilibria
International Nuclear Information System (INIS)
Short, R.W.
1979-01-01
A general method of stability analysis is described which may be applied to a large class of such problems, namely those which are described dynamically by the Vlasov equation, and geometrically by clindrical symmetry. The method is presented for the simple case of the Vlasov-Poisson (electrostatic) equations, and the results are applied to a calculation of the lower-hybrid-drift instability in a plasma with a rigid rotor distribution function. The method is extended to the full Vlasov-Maxwell (electromagnetic) equations. These results are applied to a calculation of the instability of the extraordinary electromagnetic mode in a relativistic E-layer interacting with a background plasma
Eichhorn, Ralf; Aurell, Erik
2014-04-01
theory for small deviations from equilibrium, in which a general framework is constructed from the analysis of non-equilibrium states close to equilibrium. In a next step, Prigogine and others developed linear irreversible thermodynamics, which establishes relations between transport coefficients and entropy production on a phenomenological level in terms of thermodynamic forces and fluxes. However, beyond the realm of linear response no general theoretical results were available for quite a long time. This situation has changed drastically over the last 20 years with the development of stochastic thermodynamics, revealing that the range of validity of thermodynamic statements can indeed be extended deep into the non-equilibrium regime. Early developments in that direction trace back to the observations of symmetry relations between the probabilities for entropy production and entropy annihilation in non-equilibrium steady states [5-8] (nowadays categorized in the class of so-called detailed fluctuation theorems), and the derivations of the Bochkov-Kuzovlev [9, 10] and Jarzynski relations [11] (which are now classified as so-called integral fluctuation theorems). Apart from its fundamental theoretical interest, the developments in stochastic thermodynamics have experienced an additional boost from the recent experimental progress in fabricating, manipulating, controlling and observing systems on the micro- and nano-scale. These advances are not only of formidable use for probing and monitoring biological processes on the cellular, sub-cellular and molecular level, but even include the realization of a microscopic thermodynamic heat engine [12] or the experimental verification of Landauer's principle in a colloidal system [13]. The scientific program Stochastic Thermodynamics held between 4 and 15 March 2013, and hosted by The Nordic Institute for Theoretical Physics (Nordita), was attended by more than 50 scientists from the Nordic countries and elsewhere, amongst them
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
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.
Directory of Open Access Journals (Sweden)
Otitoju, MA.
2014-01-01
Full Text Available Smallholder soybean production is investigated using an econometric analysis otherwise known as stochastic frontier analysis through transcendental logarithmic (translog production function, which incorporates an inefficiency effects model. Ninety-six farmers were randomly selected through multistage techniques in Benue State, Nigeria. Factors (socio-economic and institutional considered in the inefficiency effects model include household size, sex, age, years of schooling, farming experience in soybean production, health status, off-farm employment, non-family labour, credit accessibility, land fragmentation and extension contact. The parameters of the stochastic frontier translog production function are estimated contemporaneously with those involved in the inefficiency effects model. The results indicate that household size, age, non-family labour were significant and negatively related to the technical inefficiency while farming experience, off-farm employment, credit accessibility, land fragmentation, and extension contact were statistically significant and positively related to the inefficiency. The mean technical efficiency of the farmers is 0.84. This means that the farmers can still improve their efficiency level by 16%.
Rezaei, Satar; Zandian, Hamed; Baniasadi, Akram; Moghadam, Telma Zahirian; Delavari, Somayeh; Delavari, Sajad
2016-02-01
Hospitals are the most expensive health services provider in the world. Therefore, the evaluation of their performance can be used to reduce costs. The aim of this study was to determine the efficiency of the hospitals at the Kurdistan University of Medical Sciences using stochastic frontier analysis (SFA). This was a cross-sectional and retrospective study that assessed the performance of Kurdistan teaching hospitals (n = 12) between 2007 and 2013. The Stochastic Frontier Analysis method was used to achieve this aim. The numbers of active beds, nurses, physicians, and other staff members were considered as input variables, while the inpatient admission was considered as the output. The data were analyzed using Frontier 4.1 software. The mean technical efficiency of the hospitals we studied was 0.67. The results of the Cobb-Douglas production function showed that the maximum elasticity was related to the active beds and the elasticity of nurses was negative. Also, the return to scale was increasing. The results of this study indicated that the performances of the hospitals were not appropriate in terms of technical efficiency. In addition, there was a capacity enhancement of the output of the hospitals, compared with the most efficient hospitals studied, of about33%. It is suggested that the effect of various factors, such as the quality of health care and the patients' satisfaction, be considered in the future studies to assess hospitals' performances.
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.
Stochastic Finite element analysis of the free vibration of functionally graded material plates
Shaker, Afeefa; Abdelrahman, Wael; Tawfik, Mohammad; Sadek, Edward
2008-02-01
The superior properties of functionally graded materials (FGM) are usually accompanied by randomness in their properties due to difficulties in tailoring the gradients during manufacturing processes. Using the stochastic finite element method (SFEM) proved to be a powerful tool in studying the sensitivity of the static response of FGM plates to uncertainties in their material properties. This tool is yet to be used in studying free vibration of FGM plates. The aim of this work is to use both a First Order Reliability Method (FORM) and the Second Order Reliability Method (SORM), combined with a nine-noded isoparametric Lagrangian element based on the third order shear deformation theory to investigate sensitivity of the fundamental frequency of FGM plates to material uncertainties. These include the effect of uncertainties on both the metal and ceramic constituents. The basic random variables include ceramic and metal Young’s modulus and Poisson’s ratio, their densities and ceramic volume fraction. The developed code utilizes MATLAB capabilities to derive the derivatives of the stiffness and mass matrices symbolically with a considerable reduction in calculation time. Calculating the eigenvectors at the mean values of the variables proves to be a reasonable simplification which significantly increases solution speed. The stochastic finite element code is validated using available data in the literature, in addition to comparisons with results of the well-established Monte Carlo simulation technique with importance sampling. Results show that SORM is an excellent rapid tool in the stochastic analysis of free vibration of FGM plates, when compared to the slower Monte Carlo simulation techniques.
A stochastic analysis of the decision to produce biomass crops in Ireland
International Nuclear Information System (INIS)
Clancy, Daragh; Breen, James P.; Thorne, Fiona; Wallace, Michael
2012-01-01
There is increasing interest in biomass crops as an alternative farm activity. However farmer concerns about the production and financial risks associated with growing these crops may be impeding the actual rates of adoption. The uncertainty surrounding risky variables such as the costs of production, yield level, price per tonne and opportunity cost of land make it difficult to accurately calculate the returns to biomass crops. Their lengthy production lifespan may only serve to heighten the level of risk that affects key variables. A stochastic budgeting model is used to estimate distributions of returns from willow and miscanthus in Ireland. The opportunity cost of land is accounted for through the inclusion of the foregone returns from selected conventional agricultural activities. The impact on biomass returns of bioremediation is also examined. The Net Present Values (NPVs) of various biomass investment options are simulated to ascertain the full distribution of possible returns. The results of these simulations are then compared using their respective Cumulative Distribution Functions (CDFs) and the investments are ranked using Stochastic Efficiency with Respect to a Function (SERF). While the distributions of investment returns for miscanthus are wider than those of willow, implying greater risk, the distribution of willow returns is predominantly to the left of zero indicating that such an investment has an extremely high probability of generating a negative return. The results from the SERF analysis show that miscanthus generally has higher certainty equivalents (CEs), and therefore farmers would be more likely to invest in miscanthus rather than willow. -- Highlights: ► We develop a stochastic budgeting model to capture uncertainty in key variables. ► Farmers with higher levels of risk aversion would be unwilling to invest in biomass crops. ► Miscanthus has a greater probability of making a profit than willow. ► Bioremediation can help to offset
Dynamic Analysis of Power System Voltage Stability.
Gebreselassie, Assefa
This thesis investigates the effects of loads and voltage regulators on the dynamic voltage stability of power systems. The analysis focuses on the interactions of machine flux dynamics with loads and voltage control devices. The results are based on eigenvalue analysis of the linearized models and time simulation of the nonlinear models, using models from the Power System Toolbox, a Matlab -based package for the simulation and small signal analysis of nonlinear power systems. The voltage stability analysis results are developed using a single machine single load system with typical machine and network parameters and the NPCC 10-machine system. Dynamic models for generators, exciters and loads are used. The generator is modeled with a pair of poles and one damper circuit in both the d-axis and the q-axis. Saturation effects are included in the model. The IEEE Type DC1 DC commutator exciter model is used for all the exciters. Five different types of loads: constant impedance, constant current, constant power, a first order induction motor model (slip model) and a third order induction motor model (slip-flux model) are considered. The modes of instability and the stability limits of the different representation of loads are examined for two different operating modes of the exciters. The first, when all the exciters are on automatic control and the second when some exciters are on manual control. Modal participation factors are used to determine the characteristics of the critical modes. The characteristics of the unstable modes are verified by performing time simulation of the nonlinear models. Oscillatory and non-oscillatory instabilities are experienced by load buses when all the exciters are on automatic control and some exciters are on manual control respectively, for loads which are predominantly constant power and induction motors. It is concluded that the mode of instability does not depend on the type of loads but on the operating condition of the exciters
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...... 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...
Voltage stability analysis using a modified continuation load flow ...
African Journals Online (AJOL)
Abstract. This paper addresses the rising problem of identifying the voltage stability limits of load buses in a power system and how to optimally place capacitor banks for voltage stability improvement. This paper uses the concept of the continuation power flow analysis used in voltage stability analysis. It uses the modified ...
Stochastic higher order finite element elasto-plastic analysis of the necking phenomenon
Directory of Open Access Journals (Sweden)
Strąkowski Michał
2017-01-01
Full Text Available The principal goal of this work is to investigate an application of the stochastic perturbation technique of the 10th order in coupled thermo-elasto-plastic analysis of tension of the steel elastic bar exposed to fire with thermally dependent material characteristics. An ambient temperature, calculated from the fire curve after ISO 834-1, equivalent to the fire exposure of the steel structure is treated here as the input Gaussian random variable. It is uniquely defined by the constant mean value at outer surfaces of this element, where material parameters of the steel as Young modulus, yield strength, heat conductivity, capacity and thermal elongation are considered all as highly temperature-dependent. Computational implementation known as the Stochastic Finite Element Method is carried out with the use of the FEM system ABAQUS and computer algebra system MAPLE. It uses both polynomial and non-polynomial local response functions of stresses and displacements. The basic probabilistic characteristics of time-dependent structural response are determined (expectations, coefficients of variation, skewness and kurtosis and verified with classical Monte-Carlo simulation scheme and semi-analytical technique for input coefficient of variation not larger than 0.20. Finally, probabilistic convergence of all three methods versus increasing input uncertainty level is investigated.
Stochastic Frontier Production Analysis of Tobacco Growers in District Mardan, Pakistan
International Nuclear Information System (INIS)
Saddozai, K. N; Nasrullah, M.; Khan, N. P.
2015-01-01
The theme of this research was to analyze the stochastic frontier production of tobacco growers. This parametric approach was encompassed to investigate the technical efficiency of growers. The primary data was gleaned during 2014-15 from sampled population of three villages namely Takkar Kali, Garo Shah and Passand Kali of Takhtbhai Tehsil, Mardan district of Khyber Pakhtunkhwa province. The multi-stage sampling technique was utilized to obtain the desired sample size of 120 tobacco growers. The major findings of stochastic production frontier analysis indicate that all variables were statistically significant and have portrayed positive contribution to tobacco production except fertilizer which was found significant but has revealed inverse relation with tobacco production. The mean technical efficiency was estimated at 0.85 depicting that tobacco growers can further amplify efficiency by 15% with given level of inputs. The inefficiency model estimates demonstrate that only experience of tobacco growers in study area was significantly decreasing the inefficiency of the growers. The study has concluded that tobacco growers are operating in the second stage of production; therefore, tobacco production can still be enhanced. It is recommended that season long trainings for tobacco growers may be undertaken by the concerned authorities to enhance the crop management skills for rational use of input. (author)
International Nuclear Information System (INIS)
Liu, Shichang; Wang, Guanbo; Wu, Gaochen; Wang, Kan
2015-01-01
Highlights: • DRAGON and DONJON are applied and verified in calculations of research reactors. • Continuous-energy Monte Carlo calculations by RMC are chosen as the references. • “ECCO” option of DRAGON is suitable for the calculations of research reactors. • Manual modifications of cross-sections are not necessary with DRAGON and DONJON. • DRAGON and DONJON agree well with RMC if appropriate treatments are applied. - Abstract: Simulation of the behavior of the plate-type research reactors such as JRR-3M and CARR poses a challenge for traditional neutronics calculation tools and schemes for power reactors, due to the characteristics of complex geometry, highly heterogeneity and large leakage of the research reactors. Two different theoretical approaches, the deterministic and the stochastic methods, are used for the neutronics analysis of the JRR-3M plate-type research reactor in this paper. For the deterministic method the neutronics codes DRAGON and DONJON are used, while the continuous-energy Monte Carlo code RMC (Reactor Monte Carlo code) is employed for the stochastic approach. The goal of this research is to examine the capability of the deterministic code system DRAGON and DONJON to reliably simulate the research reactors. The results indicate that the DRAGON and DONJON code system agrees well with the continuous-energy Monte Carlo simulation on both k eff and flux distributions if the appropriate treatments (such as the ECCO option) are applied
Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors
International Nuclear Information System (INIS)
Gutierrez, R.; Gutierrez-Sanchez, R.; Nafidi, A.
2006-01-01
This paper proposes a means of using stochastic diffusion processes to model the total consumption of electrical power (including distribution and transport losses) in Morocco, as recorded by the official data for total sales published by Office Nationale de l'Electricite (ONE), the Moroccan electricity authority. Two models of univariate stochastic diffusion were used: the time-homogeneous Gompertz Diffusion Process (HGDP) and the time-non-homogeneous Gompertz Diffusion Process (NHGDP). The methodology proposed is based on the analysis of the trend function; this requires the analyst to obtain fits and forecasts for the consumption of electrical power by means of the estimated trend function (conditioned and non-conditioned). This latter function is obtained from the mean value of the process and the maximum likelihood estimators (MLE) of the parameters of the model. This estimation and the subsequent statistical inference are based on the discretised observation of the variable 'electricity consumption in Morocco', using annual data for the period 1980-2001. The fit and forecast are improved by using macroeconomic exogenous factors such as the gross domestic product per inhabitant (GDP/inhab), the final domestic consumption (FDC) and the gross fixed capital formation (GFCF). The results obtained show that NHGDP (with the above three exogenous factors) provides an adequate fit and medium-term forecast of electricity consumption in Morocco
Analysis for Ad Hoc Network Attack-Defense Based on Stochastic Game Model
Directory of Open Access Journals (Sweden)
Yuanjie LI
2014-06-01
Full Text Available The attack actions analysis for Ad Hoc networks can provide a reference for the design security mechanisms. This paper presents an analysis method of security of Ad Hoc networks based on Stochastic Game Nets (SGN. This method can establish a SGN model of Ad Hoc networks and calculate to get the Nash equilibrium strategy. After transforming the SGN model into a continuous-time Markov Chain (CTMC, the security of Ad Hoc networks can be evaluated and analyzed quantitatively by calculating the stationary probability of CTMC. Finally, the Matlab simulation results show that the probability of successful attack is related to the attack intensity and expected payoffs, but not attack rate.
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.
A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis.
Tervonen, Tommi; van Valkenhoef, Gert; Buskens, Erik; Hillege, Hans L; Postmus, Douwe
2011-05-30
Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multi-criteria model that fully takes into account the evidence on efficacy and adverse drug reactions. Our approach is based on the stochastic multi-criteria acceptability analysis methodology, which allows us to compute the typical value judgments that support a decision, to quantify decision uncertainty, and to compute a comprehensive BR profile. We construct a multi-criteria model for the therapeutic group of second-generation antidepressants. We assess fluoxetine and venlafaxine together with placebo according to incidence of treatment response and three common adverse drug reactions by using data from a published study. Our model shows that there are clear trade-offs among the treatment alternatives. Copyright © 2011 John Wiley & Sons, Ltd.
Directory of Open Access Journals (Sweden)
Shaofei Xie
2012-02-01
Full Text Available Based on the theory of stochastic resonance, an adaptive single-well stochastic resonance (ASSR coupled with genetic algorithm was developed to enhance the signal-to-noise ratio of weak chromatographic signals. In conventional stochastic resonance algorithm, there are two or more parameters needed to be optimized and the proper parameters values were obtained by a universal searching within a given range. In the developed ASSR, the optimization of system parameter was simplified and automatic implemented. The ASSR was applied to the trace analysis of clenbuterol in human urine and it helped to significantly improve the limit of detection and limit of quantification of clenbuterol. Good linearity, precision and accuracy of the proposed method ensure that it could be an effective tool for trace analysis and the improvement of detective sensibility of current detectors.
Truck Roll Stability Data Collection and Analysis
Energy Technology Data Exchange (ETDEWEB)
Stevens, SS
2001-07-02
The principal objective of this project was to collect and analyze vehicle and highway data that are relevant to the problem of truck rollover crashes, and in particular to the subset of rollover crashes that are caused by the driver error of entering a curve at a speed too great to allow safe completion of the turn. The data are of two sorts--vehicle dynamic performance data, and highway geometry data as revealed by vehicle behavior in normal driving. Vehicle dynamic performance data are relevant because the roll stability of a tractor trailer depends both on inherent physical characteristics of the vehicle and on the weight and distribution of the particular cargo that is being carried. Highway geometric data are relevant because the set of crashes of primary interest to this study are caused by lateral acceleration demand in a curve that exceeds the instantaneous roll stability of the vehicle. An analysis of data quality requires an evaluation of the equipment used to collect the data because the reliability and accuracy of both the equipment and the data could profoundly affect the safety of the driver and other highway users. Therefore, a concomitant objective was an evaluation of the performance of the set of data-collection equipment on the truck and trailer. The objective concerning evaluation of the equipment was accomplished, but the results were not entirely positive. Significant engineering apparently remains to be done before a reliable system can be fielded. Problems were identified with the trailer to tractor fiber optic connector used for this test. In an over-the-road environment, the communication between the trailer instrumentation and the tractor must be dependable. In addition, the computer in the truck must be able to withstand the rigors of the road. The major objective--data collection and analysis--was also accomplished. Using data collected by instruments on the truck, a ''bad-curve'' database can be generated. Using
Dike Strength Analysis on a Regional Scale Based On a Stochastic Subsoil Model
Koelewijn, A. R.; Vastenburg, E. W.
2013-12-01
About two-third of the Netherlands is protected against flooding by dikes and levees. The subsoil can be characterized by fluvial and marine sediments. Maintaining the safety of these dikes and levees is of vital importance. Insufficient safety is not permissible, but excessive safety would imply a waste of money and other resources. Therefore safety assessments are carried out on a regular basis. Over the past decades, a practice has grown to calculate a limited number of cross-sections, roughly one every 500 to 1000 meters. For this purpose, a representative cross-section is selected as an estimate of the most vulnerable surface geometry and the subsoil conditions determined from boreholes and cone penetration tests, for which slope stability and piping analyses are carried out. This is a time-consuming procedure which is not only expensive, but also neglects geological knowledge. A method to incorporate geological knowledge of an area, including updating on the basis of additional investigations, has been described in Koelewijn et al. [2011]. In addition, various groups have worked to incorporate geotechnical stability models and detailed Lidar-measurements of the surface into a more efficient and rational calculation process [Knoeff et al. 2011, Lam et al. 2013, van den Ham & Mastbergen, 2013]. Combining this experience with the 3D subsoil model opens possibilities for cost-effective additional soil investigations for those locations where ruling out unfavorable conditions really influences the decisions to be made regarding rejection and improvement, see the figure for examples of different subsoil profiles along a dike. The resulting system has been applied for semi-automated calculations of dikes in various parts of the Netherlands, totalling over 4000 km by now, and a part of the Mississippi levee system. [van den Ham & Mastbergen, 2013] G.A. van den Ham & D.R. Mastbergen, A semi-probabilistic assessment method for flow slides. AGU Fall meeting, 2013
Wang, Tao; Zhou, Guoqing; Wang, Jianzhou; Zhou, Lei
2018-03-01
The artificial ground freezing method (AGF) is widely used in civil and mining engineering, and the thermal regime of frozen soil around the freezing pipe affects the safety of design and construction. The thermal parameters can be truly random due to heterogeneity of the soil properties, which lead to the randomness of thermal regime of frozen soil around the freezing pipe. The purpose of this paper is to study the one-dimensional (1D) random thermal regime problem on the basis of a stochastic analysis model and the Monte Carlo (MC) method. Considering the uncertain thermal parameters of frozen soil as random variables, stochastic processes and random fields, the corresponding stochastic thermal regime of frozen soil around a single freezing pipe are obtained and analyzed. Taking the variability of each stochastic parameter into account individually, the influences of each stochastic thermal parameter on stochastic thermal regime are investigated. The results show that the mean temperatures of frozen soil around the single freezing pipe with three analogy method are the same while the standard deviations are different. The distributions of standard deviation have a great difference at different radial coordinate location and the larger standard deviations are mainly at the phase change area. The computed data with random variable method and stochastic process method have a great difference from the measured data while the computed data with random field method well agree with the measured data. Each uncertain thermal parameter has a different effect on the standard deviation of frozen soil temperature around the single freezing pipe. These results can provide a theoretical basis for the design and construction of AGF.
Bounded Linear Stability Margin Analysis of Nonlinear Hybrid Adaptive Control
Nguyen, Nhan T.; Boskovic, Jovan D.
2008-01-01
This paper presents a bounded linear stability analysis for a hybrid adaptive control that blends both direct and indirect adaptive control. Stability and convergence of nonlinear adaptive control are analyzed using an approximate linear equivalent system. A stability margin analysis shows that a large adaptive gain can lead to a reduced phase margin. This method can enable metrics-driven adaptive control whereby the adaptive gain is adjusted to meet stability margin requirements.
Stochastic Modelling, Analysis, and Simulations of the Solar Cycle Dynamic Process
Turner, Douglas C.; Ladde, Gangaram S.
2018-03-01
Analytical solutions, discretization schemes and simulation results are presented for the time delay deterministic differential equation model of the solar dynamo presented by Wilmot-Smith et al. In addition, this model is extended under stochastic Gaussian white noise parametric fluctuations. The introduction of stochastic fluctuations incorporates variables affecting the dynamo process in the solar interior, estimation error of parameters, and uncertainty of the α-effect mechanism. Simulation results are presented and analyzed to exhibit the effects of stochastic parametric volatility-dependent perturbations. The results generalize and extend the work of Hazra et al. In fact, some of these results exhibit the oscillatory dynamic behavior generated by the stochastic parametric additative perturbations in the absence of time delay. In addition, the simulation results of the modified stochastic models influence the change in behavior of the very recently developed stochastic model of Hazra et al.
Power System Transient Stability Analysis through a Homotopy Analysis Method
Energy Technology Data Exchange (ETDEWEB)
Wang, Shaobu; Du, Pengwei; Zhou, Ning
2014-04-01
As an important function of energy management systems (EMSs), online contingency analysis plays an important role in providing power system security warnings of instability. At present, N-1 contingency analysis still relies on time-consuming numerical integration. To save computational cost, the paper proposes a quasi-analytical method to evaluate transient stability through time domain periodic solutions’ frequency sensitivities against initial values. First, dynamic systems described in classical models are modified into damping free systems whose solutions are either periodic or expanded (non-convergent). Second, because the sensitivities experience sharp changes when periodic solutions vanish and turn into expanded solutions, transient stability is assessed using the sensitivity. Third, homotopy analysis is introduced to extract frequency information and evaluate the sensitivities only from initial values so that time consuming numerical integration is avoided. Finally, a simple case is presented to demonstrate application of the proposed method, and simulation results show that the proposed method is promising.
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)
A stochastic context free grammar based framework for analysis of protein sequences.
Dyrka, Witold; Nebel, Jean-Christophe
2009-10-08
In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm. This framework was implemented in a system aiming at the production of binding site descriptors. These descriptors not only allow detection of protein regions that are involved in these sites, but also provide insight in their structure. Grammars were induced using quantitative properties of amino acids to deal with the size of the protein alphabet. Moreover, we imposed some structural constraints on grammars to reduce the extent of the rule search space. Finally, grammars based on different properties were combined to convey as much information as possible. Evaluation was performed on sites of various sizes and complexity described either by PROSITE patterns, domain profiles or a set of patterns. Results show the produced binding site descriptors are human-readable and, hence, highlight biologically meaningful features. Moreover, they achieve good accuracy in both annotation and detection. In addition, findings suggest that, unlike current state-of-the-art methods, our system may be particularly suited to deal with patterns shared by non-homologous proteins. A new Stochastic Context Free Grammar based framework has been introduced allowing the
International Nuclear Information System (INIS)
Silva, Milena Wollmann da; Vilhena, Marco Tullio M.B.; Bodmann, Bardo Ernst J.; Vasques, Richard
2015-01-01
The neutron point kinetics equation, which models the time-dependent behavior of nuclear reactors, is often used to understand the dynamics of nuclear reactor operations. It consists of a system of coupled differential equations that models the interaction between (i) the neutron population; and (II) the concentration of the delayed neutron precursors, which are radioactive isotopes formed in the fission process that decay through neutron emission. These equations are deterministic in nature, and therefore can provide only average values of the modeled populations. However, the actual dynamical process is stochastic: the neutron density and the delayed neutron precursor concentrations vary randomly with time. To address this stochastic behavior, Hayes and Allen have generalized the standard deterministic point kinetics equation. They derived a system of stochastic differential equations that can accurately model the random behavior of the neutron density and the precursor concentrations in a point reactor. Due to the stiffness of these equations, this system was numerically implemented using a stochastic piecewise constant approximation method (Stochastic PCA). Here, we present a study of the influence of stochastic fluctuations on the results of the neutron point kinetics equation. We reproduce the stochastic formulation introduced by Hayes and Allen and compute Monte Carlo numerical results for examples with constant and time-dependent reactivity, comparing these results with stochastic and deterministic methods found in the literature. Moreover, we introduce a modified version of the stochastic method to obtain a non-stiff solution, analogue to a previously derived deterministic approach. (author)
Energy Technology Data Exchange (ETDEWEB)
Silva, Milena Wollmann da; Vilhena, Marco Tullio M.B.; Bodmann, Bardo Ernst J.; Vasques, Richard, E-mail: milena.wollmann@ufrgs.br, E-mail: vilhena@mat.ufrgs.br, E-mail: bardobodmann@ufrgs.br, E-mail: richard.vasques@fulbrightmail.org [Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS (Brazil). Programa de Pos-Graduacao em Engenharia Mecanica
2015-07-01
The neutron point kinetics equation, which models the time-dependent behavior of nuclear reactors, is often used to understand the dynamics of nuclear reactor operations. It consists of a system of coupled differential equations that models the interaction between (i) the neutron population; and (II) the concentration of the delayed neutron precursors, which are radioactive isotopes formed in the fission process that decay through neutron emission. These equations are deterministic in nature, and therefore can provide only average values of the modeled populations. However, the actual dynamical process is stochastic: the neutron density and the delayed neutron precursor concentrations vary randomly with time. To address this stochastic behavior, Hayes and Allen have generalized the standard deterministic point kinetics equation. They derived a system of stochastic differential equations that can accurately model the random behavior of the neutron density and the precursor concentrations in a point reactor. Due to the stiffness of these equations, this system was numerically implemented using a stochastic piecewise constant approximation method (Stochastic PCA). Here, we present a study of the influence of stochastic fluctuations on the results of the neutron point kinetics equation. We reproduce the stochastic formulation introduced by Hayes and Allen and compute Monte Carlo numerical results for examples with constant and time-dependent reactivity, comparing these results with stochastic and deterministic methods found in the literature. Moreover, we introduce a modified version of the stochastic method to obtain a non-stiff solution, analogue to a previously derived deterministic approach. (author)
Directory of Open Access Journals (Sweden)
Xiaoxuan Hu
2015-01-01
Full Text Available This paper considers a task assignment problem for multiple unmanned aerial vehicles (UAVs. The UAVs are set to perform attack tasks on a collection of ground targets in a severe uncertain environment. The UAVs have different attack capabilities and are located at different positions. Each UAV should be assigned an attack task before the mission starts. Due to uncertain information, many criteria values essential to task assignment were random or fuzzy, and the weights of criteria were not precisely known. In this study, a novel task assignment approach based on stochastic Multicriteria acceptability analysis (SMAA method was proposed to address this problem. The uncertainties in the criteria were analyzed, and a task assignment procedure was designed. The results of simulation experiments show that the proposed approach is useful for finding a satisfactory assignment under severe uncertain circumstances.
PV Hosting Capacity Analysis and Enhancement Using High Resolution Stochastic Modeling
Directory of Open Access Journals (Sweden)
Emilio J. Palacios-Garcia
2017-09-01
Full Text Available Reduction of CO2 emissions is a main target in the future smart grid. This goal is boosting the installation of renewable energy resources (RES, as well as a major consumer engagement that seeks for a more efficient utilization of these resources toward the figure of ‘prosumers’. Nevertheless, these resources present an intermittent nature, which requires the presence of an energy storage system and an energy management system (EMS to ensure an uninterrupted power supply. Moreover, network-related issues might arise due to the increasing power of renewable resources installed in the grid, the storage systems also being capable of contributing to the network stability. However, to assess these future scenarios and test the control strategies, a simulation system is needed. The aim of this paper is to analyze the interaction between residential consumers with high penetration of PV generation and distributed storage and the grid by means of a high temporal resolution simulation scenario based on a stochastic residential load model and PV production records. Results of the model are presented for different PV power rates and storage capacities, as well as a two-level charging strategy as a mechanism for increasing the hosting capacity (HC of the network.
Deng, De-Ming; Chang, Cheng-Hung
2015-05-14
Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.
Deng, De-Ming; Chang, Cheng-Hung
2015-05-01
Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.
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
A Stochastic Frontier Analysis of Output Level and Growth in Poland and Western Economies
Osiewalski, J.; Koop, G.; Steel, M.F.J.
1997-01-01
This paper uses Bayesian stochastic frontier methods to measure the productivity gap between Poland and Western countries that existed before the beginning of the main Polish economic reform. Using data for 20 Western economies, Poland and Yugoslavia (1980-1990) we estimate a translog stochastic
Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods
DEFF Research Database (Denmark)
Ordoudis, Christos; Pinson, Pierre; Zugno, Marco
2015-01-01
Owing to the massive deployment of renewable power production units over the last couple of decades, the use of stochastic optimization methods to solve the unit commitment problem has gained increasing attention. Solving stochastic unit commitment problems in large-scale power systems requires h...
Stochastic efficiency analysis of bovine tuberculosis-surveillance programs in the Netherlands
Asseldonk, van M.A.P.M.; Roermund, van H.J.W.; Fischer, E.A.J.; Jong, de M.C.M.; Huirne, R.B.M.
2005-01-01
We constructed a stochastic bio-economic model to determine the optimal cost-efficient surveillance program for bovine tuberculosis. The surveillance programs differed in combinations of one or more detection methods and/or sampling frequency. Stochastic input variables in the epidemiological module
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)
Stochastic index model for intermittent regimes: from preliminary analysis to regionalisation
Directory of Open Access Journals (Sweden)
M. Rianna
2011-04-01
Full Text Available In small and medium-sized basins or in rivers characterized by intermittent discharges, with low or negligible/null observed values for long periods of the year, the correct representation of the discharge regime is important for issues related to water management and to define the amount and quality of water available for irrigation, domestic and recreational uses. In these cases, only one index as a statistical metric is often not enough; it is thus necessary to introduce Flow Duration Curves (FDC.
The aim of this study is therefore to combine a stochastic index flow model capable of reproducing the FDC record period of a river, regardless of the persistence and seasonality of the series, with the theory of total probability in order to calculate how often a river is dry.
The paper draws from preliminary analyses, including a study to estimate the correlation between discharge indicators Q_{95}, Q_{50} and Q_{1} (discharges exceeding 95%, 50% or 1% of the time, respectively and some fundamental characteristics of the basin, as well as to identify homogeneous regions in the target area through the study of several geo-morphological features and climatic conditions. The stochastic model was then applied in one of the homogeneous regions that includes intermittent rivers.
Finally, the model was regionalized by means of regression analysis in order to calculate the FDC for ungauged basins; the reliability of this method was tested using jack-knife validation.
A Stochastic Framework for Robust Fuzzy Filtering and Analysis of Signals-Part I.
Kumar, Mohit; Stoll, Norbert; Stoll, Regina; Thurow, Kerstin
2016-05-01
There are numerous applications across all the spectrum of scientific areas that demand the mathematical study of signals/data. The two typical study areas of theoretical research on signal/data processing are of modeling (i.e., understanding of signal's behavior) and of analysis (i.e., evaluation of given signal for finding its association to existing signal models). The objective of this paper is to provide a stochastic framework to design both fuzzy filtering and analysis algorithms in a unified manner. The signals are modeled via linear-in-parameters models (e.g., a type of Takagi-Sugeno fuzzy model) based on variational Bayes (VB) methodology. This gives rise to the "negative free energy maximizing" filtering algorithm. The issue of intractability was handled first by carefully choosing the priors as conjugate to the likelihood and then by using Stirling approximation for the Gamma function. This paper highlighted that it was analytically possible to maximize the information theoretic quantity, "mutual information," exactly in the same manner as maximizing "negative free energy" in VB methodology. This gives rise to the "variational information maximizing" analysis algorithm. The robustness of the methodology against data outliers is achieved by modeling the noises with Student- t distributions. The framework takes into account the inputs noises as well apart from the usually considered output noise. The robustness of the adaptive filtering algorithm against noise is shown by a deterministic analysis where an upper bound on the magnitude of estimation errors is derived.
Unified Tractable Model for Large-Scale Networks Using Stochastic Geometry: Analysis and Design
Afify, Laila H.
2016-12-01
The ever-growing demands for wireless technologies necessitate the evolution of next generation wireless networks that fulfill the diverse wireless users requirements. However, upscaling existing wireless networks implies upscaling an intrinsic component in the wireless domain; the aggregate network interference. Being the main performance limiting factor, it becomes crucial to develop a rigorous analytical framework to accurately characterize the out-of-cell interference, to reap the benefits of emerging networks. Due to the different network setups and key performance indicators, it is essential to conduct a comprehensive study that unifies the various network configurations together with the different tangible performance metrics. In that regard, the focus of this thesis is to present a unified mathematical paradigm, based on Stochastic Geometry, for large-scale networks with different antenna/network configurations. By exploiting such a unified study, we propose an efficient automated network design strategy to satisfy the desired network objectives. First, this thesis studies the exact aggregate network interference characterization, by accounting for each of the interferers signals in the large-scale network. Second, we show that the information about the interferers symbols can be approximated via the Gaussian signaling approach. The developed mathematical model presents twofold analysis unification for uplink and downlink cellular networks literature. It aligns the tangible decoding error probability analysis with the abstract outage probability and ergodic rate analysis. Furthermore, it unifies the analysis for different antenna configurations, i.e., various multiple-input multiple-output (MIMO) systems. Accordingly, we propose a novel reliable network design strategy that is capable of appropriately adjusting the network parameters to meet desired design criteria. In addition, we discuss the diversity-multiplexing tradeoffs imposed by differently favored
Stability of fundamental couplings: A global analysis
Martins, C. J. A. P.; Pinho, A. M. M.
2017-01-01
Astrophysical tests of the stability of fundamental couplings are becoming an increasingly important probe of new physics. Motivated by the recent availability of new and stronger constraints we update previous works testing the consistency of measurements of the fine-structure constant α and the proton-to-electron mass ratio μ =mp/me (mostly obtained in the optical/ultraviolet) with combined measurements of α , μ and the proton gyromagnetic ratio gp (mostly in the radio band). We carry out a global analysis of all available data, including the 293 archival measurements of Webb et al. and 66 more recent dedicated measurements, and constraining both time and spatial variations. While nominally the full data sets show a slight statistical preference for variations of α and μ (at up to two standard deviations), we also find several inconsistencies between different subsets, likely due to hidden systematics and implying that these statistical preferences need to be taken with caution. The statistical evidence for a spatial dipole in the values of α is found at the 2.3 sigma level. Forthcoming studies with facilities such as ALMA and ESPRESSO should clarify these issues.
Effects of joint stabilizers on proprioception and stability: A systematic review and meta-analysis.
Ghai, Shashank; Driller, Matthew; Ghai, Ishan
2017-05-01
The current review and meta-analysis systematically investigated the effect of joint stabilizers on proprioception, postural stability, and neurological activity. Systematic identification of published literature was performed on online databases; Scopus, PEDro, SportDiscus, and EMBASE, followed by a critical PEDro methodological quality appraisal. Data from the studies were extracted and summarized in a tabular format. Of 2954 records, 50 studies, involving 1443 participants met our inclusion criteria. In the included studies, 60% of studies reported significant enhancements (p 0.05) and 21% of studies reported no effects of joint stabilizers on proprioception and/or postural stability. Meta-analysis of pooled studies demonstrated beneficial effects of joint stabilizers on the knee (95% CI: 0.35°-0.61°) and ankle (at 10: 0.1°-0.65°) joint proprioception, and negligible effects on postural stability (-0.28°-0.19°). The pooled evidence suggests that application of joint stabilizers enhances joint proprioception and stability by not merely altering the mechanical stability of the underlying musculoskeletal structures but by also causing subtle changes in cerebral haemodynamics and musculoskeletal activation. These findings support clinical implications of joint stabilizers as a prophylactic and rehabilitation measure in modern sports and rehabilitation settings. Copyright © 2016 Elsevier Ltd. All rights reserved.
Greenwood, Priscilla E
2016-01-01
This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain...
CAREM-25 Steam Generator Stability Analysis
International Nuclear Information System (INIS)
Rabiti, A.; Delmastro, D.
2003-01-01
In this work the stability of a once-through CAREM-25 steam generator is analyzed.A fix nodes numerical model, that allows the modelling of the liquid, two-phase and superheated steam zones, is implemented.This model was checked against a mobile finite elements model under saturated steam conditions at the channel exit and a good agreement was obtained.Finally the stability of a CAREM steam generator is studied and the range of in let restrictions that a assure the system stability is analyzed
Directory of Open Access Journals (Sweden)
GERMÁN LOBOS
2015-12-01
Full Text Available ABSTRACT The traditional method of net present value (NPV to analyze the economic profitability of an investment (based on a deterministic approach does not adequately represent the implicit risk associated with different but correlated input variables. Using a stochastic simulation approach for evaluating the profitability of blueberry (Vaccinium corymbosum L. production in Chile, the objective of this study is to illustrate the complexity of including risk in economic feasibility analysis when the project is subject to several but correlated risks. The results of the simulation analysis suggest that the non-inclusion of the intratemporal correlation between input variables underestimate the risk associated with investment decisions. The methodological contribution of this study illustrates the complexity of the interrelationships between uncertain variables and their impact on the convenience of carrying out this type of business in Chile. The steps for the analysis of economic viability were: First, adjusted probability distributions for stochastic input variables (SIV were simulated and validated. Second, the random values of SIV were used to calculate random values of variables such as production, revenues, costs, depreciation, taxes and net cash flows. Third, the complete stochastic model was simulated with 10,000 iterations using random values for SIV. This result gave information to estimate the probability distributions of the stochastic output variables (SOV such as the net present value, internal rate of return, value at risk, average cost of production, contribution margin and return on capital. Fourth, the complete stochastic model simulation results were used to analyze alternative scenarios and provide the results to decision makers in the form of probabilities, probability distributions, and for the SOV probabilistic forecasts. The main conclusion shown that this project is a profitable alternative investment in fruit trees in
Rock stability analysis – A case study
Directory of Open Access Journals (Sweden)
Lahmili A.
2018-01-01
Full Text Available The problems of the stability of the mineral-bearing structure ST2 at 560 m of depth in the east zone of Bou-azzer mine disturbs the advance of the exploitation. The geological and structural study based on field observations and the analysis of core drilling shows the presence of altered and fractured diorite surmounted by cobalt mineralization. Based on the empirical methods of Barton (Q-system and Bieniawski (RMR the bed rock is classified as poor quality. The analytical study made it possible to dimension supporting by bow-pieces and bolting. The existence of several types of discontinuities (fault, diaclases and joints has made the realization of numerical simulation by the finite elements method very difficult. These discontinuities create a network of natural fractures which cut out the blocks in various forms likely to be detached or slip into the excavation, thus encouraging the infiltration of water creating pressure on the massif. The classical studies show their limits in practice for installation of supporting because they must take into account the characteristics of discontinuities. Hence a structural analysis of the massif is essential. The cracking survey of ST2 at 560 m of depth in the east zone of Bou-azzer mine at 560m of depth, and their processing by the DIPS software, showed the existence of three main families of discontinuity NW-SE with a dip of 75SW, NS subverticale and NE-SW with a dip of 57NW, and two families of minor joints NW-SE and NE-SW with successive dips of 40SW and 75SE. The analysis of fracturing surveys allowed us to evaluate the risks of falling blocks and the families of discontinuity responsible for them, and to limit the zones presenting a risk of slip and the families responsible for them. The importance of this study is of knowing how and where to put supporting to be opposed to the risk of fall and tilting of the blocks, caused by the network of discontinuities of the massif.
Zheng, Ying; Wong, David Shan-Hill; Wang, Yan-Wei; Fang, Huajing
2014-07-01
In many batch-based industrial manufacturing processes, feedback run-to-run control is used to improve production quality. However, measurements may be expensive and cannot always be performed online. Thus, the measurement delay always exists. The metrology delay will affect the stability and performance of the process. Moreover, since quality measurements are performed offline, delay is not fixed but is stochastic in nature. In this paper, a modeling approach Takagi-Sugeno (T-S) model is presented to handle stochastic metrology delay in both single-product and mixed-product processes. Based on the Markov characteristics of the delay, the membership of the T-S model is derived. Performance indices such as the mean and the variance of the closed-loop output of the exponentially weighted moving average (EWMA) control algorithm can be derived. A steady-state error of the process output always exists, which leads the output deviating from the target. To remove the steady-state error, an algorithm called compensatory EWMA run-to-run (COM-EWMA-RtR) algorithm is proposed. The validity of the T-S model analysis and the efficiency of the proposed COM-EWMA-RtR algorithm are confirmed by simulation.
Stability analysis of impulsive parabolic complex networks
International Nuclear Information System (INIS)
Wang Jinliang; Wu Huaining
2011-01-01
Highlights: → Two impulsive parabolic complex network models are proposed. → The global exponential stability of impulsive parabolic complex networks are considered. → The robust global exponential stability of impulsive parabolic complex networks are considered. - Abstract: In the present paper, two kinds of impulsive parabolic complex networks (IPCNs) are considered. In the first one, all nodes have the same time-varying delay. In the second one, different nodes have different time-varying delays. Using the Lyapunov functional method combined with the inequality techniques, some global exponential stability criteria are derived for the IPCNs. Furthermore, several robust global exponential stability conditions are proposed to take uncertainties in the parameters of the IPCNs into account. Finally, numerical simulations are presented to illustrate the effectiveness of the results obtained here.
A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis
Directory of Open Access Journals (Sweden)
Linda J.S. Allen
2017-05-01
Full Text Available Some mathematical methods for formulation and numerical simulation of stochastic epidemic models are presented. Specifically, models are formulated for continuous-time Markov chains and stochastic differential equations. Some well-known examples are used for illustration such as an SIR epidemic model and a host-vector malaria model. Analytical methods for approximating the probability of a disease outbreak are also discussed. Keywords: Branching process, Continuous-time Markov chain, Minor outbreak, Stochastic differential equation, 2000 MSC: 60H10, 60J28, 92D30
Global stability analysis and control of leptospirosis
Directory of Open Access Journals (Sweden)
Okosun Kazeem Oare
2016-01-01
Full Text Available The aim of this paper is to investigate the effectiveness and cost-effectiveness of leptospirosis control measures, preventive vaccination and treatment of infective humans that may curtail the disease transmission. For this, a mathematical model for the transmission dynamics of the disease that includes preventive, vaccination, treatment of infective vectors and humans control measures are considered. Firstly, the constant control parameters’ case is analyzed, also calculate the basic reproduction number and investigate the existence and stability of equilibria. The threshold condition for disease-free equilibrium is found to be locally asymptotically stable and can only be achieved when the basic reproduction number is less than unity. The model is found to exhibit the existence of multiple endemic equilibria. Furthermore, to assess the relative impact of each of the constant control parameters measures the sensitivity index of the basic reproductive number to the model’s parameters are calculated. In the time-dependent constant control case, Pontryagin’s Maximum Principle is used to derive necessary conditions for the optimal control of the disease. The cost-effectiveness analysis is carried out by first of all using ANOVA to check on the mean costs. Then followed by Incremental Cost-Effectiveness Ratio (ICER for all the possible combinations of the disease control measures. Our results revealed that the most cost-effective strategy for the control of leptospirosis is the combination of the vaccination and treatment of infective livestocks. Though the combinations of all control measures is also effective, however, this strategy is not cost-effective and so too costly. Therefore, more efforts from policy makers on vaccination and treatment of infectives livestocks regime would go a long way to combat the disease epidemic.
Doberkat, Ernst-Erich
2009-01-01
Combining coalgebraic reasoning, stochastic systems and logic, this volume presents the principles of coalgebraic logic from a categorical perspective. Modal logics are also discussed, including probabilistic interpretations and an analysis of Kripke models.
Stochastic nonlinear beam equations
Czech Academy of Sciences Publication Activity Database
Brzezniak, Z.; Maslowski, Bohdan; Seidler, Jan
2005-01-01
Roč. 132, č. 1 (2005), s. 119-149 ISSN 0178-8051 R&D Projects: GA ČR(CZ) GA201/01/1197 Institutional research plan: CEZ:AV0Z10190503 Keywords : stochastic beam equation * stability Subject RIV: BA - General Mathematics Impact factor: 0.896, year: 2005
Stochastic multi-symplectic Runge-Kutta methods for stochastic Hamiltonian PDEs
Zhang, Liying; Ji, Lihai
2018-01-01
In this paper, we consider stochastic Runge-Kutta methods for stochastic Hamiltonian partial differential equations and present some sufficient conditions for multisymplecticity of stochastic Runge-Kutta methods of stochastic Hamiltonian partial differential equations. Particularly, we apply these ideas to stochastic Maxwell equations with multiplicative noise, possessing the stochastic multi-symplectic conservation law and energy conservation law. Theoretical analysis shows that the methods ...
Tang, Fiona H M; Alonso-Marroquin, Fernando; Maggi, Federico
2014-04-15
An approach based on spheropolygons (i.e., the Minkowski sum of a polygon with N vertices and a disk with spheroradius r) is presented to describe the shape of kaolinite aggregates in water and to investigate interparticle collision dynamics. Spheropolygons generated against images of kaolinite aggregates achieved an error between 0.5% and 20% as compared to at least 32% of equivalent spheres. These spheropolygons were used to investigate the probability of collision (Pr[C]) and aggregation (Pr[A]) under the action of gravitational, viscous, contact (visco-elastic), electrostatic and van der Waals forces. In ortho-axial (i.e., frontal) collision, Pr[A] of equivalent spheres was always 1, however, stochastic analysis of collision among spheropolygons showed that Pr[A] decreased asymptotically with N increasing, and decreased further in peri-axial (i.e., tangential) collision. Trajectory analysis showed that not all collisions occurring within the attraction zone of the double layer resulted in aggregation, neither all those occurring outside it led to relative departure. Rather, the relative motion on surface asperities affected the intensity of contact and attractive forces to an extent to substantially control a collision outcome in either instances. Spheropolygons revealed therefore how external shape can influence particle aggregation, and suggested that this is equally important to contact and double layer forces in determining the probability of particle aggregation. Copyright © 2014 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Reza Goudarzi
2014-07-01
Full Text Available Background Hospitals are highly resource-dependent settings, which spend a large proportion of healthcare financial resources. The analysis of hospital efficiency can provide insight into how scarce resources are used to create health values. This study examines the Technical Efficiency (TE of 12 teaching hospitals affiliated with Tehran University of Medical Sciences (TUMS between 1999 and 2011. Methods The Stochastic Frontier Analysis (SFA method was applied to estimate the efficiency of TUMS hospitals. A best function, referred to as output and input parameters, was calculated for the hospitals. Number of medical doctors, nurses, and other personnel, active beds, and outpatient admissions were considered as the input variables and number of inpatient admissions as an output variable. Results The mean level of TE was 59% (ranging from 22 to 81%. During the study period the efficiency increased from 61 to 71%. Outpatient admission, other personnel and medical doctors significantly and positively affected the production (P< 0.05. Concerning the Constant Return to Scale (CRS, an optimal production scale was found, implying that the productions of the hospitals were approximately constant. Conclusion Findings of this study show a remarkable waste of resources in the TUMS hospital during the decade considered. This warrants policy-makers and top management in TUMS to consider steps to improve the financial management of the university hospitals.
Modeling and Analysis of Cellular Networks using Stochastic Geometry: A Tutorial
Elsawy, Hesham
2016-11-03
This paper presents a tutorial on stochastic geometry (SG) based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. The paper starts by modeling and analyzing the baseband interference in a baseline single-tier downlink cellular network with single antenna base stations and universal frequency reuse. Then, it characterizes signal-to-interference-plus-noise-ratio (SINR) and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and transmission rate analysis is presented. Although the main focus of the paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. The paper then extends the unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. To this end, the paper highlights the state-of-the- art research and points out future research directions.
Stability Analysis for HIFiRE Experiments
Li, Fei; Choudhari, Meelan M.; Chang, Chau-Lyan; White, Jeffery A.; Kimmel, Roger; Adamczak, David; Borg, Matthew; Stanfield, Scott; Smith, Mark S.
2012-01-01
The HIFiRE-1 flight experiment provided a valuable database pertaining to boundary layer transition over a 7-degree half-angle, circular cone model from supersonic to hypersonic Mach numbers, and a range of Reynolds numbers and angles of attack. This paper reports selected findings from the ongoing computational analysis of the measured in-flight transition behavior. Transition during the ascent phase at nearly zero degree angle of attack is dominated by second mode instabilities except in the vicinity of the cone meridian where a roughness element was placed midway along the length of the cone. The growth of first mode instabilities is found to be weak at all trajectory points analyzed from the ascent phase. For times less than approximately 18.5 seconds into the flight, the peak amplification ratio for second mode disturbances is sufficiently small because of the lower Mach numbers at earlier times, so that the transition behavior inferred from the measurements is attributed to an unknown physical mechanism, potentially related to step discontinuities in surface height near the locations of a change in the surface material. Based on the time histories of temperature and/or heat flux at transducer locations within the aft portion of the cone, the onset of transition correlated with a linear N-factor, based on parabolized stability equations, of approximately 13.5. Due to the large angles of attack during the re-entry phase, crossflow instability may play a significant role in transition. Computations also indicate the presence of pronounced crossflow separation over a significant portion of the trajectory segment that is relevant to transition analysis. The transition behavior during this re-entry segment of HIFiRE-1 flight shares some common features with the predicted transition front along the elliptic cone shaped HIFiRE-5 flight article, which was designed to provide hypersonic transition data for a fully 3D geometric configuration. To compare and contrast the
Energy Technology Data Exchange (ETDEWEB)
Tartakovsky, Daniel
2013-08-30
We developed new CDF and PDF methods for solving non-linear stochastic hyperbolic equations that does not rely on linearization approximations and allows for rigorous formulation of the boundary conditions.
Analysis of novel stochastic switched SILI epidemic models with continuous and impulsive control
Gao, Shujing; Zhong, Deming; Zhang, Yan
2018-04-01
In this paper, we establish two new stochastic switched epidemic models with continuous and impulsive control. The stochastic perturbations are considered for the natural death rate in each equation of the models. Firstly, a stochastic switched SILI model with continuous control schemes is investigated. By using Lyapunov-Razumikhin method, the sufficient conditions for extinction in mean are established. Our result shows that the disease could be die out theoretically if threshold value R is less than one, regardless of whether the disease-free solutions of the corresponding subsystems are stable or unstable. Then, a stochastic switched SILI model with continuous control schemes and pulse vaccination is studied. The threshold value R is derived. The global attractivity of the model is also obtained. At last, numerical simulations are carried out to support our results.
Stability analysis of spacecraft power systems
Halpin, S. M.; Grigsby, L. L.; Sheble, G. B.; Nelms, R. M.
1990-01-01
The problems in applying standard electric utility models, analyses, and algorithms to the study of the stability of spacecraft power conditioning and distribution systems are discussed. Both single-phase and three-phase systems are considered. Of particular concern are the load and generator models that are used in terrestrial power system studies, as well as the standard assumptions of load and topological balance that lead to the use of the positive sequence network. The standard assumptions regarding relative speeds of subsystem dynamic responses that are made in the classical transient stability algorithm, which forms the backbone of utility-based studies, are examined. The applicability of these assumptions to a spacecraft power system stability study is discussed in detail. In addition to the classical indirect method, the applicability of Liapunov's direct methods to the stability determination of spacecraft power systems is discussed. It is pointed out that while the proposed method uses a solution process similar to the classical algorithm, the models used for the sources, loads, and networks are, in general, more accurate. Some preliminary results are given for a linear-graph, state-variable-based modeling approach to the study of the stability of space-based power distribution networks.
Chuanyi, Wang; Xiaohong, Lv; Shikui, Zhao
2016-01-01
This paper applies data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to explore the relative efficiency of China's research universities of science and technology. According to the finding, when talent training is the only output, the efficiency of research universities of science and technology is far lower than that of…
Yildirim, Necmettin; Kazanci, Caner
2011-01-01
A brief introduction to mathematical modeling of biochemical regulatory reaction networks is presented. Both deterministic and stochastic modeling techniques are covered with examples from enzyme kinetics, coupled reaction networks with oscillatory dynamics and bistability. The Yildirim-Mackey model for lactose operon is used as an example to discuss and show how deterministic and stochastic methods can be used to investigate various aspects of this bacterial circuit. © 2011 Elsevier Inc. All rights reserved.
Obour Agyekum Kwame O-B; Maxwell Oppong Afriyie; Paul Oswald Kwasi Anane; Affum Emmanuel Ampoma; Matthew Seddoh Akatey
2017-01-01
We explore the dependency of MIMO performance on azimuthal spread (AS) and elevation spread (ES) using correlated-based stochastic models (CBSMs). We represent the transmitter as uniform rectangular array (URA), and derive an analytical function for spatial correlation, in terms of maximum power when phase gradient of the incident wave follows a Student’s t-distribution. We model the correlated-based stochastic MIMO system to investigate the usefulness of the analytical function, under the co...
A linear stability analysis of supercritical water reactors, (1). Thermal-hydraulic stability
International Nuclear Information System (INIS)
Tin Tin Yi; Koshizuka, Seiichi; Oka, Yoshiaki
2004-01-01
This paper summarizes the analysis results of the thermal-hydraulic stability of a high-temperature reactor cooled and moderated by supercritical-pressure light water (SCLWR-H). A linear stability analysis code in the frequency domain was developed to study the thermal-hydraulic stability of SCLWR-H at constant supercritical pressure. The analysis method is based on linearization by perturbation of numerically-discretized one-dimensional single-channel single-phase conservation equations. The effect of water rods on stability is considered. The thermal-hydraulic stability of SCLWR-H for full-power and partial-power normal operations was investigated by frequency domain method. Our analysis reveals that though SCLWR-H has low coolant flow rate and large density change in the core, the thermal-hydraulic stability can be maintained both at normal operation and during power raising phase of constant pressure startup by applying an orifice pressure drop coefficient an the inlet of the fuel assemblies. A parametric study was also carried out to determine the parameters affecting the stability. (author)
Stability analysis of automobile driver steering control
Allen, R. W.
1981-01-01
In steering an automobile, the driver must basically control the direction of the car's trajectory (heading angle) and the lateral deviation of the car relative to a delineated pathway. A previously published linear control model of driver steering behavior which is analyzed from a stability point of view is considered. A simple approximate expression for a stability parameter, phase margin, is derived in terms of various driver and vehicle control parameters, and boundaries for stability are discussed. A field test study is reviewed that includes the measurement of driver steering control parameters. Phase margins derived for a range of vehicle characteristics are found to be generally consistent with known adaptive properties of the human operator. The implications of these results are discussed in terms of driver adaptive behavior.
Linear stability analysis of supersonic axisymmetric jets
Directory of Open Access Journals (Sweden)
Zhenhua Wan
2014-01-01
Full Text Available Stabilities of supersonic jets are examined with different velocities, momentum thicknesses, and core temperatures. Amplification rates of instability waves at inlet are evaluated by linear stability theory (LST. It is found that increased velocity and core temperature would increase amplification rates substantially and such influence varies for different azimuthal wavenumbers. The most unstable modes in thin momentum thickness cases usually have higher frequencies and azimuthal wavenumbers. Mode switching is observed for low azimuthal wavenumbers, but it appears merely in high velocity cases. In addition, the results provided by linear parabolized stability equations show that the mean-flow divergence affects the spatial evolution of instability waves greatly. The most amplified instability waves globally are sometimes found to be different from that given by LST.
Stability analysis of artificial synthetic overweight elements
International Nuclear Information System (INIS)
Zhou Jian
1990-01-01
Stability of artificial synthetic overweight elements has been analysed theoretically using a diagram of nuclear stability. It is indicated that overweight nucleus can be synthesized only when a certain amount of neutrons participate simultaneously in the synthesis. The maximum number of protons in overweight elements is 1002. The proton number of 'extreme overweight' elements of which the neutron star is possibly composed is in the range from 326 to 1002. It is expected that the mass number of the stable overweight elements with proton number 114 is in the range from 299 to 315
An analysis for crack layer stability
Sehanobish, K.; Botsis, J.; Moet, A.; Chudnovsky, A.
1986-01-01
The problem of uncontrolled crack propagation and crack arrest is considered with respect to crack layer (CL) translational stability. CL propagation is determined by the difference between the energy release rate and the amount of energy required for material transformation, and necessary and sufficient conditions for CL instability are derived. CL propagation in polystyrene is studied for two cases. For the case of remotely applied fixed load fatigue, the sufficient condition of instability is shown to be met before the necessary condition, and the necessary condition controls the stability. For the fixed displacement case, neither of the instability conditions are met, and CL propagation remains stable, resulting in crack arrest.
Directory of Open Access Journals (Sweden)
Reza Goudarzi
2014-01-01
Full Text Available Background: Full consideration of the performance and efficiency of hospital costs necessitates the application of economic analysis techniques. The aim of this study was to assess the efficiency of hospitals in Kermanshah University of Medical Sciences through Stochastic Frontier Analysis (SFA method. Methods: The performance of Kermanshah hospitals (n=7 was assessed and analyzed by Stochastic Frontier Analysis (SFA method during 2005-2011. Inpatient admission was considered as the output variable, while the number of medical doctors, nursing staff other personnel, active beds and outpatient admission were considered as the input variables. Frontier 4.1 software was used to analyze the data. Results: Based on the results of performance evaluation using Cobb-Douglas production function, the mean efficiency score of the hospitals in the SFA method was 0.63. Also, the efficiency capacity in these hospitals could be promoted up to 37 percent. Conclusion: Based on the results of Stochastic Frontier Analysis, downsizing the manpower in hospitals plays a major role in reducing hospital costs and improving their performance. Finally, it is necessary to investigate the effect of factors such as quality of service and patient satisfaction on hospital performance.
Energy Technology Data Exchange (ETDEWEB)
Carvalho, Pedro, E-mail: pedrocarv@coc.ufrj.br [Computational Modelling in Engineering and Geophysics Laboratory (LAMEMO), Department of Civil Engineering, COPPE, Federal University of Rio de Janeiro, Av. Pedro Calmon - Ilha do Fundão, 21941-596 Rio de Janeiro (Brazil); Center for Urban and Regional Systems (CESUR), CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Marques, Rui Cunha, E-mail: pedro.c.carvalho@tecnico.ulisboa.pt [Center for Urban and Regional Systems (CESUR), CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)
2016-02-15
This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. - Highlights: • This study aims to search for economies of size and scope in the water sector; • The usefulness of the application of Bayesian methods is highlighted; • Important economies of output density, economies of size, economies of vertical integration and economies of scope are found.
Stochastic Multicriteria Acceptability Analysis for Evaluation of Combined Heat and Power Units
Directory of Open Access Journals (Sweden)
Haichao Wang
2014-12-01
Full Text Available Combined heat and power (CHP is a promising technology that can contribute to energy efficiency and environmental protection. More CHP-based energy systems are planned for the future. This makes the evaluation and selection of CHP systems very important. In this paper, 16 CHP units representing different technologies are taken into account for multicriteria evaluation with respect to the end users’ requirements. These CHP technologies cover a wide range of power outputs and fuel types. They are evaluated from the energy, economy and environment (3E points of view, specifically including the criteria of efficiency, investment cost, electricity cost, heat cost, CO2 production and footprint. Uncertainties and imprecision are common both in criteria measurements and weights, therefore the stochastic multicriteria acceptability analysis (SMAA model is used in aiding this decision making problem. These uncertainties are treated better using a probability distribution function and Monte Carlo simulation in the model. Moreover, the idea of “feasible weight space (FWS” which represents the union of all preference information from decision makers (DMs is proposed. A complementary judgment matrix (CJM is introduced to determine the FWS. It can be found that the idea of FWS plus CJM is well compatible with SMAA and thus make the evaluation reliable.
Energy Technology Data Exchange (ETDEWEB)
Araujo, Leonardo Rodrigues de [Instituto Federal do Espirito Santo, Vitoria, ES (Brazil)], E-mail: leoaraujo@ifes.edu.br; Donatelli, Joao Luiz Marcon [Universidade Federal do Espirito Santo (UFES), Vitoria, ES (Brazil)], E-mail: joaoluiz@npd.ufes.br; Silva, Edmar Alino da Cruz [Instituto Tecnologico de Aeronautica (ITA/CTA), Sao Jose dos Campos, SP (Brazil); Azevedo, Joao Luiz F. [Instituto de Aeronautica e Espaco (CTA/IAE/ALA), Sao Jose dos Campos, SP (Brazil)
2010-07-01
Thermal systems are essential in facilities such as thermoelectric plants, cogeneration plants, refrigeration systems and air conditioning, among others, in which much of the energy consumed by humanity is processed. In a world with finite natural sources of fuels and growing energy demand, issues related with thermal system design, such as cost estimative, design complexity, environmental protection and optimization are becoming increasingly important. Therefore the need to understand the mechanisms that degrade energy, improve energy sources use, reduce environmental impacts and also reduce project, operation and maintenance costs. In recent years, a consistent development of procedures and techniques for computational design of thermal systems has occurred. In this context, the fundamental objective of this study is a performance comparative analysis of structural and parametric optimization of a cogeneration system using stochastic methods: genetic algorithm and simulated annealing. This research work uses a superstructure, modelled in a process simulator, IPSEpro of SimTech, in which the appropriate design case studied options are included. Accordingly, the cogeneration system optimal configuration is determined as a consequence of the optimization process, restricted within the configuration options included in the superstructure. The optimization routines are written in MsExcel Visual Basic, in order to work perfectly coupled to the simulator process. At the end of the optimization process, the system optimal configuration, given the characteristics of each specific problem, should be defined. (author)
Horng, Shih-Cheng; Lin, Shin-Yeu; Lee, Loo Hay; Chen, Chun-Hung
2013-10-01
A three-phase memetic algorithm (MA) is proposed to find a suboptimal solution for real-time combinatorial stochastic simulation optimization (CSSO) problems with large discrete solution space. In phase 1, a genetic algorithm assisted by an offline global surrogate model is applied to find N good diversified solutions. In phase 2, a probabilistic local search method integrated with an online surrogate model is used to search for the approximate corresponding local optimum of each of the N solutions resulted from phase 1. In phase 3, the optimal computing budget allocation technique is employed to simulate and identify the best solution among the N local optima from phase 2. The proposed MA is applied to an assemble-to-order problem, which is a real-world CSSO problem. Extensive simulations were performed to demonstrate its superior performance, and results showed that the obtained solution is within 1% of the true optimum with a probability of 99%. We also provide a rigorous analysis to evaluate the performance of the proposed MA.
Weiss, S. B.; Flint, A. L.; Flint, L. E.
2012-12-01
Numerous climate futures are now available from downscaled global climate models. The translation of monthly precipitation and temperatures into hydrologically and ecologically meaningful outputs for managers and planners is the next frontier. The Basin Characterization Model (BCM) is used to generate time series of annual runoff, recharge, and climatic water deficit (CWD) at the scale of small planning watersheds in the San Francisco Bay Area. These watersheds differ in climate, soils, bedrock permeability, and human use. We examine the occurrence of droughts in historical and projected climate records, and develop metrics based on multi-year running averages. Projected droughts are compared with historical droughts (1976-77, 1987-1992, and 2007-2009), providing analogs and benchmarks within recent experience. Bedrock permeability affects runoff to recharge ratios, and soils produce fine-scale variability in storage. Rising temperatures and potential evapotranspiration drive higher CWD even when average annual precipitation increases, leading to greater stress on terrestrial and aquatic ecosystems. Quantifying probabilities of drought stress by using time series analysis, extreme value statistics, and stochastic simulation defines risks at fine spatial scales relevant to water and land managers, and can be incorporated into existing water supply and flood management frameworks.
Directory of Open Access Journals (Sweden)
Sheeraz Ahmed
2016-01-01
Full Text Available Reliability is a key factor for application-oriented Underwater Sensor Networks (UWSNs which are utilized for gaining certain objectives and a demand always exists for efficient data routing mechanisms. Cooperative routing is a promising technique which utilizes the broadcast feature of wireless medium and forwards data with cooperation using sensor nodes as relays. Here, we present a cooperation-based routing protocol for underwater networks to enhance their performance called Stochastic Performance Analysis with Reliability and Cooperation (SPARCO. Cooperative communication is explored in order to design an energy-efficient routing scheme for UWSNs. Each node of the network is assumed to be consisting of a single omnidirectional antenna and multiple nodes cooperatively forward their transmissions taking advantage of spatial diversity to reduce energy consumption. Both multihop and single-hop schemes are exploited which contribute to lowering of path-losses present in the channels connecting nodes and forwarding of data. Simulations demonstrate that SPARCO protocol functions better regarding end-to-end delay, network lifetime, and energy consumption comparative to noncooperative routing protocol—improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (iAMCTD. The performance is also compared with three cooperation-based routing protocols for UWSN: Cognitive Cooperation (Cog-Coop, Cooperative Depth-Based Routing (CoDBR, and Cooperative Partner Node Selection Criteria for Cooperative Routing (Coop Re and dth.
International Nuclear Information System (INIS)
See, Kok Fong; Coelli, Tim
2013-01-01
This study examines the total factor productivity (TFP) growth of the Malaysian electricity generation industry over the 1998 to 2005 period. The stochastic frontier analysis (SFA) approach is used to measure TFP change and decompose TFP growth into efficiency change and technical progress. We find that it achieved average annual TFP growth of 2.34%, with technical change contributing the most to the TFP growth over the eight year period. We hence hypothesise that the new power plants with their newer capital-embodied technologies commencing during the sample period are likely to be the main reason for this strong technical change. In addition, it is also noted that this estimate for the Malaysian electricity generation industry is larger than the estimate obtained for the electricity sector as a whole, where we obtain 1.34% per year for a comparable period. -- Highlights: •This is the first empirical study that examines the TFP growth of the Malaysian electricity generation industry using the SFA method. •An average annual TFP change of the Malaysian electricity generation industry over eight years (1998-2005) has been achieved at 2.34% per year. •The technical progress contributing the most to the TFP growth and technical efficiency change and scale change making small contributions over the sample period
Analysis and development of stochastic multigrid methods in lattice field theory
International Nuclear Information System (INIS)
Grabenstein, M.
1994-01-01
We study the relation between the dynamical critical behavior and the kinematics of stochastic multigrid algorithms. The scale dependence of acceptance rates for nonlocal Metropolis updates is analyzed with the help of an approximation formula. A quantitative study of the kinematics of multigrid algorithms in several interacting models is performed. We find that for a critical model with Hamiltonian H(Φ) absence of critical slowing down can only be expected if the expansion of (H(Φ+ψ)) in terms of the shift ψ contains no relevant term (mass term). The predictions of this rule was verified in a multigrid Monte Carlo simulation of the Sine Gordon model in two dimensions. Our analysis can serve as a guideline for the development of new algorithms: We propose a new multigrid method for nonabelian lattice gauge theory, the time slice blocking. For SU(2) gauge fields in two dimensions, critical slowing down is almost completely eliminated by this method, in accordance with the theoretical prediction. The generalization of the time slice blocking to SU(2) in four dimensions is investigated analytically and by numerical simulations. Compared to two dimensions, the local disorder in the four dimensional gauge field leads to kinematical problems. (orig.)
Carvalho, Pedro; Marques, Rui Cunha
2016-02-15
This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. Copyright © 2015 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Carvalho, Pedro; Marques, Rui Cunha
2016-01-01
This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. - Highlights: • This study aims to search for economies of size and scope in the water sector; • The usefulness of the application of Bayesian methods is highlighted; • Important economies of output density, economies of size, economies of vertical integration and economies of scope are found.
Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis.
Wang, Gang; Li, Yuanyuan; Zou, Xiufen
2017-01-01
Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules. Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson's correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases. To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state. The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.
Stochastic analysis of 1D and 2D surface topography of x-ray mirrors
Tyurina, Anastasia Y.; Tyurin, Yury N.; Yashchuk, Valeriy V.
2017-08-01
The design and evaluation of the expected performance of new optical systems requires sophisticated and reliable information about the surface topography for planned optical elements before they are fabricated. The problem is especially complex in the case of x-ray optics, particularly for the X-ray Surveyor under development and other missions. Modern x-ray source facilities are reliant upon the availability of optics with unprecedented quality (surface slope accuracy quality optics. The uniqueness of the optics and limited number of proficient vendors makes the fabrication extremely time consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. We discuss improvements in metrology efficiency via comprehensive statistical analysis of a compact volume of metrology data. The data is considered stochastic and a new statistical model called Invertible Time Invariant Linear Filter (InTILF) is developed now for 2D surface profiles to provide compact description of the 2D data additionally to 1D data treated so far. The model captures faint patterns in the data and serves as a quality metric and feedback to polishing processes, avoiding high resolution metrology measurements over the entire optical surface. The modeling, implemented in our Beatmark software, allows simulating metrology data for optics made by the same vendor and technology. The forecast data is vital for reliable specification for optical fabrication, to be exactly adequate for the required system performance.
Methods of stability analysis in nonlinear mechanics
International Nuclear Information System (INIS)
Warnock, R.L.; Ruth, R.D.; Gabella, W.; Ecklund, K.
1989-01-01
We review our recent work on methods to study stability in nonlinear mechanics, especially for the problems of particle accelerators, and compare our ideals to those of other authors. We emphasize methods that (1) show promise as practical design tools, (2) are effective when the nonlinearity is large, and (3) have a strong theoretical basis. 24 refs., 2 figs., 2 tabs
Genotypic stability and clustering analysis of confectionery ...
African Journals Online (AJOL)
Nine groundnut genotypes were evaluated in terminal moisture-stress areas of northeastern Ethiopia during 2005 and 2006 cropping seasons with the objective of analyzing genotypic stability and clustering of confectionery groundnut for seed and protein yield. The genotypes were evaluated on a plot size of 15 m2 at Kobo ...
Kayanan, Manickavasagar; Wijekoon, Pushpakanthie
2017-01-01
In this article, the analysis of misspecification was extended to the recently introduced stochastic restricted biased estimators when multicollinearity exists among the explanatory variables. The Stochastic Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted Principal Component Regression Estimator (SRPCR), Stochastic R...
Angle Stability Analysis for Voltage-Controlled Converters
DEFF Research Database (Denmark)
Lin, Hengwei; Jia, Chenxi; Guerrero, Josep M.
2017-01-01
a criterion to analyze the quasi-steady angle stability and the direct current (DC) side stability for VSCs. The operating limit and the angle instability mechanism are revealed, which is generally applicable to the voltage-controlled converters. During the analysis, the influence of the parameters on angle...... stability is studied. Further, the difference on instability mechanism between power electronic converters and synchronous generators are explained in detail. Finally, experiment results with corrective actions verify the analysis.......Power electronics based voltage source converters (VSCs) keep increasing in modern electrical systems. As a branch of stability problems, angle stability is significant for an electrical system. Based on small disturbance analysis and time scale decomposition perspective, this paper proposes...
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Indian Academy of Sciences (India)
IAS Admin
V S Borkar is the Institute. Chair Professor of. Electrical Engineering at. IIT Bombay. His research interests are stochastic optimization, theory, algorithms and applica- tions. 1 'Markov Chain Monte Carlo' is another one (see [1]), not to mention schemes that combine both. Stochastic approximation is one of the unsung.
ANALYSIS OF THE TRANSIENT STABILITY LIMIT OF NIGERIA'S ...
African Journals Online (AJOL)
user
network engineers have to devise methodologies based on the dynamic stability analysis. This motivates the development of power system transient stability model presented herein. The developed model is thus applied to a specimen of the Nigeria's transmission power system, i.e. the Ikeja-West Sub-network. This choice ...
Performance and stability analysis of a photovoltaic power system
Merrill, W. C.; Blaha, R. J.; Pickrell, R. L.
1978-01-01
The performance and stability characteristics of a 10 kVA photovoltaic power system are studied using linear Bode analysis and a nonlinear analog simulation. Power conversion efficiencies, system stability, and system transient performance results are given for system operation at various levels of solar insolation. Additionally, system operation and the modeling of system components for the purpose of computer simulation are described.
Mathematical modelling and linear stability analysis of laser fusion cutting
International Nuclear Information System (INIS)
Hermanns, Torsten; Schulz, Wolfgang; Vossen, Georg; Thombansen, Ulrich
2016-01-01
A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process’ amount of dynamic behavior.
Linear and nonlinear stability analysis, associated to experimental fast reactors
International Nuclear Information System (INIS)
Amorim, E.S. do; Moura Neto, C. de; Rosa, M.A.P.
1980-07-01
Phenomena associated to the physics of fast neutrons were analysed by linear and nonlinear Kinetics with arbitrary feedback. The theoretical foundations of linear kinetics and transfer functions aiming at the analysis of fast reactors stability, are established. These stability conditions were analitically proposed and investigated by digital and analogic programs. (E.G.) [pt
Genotype x environment interaction and stability analysis for yield ...
African Journals Online (AJOL)
etc
2015-05-06
May 6, 2015 ... Various stability indices were used to assess stability and genotype by environment performances. Combined analysis of variance (ANOVA) for yield .... Mean grain yield (kg/ha) of 17 Kabuli-type chickpea genotypes grown at five locations in Ethiopia. ... Performance trials have to be conducted in multiple.
Riginos, Corinna; Porensky, Lauren M; Veblen, Kari E; Young, Truman P
2018-03-01
Rainfall and herbivory are fundamental drivers of grassland plant dynamics, yet few studies have examined long-term interactions between these factors in an experimental setting. Understanding such interactions is important, as rainfall is becoming increasingly erratic and native wild herbivores are being replaced by livestock. Livestock grazing and episodic low rainfall are thought to interact, leading to greater community change than either factor alone. We examined patterns of change and stability in herbaceous community composition through four dry periods, or droughts, over 15 years of the Kenya Long-term Exclosure Experiment (KLEE), which consists of six different combinations of cattle, native wild herbivores (e.g., zebras, gazelles), and mega-herbivores (giraffes, elephants). We used principal response curves to analyze the trajectory of change in each herbivore treatment relative to a common initial community and asked how droughts contributed to community change in these treatments. We examined three measures of stability (resistance, variability, and turnover) that correspond to different temporal scales and found that each had a different response to grazing. Treatments that included both cattle and wild herbivores had higher resistance (less net change over 15 years) but were more variable on shorter time scales; in contrast, the more lightly grazed treatments (no herbivores or wild herbivores only) showed lower resistance due to the accumulation of consistent, linear, short-term change. Community change was greatest during and immediately after droughts in all herbivore treatments. But, while drought contributed to directional change in the less grazed treatments, it contributed to both higher variability and resistance in the more heavily grazed treatments. Much of the community change in lightly grazed treatments (especially after droughts) was due to substantial increases in cover of the palatable grass Brachiaria lachnantha. These results
Stability analysis for downflow in heated channels
International Nuclear Information System (INIS)
Sampaio, P.A.B. de.
1985-01-01
Stability and flow distribution are analysed for downflow in heated channels. It is shown that at low flow rates instabilities associated with the buoyancy forces may appear. A computer code in FORTRAN language to determine downflow distribution among n heated channels is presented. The model used to calculate downflow distribution and the onset of instability is compared with experiments performed in a test section with two parallel channels. (Author) [pt
Stability Analysis Method of Parallel Inverter
Li, Jun; Chen, Jie; Xue, Yaru; Qiu, Ruichang; Liu, Zhigang
2017-01-01
In order to further provide theoretical support for the stability of an auxiliary inverter parallel system, a new model which covers most of control parameters needs to be established. However, the ability of the small-signal model established by the traditional method is extremely limited, so this paper proposes a new small-signal modeling method for the parallel system. The new small-signal model not only can analyze the influence of the droop parameters on the system performance, but also ...
Aerodynamic analysis of seamless horizontal stabilizer
Nithya, S.; Kanimozhi, S.
2017-05-01
This project presents an investigative view into the concept of seamless aeroelastic wing and hingeless flexible trailing edge. Wings are designed to provide maximum lift and minimal drag and weight. But with conventional wings where rivets are used and the control surfaces are separately hinged, parasite drag comes into play. This project is about analysing a smooth seamless wing with hinge-less flexible trailing edge. This type of wing reduces the drag considerably and the hinge-less trailing edge leads to a minimal control demand and reduces the noise produced when the aircraft comes for landing. Seamless aeroelastic wing will function as an integrated one piece lifting and control surface. It has been designed to enhance a desirable wing camber for control by deflecting a hinge-less flexible trailing edge part instead of a traditional hinged control surface. This kind of flexible wing can be achieved either by a curved beam and disc actuation mechanism or by piezo-electric materials, whose shape change can be achieved by electricity. The intent of this project is to analyze the effects of introducing the concept of Seamless Wing to the horizontal stabilizer. While the removal of rivets and serrations that hinge the elevators to the stabilizer reduces the overall drag by a reasonable value, the overall concept of a control surface-less stabilizer where the maneuvers are done by deflecting the trailing edge offers better maneuverability.
Stochastic and sensitivity analysis of shape error of inflatable antenna reflectors
San, Bingbing; Yang, Qingshan; Yin, Liwei
2017-03-01
Inflatable antennas are promising candidates to realize future satellite communications and space observations since they are lightweight, low-cost and small-packaged-volume. However, due to their high flexibility, inflatable reflectors are difficult to manufacture accurately, which may result in undesirable shape errors, and thus affect their performance negatively. In this paper, the stochastic characteristics of shape errors induced during manufacturing process are investigated using Latin hypercube sampling coupled with manufacture simulations. Four main random error sources are involved, including errors in membrane thickness, errors in elastic modulus of membrane, boundary deviations and pressure variations. Using regression and correlation analysis, a global sensitivity study is conducted to rank the importance of these error sources. This global sensitivity analysis is novel in that it can take into account the random variation and the interaction between error sources. Analyses are parametrically carried out with various focal-length-to-diameter ratios (F/D) and aperture sizes (D) of reflectors to investigate their effects on significance ranking of error sources. The research reveals that RMS (Root Mean Square) of shape error is a random quantity with an exponent probability distribution and features great dispersion; with the increase of F/D and D, both mean value and standard deviation of shape errors are increased; in the proposed range, the significance ranking of error sources is independent of F/D and D; boundary deviation imposes the greatest effect with a much higher weight than the others; pressure variation ranks the second; error in thickness and elastic modulus of membrane ranks the last with very close sensitivities to pressure variation. Finally, suggestions are given for the control of the shape accuracy of reflectors and allowable values of error sources are proposed from the perspective of reliability.
Energy Technology Data Exchange (ETDEWEB)
Webster, Clayton; Tempone, Raul (Florida State University, Tallahassee, FL); Nobile, Fabio (Politecnico di Milano, Italy)
2007-12-01
This work describes the convergence analysis of a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the solution of partial differential equations with random coefficients and forcing terms (input data of the model). To compute solution statistics, the sparse grid stochastic collocation method uses approximate solutions, produced here by finite elements, corresponding to a deterministic set of points in the random input space. This naturally requires solving uncoupled deterministic problems and, as such, the derived strong error estimates for the fully discrete solution are used to compare the computational efficiency of the proposed method with the Monte Carlo method. Numerical examples illustrate the theoretical results and are used to compare this approach with several others, including the standard Monte Carlo.
Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O
2016-06-01
Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.
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.
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.
Advances in power system modelling, control and stability analysis
Milano, Federico
2016-01-01
Advances in Power System Modelling, Control and Stability Analysis captures the variety of new methodologies and technologies that are changing the way modern electric power systems are modelled, simulated and operated.
SPECTRAL ANALYSIS OF GAS BEARING SYSTEMS FOR STABILITY STUDIES
latter case is given in terms of a herringbone-grooved journal bearing . This method of stability analysis is applicable to both thrust and journal bearings for both whirl and pneumatic-hammer instabilities.
Dynamics, stability analysis and quantization of β-Fermi–Pasta ...
Indian Academy of Sciences (India)
- action potential among ... linear and nonlinear forces and these give rise to a set of nonlinear coupled equations whose analytic ... Further, they also carried out stability analysis for these q-breathers by employing Floquet method [20]. Here ...
Directory of Open Access Journals (Sweden)
E. Chumak
2015-04-01
Full Text Available The author substantiates that only methodological training systems of mathematical disciplines with implementation of information and communication technologies (ICT can meet the requirements of modern educational paradigm and make possible to increase the educational efficiency. Due to this fact, the necessity of developing the methodology of theory of probability and stochastic processes computer-based learning for pre-service engineers is underlined in the paper. The results of the experimental study for analysis of the efficiency of methodological system of theory of probability and stochastic processes computer-based learning for pre-service engineers are shown. The analysis includes three main stages: ascertaining, searching and forming. The key criteria of the efficiency of designed methodological system are the level of probabilistic and stochastic skills of students and their learning motivation. The effect of implementing the methodological system of probability theory and stochastic processes computer-based learning on the level of students’ IT literacy is shown in the paper. The expanding of the range of objectives of ICT applying by students is described by author. The level of formation of students’ learning motivation on the ascertaining and forming stages of the experiment is analyzed. The level of intrinsic learning motivation for pre-service engineers is defined on these stages of the experiment. For this purpose, the methodology of testing the students’ learning motivation in the chosen specialty is presented in the paper. The increasing of intrinsic learning motivation of the experimental group students (E group against the control group students (C group is demonstrated.
Adaptive stochastic disturbance accommodating control
George, Jemin; Singla, Puneet; Crassidis, John L.
2011-02-01
This article presents a Kalman filter based adaptive disturbance accommodating stochastic control scheme for linear uncertain systems to minimise the adverse effects of both model uncertainties and external disturbances. Instead of dealing with system uncertainties and external disturbances separately, the disturbance accommodating control scheme lumps the overall effects of these errors in a to-be-determined model-error vector and then utilises a Kalman filter in the feedback loop for simultaneously estimating the system states and the model-error vector from noisy measurements. Since the model-error dynamics is unknown, the process noise covariance associated with the model-error dynamics is used to empirically tune the Kalman filter to yield accurate estimates. A rigorous stochastic stability analysis reveals a lower bound requirement on the assumed system process noise covariance to ensure the stability of the controlled system when the nominal control action on the true plant is unstable. An adaptive law is synthesised for the selection of stabilising system process noise covariance. Simulation results are presented where the proposed control scheme is implemented on a two degree-of-freedom helicopter.
Structured Modeling and Analysis of Stochastic Epidemics with Immigration and Demographic Effects.
Directory of Open Access Journals (Sweden)
Hendrik Baumann
Full Text Available Stochastic epidemics with open populations of variable population sizes are considered where due to immigration and demographic effects the epidemic does not eventually die out forever. The underlying stochastic processes are ergodic multi-dimensional continuous-time Markov chains that possess unique equilibrium probability distributions. Modeling these epidemics as level-dependent quasi-birth-and-death processes enables efficient computations of the equilibrium distributions by matrix-analytic methods. Numerical examples for specific parameter sets are provided, which demonstrates that this approach is particularly well-suited for studying the impact of varying rates for immigration, births, deaths, infection, recovery from infection, and loss of immunity.
Nonlinear analysis of the cooperation of strategic alliances through stochastic catastrophe theory
Xu, Yan; Hu, Bin; Wu, Jiang; Zhang, Jianhua
2014-04-01
The excitation intervention of strategic alliance may change with the changes in the parameters of circumstance (e.g., external alliance tasks). As a result, the stable cooperation between members may suffer a complete unplanned betrayal at last. However, current perspectives on strategic alliances cannot adequately explain this transition mechanism. This study is a first attempt to analyze this nonlinear phenomenon through stochastic catastrophe theory (SCT). A stochastic dynamics model is constructed based on the cooperation of strategic alliance from the perspective of evolutionary game theory. SCT explains the discontinuous changes caused by the changes in environmental parameters. Theoretically, we identify conditions where catastrophe can occur in the cooperation of alliance members.
Stochastic Frontier Analysis of Maize Farmers in Azad Jammu and Kashmir, Pakistan
International Nuclear Information System (INIS)
Zhang, F.; Wang, Y.; Yu, H.; Zhu, K.; Zhang, Z.; Zou, F. L. J.
2015-01-01
The research study was carried out to analyze the technical efficiency of maize growers through Cobb-Douglas type Stochastic Frontier Analysis in four villages of Muzaffarabad district, Azad Jammu and Kashmir (AJK), Pakistan. The proportional sampling allocation sampling technique was adopted to collect primary data from 80 sampled respondents in 2013-14. The maximum likelihood estimates of major inputs showed that seed, tractor hours, FYM and labor days have contributed significantly to increase the maize yield. However, the DAP and urea have shown no effect on maize yield. The mean technical efficiency was estimated at 83%, implying that the farmers can still enhance their technical efficiency by 11% within the given inputs and technology. The results have demonstrated that maize crop is lucrative crop in the study area as maize growers have received increasing return to scale i.e., 1.90 (Ep>1), hence economies of scale exists. The variance parameter lambda and gamma both were significant indicating the good fitness of model and inefficiency impact, respectively. The estimated value for gamma was 0.77 underscores that 77% variation in the production frontier was explained by technical inefficiency effect. The inefficiency indices showed that farmers with more schooling years and more number of contacts with extension agents were more efficient. Contrarily, age of the farmer and large farm size have inverse relation to technical efficiency of the farmers. This research study concludes that the use of more labor and application of farm yard manure is contributing significantly. It is recommended that the high input prices may be leveled off by the regulatory authorities so that farmers can apply the required crop inputs such as DAP and urea in study area. (author)
Xu, Lei; Zhai, Wanming
2017-10-01
This paper devotes to develop a computational model for stochastic analysis and reliability assessment of vehicle-track systems subject to earthquakes and track random irregularities. In this model, the earthquake is expressed as non-stationary random process simulated by spectral representation and random function, and the track random irregularities with ergodic properties on amplitudes, wavelengths and probabilities are characterized by a track irregularity probabilistic model, and then the number theoretical method (NTM) is applied to effectively select representative samples of earthquakes and track random irregularities. Furthermore, a vehicle-track coupled model is presented to obtain the dynamic responses of vehicle-track systems due to the earthquakes and track random irregularities at time-domain, and the probability density evolution method (PDEM) is introduced to describe the evolutionary process of probability from excitation input to response output by assuming the vehicle-track system as a probabilistic conservative system, which lays the foundation on reliability assessment of vehicle-track systems. The effectiveness of the proposed model is validated by comparing to the results of Monte-Carlo method from statistical viewpoint. As an illustrative example, the random vibrations of a high-speed railway vehicle running on the track slabs excited by lateral seismic waves and track random irregularities are analyzed, from which some significant conclusions can be drawn, e.g., track irregularities will additionally promote the dynamic influence of earthquakes especially on maximum values and dispersion degree of responses; the characteristic frequencies or frequency ranges respectively governed by earthquakes and track random irregularities are greatly different, moreover, the lateral seismic waves will dominate or even change the characteristic frequencies of system responses of some lateral dynamic indices at low frequency.
Stability analysis and the stabilization of a class of discrete-time dynamic neural networks.
Patan, Krzysztof
2007-05-01
This paper deals with problems of stability and the stabilization of discrete-time neural networks. Neural structures under consideration belong to the class of the so-called locally recurrent globally feedforward networks. The single processing unit possesses dynamic behavior. It is realized by introducing into the neuron structure a linear dynamic system in the form of an infinite impulse response filter. In this way, a dynamic neural network is obtained. It is well known that the crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates stability conditions for the analyzed class of neural networks. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem two methods are proposed. The first one is based on a gradient projection (GP) and the second one on a minimum distance projection (MDP). It is worth noting that these methods can be easily introduced into the existing learning algorithm as an additional step, and suitable convergence conditions can be developed for them. The efficiency and usefulness of the proposed approaches are justified by using a number of experiments including numerical complexity analysis, stabilization effectiveness, and the identification of an industrial process.
Parzen, Emanuel
1962-01-01
Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine
McKean, Henry P
2005-01-01
This little book is a brilliant introduction to an important boundary field between the theory of probability and differential equations. -E. B. Dynkin, Mathematical Reviews This well-written book has been used for many years to learn about stochastic integrals. The book starts with the presentation of Brownian motion, then deals with stochastic integrals and differentials, including the famous Itô lemma. The rest of the book is devoted to various topics of stochastic integral equations, including those on smooth manifolds. Originally published in 1969, this classic book is ideal for supplemen
Lin, Wen-Juan; He, Yong; Zhang, Chuan-Ke; Wu, Min
2018-01-01
This paper is concerned with the stability analysis of neural networks with a time-varying delay. To assess system stability accurately, the conservatism reduction of stability criteria has attracted many efforts, among which estimating integral terms as exact as possible is a key issue. At first, this paper develops a new relaxed integral inequality to reduce the estimation gap of popular Wirtinger-based inequality (WBI). Then, for showing the advantages of the proposed inequality over several existing inequalities that also improve the WBI, four stability criteria are derived through different inequalities and the same Lyapunov-Krasovskii functional (LKF), and the conservatism comparison of them is analyzed theoretically. Moreover, an improved criterion is established by combining the proposed inequality and an augmented LKF with delay-product-type terms. Finally, several numerical examples are used to demonstrate the advantages of the proposed method.
Modeling, Stability Analysis and Active Stabilization of Multiple DC-Microgrids Clusters
DEFF Research Database (Denmark)
Shafiee, Qobad; Dragicevic, Tomislav; Vasquez, Juan Carlos
2014-01-01
. This model can be also used to synthesis and study dynamics of control loops in dc MGs and also dc MG clusters. An active stabilization method is proposed to be implemented as a dc active power filter (APF) inside the MGs in order to not only increase damping of dc MGs at the presence of CPLs but also......DC microgrids (MGs), as an alternative option, have attracted increasing interest in recent years due to many potential advantages as compare to the ac system. Stability of these systems can be an important issue under high penetration of load converters which behaves as constant power loads (CPLs......), and more especially during interconnection with other MGs, creating dc MG clusters. This paper develops a small signal model for dc MGs from the control point of view, in order to study stability analysis and investigate effects of CPLs and line impedances between the MGs on stability of these systems...
Directory of Open Access Journals (Sweden)
Mark D McDonnell
2013-05-01
Full Text Available The release of neurotransmitter vesicles after arrival of a pre-synaptic action potential at cortical synapses is known to be a stochastic process, as is the availability of vesicles for release. These processes are known to also depend on the recent history of action-potential arrivals, and this can be described in terms of time-varying probabilities of vesicle release. Mathematical models of such synaptic dynamics frequently are based only on the mean number of vesicles released by each pre-synaptic action potential, since if it is assumed there are sufficiently many vesicle sites, then variance is small. However, it has been shown recently that variance across sites can be significant for neuron and network dynamics, and this suggests the potential importance of studying short-term plasticity using simulations that do generate trial-to-trial variability. Therefore, in this paper we study several well-known conceptual models for stochastic availability and release. We state explicitly the random variables that these models describe and propose efficient algorithms for accurately implementing stochastic simulations of these random variables in software or hardware. Our results are complemented by mathematical analysis and statement of pseudo-code algorithms.
Stochastic finite element analysis of long-span bridges with CFRP ...
Indian Academy of Sciences (India)
forced polymer (CFRP) cable; stochastic finite element method (SFEM); random variable; Monte ... 1. Introduction. The stay cables for cable-stayed bridges, and main cables and hangers for suspension bridges ..... adoption of CFRP stay cables has the effect of stiffening the structure by increasing its natural frequencies.
Directory of Open Access Journals (Sweden)
Moslem Moradi
2015-06-01
Full Text Available Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior information in Bayesian statistics. Data integration leads to a probability density function (named as a posteriori probability that can yield a model of subsurface. The Markov Chain Monte Carlo (MCMC method is used to sample the posterior probability distribution, and the subsurface model characteristics can be extracted by analyzing a set of the samples. In this study, the theory of stochastic seismic inversion in a Bayesian framework was described and applied to infer P-impedance and porosity models. The comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more detailed information of subsurface character. Since multiple realizations are extracted by this method, an estimation of pore volume and uncertainty in the estimation were analyzed.
Stochastic Volatility and Option Pricing in Heath-Jarrow-Morton Term Structure Analysis
DEFF Research Database (Denmark)
Christensen, Bent Jesper; Konaris, George; Nicolato, Elisa
We consider a generalized Heath-Jarrow-Morton bond market model which allows both for jumps and stochastic volatility. Specifications with affine and quadratic volatility are studied and explicit option pricing formulas (in the Heston (1993) sense) are derived and implemented....
Gutiérrez, M.A.; Borst, R. de
1999-01-01
This study presents some recent results on damage evolution in quasi-brittle materials including stochastic imperfections. The material strength is described as a random field and coupled to the response. The most probable configurations of imperfections leading to failure are sought by means of an
Geel, C.R.; Donselaar, M.E.
2007-01-01
Object-based stochastic modelling techniques are routinely employed to generate multiple realisations of the spatial distribution of sediment properties in settings where data density is insufficient to construct a unique deterministic facies architecture model. Challenge is to limit the wide range
Directory of Open Access Journals (Sweden)
Silvestar Šesnić
2016-01-01
Full Text Available The paper deals with the stochastic collocation analysis of a time domain response of a straight thin wire scatterer buried in a lossy half-space. The wire is excited either by a plane wave transmitted through the air-ground interface or by an equivalent current source representing direct lightning strike pulse. Transient current induced at the center of the wire, governed by corresponding Pocklington integrodifferential equation, is determined analytically. This antenna configuration suffers from uncertainties in various parameters, such as ground properties, wire dimensions, and position. The statistical processing of the results yields additional information, thus enabling more accurate and efficient analysis of buried wire configurations.
Research on nonlinear stochastic dynamical price model
International Nuclear Information System (INIS)
Li Jiaorui; Xu Wei; Xie Wenxian; Ren Zhengzheng
2008-01-01
In consideration of many uncertain factors existing in economic system, nonlinear stochastic dynamical price model which is subjected to Gaussian white noise excitation is proposed based on deterministic model. One-dimensional averaged Ito stochastic differential equation for the model is derived by using the stochastic averaging method, and applied to investigate the stability of the trivial solution and the first-passage failure of the stochastic price model. The stochastic price model and the methods presented in this paper are verified by numerical studies
Single-shell tank interim stabilization risk analysis
International Nuclear Information System (INIS)
Basche, A.D.
1998-01-01
The purpose of the Single-Shell Tank (SST) Interim Stabilization Risk Analysis is to provide a cost and schedule risk analysis of HNF-2358, Rev. 1, Single-Shell Tank Interim Stabilization Project Plan (Project Plan) (Ross et al. 1998). The analysis compares the required cost profile by fiscal year (Section 4.2) and revised schedule completion date (Section 4.5) to the Project Plan. The analysis also evaluates the executability of the Project Plan and recommends a path forward for risk mitigation
Stability analysis of a heated channel cooled by supercritical water
International Nuclear Information System (INIS)
Magni, M. C.; Delmastro, D. F; Marcel, C. P
2009-01-01
A simple model to study thermal-hydraulic stability of a heated cannel under supercritical conditions is presented. Single cannel stability analysis for the SCWR (Supercritical Water Cooled Reactor) design was performed. The drastic change of fluid density in the reactor core of a SCWR may induce DWO (Density Wave Oscillations) similar to those observed in BWRs. Due to the similarities between subcritical and supercritical systems we may treat the supercritical fluid as a pseudo two-phase system. Thus, we may extend the modeling approach often used for boiling flow stability analysis to supercritical pressure operation conditions. The model developed in this work take into account three regions: a heavy fluid region, similar to an incompressible liquid; a zone where a heavy fluid and a light fluid coexist, similar to two-phase mixture; and a light fluid region which behaves like superheated steam. It was used the homogeneous equilibrium model (HEM) for the pseudo boiling zone, and the ideal gas model for the pseudo superheated steam zone. System stability maps were obtained using linear stability analysis in the frequency domain. Two possible instability mechanisms are observed: DWO and excursive Ledinegg instabilities. Also, a sensitivity analysis showed that frictions in pseudo superheated steam zone, together with acceleration effect, are the most destabilizing effects. On the other hand, frictions in pseudo liquid zone are the most important stabilizing effect. [es
Stability analysis of Centurion electric power system
Energy Technology Data Exchange (ETDEWEB)
Galu, Y.; Munda, J.L.; Jimoh, A.A. [Tshwane Univ. of Technology, Pretoria (South Africa)
2008-07-01
A Centurion electric power system was simulated. Data from a section of the Tshwane Municipality network in South Africa were used to evaluate the use of a power system stabilizer (PSS) and a flexible AC transmission system (FACTS) controller and a thyristor controlled series compensator (TCSC). The single-machine infinite bus (SMIB) power system model was used to validate the effectiveness of the systems under various disturbance scenarios. The system's synchronous generator was characterized as a higher order model. Thevenin's equivalent of the transmission network was used to reduce the single-machine infinite bus power system in relation to the reactance of the transformer, transmission line per circuit, and the impedance of the receiving end system. Three-phase faults were applied at the generator terminal busbar in order to evaluate the model's performance. The study demonstrated that use of the PSS and TCSC-based controllers provide an improved response in terms of both overshoot and settling time. 17 refs., 10 figs.
Solar Dynamic Power System Stability Analysis and Control
Momoh, James A.; Wang, Yanchun
1996-01-01
The objective of this research is to conduct dynamic analysis, control design, and control performance test of solar power system. Solar power system consists of generation system and distribution network system. A bench mark system is used in this research, which includes a generator with excitation system and governor, an ac/dc converter, six DDCU's and forty-eight loads. A detailed model is used for modeling generator. Excitation system is represented by a third order model. DDCU is represented by a seventh order system. The load is modeled by the combination of constant power and constant impedance. Eigen-analysis and eigen-sensitivity analysis are used for system dynamic analysis. The effects of excitation system, governor, ac/dc converter control, and the type of load on system stability are discussed. In order to improve system transient stability, nonlinear ac/dc converter control is introduced. The direct linearization method is used for control design. The dynamic analysis results show that these controls affect system stability in different ways. The parameter coordination of controllers are recommended based on the dynamic analysis. It is concluded from the present studies that system stability is improved by the coordination of control parameters and the nonlinear ac/dc converter control stabilize system oscillation caused by the load change and system fault efficiently.
Stability Analysis of Neural Networks-Based System Identification
Directory of Open Access Journals (Sweden)
Talel Korkobi
2008-01-01
Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.
Linear stability analysis in a solid-propellant rocket motor
Energy Technology Data Exchange (ETDEWEB)
Kim, K.M.; Kang, K.T.; Yoon, J.K. [Agency for Defense Development, Taejon (Korea, Republic of)
1995-10-01
Combustion instability in solid-propellant rocket motors depends on the balance between acoustic energy gains and losses of the system. The objective of this paper is to demonstrate the capability of the program which predicts the standard longitudinal stability using acoustic modes based on linear stability analysis and T-burner test results of propellants. Commercial ANSYS 5.0A program can be used to calculate the acoustic characteristic of a rocket motor. The linear stability prediction was compared with the static firing test results of rocket motors. (author). 11 refs., 17 figs.
Nonlinear Stochastic stability analysis of Wind Turbine Wings by Monte Carlo Simulations
DEFF Research Database (Denmark)
Larsen, Jesper Winther; Iwankiewiczb, R.; Nielsen, Søren R.K.
2007-01-01
and inertial contributions. A reduced two-degrees-of-freedom modal expansion is used specifying the modal coordinate of the fundamental blade and edgewise fixed base eigenmodes of the beam. The rotating beam is subjected to harmonic and narrow-banded support point motion from the nacelle displacement...... under narrow-banded excitation, and it is shown that the qualitative behaviour of the strange attractor is very similar for the periodic and almost periodic responses, whereas the strange attractor for the chaotic case loses structure as the excitation becomes narrow-banded. Furthermore......, the characteristic behaviour of the strange attractor is shown to be identifiable by the so-called information dimension. Due to the complexity of the coupled nonlinear structural system all analyses are carried out via Monte Carlo simulations....
Linear modeling of rotorcraft for stability analysis and preliminary design
Wirth, Walter M.
1993-01-01
Approved for public release; distribution is unlimited. This thesis investigates linear state space modeling of single main rotor helicopters culminating in a computer program that can be used for 1) stability and control analysis for any single main rotor helicopter or 2) preliminary design of a helicopter. The trim solution for a flight condition is found, the aircraft is perturbed about the nominal point, and the stability and control derivatives are determined. State space models and ...
Stability analysis of linear switching systems with time delays
International Nuclear Information System (INIS)
Li Ping; Zhong Shouming; Cui Jinzhong
2009-01-01
The issue of stability analysis of linear switching system with discrete and distributed time delays is studied in this paper. An appropriate switching rule is applied to guarantee the stability of the whole switching system. Our results use a Riccati-type Lyapunov functional under a condition on the time delay. So, switching systems with mixed delays are developed. A numerical example is given to illustrate the effectiveness of our results.
Stability analysis for cellular neural networks with variable delays
International Nuclear Information System (INIS)
Zhang Qiang; Wei Xiaopeng; Xu Jin
2006-01-01
Some sufficient conditions for the global exponential stability of cellular neural networks with variable delay are obtained by means of a method based on delay differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result
Stochastic techno-economic analysis of alcohol-to-jet fuel production.
Yao, Guolin; Staples, Mark D; Malina, Robert; Tyner, Wallace E
2017-01-01
Alcohol-to-jet (ATJ) is one of the technical feasible biofuel technologies. It produces jet fuel from sugary, starchy, and lignocellulosic biomass, such as sugarcane, corn grain, and switchgrass, via fermentation of sugars to ethanol or other alcohols. This study assesses the ATJ biofuel production pathway for these three biomass feedstocks, and advances existing techno-economic analyses of biofuels in three ways. First, we incorporate technical uncertainty for all by-products and co-products though statistical linkages between conversion efficiencies and input and output levels. Second, future price uncertainty is based on case-by-case time-series estimation, and a local sensitivity analysis is conducted with respect to each uncertain variable. Third, breakeven price distributions are developed to communicate the inherent uncertainty in breakeven price. This research also considers uncertainties in utility input requirements, fuel and by-product outputs, as well as price uncertainties for all major inputs, products, and co-products. All analyses are done from the perspective of a private firm. The stochastic dominance results of net present values (NPV) and breakeven price distributions show that sugarcane is the lowest cost feedstock over the entire range of uncertainty with the least risks, followed by corn grain and switchgrass, with the mean breakeven jet fuel prices being $0.96/L ($3.65/gal), $1.01/L ($3.84/gal), and $1.38/L ($5.21/gal), respectively. The variation of revenues from by-products in corn grain pathway can significantly impact its profitability. Sensitivity analyses show that technical uncertainty significantly impacts breakeven price and NPV distributions. Technical uncertainty is critical in determining the economic performance of the ATJ fuel pathway. Technical uncertainty needs to be considered in future economic analyses. The variation of revenues from by-products plays a significant role in profitability. With the distribution of breakeven
Measuring efficiency of governmental hospitals in Palestine using stochastic frontier analysis.
Hamidi, Samer
2016-01-01
The Palestinian government has been under increasing pressure to improve provision of health services while seeking to effectively employ its scare resources. Governmental hospitals remain the leading costly units as they consume about 60 % of governmental health budget. A clearer understanding of the technical efficiency of hospitals is crucial to shape future health policy reforms. In this paper, we used stochastic frontier analysis to measure technical efficiency of governmental hospitals, the first of its kind nationally. We estimated maximum likelihood random-effects and time-invariant efficiency model developed by Battese and Coelli, 1988. Number of beds, number of doctors, number of nurses, and number of non-medical staff, were used as the input variables, and sum of number of treated inpatients and outpatients was used as output variable. Our dataset includes balanced panel data of 22 governmental hospitals over a period of 6 years. Cobb-Douglas function, translog function, and multi-output distance function were estimated using STATA 12. The average technical efficiency of hospitals was approximately 55 %, and ranged from 28 to 91 %. Doctors and nurses appear to be the most important factors in hospital production, as 1 % increase in number of doctors, results in an increase in the production of the hospital of 0.33 and 0.51 %, respectively. If hospitals increase all inputs by 1 %, their production would increase by 0.74 %. Hospitals production process has a decrease return to scale. Despite continued investment in governmental hospitals, they remained relatively inefficient. Using the existing amount of resources, the amount of delivered outputs can be improved 45 % which provides insight into mismanagement of available resources. To address hospital inefficiency, it is important to increase the numbers of doctors and nurses. The number of non-medical staff should be reduced. Offering the option of early retirement, limit hiring, and transfer to
Nan, Hanqing; Liang, Long; Chen, Guo; Liu, Liyu; Liu, Ruchuan; Jiao, Yang
2018-03-01
Three-dimensional (3D) collective cell migration in a collagen-based extracellular matrix (ECM) is among one of the most significant topics in developmental biology, cancer progression, tissue regeneration, and immune response. Recent studies have suggested that collagen-fiber mediated force transmission in cellularized ECM plays an important role in stress homeostasis and regulation of collective cellular behaviors. Motivated by the recent in vitro observation that oriented collagen can significantly enhance the penetration of migrating breast cancer cells into dense Matrigel which mimics the intravasation process in vivo [Han et al. Proc. Natl. Acad. Sci. USA 113, 11208 (2016), 10.1073/pnas.1610347113], we devise a procedure for generating realizations of highly heterogeneous 3D collagen networks with prescribed microstructural statistics via stochastic optimization. Specifically, a collagen network is represented via the graph (node-bond) model and the microstructural statistics considered include the cross-link (node) density, valence distribution, fiber (bond) length distribution, as well as fiber orientation distribution. An optimization problem is formulated in which the objective function is defined as the squared difference between a set of target microstructural statistics and the corresponding statistics for the simulated network. Simulated annealing is employed to solve the optimization problem by evolving an initial network via random perturbations to generate realizations of homogeneous networks with randomly oriented fibers, homogeneous networks with aligned fibers, heterogeneous networks with a continuous variation of fiber orientation along a prescribed direction, as well as a binary system containing a collagen region with aligned fibers and a dense Matrigel region with randomly oriented fibers. The generation and propagation of active forces in the simulated networks due to polarized contraction of an embedded ellipsoidal cell and a small group
Model selection for integrated pest management with stochasticity.
Akman, Olcay; Comar, Timothy D; Hrozencik, Daniel
2018-04-07
In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model. Copyright © 2017 Elsevier Ltd. All rights reserved.
Non linear stability analysis of parallel channels with natural circulation
Energy Technology Data Exchange (ETDEWEB)
Mishra, Ashish Mani; Singh, Suneet, E-mail: suneet.singh@iitb.ac.in
2016-12-01
Highlights: • Nonlinear instabilities in natural circulation loop are studied. • Generalized Hopf points, Sub and Supercritical Hopf bifurcations are identified. • Bogdanov–Taken Point (BT Point) is observed by nonlinear stability analysis. • Effect of parameters on stability of system is studied. - Abstract: Linear stability analysis of two-phase flow in natural circulation loop is quite extensively studied by many researchers in past few years. It can be noted that linear stability analysis is limited to the small perturbations only. It is pointed out that such systems typically undergo Hopf bifurcation. If the Hopf bifurcation is subcritical, then for relatively large perturbation, the system has unstable limit cycles in the (linearly) stable region in the parameter space. Hence, linear stability analysis capturing only infinitesimally small perturbations is not sufficient. In this paper, bifurcation analysis is carried out to capture the non-linear instability of the dynamical system and both subcritical and supercritical bifurcations are observed. The regions in the parameter space for which subcritical and supercritical bifurcations exist are identified. These regions are verified by numerical simulation of the time-dependent, nonlinear ODEs for the selected points in the operating parameter space using MATLAB ODE solver.
Stability analysis of sandy slope considering anisotropy effect in ...
Indian Academy of Sciences (India)
1Faculty of Engineering, Azarbaijan Shahid Madani University, ... 2School of Civil Engineering, Iran University of Science and Technology, ..... material. 4.3 Stability analysis result. For each analysis case with specified geometrical configuration for slope, a wide range of slip surfaces are considered by establishing a grid of ...
Liu, Shichao; Liu, Peter Xiaoping; Wang, Xiaoyu
2017-01-01
This survey is to summarize and compare existing and recently emerging approaches for the analysis and compensation of the effects of network-induced delays on the stability and performance of communication-based power control systems. Several important communication-based power control systems are briefly introduced. The deterministic and stochastic methodologies of analyzing the impacts of network-induced delays on the stability of the communication-based power control systems are summarized and compared. A variety of control approaches are reviewed and compared for mitigating the effects of network-induced delays, depending on several design requirements, such as model dependence and design difficulty. The summary and comparison of these control approaches in this survey provide researchers and utilities valuable guidance for designing advanced communication-based power control systems in the future. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Streeter, Lee
2017-07-01
Time-of-flight range imaging is analyzed using stochastic calculus. Through a series of interpretations and simplifications, the stochastic model leads to two methods for estimating linear radial velocity: maximum likelihood estimation on the transition probability distribution between measurements, and a new method based on analyzing the measured correlation waveform and its first derivative. The methods are tested in a simulated motion experiment from (-40)-(+40) m/s, with data from a camera imaging an object on a translation stage. In tests maximum likelihood is slow and unreliable, but when it works it estimates the linear velocity with standard deviation of 1 m/s or better. In comparison the new method is fast and reliable but works in a reduced velocity range of (-20)-(+20) m/s with standard deviation ranging from 3.5 m/s to 10 m/s.
A stochastic model for immunological feedback in carcinogenesis analysis and approximations
Dubin, Neil
1976-01-01
Stochastic processes often pose the difficulty that, as soon as a model devi ates from the simplest kinds of assumptions, the differential equations obtained for the density and the generating functions become mathematically formidable. Worse still, one is very often led to equations which have no known solution and don't yield to standard analytical methods for differential equations. In the model considered here, one for tumor growth with an immunological re sponse from the normal tissue, a nonlinear term in the transition probability for the death of a tumor cell leads to the above-mentioned complications. Despite the mathematical disadvantages of this nonlinearity, we are able to consider a more sophisticated model biologically. Ultimately, in order to achieve a more realistic representation of a complicated phenomenon, it is necessary to examine mechanisms which allow the model to deviate from the more mathematically tractable linear format. Thus far, stochastic models for tumor growth have almost ex...
A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies
International Nuclear Information System (INIS)
Chen, Zhongfei; Barros, Carlos Pestana; Borges, Maria Rosa
2015-01-01
This paper analyses the technical efficiency of Chinese fossil-fuel electricity generation companies from 1999 to 2011, using a Bayesian stochastic frontier model. The results reveal that efficiency varies among the fossil-fuel electricity generation companies that were analysed. We also focus on the factors of size, location, government ownership and mixed sources of electricity generation for the fossil-fuel electricity generation companies, and also examine their effects on the efficiency of these companies. Policy implications are derived. - Highlights: • We analyze the efficiency of 27 quoted Chinese fossil-fuel electricity generation companies during 1999–2011. • We adopt a Bayesian stochastic frontier model taking into consideration the identified heterogeneity. • With reform background in Chinese energy industry, we propose four hypotheses and check their influence on efficiency. • Big size, coastal location, government control and hydro energy sources all have increased costs
Stochastic analysis of pitch angle scattering of charged particles by transverse magnetic waves
International Nuclear Information System (INIS)
Lemons, Don S.; Liu Kaijun; Winske, Dan; Gary, S. Peter
2009-01-01
This paper describes a theory of the velocity space scattering of charged particles in a static magnetic field composed of a uniform background field and a sum of transverse, circularly polarized, magnetic waves. When that sum has many terms the autocorrelation time required for particle orbits to become effectively randomized is small compared with the time required for the particle velocity distribution to change significantly. In this regime the deterministic equations of motion can be transformed into stochastic differential equations of motion. The resulting stochastic velocity space scattering is described, in part, by a pitch angle diffusion rate that is a function of initial pitch angle and properties of the wave spectrum. Numerical solutions of the deterministic equations of motion agree with the theory at all pitch angles, for wave energy densities up to and above the energy density of the uniform field, and for different wave spectral shapes.
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.
DEFF Research Database (Denmark)
Seldin, Yevgeny; Lugosi, Gábor
We present a new strategy for gap estimation in randomized algorithms for multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and Slivkins (2014). In the stochastic regime the strategy reduces dependence of regret on a time horizon from $(ln t)^3$ to $(ln t)^2$ and eliminates an...... an additive factor of order $\\Delta e^{\\Delta^2}$, where $\\Delta$ is the minimal gap of a problem instance. In the adversarial regime regret guarantee remains unchanged....
Directory of Open Access Journals (Sweden)
P. A. Alpert
2016-02-01
Full Text Available Immersion freezing is an important ice nucleation pathway involved in the formation of cirrus and mixed-phase clouds. Laboratory immersion freezing experiments are necessary to determine the range in temperature, T, and relative humidity, RH, at which ice nucleation occurs and to quantify the associated nucleation kinetics. Typically, isothermal (applying a constant temperature and cooling-rate-dependent immersion freezing experiments are conducted. In these experiments it is usually assumed that the droplets containing ice nucleating particles (INPs all have the same INP surface area (ISA; however, the validity of this assumption or the impact it may have on analysis and interpretation of the experimental data is rarely questioned. Descriptions of ice active sites and variability of contact angles have been successfully formulated to describe ice nucleation experimental data in previous research; however, we consider the ability of a stochastic freezing model founded on classical nucleation theory to reproduce previous results and to explain experimental uncertainties and data scatter. A stochastic immersion freezing model based on first principles of statistics is presented, which accounts for variable ISA per droplet and uses parameters including the total number of droplets, Ntot, and the heterogeneous ice nucleation rate coefficient, Jhet(T. This model is applied to address if (i a time and ISA-dependent stochastic immersion freezing process can explain laboratory immersion freezing data for different experimental methods and (ii the assumption that all droplets contain identical ISA is a valid conjecture with subsequent consequences for analysis and interpretation of immersion freezing. The simple stochastic model can reproduce the observed time and surface area dependence in immersion freezing experiments for a variety of methods such as: droplets on a cold-stage exposed to air or surrounded by an oil matrix, wind and
Jacobson, R. A.
1978-01-01
The formulation of the classical Linear-Quadratic-Gaussian stochastic control problem as employed in low thrust navigation analysis is reviewed. A reformulation is then presented which eliminates a potentially unreliable matrix subtraction in the control calculations, improves the computational efficiency, and provides for a cleaner computational interface between the estimation and control processes. Lastly, the application of the U-D factorization method to the reformulated equations is examined with the objective of achieving a complete set of factored equations for the joint estimation and control problem.
Modeling and Analysis of Inter-Vehicle Communication: A Stochastic Geometry Approach
Farooq, Muhammad Junaid
2015-05-01
Vehicular communication is the enabling technology for the development of the intelligent transportation systems (ITS), which aims to improve the efficiency and safety of transportation. It can be used for a variety of useful applications such as adaptive traffic control, coordinated braking, emergency messaging, peer-to-peer networking for infotainment services and automatic toll collection etc... Accurate yet simple models for vehicular networks are required in order to understand and optimize their operation. For reliable communication between vehicles, the spectrum access is coordinated via carrier sense multiple access (CSMA) protocol. Existing models either use a simplified network abstraction and access control scheme for analysis or depend on simulation studies. Therefore it is important to develop an analytical model for CSMA coordinated communication between vehicles. In the first part of the thesis, stochastic geometry is exploited to develop a modeling framework for CSMA coordinated inter-vehicle communication (IVC) in a multi-lane highway scenario. The performance of IVC is studied in multi-lane highways taking into account the inter-lane separations and the number of traffic lanes and it is shown that for wide multi-lane highways, the line abstraction model that is widely used in literature loses accuracy and hence the analysis is not reliable. Since the analysis of CSMA in the vehicular setting makes the analysis intractable, an aggressive interference approximation and a conservative interference approximation is proposed for the probability of transmission success. These approximations are tight in the low traffic and high traffic densities respectively. In the subsequent part of the thesis, the developed model is extended to multi-hop IVC because several vehicular applications require going beyond the local communication and efficiently disseminate information across the roads via multi-hops. Two well-known greedy packet forwarding schemes are
Tsekouras, Georgios; Ioannou, Christos; Efstratiadis, Andreas; Koutsoyiannis, Demetris
2013-04-01
The drawbacks of conventional energy sources including their negative environmental impacts emphasize the need to integrate renewable energy sources into energy balance. However, the renewable sources strongly depend on time varying and uncertain hydrometeorological processes, including wind speed, sunshine duration and solar radiation. To study the design and management of hybrid energy systems we investigate the stochastic properties of these natural processes, including possible long-term persistence. We use wind speed and sunshine duration time series retrieved from a European database of daily records and we estimate representative values of the Hurst coefficient for both variables. We conduct simultaneous generation of synthetic time series of wind speed and sunshine duration, on yearly, monthly and daily scale. To this we use the Castalia software system which performs multivariate stochastic simulation. Using these time series as input, we perform stochastic simulation of an autonomous hypothetical hybrid renewable energy system and optimize its performance using genetic algorithms. For the system design we optimize the sizing of the system in order to satisfy the energy demand with high reliability also minimizing the cost. While the simulation scale is the daily, a simple method allows utilizing the subdaily distribution of the produced wind power. Various scenarios are assumed in order to examine the influence of input parameters, such as the Hurst coefficient, and design parameters such as the photovoltaic panel angle.
Kala, Zdeněk
2013-10-01
The paper deals with the statistical analysis of resistance of a hot-rolled steel IPE beam under major axis bending. The lateral-torsional buckling stability problem of imperfect beam is described. The influence of bending moments and warping torsion on the ultimate limit state of the IPE beam with random imperfections is analyzed. The resistance is calculated by means of the close form solution. The initial geometrical imperfections of the beam are considered as the formatively identical to the first eigen mode of buckling. Changes of mean values of the resistance, of mean values of internal bending moments, of the variance of resistance and of the variance of internal bending moments were studied in dependence on the beam non-dimensional slenderness. The values of non-dimensional slenderness for which the statistical characteristics of internal moments associated with random resistance are maximal were determined.
Stability Analysis for a Multi-Camera Photogrammetric System
Directory of Open Access Journals (Sweden)
Ayman Habib
2014-08-01
Full Text Available Consumer-grade digital cameras suffer from geometrical instability that may cause problems when used in photogrammetric applications. This paper provides a comprehensive review of this issue of interior orientation parameter variation over time, it explains the common ways used for coping with the issue, and describes the existing methods for performing stability analysis for a single camera. The paper then points out the lack of coverage of stability analysis for multi-camera systems, suggests a modification of the collinearity model to be used for the calibration of an entire photogrammetric system, and proposes three methods for system stability analysis. The proposed methods explore the impact of the changes in interior orientation and relative orientation/mounting parameters on the reconstruction process. Rather than relying on ground truth in real datasets to check the system calibration stability, the proposed methods are simulation-based. Experiment results are shown, where a multi-camera photogrammetric system was calibrated three times, and stability analysis was performed on the system calibration parameters from the three sessions. The proposed simulation-based methods provided results that were compatible with a real-data based approach for evaluating the impact of changes in the system calibration parameters on the three-dimensional reconstruction.
Stochastic dynamics and control
Sun, Jian-Qiao; Zaslavsky, George
2006-01-01
This book is a result of many years of author's research and teaching on random vibration and control. It was used as lecture notes for a graduate course. It provides a systematic review of theory of probability, stochastic processes, and stochastic calculus. The feedback control is also reviewed in the book. Random vibration analyses of SDOF, MDOF and continuous structural systems are presented in a pedagogical order. The application of the random vibration theory to reliability and fatigue analysis is also discussed. Recent research results on fatigue analysis of non-Gaussian stress proc
Synthesized dynamic modeling and stability analysis of novel HVDC system
Energy Technology Data Exchange (ETDEWEB)
Xu Sun; Li Kong [Inst. of Electrical Engineering, CAS, BJ (China)
2008-07-01
At the present time, many projects large offshore wind power fields connecting to the grid adopt the novel HVDC technology. Voltage source converter structure and PWM modulation technology are used in the system and active power and reactive power can be controlled respectively, so it can ensure the excellent performance of the projects. It is very necessary to build its detailed dynamic model and analyze its stability to be the base for further research. In this paper, firstly, the switch function model is established as the base of further analysis. Secondly, the steady model, small signal model and high frequency dynamic model of novel HVDC based on state space average method are established respectively. Thirdly, the stability of the whole system is analyzed on the base of above models of the novel HVDC. Finally, the whole system is validated practically by simulation analysis to prove the validity of model and stability analysis. (orig.)
Probabilistic approaches for geotechnical site characterization and slope stability analysis
Cao, Zijun; Li, Dianqing
2017-01-01
This is the first book to revisit geotechnical site characterization from a probabilistic point of view and provide rational tools to probabilistically characterize geotechnical properties and underground stratigraphy using limited information obtained from a specific site. This book not only provides new probabilistic approaches for geotechnical site characterization and slope stability analysis, but also tackles the difficulties in practical implementation of these approaches. In addition, this book also develops efficient Monte Carlo simulation approaches for slope stability analysis and implements these approaches in a commonly available spreadsheet environment. These approaches and the software package are readily available to geotechnical practitioners and alleviate them from reliability computational algorithms. The readers will find useful information for a non-specialist to determine project-specific statistics of geotechnical properties and to perform probabilistic analysis of slope stability.
Static Voltage Stability Analysis by Using SVM and Neural Network
Directory of Open Access Journals (Sweden)
Mehdi Hajian
2013-01-01
Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.
Cheng, Yang; Wong, Michael T; van der Maaten, Laurens; Newell, Evan W
2016-01-15
Rapid progress in single-cell analysis methods allow for exploration of cellular diversity at unprecedented depth and throughput. Visualizing and understanding these large, high-dimensional datasets poses a major analytical challenge. Mass cytometry allows for simultaneous measurement of >40 different proteins, permitting in-depth analysis of multiple aspects of cellular diversity. In this article, we present one-dimensional soli-expression by nonlinear stochastic embedding (One-SENSE), a dimensionality reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. In contrast with higher-dimensional t-SNE, each dimension (plot axis) in One-SENSE has biological meaning that can be easily annotated with binned heat plots. We applied One-SENSE to probe relationships between categories of human T cell phenotypes and observed previously unappreciated cellular populations within an orchestrated view of immune cell diversity. The presentation of high-dimensional cytometric data using One-SENSE showed a significant improvement in distinguished T cell diversity compared with the original t-SNE algorithm and could be useful for any high-dimensional dataset. Copyright © 2016 by The American Association of Immunologists, Inc.
Stability Analysis of Nonuniform Rectangular Beams Using Homotopy Perturbation Method
Directory of Open Access Journals (Sweden)
Seval Pinarbasi
2012-01-01
Full Text Available The design of slender beams, that is, beams with large laterally unsupported lengths, is commonly controlled by stability limit states. Beam buckling, also called “lateral torsional buckling,” is different from column buckling in that a beam not only displaces laterally but also twists about its axis during buckling. The coupling between twist and lateral displacement makes stability analysis of beams more complex than that of columns. For this reason, most of the analytical studies in the literature on beam stability are concentrated on simple cases: uniform beams with ideal boundary conditions and simple loadings. This paper shows that complex beam stability problems, such as lateral torsional buckling of rectangular beams with variable cross-sections, can successfully be solved using homotopy perturbation method (HPM.
Global robust exponential stability analysis for interval recurrent neural networks
International Nuclear Information System (INIS)
Xu Shengyuan; Lam, James; Ho, Daniel W.C.; Zou Yun
2004-01-01
This Letter investigates the problem of robust global exponential stability analysis for interval recurrent neural networks (RNNs) via the linear matrix inequality (LMI) approach. The values of the time-invariant uncertain parameters are assumed to be bounded within given compact sets. An improved condition for the existence of a unique equilibrium point and its global exponential stability of RNNs with known parameters is proposed. Based on this, a sufficient condition for the global robust exponential stability for interval RNNs is obtained. Both of the conditions are expressed in terms of LMIs, which can be checked easily by various recently developed convex optimization algorithms. Examples are provided to demonstrate the reduced conservatism of the proposed exponential stability condition
Comprehensive wellbore stability analysis utilizing Quantitative Risk Assessment
Energy Technology Data Exchange (ETDEWEB)
Moos, Daniel; Peska, Pavel; Finkbeiner, Thomas [GeoMechanics International, Palo Alto, CA 94303 (United States); Zoback, Mark [Stanford University, Stanford, CA 94305 (United States)
2003-06-01
A comprehensive geomechanical approach to wellbore stability requires knowledge of rock strength, pore pressure and the magnitude and orientation of the three principal stresses. These parameters are often uncertain, making confidence in deterministic predictions of the risks associated with instabilities during drilling and production difficult to assess. This paper demonstrates the use of Quantitative Risk Assessment (QRA) to formally account for the uncertainty in each input parameter to assess the probability of achieving a desired degree of wellbore stability at a given mud weight. We also utilize QRA to assess how the uncertainty in each parameter affects the mud weight calculated to maintain stability. In one case study, we illustrate how this approach allows us to compute optimal mud weight windows and casing set points at a deep-water site. In another case study, we demonstrate how to assess the feasibility of underbalanced drilling and open-hole completion of horizontal wells utilizing a comprehensive stability analysis that includes application of QRA.
Stability analysis of embedded nonlinear predictor neural generalized predictive controller
Directory of Open Access Journals (Sweden)
Hesham F. Abdel Ghaffar
2014-03-01
Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.
International Nuclear Information System (INIS)
Rumpf, H.
1987-01-01
We begin with a naive application of the Parisi-Wu scheme to linearized gravity. This will lead into trouble as one peculiarity of the full theory, the indefiniteness of the Euclidean action, shows up already at this level. After discussing some proposals to overcome this problem, Minkowski space stochastic quantization will be introduced. This will still not result in an acceptable quantum theory of linearized gravity, as the Feynman propagator turns out to be non-causal. This defect will be remedied only after a careful analysis of general covariance in stochastic quantization has been performed. The analysis requires the notion of a metric on the manifold of metrics, and a natural candidate for this is singled out. With this a consistent stochastic quantization of Einstein gravity becomes possible. It is even possible, at least perturbatively, to return to the Euclidean regime. 25 refs. (Author)
Power system small signal stability analysis and control
Mondal, Debasish; Sengupta, Aparajita
2014-01-01
Power System Small Signal Stability Analysis and Control presents a detailed analysis of the problem of severe outages due to the sustained growth of small signal oscillations in modern interconnected power systems. The ever-expanding nature of power systems and the rapid upgrade to smart grid technologies call for the implementation of robust and optimal controls. Power systems that are forced to operate close to their stability limit have resulted in the use of control devices by utility companies to improve the performance of the transmission system against commonly occurring power system
Stability Analysis on Sparsely Encoded Associative Memory with Short-Term Synaptic Dynamics
Xu, Muyuan; Katori, Yuichi; Aihara, Kazuyuki
This study investigates the stability of sparsely encoded associative memory in a network composed of stochastic neurons. The incorporation of short-term synaptic dynamics significantly changes the stability with respect to synaptic properties. Various states including static and oscillatory states are found in the network dynamics. Specifically, the sparseness of memory patterns raises the problem of spurious states. A mean field model is used to analyze the detailed structure in the stability and show that the performance of memory retrieval is recovered by appropriate feedback.
Surficial Stability Analysis for Landslide Prediction
Cho, Sung Eun
2017-04-01
In Korea where rainfall of strong intensities is frequent, the depth of weathered residual soil is shallow in mountainous region. Therefore, full saturation of soil layer caused by the reaching of rainwater from the slope surface to impermeable bedrock is one of important causes of landslide. In this study, a shallow slope failure analysis method for slopes with shallow bedrock was developed to predict landslide based on one-dimensional Green-Ampt model. Constant intensities of rainfall were considered and shallow impermeable boundary condition was imposed on the Green-Ampt model to simulate the impermeable bedrock underlying the shallow weathered residual soil. The prediction results showed that the proposed method can be used to predict the landslide due to rainfall infiltration by efficiently considering the movement of the saturated region in the hillslope with shallow impermeable bedrock. Acknowledgements This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2012M3A2A1050981).
Numerical methods for stochastic differential equations.
Wilkie, Joshua
2004-01-01
Stochastic differential equations (SDE's) play an important role in physics but existing numerical methods for solving such equations are of low accuracy and poor stability. A general strategy for developing accurate and efficient schemes for solving stochastic equations is outlined here. High-order numerical methods are developed for the integration of stochastic differential equations with strong solutions. We demonstrate the accuracy of the resulting integration schemes by computing the errors in approximate solutions for SDE's which have known exact solutions.
Lukacs, Eugene; Lukacs, E
1975-01-01
Stochastic Convergence, Second Edition covers the theoretical aspects of random power series dealing with convergence problems. This edition contains eight chapters and starts with an introduction to the basic concepts of stochastic convergence. The succeeding chapters deal with infinite sequences of random variables and their convergences, as well as the consideration of certain sets of random variables as a space. These topics are followed by discussions of the infinite series of random variables, specifically the lemmas of Borel-Cantelli and the zero-one laws. Other chapters evaluate the po
Stochastic partial differential equations
Chow, Pao-Liu
2014-01-01
Preliminaries Introduction Some Examples Brownian Motions and Martingales Stochastic Integrals Stochastic Differential Equations of Itô Type Lévy Processes and Stochastic IntegralsStochastic Differential Equations of Lévy Type Comments Scalar Equations of First Order Introduction Generalized Itô's Formula Linear Stochastic Equations Quasilinear Equations General Remarks Stochastic Parabolic Equations Introduction Preliminaries Solution of Stochastic Heat EquationLinear Equations with Additive Noise Some Regularity Properties Stochastic Reaction-Diffusion Equations Parabolic Equations with Grad
Analysis of a Stochastic Chemical System Close to a SNIPER Bifurcation of Its Mean-Field Model
Erban, Radek
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, in the modeling of cell-cycle regulation. It is shown that the stochastic system possesses oscillatory solutions even for parameter values for which the mean-field model does not oscillate. The dependence of the mean period of these oscillations on the parameters of the model (kinetic rate constants) and the size of the system (number of molecules present) are studied. Our approach is based on the chemical Fokker-Planck equation. To gain some insight into the advantages and disadvantages of the method, a simple one-dimensional chemical switch is first analyzed, and then the chemical SNIPER problem is studied in detail. First, results obtained by solving the Fokker-Planck equation numerically are presented. Then an asymptotic analysis of the Fokker-Planck equation is used to derive explicit formulae for the period of oscillation as a function of the rate constants and as a function of the system size. © 2009 Society for Industrial and Applied Mathematics.
Pitching stability analysis of half-rotating wing air vehicle
Wang, Xiaoyi; Wu, Yang; Li, Qian; Li, Congmin; Qiu, Zhizhen
2017-06-01
Half-Rotating Wing (HRW) is a new power wing which had been developed by our work team using rotating-type flapping instead of oscillating-type flapping. Half-Rotating Wing Air Vehicle (HRWAV) is similar as Bionic Flapping Wing Air Vehicle (BFWAV). It is necessary to guarantee pitching stability of HRWAV to maintain flight stability. The working principle of HRW was firstly introduced in this paper. The rule of motion indicated that the fuselage of HRWAV without empennage would overturn forward as it generated increased pitching movement. Therefore, the empennage was added on the tail of HRWAV to balance the additional moment generated by aerodynamic force during flight. The stability analysis further shows that empennage could weaken rapidly the pitching disturbance on HRWAV and a new balance of fuselage could be achieved in a short time. Case study using numerical analysis verified correctness and validity of research results mentioned above, which could provide theoretical guidance to design and control HRWAV.
Stability Analysis for Compliant Constant-Force Compression Mechanisms
Directory of Open Access Journals (Sweden)
Ikechukwu Celestine UGWUOKE
2009-12-01
Full Text Available Stability analysis in compliant mechanism (CM design is of utmostimportance. From a practical point of view, a CM that is unstable is of nosignificance (has no practical value. Three useful plots were considered in theevaluation of each of the dynamic models of nine configurations of compliantconstant-force compression mechanisms (CCFCMs for their stabilitycharacteristics, which includes the polar plot based on the Routh-Hurwitzstability criterion, the Bode plot, and the Nyquist diagram which considersstability in the real frequency domain. Frequency-domain stability criterion isvery useful for determining suitable approaches to adjusting the CCFCMparameters in order to increase its relative stability. The results obtained showthat the CCFCMs investigated do exhibit higher relative stability for highervalues of damping ratio, and for zero damping ratio, all the CCFCMsinvestigated were unstable. The result also show that for the CCFCMsinvestigated to be stable, damping ratio must be greater than 0.03 (ξ > 0.03and depending on what attributes are most desirable, the CCFCM parameterscan be optimized to achieve the desired results. Nyquist criterion provides uswith suitable information concerning the absolute stability and furthermore,can be utilized to define and ascertain the relative stability of a system.