Modelling tourists arrival using time varying parameter
Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.
2017-06-01
The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.
Model Identification of Linear Parameter Varying Aircraft Systems
Fujimore, Atsushi; Ljung, Lennart
2007-01-01
This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...
Robust linear parameter varying induction motor control with polytopic models
Directory of Open Access Journals (Sweden)
Dalila Khamari
2013-01-01
Full Text Available This paper deals with a robust controller for an induction motor which is represented as a linear parameter varying systems. To do so linear matrix inequality (LMI based approach and robust Lyapunov feedback controller are associated. This new approach is related to the fact that the synthesis of a linear parameter varying (LPV feedback controller for the inner loop take into account rotor resistance and mechanical speed as varying parameter. An LPV flux observer is also synthesized to estimate rotor flux providing reference to cited above regulator. The induction motor is described as a polytopic model because of speed and rotor resistance affine dependence their values can be estimated on line during systems operations. The simulation results are presented to confirm the effectiveness of the proposed approach where robustness stability and high performances have been achieved over the entire operating range of the induction motor.
Varying parameter models to accommodate dynamic promotion effects
Foekens, E.W.; Leeflang, P.S.H.; Wittink, D.R.
1999-01-01
The purpose of this paper is to examine the dynamic effects of sales promotions. We create dynamic brand sales models (for weekly store-level scanner data) by relating store intercepts and a brand's own price elasticity to a measure of the cumulated previous price discounts - amount and time - for t
Varying parameter models to accommodate dynamic promotion effects
Foekens, E.W.; Leeflang, P.S.H.; Wittink, D.R.
1999-01-01
The purpose of this paper is to examine the dynamic effects of sales promotions. We create dynamic brand sales models (for weekly store-level scanner data) by relating store intercepts and a brand's own price elasticity to a measure of the cumulated previous price discounts - amount and time - for
Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters
DEFF Research Database (Denmark)
Nystrup, Peter; Madsen, Henrik; Lindström, Erik
2016-01-01
estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact...... to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations....
varying elastic parameters distributions
Moussawi, Ali
2014-12-01
The experimental identication of mechanical properties is crucial in mechanics for understanding material behavior and for the development of numerical models. Classical identi cation procedures employ standard shaped specimens, assume that the mechanical elds in the object are homogeneous, and recover global properties. Thus, multiple tests are required for full characterization of a heterogeneous object, leading to a time consuming and costly process. The development of non-contact, full- eld measurement techniques from which complex kinematic elds can be recorded has opened the door to a new way of thinking. From the identi cation point of view, suitable methods can be used to process these complex kinematic elds in order to recover multiple spatially varying parameters through one test or a few tests. The requirement is the development of identi cation techniques that can process these complex experimental data. This thesis introduces a novel identi cation technique called the constitutive compatibility method. The key idea is to de ne stresses as compatible with the observed kinematic eld through the chosen class of constitutive equation, making possible the uncoupling of the identi cation of stress from the identi cation of the material parameters. This uncoupling leads to parametrized solutions in cases where 5 the solution is non-unique (due to unknown traction boundary conditions) as demonstrated on 2D numerical examples. First the theory is outlined and the method is demonstrated in 2D applications. Second, the method is implemented within a domain decomposition framework in order to reduce the cost for processing very large problems. Finally, it is extended to 3D numerical examples. Promising results are shown for 2D and 3D problems.
Long memory of financial time series and hidden Markov models with time-varying parameters
DEFF Research Database (Denmark)
Nystrup, Peter; Madsen, Henrik; Lindström, Erik
facts have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time-varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared...... daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step predictions....
A new switching parameter varying optoelectronic delayed feedback model with computer simulation
Liu, Lingfeng; Miao, Suoxia; Cheng, Mengfan; Gao, Xiaojing
2016-02-01
In this paper, a new switching parameter varying optoelectronic delayed feedback model is proposed and analyzed by computer simulation. This model is switching between two parameter varying optoelectronic delayed feedback models based on chaotic pseudorandom sequences. Complexity performance results show that this model has a high complexity compared to the original model. Furthermore, this model can conceal the time delay effectively against the auto-correlation function, delayed mutual information and permutation information analysis methods, and can extent the key space, which greatly improve its security.
Time-varying parameter auto-regressive models for autocovariance nonstationary time series
Institute of Scientific and Technical Information of China (English)
FEI WanChun; BAI Lun
2009-01-01
In this paper,autocovariance nonstationary time series is clearly defined on a family of time series.We propose three types of TVPAR (time-varying parameter auto-regressive) models:the full order TVPAR model,the time-unvarying order TVPAR model and the time-varying order TVPAR model for autocovariance nonstationary time series.Related minimum AIC (Akaike information criterion) estimations are carried out.
Time-varying parameter auto-regressive models for autocovariance nonstationary time series
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.
Nonlinear model predictive control using parameter varying BP-ARX combination model
Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.
2012-03-01
A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.
Bianchi type-V dark energy model with varying EoS parameter
Saha, Bijan
2012-01-01
Within the scope of an anisotropic Bianchi type-V cosmological model we have studied the evolution of the universe. The assumption of a diagonal energy-momentum tensor leads to some severe restriction on the metric functions, which on its part imposes restriction on the components of the energy momentum tensor. This model allows anisotropic matter distribution. Further using the proportionality condition that relates the shear scalar $(\\sigma)$ in the model is proportional to expansion scalar $(\\vartheta)$ and the variation law of Hubble parameter, connecting Hubble parameter with volume scale. Exact solution to the corresponding equations are obtained. The EoS parameter for dark energy as well as deceleration parameter is found to be the time varying functions. A qualitative picture of the evolution of the universe corresponding to different of its stages is given using the latest observational data.
She, M.; Jiang, L. P.
2014-12-01
In this paper, an oscillating dark energy model is presented in an isotropic but inhomogeneous plane symmetric space-time by considering a time periodic varying deceleration parameter. We find three different types of new solutions which describe different scenarios of oscillating universe. The first two solutions show an oscillating universe with singularities. For the third one, the universe is singularity-free during the whole evolution. Moreover, the Hubble parameter oscillates and keeps positive which explores an interesting possibility to unify the early inflation and late time acceleration of the universe.
Time-Varying FOPDT Modeling and On-line Parameter Identification
DEFF Research Database (Denmark)
Yang, Zhenyu; Sun, Zhen
2013-01-01
A type of Time-Varying First-Order Plus Dead-Time (TV-FOPDT) model is extended from SISO format into a MISO version by explicitly taking the disturbance input into consideration. Correspondingly, a set of on-line parameter identification algorithms oriented to MISO TV-FOPDT model are proposed bas...... are firstly illustrated through a numerical example, and then applied to investigate transient superheat dynamic modeling in a supermarket refrigeration system....... on the Mixed-Integer-Nonlinear Programming, Least-Mean-Square and sliding window techniques. The proposed approaches can simultaneously estimate the time-dependent system parameters, as well as the unknown disturbance input if it is the case, in an on-line manner. The proposed concepts and algorithms...
National Aeronautics and Space Administration — The overall goal of the project is to develop reliable reduced order modeling technologies to automatically generate nonlinear, parameter-varying (PV),...
A Time-Varying Parameter Vector Autoregression Model for Forecasting Emerging Market Exchange Rates
Directory of Open Access Journals (Sweden)
Manish Kumar
2010-12-01
Full Text Available In this study, a vector autoregression (VAR model with time-varying parameters (TVP to predict the daily Indian rupee (INR/US dollar (USD exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a blend of the flexible least squares and Kalman filter techniques. The out-of-sample forecasting performance of the TVP-VAR model is evaluated against the simple VAR and ARIMA models, by employing a cross-validation process and metrics such as mean absolute error, root mean square error, and directional accuracy. Outof-sample results in terms of conventional forecast evaluation statistics and directional accuracy show TVP-VAR model consistently outperforms the simple VAR and ARIMA models.
Linear Parameter Varying Model Identification for Control of Rotorcraft-based UAV
Budiyono, Agus
2008-01-01
A rotorcraft-based unmanned aerial vehicle exhibits more complex properties compared to its full-size counterparts due to its increased sensitivity to control inputs and disturbances and higher bandwidth of its dynamics. As an aerial vehicle with vertical take-off and landing capability, the helicopter specifically poses a difficult problem of transition between forward flight and unstable hover and vice versa. The LPV control technique explicitly takes into account the change in performance due to the real-time parameter variations. The technique therefore theoretically guarantees the performance and robustness over the entire operating envelope. In this study, we investigate a new approach implementing model identification for use in the LPV control framework. The identification scheme employs recursive least square technique implemented on the LPV system represented by dynamics of helicopter during a transition. The airspeed as the scheduling of parameter trajectory is not assumed to vary slowly. The exclu...
Li, Shanzhi; Wang, Haoping; Aitouche, Abdel; Tian, Yang; Christov, Nicolai
2017-01-01
This paper proposes a robust unknown input observer for state estimation and fault detection using linear parameter varying model. Since the disturbance and actuator fault is mixed together in the physical system, it is difficult to isolate the fault from the disturbance. Using the state transforation, the estimation of the original state becomes to associate with the transform state. By solving the linear matrix inequalities (LMIs)and linear matrix equalities (LMEs), the parameters of the UIO can be obtained. The convergence of the UIO is also analysed by the Layapunov theory. Finally, a wind turbine system with disturbance and actuator fault is tested for the proposed method. From the simulations, it demonstrates the effectiveness and performances of the proposed method.
Linear parameter-varying modeling and control of the steam temperature in a Canadian SCWR
Energy Technology Data Exchange (ETDEWEB)
Sun, Peiwei, E-mail: sunpeiwei@mail.xjtu.edu.cn; Zhang, Jianmin; Su, Guanghui
2017-03-15
Highlights: • Nonlinearity of Canadian SCWR is analyzed based on step responses and Nyquist plots. • LPV model is derived through Jacobian linearization and curve fitting. • An output feedback H{sub ∞} controller is synthesized for the steam temperature. • The control performance is evaluated by step disturbances and wide range operation. • The controller can stabilize the system and reject the reactor power disturbance. - Abstract: The Canadian direct-cycle Supercritical Water-cooled Reactor (SCWR) is a pressure-tube type SCWR under development in Canada. The dynamics of the steam temperature have a high degree of nonlinearity and are highly sensitive to reactor power disturbances. Traditional gain scheduling control cannot theoretically guarantee stability for all operating regions. The control performance can also be deteriorated when the controllers are switched. In this paper, a linear parameter-varying (LPV) strategy is proposed to solve such problems. Jacobian linearization and curve fitting are applied to derive the LPV model, which is verified using a nonlinear dynamic model and determined to be sufficiently accurate for control studies. An output feedback H{sub ∞} controller is synthesized to stabilize the steam temperature system and reject reactor power disturbances. The LPV steam temperature controller is implemented using a nonlinear dynamic model, and step changes in the setpoints and typical load patterns are carried out in the testing process. It is demonstrated through numerical simulation that the LPV controller not only stabilizes the steam temperature under different disturbances but also efficiently rejects reactor power disturbances and suppresses the steam temperature variation at different power levels. The LPV approach is effective in solving control problems of the steam temperature in the Canadian SCWR.
Directory of Open Access Journals (Sweden)
Huiguo Chen
2017-01-01
Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.
Directory of Open Access Journals (Sweden)
Miholca CONSTANTIN
2008-07-01
Full Text Available The paper presents a method of mathematical modelling of a solar converter using the results of full-scale testing. The advantages of analytical modelling method applied to photovoltaic systems are also presented; this is because the model parameters are directly measurable by data acquisition from the photovoltaic field consisting of photovoltaic cells type Z - (mono-crystalline photovoltaic. The model parameter also includes both the photovoltaic cell characteristics as a device (forming the photovoltaic field and the temperature influence on the photovoltaic field performance. The results of the photovoltaic model numerical simulation (PV to the major parameters conversion variation can also be used to design and assess the performance of low and medium - power photovoltaic systems operating in single regime (to supply the home appliances.
Artificial neural network modeling of DDGS flowability with varying process and storage parameters
Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels....
Institute of Scientific and Technical Information of China (English)
Zheng-yan Lin; Yu-ze Yuan
2012-01-01
Semiparametric models with diverging number of predictors arise in many contemporary scientific areas. Variable selection for these models consists of two components: model selection for non-parametric components and selection of significant variables for the parametric portion.In this paper,we consider a variable selection procedure by combining basis function approximation with SCAD penalty.The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components.With appropriate selection of tuning parameters,we establish the consistency and sparseness of this procedure.
The Impact of Varied Discrimination Parameters on Mixed-Format Item Response Theory Model Selection
Whittaker, Tiffany A.; Chang, Wanchen; Dodd, Barbara G.
2013-01-01
Whittaker, Chang, and Dodd compared the performance of model selection criteria when selecting among mixed-format IRT models and found that the criteria did not perform adequately when selecting the more parameterized models. It was suggested by M. S. Johnson that the problems when selecting the more parameterized models may be because of the low…
Early marketing matters : A time-varying parameter approach to persistence modeling
Osinga, E.C.; Leeflang, P.S.H.; Wieringa, J.E.
Are persistent marketing effects most likely to appear right after the introduction of a product? The authors give an affirmative answer to this question by developing a model that explicitly reports how persistent and transient marketing effects evolve over time. The proposed model provides
Early marketing matters : A time-varying parameter approach to persistence modeling
Osinga, E.C.; Leeflang, P.S.H.; Wieringa, J.E.
2010-01-01
Are persistent marketing effects most likely to appear right after the introduction of a product? The authors give an affirmative answer to this question by developing a model that explicitly reports how persistent and transient marketing effects evolve over time. The proposed model provides manager
Borgonovo, Emanuele
2010-03-01
In risk analysis problems, the decision-making process is supported by the utilization of quantitative models. Assessing the relevance of interactions is an essential information in the interpretation of model results. By such knowledge, analysts and decisionmakers are able to understand whether risk is apportioned by individual factor contributions or by their joint action. However, models are oftentimes large, requiring a high number of input parameters, and complex, with individual model runs being time consuming. Computational complexity leads analysts to utilize one-parameter-at-a-time sensitivity methods, which prevent one from assessing interactions. In this work, we illustrate a methodology to quantify interactions in probabilistic safety assessment (PSA) models by varying one parameter at a time. The method is based on a property of the functional ANOVA decomposition of a finite change that allows to exactly determine the relevance of factors when considered individually or together with their interactions with all other factors. A set of test cases illustrates the technique. We apply the methodology to the analysis of the core damage frequency of the large loss of coolant accident of a nuclear reactor. Numerical results reveal the nonadditive model structure, allow to quantify the relevance of interactions, and to identify the direction of change (increase or decrease in risk) implied by individual factor variations and by their cooperation.
Avendaño-Valencia, L. D.; Fassois, S. D.
2015-07-01
The problem of damage detection in an operating wind turbine under normal operating conditions is addressed. This is characterized by difficulties associated with the lack of measurable excitation(s), the vibration response non-stationary nature, and its dependence on various types of uncertainties. To overcome these difficulties a stochastic approach based on Random Coefficient (RC) Linear Parameter Varying (LPV) AutoRegressive (AR) models is postulated. These models may effectively represent the non-stationary random vibration response under healthy conditions and subsequently used for damage detection through hypothesis testing. The performance of the method for damage and fault detection in an operating wind turbine is subsequently assessed via Monte Carlo simulations using the FAST simulation package.
DEFF Research Database (Denmark)
Matzuka, Brett; Mehlsen, Jesper; Tran, Hien
2015-01-01
, while the second uses the ensemble transform Kalman filter (ETKF) [1], [12], [13], [35]. In addition, we show that the delayed rejection adaptive Metropolis (DRAM) algorithm can be used for predicting parameter uncertainties within the spline methodology, which is compared with the variability obtained...
Linear, Parameter-Varying Control of Aeroservoelastic Systems
Moreno Chicunque, Claudia Patricia
Modern aircraft designers are adopting light-weight, high-aspect ratio flexible wings to improve performance and reduce operation costs. A technical challenge associated with these designs is that the large deformations in flight of the wings lead to adverse interactions between the aircraft aerodynamic forces and structural forces. These adverse interactions produce excessive vibrations that can degrade flying qualities and may result in severe structural damages or catastrophic failure. This dissertation is focused on the application of multivariable robust control techniques for suppression of these adverse interactions in flexible aircraft. Here, the aircraft coupled nonlinear equations of motion are represented in the linear, parameter-varying framework. These equations account for the coupled aerodynamics, rigid body dynamics, and deformable body dynamics of the aircraft. Unfortunately, the inclusion of this coupled dynamics results in high-order models that increase the computational complexity of linear, parameter-varying control techniques. This dissertation addresses three key technologies for linear, parameter-varying control of flexible aircraft: (i) linear, parameter-varying model reduction; (ii) selection of actuators and sensors for vibration suppression; and (iii) design of linear, parameter-varying controllers for vibration suppression. All of these three technologies are applied to an experimental research platform located at the University of Minnesota. The objective of this dissertation is to provide to the flight control community with a set of design methodologies to safely exploit the benefits of light-weight flexible aircraft.
Structured Linear Parameter Varying Control of Wind Turbines
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Sloth, Christoffer; Stoustrup, Jakob
2012-01-01
is presented. We specifically consider variable-speed, variable-pitch wind turbines with faults on actuators and sensors. Linear parameter-varying (LPV) controllers can be designed by a proposed method that allows the inclusion of faults in the LPV controller design. Moreover, the controller structure can......High performance and reliability are required for wind turbines to be competitive within the energy market. To capture their nonlinear behavior, wind turbines are often modeled using parameter-varying models. In this chapter, a framework for modelling and controller design of wind turbines...... be arbitrarily chosen: static output feedback, dynamic (reduced order) output feedback, decentralized, among others. The controllers are scheduled on an estimated wind speed to manage the parametervarying nature of the model and on information from a fault diagnosis system. The optimization problems involved...
Tracking time-varying parameters with local regression
DEFF Research Database (Denmark)
Joensen, Alfred Karsten; Nielsen, Henrik Aalborg; Nielsen, Torben Skov;
2000-01-01
This paper shows that the recursive least-squares (RLS) algorithm with forgetting factor is a special case of a varying-coe\\$cient model, and a model which can easily be estimated via simple local regression. This observation allows us to formulate a new method which retains the RLS algorithm, bu......, but extends the algorithm by including polynomial approximations. Simulation results are provided, which indicates that this new method is superior to the classical RLS method, if the parameter variations are smooth....
Identification and robust control of linear parameter-varying systems
Lee, Lawton Hubert
This dissertation deals with linear parameter-varying (LPV) systems: linear dynamic systems that depend on time-varying parameters. These systems appear in gain scheduling problems, and much recent research has been devoted to their prospective usefulness for systematic gain scheduling. We primarily focus on robust control of uncertain LPV systems and identification of LPV systems that are modelable as linear-fractional transformations (LFTs). Using parameter-dependent quadratic Lyapunov functions, linear matrix inequalities (LMIs), and scaled small-gain arguments, we define notions of stability and induced-{cal L}sb2 performance for uncertain LPV systems whose parameters and rates of parameter variation satisfy given bounds. The performance criterion involves integral quadratic constraints and implies naturally parameter-dependent induced-{cal L}sb2 norm bounds. We formulate and solve an {cal H}sb{infty}-like control problem for an LPV plant with measurable parameters and an "Output/State Feedback" structure: the feedback outputs include some noiselessly measured states. Necessary and sufficient solvability conditions reduce to LMIs that can be solved approximately using finite-dimensional convex programming. Reduced-order LPV controllers are constructed from the LMI solutions. A D-K iteration-like procedure provides robustness to structured, time-varying, parametric uncertainty. The design method is applied to a motivating example: flight control for the F-16 VISTA throughout its subsonic flight envelope. Parameter-dependent weights and {cal H}sb{infty} design principles describe the performance objectives. Closed-loop responses exhibited by nonlinear simulations indicate satisfactory flying qualities. Identification of linear-fractional LPV systems is treated using maximum-likelihood parameter estimation. Computing the gradient and Hessian of a maximum-likelihood cost function reduces to simulating one LPV filter per identified parameter. We use nonlinear
Control of Linear Parameter Varying Systems with Applications
Mohammadpour, Javad
2012-01-01
Control of Linear Parameter Varying Systems with Applications compiles state-of-the-art contributions on novel analytical and computational methods to address system modeling and identification, complexity reduction, performance analysis and control design for time-varying and nonlinear systems in the LPV framework. The book has an interdisciplinary character by emphasizing techniques that can be commonly applied in various engineering fields. It also includes a rich collection of illustrative applications in diverse domains to substantiate the effectiveness of the design methodologies and provide pointers to open research directions. The book is divided into three parts. The first part collects chapters of a more tutorial character on the background of LPV systems modeling and control. The second part gathers chapters devoted to the theoretical advancement of LPV analysis and synthesis methods to cope with the design constraints such as uncertainties and time delay. The third part of the volume showcases con...
Expected optimal feedback with Time-Varying Parameters
Tucci, M.P.; Kendrick, D.A.; Amman, H.M.
2011-01-01
In this paper we derive the closed loop form of the Expected Optimal Feedback rule, sometimes called passive learning stochastic control, with time varying parameters. As such this paper extends the work of Kendrick (1981,2002, Chapter 6) where parameters are assumed to vary randomly around a known
Linear Parameter Varying Control of Doubly Fed Induction Machines
Tien, H. Nguyen; Scherer, Carsten W.; Scherpen, Jacquelien M.A.; Müller, Volkmar
2016-01-01
This paper is concerned with the design of a self-scheduled current controller for doubly fed induction machines. The design is based on the framework of linear parameter-varying systems where the mechanical angular speed is considered to be a measurable time-varying parameter. The objective is to o
Varying Alpha and the Electroweak Model
Kimberly, D; Kimberly, Dagny; Magueijo, Joao
2003-01-01
Inspired by recent claims for a varying fine structure constant, alpha, we investigate the effect of ``promoting coupling constants to variables'' upon various parameters of the standard model. We first consider a toy model: Proca's theory of the massive photon. We then explore the electroweak theory with one and two dilaton fields. We find that a varying alpha unavoidably implies varying W and Z masses. This follows from gauge invariance, and is to be contrasted with Proca' theory. For the two dilaton theory the Weinberg angle is also variable, but Fermi's constant and the tree level fermion masses remain constant unless the Higgs' potential becomes dynamical. We outline some cosmological implications.
Linearized Bekenstein Varying Alpha Models
Pina-Avelino, P; Oliveira, J C
2004-01-01
We study the simplest class of Bekenstein-type, varying $\\alpha$ models, in which the two available free functions (potential and gauge kinetic function) are Taylor-expanded up to linear order. Any realistic model of this type reduces to a model in this class for a certain time interval around the present day. Nevertheless, we show that no such model is consistent with all existing observational results. We discuss possible implications of these findings, and in particular clarify the ambiguous statement (often found in the literature) that ``the Webb results are inconsistent with Oklo''.
Linearized Bekenstein varying α models
Avelino, P. P.; Martins, C. J.; Oliveira, J. C.
2004-10-01
We study the simplest class of Bekenstein-type, varying α models, in which the two available free functions (potential and gauge kinetic function) are Taylor-expanded up to linear order. Any realistic model of this type reduces to a model in this class for a certain time interval around the present day. Nevertheless, we show that no such model is consistent with all existing observational results. We discuss possible implications of these findings, and, in particular, clarify the ambiguous statement (often found in the literature) that “the Webb results are inconsistent with Oklo.”
Cooperative Output Regulation of Multiagent Linear Parameter-Varying Systems
Directory of Open Access Journals (Sweden)
Afshin Mesbahi
2017-01-01
Full Text Available The output regulation problem is examined in this paper for a class of heterogeneous multiagent systems whose dynamics are governed by polytopic linear parameter-varying (LPV models. The dynamics of the agents are decoupled from each other but the agents’ controllers are assumed to communicate. To design the cooperative LPV controllers, analysis conditions for closed-loop system are first established to ensure stability and reference tracking. Then, the LPV control synthesis problem is addressed, where the offline solution to a time-varying Sylvester equation will be used to determine and update in real time the controller state-space matrices. Two numerical examples will be finally given to demonstrate the efficacy of the proposed cooperative design method.
Observer-based linear parameter varying H∞ tracking control for hypersonic vehicles
Directory of Open Access Journals (Sweden)
Yiqing Huang
2016-11-01
Full Text Available This article aims to develop observer-based linear parameter varying output feedback H∞ tracking controller for hypersonic vehicles. Due to the complexity of an original nonlinear model of the hypersonic vehicle dynamics, a slow–fast loop linear parameter varying polytopic model is introduced for system stability analysis and controller design. Then, a state observer is developed by linear parameter varying technique in order to estimate the unmeasured attitude angular for slow loop system. Also, based on the designed linear parameter varying state observer, a kind of attitude tracking controller is presented to reduce tracking errors for all bounded reference attitude angular inputs. The closed-loop linear parameter varying system is proved to be quadratically stable by Lypapunov function technique. Finally, simulation results show that the developed linear parameter varying H∞ controller has good tracking capability for reference commands.
Varying alpha and the electroweak model
Energy Technology Data Exchange (ETDEWEB)
Kimberly, Dagny; Magueijo, Joao
2004-03-25
Inspired by recent claims for a varying fine structure constant, alpha, we investigate the effect of 'promoting coupling constants to variables' upon various parameters of the standard model. We first consider a toy model: Proca theory of the massive photon. We then explore the electroweak theory with one and two dilaton fields. We find that a varying alpha unavoidably implies varying W and Z masses. This follows from gauge invariance, and is to be contrasted with Proca theory. For the two dilaton theory the Weinberg angle is also variable, but Fermi's constant and the tree level fermion masses remain constant unless the Higgs potential becomes dynamical. We outline some cosmological implications.
Incremental Closed-loop Identification of Linear Parameter Varying Systems
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2011-01-01
This paper deals with system identification for control of linear parameter varying systems. In practical applications, it is often important to be able to identify small plant changes in an incremental manner without shutting down the system and/or disconnecting the controller; unfortunately, cl...
Directory of Open Access Journals (Sweden)
Lei Hu
2015-01-01
Full Text Available Rotational speed and load usually change when rotating machinery works. Both this kind of changing operational conditions and machine fault could make the mechanical vibration characteristics change. Therefore, effective health monitoring method for rotating machinery must be able to adjust during the change of operational conditions. This paper presents an adaptive threshold model for the health monitoring of bearings under changing operational conditions. Relevance vector machines (RVMs are used for regression of the relationships between the adaptive parameters of the threshold model and the statistical characteristics of vibration features. The adaptive threshold model is constructed based on these relationships. The health status of bearings can be indicated via detecting whether vibration features exceed the adaptive threshold. This method is validated on bearings running at changing speeds. The monitoring results show that this method is effective as long as the rotational speed is higher than a relative small value.
Linear parameter-varying control for engineering applications
White, Andrew P; Choi, Jongeun
2013-01-01
The objective of this brief is to carefully illustrate a procedure of applying linear parameter-varying (LPV) control to a class of dynamic systems via a systematic synthesis of gain-scheduling controllers with guaranteed stability and performance. The existing LPV control theories rely on the use of either H-infinity or H2 norm to specify the performance of the LPV system. The challenge that arises with LPV control for engineers is twofold. First, there is no systematic procedure for applying existing LPV control system theory to solve practical engineering problems from modeling to control design. Second, there exists no LPV control synthesis theory to design LPV controllers with hard constraints. For example, physical systems usually have hard constraints on their required performance outputs along with their sensors and actuators. Furthermore, the H-infinity and H2 performance criteria cannot provide hard constraints on system outputs. As a result, engineers in industry could find it difficult to utiliz...
Free electron lasers with slowly varying beam and undulator parameters
Directory of Open Access Journals (Sweden)
Z. Huang
2005-04-01
Full Text Available A self-consistent theory of a free electron laser (FEL with slowly varying beam and undulator parameters is developed using the WKB approximation. The theory is applied to study the performance of a self-amplified spontaneous emission (SASE FEL when the electron beam energy varies along the undulator as would be caused by vacuum pipe wakefields and/or when the undulator strength parameter is tapered in the small signal regime before FEL saturation. We find that a small energy gain or an equivalent undulator taper slightly reduces the power gain length in the exponential growth regime and can increase the saturated SASE power by about a factor of 2. Power degradation away from the optimal performance can be estimated based upon knowledge of the SASE bandwidth. The analytical results, which agree with numerical simulations, are used to optimize the undulator taper and to evaluate wakefield effects.
Institute of Scientific and Technical Information of China (English)
牟静; 陶超; 杜功焕
2003-01-01
In this paper we propose and investigate the synchronization of a new chaotic model with time-varying parameters and apply it to improve the security of chaotic communication. In this model, the chaotic system is modulated by both the message and the varying parameters. The varying parameters distort the phase space so heavily that they prevent the carrier from being broken by nonlinear dynamic forecasting method. Theory and simulation experiments with speech signal communication indicate that the receiver can gain a perfect synchronization with the transmitter, and the intruder cannot break down this communication system. We also discuss the robustness of the new communication system.
Institute of Scientific and Technical Information of China (English)
Mujing; TaoChao; DuGong-Huan
2003-01-01
In this paper we propose and investigate the synchronization of a new chaotic model with time-varying parameters and apply it to improve the security of chaotic communication. In this model, the chaotic system is modulated by both the message and the varying parameters. The varying parameters distort the phase space so heavily that they prevent the carrier from being broken by nonlinear dynamic forecasting method. Theory and simulation experiments with speech signal communication indicate that the receiver can gain a perfect synchronization with the transmitter, and the intruder cannot break down this communication system. We also discuss the robustness of the new communication system.
Energy Technology Data Exchange (ETDEWEB)
Ibsen, Lars Bo; Liingaard, M.
2006-12-15
A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. In this technical report the steps of establishing a lumped-parameter model are presented. Following sections are included in this report: Static and dynamic formulation, Simple lumped-parameter models and Advanced lumped-parameter models. (au)
Inhomogeneous Universe Models with Varying Cosmological Term
Chimento, L P; Chimento, Luis P.; Pavon, Diego
1998-01-01
The evolution of a class of inhomogeneous spherically symmetric universe models possessing a varying cosmological term and a material fluid, with an adiabatic index either constant or not, is studied.
Modified Hubble law, the time-varying Hubble parameter and the problem of dark energy
Liu, Jian-Miin
2005-01-01
In the framework of the solvable model of cosmology constructed in the Earth-related coordinate system, we derive the modified Hubble law. This law carries the slowly time-varying Hubble parameter. The modified Hubble law eliminates the need for dark energy.
Modified Hubble law, the time-varying Hubble parameter and the problem of dark energy
Liu, Jian-Miin
2005-01-01
In the framework of the solvable model of cosmology constructed in the Earth-related coordinate system, we derive the modified Hubble law. This law carries the slowly time-varying Hubble parameter. The modified Hubble law eliminates the need for dark energy.
DEFF Research Database (Denmark)
Ibsen, Lars Bo; Liingaard, Morten
A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. The lumped-parameter model development have been reported by (Wolf 1991b; Wolf 1991a; Wolf and Paronesso 1991; Wolf and Paronesso 19...
Efficient Estimation in Heteroscedastic Varying Coefficient Models
Directory of Open Access Journals (Sweden)
Chuanhua Wei
2015-07-01
Full Text Available This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the performance of the proposed method.
EOQ Models with Varying Holding Cost
Directory of Open Access Journals (Sweden)
Naser Ghasemi
2013-01-01
Full Text Available Models of inventory management contain different parameters. An issue is observable in the classical models which can be related to the determination of the quantity of the economic order and the quantity of the economic production. In these models, the parameters like setup and holding costs and also the rate of demands are fixed. This matter causes the quantity of the economic ordering in classic model to have some differences in comparison with the real-world conditions. It should be stated that holding cost of spoiled and useless products is not always fixed and so the costs increase by passing the time. This paper is an attempt to develop classical EOQ models by considering holding cost as an increasing function of the ordering cycle length. So the classical EOQ models are developed, and the related optimum quantity to the ordering cycle length, economic ordering quantity, and the optimum total cost are determined.
An ETAS model with varying productivity rates
Harte, D. S.
2014-07-01
We present an epidemic type aftershock sequenc (ETAS) model where the offspring rates vary both spatially and temporally. This is achieved by distinguishing between those space-time volumes where the interpoint space and time distances are small, and those where they are considerably larger. We also question the nature of the background component in the ETAS model. Is it simply a temporal boundary correction (t = 0) or does it represent an additional tectonic process not described by the aftershock component? The form of these stochastic models should not be considered to be fixed. As we accumulate larger and better earthquake catalogues, GPS data, strain rates, etc., we have the ability to ask more complex questions about the nature of the process. By fitting modified models consistent with such questions, we should gain a better insight into the earthquake process. Hence, we consider a sequence of incrementally modified ETAS type models rather than `the' ETAS model.
CMB Constraints on Reheating Models with Varying Equation of State
de Freitas, Rodolfo C
2015-01-01
The temperature at the end of reheating and the length of this cosmological phase can be bound to the inflationary observables if one considers the cosmological evolution from the time of Hubble crossing until today. There are many examples in the literature where it is made for single-field inflationary models and a constant equation of state during reheating. We adopt two simple varying equation of state parameters during reheating, combine the allowed range of the reheating parameters with the observational limits of the scalar perturbations spectral index and compare the constraints of some inflationary models with the case of a constant equation of state parameter during reheating.
Response model parameter linking
Barrett, Michelle Derbenwick
2015-01-01
With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of equating observed scores on different test forms. This thesis argues, however, that the use of item response models does not require
Local Rank Inference for Varying Coefficient Models.
Wang, Lan; Kai, Bo; Li, Runze
2009-12-01
By allowing the regression coefficients to change with certain covariates, the class of varying coefficient models offers a flexible approach to modeling nonlinearity and interactions between covariates. This paper proposes a novel estimation procedure for the varying coefficient models based on local ranks. The new procedure provides a highly efficient and robust alternative to the local linear least squares method, and can be conveniently implemented using existing R software package. Theoretical analysis and numerical simulations both reveal that the gain of the local rank estimator over the local linear least squares estimator, measured by the asymptotic mean squared error or the asymptotic mean integrated squared error, can be substantial. In the normal error case, the asymptotic relative efficiency for estimating both the coefficient functions and the derivative of the coefficient functions is above 96%; even in the worst case scenarios, the asymptotic relative efficiency has a lower bound 88.96% for estimating the coefficient functions, and a lower bound 89.91% for estimating their derivatives. The new estimator may achieve the nonparametric convergence rate even when the local linear least squares method fails due to infinite random error variance. We establish the large sample theory of the proposed procedure by utilizing results from generalized U-statistics, whose kernel function may depend on the sample size. We also extend a resampling approach, which perturbs the objective function repeatedly, to the generalized U-statistics setting; and demonstrate that it can accurately estimate the asymptotic covariance matrix.
Flexible-link Robot Control Using a Linear Parameter Varying Systems Methodology
Directory of Open Access Journals (Sweden)
Houssem Halalchi
2014-03-01
Full Text Available This paper addresses the issues of the Linear Parameter Varying (LPV modelling and control of flexible-link robot manipulators. The LPV formalism allows the synthesis of nonlinear control laws and the assessment of their closed-loop stability and performances in a simple and effective manner, based on the use of Linear Matrix Inequalities (LMI. Following the quasi-LPV modelling approach, an LPV model of a flexible manipulator is obtained, starting from the nonlinear dynamic model stemming from Euler-Lagrange equations. Based on this LPV model, which has a rational dependence in terms of the varying parameters, two different methods for the synthesis of LPV controllers are explored. They guarantee the asymptotic stability and some level of closed-loop L2-gain performance on a bounded parametric set. The first method exploits a descriptor representation that simplifies the rational dependence of the LPV model, whereas the second one manages the troublesome rational dependence by using dilated LMI conditions and taking the particular structure of the model into account. The resulting controllers involve the measured state variables only, namely the joint positions and velocities. Simulation results are presented that illustrate the validity of the proposed control methodology. Comparisons with an inversion-based nonlinear control method are performed in the presence of velocity measurement noise, model uncertainties and high-frequency inputs.
National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes to develop an on-line flutter prediction tool for wind tunnel model using the parameter varying estimation (PVE) technique to...
Robust and Fault-Tolerant Linear Parameter-Varying Control of Wind Turbines
DEFF Research Database (Denmark)
Sloth, Christoffer; Esbensen, Thomas; Stoustrup, Jakob
2011-01-01
, designed using a proposed method that allows the inclusion of both faults and uncertainties in the LPV controller design. We specifically consider a 4.8 MW, variable-speed, variable-pitch wind turbine model with a fault in the pitch system. We propose the design of a nominal controller (NC), handling...... the parameter variations along the nominal operating trajectory caused by nonlinear aerodynamics. To accommodate the fault in the pitch system, an active fault-tolerant controller (AFTC) and a passive fault-tolerant controller (PFTC) are designed. In addition to the nominal LPV controller, we also propose...... a robust controller (RC). This controller is able to take into account model uncertainties in the aerodynamic model. The controllers are based on output feedback and are scheduled on an estimated wind speed to manage the parameter-varying nature of the model. Furthermore, the AFTC relies on information...
Crops Models for Varying Environmental Conditions
Jones, Harry; Cavazzoni, James; Keas, Paul
2001-01-01
New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allows changes in light energy but uses a less accurate linear approximation. The simulation outputs of the new MEC models for constant nominal environmental conditions are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have realistic exponential canopy growth, and have corrected harvest dates for potato and tomato. The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures, The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to- day to accommodate power shortages or Lise excess power while monitoring and controlling edible biomass production.
Franzè, Giuseppe; Lucia, Walter; Tedesco, Francesco
2014-12-01
This paper proposes a Model Predictive Control (MPC) strategy to address regulation problems for constrained polytopic Linear Parameter Varying (LPV) systems subject to input and state constraints in which both plant measurements and command signals in the loop are sent through communication channels subject to time-varying delays (Networked Control System (NCS)). The results here proposed represent a significant extension to the LPV framework of a recent Receding Horizon Control (RHC) scheme developed for the so-called robust case. By exploiting the parameter availability, the pre-computed sequences of one- step controllable sets inner approximations are less conservative than the robust counterpart. The resulting framework guarantees asymptotic stability and constraints fulfilment regardless of plant uncertainties and time-delay occurrences. Finally, experimental results on a laboratory two-tank test-bed show the effectiveness of the proposed approach.
MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES.
Zhu, Hongtu; Li, Runze; Kong, Linglong
2012-10-01
Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.
Time-varying modeling of cerebral hemodynamics.
Marmarelis, Vasilis Z; Shin, Dae C; Orme, Melissa; Rong Zhang
2014-03-01
The scientific and clinical importance of cerebral hemodynamics has generated considerable interest in their quantitative understanding via computational modeling. In particular, two aspects of cerebral hemodynamics, cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity (CVR), have attracted much attention because they are implicated in many important clinical conditions and pathologies (orthostatic intolerance, syncope, hypertension, stroke, vascular dementia, mild cognitive impairment, Alzheimer's disease, and other neurodegenerative diseases with cerebrovascular components). Both CFA and CVR are dynamic physiological processes by which cerebral blood flow is regulated in response to fluctuations in cerebral perfusion pressure and blood CO2 tension. Several modeling studies to date have analyzed beat-to-beat hemodynamic data in order to advance our quantitative understanding of CFA-CVR dynamics. A confounding factor in these studies is the fact that the dynamics of the CFA-CVR processes appear to vary with time (i.e., changes in cerebrovascular characteristics) due to neural, endocrine, and metabolic effects. This paper seeks to address this issue by tracking the changes in linear time-invariant models obtained from short successive segments of data from ten healthy human subjects. The results suggest that systemic variations exist but have stationary statistics and, therefore, the use of time-invariant modeling yields "time-averaged models" of physiological and clinical utility.
Distributed Parameter Modelling Applications
DEFF Research Database (Denmark)
2011-01-01
Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers and the d......Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers...... sands processing. The fertilizer granulation model considers the dynamics of MAP-DAP (mono and diammonium phosphates) production within an industrial granulator, that involves complex crystallisation, chemical reaction and particle growth, captured through population balances. A final example considers...
Indian Academy of Sciences (India)
HAIBIN ZHANG; WEI XIONG; SHANGBIN ZHANG; QINGBO HE; FANRANG KONG
2016-06-01
The nonlinear stochastic resonance system possesses the ability of taking advantage of background noise to enhance the weak signal. It provides a new approach to detect the weak signal embedded with heavy noise. This study proposes a new varying parameter stochastic resonance employing the fourth-order Runge–Kutta numerical method as well as the normalized transformation of a bistable stochastic resonance system. The model performs well in the detection of a time-varying signal with background noise for denoising and signal recovery. We take the fitness coefficient and cross-correlation coefficient as the criteria and analyze the influence of different parameters. The simulating results indicate its availability, validity and that it generates a betterperformance than the traditional stochastic resonance. The method develops the area of time-varying signal detection with stochastic resonance and presents new strategy for detection and denoising of a time-varying signal. It can be expected to be widely used in the areas of aperiodic signal processing, radar communication,etc
Nonlinear systems time-varying parameter estimation: Application to induction motors
Energy Technology Data Exchange (ETDEWEB)
Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, IUT FOTSO Victor, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Ahmed-Ali, Tarek [Ecole Nationale Superieure des Ingenieurs des Etudes et Techniques d' Armement (ENSIETA), 2 Rue Francois Verny, 29806 Brest Cedex 9 (France); Lamnabhi-Lagarrigue, F. [Laboratoire des Signaux et Systemes (L2S), C.N.R.S-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)
2008-11-15
In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines. (author)
Finite mixture varying coefficient models for analyzing longitudinal heterogenous data.
Lu, Zhaohua; Song, Xinyuan
2012-03-15
This paper aims to develop a mixture model to study heterogeneous longitudinal data on the treatment effect of heroin use from a California Civil Addict Program. Each component of the mixture is characterized by a varying coefficient mixed effect model. We use the Bayesian P-splines approach to approximate the varying coefficient functions. We develop Markov chain Monte Carlo algorithms to estimate the smooth functions, unknown parameters, and latent variables in the model. We use modified deviance information criterion to determine the number of components in the mixture. A simulation study demonstrates that the modified deviance information criterion selects the correct number of components and the estimation of unknown quantities is accurate. We apply the proposed model to the heroin treatment study. Furthermore, we identify heterogeneous longitudinal patterns.
LMI-based gain scheduled controller synthesis for a class of linear parameter varying systems
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Anderson, Brian; Lanzon, Alexander
2006-01-01
This paper presents a novel method for constructing controllers for a class of single-input multiple-output (SIMO) linear parameter varying (LPV) systems. This class of systems encompasses many physical systems, in particular systems where individual components vary with time, and is therefore...... the parameters are varying, with the degree of stability related directly to a bound on the average rate of allowable parameter variations. Thus, if knowledge of the parameter variations is available, the conservativeness of the design can be kept at a minimum. The construction of the controller is formulated...
A time-varying parameter VAR investigation of the exchange rate pass-through in Turkey
Directory of Open Access Journals (Sweden)
Çatık Abdurrahman Nazif
2016-01-01
Full Text Available The effects of exchange rate movements on price levels have important implications to macroeconomic policies through the impacts on trade balance and inflation. In contrast to previous studies, we employ a VAR model with time-varying parameters to measure the magnitude of exchange rate pass-through (ERPT. The findings confirm the time-varying pattern in the ERPT, as the magnitude of ERPT has reached its maximum value during the 1994 financial crisis. The decline in the magnitude of ERPT has become more pronounced after the 2001 financial crisis as a result of the implementation of inflation targeting, which has shifted Turkey’s economy from a long lasting high inflationary phase to a low inflationary economic environment.
A new image encryption algorithm based on logistic chaotic map with varying parameter.
Liu, Lingfeng; Miao, Suoxia
2016-01-01
In this paper, we proposed a new image encryption algorithm based on parameter-varied logistic chaotic map and dynamical algorithm. The parameter-varied logistic map can cure the weaknesses of logistic map and resist the phase space reconstruction attack. We use the parameter-varied logistic map to shuffle the plain image, and then use a dynamical algorithm to encrypt the image. We carry out several experiments, including Histogram analysis, information entropy analysis, sensitivity analysis, key space analysis, correlation analysis and computational complexity to evaluate its performances. The experiment results show that this algorithm is with high security and can be competitive for image encryption.
Cao, Jiguo
2012-01-01
Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.
Cao, Jiguo; Huang, Jianhua Z; Wu, Hulin
2012-01-01
Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.
Robust control design for active driver assistance systems a linear-parameter-varying approach
Gáspár, Péter; Bokor, József; Nemeth, Balazs
2017-01-01
This monograph focuses on control methods that influence vehicle dynamics to assist the driver in enhancing passenger comfort, road holding, efficiency and safety of transport, etc., while maintaining the driver’s ability to override that assistance. On individual-vehicle-component level the control problem is formulated and solved by a unified modelling and design method provided by the linear parameter varying (LPV) framework. The global behaviour desired is achieved by a judicious interplay between the individual components, guaranteed by an integrated control mechanism. The integrated control problem is also formalized and solved in the LPV framework. Most important among the ideas expounded in the book are: application of the LPV paradigm in the modelling and control design methodology; application of the robust LPV design as a unified framework for setting control tasks related to active driver assistance; formulation and solution proposals for the integrated vehicle control problem; proposal for a re...
Xie, Hui; Shen, Zhenyao; Chen, Lei; Qiu, Jiali; Dong, Jianwei
2017-11-15
Environmental models can be used to better understand the hydrologic and sediment behavior in a watershed system. However, different processes may dominate at different time periods and timescales, which highly complicate the model interpretation. The related parameter uncertainty may be significant and needs to be addressed to avoid bias in the watershed management. In this study, we used the time-varying and multi-timescale (TVMT) method to characterize the temporal dynamics of parameter sensitivity at different timescales in hydrologic and sediment modeling. As a case study, the first order sensitivity indices were estimated with the Fourier amplitude sensitivity test (FAST) method for the Hydrological Simulation Program - Fortran (HSPF) model in the Zhangjiachong catchment in the Three Gorge Reservoir Region (TGRR) in China. The results were compared to those of the traditional aggregate method to demonstrate the merits of the TVMT method. The time-varying nature of the hydrologic and sediment parameters was revealed and explained mainly by the variation of hydro-climatic conditions. The baseflow recession parameter, evapotranspiration (ET) parameter for the soil storage, and sediment washoff parameter showed high sensitivities almost across the whole period. However, parameters related to canopy interception and channel sediment scour varied notably over time due to changes in the climate forcing. The timescale-dependent characteristics was observed and was most evident for the baseflow recession parameter and ET parameter. At last, the parameters affecting the sediment export and transport were discussed together with the inferred conservation practices. Reasonable controls for sediment must be storm-dependent. Compared to management practices on the land surface, practices affecting channel process would be more effective during storm events. Our results present one of the first investigations for sediment modeling in terms of the importance of parameter
TESTS FOR VARIANCE COMPONENTS IN VARYING COEFFICIENT MIXED MODELS
National Research Council Canada - National Science Library
Zaixing Li; Yuedong Wang; Ping Wu; Wangli Xu; Lixing Zhu
2012-01-01
.... To address the question of whether a varying coefficient mixed model can be reduced to a simpler varying coefficient model, we develop one-sided tests for the null hypothesis that all the variance components are zero...
IDENTIFICATION OF TIME-VARYING MODAL PARAMETERS USING LINEAR TIME-FREQUENCY REPRESENTATION
Institute of Scientific and Technical Information of China (English)
Xu Xiuzhong; Zhang Zhiyi; Hua Hongxing; Chen Zhaoneng
2003-01-01
A new method of parameter identification based on linear time-frequency representation and Hilbert transform is proposed to identify modal parameters of linear time-varying systems from measured vibration responses. Using Gabor expansion and synthesis theory, measured responses are represented in the time-frequency domain and modal components are reconstructed by time-frequency filtering. The Hilbert transform is applied to obtain time histories of the amplitude and phase angle of each modal component, from which time-varying frequencies and damping ratios are identified. The proposed method has been demonstrated with a numerical example in which a linear time-varying system of two degrees of freedom is used to validate the identification scheme based on time-frequency representation. Simulation results have indicated that time-frequency representation presents an effective tool for modal parameter identification of time-varying systems.
Effect of varying two key parameters in simulating evacuation for a dormitory in China
Lei, Wenjun; Li, Angui; Gao, Ran
2013-01-01
Student dormitories are both living and resting areas for students in their spare time. There are many small rooms in the dormitories. And the students are distributed densely in the dormitories. High occupant density is the main characteristic of student dormitories. Once there is an accident, such as fire or earthquake, the losses will be cruel. Computer evacuation models developed overseas are commonly applied in working out safety management schemes. The average minimum widths of corridor and exit are the two key parameters affecting the evacuation for the dormitory. The effect of varying these two parameters will be studied in this paper by taking a dormitory in our university as an example. Evacuation performance is predicted with the software FDS + Evac. The default values in the software are used and adjusted through a field survey. The effect of varying either of the two parameters is discussed. It is found that the simulated results agree well with the experimental results. From our study it seems that the evacuation time is not in proportion to the evacuation distance. And we also named a phenomenon of “the closer is not the faster”. For the building researched in this article, a corridor width of 3 m is the most appropriate. And the suitable exit width of the dormitory for evacuation is about 2.5 to 3 m. The number of people has great influence on the walking speed of people. The purpose of this study is to optimize the building, and to make the building in favor of personnel evacuation. Then the damage could be minimized.
A simple strategy for varying the restart parameter in GMRES(m)
Energy Technology Data Exchange (ETDEWEB)
Baker, A H; Jessup, E R; Kolev, T V
2007-10-02
When solving a system of linear equations with the restarted GMRES method, a fixed restart parameter is typically chosen. We present numerical experiments that demonstrate the beneficial effects of changing the value of the restart parameter in each restart cycle on the total time to solution. We propose a simple strategy for varying the restart parameter and provide some heuristic explanations for its effectiveness based on analysis of the symmetric case.
A hierarchical nest survival model integrating incomplete temporally varying covariates
Converse, Sarah J.; Royle, J. Andrew; Adler, Peter H.; Urbanek, Richard P.; Barzan, Jeb A.
2013-01-01
Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the
Filtering and fault tolerant control of parameter-varying time-delay systems and applications
Mohammadpour Velni, Javad
This dissertation addresses some open problems in control systems theory. The problems considered include the dynamic controller and filter design for Linear Parameter Varying (LPV) time-delay systems, the reconfigurable control design in Fault Tolerant Control Systems (FTCS) and fault diagnostics in Diesel engines. In the first part of this thesis, we investigate the problem of designing parameter-dependent filters for output estimation of LPV time-delay systems. The filters are designed such that the filtering error system guarantees an optimum level of H2 or Hinfinity performance. A state-delay term is included in the filter dynamics to reduce the design conservatism and improve the performance. The Linear Matrix Inequality (LMI)-based synthesis conditions developed for the filter design purposes are categorized into the rate-dependent and delay-dependent conditions which could handle the time-varying state-delay and bounded small delay cases, respectively. Among these two, the latter one is shown to provide a significant reduction in the conservativeness in the filter design. The second part of the thesis examines the analysis and synthesis of Fault Tolerant Control (FTC) systems in an LPV framework. For reconfigurable control design purposes, the information from Fault Detection and Isolation (FDI) module, that provides an estimate of the fault parameters, is utilized to schedule the controller matrices. We will also present a formulation that incorporates the factor of detection delay in the FTC supervisory system. It is shown that including this delay in the synthesis conditions leads to improved performance and reduced control effort. For analysis of the FTC systems including time-delay, where the fault parameters might be identified inaccurately, we first introduce the notion of brief instability for LPV time-delay systems. In these systems it is possible that the output trajectory converges to zero even though there are parameter trajectories for which
Bianchi Type-V Bulk Viscous Cosmic String in f(R,T Gravity with Time Varying Deceleration Parameter
Directory of Open Access Journals (Sweden)
Bïnaya K. Bishi
2015-01-01
Full Text Available We study the Bianchi type-V string cosmological model with bulk viscosity in f(R,T theory of gravity by considering a special form and linearly varying deceleration parameter. This is an extension of the earlier work of Naidu et al., 2013, where they have constructed the model by considering a constant deceleration parameter. Here we find that the cosmic strings do not survive in both models. In addition we study some physical and kinematical properties of both models. We observe that in one of our models these properties are identical to the model obtained by Naidu et al., 2013, and in the other model the behavior of these parameters is different.
Closed-loop Identification for Control of Linear Parameter Varying Systems
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2014-01-01
, closed- loop system identification is more difficult than open-loop identification. In this paper we prove that the so-called Hansen Scheme, a technique known from linear time-invariant systems theory for transforming closed-loop system identification problems into open-loop-like problems, can......This paper deals with system identification for control of linear parameter varying systems. In practical applications, it is often important to be able to identify small plant changes in an incremental manner without shutting down the system and/or disconnecting the controller; unfortunately...... be extended to accommodate linear parameter varying systems as well. We investigate the identified subsystem’s parameter dependency and observe that, under mild assumptions, the identified subsystem is affine in the parameter vector. Various identification methods are compared in direct and Hansen Scheme...
Turso, James A.; Litt, Jonathan S.
2004-01-01
A method for accommodating engine deterioration via a scheduled Linear Parameter Varying Quadratic Lyapunov Function (LPVQLF)-Based controller is presented. The LPVQLF design methodology provides a means for developing unconditionally stable, robust control of Linear Parameter Varying (LPV) systems. The controller is scheduled on the Engine Deterioration Index, a function of estimated parameters that relate to engine health, and is computed using a multilayer feedforward neural network. Acceptable thrust response and tight control of exhaust gas temperature (EGT) is accomplished by adjusting the performance weights on these parameters for different levels of engine degradation. Nonlinear simulations demonstrate that the controller achieves specified performance objectives while being robust to engine deterioration as well as engine-to-engine variations.
Development of linear parameter varying control system for autonomous underwater vehicle
Sutarto, Herman; Budiyono, Agus
2011-01-01
The development and application of Linear Parameter Varying (LPV) control system for robust longitudinal control system on an Autonomous Underwater Vehicle (AUV) are presented. The LPV system is represented as Linear Fractional Transformation (LFT) on its parameter set. The LPV control system combines LPV theory based upon Linear Matrix Inequalities (LMIs) and - synthesis to form a robust LPV control system. The LPV control design is applied for a pitch control of the AUV to fulfill control...
Scalable Video Streaming Adaptive to Time-Varying IEEE 802.11 MAC Parameters
Lee, Kyung-Jun; Suh, Doug-Young; Park, Gwang-Hoon; Huh, Jae-Doo
This letter proposes a QoS control method for video streaming service over wireless networks. Based on statistical analysis, the time-varying MAC parameters highly related to channel condition are selected to predict available bitrate. Adaptive bitrate control of scalably-encoded video guarantees continuity in streaming service even if the channel condition changes abruptly.
DEFF Research Database (Denmark)
Callot, Laurent; Kristensen, Johannes Tang
the monetary policy response to inflation and business cycle fluctuations in the US by estimating a parsimoniously time varying parameter Taylor rule.We document substantial changes in the policy response of the Fed in the 1970s and 1980s, and since 2007, but also document the stability of this response...
Ali, M Syed; Rani, M Esther
2015-01-01
This paper investigates the problem of robust passivity of uncertain stochastic neural networks with time-varying delays and Markovian jumping parameters. To reflect most of the dynamical behaviors of the system, both parameter uncertainties and stochastic disturbances are considered; stochastic disturbances are given in the form of a Brownian motion. By utilizing the Lyapunov functional method, the Itô differential rule, and matrix analysis techniques, we establish a sufficient criterion such that, for all admissible parameter uncertainties and stochastic disturbances, the stochastic neural network is robustly passive in the sense of expectation. A delay-dependent stability condition is formulated, in which the restriction of the derivative of the time-varying delay should be less than 1 is removed. The derived criteria are expressed in terms of linear matrix inequalities that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results.
Robust control and linear parameter varying approaches application to vehicle dynamics
Gaspar, Peter; Bokor, József
2013-01-01
Vehicles are complex systems (non-linear, multi-variable) where the abundance of embedded controllers should ensure better security. This book aims at emphasizing the interest and potential of Linear Parameter Varying methods within the framework of vehicle dynamics, e.g. · proposed control-oriented model, complex enough to handle some system non linearities but still simple for control or observer design, · take into account the adaptability of the vehicle's response to driving situations, to the driver request and/or to the road sollicitations, · manage interactions between various actuators to optimize the dynamic behavior of vehicles. This book results from the 32th International Summer School in Automatic that held in Grenoble, France, in September 2011, where recent methods (based on robust control and LPV technics), then applied to the control of vehicle dynamics, have been presented. After some theoretical background and a view on so...
Wei, H. L.; Balikhin, M.; S. A. Billings
2003-01-01
Identification techniques for nonlinear time-varying systems are investigated based on the NARMAX model and multiresolution wavelet expansions. It is shown that a NARMAX model with time-varying coefficients can be reduced to a time-invariant linear-in-the-parameters analysis problem by then adapted to estimate the parameters. An application data relating to magnetic storms is used to illustrate the realistic application of the new identification technique.
TESTING FOR VARYING DISPERSION IN DISCRETE EXPONENTIAL FAMILY NONLINEAR MODELS
Institute of Scientific and Technical Information of China (English)
LinJinguan; WeiBocheng; ZhangNansong
2003-01-01
It is necessary to test for varying dispersion in generalized nonlinear models. Wei ,et al(1998) developed a likelihood ratio test,a score test and their adjustments to test for varying dispersion in continuous exponential family nonlinear models. This type of problem in the framework of general discrete exponential family nonlinear models is discussed. Two types of varying dispersion, which are random coefficients model and random effects model, are proposed,and corresponding score test statistics are constructed and expressed in simple ,easy to use ,matrix formulas.
Modeling non-Gaussian time-varying vector autoregressive process
National Aeronautics and Space Administration — We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical...
Petri nets extension to model state-varying failure rates
DEFF Research Database (Denmark)
Lazarova-Molnar, Sanja
2013-01-01
One of the most common assumptions in reliability modeling is the constant failure rate. This has been increasingly changing lately, yielding significant research towards abandoning simulation results based on this assumption; thus, deeming constant failure rates as inadequate to model failures......-varying failure rates and extend the formalism of Petri nets to model them. To illustrate our approach we provide an example model that features state-varying failure rates....
NOISE-INDUCED CHAOTIC MOTIONS IN HAMILTONIAN SYSTEMS WITH SLOW-VARYING PARAMETERS
Institute of Scientific and Technical Information of China (English)
王双连; 郭乙木; 甘春标
2001-01-01
This paper studies chaotic motions in quasi-integrable Hamiltonian systems with slow-varying parameters under both harmonic and noise excitations.Based on the dynamic theory and some assumptions of excited noises, an extended form of the stochastic Melnikov method is presented. Using this extended method, the homoclinic bifurcations and chaotic behavior of a nonlinear Hamiltonian system with weak feed-back control under both harmonic and Gaussian white noise excitations are analyzed in detail. It is shown that the addition of stochastic excitations can make the parameter threshold value for the occurrence of chaotic motions vary in a wider region. Therefore, chaotic motions may arise easily in the system. By the Monte-Carlo method, the numerical results for the time-history and the maximum Lyapunov exponents of an example system are finally given to illustrate that the presented method is effective.
Linear parameter-varying and time-delay systems analysis, observation, filtering & control
Briat, Corentin
2015-01-01
This book provides an introduction to the analysis and control of Linear Parameter-Varying Systems and Time-Delay Systems and their interactions. The purpose is to give the readers some fundamental theoretical background on these topics and to give more insights on the possible applications of these theories. This self-contained monograph is written in an accessible way for readers ranging from undergraduate/PhD students to engineers and researchers willing to know more about the fields of time-delay systems, parameter-varying systems, robust analysis, robust control, gain-scheduling techniques in the LPV fashion and LMI based approaches. The only prerequisites are basic knowledge in linear algebra, ordinary differential equations and (linear) dynamical systems. Most of the results are proved unless the proof is too complex or not necessary for a good understanding of the results. In the latter cases, suitable references are systematically provided. The first part pertains on the representation, analysis and ...
Photovoltaic module parameters acquisition model
Cibira, Gabriel; Koščová, Marcela
2014-09-01
This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I-V and P-V characteristics for PV module based on equivalent electrical circuit. Then, limited I-V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model.
DEFF Research Database (Denmark)
Østergaard, Kasper Zinck; Stoustrup, Jakob; Brath, Per
2009-01-01
as a function of estimated wind speed. The dynamic control law is based on LPV controller synthesis with general parameter dependency by gridding the parameter space.The controller construction can, for medium- to large-scale systems, be difficult from a numerical point of view, because the involved matrix......This paper considers the design of linear parameter varying (LPV) controllers for wind turbines in order to obtain a multivariable control law that covers the entire nominal operating trajectory.The paper first presents a controller structure for selecting a proper operating trajectory...... operations tend to be ill-conditioned. The paper proposes a controller construction algorithm together with various remedies for improving the numerical conditioning the algorithm.The proposed algorithm is applied to the design of a LPV controller for wind turbines, and a comparison is made with a controller...
Directory of Open Access Journals (Sweden)
Linlin Gao
2015-11-01
Full Text Available From the perspective of vehicle dynamics, the four-wheel independent steering vehicle dynamics stability control method is studied, and a four-wheel independent steering varying parameter linear quadratic regulator control system is proposed with the help of expert control method. In the article, a four-wheel independent steering linear quadratic regulator controller for model following purpose is designed first. Then, by analyzing the four-wheel independent steering vehicle dynamic characteristics and the influence of linear quadratic regulator control parameters on control performance, a linear quadratic regulator control parameter adjustment strategy based on vehicle steering state is proposed to achieve the adaptive adjustment of linear quadratic regulator control parameters. In addition, to further improve the control performance, the proposed varying parameter linear quadratic regulator control system is optimized by genetic algorithm. Finally, simulation studies have been conducted by applying the proposed control system to the 8-degree-of-freedom four-wheel independent steering vehicle dynamics model. The simulation results indicate that the proposed control system has better performance and robustness and can effectively improve the stability and steering safety of the four-wheel independent steering vehicle.
Mode choice model parameters estimation
Strnad, Irena
2010-01-01
The present work focuses on parameter estimation of two mode choice models: multinomial logit and EVA 2 model, where four different modes and five different trip purposes are taken into account. Mode choice model discusses the behavioral aspect of mode choice making and enables its application to a traffic model. Mode choice model includes mode choice affecting trip factors by using each mode and their relative importance to choice made. When trip factor values are known, it...
Cosmological models with interacting components and mass-varying neutrinos
Collodel, Lucas G
2012-01-01
A model for a homogeneous and isotropic spatially flat Universe, composed of baryons, radiation, neutrinos, dark matter and dark energy is analyzed. We infer that dark energy (considered to behave as a scalar field) interacts with dark matter (either by the Wetterich model, or by the Anderson and Carroll model) and with neutrinos by a model proposed by Brookfield et al.. The latter is understood to have a mass-varying behavior. We show that for a very-softly varying field, both interacting models for dark matter give the same results. The models reproduce the expected red-shift performances of the present behavior of the Universe.
Sen, Subhamoy; Crinière, Antoine; Mevel, Laurent; Cerou, Frederic; Dumoulin, Jean
2017-04-01
Keywords: Parameter estimation; Kalman filter; Particle filter; Particle-Kalman filter; Correlated noise Although Kalman filter (KF) was originally proposed for system control i.e. steering a system as desired by monitoring the system states, its application for parameter estimation problems is widespread because of the excellent similarity between these two apparently different problem types in state space description. In standard Kalman filter, system dynamics is described through the dynamics of certain internal variable, termed as states, evolving over time as defined by an assumed process model, while a measurement model maps these states to measurements. In some parameter estimation problems, the system is replaced by a state space formulation of the dynamic model with parameters appended in the unobserved states and collectively observed through the response measurements. Filtering based parameter estimation problems are thus inherently nonlinear due to the required nonlinear mapping of parameters to the corresponding observations. Being a linear estimator, Kalman Filter (KF) cannot be employed for such nonlinear system estimation and alternative filtering algorithms (eg. Particle filter) are therefore generally used. However, being model based, these filters optimally estimate the parameters of a quasi-static model of the real dynamic system. Consequently, any time variation in the system dynamics may completely diverge the estimation yielding a false or infeasible solution. By decoupling the estimation of system states and parameters, and applying concurrent filtering strategy that attempts conditional estimation of states based on parameters and vice versa, time varying systems can be estimated. This article attempts to combine KF with Particle filter (PF) and apply them for estimation of states and system parameters respectively on a system with correlated noise in process and measurement. The idea is to nest a bank of linear KFs for state estimation
Transient,spatially-varied recharge for groundwater modeling
Assefa, Kibreab; Woodbury, Allan
2013-04-01
This study is aimed at producing spatially and temporally varying groundwater recharge for transient groundwater modeling in a pilot watershed in the North Okanagan, Canada. The recharge modeling is undertaken by using a Richard's equation based finite element code (HYDRUS-1D) [Simunek et al., 2002], ArcGISTM [ESRI, 2011], ROSETTA [Schaap et al., 2001], in situ observations of soil temperature and soil moisture and a long term gridded climate data [Nielsen et al., 2010]. The public version of HYDUS-1D [Simunek et al., 2002] and another beta version with a detailed freezing and thawing module [Hansson et al., 2004] are first used to simulate soil temperature, snow pack and soil moisture over a one year experimental period. Statistical analysis of the results show both versions of HYDRUS-1D reproduce observed variables to the same degree. Correlation coefficients for soil temperature simulation were estimated at 0.9 and 0.8, at depths of 10 cm and 50 cm respectively; and for soil moisture, 0.8 and 0.6 at 10 cm and 50 cm respectively. This and other standard measures of model performance (root mean square error and average error) showed a promising performance of the HYDRUS-1D code in our pilot watershed. After evaluating model performance using field data and ROSETTA derived soil hydraulic parameters, the HYDRUS-1D code is coupled with ArcGISTM to produce spatially and temporally varying recharge maps throughout the Deep Creek watershed. Temporal and spatial analysis of 25 years daily recharge results at various representative points across the study watershed reveal significant temporal and spatial variations; average recharge estimated at 77.8 ± 50.8mm /year. This significant variation over the years, caused by antecedent soil moisture condition and climatic condition, illustrates the common flaw of assigning a constant percentage of precipitation throughout the simulation period. Groundwater recharge modeling has previously been attempted in the Okanagan Basin
Directory of Open Access Journals (Sweden)
Jianping Cai
2003-01-01
Full Text Available A method of approximate potential is presented for the study of certain kinds of strongly nonlinear oscillators. This method is to express the potential for an oscillatory system by a polynomial of degree four such that the leading approximation may be derived in terms of elliptic functions. The advantage of present method is that it is valid for relatively large oscillations. As an application, the elapsed time of periodic motion of a strongly nonlinear oscillator with slowly varying parameters is studied in detail. Comparisons are made with other methods to assess the accuracy of the present method.
Probing kinematics and fate of the Universe with linearly time-varying deceleration parameter
Dereli, Tekin; Akarsu, Özgür; Kumar, Suresh; Xu, Lixin
2013-01-01
arXiv:1305.5190v3 [gr-qc] 4 Feb 2014 Probing kinematics and fate of the Universe with linearly time-varying deceleration parameter Özgür Akarsua, Tekin Derelia, Suresh Kumarb, Lixin Xuc a Department of Physics, Koç University, 34450 Sarıyer, İstanbul, Turkey. b Department of Mathematics, BITS Pilani, Pilani Campus, Rajasthan-333031, India. c Institute of Theoretical Physics, Dalian University of Technology, Dalian, 116024, P. R. China. E-Mail: , tdereli@k...
Parameters estimation of a noisy sinusoidal signal with time-varying amplitude
Liu, Da-Yan; Perruquetti, Wilfrid
2011-01-01
In this paper, we give estimators of the frequency, amplitude and phase of a noisy sinusoidal signal with time-varying amplitude by using the algebraic parametric techniques introduced by Fliess and Sira-Ramirez. We apply a similar strategy to estimate these parameters by using modulating functions method. The convergence of the noise error part due to a large class of noises is studied to show the robustness and the stability of these methods. We also show that the estimators obtained by modulating functions method are robust to "large" sampling period and to non zero-mean noises.
Moussawi, Ali
2015-02-24
Summary: The post-treatment of (3D) displacement fields for the identification of spatially varying elastic material parameters is a large inverse problem that remains out of reach for massive 3D structures. We explore here the potential of the constitutive compatibility method for tackling such an inverse problem, provided an appropriate domain decomposition technique is introduced. In the method described here, the statically admissible stress field that can be related through the known constitutive symmetry to the kinematic observations is sought through minimization of an objective function, which measures the violation of constitutive compatibility. After this stress reconstruction, the local material parameters are identified with the given kinematic observations using the constitutive equation. Here, we first adapt this method to solve 3D identification problems and then implement it within a domain decomposition framework which allows for reduced computational load when handling larger problems.
Pourbabaee, Bahareh; Meskin, Nader; Khorasani, Khashayar
2016-08-01
In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature.
Partially linear varying coefficient models stratified by a functional covariate
Maity, Arnab
2012-10-01
We consider the problem of estimation in semiparametric varying coefficient models where the covariate modifying the varying coefficients is functional and is modeled nonparametrically. We develop a kernel-based estimator of the nonparametric component and a profiling estimator of the parametric component of the model and derive their asymptotic properties. Specifically, we show the consistency of the nonparametric functional estimates and derive the asymptotic expansion of the estimates of the parametric component. We illustrate the performance of our methodology using a simulation study and a real data application.
Partially Linear Varying Coefficient Models Stratified by a Functional Covariate.
Maity, Arnab; Huang, Jianhua Z
2012-10-01
We consider the problem of estimation in semiparametric varying coefficient models where the covariate modifying the varying coefficients is functional and is modeled nonparametrically. We develop a kernel-based estimator of the nonparametric component and a profiling estimator of the parametric component of the model and derive their asymptotic properties. Specifically, we show the consistency of the nonparametric functional estimates and derive the asymptotic expansion of the estimates of the parametric component. We illustrate the performance of our methodology using a simulation study and a real data application.
A blind separation method of overlapped multi-components based on time varying AR model
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.
Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene
Directory of Open Access Journals (Sweden)
B. SATOURI
2016-09-01
Full Text Available In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.
Akarsu, Ozgur; Kumar, Suresh; Xu, Lixin
2014-01-01
We study linearly varying deceleration parameter in terms of cosmic time t (LVDPt) with the companion linearly varying deceleration parameter in terms of cosmic redshift z (LVDPz) and in terms of cosmic scale factor a (LVDPa). We investigate in detail the kinematics and the fate of the Universe by confronting the three LVDP laws with the latest observational data from H(z) compilation (25 data points) and SN Ia Union2.1 compilation (580 data points). The study reveals that the LVDPt law is superior than LVDPz and LVDPa laws in many aspects. In particular, the goodness of fit to the observational data is found to be the best for the LVDPt law. The kinematics and dynamics (assuming general relativity) of the Universe is further studied by considering the LVDPt law in comparison with the standard LCDM model. It is found that these two models are observationally indistinguishable but the LVDPt fits the data slightly better than the LCDM model. These two models exhibit a very similar behavior for a long passage of...
An observer for an occluded reaction-diffusion system with spatially varying parameters
Kramer, Sean; Bollt, Erik M.
2017-03-01
Spatially dependent parameters of a two-component chaotic reaction-diffusion partial differential equation (PDE) model describing ocean ecology are observed by sampling a single species. We estimate the model parameters and the other species in the system by autosynchronization, where quantities of interest are evolved according to misfit between model and observations, to only partially observed data. Our motivating example comes from oceanic ecology as viewed by remote sensing data, but where noisy occluded data are realized in the form of cloud cover. We demonstrate a method to learn a large-scale coupled synchronizing system that represents the spatio-temporal dynamics and apply a network approach to analyze manifold stability.
The Oil Price and Exchange Rate Relationship Revisited: A time-varying VAR parameter approach
Directory of Open Access Journals (Sweden)
Vincent Brémond
2016-07-01
Full Text Available The aim of this paper is to study the relationship between the effective exchange rate of the dollar and the oil price dynamics from 1976 to 2013. We explore the links between financial factors (exchange rate, monetary policy, international liquidity and the oil price volatility. Using a Bayesian time-varying parameter vector auto-regressive estimation we demonstrate that the “historical coincidence” of oil and financial crises can be explained by the specificities of the relationship between these two commodities. The results of this paper are twofold. The US Dollar effective exchange rate elasticity of crude oil prices is not constant across time and remains negative from 1989: a depreciation of the effective exchange rate of the dollar triggers an increase of crude oil prices. This paper also demonstrates the contagion of financial commodities markets development upon the global economy.
Dynamical bifurcation in a system of coupled oscillators with slowly varying parameters
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Igor Parasyuk
2016-08-01
Full Text Available This paper deals with a fast-slow system representing n nonlinearly coupled oscillators with slowly varying parameters. We find conditions which guarantee that all omega-limit sets near the slow surface of the system are equilibria and invariant tori of all dimensions not exceeding n, the tori of dimensions less then n being hyperbolic. We show that a typical trajectory demonstrates the following transient process: while its slow component is far from the stationary points of the slow vector field, the fast component exhibits damping oscillations; afterwards, the former component enters and stays in a small neighborhood of some stationary point, and the oscillation amplitude of the latter begins to increase; eventually the trajectory is attracted by an n-dimesional invariant torus and a multi-frequency oscillatory regime is established.
Directory of Open Access Journals (Sweden)
Godwin Onyeamaechi Egwu
2011-01-01
Full Text Available The effects of vitamin C administration at varying time intervals on rectal temperature, respiratory rates, heart rates and sleeping time following xylazine anaesthesia was evaluated in rabbits. A total of 36 rabbits placed in six groups(A-F with 6 animals per group each were used. Groups A and B were used as controls for vitamin C (120 mg/kg, oral and xylazine (4 mg/kg, intramuscular treatments, respectively, while groups C-F received vitamin C at four intervals prior to xylazine anaesthesia. The result of the study showed that vitamin Cpre-medication prior to xylazine anaesthesia induced depression in respiratory and heart rates and a slight increase in rectal temperature. It also significantly increased sleeping time in rabbits (p0.05 in temperature between groups either before or after xylazine administration. It was concluded that vitamin C alters the clinical parameters as well as the sleeping time in rabbits under xylazine anaesthesia.
Escape time from potential wells of strongly nonlinear oscillators with slowly varying parameters
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Cai Jianping
2005-01-01
Full Text Available The effect of negative damping to an oscillatory system is to force the amplitude to increase gradually and the motion will be out of the potential well of the oscillatory system eventually. In order to deduce the escape time from the potential well of quadratic or cubic nonlinear oscillator, the multiple scales method is firstly used to obtain the asymptotic solutions of strongly nonlinear oscillators with slowly varying parameters, and secondly the character of modulus of Jacobian elliptic function is applied to derive the equations governing the escape time. The approximate potential method, instead of Taylor series expansion, is used to approximate the potential of an oscillation system such that the asymptotic solution can be expressed in terms of Jacobian elliptic function. Numerical examples verify the efficiency of the present method.
Effect of varying geometrical parameters of trapezoidal corrugated-core sandwich structure
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Zaid N.Z.M.
2017-01-01
Full Text Available Sandwich structure is an attractive alternative that increasingly used in the transportation and aerospace industry. Corrugated-core with trapezoidal shape allows enhancing the damage resistance to the sandwich structure, but on the other hand, it changes the structural response of the sandwich structure. The aim of this paper is to study the effect of varying geometrical parameters of trapezoidal corrugated-core sandwich structure under compression loading. The corrugated-core specimen was fabricated using press technique, following the shape of trapezoidal shape. Two different materials were used in the study, glass fibre reinforced plastic (GFRP and carbon fibre reinforced plastic (CFRP. The result shows that the mechanical properties of the core in compression loading are sensitive to the variation of a number of unit cells and the core thickness.
Estimation of Model Parameters for Steerable Needles
Park, Wooram; Reed, Kyle B.; Okamura, Allison M.; Chirikjian, Gregory S.
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%. PMID:21643451
Estimation of Model Parameters for Steerable Needles.
Park, Wooram; Reed, Kyle B; Okamura, Allison M; Chirikjian, Gregory S
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.
Late time attractors of some varying Chaplygin gas cosmological models
Khurshudyan, M
2015-01-01
Varying Chaplygin gas is one of the dark fluids actively studied in modern cosmology. It does belong to the group of the fluids which has an explicitly given EoS. From the other hand phase space does contain all possible states of the system. Therefore, phase space analysis of the cosmological models does allow to understand qualitative behavior and estimate required characteristics of the models. Phase space analysis is a convenient approach to study a cosmological model, because we do not need to solve a system of differential equations for a given initial conditions, instead, we need to deal with appropriate algebraic equations. The goal of this paper is to find late time attractors for the cosmological models, where a varying Chaplygin gas is one of the components of the large sale universe. We will pay our attention to some non linear interacting models.
Energy Technology Data Exchange (ETDEWEB)
McConathy, R.K.
1983-03-01
The study describes the gradients of stomatal size and density in the crown of a mature forest-grown tulip-poplar (Liriodendron tulipifera L.) in eastern Tennessee. These data are used to predict leaf resistance to vapor diffusion in relation to stomatal width and boundary layer resistance. Stomatal density on individual leaves did not vary, but density increased with increasing crown height. Stomatal size decreased with increasing height of leaves within the crown. Stomatal size and density variations interacted to result in a constant number of stomata per leaf at all crown heights. Stomatal diffusive resistance values calculated from stomatal measurements and varying environmental parameters indicated that stomatal resistance controlled transpiration water losses only at small apertures (<0.6 ..mu..m). Boundary layer resistance was controlling at large stomatal apertures (>0.6 ..mu..m) and at low wind speeds (approx.100 cm/s). Under normal forest conditions tulip-poplar stomatal resistance exercised more control over transpiration than did boundary layer resistance.
Using time-varying covariates in multilevel growth models
Directory of Open Access Journals (Sweden)
D. Betsy McCoach
2010-06-01
Full Text Available This article provides an illustration of growth curve modeling within a multilevel framework. Specifically, we demonstrate coding schemes that allow the researcher to model discontinuous longitudinal data using a linear growth model in conjunction with time varying covariates. Our focus is on developing a level-1 model that accurately reflects the shape of the growth trajectory. We demonstrate the importance of adequately modeling the shape of the level-1 growth trajectory in order to make inferences about the importance of both level-1 and level-2 predictors.
Exploring vibration control strategies for a footbridge with time-varying modal parameters
Soria, Jose M.; Díaz, Ivan M.; Pereira, Emiliano; García-Palacios, Jaime H.; Wang, Xidong
2016-09-01
This paper explores different vibration control strategies for the cancellation of human-induced vibration of a structure with time-varying modal parameters. The motivation of this study is an urban stress-ribbon footbridge (Pedro Gomez Bosque, Valladolid, Spain) that, after a whole-year monitoring, it has been obtained that the natural frequency of a vibration mode at approximately 1.8 Hz (within the normal range of walking) changes up to 20%, mainly due to temperature variations. Thus, this paper takes the annual modal parameter estimates (aprox. 14000 estimations) of this mode and designs three control strategies: a) a tuned mass damper (TMD) tuned to the aforementioned mode using its most-repeated modal properties, b) a semi-active TMD with an on-off control law for the TMD damping, and c) an active mass damper designed using the well-known velocity feedback control strategy with a saturation nonlinearity. Illustrative results have been reported from this preliminary study.
Directory of Open Access Journals (Sweden)
Nicholas Curry
2015-07-01
Full Text Available Suspension plasma spraying has become an emerging technology for the production of thermal barrier coatings for the gas turbine industry. Presently, though commercial systems for coating production are available, coatings remain in the development stage. Suitable suspension parameters for coating production remain an outstanding question and the influence of suspension properties on the final coatings is not well known. For this study, a number of suspensions were produced with varied solid loadings, powder size distributions and solvents. Suspensions were sprayed onto superalloy substrates coated with high velocity air fuel (HVAF -sprayed bond coats. Plasma spray parameters were selected to generate columnar structures based on previous experiments and were maintained at constant to discover the influence of the suspension behavior on coating microstructures. Testing of the produced thermal barrier coating (TBC systems has included thermal cyclic fatigue testing and thermal conductivity analysis. Pore size distribution has been characterized by mercury infiltration porosimetry. Results show a strong influence of suspension viscosity and surface tension on the microstructure of the produced coatings.
Maximum Likelihood Estimation of Time-Varying Loadings in High-Dimensional Factor Models
DEFF Research Database (Denmark)
Mikkelsen, Jakob Guldbæk; Hillebrand, Eric; Urga, Giovanni
In this paper, we develop a maximum likelihood estimator of time-varying loadings in high-dimensional factor models. We specify the loadings to evolve as stationary vector autoregressions (VAR) and show that consistent estimates of the loadings parameters can be obtained by a two-step maximum...... likelihood estimation procedure. In the first step, principal components are extracted from the data to form factor estimates. In the second step, the parameters of the loadings VARs are estimated as a set of univariate regression models with time-varying coefficients. We document the finite...
Modelling Acoustic Wave Propagation in Axisymmetric Varying-Radius Waveguides
DEFF Research Database (Denmark)
Bæk, David; Willatzen, Morten
2008-01-01
A computationally fast and accurate model (a set of coupled ordinary differential equations) for fluid sound-wave propagation in infinite axisymmetric waveguides of varying radius is proposed. The model accounts for fluid heat conduction and fluid irrotational viscosity. The model problem is solved...... by expanding solutions in terms of cross-sectional eigenfunctions following Stevenson’s method. A transfer matrix can be easily constructed from simple model responses of a given waveguide and later used in computing the response to any complex wave input. Energy losses due to heat conduction and viscous...
Mosher, Mark
Within a wave energy converter's operational bandwidth, device operation tends to be optimal in converting mechanical energy into a more useful form at an incident wave period that is proximal to that of a power-producing mode of motion. Point absorbers, a particular classification of wave energy converters, tend to have a relative narrow optimal bandwidth. When not operating within the narrow optimal bandwidth, a point absorber's response and efficiency is attenuated. Given the wide range of sea-states that can be expected during a point absorber's operational life, these devices require a means to adjust, or control, their natural response to maximize the amount of energy absorbed in the large population of non-optimal conditions. In the field of wave energy research, there is considerable interest in the use of non-linear control techniques to this end. Non-linear control techniques introduce time-varying and state dependent control parameters into the point absorber motion equations, which usually motivates a computationally expensive numerical integration to determine the response of the device - important metrics such as gross converted power and relative travels of the device's pieces are extracted through post processing of the time series data. As an alternative, the work presented in this thesis was based on a closed form perturbation based approach for analysis of the response of a device with periodically-varying control parameters, subject to regular wave forcing, in the frequency domain. The proposed perturbation based method provides significant savings in computational time and enables the device's response to be represented in a closed form manner with a relatively small number of solution components - each component is comprised of a complex amplitude and oscillation frequency. This representation of the solution was found to be very concise and descriptive, and to lend itself to the calculation of gross absorbed power and travel constraint
Masmoudi, Nabil
2014-05-01
Traveltimes are conventionally evaluated by solving the zero-order approximation of the Wentzel, Kramers and Brillouin (WKB) expansion of the wave equation. This high frequency approximation is good enough for most imaging applications and provides us with a traveltime equation called the eikonal equation. The eikonal equation is a non-linear partial differential equation which can be solved by any of the familiar numerical methods. Among the most popular of these methods is the method of characteristics which yields the ray tracing equations and the finite difference approaches. In the first part of the Master Thesis, we use the ray tracing method to solve the eikonal equation to get P-waves traveltimes for orthorhombic models with arbitrary orientation of symmetry planes. We start with a ray tracing procedure specified in curvilinear coordinate system valid for anisotropy of arbitrary symmetry. The coordinate system is constructed so that the coordinate lines are perpendicular to the symmetry planes of an orthorohombic medium. Advantages of this approach are the conservation of orthorhombic symmetry throughout the model and reduction of the number of parameters specifying the model. We combine this procedure with first-order ray tracing and dynamic ray tracing equations for P waves propagating in smooth, inhomogeneous, weakly anisotropic media. The first-order ray tracing and dynamic ray tracing equations are derived from the exact ones by replacing the exact P-wave eigenvalue of the Christoffel matrix by its first-order approximation. In the second part of the Master Thesis, we compute traveltimes using the fast marching method and we develop an approach to estimate the anisotropy parameters. The idea is to relate them analytically to traveltimes which is challenging in inhomogeneous media. Using perturbation theory, we develop traveltime approximations for transversely isotropic media with horizontal symmetry axis (HTI) as explicit functions of the
Roe, Byron
2013-01-01
The effect of correlations between model parameters and nuisance parameters is discussed, in the context of fitting model parameters to data. Modifications to the usual $\\chi^2$ method are required. Fake data studies, as used at present, will not be optimum. Problems will occur for applications of the Maltoni-Schwetz \\cite{ms} theorem. Neutrino oscillations are used as examples, but the problems discussed here are general ones, which are often not addressed.
STATISTICAL INFERENCES FOR VARYING-COEFFICINT MODELS BASED ON LOCALLY WEIGHTED REGRESSION TECHNIQUE
Institute of Scientific and Technical Information of China (English)
梅长林; 张文修; 梁怡
2001-01-01
Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fired by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.
Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models.
Xie, Minge; Simpson, Douglas G; Carroll, Raymond J
2008-01-01
This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scaled link function models as special cases. We present an algorithm to estimate the scale function nonparametrically, and obtain asymptotic distribution theory for regression parameter estimates. In particular, we establish that the asymptotic covariance of the semiparametric estimator for the parametric part of the model achieves the semiparametric lower bound. We also describe bootstrap-based goodness-of-scale test. We illustrate the methodology with simulations, published data, and data from collaborative research on ultrasound safety.
Egwu, Godwin Onyeamaechi; Mshelia, Gideon Dauda; Sanni, Saka; Onyeyili, Patrick Azubuike; Adeyanju, Gladys Taiwo
2011-01-01
The effects of vitamin C administration at varying time intervals on rectal temperature, respiratory rates, heart rates and sleeping time following xylazine anaesthesia was evaluated in rabbits. A total of 36 rabbits placed in six groups (A-F) with 6 animals per group each were used. Groups A and B were used as controls for vitamin C (120 mg/kg, oral) and xylazine (4 mg/kg, intramuscular) treatments, respectively, while groups C-F received vitamin C at four intervals prior to xylazine anaesthesia. The result of the study showed that vitamin C pre-medication prior to xylazine anaesthesia induced depression in respiratory and heart rates and a slight increase in rectal temperature. It also significantly increased sleeping time in rabbits (prabbits that received vitamin C 60 min prior to xylazine anaesthesia. Vitamin C administration 10 min prior to xylazine anaesthesia in rabbits induced a sleeping time three times the value compared to those animals that had received xylazine anaesthesia alone. However, the study did not observe a significant difference (p>0.05) in temperature between groups either before or after xylazine administration. It was concluded that vitamin C alters the clinical parameters as well as the sleeping time in rabbits under xylazine anaesthesia.
Modelling Time-Varying Volatility in Financial Returns
DEFF Research Database (Denmark)
Amado, Cristina; Laakkonen, Helinä
2014-01-01
The “unusually uncertain” phase in the global financial markets has inspired many researchers to study the effects of ambiguity (or “Knightian uncertainty”) on the decisions made by investors and their implications for the capital markets. We contribute to this literature by using a modified...... version of the time-varying GARCH model of Amado and Teräsvirta (2013) to analyze whether the increasing uncertainty has caused excess volatility in the US and European government bond markets. In our model, volatility is multiplicatively decomposed into two time-varying conditional components: the first...... being captured by a stable GARCH(1,1) process and the second driven by the level of uncertainty in the financial market....
National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes to develop an on-line flutter prediction tool using the parameter varying estimation (PVE) methodology, called the PVE Toolbox,...
Simple Model with Time-Varying Fine-Structure ``Constant''
Berman, M. S.
2009-10-01
Extending the original version written in colaboration with L.A. Trevisan, we study the generalisation of Dirac's LNH, so that time-variation of the fine-structure constant, due to varying electrical and magnetic permittivities is included along with other variations (cosmological and gravitational ``constants''), etc. We consider the present Universe, and also an inflationary scenario. Rotation of the Universe is a given possibility in this model.
Pineda, Evan J.; Mital, Subodh K.; Bednarcyk, Brett A.; Arnold, Steven M.
2015-01-01
Constituent properties, along with volume fraction, have a first order effect on the microscale fields within a composite material and influence the macroscopic response. Therefore, there is a need to assess the significance of stochastic variation in the constituent properties of composites at the higher scales. The effect of variability in the parameters controlling the time-dependent behavior, in a unidirectional SCS-6 SiC fiber-reinforced RBSN matrix composite lamina, on the residual stresses induced during processing is investigated numerically. The generalized method of cells micromechanics theory is utilized to model the ceramic matrix composite lamina using a repeating unit cell. The primary creep phases of the constituents are approximated using a Norton-Bailey, steady state, power law creep model. The effect of residual stresses on the proportional limit stress and strain to failure of the composite is demonstrated. Monte Carlo simulations were conducted using a normal distribution for the power law parameters and the resulting residual stress distributions were predicted.
A von Bertalanffy growth model with a seasonally varying coefficient
Cloern, James E.; Nichols, Frederic H.
1978-01-01
The von Bertalanffy model of body growth is inappropriate for organisms whose growth is restricted to a seasonal period because it assumes that growth rate is invariant with time. Incorporation of a time-varying coefficient significantly improves the capability of the von Bertalanffy equation to describe changing body size of both the bivalve mollusc Macoma balthicain San Francisco Bay and the flathead sole, Hippoglossoides elassodon, in Washington state. This simple modification of the von Bertalanffy model should offer improved predictions of body growth for a variety of other aquatic animals.
Influence Diagnostics in Partially Varying-Coefficient Models
Institute of Scientific and Technical Information of China (English)
2007-01-01
When a real-world data set is fitted to a specific type of models, it is often encountered that one or a set of observations have undue influence on the model fitting, which may lead to misleading conclusions. Therefore, it is necessary for data analysts to identify these influential observations and assess their impact on various aspects of model fitting. In this paper, one type of modified Cook's distances is defined to gauge the influence of one or a set observations on the estimate of the constant coefficient part in partially varying-coefficient models, and the Cook's distances are expressed as functions of the corresponding residuals and leverages. Meanwhile, a bootstrap procedure is suggested to derive the reference values for the proposed Cook's distances. Some simulations are conducted, and a real-world data set is further analyzed to examine the performance of the proposed method. The experimental results are satisfactory.
Modeling and Analysis of Time-Varying Graphs
Basu, Prithwish; Ramanathan, Ram; Johnson, Matthew P
2010-01-01
We live in a world increasingly dominated by networks -- communications, social, information, biological etc. A central attribute of many of these networks is that they are dynamic, that is, they exhibit structural changes over time. While the practice of dynamic networks has proliferated, we lag behind in the fundamental, mathematical understanding of network dynamism. Existing research on time-varying graphs ranges from preliminary algorithmic studies (e.g., Ferreira's work on evolving graphs) to analysis of specific properties such as flooding time in dynamic random graphs. A popular model for studying dynamic graphs is a sequence of graphs arranged by increasing snapshots of time. In this paper, we study the fundamental property of reachability in a time-varying graph over time and characterize the latency with respect to two metrics, namely store-or-advance latency and cut-through latency. Instead of expected value analysis, we concentrate on characterizing the exact probability distribution of routing l...
Institute of Scientific and Technical Information of China (English)
CHEN Xiongzi; YU Jinsong; TANG Diyin; WANG Yingxun
2012-01-01
Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis.However,for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters,the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models.To address the problem,this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis.This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances.Meantime,the propagation of prediction uncertainty is emphatically taken into account.Therefore,PF-LSSVR avoids over-dependency on system state models in prediction phase.With a two-sided failure definition,the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward.The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.
Climatic influence on demographic parameters of a tropical seabird varies with age and sex.
Oro, Daniel; Torres, Roxana; Rodríguez, Cristina; Drummond, Hugh
2010-04-01
In marine ecosystems climatic fluctuation and other physical variables greatly influence population dynamics, but differential effects of physical variables on the demographic parameters of the two sexes and different age classes are largely unexplored. We analyzed the effects of climate on the survival and recruitment of both sexes and several age classes of a long-lived tropical seabird, the Blue-footed Booby (Sula nebouxii), using long-term observations on marked individuals. Results demonstrated a complex interaction between yearly fluctuations in climate (both local and global indexes, during both winter and breeding season) and the sex and age of individuals. Youngest birds' survival and recruitment were commonly affected by local climate, whereas oldest birds' parameters tended to be constant and less influenced by environmental variables. These results confirm the theoretical prediction that sex- and age-related variation in life-history demographic traits is greater under poor environmental conditions, and they highlight the importance of including variability in fitness components in demographic and evolutionary models. Males and females showed similar variation in survival but different recruitment patterns, in relation to both age and the spatial scale of climatic influence (local or global). Results indicate different life-history tactics for each sex and different ages, with birds likely trying to maximize their fitness by responding to the environmental contingencies of each year.
Directory of Open Access Journals (Sweden)
Ady Marinho Bezerra
2007-03-01
Full Text Available O objetivo deste trabalho foi selecionar as variáveis de manejo do camarão marinho Litopenaeus vannamei que mais influenciaram nas variáveis-respostas ao cultivo (produção, produtividade, peso final e taxa de sobrevivência, em modelos matemáticos. O banco de dados foi composto por 83 cultivos, realizados no período de 2003 a 2005, obtidos de uma fazenda comercial localizada no litoral sul de Pernambuco. Para estimar os parâmetros dos modelos, utilizou-se a técnica dos mínimos quadrados. A seleção das variáveis foi realizada com o processo "backward elimination" associado ao método de transformação de Box e Cox. A adequação das equações e os pressupostos de normalidade e homocedasticidade, para os erros, foram analisadas com base na análise de variância e análise de resíduo. É possível relacionar essas variáveis e estabelecer predições com as equações.The objective of this work was to select management variables of the marine shrimp Litopenaeus vannamei that most influenced culture variable responses (production, productivity, final weight and survival rate, in mathematical models. The database was composed of 83 cultures in the period of 2003 to 2005, obtained from a shrimp farm located in the South coast of Pernambuco. To estimate the parameters of the models it was used the technique of least square. The selection of variable was carried through the backward elimination process associated to the Box and Cox transformation. The adequacy of the equations and the hypothesis of normality and homogeneous variance for the errors were analyzed based on the analysis of variance and on the analysis of residuals. It is possible to correlate those variables and to establish predictions with the equations.
Institute of Scientific and Technical Information of China (English)
Tao Hu; Heng-jian Cui; Xing-wei Tong
2009-01-01
This article considers a semiparametric varying-coefficient partially linear regression model with current status data. The semiparametric varying-coefficient partially linear regression model which is a gen-eralization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estima-tor for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are conducted to examine the small-sample properties of the proposed estimates and a real dataset is used to illustrate our approach.
Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks
Energy Technology Data Exchange (ETDEWEB)
Olama, Mohammed M [ORNL; Djouadi, Seddik M [ORNL; Charalambous, Prof. Charalambos [University of Cyprus
2014-01-01
Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.
A hepatitis C virus infection model with time-varying drug effectiveness: solution and analysis.
Directory of Open Access Journals (Sweden)
Jessica M Conway
2014-08-01
Full Text Available Simple models of therapy for viral diseases such as hepatitis C virus (HCV or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time.
Reducing component estimation for varying coefficient models with longitudinal data
Institute of Scientific and Technical Information of China (English)
2008-01-01
Varying-coefficient models with longitudinal observations are very useful in epidemiology and some other practical fields.In this paper,a reducing component procedure is proposed for es- timating the unknown functions and their derivatives in very general models,in which the unknown coefficient functions admit different or the same degrees of smoothness and the covariates can be time- dependent.The asymptotic properties of the estimators,such as consistency,rate of convergence and asymptotic distribution,are derived.The asymptotic results show that the asymptotic variance of the reducing component estimators is smaller than that of the existing estimators when the coefficient functions admit different degrees of smoothness.Finite sample properties of our procedures are studied through Monte Carlo simulations.
Time-varying priority queuing models for human dynamics.
Jo, Hang-Hyun; Pan, Raj Kumar; Kaski, Kimmo
2012-06-01
Queuing models provide insight into the temporal inhomogeneity of human dynamics, characterized by the broad distribution of waiting times of individuals performing tasks. We theoretically study the queuing model of an agent trying to execute a task of interest, the priority of which may vary with time due to the agent's "state of mind." However, its execution is disrupted by other tasks of random priorities. By considering the priority of the task of interest either decreasing or increasing algebraically in time, we analytically obtain and numerically confirm the bimodal and unimodal waiting time distributions with power-law decaying tails, respectively. These results are also compared to the updating time distribution of papers in arXiv.org and the processing time distribution of papers in Physical Review journals. Our analysis helps to understand human task execution in a more realistic scenario.
Time-Varying Priority Queuing Models for Human Dynamics
Jo, Hang-Hyun; Kaski, Kimmo
2011-01-01
Queuing models provide insight into the temporal inhomogeneity of human dynamics, characterized by the broad distribution of waiting times of individuals performing tasks. We study the queuing model of an agent trying to execute a task of interest, the priority of which may vary with time due to the agent's "state of mind." However, its execution can be disrupted by other tasks of random priorities. By considering the priority of the task of interest either decreasing or increasing algebraically in time, we analytically obtain and numerically confirm the bimodal and unimodal waiting time distributions with power-law decaying tails, respectively. These results are also compared to the updating time distribution of papers in the arXiv and the processing time distribution of papers in Physical Review journals. Our analysis helps to understand the human task execution behavior in a more realistic scenario.
Hart, Sean; Ren, Hechen; Kosowsky, Michael; Ben-Shach, Gilad; Leubner, Philipp; Brüne, Christoph; Buhmann, Hartmut; Molenkamp, Laurens W.; Halperin, Bertrand I.; Yacoby, Amir
2017-01-01
Conventional s-wave superconductivity arises from singlet pairing of electrons with opposite Fermi momenta, forming Cooper pairs with zero net momentum. Recent studies have focused on coupling s-wave superconductors to systems with an unusual configuration of electronic spin and momentum at the Fermi surface, where the nature of the paired state can be modified and the system may even undergo a topological phase transition. Here we present measurements and theoretical calculations of HgTe quantum wells coupled to aluminium or niobium superconductors and subject to a magnetic field in the plane of the quantum well. We find that this magnetic field tunes the momentum of Cooper pairs in the quantum well, directly reflecting the response of the spin-dependent Fermi surfaces. In the high electron density regime, the induced superconductivity evolves with electron density in agreement with our model based on the Hamiltonian of Bernevig, Hughes and Zhang. This agreement provides a quantitative value for g ˜/vF, where g ˜ is the effective g-factor and vF is the Fermi velocity. Our new understanding of the interplay between spin physics and superconductivity introduces a way to spatially engineer the order parameter from singlet to triplet pairing, and in general allows investigation of electronic spin texture at the Fermi surface of materials.
Thermohaline feedbacks in ocean-climate models of varying complexity
den Toom, M.
2013-03-01
explicitly resolves eddies, and a model in which eddies are parameterized. It is found that the behavior of an eddy-resolving model is qualitatively different from that of a non-eddying model. What is clear at this point, is that the AMOC is governed by non-linear dynamics. As a result, its simulated behavior depends in a non-trivial way on how unresolved processes are represented in a model. As demonstrated in this thesis, model fidelity can be effectively assessed by examining models of varying complexity.
Projection-type estimation for varying coefficient regression models
Lee, Young K; Park, Byeong U; 10.3150/10-BEJ331
2012-01-01
In this paper we introduce new estimators of the coefficient functions in the varying coefficient regression model. The proposed estimators are obtained by projecting the vector of the full-dimensional kernel-weighted local polynomial estimators of the coefficient functions onto a Hilbert space with a suitable norm. We provide a backfitting algorithm to compute the estimators. We show that the algorithm converges at a geometric rate under weak conditions. We derive the asymptotic distributions of the estimators and show that the estimators have the oracle properties. This is done for the general order of local polynomial fitting and for the estimation of the derivatives of the coefficient functions, as well as the coefficient functions themselves. The estimators turn out to have several theoretical and numerical advantages over the marginal integration estimators studied by Yang, Park, Xue and H\\"{a}rdle [J. Amer. Statist. Assoc. 101 (2006) 1212--1227].
Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm
Jin, Ick Hoon
2013-10-01
The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing algorithms, such as Monte Carlo maximum likelihood estimation (MCMLE) and stochastic approximation, often fail for this problem in the presence of model degeneracy. In this article, we introduce the varying truncation stochastic approximation Markov chain Monte Carlo (SAMCMC) algorithm to tackle this problem. The varying truncation mechanism enables the algorithm to choose an appropriate starting point and an appropriate gain factor sequence, and thus to produce a reasonable parameter estimate for the ERGM even in the presence of model degeneracy. The numerical results indicate that the varying truncation SAMCMC algorithm can significantly outperform the MCMLE and stochastic approximation algorithms: for degenerate ERGMs, MCMLE and stochastic approximation often fail to produce any reasonable parameter estimates, while SAMCMC can do; for nondegenerate ERGMs, SAMCMC can work as well as or better than MCMLE and stochastic approximation. The data and source codes used for this article are available online as supplementary materials. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
PARAMETER ESTIMATION OF ENGINEERING TURBULENCE MODEL
Institute of Scientific and Technical Information of China (English)
钱炜祺; 蔡金狮
2001-01-01
A parameter estimation algorithm is introduced and used to determine the parameters in the standard k-ε two equation turbulence model (SKE). It can be found from the estimation results that although the parameter estimation method is an effective method to determine model parameters, it is difficult to obtain a set of parameters for SKE to suit all kinds of separated flow and a modification of the turbulence model structure should be considered. So, a new nonlinear k-ε two-equation model (NNKE) is put forward in this paper and the corresponding parameter estimation technique is applied to determine the model parameters. By implementing the NNKE to solve some engineering turbulent flows, it is shown that NNKE is more accurate and versatile than SKE. Thus, the success of NNKE implies that the parameter estimation technique may have a bright prospect in engineering turbulence model research.
Variable Selection for Varying-Coefficient Models with Missing Response at Random
Institute of Scientific and Technical Information of China (English)
Pei Xin ZHAO; Liu Gen XUE
2011-01-01
In this paper, we present a variable selection procedure by combining basis function approximations with penalized estimating equations for varying-coefficient models with missing response at random. With appropriate selection of the tuning parameters, we establish the consistency of the variable selection procedure and the optimal convergence rate of the regularized estimators. A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.
Energy Technology Data Exchange (ETDEWEB)
Kang, H.J. [Mando Machinery Co., Pyungtaek (Korea); Ko, J.W. [Yuhan College, Buchon (Korea); Park, M.N. [Yonsei University, Seoul (Korea)
1999-06-01
This note presents an efficient numerical method of determining a quadratic stability bound for SISO linear system with time-varying uncertainties. Based on the quadratic stability condition in Linear Matrix Inequalities (LMI) form, the proposed method gives a quadratic stability bound for each system parameter. As an example, the state feedback regulation problem is presented. (author). 12 refs.
effect of varying controller parameters on the performance of a fuzzy ...
African Journals Online (AJOL)
Dr Obe
This paper presents the results of computer simulation studies designed to isolate the effects of the major parameters of a fuzzy logic controller namely the range of the universe of discourse, the ... rule-based expert system, which is a logical.
Marching on in anything : Solving electromagnetic field equations with a varying physical parameter
Tijhuis, A.G.; Zwamborn, A.P.M.
2003-01-01
In this paper, we consider the determination of electromagnetic flelds for a (large) number of values of a physical parameter. We restrict ourselves to the case where the linear system originates from one or more integral equations. we apply an iterative procedure based on the minimization of an int
Cascades in the Threshold Model for varying system sizes
Karampourniotis, Panagiotis; Sreenivasan, Sameet; Szymanski, Boleslaw; Korniss, Gyorgy
2015-03-01
A classical model in opinion dynamics is the Threshold Model (TM) aiming to model the spread of a new opinion based on the social drive of peer pressure. Under the TM a node adopts a new opinion only when the fraction of its first neighbors possessing that opinion exceeds a pre-assigned threshold. Cascades in the TM depend on multiple parameters, such as the number and selection strategy of the initially active nodes (initiators), and the threshold distribution of the nodes. For a uniform threshold in the network there is a critical fraction of initiators for which a transition from small to large cascades occurs, which for ER graphs is largerly independent of the system size. Here, we study the spread contribution of each newly assigned initiator under the TM for different initiator selection strategies for synthetic graphs of various sizes. We observe that for ER graphs when large cascades occur, the spread contribution of the added initiator on the transition point is independent of the system size, while the contribution of the rest of the initiators converges to zero at infinite system size. This property is used for the identification of large transitions for various threshold distributions. Supported in part by ARL NS-CTA, ARO, ONR, and DARPA.
Normalized least-squares estimation in time-varying ARCH models
Fryzlewicz, Piotr; Sapatinas, Theofanis; Subba Rao, Suhasini
2008-01-01
We investigate the time-varying ARCH (tvARCH) process. It is shown that it can be used to describe the slow decay of the sample autocorrelations of the squared returns often observed in financial time series, which warrants the further study of parameter estimation methods for the model. ¶ Since the parameters are changing over time, a successful estimator needs to perform well for small samples. We propose a kernel normalized-least-squares (kernel-NLS) estimator which has a closed form...
Models wagging the dog: are circuits constructed with disparate parameters?
Nowotny, Thomas; Szücs, Attila; Levi, Rafael; Selverston, Allen I
2007-08-01
In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.
Institute of Scientific and Technical Information of China (English)
YUEYuncan; QIANJixin
2002-01-01
Based on the idea of the set-membership identification,a modified recursive least squares algorithm with variable gain, variable forgetting factor and resetting is presented.The concept of the error tolerance level is proposed.The selection criteria of the error tolerance level are also given according to the min-max principle.The algorithm is particularly suitable for tracing time-varying systems and is similar in computational complexity to the standard recursive least squares algorithm.The superior performance of the algorithm is verified via simulation studies on a dynamic fermentation process.
Sensorless Modeling of Varying Pulse Width Modulator Resolutions in Three-Phase Induction Motors
Marko, Matthew David; Shevach, Glenn
2017-01-01
A sensorless algorithm was developed to predict rotor speeds in an electric three-phase induction motor. This sensorless model requires a measurement of the stator currents and voltages, and the rotor speed is predicted accurately without any mechanical measurement of the rotor speed. A model of an electric vehicle undergoing acceleration was built, and the sensorless prediction of the simulation rotor speed was determined to be robust even in the presence of fluctuating motor parameters and significant sensor errors. Studies were conducted for varying pulse width modulator resolutions, and the sensorless model was accurate for all resolutions of sinusoidal voltage functions. PMID:28076418
Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang
2017-05-18
This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.
Velazquez, Antonio; Swartz, R. Andrew
2013-04-01
Wind energy is becoming increasingly important worldwide as an alternative renewable energy source. Economical, maintenance and operation are critical issues for large slender dynamic structures, especially for remote offshore wind farms. Health monitoring systems are very promising instruments to assure reliability and good performance of the structure. These sensing and control technologies are typically informed by models based on mechanics or data-driven identification techniques in the time and/or frequency domain. Frequency response functions are popular but are difficult to realize autonomously for structures of higher order and having overlapping frequency content. Instead, time-domain techniques have shown powerful advantages from a practical point of view (e.g. embedded algorithms in wireless-sensor networks), being more suitable to differentiate closely-related modes. Customarily, time-varying effects are often neglected or dismissed to simplify the analysis, but such is not the case for wind loaded structures with spinning multibodies. A more complex scenario is constituted when dealing with both periodic mechanisms responsible for the vibration shaft of the rotor-blade system, and the wind tower substructure interaction. Transformations of the cyclic effects on the vibration data can be applied to isolate inertia quantities different from rotating-generated forces that are typically non-stationary in nature. After applying these transformations, structural identification can be carried out by stationary techniques via data-correlated Eigensystem realizations. In this paper an exploration of a periodic stationary or cyclo-stationary subspace identification technique is presented here by means of a modified Eigensystem Realization Algorithm (ERA) via Stochastic Subspace Identification (SSI) and Linear Parameter Time-Varying (LPTV) techniques. Structural response is assumed under stationary ambient excitation produced by a Gaussian (white) noise assembled
Sieve M-estimation for semiparametric varying-coefficient partially linear regression model
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This article considers a semiparametric varying-coefficient partially linear regression model.The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable.A sieve M-estimation method is proposed and the asymptotic properties of the proposed estimators are discussed.Our main object is to estimate the nonparametric component and the unknown parameters simultaneously.It is easier to compute and the required computation burden is much less than the existing two-stage estimation method.Furthermore,the sieve M-estimation is robust in the presence of outliers if we choose appropriate ρ(·).Under some mild conditions,the estimators are shown to be strongly consistent;the convergence rate of the estimator for the unknown nonparametric component is obtained and the estimator for the unknown parameter is shown to be asymptotically normally distributed.Numerical experiments are carried out to investigate the performance of the proposed method.
Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.
Kyle, Ryan P; Moodie, Erica E M; Klein, Marina B; Abrahamowicz, Michał
2016-08-01
Unbiased estimation of causal parameters from marginal structural models (MSMs) requires a fundamental assumption of no unmeasured confounding. Unfortunately, the time-varying covariates used to obtain inverse probability weights are often error-prone. Although substantial measurement error in important confounders is known to undermine control of confounders in conventional unweighted regression models, this issue has received comparatively limited attention in the MSM literature. Here we propose a novel application of the simulation-extrapolation (SIMEX) procedure to address measurement error in time-varying covariates, and we compare 2 approaches. The direct approach to SIMEX-based correction targets outcome model parameters, while the indirect approach corrects the weights estimated using the exposure model. We assess the performance of the proposed methods in simulations under different clinically plausible assumptions. The simulations demonstrate that measurement errors in time-dependent covariates may induce substantial bias in MSM estimators of causal effects of time-varying exposures, and that both proposed SIMEX approaches yield practically unbiased estimates in scenarios featuring low-to-moderate degrees of error. We illustrate the proposed approach in a simple analysis of the relationship between sustained virological response and liver fibrosis progression among persons infected with hepatitis C virus, while accounting for measurement error in γ-glutamyltransferase, using data collected in the Canadian Co-infection Cohort Study from 2003 to 2014.
Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
Directory of Open Access Journals (Sweden)
Shuang Zhao
2015-01-01
Full Text Available Task resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO algorithms with adaptive change of parameter (viz., inertial weight and acceleration coefficients according to the evolution state evaluation is presented. This adaptation helps to avoid premature convergence and explore the search space more efficiently. Simulations are carried out to test proposed algorithm, test reveal that the algorithm can achieving significant optimization of makespan.
Helical Milling of CFRP/Ti-6Al-4V Stacks with Varying Machining Parameters
Institute of Scientific and Technical Information of China (English)
He Gaiyun; Li Hao; Jiang Yuedong; Qin Xuda; Zhang Xinpei; Guan Yi
2015-01-01
The hole-making process in stack materials consisting of carbon fiber reinforced plastics(CFRP) and Ti-6Al-4V remains a critical challenge. In this paper, an experimental study on the helical milling of CFRP/Ti-6Al-4V stacks was conducted by using two different machining strategies. Helical milling strategyⅠmachines both materials with identical machining parameters, while machining strategyⅡuses two sets of machining parameters to machine each material. Helical milling performance was evaluated by the following indicators: tool life, cutting forces, hole quality(including diameter deviation, roundness, roughness, and hole edge quality). The results demonstrate that heli-cal milling strategyⅡoutperformed strategyⅠ, leading to longer tool life(up to 48 holes), smaller cutting forces and better hole quality with higher geometric accuracy and smoother surface finish(Ra≤0.58μm for Ti-6Al-4V and Ra≤0.81μm for CFRP), eliminating the need for reaming or de-burring.
a Linear Model for Meandering Rivers with Arbitrarily Varying Width
Frascati, A.; Lanzoni, S.
2011-12-01
are derived by depth-averaging the three-dimensional conservation equations and accounting for the dynamic effects of secondary flows. A two-parameter perturbation expansion technique, considering perturbations induced by curvature and spatial channel width variations, is adopted to linearize the governing equations. The model predictions are then tested by a direct application to a test case. We show that the present model provides a further step towards the development of a computationally sustainable and robust framework to simulate the long term evolution of alluvial rivers.
Vaskovskaya, T. A.
2014-12-01
This paper offers a new approach to the analysis of price signals from the wholesale electricity and capacity market that is based on the analysis of the influence exerted by input data used in the problem of optimization of the power system operating conditions, namely: parameters of a power grid and power-receiving equipment that might vary under the effect of control devices. It is shown that it would be possible to control nonregulated prices for electricity in the wholesale electricity market by varying the parameters of control devices and energy-receiving equipment. An increase in the effectiveness of power transmission and the cost-effective use of fuel-and-energy resources (energy saving) can become an additional effect of controlling the nonregulated prices.
Global Stability Analysis for an Internet Congestion Control Model with a Time-Varying Link Capacity
Rezaie, B; Analoui, M; Khorsandi, S
2009-01-01
In this paper, a global stability analysis is given for a rate-based congestion control system modeled by a nonlinear delayed differential equation. The model determines the dynamics of a single-source single-link network, with a time-varying capacity of link and a fixed communication delay. We obtain a sufficient delay-independent conditions on system parameters under which global asymptotic stability of the system is guarantied. The proof is based on an extension of Lyapunov-Krasovskii theorem for a class of nonlinear time-delay systems. The numerical simulations for a typical scenario justify the theoretical results.
Directory of Open Access Journals (Sweden)
Wu Kun-Shan
2002-01-01
Full Text Available In this paper, an EOQ inventory model is depleted not only by time varying demand but also by Weibull distribution deterioration, in which the inventory is permitted to start with shortages and end without shortages. A theory is developed to obtain the optimal solution of the problem; it is then illustrated with the aid of several numerical examples. Moreover, we also assume that the holding cost is a continuous, non-negative and non-decreasing function of time in order to extend the EOQ model. Finally, sensitivity of the optimal solution to changes in the values of different system parameters is also studied.
Surface Wave Tomography with Spatially Varying Smoothing Based on Continuous Model Regionalization
Liu, Chuanming; Yao, Huajian
2017-03-01
Surface wave tomography based on continuous regionalization of model parameters is widely used to invert for 2-D phase or group velocity maps. An inevitable problem is that the distribution of ray paths is far from homogeneous due to the spatially uneven distribution of stations and seismic events, which often affects the spatial resolution of the tomographic model. We present an improved tomographic method with a spatially varying smoothing scheme that is based on the continuous regionalization approach. The smoothness of the inverted model is constrained by the Gaussian a priori model covariance function with spatially varying correlation lengths based on ray path density. In addition, a two-step inversion procedure is used to suppress the effects of data outliers on tomographic models. Both synthetic and real data are used to evaluate this newly developed tomographic algorithm. In the synthetic tests, when the contrived model has different scales of anomalies but with uneven ray path distribution, we compare the performance of our spatially varying smoothing method with the traditional inversion method, and show that the new method is capable of improving the recovery in regions of dense ray sampling. For real data applications, the resulting phase velocity maps of Rayleigh waves in SE Tibet produced using the spatially varying smoothing method show similar features to the results with the traditional method. However, the new results contain more detailed structures and appears to better resolve the amplitude of anomalies. From both synthetic and real data tests we demonstrate that our new approach is useful to achieve spatially varying resolution when used in regions with heterogeneous ray path distribution.
Robust estimation of hydrological model parameters
Directory of Open Access Journals (Sweden)
A. Bárdossy
2008-11-01
Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.
Structural studies of ZnO nanostructures by varying the deposition parameters
Yunus, S. H. A.; Sahdan, M. Z.; Ichimura, M.; Supee, A.; Rahim, S.
2017-01-01
The effect of Zinc Oxide (ZnO) thin film on the growth of ZnO nanorods (NRs) was investigated. The structures of ZnO NRs were synthesized by chemical bath deposition (CBD) method in aqueous solution of N2O6Zn.6H2O and C6H12N4 at 90°C of deposition temperature. One of the ZnO NRs samples was deposited on a ZnO seed layer coated on a glass substrate to investigate the properties of ZnO NRs without receiving effect of other materials. Next, for diode application, the ZnO NRs was deposited on tin monosulfide (SnS) coated on indium-tin-oxide (ITO) coated glass substrate (SnS/ITO). The next, the ZnO structural properties were studied from surface morphology, X-ray diffractometer (XRD) spectra, and chemical composition by using field emission scanning electron microscope (FESEM), XRD and energy dispersive X-ray Spectroscopy (EDX). The growth of ZnO NRs on ZnO seed layer was investigated by ZnO seed layer condition while the growth of ZnO NRs on SnS/ITO was investigated by deposition time and deposition temperature parameters. From FESEM images, aligned ZnO NRs were obtained, and the diameters of ZnO NRs were 0.024-3.94 µm. The SnS thin film was affected by the diameter of ZnO NRs which are the ZnO NRs grow on SnS thin films has a larger diameter compared to ZnO NRs grow on ZnO seed layer. Besides that, all of ZnO peaks observed from XRD corresponding to the wurzite structure and preferentially oriented along the c-axis. In addition, EDX shows a high composition of zinc (Zn) and oxygen (O) signals, which indicated that the NRs are indeed made up of Zn and O.
Varactor Modelling for Power Factor Correction in a Varying Load
Directory of Open Access Journals (Sweden)
Agwu D. D.
2016-06-01
Full Text Available : For efficient system operation, it is desirable to keep the power factor at, or very close to unity. One of the very often used methods is application of suitable power factor correction technology. Capacitors are good candidate for constant load power factor correction due to suitability and cost effectiveness. However for varying loads, synchronous condensers are preferred despite having high initial cost as a result of their being able to supply varying leading or lagging reactive power; according to their field excitation. Due to the high acquisition and operation cost of synchronous condensers, this paper presents varactors as a possible alternative for power factor correction. These are diodes that vary their capacitances and leading reactive power according to supply voltage. Applying this involves looking at variation of power factor with supply voltage; and the option of aggregating and harnessing the junction capacitance of varactors for power factor correction of varying loads at low voltage AC levels. This innovation may lead to great improvement in distribution systems requiring quality power supply
modelling flow over stepped spillway with varying chute geometry
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... to obtain some varying flow data in 36 different experiments. These obtained flow ... The percentage difference between the values predicted by each of these ..... d1 the discharge per unit width q, gravity accelera- tion g, and ...
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Hadiyanto Hadiyanto; AJB van Boxtel
2012-01-01
Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally pro...
Parameter counting in models with global symmetries
Energy Technology Data Exchange (ETDEWEB)
Berger, Joshua [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: jb454@cornell.edu; Grossman, Yuval [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: yuvalg@lepp.cornell.edu
2009-05-18
We present rules for determining the number of physical parameters in models with exact flavor symmetries. In such models the total number of parameters (physical and unphysical) needed to described a matrix is less than in a model without the symmetries. Several toy examples are studied in order to demonstrate the rules. The use of global symmetries in studying the minimally supersymmetric standard model (MSSM) is examined.
On parameter estimation in deformable models
DEFF Research Database (Denmark)
Fisker, Rune; Carstensen, Jens Michael
1998-01-01
Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian...... method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented...
Cosmological models with constant deceleration parameter
Energy Technology Data Exchange (ETDEWEB)
Berman, M.S.; de Mello Gomide, F.
1988-02-01
Berman presented elsewhere a law of variation for Hubble's parameter that yields constant deceleration parameter models of the universe. By analyzing Einstein, Pryce-Hoyle and Brans-Dicke cosmologies, we derive here the necessary relations in each model, considering a perfect fluid.
Testing for time-varying loadings in dynamic factor models
DEFF Research Database (Denmark)
Mikkelsen, Jakob Guldbæk
factors. The squared correlation coefficient times the sample size has a limiting chi-squared distribution. The test can be made robust to serial correlation in the idiosyncratic errors. We find evidence for factor loadings variance in over half of the variables in a dataset for the US economy, while...... there is evidence of time-varying loadings on the risk factors underlying portfolio returns for around 80% of the portfolios....
Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.
2015-01-01
Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
Modeling Potentially Time-Varying Effects of Promotions on Sales
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard); Ph.A. Sijthoff
2001-01-01
textabstractA commonly applied modeling tool for the analysis of promotional effects on weekly sales data is a linear regression model. Usually, such a model includes 0/1 dummy variables for promotions, where weeks with a promotion get a value of 1. When these variables are included in a model with
Directory of Open Access Journals (Sweden)
Gabriel Rodríguez
2016-06-01
Full Text Available Following Xu and Perron (2014, I applied the extended RLS model to the daily stock market returns of Argentina, Brazil, Chile, Mexico and Peru. This model replaces the constant probability of level shifts for the entire sample with varying probabilities that record periods with extremely negative returns. Furthermore, it incorporates a mean reversion mechanism with which the magnitude and the sign of the level shift component vary in accordance with past level shifts that deviate from the long-term mean. Therefore, four RLS models are estimated: the Basic RLS, the RLS with varying probabilities, the RLS with mean reversion, and a combined RLS model with mean reversion and varying probabilities. The results show that the estimated parameters are highly significant, especially that of the mean reversion model. An analysis of ARFIMA and GARCH models is also performed in the presence of level shifts, which shows that once these shifts are taken into account in the modeling, the long memory characteristics and GARCH effects disappear. Also, I find that the performance prediction of the RLS models is superior to the classic models involving long memory as the ARFIMA(p,d,q models, the GARCH and the FIGARCH models. The evidence indicates that except in rare exceptions, the RLS models (in all its variants are showing the best performance or belong to the 10% of the Model Confidence Set (MCS. On rare occasions the GARCH and the ARFIMA models appear to dominate but they are rare exceptions. When the volatility is measured by the squared returns, the great exception is Argentina where a dominance of GARCH and FIGARCH models is appreciated.
Trait Characteristics of Diffusion Model Parameters
Directory of Open Access Journals (Sweden)
Anna-Lena Schubert
2016-07-01
Full Text Available Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence.
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Application of lumped-parameter models
Energy Technology Data Exchange (ETDEWEB)
Ibsen, Lars Bo; Liingaard, M.
2006-12-15
This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil. Subsequently, the assembly of the dynamic stiffness matrix for the foundation is considered, and the solution for obtaining the steady state response, when using lumped-parameter models is given. (au)
Estimation of Semi-Varying Coefficient Model with Surrogate Data and Validation Sampling
Institute of Scientific and Technical Information of China (English)
Ya-zhao L(U); Ri-quan ZHANG; Zhen-sheng HUANG
2013-01-01
In this paper,we investigate the estimation of semi-varying coefficient models when the nonlinear covariates are prone to measurement error.With the help of validation sampling,we propose two estimators of the parameter and the coefficient functions by combining dimension reduction and the profile likelihood methods without any error structure equation specification or error distribution assumption.We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the proposed estimators achieves the best convergence rate.Data-driven bandwidth selection methods are also discussed.Simulations are conducted to evaluate the finite sample property of the estimation methods proposed.
Institute of Scientific and Technical Information of China (English)
Pei Xin ZHAO; Liu Gen XUE
2011-01-01
In this paper,we present a variable selection procedure by combining basis function approximations with penalized estimating equations for semiparametric varying-coefficient partially linear models with missing response at random.The proposed procedure simultaneously selects significant variables in parametric components and nonparametric components.With appropriate selection of the tuning parameters,we establish the consistency of the variable selection procedure and the convergence rate of the regularized estimators.A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.
Mainardi, Luca T
2009-01-28
In the last decades, one of the main challenges in the study of heart rate variability (HRV) signals has been the quantification of the low-frequency (LF) and high-frequency (HF) components of the HRV spectrum during non-stationary events. At this regard, different time-frequency and time-varying approaches have been proposed with the aim to track the modification of the HRV spectra during ischaemic attacks, provocative stress testing, sleep or daily-life activities. The quantitative evaluation of power (and frequencies) of the LF and HF components has been approached in various ways depending on the selected time-frequency method. This paper is an excursus through the most common time-frequency/time-varying representation of the HRV signal with a special emphasis on the algorithms employed for the reliable quantification of the LF and HF parameters and their tracking.
Yang, Wu; Liu, Li; Zhou, Si-Da; Ma, Zhi-Sai
2015-10-01
This work proposes a Moving Kriging (MK) shape function modeling method for modal identification of linear time-varying (LTV) structural systems based on vector time-dependent autoregressive moving average (VTARMA) models. It aims to avoid the functional subspaces selection of the conventional functional series VTARMA (FS-VTARMA) models. Instead of the common basis functions, it constructs the time-varying coefficients on the time nodes with the MK shape functions in a compact support domain. The merit of the MK shape function is to determine its shape parameters upon vector random vibration signals adaptively. Model identification is effectively dealt with through an optimization scheme that decomposes the identification problem into two subproblems: estimating model parameters via two-stage least squares (2SLS) method and estimating shape function parameters via a discrete-continuous-variable hybrid optimization. In addition, the model order selection is achieved by the optimization scheme. This method has been validated by a Monte Carlo study of simulation case and further by an experimental test case, and the performance and potential advantages are illustrated.
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Directory of Open Access Journals (Sweden)
Hadiyanto Hadiyanto
2012-05-01
Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels. Abstrak PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan
A potato model intercomparison across varying climates and productivity levels
DEFF Research Database (Denmark)
H. Fleisher, David; Condori, Bruno; Quiroz, Roberto
2017-01-01
A potato crop multi-model assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low- (Chinoli, Bolivia and Gisozi, Burundi) and high- (Jyndevad, Denmark and Washington, United States...
Potato model uncertainty across common datasets and varying climate
A potato crop multi-model assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low- (Chinoli, Bolivia and Gisozi, Burundi) and high- (Jyndevad, Denmark and Washington, United States) ...
EOQ Models for Deteriorating Items with Time-varying Demand and Partial Backorder under Inflation
Institute of Scientific and Technical Information of China (English)
ShanlinYang; YongwuZhou
2004-01-01
This paper, through the discounted cash flow (DCF) approach, considers inventory replenishment problems for deteriorating items with general time-varying demand over a finite planning horizon under inflation. The optimal replenishment policies for the total profit of system to be maximum are presented with partial backlogging. Moreover, the fraction of demand backlogged is assumed to be a non-increasing function of waiting time. Firstly, the models with a fixed fraction backorder and complete backorder are provided. Then the model is presented with assuming that the fraction of demand backlogged is an exponentially decreasing function of waiting time. The solution procedures of models are proposed. The effect of inflation on the optimal policies is shown. The models are illustrated through numerical examples and sensitivity analysis of parameters is given.
Chen, Lihong; Wei, Fengying
2017-10-01
In this paper, the dynamics of a stochastic susceptible-infected-removed model in a population with varying size is investigated. We firstly show that the stochastic epidemic model has a unique global positive solution with any positive initial value. Then we verify that random perturbations lead to extinction when some conditions are being valid. Moreover, we prove that the solution of the stochastic epidemic model is persistent in the mean by building up a suitable Lyapunov function and using generalized Itô's formula. Further, the stochastic epidemic model admits a stationary distribution around the endemic equilibrium when parameters satisfy some sufficient conditions. To end this contribution and to check the validity of the main results, numerical simulations are separately carried out to illustrate these results.
Identification of hydrological model parameter variation using ensemble Kalman filter
Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao
2016-12-01
Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.
Nonstationary conditional models for spatial data based on varying coefficients
Dreesman, J; Tutz, Gerhard
1999-01-01
The analysis of spatial data by means of Markov random fields usually is based on strict stationarity assumptions. Although these assumptions rarely hold, they are necessary in order to obtain parameter estimates. For Gaussian data the necessary assumptions are mean- and covariance stationarity. While simple techniques are available to deal with violations of mean stationarity, the same is not true for covariance stationarity. In order to handle mean nonstationarity as well as covariance nons...
Performance of growth mixture models in the presence of time-varying covariates.
Diallo, Thierno M O; Morin, Alexandre J S; Lu, HuiZhong
2016-10-31
Growth mixture modeling is often used to identify unobserved heterogeneity in populations. Despite the usefulness of growth mixture modeling in practice, little is known about the performance of this data analysis technique in the presence of time-varying covariates. In the present simulation study, we examined the impacts of five design factors: the proportion of the total variance of the outcome explained by the time-varying covariates, the number of time points, the error structure, the sample size, and the mixing ratio. More precisely, we examined the impact of these factors on the accuracy of parameter and standard error estimates, as well as on the class enumeration accuracy. Our results showed that the consistent Akaike information criterion (CAIC), the sample-size-adjusted CAIC (SCAIC), the Bayesian information criterion (BIC), and the integrated completed likelihood criterion (ICL-BIC) proved to be highly reliable indicators of the true number of latent classes in the data, across design conditions, and that the sample-size-adjusted BIC (SBIC) also proved quite accurate, especially in larger samples. In contrast, the Akaike information criterion (AIC), the entropy, the normalized entropy criterion (NEC), and the classification likelihood criterion (CLC) proved to be unreliable indicators of the true number of latent classes in the data. Our results also showed that substantial biases in the parameter and standard error estimates tended to be associated with growth mixture models that included only four time points.
He, Fei; Wei, Hua-Liang; Billings, Stephen A.
2015-08-01
This paper introduces a new approach for nonlinear and non-stationary (time-varying) system identification based on time-varying nonlinear autoregressive moving average with exogenous variable (TV-NARMAX) models. The challenging model structure selection and parameter tracking problems are solved by combining a multiwavelet basis function expansion of the time-varying parameters with an orthogonal least squares algorithm. Numerical examples demonstrate that the proposed approach can track rapid time-varying effects in nonlinear systems more accurately than the standard recursive algorithms. Based on the identified time domain model, a new frequency domain analysis approach is introduced based on a time-varying generalised frequency response function (TV-GFRF) concept, which enables the analysis of nonlinear, non-stationary systems in the frequency domain. Features in the TV-GFRFs which depend on the TV-NARMAX model structure and time-varying parameters are investigated. It is shown that the high-dimensional frequency features can be visualised in a low-dimensional time-frequency space.
Energy Technology Data Exchange (ETDEWEB)
Macabebe, E.Q.B. [Department of Physics, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth 6031 (South Africa); Sheppard, C.J. [Department of Physics, University of Johannesburg, P.O. Box 524, Auckland Park 2006 (South Africa); Dyk, E.E. van, E-mail: ernest.vandyk@nmmu.ac.z [Department of Physics, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth 6031 (South Africa)
2009-12-01
In pursuit of low-cost and highly efficient thin film solar cells, Cu(In,Ga)(Se,S){sub 2}/CdS/i-ZnO/ZnO:Al (CIGSS) solar cells were fabricated using a two-step process. The thickness of i-ZnO layer was varied from 0 to 454 nm. The current density-voltage (J-V) characteristics of the devices were measured, and the device and performance parameters of the solar cells were obtained from the J-V curves to analyze the effect of varying i-ZnO layer thickness. The device parameters were determined using a parameter extraction method that utilized particle swarm optimization. The method is a curve-fitting routine that employed the two-diode model. The J-V curves of the solar cells were fitted with the model and the parameters were determined. Results show that as the thickness of i-ZnO was increased, the average efficiency and the fill factor (FF) of the solar cells increase. Device parameters reveal that although the series resistance increased with thicker i-ZnO layer, the solar cells absorbed more photons resulting in higher short-circuit current density (J{sub sc}) and, consequently, higher photo-generated current density (J{sub L}). For solar cells with 303-454 nm-thick i-ZnO layer, the best devices achieved efficiency between 15.24% and 15.73% and the fill factor varied between 0.65 and 0.67.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models.
Fan, Jianqing; Ma, Yunbei; Dai, Wei
2014-01-01
The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high dimensional varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications.
Statefinder parameters in two dark energy models
Panotopoulos, Grigoris
2007-01-01
The statefinder parameters ($r,s$) in two dark energy models are studied. In the first, we discuss in four-dimensional General Relativity a two fluid model, in which dark energy and dark matter are allowed to interact with each other. In the second model, we consider the DGP brane model generalized by taking a possible energy exchange between the brane and the bulk into account. We determine the values of the statefinder parameters that correspond to the unique attractor of the system at hand. Furthermore, we produce plots in which we show $s,r$ as functions of red-shift, and the ($s-r$) plane for each model.
Parameter Symmetry of the Interacting Boson Model
Shirokov, A M; Smirnov, Yu F; Shirokov, Andrey M.; Smirnov, Yu. F.
1998-01-01
We discuss the symmetry of the parameter space of the interacting boson model (IBM). It is shown that for any set of the IBM Hamiltonian parameters (with the only exception of the U(5) dynamical symmetry limit) one can always find another set that generates the equivalent spectrum. We discuss the origin of the symmetry and its relevance for physical applications.
A potato model intercomparison across varying climates and productivity levels
Fleisher, David H.; Condori, Bruno; Quiroz, Roberto; Alva, Ashok; Asseng, Senthold; Barreda, Carolina; Bindi, Marco; Boote, Kenneth J.; Ferrise, Roberto; Franke, Angelinus C.; Govindakrishnan, Panamanna M.; Harahagazwe, Dieudonne; Hoogenboom, Gerrit; Naresh Kumar, Soora; Merante, Paolo; Nendel, Claas; Olesen, Jorgen E.; Parker, Phillip S.; Raes, Dirk; Raymundo, Rubi; Ruane, Alex C.; Stockle, Claudio; Supit, Iwan; Vanuytrecht, Eline; Wolf, Joost; Woli, Prem
2016-01-01
A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United
Wind Farm Decentralized Dynamic Modeling With Parameters
DEFF Research Database (Denmark)
Soltani, Mohsen; Shakeri, Sayyed Mojtaba; Grunnet, Jacob Deleuran;
2010-01-01
Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...
Setting Parameters for Biological Models With ANIMO
Schivo, Stefano; Scholma, Jetse; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole; van de Pol, Jan Cornelis; Langerak, Romanus; André, Étienne; Frehse, Goran
2014-01-01
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions
Delineating Parameter Unidentifiabilities in Complex Models
Raman, Dhruva V; Papachristodoulou, Antonis
2016-01-01
Scientists use mathematical modelling to understand and predict the properties of complex physical systems. In highly parameterised models there often exist relationships between parameters over which model predictions are identical, or nearly so. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, and the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast timescale subsystems, as well as the regimes in which such approximations are valid. We base our algorithm on a novel quantification of regional parametric sensitivity: multiscale sloppiness. Traditional...
Model independent constraints on mass-varying neutrino scenarios
Franca, Urbano; Lesgourgues, Julien; Pastor, Sergio
2009-01-01
Models of dark energy in which neutrinos interact with the scalar field supposed to be responsible for the acceleration of the universe usually imply a variation of the neutrino masses on cosmological time scales. In this work we propose a parameterization for the neutrino mass variation that captures the essentials of those scenarios and allows to constrain them in a model independent way, that is, without resorting to any particular scalar field model. Using WMAP 5yr data combined with the matter power spectrum of SDSS and 2dFGRS, the limit on the present value of the neutrino mass is $m_0 \\equiv m_{\
Parameter Estimation, Model Reduction and Quantum Filtering
Chase, Bradley A
2009-01-01
This dissertation explores the topics of parameter estimation and model reduction in the context of quantum filtering. Chapters 2 and 3 provide a review of classical and quantum probability theory, stochastic calculus and filtering. Chapter 4 studies the problem of quantum parameter estimation and introduces the quantum particle filter as a practical computational method for parameter estimation via continuous measurement. Chapter 5 applies these techniques in magnetometry and studies the estimator's uncertainty scalings in a double-pass atomic magnetometer. Chapter 6 presents an efficient feedback controller for continuous-time quantum error correction. Chapter 7 presents an exact model of symmetric processes of collective qubit systems.
Model transcriptional networks with continuously varying expression levels
Directory of Open Access Journals (Sweden)
Carneiro Mauricio O
2011-12-01
Full Text Available Abstract Background At a time when genomes are being sequenced by the hundreds, much attention has shifted from identifying genes and phenotypes to understanding the networks of interactions among genes. We developed a gene network developmental model expanding on previous models of transcription regulatory networks. In our model, each network is described by a matrix representing the interactions between transcription factors, and a vector of continuous values representing the transcription factor expression in an individual. Results In this work we used the gene network model to look at the impact of mating as well as insertions and deletions of genes in the evolution of complexity of these networks. We found that the natural process of diploid mating increases the likelihood of maintaining complexity, especially in higher order networks (more than 10 genes. We also show that gene insertion is a very efficient way to add more genes to a network as it provides a much higher chance of developmental stability. Conclusions The continuous model affords a more complete view of the evolution of interacting genes. The notion of a continuous output vector also incorporates the reality of gene networks and graded concentrations of gene products.
Morphology of Rain Water Channeling in Systematically Varied Model Sandy Soils
Wei, Yuli; Cejas, Cesare M.; Barrois, Rémi; Dreyfus, Rémi; Durian, Douglas J.
2014-10-01
We visualize the formation of fingered flow in dry model sandy soils under different rain conditions using a quasi-2D experimental setup and systematically determine the impact of the soil grain diameter and surface wetting properties on the water channeling phenomenon. The model sandy soils we use are random closely packed glass beads with varied diameters and surface treatments. For hydrophilic sandy soils, our experiments show that rain water infiltrates a shallow top layer of soil and creates a horizontal water wetting front that grows downward homogeneously until instabilities occur to form fingered flows. For hydrophobic sandy soils, in contrast, we observe that rain water ponds on the top of the soil surface until the hydraulic pressure is strong enough to overcome the capillary repellency of soil and create narrow water channels that penetrate the soil packing. Varying the raindrop impinging speed has little influence on water channel formation. However, varying the rain rate causes significant changes in the water infiltration depth, water channel width, and water channel separation. At a fixed rain condition, we combine the effects of the grain diameter and surface hydrophobicity into a single parameter and determine its influence on the water infiltration depth, water channel width, and water channel separation. We also demonstrate the efficiency of several soil water improvement methods that relate to the rain water channeling phenomenon, including prewetting sandy soils at different levels before rainfall, modifying soil surface flatness, and applying superabsorbent hydrogel particles as soil modifiers.
Parameter Estimation for Thurstone Choice Models
Energy Technology Data Exchange (ETDEWEB)
Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-04-24
We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.
Phenomenologically varying \\Lambda and a toy model for the Universe
Khurshudyan, M; Chubaryan, E; Farahani, H
2014-01-01
We consider a model of the Universe with variable G and {\\Lambda}. Subject of our interest is a phenomenological model for {\\Lambda} proposed and considered in this article first time (up to our knowledge). Modification based on an assumption that ghost dark energy exists and Universe will feel it through {\\Lambda}. In that case we would like to consider possibility that there exist some unusual connections between different components of the fluids existing in Universe. We would like to stress, that this is just an assumption and could be very far from the reality. We are interested by this model as a phenomenological and mathematical and unfortunately, we will not discuss about physical conditions and possibilities of having such modifications. To test our assumption and to observe behavior of the Universe, we will consider toy models filled by a barotropic fluid and modified Chaplyagin gas. To complete the logic of the research we will consider interaction between barotropic fluid or Chaplygin gas with gho...
Delineating parameter unidentifiabilities in complex models
Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis
2017-03-01
Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em
2017-02-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are "sparse". The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.
Estes, Jason P; Nguyen, Danh V; Dalrymple, Lorien S; Mu, Yi; Şentürk, Damla
2016-05-20
Recent studies found that infection-related hospitalization was associated with increased risk of cardiovascular (CV) events, such as myocardial infarction and stroke in the dialysis population. In this work, we develop time-varying effects modeling tools in order to examine the CV outcome risk trajectories during the time periods before and after an initial infection-related hospitalization. For this, we propose partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs) for modeling time-varying effects in longitudinal data with substantial follow-up truncation by death. Unconditional models that implicitly target an immortal population is not a relevant target of inference in applications involving a population with high mortality, like the dialysis population. A partly conditional model characterizes the outcome trajectory for the dynamic cohort of survivors, where each point in the longitudinal trajectory represents a snapshot of the population relationships among subjects who are alive at that time point. In contrast, a fully conditional approach models the time-varying effects of the population stratified by the actual time of death, where the mean response characterizes individual trends in each cohort stratum. We compare and contrast partly and fully conditional PL-GVCMs in our aforementioned application using hospitalization data from the United States Renal Data System. For inference, we develop generalized likelihood ratio tests. Simulation studies examine the efficacy of estimation and inference procedures.
Abul Kashem, Saad Bin; Ektesabi, Mehran; Nagarajah, Romesh
2012-07-01
This study examines the uncertainties in modelling a quarter car suspension system caused by the effect of different sets of suspension parameters of a corresponding mathematical model. To overcome this problem, 11 sets of identified parameters of a suspension system have been compared, taken from the most recent published work. From this investigation, a set of parameters were chosen which showed a better performance than others in respect of peak amplitude and settling time. These chosen parameters were then used to investigate the performance of a new modified continuous skyhook control strategy with adaptive gain that dictates the vehicle's semi-active suspension system. The proposed system first captures the road profile input over a certain period. Then it calculates the best possible value of the skyhook gain (SG) for the subsequent process. Meanwhile the system is controlled according to the new modified skyhook control law using an initial or previous value of the SG. In this study, the proposed suspension system is compared with passive and other recently reported skyhook controlled semi-active suspension systems. Its performances have been evaluated in terms of ride comfort and road handling performance. The model has been validated in accordance with the international standards of admissible acceleration levels ISO2631 and human vibration perception.
Application of lumped-parameter models
DEFF Research Database (Denmark)
Ibsen, Lars Bo; Liingaard, Morten
This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil (section 1.1). Subse...
Models and parameters for environmental radiological assessments
Energy Technology Data Exchange (ETDEWEB)
Miller, C W [ed.
1984-01-01
This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base. (ACR)
Modeling deep ocean shipping noise in varying acidity conditions.
Udovydchenkov, Ilya A; Duda, Timothy F; Doney, Scott C; Lima, Ivan D
2010-09-01
Possible future changes of ambient shipping noise at 0.1-1 kHz in the North Pacific caused by changing seawater chemistry conditions are analyzed with a simplified propagation model. Probable decreases of pH would cause meaningful reduction of the sound absorption coefficient in near-surface ocean water for these frequencies. The results show that a few decibels of increase may occur in 100 years in some very quiet areas very far from noise sources, with small effects closer to noise sources. The use of ray physics allows sound energy attenuated via volume absorption and by the seafloor to be compared.
Cheong, Chin Wen
2008-02-01
This article investigated the influences of structural breaks on the fractionally integrated time-varying volatility model in the Malaysian stock markets which included the Kuala Lumpur composite index and four major sectoral indices. A fractionally integrated time-varying volatility model combined with sudden changes is developed to study the possibility of structural change in the empirical data sets. Our empirical results showed substantial reduction in fractional differencing parameters after the inclusion of structural change during the Asian financial and currency crises. Moreover, the fractionally integrated model with sudden change in volatility performed better in the estimation and specification evaluations.
Modeling the surface photovoltage of silicon slabs with varying thickness.
Vazhappilly, Tijo; Kilin, Dmitri S; Micha, David A
2015-04-10
The variation with thickness of the energy band gap and photovoltage at the surface of a thin semiconductor film are of great interest in connection with their surface electronic structure and optical properties. In this work, the change of a surface photovoltage (SPV) with the number of layers of a crystalline silicon slab is extracted from models based on their atomic structure. Electronic properties of photoexcited slabs are investigated using generalized gradient and hybrid density functionals, and plane wave basis sets. Si(1 1 1) surfaces have been terminated by hydrogen atoms to compensate for dangling bonds and have been described by large supercells with periodic boundary conditions. Calculations of the SPV of the Si slabs have been done in terms of the reduced density matrix of the photoactive electrons including dissipative effects due to their interaction with medium phonons and excitons. Surface photovoltages have been calculated for model Si slabs with 4-12, and 16 layers, to determine convergence trends versus slab thickness. Band gaps and the inverse of the SPVs have been found to scale nearly linearly with the inverse thickness of the slab, while the electronic density of states increases quadratically with thickness. Our calculations show the same trends as experimental values indicating band gap reduction and absorption enhancement for Si films of increasing thickness. Simple arguments on confined electronic structures have been used to explain the main effects of changes with slab thickness. A procedure involving shifted electron excitation energies is described to improve results from generalized gradient functionals so they can be in better agreement with the more accurate but also more computer intensive values from screened exchange hybrid functionals.
Do land parameters matter in large-scale hydrological modelling?
Gudmundsson, Lukas; Seneviratne, Sonia I.
2013-04-01
Many of the most pending issues in large-scale hydrology are concerned with predicting hydrological variability at ungauged locations. However, current-generation hydrological and land surface models that are used for their estimation suffer from large uncertainties. These models rely on mathematical approximations of the physical system as well as on mapped values of land parameters (e.g. topography, soil types, land cover) to predict hydrological variables (e.g. evapotranspiration, soil moisture, stream flow) as a function of atmospheric forcing (e.g. precipitation, temperature, humidity). Despite considerable progress in recent years, it remains unclear whether better estimates of land parameters can improve predictions - or - if a refinement of model physics is necessary. To approach this question we suggest scrutinizing our perception of hydrological systems by confronting it with the radical assumption that hydrological variability at any location in space depends on past and present atmospheric forcing only, and not on location-specific land parameters. This so called "Constant Land Parameter Hypothesis (CLPH)" assumes that variables like runoff can be predicted without taking location specific factors such as topography or soil types into account. We demonstrate, using a modern statistical tool, that monthly runoff in Europe can be skilfully estimated using atmospheric forcing alone, without accounting for locally varying land parameters. The resulting runoff estimates are used to benchmark state-of-the-art process models. These are found to have inferior performance, despite their explicit process representation, which accounts for locally varying land parameters. This suggests that progress in the theory of hydrological systems is likely to yield larger improvements in model performance than more precise land parameter estimates. The results also question the current modelling paradigm that is dominated by the attempt to account for locally varying land
Aminov, R. Z.; Shkret, A. F.; Garievskii, M. V.
2016-08-01
The use of potent power units in thermal and nuclear power plants in order to regulate the loads results in intense wear of power generating equipment and reduction in cost efficiency of their operation. We review the methodology of a quantitative assessment of the lifespan and wear of steam-turbine power units and estimate the effect of various operation regimes upon their efficiency. To assess the power units' equipment wear, we suggest using the concept of a turbine's equivalent lifespan. We give calculation formulae and an example of calculation of the lifespan of a steam-turbine power unit for supercritical parameters of steam for different options of its loading. The equivalent lifespan exceeds the turbine's assigned lifespan only provided daily shutdown of the power unit during the night off-peak time. We obtained the engineering and economical indices of the power unit operation for different loading regulation options in daily and weekly diagrams. We proved the change in the prime cost of electric power depending on the operation regimes and annual daily number of unloading (non-use) of the power unit's installed capacity. According to the calculation results, the prime cost of electric power for the assumed initial data varies from 11.3 cents/(kW h) in the basic regime of power unit operation (with an equivalent operation time of 166700 hours) to 15.5 cents/(kW h) in the regime with night and holiday shutdowns. The reduction of using the installed capacity of power unit at varying regimes from 3.5 to 11.9 hours per day can increase the prime cost of energy from 4.2 to 37.4%. Furthermore, repair and maintenance costs grow by 4.5% and by 3 times, respectively, in comparison with the basic regime. These results indicate the need to create special maneuverable equipment for working in the varying section of the electric load diagram.
Directory of Open Access Journals (Sweden)
Muhammad Aamir
2014-01-01
Full Text Available An experimental study was carried out to investigate the effects of inlet pressure, sample thickness, initial sample temperature, and temperature sensor location on the surface heat flux, surface temperature, and surface ultrafast cooling rate using stainless steel samples of diameter 27 mm and thickness (mm 8.5, 13, 17.5, and 22, respectively. Inlet pressure was varied from 0.2 MPa to 1.8 MPa, while sample initial temperature varied from 600°C to 900°C. Beck’s sequential function specification method was utilized to estimate surface heat flux and surface temperature. Inlet pressure has a positive effect on surface heat flux (SHF within a critical value of pressure. Thickness of the sample affects the maximum achieved SHF negatively. Surface heat flux as high as 0.4024 MW/m2 was estimated for a thickness of 8.5 mm. Insulation effects of vapor film become apparent in the sample initial temperature range of 900°C causing reduction in surface heat flux and cooling rate of the sample. A sensor location near to quenched surface is found to be a better choice to visualize the effects of spray parameters on surface heat flux and surface temperature. Cooling rate showed a profound increase for an inlet pressure of 0.8 MPa.
Aamir, Muhammad; Liao, Qiang; Zhu, Xun; Aqeel-ur-Rehman; Wang, Hong; Zubair, Muhammad
2014-01-01
An experimental study was carried out to investigate the effects of inlet pressure, sample thickness, initial sample temperature, and temperature sensor location on the surface heat flux, surface temperature, and surface ultrafast cooling rate using stainless steel samples of diameter 27 mm and thickness (mm) 8.5, 13, 17.5, and 22, respectively. Inlet pressure was varied from 0.2 MPa to 1.8 MPa, while sample initial temperature varied from 600°C to 900°C. Beck's sequential function specification method was utilized to estimate surface heat flux and surface temperature. Inlet pressure has a positive effect on surface heat flux (SHF) within a critical value of pressure. Thickness of the sample affects the maximum achieved SHF negatively. Surface heat flux as high as 0.4024 MW/m(2) was estimated for a thickness of 8.5 mm. Insulation effects of vapor film become apparent in the sample initial temperature range of 900°C causing reduction in surface heat flux and cooling rate of the sample. A sensor location near to quenched surface is found to be a better choice to visualize the effects of spray parameters on surface heat flux and surface temperature. Cooling rate showed a profound increase for an inlet pressure of 0.8 MPa.
Modelling suction instabilities in soils at varying degrees of saturation
Directory of Open Access Journals (Sweden)
Buscarnera Giuseppe
2016-01-01
Full Text Available Wetting paths imparted by the natural environment and/or human activities affect the state of soils in the near-surface, promoting transitions across different regimes of saturation. This paper discusses a set of techniques aimed at quantifying the role of hydrologic processes on the hydro-mechanical stability of soil specimens subjected to saturation events. Emphasis is given to the mechanical conditions leading to coupled flow/deformation instabilities. For this purpose, energy balance arguments for three-phase systems are used to derive second-order work expressions applicable to various regimes of saturation. Controllability analyses are then performed to relate such work input with constitutive singularities that reflect the loss of strength under coupled and/or uncoupled hydro-mechanical forcing. A suction-dependent plastic model is finally used to track the evolution of stability conditions in samples subjected to wetting, thus quantifying the growth of the potential for coupled failure modes upon increasing degree of saturation. These findings are eventually linked with the properties of the field equations that govern pore pressure transients, thus disclosing a conceptual link between the onset of coupled hydro-mechanical failures and the evolution of suction with time. Such results point out that mathematical instabilities caused by a non-linear suction dependent behaviour play an important role in the advanced constitutive and/or numerical tools that are commonly used for the analysis of geomechanical problems in the unsaturated zone, and further stress that the relation between suction transients and soil deformations is a key factor for the interpretation of runaway failures caused by intense saturation events.
An Optimization Model of Tunnel Support Parameters
Directory of Open Access Journals (Sweden)
Su Lijuan
2015-05-01
Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.
Fang-Xiang Wu
2011-08-01
The study of stability is essential for designing or controlling genetic regulatory networks. This paper addresses global and robust stability of genetic regulatory networks with time delays and parameter uncertainties. Most existing results on this issue are based on the linear matrix inequalities (LMIs) approach, which results in checking the existence of a feasible solution to high dimensional LMIs. Based on M-matrix theory, we will present several novel global stability conditions for genetic regulatory networks with time-varying and time-invariant delays. All of these stability conditions are given in terms of M-matrices, for which there are many and very easy ways to be verified. Then, we extend these results to genetic regulatory networks with time delays and parameter uncertainties. To illustrate the effectiveness of our theoretical results, several genetic regulatory networks are analyzed. Compared with existing results in the literature, we also show that our results are less conservative than existing ones with these illustrative genetic regulatory networks.
Analysis of Modeling Parameters on Threaded Screws.
Energy Technology Data Exchange (ETDEWEB)
Vigil, Miquela S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vangoethem, Douglas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-06-01
Assembled mechanical systems often contain a large number of bolted connections. These bolted connections (joints) are integral aspects of the load path for structural dynamics, and, consequently, are paramount for calculating a structure's stiffness and energy dissipation prop- erties. However, analysts have not found the optimal method to model appropriately these bolted joints. The complexity of the screw geometry cause issues when generating a mesh of the model. This paper will explore different approaches to model a screw-substrate connec- tion. Model parameters such as mesh continuity, node alignment, wedge angles, and thread to body element size ratios are examined. The results of this study will give analysts a better understanding of the influences of these parameters and will aide in finding the optimal method to model bolted connections.
Testing Serial Correlation in Semiparametric Varying-Coefficient Partially Linear EV Models
Institute of Scientific and Technical Information of China (English)
Xue-mei Hu; Zhi-zhong Wang; Feng Liu
2008-01-01
This paper studies estimation and serial correlation test of a semiparametric varying-coefficient partially linear EV model of the form Y = Xτβ + Zτα(T) + ε,ξ = X + η with the identifying condition E[(ε,ητ)τ] = 0, Cov[(ε,ητ)τ] = σ2Iρ+1. The estimators of interested regression parameters β, and the model error variance σ2, as well as the nonparametric components α(T), are constructed. Under some regular conditions, we show that the estimators of the unknown vector β and the unknown parameter σ2 are strongly consistent and asymptotically normal and that the estimator of α(T) achieves the optimal strong convergence rate of the usual nonparametric regression. Based on these estimators and asymptotic properties, we propose the VN,p test statistic and empirical log-likelihood ratio statistic for testing serial correlation in the model. The proposed statistics are shown to have asymptotic normal or chi-square distributions under the null hypothesis of no serial correlation. Some simulation studies are conducted to illustrate the finite sample performance of the proposed tests.
Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series
Cizek, Pavel; Koo, Chao
2017-01-01
An important and widely used class of semiparametric models is formed by the varyingcoefficient models. Although the varying coefficients are traditionally assumed to be smooth functions, the varying-coefficient model is considered here with the coefficient functions containing a finite set of disco
ASYMPTOTICS FOR CHANGE-POINT MODELS UNDER VARYING DEGREES OF MIS-SPECIFICATION.
Song, Rui; Banerjee, Moulinath; Kosorok, Michael R
2016-02-01
Change-point models are widely used by statisticians to model drastic changes in the pattern of observed data. Least squares/maximum likelihood based estimation of change-points leads to curious asymptotic phenomena. When the change-point model is correctly specified, such estimates generally converge at a fast rate (n) and are asymptotically described by minimizers of a jump process. Under complete mis-specification by a smooth curve, i.e. when a change-point model is fitted to data described by a smooth curve, the rate of convergence slows down to n(1/3) and the limit distribution changes to that of the minimizer of a continuous Gaussian process. In this paper we provide a bridge between these two extreme scenarios by studying the limit behavior of change-point estimates under varying degrees of model mis-specification by smooth curves, which can be viewed as local alternatives. We find that the limiting regime depends on how quickly the alternatives approach a change-point model. We unravel a family of 'intermediate' limits that can transition, at least qualitatively, to the limits in the two extreme scenarios. The theoretical results are illustrated via a set of carefully designed simulations. We also demonstrate how inference for the change-point parameter can be performed in absence of knowledge of the underlying scenario by resorting to subsampling techniques that involve estimation of the convergence rate.
MODELING PARAMETERS OF ARC OF ELECTRIC ARC FURNACE
Directory of Open Access Journals (Sweden)
R.N. Khrestin
2015-08-01
Full Text Available Purpose. The aim is to build a mathematical model of the electric arc of arc furnace (EAF. The model should clearly show the relationship between the main parameters of the arc. These parameters determine the properties of the arc and the possibility of optimization of melting mode. Methodology. We have built a fairly simple model of the arc, which satisfies the above requirements. The model is designed for the analysis of electromagnetic processes arc of varying length. We have compared the results obtained when testing the model with the results obtained on actual furnaces. Results. During melting in real chipboard under the influence of changes in temperature changes its properties arc plasma. The proposed model takes into account these changes. Adjusting the length of the arc is the main way to regulate the mode of smelting chipboard. The arc length is controlled by the movement of the drive electrode. The model reflects the dynamic changes in the parameters of the arc when changing her length. We got the dynamic current-voltage characteristics (CVC of the arc for the different stages of melting. We got the arc voltage waveform and identified criteria by which possible identified stage of smelting. Originality. In contrast to the previously known models, this model clearly shows the relationship between the main parameters of the arc EAF: arc voltage Ud, amperage arc id and length arc d. Comparison of the simulation results and experimental data obtained from real particleboard showed the adequacy of the constructed model. It was found that character of change of magnitude Md, helps determine the stage of melting. Practical value. It turned out that the model can be used to simulate smelting in EAF any capacity. Thus, when designing the system of control mechanism for moving the electrode, the model takes into account changes in the parameters of the arc and it can significantly reduce electrode material consumption and energy consumption
Directory of Open Access Journals (Sweden)
Fengxia Xu
2014-01-01
Full Text Available U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.
The Lund Model at Nonzero Impact Parameter
Janik, R A; Janik, Romuald A.; Peschanski, Robi
2003-01-01
We extend the formulation of the longitudinal 1+1 dimensional Lund model to nonzero impact parameter using the minimal area assumption. Complete formulae for the string breaking probability and the momenta of the produced mesons are derived using the string worldsheet Minkowskian helicoid geometry. For strings stretched into the transverse dimension, we find probability distribution with slope linear in m_T similar to the statistical models but without any thermalization assumptions.
IMPROVEMENT OF FLUID PIPE LUMPED PARAMETER MODEL
Institute of Scientific and Technical Information of China (English)
Kong Xiaowu; Wei Jianhua; Qiu Minxiu; Wu Genmao
2004-01-01
The traditional lumped parameter model of fluid pipe is introduced and its drawbacks are pointed out.Furthermore, two suggestions are put forward to remove these drawbacks.Firstly, the structure of equivalent circuit is modified, and then the evaluation of equivalent fluid resistance is change to take the frequency-dependent friction into account.Both simulation and experiment prove that this model is precise to characterize the dynamic behaviors of fluid in pipe.
Consistent Stochastic Modelling of Meteocean Design Parameters
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Sterndorff, M. J.
2000-01-01
Consistent stochastic models of metocean design parameters and their directional dependencies are essential for reliability assessment of offshore structures. In this paper a stochastic model for the annual maximum values of the significant wave height, and the associated wind velocity, current...... velocity, and water level is presented. The stochastic model includes statistical uncertainty and dependency between the four stochastic variables. Further, a new stochastic model for annual maximum directional significant wave heights is presented. The model includes dependency between the maximum wave...... height from neighboring directional sectors. Numerical examples are presented where the models are calibrated using the Maximum Likelihood method to data from the central part of the North Sea. The calibration of the directional distributions is made such that the stochastic model for the omnidirectional...
Sulaksono, A; Agrawal, B K
2014-01-01
The model dependence and the symmetry energy dependence of the core-crust transition properties for the neutron stars are studied using three different families of systematically varied extended relativistic mean field model. Several forces within each of the families are so considered that they yield wide variations in the values of the nuclear symmetry energy $a_{\\rm sym}$ and its slope parameter $L$ at the saturation density. The core-crust transition density is calculated using a method based on random-phase-approximation. The core-crust transition density is strongly correlated, in a model independent manner, with the symmetry energy slope parameter evaluated at the saturation density. The pressure at the transition point dose not show any meaningful correlations with the symmetry energy parameters at the saturation density. At best, pressure at the transition point is correlated with the symmetry energy parameters and their linear combination evaluated at the some sub-saturation density. Yet, such corre...
Ferranti, Francesco; Rolain, Yves
2017-01-01
This paper proposes a novel state-space matrix interpolation technique to generate linear parameter-varying (LPV) models starting from a set of local linear time-invariant (LTI) models estimated at fixed operating conditions. Since the state-space representation of LTI models is unique up to a similarity transformation, the state-space matrices need to be represented in a common state-space form. This is needed to avoid potentially large variations as a function of the scheduling parameters of the state-space matrices to be interpolated due to underlying similarity transformations, which might degrade the accuracy of the interpolation significantly. Underlying linear state coordinate transformations for a set of local LTI models are extracted by the computation of similarity transformation matrices by means of linear least-squares approximations. These matrices are then used to transform the local LTI state-space matrices into a form suitable to achieve accurate interpolation results. The proposed LPV modeling technique is validated by pertinent numerical results.
Directory of Open Access Journals (Sweden)
Lili Jiang
2010-04-01
Full Text Available Escherichia coli chemotactic motion in spatiotemporally varying environments is studied by using a computational model based on a coarse-grained description of the intracellular signaling pathway dynamics. We find that the cell's chemotaxis drift velocity v(d is a constant in an exponential attractant concentration gradient [L] proportional, variantexp(Gx. v(d depends linearly on the exponential gradient G before it saturates when G is larger than a critical value G(C. We find that G(C is determined by the intracellular adaptation rate k(R with a simple scaling law: G(C infinity k(1/2(R. The linear dependence of v(d on G = d(ln[L]/dx directly demonstrates E. coli's ability in sensing the derivative of the logarithmic attractant concentration. The existence of the limiting gradient G(C and its scaling with k(R are explained by the underlying intracellular adaptation dynamics and the flagellar motor response characteristics. For individual cells, we find that the overall average run length in an exponential gradient is longer than that in a homogeneous environment, which is caused by the constant kinase activity shift (decrease. The forward runs (up the gradient are longer than the backward runs, as expected; and depending on the exact gradient, the (shorter backward runs can be comparable to runs in a spatially homogeneous environment, consistent with previous experiments. In (spatial ligand gradients that also vary in time, the chemotaxis motion is damped as the frequency omega of the time-varying spatial gradient becomes faster than a critical value omega(c, which is controlled by the cell's chemotaxis adaptation rate k(R. Finally, our model, with no adjustable parameters, agrees quantitatively with the classical capillary assay experiments where the attractant concentration changes both in space and time. Our model can thus be used to study E. coli chemotaxis behavior in arbitrary spatiotemporally varying environments. Further experiments are
Order Parameters of the Dilute A Models
Warnaar, S O; Seaton, K A; Nienhuis, B
1993-01-01
The free energy and local height probabilities of the dilute A models with broken $\\Integer_2$ symmetry are calculated analytically using inversion and corner transfer matrix methods. These models possess four critical branches. The first two branches provide new realisations of the unitary minimal series and the other two branches give a direct product of this series with an Ising model. We identify the integrable perturbations which move the dilute A models away from the critical limit. Generalised order parameters are defined and their critical exponents extracted. The associated conformal weights are found to occur on the diagonal of the relevant Kac table. In an appropriate regime the dilute A$_3$ model lies in the universality class of the Ising model in a magnetic field. In this case we obtain the magnetic exponent $\\delta=15$ directly, without the use of scaling relations.
Damour, Thibault Marie Alban Guillaume
2003-01-01
We review some string-inspired theoretical models which incorporate a correlated spacetime variation of coupling constants while remaining naturally compatible both with phenomenological constraints coming from geochemical data (Oklo; Rhenium decay) and with present equivalence principle tests. Barring unnatural fine-tunings of parameters, a variation of the fine-structure constant as large as that recently ``observed'' by Webb et al. in quasar absorption spectra appears to be incompatible with these phenomenological constraints. Independently of any model, it is emphasized that the best experimental probe of varying constants are high-precision tests of the universality of free fall, such as MICROSCOPE and STEP. Recent claims by Bekenstein that fine-structure-constant variability does not imply detectable violations of the equivalence principle are shown to be untenable.
Testing Linear Models for Ability Parameters in Item Response Models
Glas, Cees A.W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum like
Estimation of time-varying selectivity in stock assessments using state-space models
DEFF Research Database (Denmark)
Nielsen, Anders; Berg, Casper Willestofte
2014-01-01
-varying selectivity pattern. The fishing mortality rates are considered (possibly correlated) stochastic processes, and the corresponding process variances are estimated within the model. The model is applied to North Sea cod and it is verified from simulations that time-varying selectivity can be estimated...
Prediction of mortality rates using a model with stochastic parameters
Tan, Chon Sern; Pooi, Ah Hin
2016-10-01
Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths.
Modelling spin Hamiltonian parameters of molecular nanomagnets.
Gupta, Tulika; Rajaraman, Gopalan
2016-07-12
Molecular nanomagnets encompass a wide range of coordination complexes possessing several potential applications. A formidable challenge in realizing these potential applications lies in controlling the magnetic properties of these clusters. Microscopic spin Hamiltonian (SH) parameters describe the magnetic properties of these clusters, and viable ways to control these SH parameters are highly desirable. Computational tools play a proactive role in this area, where SH parameters such as isotropic exchange interaction (J), anisotropic exchange interaction (Jx, Jy, Jz), double exchange interaction (B), zero-field splitting parameters (D, E) and g-tensors can be computed reliably using X-ray structures. In this feature article, we have attempted to provide a holistic view of the modelling of these SH parameters of molecular magnets. The determination of J includes various class of molecules, from di- and polynuclear Mn complexes to the {3d-Gd}, {Gd-Gd} and {Gd-2p} class of complexes. The estimation of anisotropic exchange coupling includes the exchange between an isotropic metal ion and an orbitally degenerate 3d/4d/5d metal ion. The double-exchange section contains some illustrative examples of mixed valance systems, and the section on the estimation of zfs parameters covers some mononuclear transition metal complexes possessing very large axial zfs parameters. The section on the computation of g-anisotropy exclusively covers studies on mononuclear Dy(III) and Er(III) single-ion magnets. The examples depicted in this article clearly illustrate that computational tools not only aid in interpreting and rationalizing the observed magnetic properties but possess the potential to predict new generation MNMs.
Institute of Scientific and Technical Information of China (English)
LI JunWei; LIN BoLiang; SUN ZhiHui; GENG XueFei
2009-01-01
On the basis of measurable time series of mainline and ramp flows from traffic counts and the assumption of travel time distributions, this research presents a dynamic system model and its on-line estimation algorithm for recursive estimation of Ume-varying origin-destination (OD) matrices in expressway corridors. The proposed model employs a macro-traffic flow model to estimate travel times of OD flows and uses parameters of the traffic model as state variables, which are added to the constrained function of the system. To improve the model efficiency, we revise the travel time distribution based on the feature of normal distribution. The research employs a newly developed filtering technique, called unscented Kalman filter. The proposed model is evaluated with simulation experiments.Numerical analyses with respect to the sensitivity of the selection of initial parameters on the estimation results indicate that the proposed model is sufficiently reasonable and stable for real-world applications.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
On the basis of measurable time series of mainline and ramp flows from traffic counts and the assumption of travel time distributions, this research presents a dynamic system model and its on-line estimation algorithm for recursive estimation of time-varying origin-destination (OD) matrices in expressway corridors. The proposed model employs a macro-traffic flow model to estimate travel times of OD flows and uses parameters of the traffic model as state variables, which are added to the constrained function of the system. To improve the model efficiency, we revise the travel time distribution based on the feature of normal distribution. The research employs a newly developed filtering technique, called unscented Kalman filter. The proposed model is evaluated with simulation experiments. Numerical analyses with respect to the sensitivity of the selection of initial parameters on the estimation results indicate that the proposed model is sufficiently reasonable and stable for real-world appli-cations.
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Li, Zhen; Karniadakis, George
2016-01-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are sparse. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space....
Dynamic linear models to explore time-varying suspended sediment-discharge rating curves
Ahn, Kuk-Hyun; Yellen, Brian; Steinschneider, Scott
2017-06-01
This study presents a new method to examine long-term dynamics in sediment yield using time-varying sediment-discharge rating curves. Dynamic linear models (DLMs) are introduced as a time series filter that can assess how the relationship between streamflow and sediment concentration or load changes over time in response to a wide variety of natural and anthropogenic watershed disturbances or long-term changes. The filter operates by updating parameter values using a recursive Bayesian design that responds to 1 day-ahead forecast errors while also accounting for observational noise. The estimated time series of rating curve parameters can then be used to diagnose multiscale (daily-decadal) variability in sediment yield after accounting for fluctuations in streamflow. The technique is applied in a case study examining changes in turbidity load, a proxy for sediment load, in the Esopus Creek watershed, part of the New York City drinking water supply system. The results show that turbidity load exhibits a complex array of variability across time scales. The DLM highlights flood event-driven positive hysteresis, where turbidity load remained elevated for months after large flood events, as a major component of dynamic behavior in the rating curve relationship. The DLM also produces more accurate 1 day-ahead loading forecasts compared to other static and time-varying rating curve methods. The results suggest that DLMs provide a useful tool for diagnosing changes in sediment-discharge relationships over time and may help identify variability in sediment concentrations and loads that can be used to inform dynamic water quality management.
Walker, Christoph
2010-01-01
A parameter-dependent model involving nonlinear diffusion for an age-structured population is studied. The parameter measures the intensity of the mortality. A bifurcation approach is used to establish existence of positive equilibrium solutions.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
Directory of Open Access Journals (Sweden)
Qi-an Chen
2015-01-01
Full Text Available Taking full advantage of the strengths of G-H distribution, Copula function, and GARCH model in depicting the return distribution of financial asset, we construct the multivariate time-varying G-H Copula GARCH model which can comprehensively describe “asymmetric, leptokurtic, and heavy-tail” characteristics, the time-varying volatility characteristics, and the extreme-tail dependence characteristics of financial asset return. Based on the conditional maximum likelihood estimator and IFM method, we propose the estimation algorithm of model parameters. Using the quantile function and simulation method, we propose the calculation algorithm of VaR on the basis of this model. To apply this model on studying a real financial market risk, we select the SSCI (China, HSI (Hong Kong, China, TAIEX (Taiwan, China, and SP500 (USA from January 3, 2000, to June 18, 2010, as the samples to estimate the model parameters and to measure the VaRs of various index risk portfolios under different confidence levels empirically. The results of the application example are in line with the actual situation and the risk diversification theory of portfolio. To a certain extent, these results also justify the feasibility and effectiveness of the multivariate time-varying G-H Copula GARCH model in depicting the return distribution of financial assets.
Bursting-like motion induced by time-varying delay in an internet congestion control model
Institute of Scientific and Technical Information of China (English)
Shu Zhang; Jian Xu
2012-01-01
Time delay is an important parameter in the problem of internet congestion control.According to some researches,time delay is not always constant and can be viewed as a periodic function of time for some cases.In this work,an internet congestion control model is considered to study the time-varying delay induced bursting-like motion,which consists of a rapid oscillation burst and quiescent steady state.Then,for the system with periodic delay of small amplitude and low frequency,the method of multiple scales is employed to obtain the amplitude of the oscillation.Based on the expression of the asymptotic solution,it can be found that the relative length of the steady state increases with amplitude of the variation of time delay and decreases with frequency of the variation of time delay.Finally,an effective method to control the bursting-like motion is proposed by introducing a periodic gain parameter with appropriate amplitude.Theoretical results are in agreement with that from numerical method.
Li, Yang; Wei, Hua-Liang; Billings, Stephen. A.; Sarrigiannis, P. G.
2016-08-01
The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.
Resampling: An improvement of importance sampling in varying population size models.
Merle, C; Leblois, R; Rousset, F; Pudlo, P
2017-04-01
Sequential importance sampling algorithms have been defined to estimate likelihoods in models of ancestral population processes. However, these algorithms are based on features of the models with constant population size, and become inefficient when the population size varies in time, making likelihood-based inferences difficult in many demographic situations. In this work, we modify a previous sequential importance sampling algorithm to improve the efficiency of the likelihood estimation. Our procedure is still based on features of the model with constant size, but uses a resampling technique with a new resampling probability distribution depending on the pairwise composite likelihood. We tested our algorithm, called sequential importance sampling with resampling (SISR) on simulated data sets under different demographic cases. In most cases, we divided the computational cost by two for the same accuracy of inference, in some cases even by one hundred. This study provides the first assessment of the impact of such resampling techniques on parameter inference using sequential importance sampling, and extends the range of situations where likelihood inferences can be easily performed.
Considerations for parameter optimization and sensitivity in climate models.
Neelin, J David; Bracco, Annalisa; Luo, Hao; McWilliams, James C; Meyerson, Joyce E
2010-12-14
Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.
Pianosi, Francesca; Wagener, Thorsten
2016-04-01
Simulations from environmental models are affected by potentially large uncertainties stemming from various sources, including model parameters and observational uncertainty in the input/output data. Understanding the relative importance of such sources of uncertainty is essential to support model calibration, validation and diagnostic evaluation, and to prioritize efforts for uncertainty reduction. Global Sensitivity Analysis (GSA) provides the theoretical framework and the numerical tools to gain this understanding. However, in traditional applications of GSA, model outputs are an aggregation of the full set of simulated variables. This aggregation of propagated uncertainties prior to GSA may lead to a significant loss of information and may cover up local behaviour that could be of great interest. In this work, we propose a time-varying version of a recently developed density-based GSA method, called PAWN, as a viable option to reduce this loss of information. We apply our approach to a medium-complexity hydrological model in order to address two questions: [1] Can we distinguish between the relative importance of parameter uncertainty versus data uncertainty in time? [2] Do these influences change in catchments with different characteristics? The results present the first quantitative investigation on the relative importance of parameter and data uncertainty across time. They also provide a demonstration of the value of time-varying GSA to investigate the propagation of uncertainty through numerical models and therefore guide additional data collection needs and model calibration/assessment.
Directory of Open Access Journals (Sweden)
Sudeep Sharma
2012-07-01
Full Text Available Generalized Adaptive Linear Element (GADALINE Artificial Neural Network (ANN as an Artificial Intelligence (AI technique is used in this paper to online adaptive control of a Non-linear Inverted Pendulum (IP system. The ANN controller is designed with specifications as: network type is three (Input, Hidden and Output layered Feed-Forward Network (FFN, training is done by Widrow-Hoffs delta rule or Least Mean Square algorithm (LMS, that updates weight and bias states to minimize the error function. The research is focused on how to adapt the control actions to solve the problem of “parameter variations”. The method is applied to the Nonlinear IP model with the application of some uncertainties, and the experimental results show that the system responds very well to handle those uncertainties.
Parameter estimation, model reduction and quantum filtering
Chase, Bradley A.
This thesis explores the topics of parameter estimation and model reduction in the context of quantum filtering. The last is a mathematically rigorous formulation of continuous quantum measurement, in which a stream of auxiliary quantum systems is used to infer the state of a target quantum system. Fundamental quantum uncertainties appear as noise which corrupts the probe observations and therefore must be filtered in order to extract information about the target system. This is analogous to the classical filtering problem in which techniques of inference are used to process noisy observations of a system in order to estimate its state. Given the clear similarities between the two filtering problems, I devote the beginning of this thesis to a review of classical and quantum probability theory, stochastic calculus and filtering. This allows for a mathematically rigorous and technically adroit presentation of the quantum filtering problem and solution. Given this foundation, I next consider the related problem of quantum parameter estimation, in which one seeks to infer the strength of a parameter that drives the evolution of a probe quantum system. By embedding this problem in the state estimation problem solved by the quantum filter, I present the optimal Bayesian estimator for a parameter when given continuous measurements of the probe system to which it couples. For cases when the probe takes on a finite number of values, I review a set of sufficient conditions for asymptotic convergence of the estimator. For a continuous-valued parameter, I present a computational method called quantum particle filtering for practical estimation of the parameter. Using these methods, I then study the particular problem of atomic magnetometry and review an experimental method for potentially reducing the uncertainty in the estimate of the magnetic field beyond the standard quantum limit. The technique involves double-passing a probe laser field through the atomic system, giving
Physiologically based pharmacokinetic modeling of PLGA nanoparticles with varied mPEG content
Directory of Open Access Journals (Sweden)
Avgoustakis K
2012-03-01
Full Text Available Mingguang Li1, Zoi Panagi2, Konstantinos Avgoustakis2, Joshua Reineke11Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI, USA; 2Pharmaceutical Technology Laboratory, Department of Pharmacy, University of Patras, Rion, Patras, GreeceAbstract: Biodistribution of nanoparticles is dependent on their physicochemical properties (such as size, surface charge, and surface hydrophilicity. Clear and systematic understanding of nanoparticle properties' effects on their in vivo performance is of fundamental significance in nanoparticle design, development and optimization for medical applications, and toxicity evaluation. In the present study, a physiologically based pharmacokinetic model was utilized to interpret the effects of nanoparticle properties on previously published biodistribution data. Biodistribution data for five poly(lactic-co-glycolic acid (PLGA nanoparticle formulations prepared with varied content of monomethoxypoly (ethyleneglycol (mPEG (PLGA, PLGA-mPEG256, PLGA-mPEG153, PLGA-mPEG51, PLGA-mPEG34 were collected in mice after intravenous injection. A physiologically based pharmacokinetic model was developed and evaluated to simulate the mass-time profiles of nanoparticle distribution in tissues. In anticipation that the biodistribution of new nanoparticle formulations could be predicted from the physiologically based pharmacokinetic model, multivariate regression analysis was performed to build the relationship between nanoparticle properties (size, zeta potential, and number of PEG molecules per unit surface area and biodistribution parameters. Based on these relationships, characterized physicochemical properties of PLGA-mPEG495 nanoparticles (a sixth formulation were used to calculate (predict biodistribution profiles. For all five initial formulations, the developed model adequately simulates the experimental data indicating that the model is suitable for
Yu, Xiaolin; Zhang, Shaoqing; Lin, Xiaopei; Li, Mingkui
2017-03-01
The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slow-varying ocean, this study examines the sensitivity of state-parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying atmosphere with a chaotic nature is the major source of the inaccuracy of estimated state-parameter covariance. Thus, enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.
Parameter optimization in S-system models
Directory of Open Access Journals (Sweden)
Vasconcelos Ana
2008-04-01
Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
Cosmological effects of scalar-photon couplings: dark energy and varying-α Models
Energy Technology Data Exchange (ETDEWEB)
Avgoustidis, A. [School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD (United Kingdom); Martins, C.J.A.P.; Monteiro, A.M.R.V.L.; Vielzeuf, P.E. [Centro de Astrofísica, Universidade do Porto, Rua das Estrelas, 4150-762 Porto (Portugal); Luzzi, G., E-mail: tavgoust@gmail.com, E-mail: Carlos.Martins@astro.up.pt, E-mail: mmonteiro@fc.up.pt, E-mail: up110370652@alunos.fc.up.pt, E-mail: gluzzi@lal.in2p3.fr [Laboratoire de l' Accélérateur Linéaire, Université de Paris-Sud, CNRS/IN2P3, Bâtiment 200, BP 34, 91898 Orsay Cedex (France)
2014-06-01
We study cosmological models involving scalar fields coupled to radiation and discuss their effect on the redshift evolution of the cosmic microwave background temperature, focusing on links with varying fundamental constants and dynamical dark energy. We quantify how allowing for the coupling of scalar fields to photons, and its important effect on luminosity distances, weakens current and future constraints on cosmological parameters. In particular, for evolving dark energy models, joint constraints on the dark energy equation of state combining BAO radial distance and SN luminosity distance determinations, will be strongly dominated by BAO. Thus, to fully exploit future SN data one must also independently constrain photon number non-conservation arising from the possible coupling of SN photons to the dark energy scalar field. We discuss how observational determinations of the background temperature at different redshifts can, in combination with distance measures data, set tight constraints on interactions between scalar fields and photons, thus breaking this degeneracy. We also discuss prospects for future improvements, particularly in the context of Euclid and the E-ELT and show that Euclid can, even on its own, provide useful dark energy constraints while allowing for photon number non-conservation.
Modeling of Parameters of Subcritical Assembly SAD
Petrochenkov, S; Puzynin, I
2005-01-01
The accepted conceptual design of the experimental Subcritical Assembly in Dubna (SAD) is based on the MOX core with a nominal unit capacity of 25 kW (thermal). This corresponds to the multiplication coefficient $k_{\\rm eff} =0.95$ and accelerator beam power 1 kW. A subcritical assembly driven with the existing 660 MeV proton accelerator at the Joint Institute for Nuclear Research has been modelled in order to make choice of the optimal parameters for the future experiments. The Monte Carlo method was used to simulate neutron spectra, energy deposition and doses calculations. Some of the calculation results are presented in the paper.
Positive Almost Periodic Solutions for a Time-Varying Fishing Model with Delay
Directory of Open Access Journals (Sweden)
Xia Li
2011-01-01
Full Text Available This paper is concerned with a time-varying fishing model with delay. By means of the continuation theorem of coincidence degree theory, we prove that it has at least one positive almost periodic solution.
Baker Syed; Poskar C; Junker Björn
2011-01-01
Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. Wh...
Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance
Lin, Julia Y.; Ten Have, Thomas R.; ELLIOTT, MICHAEL R.
2009-01-01
We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens-Rubin (1997) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model where we use time-invariant subject-specific complianc...
Moose models with vanishing $S$ parameter
Casalbuoni, R; Dominici, Daniele
2004-01-01
In the linear moose framework, which naturally emerges in deconstruction models, we show that there is a unique solution for the vanishing of the $S$ parameter at the lowest order in the weak interactions. We consider an effective gauge theory based on $K$ SU(2) gauge groups, $K+1$ chiral fields and electroweak groups $SU(2)_L$ and $U(1)_Y$ at the ends of the chain of the moose. $S$ vanishes when a link in the moose chain is cut. As a consequence one has to introduce a dynamical non local field connecting the two ends of the moose. Then the model acquires an additional custodial symmetry which protects this result. We examine also the possibility of a strong suppression of $S$ through an exponential behavior of the link couplings as suggested by Randall Sundrum metric.
Model parameters for simulation of physiological lipids
McGlinchey, Nicholas
2016-01-01
Coarse grain simulation of proteins in their physiological membrane environment can offer insight across timescales, but requires a comprehensive force field. Parameters are explored for multicomponent bilayers composed of unsaturated lipids DOPC and DOPE, mixed‐chain saturation POPC and POPE, and anionic lipids found in bacteria: POPG and cardiolipin. A nonbond representation obtained from multiscale force matching is adapted for these lipids and combined with an improved bonding description of cholesterol. Equilibrating the area per lipid yields robust bilayer simulations and properties for common lipid mixtures with the exception of pure DOPE, which has a known tendency to form nonlamellar phase. The models maintain consistency with an existing lipid–protein interaction model, making the force field of general utility for studying membrane proteins in physiologically representative bilayers. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26864972
A robust algorithm for time varying parameter estimation%具有鲁棒性的一种时变参数估计算法
Institute of Scientific and Technical Information of China (English)
夏传良
2001-01-01
Time varying parameter estimation is very important to the control of dynamic system. Considering a general model,Z.G.Han put up a time varying param ete r estimation algorithm which in some given conditions, has good properties, but doesn′t consider the robustness. For another model, Goodwin put up a proje ct ive algorithm with deadline, so the algorithm has rubustness. Based on the proje ctive alg orithm with deadline by Goodwin, and deadline being put into the algorithm of dy nami c system parameter estimation by Z. G Han, a new algorithm is achieved. The new algorithm both has the time-varying property and robustness property, and also at given condition has quickly tracing property.%时变参数的估计问题，对于动态系统的控制是十分重要的。针对一种基本模型，韩志 刚给出了一种时变参数估计算法，该算法在一定条件下具有一些优良性质，但是没有考虑算 法的鲁棒性。针对另一种基本模型Goodwin给出了一种带死区的投影算法，由于引入了死区 而使该算法具有鲁棒性。本文基于Goodwin给出的带死区的投影算法，在韩志刚给出的动态 系统时变参数估计算法中引入死区，得到了一组新的算法，该算法既能反映动态系统时变参 数的时变特性，又具有一定的鲁棒性，并且在一定条件下具有快速跟踪性质。
From calls to communities: a model for time varying social networks
Laurent, Guillaume; Karsai, Márton
2015-01-01
Social interactions vary in time and appear to be driven by intrinsic mechanisms, which in turn shape the emerging structure of the social network. Large-scale empirical observations of social interaction structure have become possible only recently, and modelling their dynamics is an actual challenge. Here we propose a temporal network model which builds on the framework of activity-driven time-varying networks with memory. The model also integrates key mechanisms that drive the formation of social ties - social reinforcement, focal closure and cyclic closure, which have been shown to give rise to community structure and the global connectedness of the network. We compare the proposed model with a real-world time-varying network of mobile phone communication and show that they share several characteristics from heterogeneous degrees and weights to rich community structure. Further, the strong and weak ties that emerge from the model follow similar weight-topology correlations as real-world social networks, i...
Changing dynamics: Time-varying autoregressive models using generalized additive modeling.
Bringmann, Laura F; Hamaker, Ellen L; Vigo, Daniel E; Aubert, André; Borsboom, Denny; Tuerlinckx, Francis
2017-09-01
In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Uncertainty Quantification for Optical Model Parameters
Lovell, A E; Sarich, J; Wild, S M
2016-01-01
Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of this work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fit and create corresponding 95\\% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. We study a number of reactions involving neutron and deuteron p...
Numerical modeling of partial discharges parameters
Directory of Open Access Journals (Sweden)
Kartalović Nenad M.
2016-01-01
Full Text Available In recent testing of the partial discharges or the use for the diagnosis of insulation condition of high voltage generators, transformers, cables and high voltage equipment develops rapidly. It is a result of the development of electronics, as well as, the development of knowledge about the processes of partial discharges. The aim of this paper is to contribute the better understanding of this phenomenon of partial discharges by consideration of the relevant physical processes in isolation materials and isolation systems. Prebreakdown considers specific processes, and development processes at the local level and their impact on specific isolation material. This approach to the phenomenon of partial discharges needed to allow better take into account relevant discharge parameters as well as better numerical model of partial discharges.
On the Influence of Material Parameters in a Complex Material Model for Powder Compaction
Staf, Hjalmar; Lindskog, Per; Andersson, Daniel C.; Larsson, Per-Lennart
2016-10-01
Parameters in a complex material model for powder compaction, based on a continuum mechanics approach, are evaluated using real insert geometries. The parameter sensitivity with respect to density and stress after compaction, pertinent to a wide range of geometries, is studied in order to investigate completeness and limitations of the material model. Finite element simulations with varied material parameters are used to build surrogate models for the sensitivity study. The conclusion from this analysis is that a simplification of the material model is relevant, especially for simple insert geometries. Parameters linked to anisotropy and the plastic strain evolution angle have a small impact on the final result.
DEFF Research Database (Denmark)
Jensen, Jakob Søndergaard
2010-01-01
Results are presented for optimal layout of materials in the spatial and temporal domains for a 1D structure subjected to transient wave propagation. A general optimization procedure is outlined including derivation of design sensitivities for the case when the mass density and stiffness vary in ...
Institute of Scientific and Technical Information of China (English)
Baocang Ding; Hongguang Pan
2016-01-01
The output feedback model predictive control (MPC), for a linear parameter varying (LPV) process system including unmeasurable model parameters and disturbance (all lying in known polytopes), is considered. Some previously developed tools, including the norm-bounding technique for relaxing the disturbance-related constraint handling, the dynamic output feedback law, the notion of quadratic boundedness for specifying the closed-loop stability, and the el ipsoidal state estimation error bound for guaranteeing the recursive feasibility, are merged in the control design. Some previous approaches are shown to be the special cases. An example of continuous stirred tank reactor (CSTR) is given to show the effectiveness of the proposed approaches.
Long-time behavior of a stochastic epidemic model with varying population size
Wei, Fengying; Liu, Jiamin
2017-03-01
In this paper we investigate the persistence and extinction of a stochastic epidemic model with a varying population environment in the long-term behavior. Our model consists of two stochastic differential equations: one for the susceptible individuals in which the transmission rate is disturbed by white noise, one for the exposed individuals in which the same perturbation occurs, and one ordinary differential equation in which describes the infective individuals in a varying population environment. We derive sufficient conditions for the extinction and persistence of the epidemic model depending on the constant contact rate. Moreover, we carry out several numerical simulations to illustrate the main results of this contribution.
Wheeler, David C.; Calder, Catherine A.
2007-06-01
The realization in the statistical and geographical sciences that a relationship between an explanatory variable and a response variable in a linear regression model is not always constant across a study area has led to the development of regression models that allow for spatially varying coefficients. Two competing models of this type are geographically weighted regression (GWR) and Bayesian regression models with spatially varying coefficient processes (SVCP). In the application of these spatially varying coefficient models, marginal inference on the regression coefficient spatial processes is typically of primary interest. In light of this fact, there is a need to assess the validity of such marginal inferences, since these inferences may be misleading in the presence of explanatory variable collinearity. In this paper, we present the results of a simulation study designed to evaluate the sensitivity of the spatially varying coefficients in the competing models to various levels of collinearity. The simulation study results show that the Bayesian regression model produces more accurate inferences on the regression coefficients than does GWR. In addition, the Bayesian regression model is overall fairly robust in terms of marginal coefficient inference to moderate levels of collinearity, and degrades less substantially than GWR with strong collinearity.
Helbich, M; Griffith, D
2016-01-01
Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns
Liu, Qun; Jiang, Daqing; Shi, Ningzhong; Hayat, Tasawar; Alsaedi, Ahmed
2017-03-01
In this paper, we develop a mathematical model for a tuberculosis model with constant recruitment and varying total population size by incorporating stochastic perturbations. By constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of an ergodic stationary distribution as well as extinction of the disease to the stochastic system.
Evans, Daniel J; Manwaring, Mark L
2007-01-01
Time varying computer models of the interaction of electric current and tissue are very valuable in helping to understand the complexity of the human body and biological tissue. The electrical properties of tissue, permittivity and conductivity, are vital to accurately modeling the interaction of the human tissue with electric current. Past models have represented the electric properties of the tissue as constant or temperature dependent. This paper presents time dependent electric properties that change as a result of tissue damage, temperature, blood flow, blood vessels, and tissue property. Six models are compared to emphasize the importance of accounting for these different tissue properties in the computer model. In particular, incorporating the time varying nature of the electric properties of human tissue into the model leads to a significant increase in tissue damage. An important feature of the model is the feedback loop created between the electric properties, tissue damage, and temperature.
Optimization of a simplified automobile finite element model using time varying injury metrics.
Gaewsky, James P; Danelson, Kerry A; Weaver, Caitlin M; Stitzel, Joel D
2014-01-01
In 2011, frontal crashes resulted in 55% of passenger car injuries with 10,277 fatalities and 866,000 injuries in the United States. To better understand frontal crash injury mechanisms, human body finite element models (FEMs) can be used to reconstruct Crash Injury Research and Engineering Network (CIREN) cases. A limitation of this method is the paucity of vehicle FEMs; therefore, we developed a functionally equivalent simplified vehicle model. The New Car Assessment Program (NCAP) data for our selected vehicle was from a frontal collision with Hybrid III (H3) Anthropomorphic Test Device (ATD) occupant. From NCAP test reports, the vehicle geometry was created and the H3 ATD was positioned. The material and component properties optimized using a variation study process were: steering column shear bolt fracture force and stroke resistance, seatbelt pretensioner force, frontal and knee bolster airbag stiffness, and belt friction through the D-ring. These parameters were varied using three successive Latin Hypercube Designs of Experiments with 130-200 simulations each. The H3 injury response was compared to the reported NCAP frontal test results for the head, chest and pelvis accelerations, and seat belt and femur forces. The phase, magnitude, and comprehensive error factors, from a Sprague and Geers analysis were calculated for each injury metric and then combined to determine the simulations with the best match to the crash test. The Sprague and Geers analyses typically yield error factors ranging from 0 to 1 with lower scores being more optimized. The total body injury response error factor for the most optimized simulation from each round of the variation study decreased from 0.466 to 0.395 to 0.360. This procedure to optimize vehicle FEMs is a valuable tool to conduct future CIREN case reconstructions in a variety of vehicles.
Parameter Optimisation for the Behaviour of Elastic Models over Time
DEFF Research Database (Denmark)
Mosegaard, Jesper
2004-01-01
Optimisation of parameters for elastic models is essential for comparison or finding equivalent behaviour of elastic models when parameters cannot simply be transferred or converted. This is the case with a large range of commonly used elastic models. In this paper we present a general method...... that will optimise parameters based on the behaviour of the elastic models over time....
Analytic model of acoustic streaming in thermoacoustic waveguides with slowly varying cross-section
Institute of Scientific and Technical Information of China (English)
FAN Yuxian; LIU Ke; YANG Jun
2012-01-01
An analytic model of acoustic streaming generated in two-dimensional thermoa- coustic waveguides with slowly varying cross-section was developed for more general applica- tions. The analytical solutions of acoustic streaming characteristics in the closed straight tube and the annular tube are given based on the model. The solution for the closed straight tube can be applied to the case with any transverse scale. The solution for the annular tube is obtained under the assumption that the width of the varying cross-section part is much larger than the viscous and thermal penetration depths. The effects of cross-section variation, time-averaged temperature distribution and components of sound field are reflected in the analytic solutions. The magnitude and distribution of acoustic streaming velocity vary with the characteristic scale of the waveguides. The analytic model of acoustic streaming can be applied in research under thermoacoustic and other physical backgrounds.
Lee, Chu-Yu; Bennett, Kevin M; Debbins, Josef P
2013-05-01
The aim of this study was to investigate the microstructural sensitivity of the statistical distribution and diffusion kurtosis (DKI) models of non-monoexponential signal attenuation in the brain using diffusion-weighted MRI (DWI). We first developed a simulation of 2-D water diffusion inside simulated tissue consisting of semi-permeable cells and a variable cell size. We simulated a DWI acquisition of the signal in a volume using a pulsed gradient spin echo (PGSE) pulse sequence, and fitted the models to the simulated DWI signals using b-values up to 2500 s/mm(2). For comparison, we calculated the apparent diffusion coefficient (ADC) of the monoexponential model (b-value=1000 s/mm(2)). In separate experiments, we varied the cell size (5-10-15 μm), cell volume fraction (0.50-0.65-0.80), and membrane permeability (0.001-0.01-0.1mm/s) to study how the fitted parameters tracked simulated microstructural changes. The ADC was sensitive to all the simulated microstructural changes except the decrease in membrane permeability. The ADC increased with larger cell size, smaller cell volume fraction, and larger membrane permeability. The σstat of the statistical distribution model increased exclusively with a decrease in cell volume fraction. The Kapp of the DKI model was exclusively increased with decreased cell size and decreased with increasing membrane permeability. These results suggest that the non-monoexponential models of water diffusion have different, specific microstructural sensitivity, and a combination of the models may give insights into the microstructural underpinning of tissue pathology.
An Improved NHPP Model with Time-Varying Fault Removal Delay
Institute of Scientific and Technical Information of China (English)
Xue Yang; Nan Sang; Hang Lei
2008-01-01
In this paper, an improved NHPP model isproposed by replacing constant fault removal time withtime-varying fault removal delay in NHPP model,proposed by Daniel R Jeske. In our model, a time-dependent delay function is established to fit the faultremoval process. By using two sets of practical data, thedescriptive and predictive abilities of the improved NHPPmodel are compared with those of the NHPP model, G-Omodel, and delayed S-shape model. The results show that the improved model can fit and predict the data better.
Velazquez, Antonio; Swartz, R. Andrew
2015-02-01
stochastic subspace identification (SSI) and linear parameter time-varying (LPTV) techniques. Structural response is assumed to be stationary ambient excitation produced by a Gaussian (white) noise within the operative range bandwidth of the machinery or structure in study. ERA-OKID analysis is driven by correlation-function matrices from the stationary ambient response aiming to reduce noise effects. Singular value decomposition (SVD) and eigenvalue analysis are computed in a last stage to identify frequencies and complex-valued mode shapes. Proposed assumptions are carefully weighted to account for the uncertainty of the environment. A numerical example is carried out based a spinning finite element (SFE) model, and verified using ANSYS® Ver. 12. Finally, comments and observations are provided on how this subspace realization technique can be extended to the problem of modal-parameter identification using only ambient vibration data.
Investigations of the sensitivity of a coronal mass ejection model (ENLIL) to solar input parameters
DEFF Research Database (Denmark)
Falkenberg, Thea Vilstrup; Vršnak, B.; Taktakishvili, A.;
2010-01-01
investigate the parameter space of the ENLILv2.5b model using the CME event of 25 July 2004. ENLIL is a time‐dependent 3‐D MHD model that can simulate the propagation of cone‐shaped interplanetary coronal mass ejections (ICMEs) through the solar system. Excepting the cone parameters (radius, position...... (CMEs), but in order to predict the caused effects, we need to be able to model their propagation from their origin in the solar corona to the point of interest, e.g., Earth. Many such models exist, but to understand the models in detail we must understand the primary input parameters. Here we......, and initial velocity), all remaining parameters are varied, resulting in more than 20 runs investigated here. The output parameters considered are velocity, density, magnetic field strength, and temperature. We find that the largest effects on the model output are the input parameters of upper limit...
[Calculation of parameters in forest evapotranspiration model].
Wang, Anzhi; Pei, Tiefan
2003-12-01
Forest evapotranspiration is an important component not only in water balance, but also in energy balance. It is a great demand for the development of forest hydrology and forest meteorology to simulate the forest evapotranspiration accurately, which is also a theoretical basis for the management and utilization of water resources and forest ecosystem. Taking the broadleaved Korean pine forest on Changbai Mountain as an example, this paper constructed a mechanism model for estimating forest evapotranspiration, based on the aerodynamic principle and energy balance equation. Using the data measured by the Routine Meteorological Measurement System and Open-Path Eddy Covariance Measurement System mounted on the tower in the broadleaved Korean pine forest, the parameters displacement height d, stability functions for momentum phi m, and stability functions for heat phi h were ascertained. The displacement height of the study site was equal to 17.8 m, near to the mean canopy height, and the functions of phi m and phi h changing with gradient Richarson number R i were constructed.
Multiobjective Automatic Parameter Calibration of a Hydrological Model
Directory of Open Access Journals (Sweden)
Donghwi Jung
2017-03-01
Full Text Available This study proposes variable balancing approaches for the exploration (diversification and exploitation (intensification of the non-dominated sorting genetic algorithm-II (NSGA-II with simulated binary crossover (SBX and polynomial mutation (PM in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives—minimizing the percent bias and minimizing three peak flow differences—are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization in a benchmark hydrological calibration problem for the Leaf River (1950 km2 near Collins, Mississippi. Three performance metrics—solution quality, spacing, and convergence—are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.
Franceschini, Alexandre; Filippidi, Emmanouela; Guazzelli, Elisabeth; Pine, David
2011-11-01
Shearing fibers and polymer solutions tends to align particles with the flow direction. Here, we report that neutrally buoyant non-Brownian fibers subjected to oscillatory shear are observed to align perpendicular to the flow. This alignment occurs over a finite range of strain amplitudes and is governed by a subtle interplay between fiber orientation and short-range interactions through an athermal (non-equilibrium) process known as random organization. For a given strain amplitude and concentration, the mean field orientation defines a time-dependant control parameter that can drive the suspension through an absorbing phase transition. The slow drift of the control parameter does not influence the class of the transition. The measured critical threshold and exponents are consistent with the one reported for sphere suspensions. This work was supported by the NSF through the NYU MRSEC, Award DMR:0820341. Additional support was provided by a Lavoisier Fellowship (AF) and from the Onassis Foundation (EF).
Using video modeling with substitutable loops to teach varied play to children with autism.
Dupere, Sally; MacDonald, Rebecca P F; Ahearn, William H
2013-01-01
Children with autism often engage in repetitive play with little variation in the actions performed or items used. This study examined the use of video modeling with scripted substitutable loops on children's pretend play with trained and untrained characters. Three young children with autism were shown a video model of scripted toy play that included a substitutable loop that allowed various characters to perform the same actions and vocalizations. Three characters were modeled with the substitutable loop during training sessions, and 3 additional characters were present in the video but never modeled. Following video modeling, all the participants incorporated untrained characters into their play, but the extent to which they did so varied.
An approach to measure parameter sensitivity in watershed hydrologic modeling
U.S. Environmental Protection Agency — Abstract Hydrologic responses vary spatially and temporally according to watershed characteristics. In this study, the hydrologic models that we developed earlier...
Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions.
Fergany, Hala A
2016-01-01
This paper proposed a new general probabilistic multi-item, single-source inventory model with varying mixture shortage cost under two restrictions. One of them is on the expected varying backorder cost and the other is on the expected varying lost sales cost. This model is formulated to analyze how the firm can deduce the optimal order quantity and the optimal reorder point for each item to reach the main goal of minimizing the expected total cost. The demand is a random variable and the lead time is a constant. The demand during the lead time is a random variable that follows any continuous distribution, for example; the normal distribution, the exponential distribution and the Chi square distribution. An application with real data is analyzed and the goal of minimization the expected total cost is achieved. Two special cases are deduced.
Hagen, Katja; Ehlis, Ann-Christine; Schneider, Sabrina; Haeussinger, Florian B; Fallgatter, Andreas J; Metzger, Florian G
2014-04-01
Vagus somatosensory evoked potentials are a method for assessing the function of the vagus nerve, which were shown to be altered in neurodegenerative diseases like Alzheimer's and Parkinson's disease. Various parameters of the stimulation such as the electrode position and the stimulus intensity have already been investigated. In this study, the focus is on the systematic examination of the other parameters of the stimulation of the vagus somatosensory evoked potentials: stimulus duration, interstimulus interval, and, again, the stimulation intensity. Thirty young and healthy subjects were examined using five different sets of stimulation parameters, and 24 were included in the further analysis. The results show that a reduction of the stimulus duration and a decrease in stimulus intensity have a significant effect on the amplitudes. A shortening of the interstimulus interval does not seem to have such an effect, but this stimulation is rated more painful and unpleasant than the standard stimulation. Overall, the standard stimulation used so far seems to be the most preferable condition.
Transfer function modeling of damping mechanisms in distributed parameter models
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
Kai, Bo; Li, Runze; Zou, Hui
2011-02-01
The complexity of semiparametric models poses new challenges to statistical inference and model selection that frequently arise from real applications. In this work, we propose new estimation and variable selection procedures for the semiparametric varying-coefficient partially linear model. We first study quantile regression estimates for the nonparametric varying-coefficient functions and the parametric regression coefficients. To achieve nice efficiency properties, we further develop a semiparametric composite quantile regression procedure. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the estimators achieve the best convergence rate. Moreover, we show that the proposed method is much more efficient than the least-squares-based method for many non-normal errors and that it only loses a small amount of efficiency for normal errors. In addition, it is shown that the loss in efficiency is at most 11.1% for estimating varying coefficient functions and is no greater than 13.6% for estimating parametric components. To achieve sparsity with high-dimensional covariates, we propose adaptive penalization methods for variable selection in the semiparametric varying-coefficient partially linear model and prove that the methods possess the oracle property. Extensive Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedures. Finally, we apply the new methods to analyze the plasma beta-carotene level data.
Dynamics in a Lotka-Volterra Predator-Prey Model with Time-Varying Delays
Directory of Open Access Journals (Sweden)
Changjin Xu
2013-01-01
Full Text Available A Lotka-Volterra predator-prey model with time-varying delays is investigated. By using the differential inequality theory, some sufficient conditions which ensure the permanence and global asymptotic stability of the system are established. The paper ends with some interesting numerical simulations that illustrate our analytical predictions.
Ammonia volatilization from treatment lagoons varies widely with the total ammonia concentration, pH, temperature, suspended solids, atmospheric ammonia concentration above the water surface, and wind speed. Ammonia emissions were estimated with a process-based mechanistic model integrating ammonia ...
Kosterlitz-Thouless transitions in simple spin-models with strongly varying vortex densities
Himbergen, J.E.J.M. van
1985-01-01
A generalized XY-model, consisting of a family of nearest neighbour potentials of varying shape, for classical planar spins on a two-dimensional square lattice is analysed by a combination of Migdal-Kadanoff real-space renormalization and Monte Carlo simulations on a sequence of finite lattices of
Institute of Scientific and Technical Information of China (English)
Peixin ZHAO
2013-01-01
In this paper,we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data.By constructing a penalized auxiliary vector ingeniously,we propose an empirical likelihood based variable selection procedure,and show that it is consistent and satisfies the sparsity.The simulation studies show that the proposed variable selection method is workable.
Generating survival times to simulate Cox proportional hazards models with time-varying covariates.
Austin, Peter C
2012-12-20
Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow for an examination of the performance of statistical procedures in settings in which analytic and mathematical derivations may not be feasible. A key element in any statistical simulation is the existence of an appropriate data-generating process: one must be able to simulate data from a specified statistical model. We describe data-generating processes for the Cox proportional hazards model with time-varying covariates when event times follow an exponential, Weibull, or Gompertz distribution. We consider three types of time-varying covariates: first, a dichotomous time-varying covariate that can change at most once from untreated to treated (e.g., organ transplant); second, a continuous time-varying covariate such as cumulative exposure at a constant dose to radiation or to a pharmaceutical agent used for a chronic condition; third, a dichotomous time-varying covariate with a subject being able to move repeatedly between treatment states (e.g., current compliance or use of a medication). In each setting, we derive closed-form expressions that allow one to simulate survival times so that survival times are related to a vector of fixed or time-invariant covariates and to a single time-varying covariate. We illustrate the utility of our closed-form expressions for simulating event times by using Monte Carlo simulations to estimate the statistical power to detect as statistically significant the effect of different types of binary time-varying covariates. This is compared with the statistical power to detect as statistically significant a binary time-invariant covariate.
On the modeling of internal parameters in hyperelastic biological materials
Giantesio, Giulia
2016-01-01
This paper concerns the behavior of hyperelastic energies depending on an internal parameter. First, the situation in which the internal parameter is a function of the gradient of the deformation is presented. Second, two models where the parameter describes the activation of skeletal muscle tissue are analyzed. In those models, the activation parameter depends on the strain and it is important to consider the derivative of the parameter with respect to the strain in order to capture the proper behavior of the stress.
Determining extreme parameter correlation in ground water models
DEFF Research Database (Denmark)
Hill, Mary Cole; Østerby, Ole
2003-01-01
In ground water flow system models with hydraulic-head observations but without significant imposed or observed flows, extreme parameter correlation generally exists. As a result, hydraulic conductivity and recharge parameters cannot be uniquely estimated. In complicated problems, such correlation...... correlation coefficients, but it required sensitivities that were one to two significant digits less accurate than those that required using parameter correlation coefficients; and (3) both the SVD and parameter correlation coefficients identified extremely correlated parameters better when the parameters...
Morphology of rain water channelization in systematically varied model sandy soils
Wei, Y.; Cejas, C. M.; Barrois, R.; Dreyfus, R.; Durian, D. J.
2014-01-01
We visualize the formation of fingered flow in dry model sandy soils under different raining conditions using a quasi-2d experimental set-up, and systematically determine the impact of soil grain diameter and surface wetting property on water channelization phenomenon. The model sandy soils we use are random closely-packed glass beads with varied diameters and surface treatments. For hydrophilic sandy soils, our experiments show that rain water infiltrates into a shallow top layer of soil and...
Perfect fluid Bianchi Type-I cosmological models with time varying and
Indian Academy of Sciences (India)
J P Singh; R K Tiwari
2008-04-01
Bianchi Type-I cosmological models containing perfect fluid with time varying and have been presented. The solutions obtained represent an expansion scalar bearing a constant ratio to the anisotropy in the direction of space-like unit vector . Of the two models obtained, one has negative vacuum energy density, which decays numerically. In this model, we obtain ∼ 2, ∼ 44/ and ∼ -2 ( is the cosmic time) which is in accordance with the main dynamical laws for the decay of . The second model reduces to a static solution with repulsive gravity.
Directory of Open Access Journals (Sweden)
Yeong Shiong Chiew
Full Text Available BACKGROUND: Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV, but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E(drs model that can be used in spontaneously breathing patients to determine their respiratory mechanics. METHODS: A time-varying respiratory elastance model is developed with a negative elastic component (E(demand, to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS and Neurally Adjusted Ventilatory Assist (NAVA are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs of every breathing cycle for each patient at different ventilation modes are presented for comparison. RESULTS: At the start of every breathing cycle initiated by patient, E(drs is 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS severity indicator. CONCLUSION: The E(drs model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.
Model comparisons and genetic and environmental parameter ...
African Journals Online (AJOL)
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South African Journal of Animal Science 2005, 35 (1) ... Genetic and environmental parameters were estimated for pre- and post-weaning average daily gain ..... and BWT (and medium maternal genetic correlations) indicates that these traits ...
NEW DOCTORAL DEGREE Parameter estimation problem in the Weibull model
Marković, Darija
2009-01-01
In this dissertation we consider the problem of the existence of best parameters in the Weibull model, one of the most widely used statistical models in reliability theory and life data theory. Particular attention is given to a 3-parameter Weibull model. We have listed some of the many applications of this model. We have described some of the classical methods for estimating parameters of the Weibull model, two graphical methods (Weibull probability plot and hazard plot), and two analyt...
Observations from using models to fit the gas production of varying volume test cells and landfills.
Lamborn, Julia
2012-12-01
Landfill operators are looking for more accurate models to predict waste degradation and landfill gas production. The simple microbial growth and decay models, whilst being easy to use, have been shown to be inaccurate. Many of the newer and more complex (component) models are highly parameter hungry and many of the required parameters have not been collected or measured at full-scale landfills. This paper compares the results of using different models (LANDGEM, HBM, and two Monod models developed by the author) to fit the gas production of laboratory scale, field test cell and full-scale landfills and discusses some observations that can be made regarding the scalability of gas generation rates. The comparison of these results show that the fast degradation rate that occurs at laboratory scale is not replicated at field-test cell and full-scale landfills. At small scale, all the models predict a slower rate of gas generation than actually occurs. At field test cell and full-scale a number of models predict a faster gas generation than actually occurs. Areas for future work have been identified, which include investigations into the capture efficiency of gas extraction systems and into the parameter sensitivity and identification of the critical parameters for field-test cell and full-scale landfill predication.
A parameter model for dredge plume sediment source terms
Decrop, Boudewijn; De Mulder, Tom; Toorman, Erik; Sas, Marc
2017-01-01
The presented model allows for fast simulations of the near-field behaviour of overflow dredging plumes. Overflow dredging plumes occur when dredging vessels employ a dropshaft release system to discharge the excess sea water, which is pumped into the trailing suction hopper dredger (TSHD) along with the dredged sediments. The fine sediment fraction in the loaded water-sediment mixture does not fully settle before it reaches the overflow shaft. By consequence, the released water contains a fine sediment fraction of time-varying concentration. The sediment grain size is in the range of clays, silt and fine sand; the sediment concentration varies roughly between 10 and 200 g/l in most cases, peaking at even higher value with short duration. In order to assess the environmental impact of the increased turbidity caused by this release, plume dispersion predictions are often carried out. These predictions are usually executed with a large-scale model covering a complete coastal zone, bay, or estuary. A source term of fine sediments is implemented in the hydrodynamic model to simulate the fine sediment dispersion. The large-scale model mesh resolution and governing equations, however, do not allow to simulate the near-field plume behaviour in the vicinity of the ship hull and propellers. Moreover, in the near-field, these plumes are under influence of buoyancy forces and air bubbles. The initial distribution of sediments is therefore unknown and has to be based on crude assumptions at present. The initial (vertical) distribution of the sediment source is indeed of great influence on the final far-field plume dispersion results. In order to study this near-field behaviour, a highly-detailed computationally fluid dynamics (CFD) model was developed. This model contains a realistic geometry of a dredging vessel, buoyancy effects, air bubbles and propeller action, and was validated earlier by comparing with field measurements. A CFD model requires significant simulation times
Institute of Scientific and Technical Information of China (English)
林金官; 韦博成
2004-01-01
In this paper, it is discussed that two tests for varying dispersion of binomial data in the framework of nonlinear logistic models with random effects, which are widely used in analyzing longitudinal binomial data. One is the individual test and power calculation for varying dispersion through testing the randomness of cluster effects, which is extensions of Dean(1992) and Commenges et al (1994). The second test is the composite test for varying dispersion through simultaneously testing the randomness of cluster effects and the equality of random-effect means. The score test statistics are constructed and expressed in simple, easy to use, matrix formulas. The authors illustrate their test methods using the insecticide data (Giltinan, Capizzi & Malani (1988)).
Domain selection for the varying coefficient model via local polynomial regression.
Kong, Dehan; Bondell, Howard; Wu, Yichao
2015-03-01
In this article, we consider the varying coefficient model, which allows the relationship between the predictors and response to vary across the domain of interest, such as time. In applications, it is possible that certain predictors only affect the response in particular regions and not everywhere. This corresponds to identifying the domain where the varying coefficient is nonzero. Towards this goal, local polynomial smoothing and penalized regression are incorporated into one framework. Asymptotic properties of our penalized estimators are provided. Specifically, the estimators enjoy the oracle properties in the sense that they have the same bias and asymptotic variance as the local polynomial estimators as if the sparsity is known as a priori. The choice of appropriate bandwidth and computational algorithms are discussed. The proposed method is examined via simulations and a real data example.
New Inference Procedures for Semiparametric Varying-Coefficient Partially Linear Cox Models
Directory of Open Access Journals (Sweden)
Yunbei Ma
2014-01-01
Full Text Available In biomedical research, one major objective is to identify risk factors and study their risk impacts, as this identification can help clinicians to both properly make a decision and increase efficiency of treatments and resource allocation. A two-step penalized-based procedure is proposed to select linear regression coefficients for linear components and to identify significant nonparametric varying-coefficient functions for semiparametric varying-coefficient partially linear Cox models. It is shown that the penalized-based resulting estimators of the linear regression coefficients are asymptotically normal and have oracle properties, and the resulting estimators of the varying-coefficient functions have optimal convergence rates. A simulation study and an empirical example are presented for illustration.
Parameter optimization model in electrical discharge machining process
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.
Modeling and Analysis of a Piezoelectric Energy Harvester with Varying Cross-Sectional Area
Directory of Open Access Journals (Sweden)
Maiara Rosa
2014-01-01
Full Text Available This paper reports on the modeling and on the experimental verification of electromechanically coupled beams with varying cross-sectional area for piezoelectric energy harvesting. The governing equations are formulated using the Rayleigh-Ritz method and Euler-Bernoulli assumptions. A load resistance is considered in the electrical domain for the estimate of the electric power output of each geometric configuration. The model is first verified against the analytical results for a rectangular bimorph with tip mass reported in the literature. The experimental verification of the model is also reported for a tapered bimorph cantilever with tip mass. The effects of varying cross-sectional area and tip mass on the electromechanical behavior of piezoelectric energy harvesters are also discussed. An issue related to the estimation of the optimal load resistance (that gives the maximum power output on beam shape optimization problems is also discussed.
VARYING COEFFICIENT MODELS FOR DATA WITH AUTO-CORRELATED ERROR PROCESS.
Chen, Zhao; Li, Runze; Li, Yan
2015-04-01
Varying coefficient model has been popular in the literature. In this paper, we propose a profile least squares estimation procedure to its regression coefficients when its random error is an auto-regressive (AR) process. We further study the asymptotic properties of the proposed procedure, and establish the asymptotic normality for the resulting estimate. We show that the resulting estimate for the regression coefficients has the same asymptotic bias and variance as the local linear estimate for varying coefficient models with independent and identically distributed observations. We apply the SCAD variable selection procedure (Fan and Li, 2001) to reduce model complexity of the AR error process. Numerical comparison and finite sample performance of the resulting estimate are examined by Monte Carlo studies. Our simulation results demonstrate the proposed procedure is much more efficient than the one ignoring the error correlation. The proposed methodology is illustrated by a real data example.
Sensitivity of a Shallow-Water Model to Parameters
Kazantsev, Eugene
2011-01-01
An adjoint based technique is applied to a shallow water model in order to estimate the influence of the model's parameters on the solution. Among parameters the bottom topography, initial conditions, boundary conditions on rigid boundaries, viscosity coefficients Coriolis parameter and the amplitude of the wind stress tension are considered. Their influence is analyzed from three points of view: 1. flexibility of the model with respect to a parameter that is related to the lowest value of the cost function that can be obtained in the data assimilation experiment that controls this parameter; 2. possibility to improve the model by the parameter's control, i.e. whether the solution with the optimal parameter remains close to observations after the end of control; 3. sensitivity of the model solution to the parameter in a classical sense. That implies the analysis of the sensitivity estimates and their comparison with each other and with the local Lyapunov exponents that characterize the sensitivity of the mode...
Estimation of shape model parameters for 3D surfaces
DEFF Research Database (Denmark)
Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen;
2008-01-01
Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D s...
Modelling time-varying volatility in the Indian stock returns: Some empirical evidence
Directory of Open Access Journals (Sweden)
Trilochan Tripathy
2015-12-01
Full Text Available This paper models time-varying volatility in one of the Indian main stock markets, namely, the National Stock Exchange (NSE located in Mumbai, investigating whether it has been affected by the recent global financial crisis. A Chow test indicates the presence of a structural break. Both symmetric and asymmetric GARCH models suggest that the volatility of NSE returns is persistent and asymmetric and has increased as a result of the crisis. The model under the Generalized Error Distribution appears to be the most suitable one. However, its out-of-sample forecasting performance is relatively poor.
Compositional modelling of distributed-parameter systems
Maschke, Bernhard; Schaft, van der Arjan; Lamnabhi-Lagarrigue, F.; Loría, A.; Panteley, E.
2005-01-01
The Hamiltonian formulation of distributed-parameter systems has been a challenging reserach area for quite some time. (A nice introduction, especially with respect to systems stemming from fluid dynamics, can be found in [26], where also a historical account is provided.) The identification of the
Parameter Estimation and Experimental Design in Groundwater Modeling
Institute of Scientific and Technical Information of China (English)
SUN Ne-zheng
2004-01-01
This paper reviews the latest developments on parameter estimation and experimental design in the field of groundwater modeling. Special considerations are given when the structure of the identified parameter is complex and unknown. A new methodology for constructing useful groundwater models is described, which is based on the quantitative relationships among the complexity of model structure, the identifiability of parameter, the sufficiency of data, and the reliability of model application.
Bayesian approach to decompression sickness model parameter estimation.
Howle, L E; Weber, P W; Nichols, J M
2017-03-01
We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.
A river water quality model for time varying BOD discharge concentration
Directory of Open Access Journals (Sweden)
Oppenheimer Seth F.
1999-01-01
Full Text Available We consider a model for biochemical oxygen demand (BOD in a semi-infinite river where the BOD is prescribed by a time varying function at the left endpoint. That is, we study the problem with a time varying boundary loading. We obtain the well-posedness for the model when the boundary loading is smooth in time. We also obtain various qualitative results such as ordering, positivity, and boundedness. Of greatest interest, we show that a periodic loading function admits a unique asymptotically attracting periodic solution. For non-smooth loading functions, we obtain weak solutions. Finally, for certain special cases, we show how to obtain explicit solutions in the form of infinite series.
Delay-Dependent Asymptotic Stability of Cohen-Grossberg Models with Multiple Time-Varying Delays
Directory of Open Access Journals (Sweden)
Xiaofeng Liao
2007-01-01
Full Text Available Dynamical behavior of a class of Cohen-Grossberg models with multiple time-varying delays is studied in detail. Sufficient delay-dependent criteria to ensure local and global asymptotic stabilities of the equilibrium of this network are derived by constructing suitable Lyapunov functionals. The obtained conditions are shown to be less conservative and restrictive than those reported in the known literature. Some numerical examples are included to demonstrate our results.
Estimation and Properties of a Time-Varying GQARCH(1,1-M Model
Directory of Open Access Journals (Sweden)
Sofia Anyfantaki
2011-01-01
analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only ( computational operations, where is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.
Institute of Scientific and Technical Information of China (English)
Xuemei HU; Feng LIU; Zhizhong WANG
2009-01-01
The authors propose a V_(N,P) test statistic for testing finite-order serial correlation in a semiparametric varying coefficient partially linear errors-in-variables model. The test statistic is shown to have asymptotic normal distribution under the null hypothesis of no serial correlation. Some Monte Carlo experiments are conducted to examine the finite sample performance of the proposed V_(N,P) test statistic. Simulation results confirm that the proposed test performs satisfactorily in estimated size and power.
Time-varying tolls in a dynamic model of road traffic congestion with elastic demand
Verhoef, E.T.
1997-01-01
In this paper, a dynamic model of road traffic congestion is presented, with an elastic overall demand for morning peak road usage, and with the congestion technology used being 'flow congestion'. It is demonstrated that in such a case, the optimal time-varying toll should include a 'flat', time-invariant component when road users share the same desired arrival time. This has important consequences for the design of optimal toll schemes in reality, because it implies that optimal tolls cannot...
Modeling soil detachment capacity by rill flow using hydraulic parameters
Wang, Dongdong; Wang, Zhanli; Shen, Nan; Chen, Hao
2016-04-01
The relationship between soil detachment capacity (Dc) by rill flow and hydraulic parameters (e.g., flow velocity, shear stress, unit stream power, stream power, and unit energy) at low flow rates is investigated to establish an accurate experimental model. Experiments are conducted using a 4 × 0.1 m rill hydraulic flume with a constant artificial roughness on the flume bed. The flow rates range from 0.22 × 10-3 m2 s-1 to 0.67 × 10-3 m2 s-1, and the slope gradients vary from 15.8% to 38.4%. Regression analysis indicates that the Dc by rill flow can be predicted using the linear equations of flow velocity, stream power, unit stream power, and unit energy. Dc by rill flow that is fitted to shear stress can be predicted with a power function equation. Predictions based on flow velocity, unit energy, and stream power are powerful, but those based on shear stress, especially on unit stream power, are relatively poor. The prediction based on flow velocity provides the best estimates of Dc by rill flow because of the simplicity and availability of its measurements. Owing to error in measuring flow velocity at low flow rates, the predictive abilities of Dc by rill flow using all hydraulic parameters are relatively lower in this study compared with the results of previous research. The measuring accuracy of experiments for flow velocity should be improved in future research.
Variational methods to estimate terrestrial ecosystem model parameters
Delahaies, Sylvain; Roulstone, Ian
2016-04-01
Carbon is at the basis of the chemistry of life. Its ubiquity in the Earth system is the result of complex recycling processes. Present in the atmosphere in the form of carbon dioxide it is adsorbed by marine and terrestrial ecosystems and stored within living biomass and decaying organic matter. Then soil chemistry and a non negligible amount of time transform the dead matter into fossil fuels. Throughout this cycle, carbon dioxide is released in the atmosphere through respiration and combustion of fossils fuels. Model-data fusion techniques allow us to combine our understanding of these complex processes with an ever-growing amount of observational data to help improving models and predictions. The data assimilation linked ecosystem carbon (DALEC) model is a simple box model simulating the carbon budget allocation for terrestrial ecosystems. Over the last decade several studies have demonstrated the relative merit of various inverse modelling strategies (MCMC, ENKF, 4DVAR) to estimate model parameters and initial carbon stocks for DALEC and to quantify the uncertainty in the predictions. Despite its simplicity, DALEC represents the basic processes at the heart of more sophisticated models of the carbon cycle. Using adjoint based methods we study inverse problems for DALEC with various data streams (8 days MODIS LAI, monthly MODIS LAI, NEE). The framework of constraint optimization allows us to incorporate ecological common sense into the variational framework. We use resolution matrices to study the nature of the inverse problems and to obtain data importance and information content for the different type of data. We study how varying the time step affect the solutions, and we show how "spin up" naturally improves the conditioning of the inverse problems.
Modelling the solidification of ductile cast iron parts with varying wall thicknesses
DEFF Research Database (Denmark)
Bjerre, Mathias Karsten; Tiedje, Niels Skat; Thorborg, Jesper
2015-01-01
] with a 2D FE solution of the heat conduction equation is developed in an in-house code and model parameters are calibrated using experimental data from representative castings made of ductile cast iron. The main focus is on the influence of casting thickness and resulting local cooling conditions......In the present paper modelling the solidification of cast iron parts is considered. Common for previous efforts in this field is that they have mainly considered thin walled to medium thickness castings. Hence, a numerical model combining the solidification model presented by Lesoultet al. [1...
Modeling rainfall-runoff processes using smoothed particle hydrodynamics with mass-varied particles
Chang, Tsang-Jung; Chang, Yu-Sheng; Chang, Kao-Hua
2016-12-01
In this study, a novel treatment of adopting mass-varied particles in smoothed particle hydrodynamics (SPH) is proposed to solve the shallow water equations (SWEs) and model the rainfall-runoff process. Since SWEs have depth-averaged or cross-section-averaged features, there is no sufficient dimension to add rainfall particles. Thus, SPH-SWE methods have focused on modeling discharge flows in open channels or floodplains without rainfall. With the proposed treatment, the application of SPH-SWEs can be extended to rainfall-runoff processes in watersheds. First, the numerical procedures associated with using mass-varied particles in SPH-SWEs are introduced and derived. Then, numerical validations are conducted for three benchmark problems, including uniform rainfall over a 1D flat sloping channel, nonuniform rain falling over a 1D three-slope channel with different rainfall durations, and uniform rainfall over a 2D plot with complex topography. The simulated results indicate that the proposed treatment can avoid the necessity of a source term function of mass variation, and no additional particles are needed for the increase of mass. Rainfall-runoff processes can be well captured in the presence of hydraulic jumps, dry/wet bed flows, and supercritical/subcritical/transcritical flows. The proposed treatment using mass-varied particles was proven robust and reliable for modeling rainfall-runoff processes. It can provide a new alternative for investigating practical hydrological problems.
First order coupled dynamic model of flexible space structures with time-varying configurations
Wang, Jie; Li, Dongxu; Jiang, Jianping
2017-03-01
This paper proposes a first order coupled dynamic modeling method for flexible space structures with time-varying configurations for the purpose of deriving the characteristics of the system. The model considers the first time derivative of the coordinate transformation matrix between the platform's body frame and the appendage's floating frame. As a result it can accurately predict characteristics of the system even if flexible appendages rotate with complex trajectory relative to the rigid part. In general, flexible appendages are fixed on the rigid platform or forced to rotate with a slow angular velocity. So only the zero order of the transformation matrix is considered in conventional models. However, due to neglecting of time-varying terms of the transformation matrix, these models introduce severe error when appendages, like antennas, for example, rotate with a fast speed relative to the platform. The first order coupled dynamic model for flexible space structures proposed in this paper resolve this problem by introducing the first time derivative of the transformation matrix. As a numerical example, a central core with a rotating solar panel is considered and the results are compared with those given by the conventional model. It has been shown that the first order terms are of great importance on the attitude of the rigid body and dynamic response of the flexible appendage.
Leyk, S; McCormick, BJJ; Nuckols, JR
2011-01-01
Public health data is often highly aggregated in time and space. The consequences of temporal aggregation for modeling in support of policy decisions have largely been overlooked. We examine the effects of changing temporal scale on spatial regression models of pediatric diarrhea mortality patterns, mortality rates and mortality peak timing, in Mexico. We compare annual and decadal level univariate models that incorporate known risk factors. Based on normalized sums of squared differences we compare between annual and decadal coefficients for variables that were significant in decadal models. We observed that spurious relationships might be created through aggregating time scales; eliminating inter-annual variation and resulting in inflated model diagnostics. In fact, variable selection and coefficient values can vary with changing temporal aggregation. Some variables that were significant at the decadal level were not significant at the annual level. Implications of such aggregation should be part of risk communication to policy makers. PMID:22623950
Evaluation model for service life of dam based on time-varying risk probability
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
For many dam projects in China, the 50-year designed life time is coming to an end. It is urgent to study the theory and method to evaluate the dam service life. In this paper, firstly, the probability theory of fuzzy event and time-varying effect theory are used to analyze the time-variety of various risk factors in the process of dam operations. A method is proposed to quantify the above time-variety and a model to describe the fuzzy time-varying risk probability for the dam structure is also built. Secondly, the information entropy theory is used to analyze the uncertain degree relationship between the characteristic value of membership function and fuzzy risk probability, and a mathematical method is presented to calculate the time-varying risk probability accordingly. Thirdly, the relation mode between time-varying risk probability and service life is discussed. Based on this relation mode and the acceptable risk probability of dams in China, a method is put forward to evaluate and forecast the dam service life. Finally, the proposed theory and method are used to analyze one concrete dam. The dynamic variability and mutation feature of the dam risk probability are analyzed. The remaining service life of this dam is forecasted. The obtained results can provide technology support for the project management department to make treatment measures of engineering and reasonably arrange reinforce cost. The principles in this paper have wide applicability and can be used in risk analysis for slope instability and other fields.
Use of the varying coefficient model in an exercise and depression meta-analysis.
Kelley, George A; Kelley, Kristi S
2012-06-26
Use a recently developed varying coefficient model to determine the effects of exercise in adults with depression. Data from a recent meta-analysis addressing the effects of exercise on depression in adults were used. Studies were limited to randomized controlled intervention trials of any type of chronic exercise (for example, walking and jogging) in adults greater than or equal to 18 years of age with a diagnosis of depression. For each study, the standardized mean difference (exercise minus control) effect size for depression, adjusted for small-sample bias, was calculated. Variance statistics for each effect size and pooling of results were calculated using the recently proposed varying coefficient (VC) model for standardized mean differences. Standardized effect-sizes of 0.20, 0.50 and 0.80 were considered to represent small, medium and large effects. Results were considered statistically significant if the 95% confidence intervals did not cross 0, with negative results indicative of reductions in depression. These findings were then compared with results using traditional random-effects (RE) models. A total of 23 studies representing 907 men and women (476 exercise, 431 control) were pooled for analysis. Both RE and VC models resulted in large, statistically significant improvements in depression as a result of exercise in adults. However, the VC model resulted in a larger overall effect size as well as confidence intervals that were narrower than previously reported using the RE model. The overall mean effect size for the RE model was -0.82 with a 95% confidence interval of -1.12 to -0.51. For the VC model, overall mean effect size was -0.88 with a 95% confidence interval of -1.08 to -0.68. The relative difference between the RE and VC approaches was 7.3%. The VC model, a potentially preferable model, confirms the positive effects of exercise on depression in adults.
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... and uncertainty estimation. Essential issues relating to calibration are discussed. The classical regression methods are described; however, the main focus is on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The next two chapters describe case studies in which the GLUE methodology...
Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models.
Hildebrandt, Andrea; Lüdtke, Oliver; Robitzsch, Alexander; Sommer, Christopher; Wilhelm, Oliver
2016-01-01
Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.
Parameter redundancy in discrete state‐space and integrated models
McCrea, Rachel S.
2016-01-01
Discrete state‐space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state‐space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state‐space models using discrete analogues of methods for continuous state‐space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. PMID:27362826
Parameter redundancy in discrete state-space and integrated models.
Cole, Diana J; McCrea, Rachel S
2016-09-01
Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-11-01
simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.
Segmentation of complex objects' sonar images using parameter-fixed MRF model
Institute of Scientific and Technical Information of China (English)
YAO Bin; LI Hai-sen; ZHOU Tian; SUN SHENG-he
2006-01-01
The effective method of the recognition of underwater complex objects in sonar image is to segment sonar image into target, shadow and sea-bottom reverberation regions and then extract the edge of the object. Because of the time-varying and space-varying characters of underwater acoustics environment, the sonar images have poor quality and serious speckle noise, so traditional image segmentation is unable to achieve precise segmentation. In the paper, the image segmentation process based on MRF (Markov random field) model is studied, and a practical method of estimating model parameters is proposed. Through analyzing the impact of chosen model parameters, a sonar imagery segmentation algorithm based on fixed parameters' MRF model is proposed. Both of the segmentation effect and the low computing load are gained. By applying the algorithm to the synthesized texture image and actual side-scan sonar image, the algorithm can be achieved with precise segmentation result.
Ternary interaction parameters in calphad solution models
Energy Technology Data Exchange (ETDEWEB)
Eleno, Luiz T.F., E-mail: luizeleno@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica; Schön, Claudio G., E-mail: schoen@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Computational Materials Science Laboratory. Department of Metallurgical and Materials Engineering
2014-07-01
For random, diluted, multicomponent solutions, the excess chemical potentials can be expanded in power series of the composition, with coefficients that are pressure- and temperature-dependent. For a binary system, this approach is equivalent to using polynomial truncated expansions, such as the Redlich-Kister series for describing integral thermodynamic quantities. For ternary systems, an equivalent expansion of the excess chemical potentials clearly justifies the inclusion of ternary interaction parameters, which arise naturally in the form of correction terms in higher-order power expansions. To demonstrate this, we carry out truncated polynomial expansions of the excess chemical potential up to the sixth power of the composition variables. (author)
Institute of Scientific and Technical Information of China (English)
Li-liang REN; Gui-zuo WANG; Fang LU; Tian-fang FANG
2009-01-01
This paper introduces the method of designation of water storage capacity for each grid cell within a catchment, which considers topography, vegetation and soil synthetically. For the purpose of hydrological process simulation in semi-arid regions, a spatially varying storage capacity (VSC) model was developed based on the spatial distribution of water storage capacity and the vertical hybrid runoff mechanism. To verify the applicability of the VSC model, both the VSC model and a hybrid runoff model were used to simulate daily runoff processes in the catchment upstream of the Dianzi hydrological station from 1973 to 1979. The results showed that the annual average Nash-Sutcliffe coefficient was 0.80 for the VSC model, and only 0.67 for the hybrid runoff model. The higher annual average Nash-Sutcliffe coefficient of the VSC model means that this hydrological model can better simulate daily runoff processes in semi-arid regions. Furthermore, as a distributed hydrological model, the VSC model can be applied in regional water resource management.
Directory of Open Access Journals (Sweden)
Li-liang REN
2009-06-01
Full Text Available This paper introduces the method of designation of water storage capacity for each grid cell within a catchment, which considers topography, vegetation and soil synthetically. For the purpose of hydrological process simulation in semi-arid regions, a spatially varying storage capacity (VSC model was developed based on the spatial distribution of water storage capacity and the vertical hybrid runoff mechanism. To verify the applicability of the VSC model, both the VSC model and a hybrid runoff model were used to simulate daily runoff processes in the catchment upstream of the Dianzi hydrological station from 1973 to 1979. The results showed that the annual average Nash-Sutcliffe coefficient was 0.80 for the VSC model, and only 0.67 for the hybrid runoff model. The higher annual average Nash-Sutcliffe coefficient of the VSC model means that this hydrological model can better simulate daily runoff processes in semi-arid regions. Furthermore, as a distributed hydrological model, the VSC model can be applied in regional water resource management.
A behavioral asset pricing model with a time-varying second moment
Energy Technology Data Exchange (ETDEWEB)
Chiarella, Carl [School of Finance and Economics, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007 (Australia)]. E-mail: carl.chiarella@uts.edu.au; He Xuezhong [School of Finance and Economics, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007 (Australia); Wang, Duo [LMAM, Department of Financial Mathematics, School of Mathematical Sciences, Peking University, Beijing 100871 (China)
2006-08-15
We develop a simple behavioral asset pricing model with fundamentalists and chartists in order to study price behavior in financial markets when chartists estimate both conditional mean and variance by using a weighted averaging process. Through a stability, bifurcation, and normal form analysis, the market impact of the weighting process and time-varying second moment are examined. It is found that the fundamental price becomes stable (unstable) when the activities from both types of traders are balanced (unbalanced). When the fundamental price becomes unstable, the weighting process leads to different price dynamics, depending on whether the chartists act as either trend followers or contrarians. It is also found that a time-varying second moment of the chartists does not change the stability of the fundamental price, but it does influence the stability of the bifurcations. The bifurcation becomes stable (unstable) when the chartists are more (less) concerned about the market risk characterized by the time-varying second moment. Different routes to complicated price dynamics are also observed. The analysis provides an analytical foundation for the statistical analysis of the corresponding stochastic version of this type of behavioral model.
Parameter estimation and error analysis in environmental modeling and computation
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
A composite numerical model for wave diffraction in a harbor with varying water depth
Institute of Scientific and Technical Information of China (English)
ZHAO Ming; TENG Bin
2004-01-01
A composite numerical model is presented for computing the wave field in a harbor. The mild slope equation is discretized by a finite element method in the domain concerned. Out of the computational domain, the water depth is assumed to be constant. The boundary element method is applied to the outer boundary for dealing with the infinite boundary condition. Because the model satisfies strictly the infinite boundary condition, more accurate results can be obtained. The model is firstly applied to compute the wave diffraction in a narrow rectangular bay and the wave diffraction from a porous cylinder. The numerical results are compared with the analytical solution, experimental data and other numerical results. Good agreements are obtained. Then the model is applied to computing the wave diffraction in a square harbor with varying water depth. The effects of the water depth in the harbor and the incoming wave direction on the wave height distribution are discussed.
Energy Technology Data Exchange (ETDEWEB)
Buerger, R.; Damasceno, J.J.R.; Karlesen, K.H.
2001-10-01
The phenomenological theory of continuous thickening of flocculated suspensions in an ideal cylindrical thickener is extended to vessels having varying cross-section, including divergent or convergent conical vessels. The purpose of this contribution is to draw attention to the corresponding mathematical model, whose key ingredient is a strongly degenerate parabolic partial differential equation. For ideal (non-flocculated) suspensions, which do not form co compressible sediments, the mathematical model reduces to the kinematic approach by Anestis, who developed a method of construction of exact solution by the method of characteristics. The difficulty lies in the fact that characteristics and iso-concentration lines, unlike the conventional Kynch model for cylindrical vessels, do not coincide, and one has to resort to numerical methods to simulate the thickening process. A numerical algorithm is presented and employed for simulations of continuous thickening. Implications of the mathematical model are also demonstrated by steady-state calculations, which lead to new possibilities in thickener design. (author)
Pola, Giordano; Di Benedetto, Maria Domenica
2010-01-01
Time-delay systems are an important class of dynamical systems that provide a solid mathematical framework to deal with many application domains of interest. In this paper we focus on nonlinear control systems with unknown and time-varying delay signals and we propose one approach to the control design of such systems, which is based on the construction of symbolic models. Symbolic models are abstract descriptions of dynamical systems in which one symbolic state and one symbolic input correspond to an aggregate of states and an aggregate of inputs. We first introduce the notion of incremental input-delay-to-state stability and characterize it by means of Liapunov-Krasovskii functionals. We then derive sufficient conditions for the existence of symbolic models that are shown to be alternating approximately bisimilar to the original system. Further results are also derived which prove the computability of the proposed symbolic models in a finite number of steps.
Modeling the Plasma Flow in the Inner Heliosheath with a Spatially Varying Compression Ratio
Nicolaou, G.; Livadiotis, G.
2017-03-01
We examine a semi-analytical non-magnetic model of the termination shock location previously developed by Exarhos & Moussas. In their study, the plasma flow beyond the shock is considered incompressible and irrotational, thus the flow potential is analytically derived from the Laplace equation. Here we examine the characteristics of the downstream flow in the heliosheath in order to resolve several inconsistencies existing in the Exarhos & Moussas model. In particular, the model is modified in order to be consistent with the Rankine-Hugoniot jump conditions and the geometry of the termination shock. It is shown that a shock compression ratio varying along the latitude can lead to physically correct results. We describe the new model and present several simplified examples for a nearly spherical, strong termination shock. Under those simplifications, the upstream plasma is nearly adiabatic for large (˜100 AU) heliosheath thickness.
Wasch, L.J.; Koenen, M.; Wollenweber, J.; Tambach, T.J.
2015-01-01
To ensure the safety of a CO 2 storage site and containment of CO 2 in the subsurface, the integrity of wellbore materials must be maintained. Field and laboratory studies have shown CO 2 -induced reactivity of wellbore cement, but these results have to be extrapolated to the extended time span of C
Schaefer, Jacob; Hanson, Curt; Johnson, Marcus A.; Nguyen, Nhan
2011-01-01
Three model reference adaptive controllers (MRAC) with varying levels of complexity were evaluated on a high performance jet aircraft and compared along with a baseline nonlinear dynamic inversion controller. The handling qualities and performance of the controllers were examined during failure conditions that induce coupling between the pitch and roll axes. Results from flight tests showed with a roll to pitch input coupling failure, the handling qualities went from Level 2 with the baseline controller to Level 1 with the most complex MRAC tested. A failure scenario with the left stabilator frozen also showed improvement with the MRAC. Improvement in performance and handling qualities was generally seen as complexity was incrementally added; however, added complexity usually corresponds to increased verification and validation effort required for certification. The tradeoff between complexity and performance is thus important to a controls system designer when implementing an adaptive controller on an aircraft. This paper investigates this relation through flight testing of several controllers of vary complexity.
Li, Huicong; Peng, Rui; Wang, Feng-Bin
2017-01-01
This paper performs qualitative analysis on an SIS epidemic reaction-diffusion system with a linear source in spatially heterogeneous environment. The main feature of our model lies in that its total population number varies, compared to its counterpart proposed by Allen et al. [2]. The uniform bounds of solutions are derived, based on which, the threshold dynamics in terms of the basic reproduction number is established and the global stability of the unique endemic equilibrium is discussed when spatial environment is homogeneous. In particular, the asymptotic profile of endemic equilibria is determined if the diffusion rate of the susceptible or infected population is small or large. The theoretical results show that a varying total population can enhance persistence of infectious disease, and therefore the disease becomes more threatening and harder to control.
Spectrum Sensing Based on Censored Observations in Time-Varying Channels using AR-1 Model
Directory of Open Access Journals (Sweden)
Dhaval K Patel
2015-01-01
Full Text Available Non-parametric sensing algorithms are preferred in cognitive radio. In this paper, spectrum sensing method based on censored observations is proposed. We evaluate the performance of Censored Anderson-Darling (CAD sensing method in time-varying and flat-fading channel using Monte Carlo simulations. We have shown the performance of the CAD sensing in terms of receiver operating characteristic (ROC. The considered channel is modeled by Gaussian variables and characterized by a first ordered autoregressive process ($AR1$. It is shown that the proposed method outperforms prevailing techniques such as the Energy detection (ED sensing and Order-statistic (OS based sensing in time-varying channel at lower signal to noise ratio.
Quantifying Key Climate Parameter Uncertainties Using an Earth System Model with a Dynamic 3D Ocean
Olson, R.; Sriver, R. L.; Goes, M. P.; Urban, N.; Matthews, D.; Haran, M.; Keller, K.
2011-12-01
Climate projections hinge critically on uncertain climate model parameters such as climate sensitivity, vertical ocean diffusivity and anthropogenic sulfate aerosol forcings. Climate sensitivity is defined as the equilibrium global mean temperature response to a doubling of atmospheric CO2 concentrations. Vertical ocean diffusivity parameterizes sub-grid scale ocean vertical mixing processes. These parameters are typically estimated using Intermediate Complexity Earth System Models (EMICs) that lack a full 3D representation of the oceans, thereby neglecting the effects of mixing on ocean dynamics and meridional overturning. We improve on these studies by employing an EMIC with a dynamic 3D ocean model to estimate these parameters. We carry out historical climate simulations with the University of Victoria Earth System Climate Model (UVic ESCM) varying parameters that affect climate sensitivity, vertical ocean mixing, and effects of anthropogenic sulfate aerosols. We use a Bayesian approach whereby the likelihood of each parameter combination depends on how well the model simulates surface air temperature and upper ocean heat content. We use a Gaussian process emulator to interpolate the model output to an arbitrary parameter setting. We use Markov Chain Monte Carlo method to estimate the posterior probability distribution function (pdf) of these parameters. We explore the sensitivity of the results to prior assumptions about the parameters. In addition, we estimate the relative skill of different observations to constrain the parameters. We quantify the uncertainty in parameter estimates stemming from climate variability, model and observational errors. We explore the sensitivity of key decision-relevant climate projections to these parameters. We find that climate sensitivity and vertical ocean diffusivity estimates are consistent with previously published results. The climate sensitivity pdf is strongly affected by the prior assumptions, and by the scaling
Directory of Open Access Journals (Sweden)
Jonathan R Karr
2015-05-01
Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.
Parameter estimation of hydrologic models using data assimilation
Kaheil, Y. H.
2005-12-01
The uncertainties associated with the modeling of hydrologic systems sometimes demand that data should be incorporated in an on-line fashion in order to understand the behavior of the system. This paper represents a Bayesian strategy to estimate parameters for hydrologic models in an iterative mode. The paper presents a modified technique called localized Bayesian recursive estimation (LoBaRE) that efficiently identifies the optimum parameter region, avoiding convergence to a single best parameter set. The LoBaRE methodology is tested for parameter estimation for two different types of models: a support vector machine (SVM) model for predicting soil moisture, and the Sacramento Soil Moisture Accounting (SAC-SMA) model for estimating streamflow. The SAC-SMA model has 13 parameters that must be determined. The SVM model has three parameters. Bayesian inference is used to estimate the best parameter set in an iterative fashion. This is done by narrowing the sampling space by imposing uncertainty bounds on the posterior best parameter set and/or updating the "parent" bounds based on their fitness. The new approach results in fast convergence towards the optimal parameter set using minimum training/calibration data and evaluation of fewer parameter sets. The efficacy of the localized methodology is also compared with the previously used Bayesian recursive estimation (BaRE) algorithm.
GIS-Based Hydrogeological-Parameter Modeling
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A regression model is proposed to relate the variation of water well depth with topographic properties (area and slope), the variation of hydraulic conductivity and vertical decay factor. The implementation of this model in GIS environment (ARC/TNFO) based on known water data and DEM is used to estimate the variation of hydraulic conductivity and decay factor of different lithoiogy units in watershed context.
Fecher, T.; Pail, R.; Gruber, T.
2017-05-01
GOCO05c is a gravity field model computed as a combined solution of a satellite-only model and a global data set of gravity anomalies. It is resolved up to degree and order 720. It is the first model applying regionally varying weighting. Since this causes strong correlations among all gravity field parameters, the resulting full normal equation system with a size of 2 TB had to be solved rigorously by applying high-performance computing. GOCO05c is the first combined gravity field model independent of EGM2008 that contains GOCE data of the whole mission period. The performance of GOCO05c is externally validated by GNSS-levelling comparisons, orbit tests, and computation of the mean dynamic topography, achieving at least the quality of existing high-resolution models. Results show that the additional GOCE information is highly beneficial in insufficiently observed areas, and that due to the weighting scheme of individual data the spectral and spatial consistency of the model is significantly improved. Due to usage of fill-in data in specific regions, the model cannot be used for physical interpretations in these regions.
Density perturbations in f (R ,ϕ ) gravity with an application to the varying-power-law model
Hammad, Fayçal
2017-09-01
Density perturbations in the cosmic microwave background within general f (R ,ϕ ) models of gravity are investigated. The general dynamical equations for the tensor and scalar modes in any f (R ,ϕ )-gravity model are derived. An application of the equations to the varying-power-law modified gravity toy model is then made. Formulas and numerical values for the tensor-to-scalar ratio, the scalar tilt, and the tensor tilt are all obtained within this specific model. While the model cannot provide a theoretical reason for the value of the energy scale at which inflation should occur, it is found, based on the latest observations of the density perturbations in the sky, that the model requires inflation to occur at an energy scale less than the grand unified theory scale, namely, ˜1014 GeV . The different energy intervals examined here show that the density perturbations recently obtained from observations are recovered naturally, with very high precision, and without fine tuning the model's parameters.
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens;
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests, or p...
Dynamical System Approach to Cosmological Models with a Varying Speed of Light
Szydlowski, M; Szydlowski, Marek; Krawiec, Adam
2003-01-01
Methods of dynamical systems have been used to study homogeneous and isotropic cosmological models with a varying speed of light (VSL). We propose two methods of reduction of dynamics to the form of planar Hamiltonian dynamical systems for models with a time dependent equation of state. The solutions are analyzed on two-dimensional phase space in the variables $(x, \\dot{x})$ where $x$ is a function of a scale factor $a$. Then we show how the horizon problem may be solved on some evolutional paths. It is shown that the models with negative curvature overcome the horizon and flatness problems. The presented method of reduction can be adopted to the analysis of dynamics of the universe with the general form of the equation of state $p=\\gamma(a)\\epsilon$. This is demonstrated using as an example the dynamics of VSL models filled with a non-interacting fluid. We demonstrate a new type of evolution near the initial singularity caused by a varying speed of light. The singularity-free oscillating universes are also a...
Modeling ventricular function during cardiac assist: does time-varying elastance work?
Vandenberghe, Stijn; Segers, Patrick; Steendijk, Paul; Meyns, Bart; Dion, Robert A E; Antaki, James F; Verdonck, Pascal
2006-01-01
The time-varying elastance theory of Suga et al. is widely used to simulate left ventricular function in mathematical models and in contemporary in vitro models. We investigated the validity of this theory in the presence of a left ventricular assist device. Left ventricular pressure and volume data are presented that demonstrate the heart-device interaction for a positive-displacement pump (Novacor) and a rotary blood pump (Medos). The Novacor was implanted in a calf and used in fixed-rate mode (85 BPM), whereas the Medos was used at several flow levels (0-3 l/min) in seven healthy sheep. The Novacor data display high beat-to-beat variations in the amplitude of the elastance curve, and the normalized curves deviate strongly from the typical bovine curve. The Medos data show how the maximum elastance depends on the pump flow level. We conclude that the original time-varying elastance theory insufficiently models the complex hemodynamic behavior of a left ventricle that is mechanically assisted, and that there is need for an updated ventricular model to simulate the heart-device interaction.
Mirror symmetry for two parameter models, 2
Candelas, Philip; Katz, S; Morrison, Douglas Robert Ogston; Philip Candelas; Anamaria Font; Sheldon Katz; David R Morrison
1994-01-01
We describe in detail the space of the two K\\"ahler parameters of the Calabi--Yau manifold \\P_4^{(1,1,1,6,9)}[18] by exploiting mirror symmetry. The large complex structure limit of the mirror, which corresponds to the classical large radius limit, is found by studying the monodromy of the periods about the discriminant locus, the boundary of the moduli space corresponding to singular Calabi--Yau manifolds. A symplectic basis of periods is found and the action of the Sp(6,\\Z) generators of the modular group is determined. From the mirror map we compute the instanton expansion of the Yukawa couplings and the generalized N=2 index, arriving at the numbers of instantons of genus zero and genus one of each degree. We also investigate an SL(2,\\Z) symmetry that acts on a boundary of the moduli space.
A boundary element model for diffraction of water waves on varying water depth
Energy Technology Data Exchange (ETDEWEB)
Poulin, Sanne
1997-12-31
In this thesis a boundary element model for calculating diffraction of water waves on varying water depth is presented. The varying water depth is approximated with a perturbed constant depth in the mild-slope wave equation. By doing this, the domain integral which is a result of the varying depth is no longer a function of the unknown wave potential but only a function of position and the constant depth wave potential. The number of unknowns is the resulting system of equations is thus reduced significantly. The integration procedures in the model are tested very thoroughly and it is found that a combination of analytical integration in the singular region and standard numerical integration outside works very well. The gradient of the wave potential is evaluated successfully using a hypersingular integral equation. Deviations from the analytical solution are only found on the boundary or very close to, but these deviations have no significant influence on the accuracy of the solution. The domain integral is evaluated using the dual reciprocity method. The results are compared with a direct integration of the integral, and the accuracy is quite satisfactory. The problem with irregular frequencies is taken care of by the CBIEM (or CHIEF-method) together with a singular value decomposition technique. This method is simple to implement and works very well. The model is verified using Homma`s island as a test case. The test cases are limited to shallow water since the analytical solution is only valid in this region. Several depth ratios are examined, and it is found that the accuracy of the model increases with increasing wave period and decreasing depth ratio. Short waves, e.g. wind generated waves, can allow depth variations up to approximately 2 before the error exceeds 10%, while long waves can allow larger depth ratios. It is concluded that the perturbation idea is highly usable. A study of (partially) absorbing boundary conditions is also conducted. (EG)
Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.
Kim, Seock-Ho; Cohen, Allan S.
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
On linear models and parameter identifiability in experimental biological systems.
Lamberton, Timothy O; Condon, Nicholas D; Stow, Jennifer L; Hamilton, Nicholas A
2014-10-07
A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.
Modeling Water Flux at the Base of the Rooting Zone for Soils with Varying Glacial Parent Materials
Naylor, S.; Ellett, K. M.; Ficklin, D. L.; Olyphant, G. A.
2013-12-01
Soils of varying glacial parent materials in the Great Lakes Region (USA) are characterized by thin unsaturated zones and widespread use of agricultural pesticides and nutrients that affect shallow groundwater. To better our understanding of the fate and transport of contaminants, improved models of water fluxes through the vadose zones of various hydrogeologic settings are warranted. Furthermore, calibrated unsaturated zone models can be coupled with watershed models, providing a means for predicting the impact of varying climate scenarios on agriculture in the region. To address these issues, a network of monitoring sites was developed in Indiana that provides continuous measurements of precipitation, potential evapotranspiration (PET), soil volumetric water content (VWC), and soil matric potential to parameterize and calibrate models. Flux at the base of the root zone is simulated using two models of varying complexity: 1) the HYDRUS model, which numerically solves the Richards equation, and 2) the soil-water-balance (SWB) model, which assumes vertical flow under a unit gradient with infiltration and evapotranspiration treated as separate, sequential processes. Soil hydraulic parameters are determined based on laboratory data, a pedo-transfer function (ROSETTA), field measurements (Guelph permeameter), and parameter optimization. Groundwater elevation data are available at three of six sites to establish the base of the unsaturated zone model domain. Initial modeling focused on the groundwater recharge season (Nov-Feb) when PET is limited and much of the annual vertical flux occurs. HYDRUS results indicate that base of root zone fluxes at a site underlain by glacial ice-contact parent materials are 48% of recharge season precipitation (VWC RMSE=8.2%), while SWB results indicate that fluxes are 43% (VWC RMSE=3.7%). Due in part to variations in surface boundary conditions, more variable fluxes were obtained for a site underlain by alluvium with the SWB model (68
Christensen, H. M.; Moroz, I.; Palmer, T.
2015-12-01
It is now acknowledged that representing model uncertainty in atmospheric simulators is essential for the production of reliable probabilistic ensemble forecasts, and a number of different techniques have been proposed for this purpose. Stochastic convection parameterization schemes use random numbers to represent the difference between a deterministic parameterization scheme and the true atmosphere, accounting for the unresolved sub grid-scale variability associated with convective clouds. An alternative approach varies the values of poorly constrained physical parameters in the model to represent the uncertainty in these parameters. This study presents new perturbed parameter schemes for use in the European Centre for Medium Range Weather Forecasts (ECMWF) convection scheme. Two types of scheme are developed and implemented. Both schemes represent the joint uncertainty in four of the parameters in the convection parametrisation scheme, which was estimated using the Ensemble Prediction and Parameter Estimation System (EPPES). The first scheme developed is a fixed perturbed parameter scheme, where the values of uncertain parameters are changed between ensemble members, but held constant over the duration of the forecast. The second is a stochastically varying perturbed parameter scheme. The performance of these schemes was compared to the ECMWF operational stochastic scheme, Stochastically Perturbed Parametrisation Tendencies (SPPT), and to a model which does not represent uncertainty in convection. The skill of probabilistic forecasts made using the different models was evaluated. While the perturbed parameter schemes improve on the stochastic parametrisation in some regards, the SPPT scheme outperforms the perturbed parameter approaches when considering forecast variables that are particularly sensitive to convection. Overall, SPPT schemes are the most skilful representations of model uncertainty due to convection parametrisation. Reference: H. M. Christensen, I
Multi-objective global sensitivity analysis of the WRF model parameters
Quan, Jiping; Di, Zhenhua; Duan, Qingyun; Gong, Wei; Wang, Chen
2015-04-01
Tuning model parameters to match model simulations with observations can be an effective way to enhance the performance of numerical weather prediction (NWP) models such as Weather Research and Forecasting (WRF) model. However, this is a very complicated process as a typical NWP model involves many model parameters and many output variables. One must take a multi-objective approach to ensure all of the major simulated model outputs are satisfactory. This talk presents the results of an investigation of multi-objective parameter sensitivity analysis of the WRF model to different model outputs, including conventional surface meteorological variables such as precipitation, surface temperature, humidity and wind speed, as well as atmospheric variables such as total precipitable water, cloud cover, boundary layer height and outgoing long radiation at the top of the atmosphere. The goal of this study is to identify the most important parameters that affect the predictive skill of short-range meteorological forecasts by the WRF model. The study was performed over the Greater Beijing Region of China. A total of 23 adjustable parameters from seven different physical parameterization schemes were considered. Using a multi-objective global sensitivity analysis method, we examined the WRF model parameter sensitivities to the 5-day simulations of the aforementioned model outputs. The results show that parameter sensitivities vary with different model outputs. But three to four of the parameters are shown to be sensitive to all model outputs considered. The sensitivity results from this research can be the basis for future model parameter optimization of the WRF model.
Kumar, B Shiva; Venkateswarlu, Ch
2014-08-01
The complex nature of biological reactions in biofilm reactors often poses difficulties in analyzing such reactors experimentally. Mathematical models could be very useful for their design and analysis. However, application of biofilm reactor models to practical problems proves somewhat ineffective due to the lack of knowledge of accurate kinetic models and uncertainty in model parameters. In this work, we propose an inverse modeling approach based on tabu search (TS) to estimate the parameters of kinetic and film thickness models. TS is used to estimate these parameters as a consequence of the validation of the mathematical models of the process with the aid of measured data obtained from an experimental fixed-bed anaerobic biofilm reactor involving the treatment of pharmaceutical industry wastewater. The results evaluated for different modeling configurations of varying degrees of complexity illustrate the effectiveness of TS for accurate estimation of kinetic and film thickness model parameters of the biofilm process. The results show that the two-dimensional mathematical model with Edward kinetics (with its optimum parameters as mu(max)rho(s)/Y = 24.57, Ks = 1.352 and Ki = 102.36) and three-parameter film thickness expression (with its estimated parameters as a = 0.289 x 10(-5), b = 1.55 x 10(-4) and c = 15.2 x 10(-6)) better describes the biofilm reactor treating the industry wastewater.
CHAMP: Changepoint Detection Using Approximate Model Parameters
2014-06-01
positions as a Markov chain in which the transition probabilities are defined by the time since the last changepoint: p(τi+1 = t|τi = s) = g(t− s), (1...experimentally verified using artifi- cially generated data and are compared to those of Fearnhead and Liu [5]. 2 Related work Hidden Markov Models (HMMs) are...length α, and maximum number of particles M . Output: Viterbi path of changepoint times and models // Initialize data structures 1: max path, prev queue
Marginal Maximum A Posteriori Item Parameter Estimation for the Generalized Graded Unfolding Model
Roberts, James S.; Thompson, Vanessa M.
2011-01-01
A marginal maximum a posteriori (MMAP) procedure was implemented to estimate item parameters in the generalized graded unfolding model (GGUM). Estimates from the MMAP method were compared with those derived from marginal maximum likelihood (MML) and Markov chain Monte Carlo (MCMC) procedures in a recovery simulation that varied sample size,…
Model Study of Wave Overtopping of Marine Structure for a Wide Range of Geometric Parameters
DEFF Research Database (Denmark)
Kofoed, Jens Peter
2000-01-01
The objective of the study described in this paper is to enable estimation of wave overtopping rates for slopes/ramps given by a wide range of geometric parameters when subjected to varying wave conditions. To achieve this a great number of model tests are carried out in a wave tank using irregular...
WINKLER'S SINGLE-PARAMETER SUBGRADE MODEL FROM ...
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[3, 9]. However, mainly due to the simplicity of Winkler's model in practical applications and .... this case, the coefficient B takes the dimension of a ... In plane-strain problems, the assumption of ... loaded circular region; s is the radial coordinate.
Current and future constraints on Bekenstein-type models for varying couplings
Leite, A C O
2016-01-01
Astrophysical tests of the stability of dimensionless fundamental couplings, such as the fine-structure constant $\\alpha$ and the proton-to-electron mass ratio $\\mu$, are an optimal probe of new physics. There is a growing interest in these tests, following indications of possible spacetime variations at the few parts per million level. Here we make use of the latest astrophysical measurements, combined with background cosmological observations, to obtain improved constraints on Bekenstein-type models for the evolution of both couplings. These are arguably the simplest models allowing for $\\alpha$ and $\\mu$ variations, and are characterized by a single free dimensionless parameter, $\\zeta$, describing the coupling of the underlying dynamical degree of freedom to the electromagnetic sector. In the former case we find that this parameter is constrained to be $|\\zeta_\\alpha|<4.8\\times10^{-6}$ (improving previous constraints by a factor of 6), while in the latter (which we quantitatively compare to astrophysic...
De la Sen, M
2008-01-01
This paper deals with a unifying approach to the problems of computing the admissible sets of parametrical multi perturbations in appropriate bounded sets such that some fundamental properties of parameter-varying linear dynamic systems are maintained provided that the so-called (i.e. perturbation-free) nominal system possesses such properties. The sets of parametrical multi perturbations include any combinations of parametrical multi perturbations in the matrix of dynamics as well as in the control, output and input-output interconnection matrices which belong to some prescribed bounded domain in the complex space. The various properties which are investigated are controllability, observability, output controllability and existence of minimal state-space realizations together with the associate existence or not of associate decoupling, transmission and invariant zeros. All the matrices of parameters including the nominal and the disturbed ones which parameterize the dynamic system may be real or complex. The...
Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models
Bates, Bryson C.
1992-01-01
A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.
Dziak, John J.; Li, Runze; Tan, Xianming; Shiffman, Saul; Shiyko, Mariya P.
2015-01-01
Behavioral scientists increasingly collect intensive longitudinal data (ILD), in which phenomena are measured at high frequency and in real time. In many such studies, it is of interest to describe the pattern of change over time in important variables as well as the changing nature of the relationship between variables. Individuals' trajectories on variables of interest may be far from linear, and the predictive relationship between variables of interest and related covariates may also change over time in a nonlinear way. Time-varying effect models (TVEMs; see Tan, Shiyko, Li, Li, & Dierker, 2012) address these needs by allowing regression coefficients to be smooth, nonlinear functions of time rather than constants. However, it is possible that not only observed covariates but also unknown, latent variables may be related to the outcome. That is, regression coefficients may change over time and also vary for different kinds of individuals. Therefore, we describe a finite mixture version of TVEM for situations in which the population is heterogeneous and in which a single trajectory would conceal important, inter-individual differences. This extended approach, MixTVEM, combines finite mixture modeling with non- or semi-parametric regression modeling, in order to describe a complex pattern of change over time for distinct latent classes of individuals. The usefulness of the method is demonstrated in an empirical example from a smoking cessation study. We provide a versatile SAS macro and R function for fitting MixTVEMs. PMID:26390169
Modeling brain injury response for rotational velocities of varying directions and magnitudes.
Weaver, Ashley A; Danelson, Kerry A; Stitzel, Joel D
2012-09-01
An estimated 1.7 million people in the United States sustain a traumatic brain injury (TBI) annually. To investigate the effects of rotational motions on TBI risk and location, this study modeled rotational velocities of five magnitudes and 26 directions of rotation using the Simulated Injury Monitor finite element brain model. The volume fraction of the total brain exceeding a predetermined strain threshold, the Cumulative Strain Damage Measure (CSDM), was investigated to evaluate global model response. To evaluate regional response, this metric was computed relative to individual brain structures and termed the Structure Cumulative Strain Damage Measure (SCSDM). CSDM increased as input magnitude increased and varied with the direction of rotation. CSDM was 0.55-1.7 times larger in simulations with transverse plane rotation compared to those without transverse plane rotation. The largest SCSDM in the cerebrum and brainstem occurred with rotations in the transverse and sagittal planes, respectively. Velocities causing medial rotation of the cerebellum resulted in the largest SCSDM in this structure. For velocities of the same magnitude, injury risk calculated from CSDM varied from 0 to 97% with variations in the direction of rotation. These findings demonstrate injury risk, as estimated by CSDM and SCSDM, is affected by the direction of rotation and input magnitude, and these may be important considerations for injury prediction.
Estimation of Bid Curves in Power Exchanges using Time-varying Simultaneous-Equations Models
Ofuji, Kenta; Yamaguchi, Nobuyuki
Simultaneous-equations model (SEM) is generally used in economics to estimate interdependent endogenous variables such as price and quantity in a competitive, equilibrium market. In this paper, we have attempted to apply SEM to JEPX (Japan Electric Power eXchange) spot market, a single-price auction market, using the publicly available data of selling and buying bid volumes, system price and traded quantity. The aim of this analysis is to understand the magnitude of influences to the auctioned prices and quantity from the selling and buying bids, than to forecast prices and quantity for risk management purposes. In comparison with the Ordinary Least Squares (OLS) estimation where the estimation results represent average values that are independent of time, we employ a time-varying simultaneous-equations model (TV-SEM) to capture structural changes inherent in those influences, using State Space models with Kalman filter stepwise estimation. The results showed that the buying bid volumes has that highest magnitude of influences among the factors considered, exhibiting time-dependent changes, ranging as broad as about 240% of its average. The slope of the supply curve also varies across time, implying the elastic property of the supply commodity, while the demand curve remains comparatively inelastic and stable over time.
A simulation of water pollution model parameter estimation
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
On retrial queueing model with fuzzy parameters
Ke, Jau-Chuan; Huang, Hsin-I.; Lin, Chuen-Horng
2007-01-01
This work constructs the membership functions of the system characteristics of a retrial queueing model with fuzzy customer arrival, retrial and service rates. The α-cut approach is used to transform a fuzzy retrial-queue into a family of conventional crisp retrial queues in this context. By means of the membership functions of the system characteristics, a set of parametric non-linear programs is developed to describe the family of crisp retrial queues. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the system characteristics are expressed and governed by the membership functions, more information is provided for use by management. By extending this model to the fuzzy environment, fuzzy retrial-queue is represented more accurately and analytic results are more useful for system designers and practitioners.
Solar parameters for modeling interplanetary background
Bzowski, M; Tokumaru, M; Fujiki, K; Quemerais, E; Lallement, R; Ferron, S; Bochsler, P; McComas, D J
2011-01-01
The goal of the Fully Online Datacenter of Ultraviolet Emissions (FONDUE) Working Team of the International Space Science Institute in Bern, Switzerland, was to establish a common calibration of various UV and EUV heliospheric observations, both spectroscopic and photometric. Realization of this goal required an up-to-date model of spatial distribution of neutral interstellar hydrogen in the heliosphere, and to that end, a credible model of the radiation pressure and ionization processes was needed. This chapter describes the solar factors shaping the distribution of neutral interstellar H in the heliosphere. Presented are the solar Lyman-alpha flux and the solar Lyman-alpha resonant radiation pressure force acting on neutral H atoms in the heliosphere, solar EUV radiation and the photoionization of heliospheric hydrogen, and their evolution in time and the still hypothetical variation with heliolatitude. Further, solar wind and its evolution with solar activity is presented in the context of the charge excha...
Ngonghala, Calistus N; Teboh-Ewungkem, Miranda I; Ngwa, Gideon A
2015-06-01
We derive and study a deterministic compartmental model for malaria transmission with varying human and mosquito populations. Our model considers disease-related deaths, asymptomatic immune humans who are also infectious, as well as mosquito demography, reproduction and feeding habits. Analysis of the model reveals the existence of a backward bifurcation and persistent limit cycles whose period and size is determined by two threshold parameters: the vectorial basic reproduction number Rm, and the disease basic reproduction number R0, whose size can be reduced by reducing Rm. We conclude that malaria dynamics are indeed oscillatory when the methodology of explicitly incorporating the mosquito's demography, feeding and reproductive patterns is considered in modeling the mosquito population dynamics. A sensitivity analysis reveals important control parameters that can affect the magnitudes of Rm and R0, threshold quantities to be taken into consideration when designing control strategies. Both Rm and the intrinsic period of oscillation are shown to be highly sensitive to the mosquito's birth constant λm and the mosquito's feeding success probability pw. Control of λm can be achieved by spraying, eliminating breeding sites or moving them away from human habitats, while pw can be controlled via the use of mosquito repellant and insecticide-treated bed-nets. The disease threshold parameter R0 is shown to be highly sensitive to pw, and the intrinsic period of oscillation is also sensitive to the rate at which reproducing mosquitoes return to breeding sites. A global sensitivity and uncertainty analysis reveals that the ability of the mosquito to reproduce and uncertainties in the estimations of the rates at which exposed humans become infectious and infectious humans recover from malaria are critical in generating uncertainties in the disease classes.
Evaluation model for service life of dam based on time-varying risk probability
Institute of Scientific and Technical Information of China (English)
SU HuaiZhi; WEN ZhiPing; HU Jiang; WU ZhongRu
2009-01-01
For many dam projects in China, the 50-year designed life time is coming to an end. It is urgent to study the theory and method to evaluate the dam service life. In this paper, firstly, the probability theory of fuzzy event and time-varying effect theory are used to analyze the time-variety of various risk factors in the process of dam operations. A method is proposed to quantify the above time-variety and a model to describe the fuzzy time-varying risk probability for the dam structure is also built. Secondly, the information entropy theory is used to analyze the uncertain degree relationship between the characteristic value of membership function and fuzzy risk probability, and a mathematical method is presented to calculate the time-varying risk probability accordingly. Thirdly, the relation mode between time-varying risk probability and service life is discussed. Based on this relation mode and the acceptable risk probability of dams in China, a method is put forward to evaluate and forecast the dam service life.Finally, the proposed theory and method are used to analyze one concrete dam. The dynamic variability and mutation feature of the dam risk probability are analyzed. The remaining service life of this dam is forecasted. The obtained results can provide technology support for the project management department to make treatment measures of engineering and reasonably arrange reinforce cost. The principles in this paper have wide applicability and can be used in risk analysis for slope instability and other fields.
Linear Sigma Models With Strongly Coupled Phases -- One Parameter Models
Hori, Kentaro
2013-01-01
We systematically construct a class of two-dimensional $(2,2)$ supersymmetric gauged linear sigma models with phases in which a continuous subgroup of the gauge group is totally unbroken. We study some of their properties by employing a recently developed technique. The focus of the present work is on models with one K\\"ahler parameter. The models include those corresponding to Calabi-Yau threefolds, extending three examples found earlier by a few more, as well as Calabi-Yau manifolds of other dimensions and non-Calabi-Yau manifolds. The construction leads to predictions of equivalences of D-brane categories, systematically extending earlier examples. There is another type of surprise. Two distinct superconformal field theories corresponding to Calabi-Yau threefolds with different Hodge numbers, $h^{2,1}=23$ versus $h^{2,1}=59$, have exactly the same quantum K\\"ahler moduli space. The strong-weak duality plays a crucial r\\^ole in confirming this, and also is useful in the actual computation of the metric on t...
Scheibehenne, Benjamin; Pachur, Thorsten
2015-04-01
To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers' estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choice-cumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test-retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; ten Brummelhuis, P.G.J.; ten Brummelhuis, Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The
Exploring the interdependencies between parameters in a material model.
Energy Technology Data Exchange (ETDEWEB)
Silling, Stewart Andrew; Fermen-Coker, Muge
2014-01-01
A method is investigated to reduce the number of numerical parameters in a material model for a solid. The basis of the method is to detect interdependencies between parameters within a class of materials of interest. The method is demonstrated for a set of material property data for iron and steel using the Johnson-Cook plasticity model.
An Alternative Three-Parameter Logistic Item Response Model.
Pashley, Peter J.
Birnbaum's three-parameter logistic function has become a common basis for item response theory modeling, especially within situations where significant guessing behavior is evident. This model is formed through a linear transformation of the two-parameter logistic function in order to facilitate a lower asymptote. This paper discusses an…
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; Brummelhuis, ten Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The hyperboli
A compact cyclic plasticity model with parameter evolution
DEFF Research Database (Denmark)
Krenk, Steen; Tidemann, L.
2017-01-01
, and it is demonstrated that this simple formulation enables very accurate representation of experimental results. An extension of the theory to account for model parameter evolution effects, e.g. in the form of changing yield level, is included in the form of extended evolution equations for the model parameters...
Regionalization of SWAT Model Parameters for Use in Ungauged Watersheds
Directory of Open Access Journals (Sweden)
Indrajeet Chaubey
2010-11-01
Full Text Available There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and therefore BMP representation. This is, however, only possible for gauged watersheds. There are many watersheds for which there are very little or no monitoring data available, thus the question as to whether it would be possible to extend and/or generalize model parameters obtained through calibration of gauged watersheds to ungauged watersheds within the same region. This study explored the possibility of developing regionalized model parameter sets for use in ungauged watersheds. The study evaluated two regionalization methods: global averaging, and regression-based parameters, on the SWAT model using data from priority watersheds in Arkansas. Resulting parameters were tested and model performance determined on three gauged watersheds. Nash-Sutcliffe efficiencies (NS for stream flow obtained using regression-based parameters (0.53–0.83 compared well with corresponding values obtained through model calibration (0.45–0.90. Model performance obtained using global averaged parameter values was also generally acceptable (0.4 ≤ NS ≤ 0.75. Results from this study indicate that regionalized parameter sets for the SWAT model can be obtained and used for making satisfactory hydrologic response predictions in ungauged watersheds.
NWP model forecast skill optimization via closure parameter variations
Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.
2012-04-01
We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.
Bayesian estimation of parameters in a regional hydrological model
Directory of Open Access Journals (Sweden)
K. Engeland
2002-01-01
Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis
Dawson, John R; Mckann, Robert; Hay, Elizabeth S
1946-01-01
The second part of a series of tests made in Langley tank no. 2 to determine the effect of varying design parameters of planing-tail hulls is presented. Results are given to show the effects on resistance characteristics of varying angle of afterbody keel, depth of step, and length of afterbody chine. The effect of varying the gross load is shown for one configuration. The resistance characteristics of planing-tail hulls are compared with those of a conventional flying-boat hull. The forces on the forebody and afterbody of one configuration are compared with the forces on a conventional hull. Increasing the angle of afterbody keel had small effect on hump resistance and no effect on high-speed resistance but increased free-to-trim resistance at intermediate speeds. Increasing the depth of step increased hump resistance, had little effect on high-speed resistance, and increased free-to-trim resistance at intermediate speeds. Omitting the chines on the forward 25 percent of the afterbody had no appreciable effect on resistance. Omitting 70 percent of the chine length had almost no effect on maximum resistance but broadened the hump and increased spray around the afterbody. Load-resistance ratio at the hump decreased more rapidly with increasing load coefficient for the planing-tail hull than for the representative conventional hull, although the load-resistance ratio at the hump was greater for the planing-tail hull than for the conventional hull throughout the range of loads tested. At speeds higher than hump speed, load-resistance ratio for the planing-tail hull was a maximum at a particular gross load and was slightly less at heavier and lighter gross loads. The planing-tail hull was found to have lower resistance than the conventional hull at both the hump and at high speeds, but at intermediate speeds there was little difference. The lower hump resistance of the planing-tail hull was attributed to the ability of the afterbody to carry a greater percentage of the
Varying facets of a model of competitive learning: the role of updates and memory
Bhat, Ajaz Ahmad
2011-01-01
The effects of memory and different updating paradigms in a game-theoretic model of competitive learning, comprising two distinct agent types, are analysed. For nearly all the updating schemes, the phase diagram of the model consists of a disordered phase separating two ordered phases at coexistence: the critical exponents of these transitions belong to the generalised universality class of the voter model. Also, as appropriate for a model of competing strategies, we examine the situation when the two types have different characteristics, i.e. their parameters are chosen to be away from coexistence. We find linear response behaviour in the expected regimes but, more interestingly, are able to probe the effect of memory. This suggests that even the less successful agent types can win over the more successful ones, provided they have better retentive powers.
AST: Activity-Security-Trust driven modeling of time varying networks.
Wang, Jian; Xu, Jiake; Liu, Yanheng; Deng, Weiwen
2016-02-18
Network modeling is a flexible mathematical structure that enables to identify statistical regularities and structural principles hidden in complex systems. The majority of recent driving forces in modeling complex networks are originated from activity, in which an activity potential of a time invariant function is introduced to identify agents' interactions and to construct an activity-driven model. However, the new-emerging network evolutions are already deeply coupled with not only the explicit factors (e.g. activity) but also the implicit considerations (e.g. security and trust), so more intrinsic driving forces behind should be integrated into the modeling of time varying networks. The agents undoubtedly seek to build a time-dependent trade-off among activity, security, and trust in generating a new connection to another. Thus, we reasonably propose the Activity-Security-Trust (AST) driven model through synthetically considering the explicit and implicit driving forces (e.g. activity, security, and trust) underlying the decision process. AST-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the effects of security and trust in driving network evolution, and improving the biases induced by only involving activity representations in analyzing the dynamical processes.
A time-varying subjective quality model for mobile streaming videos with stalling events
Ghadiyaram, Deepti; Pan, Janice; Bovik, Alan C.
2015-09-01
Over-the-top mobile video streaming is invariably influenced by volatile network conditions which cause playback interruptions (stalling events), thereby impairing users' quality of experience (QoE). Developing models that can accurately predict users' QoE could enable the more efficient design of quality-control protocols for video streaming networks that reduce network operational costs while still delivering high-quality video content to the customers. Existing objective models that predict QoE are based on global video features, such as the number of stall events and their lengths, and are trained and validated on a small pool of ad hoc video datasets, most of which are not publicly available. The model we propose in this work goes beyond previous models as it also accounts for the fundamental effect that a viewer's recent level of satisfaction or dissatisfaction has on their overall viewing experience. In other words, the proposed model accounts for and adapts to the recency, or hysteresis effect caused by a stall event in addition to accounting for the lengths, frequency of occurrence, and the positions of stall events - factors that interact in a complex way to affect a user's QoE. On the recently introduced LIVE-Avvasi Mobile Video Database, which consists of 180 distorted videos of varied content that are afflicted solely with over 25 unique realistic stalling events, we trained and validated our model to accurately predict the QoE, attaining standout QoE prediction performance.
Specification and testing of Multiplicative Time-Varying GARCH models with applications
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
2017-01-01
In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smoothly...... over time. This nonstationary component is defined as a linear combination of logistic transition functions with time as the transition variable. The appropriate number of transition functions is determined by a sequence of specification tests. For that purpose, a coherent modelling strategy based...... is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns....
Some tests for parameter constancy in cointegrated VAR-models
DEFF Research Database (Denmark)
Hansen, Henrik; Johansen, Søren
1999-01-01
Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations, and anot...... be applied to test the constancy of the long-run parameters in the cointegrated VAR-model. All results are illustrated using a model for the term structure of interest rates on US Treasury securities. ...
Spatio-temporal modeling of nonlinear distributed parameter systems
Li, Han-Xiong
2011-01-01
The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s
Directory of Open Access Journals (Sweden)
Baker Syed
2011-01-01
Full Text Available Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF, rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Baker, Syed Murtuza; Poskar, C Hart; Junker, Björn H
2011-10-11
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
A multiscale MDCT image-based breathing lung model with time-varying regional ventilation
Energy Technology Data Exchange (ETDEWEB)
Yin, Youbing, E-mail: youbing-yin@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Radiology, The University of Iowa, Iowa City, IA 52242 (United States); Choi, Jiwoong, E-mail: jiwoong-choi@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Hoffman, Eric A., E-mail: eric-hoffman@uiowa.edu [Department of Radiology, The University of Iowa, Iowa City, IA 52242 (United States); Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Internal Medicine, The University of Iowa, Iowa City, IA 52242 (United States); Tawhai, Merryn H., E-mail: m.tawhai@auckland.ac.nz [Auckland Bioengineering Institute, The University of Auckland, Auckland (New Zealand); Lin, Ching-Long, E-mail: ching-long-lin@uiowa.edu [Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242 (United States); IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 (United States)
2013-07-01
A novel algorithm is presented that links local structural variables (regional ventilation and deforming central airways) to global function (total lung volume) in the lung over three imaged lung volumes, to derive a breathing lung model for computational fluid dynamics simulation. The algorithm constitutes the core of an integrative, image-based computational framework for subject-specific simulation of the breathing lung. For the first time, the algorithm is applied to three multi-detector row computed tomography (MDCT) volumetric lung images of the same individual. A key technique in linking global and local variables over multiple images is an in-house mass-preserving image registration method. Throughout breathing cycles, cubic interpolation is employed to ensure C{sub 1} continuity in constructing time-varying regional ventilation at the whole lung level, flow rate fractions exiting the terminal airways, and airway deformation. The imaged exit airway flow rate fractions are derived from regional ventilation with the aid of a three-dimensional (3D) and one-dimensional (1D) coupled airway tree that connects the airways to the alveolar tissue. An in-house parallel large-eddy simulation (LES) technique is adopted to capture turbulent-transitional-laminar flows in both normal and deep breathing conditions. The results obtained by the proposed algorithm when using three lung volume images are compared with those using only one or two volume images. The three-volume-based lung model produces physiologically-consistent time-varying pressure and ventilation distribution. The one-volume-based lung model under-predicts pressure drop and yields un-physiological lobar ventilation. The two-volume-based model can account for airway deformation and non-uniform regional ventilation to some extent, but does not capture the non-linear features of the lung.
Weigand, M.; Kemna, A.
2016-06-01
Spectral induced polarization (SIP) data are commonly analysed using phenomenological models. Among these models the Cole-Cole (CC) model is the most popular choice to describe the strength and frequency dependence of distinct polarization peaks in the data. More flexibility regarding the shape of the spectrum is provided by decomposition schemes. Here the spectral response is decomposed into individual responses of a chosen elementary relaxation model, mathematically acting as kernel in the involved integral, based on a broad range of relaxation times. A frequently used kernel function is the Debye model, but also the CC model with some other a priorly specified frequency dispersion (e.g. Warburg model) has been proposed as kernel in the decomposition. The different decomposition approaches in use, also including conductivity and resistivity formulations, pose the question to which degree the integral spectral parameters typically derived from the obtained relaxation time distribution are biased by the approach itself. Based on synthetic SIP data sampled from an ideal CC response, we here investigate how the two most important integral output parameters deviate from the corresponding CC input parameters. We find that the total chargeability may be underestimated by up to 80 per cent and the mean relaxation time may be off by up to three orders of magnitude relative to the original values, depending on the frequency dispersion of the analysed spectrum and the proximity of its peak to the frequency range limits considered in the decomposition. We conclude that a quantitative comparison of SIP parameters across different studies, or the adoption of parameter relationships from other studies, for example when transferring laboratory results to the field, is only possible on the basis of a consistent spectral analysis procedure. This is particularly important when comparing effective CC parameters with spectral parameters derived from decomposition results.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
参数可变系统时间序列短期预测方法%An Approach for Short-Term Prediction on Time Series from Parameter-Varying Systems
Institute of Scientific and Technical Information of China (English)
肖芬; 高协平
2006-01-01
时间序列预测是一类非常重要的问题,但基本上局限于参数不可变问题的研究,而对实际问题中经常出现的更重要的参数可变系统的预测,由于构成几乎所有已有预测技术基础的Taken嵌入定理不再成立,所以这方面的研究成果极少.使用一种将(多)小波变换与反向传播神经网络相结合的新型网络结构--(多)小波神经网络,尝试对参数可变时间序列的预测.因为(多)小波神经网络的误差函数是一个凸函数,这在一定程度上可以避免经典神经网络容易陷入局部极小、收敛速度慢等问题.对著名的Ikeda参数可变系统的实验表明,多小波神经网络的预测性能较单小波神经网络要好,而单小波神经网络的性能较BP网要好.因此,该方法不失为时间可变系统预测的一种好的推荐.%Time series prediction is a very important problem in many applications and the current prediction techniques are nearly all based on the Takens' embedding theorem. Many realistic systems are parameter-varying systems, and the embedding theorems are invalid, predicting the behavior of parameter-varying systems is more difficult. This paper proposes the novel prediction techniques for parameter-varying systems reconstruction, which are based on wavelet neural network (WNN) and multiwavelets neural network (MWNN). These techniques absorb the advantages of high resolution of wavelet and learning of neural networks. The significant improvement is that the error's functions of both networks are convex, and the problem of poor convergence and undesired local minimum can be solved remarkably. Ikeda time series generated by the parameter-varying systems is adopted to check the prediction performance of the proposed models. The numerical experiments show that the three proposed models are feasible, MWNN has the top performance, and WNN could lead the better results than NN in the prediction of the parameter-varying systems.
Universally sloppy parameter sensitivities in systems biology models.
Directory of Open Access Journals (Sweden)
Ryan N Gutenkunst
2007-10-01
Full Text Available Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Directory of Open Access Journals (Sweden)
Guanqun eZhang
2011-11-01
Full Text Available A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel while being defined by only a few parameters (unlike comprehensive distributed-parameter models. As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Zhang, Guanqun; Hahn, Jin-Oh; Mukkamala, Ramakrishna
2011-01-01
A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel) while being defined by only a few parameters (unlike comprehensive distributed-parameter models). As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications. PMID:22053157
Shah, Kushal S; Saranathan, Archana; Koya, Bharath; Elias, John J
2015-01-01
A finite element analysis (FEA) modeling technique has been developed to characterize how varying the orientation of the patellar tendon influences the patellofemoral pressure distribution. To evaluate the accuracy of the technique, models were created from MRI images to represent five knees that were previously tested in vitro to determine the influence of hamstrings loading on patellofemoral contact pressures. Hamstrings loading increased the lateral and posterior orientation of the patellar tendon. Each model was loaded at 40°, 60°, and 80° of flexion with quadriceps force vectors representing the experimental loading conditions. The orientation of the patellar tendon was represented for the loaded and unloaded hamstrings conditions based on experimental measures of tibiofemoral alignment. Similar to the experimental data, simulated loading of the hamstrings within the FEA models shifted the center of pressure laterally and increased the maximum lateral pressure. Significant (p pressure and maximum lateral pressure from paired t-tests carried out at the individual flexion angles. The ability to replicate experimental trends indicates that the FEA models can be used for future studies focused on determining how variations in the orientation of the patellar tendon related to anatomical or loading variations or surgical procedures influence the patellofemoral pressure distribution.
Parameter estimation and investigation of a bolted joint model
Shiryayev, O. V.; Page, S. M.; Pettit, C. L.; Slater, J. C.
2007-11-01
Mechanical joints are a primary source of variability in the dynamics of built-up structures. Physical phenomena in the joint are quite complex and therefore too impractical to model at the micro-scale. This motivates the development of lumped parameter joint models with discrete interfaces so that they can be easily implemented in finite element codes. Among the most important considerations in choosing a model for dynamically excited systems is its ability to model energy dissipation. This translates into the need for accurate and reliable methods to measure model parameters and estimate their inherent variability from experiments. The adjusted Iwan model was identified as a promising candidate for representing joint dynamics. Recent research focused on this model has exclusively employed impulse excitation in conjunction with neural networks to identify the model parameters. This paper presents an investigation of an alternative parameter estimation approach for the adjusted Iwan model, which employs data from oscillatory forcing. This approach is shown to produce parameter estimates with precision similar to the impulse excitation method for a range of model parameters.
Modeling and Parameter Estimation of a Small Wind Generation System
Directory of Open Access Journals (Sweden)
Carlos A. Ramírez Gómez
2013-11-01
Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.
Parameter estimation of hidden periodic model in random fields
Institute of Scientific and Technical Information of China (English)
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
Identification of parameters of discrete-continuous models
Energy Technology Data Exchange (ETDEWEB)
Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)
2015-03-10
In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.
Estimating parameters for generalized mass action models with connectivity information
Directory of Open Access Journals (Sweden)
Voit Eberhard O
2009-05-01
Full Text Available Abstract Background Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems. Results In this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters. Conclusion The method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out
Parameter sensitivity analysis of stochastic models provides insights into cardiac calcium sparks.
Lee, Young-Seon; Liu, Ona Z; Hwang, Hyun Seok; Knollmann, Bjorn C; Sobie, Eric A
2013-03-05
We present a parameter sensitivity analysis method that is appropriate for stochastic models, and we demonstrate how this analysis generates experimentally testable predictions about the factors that influence local Ca(2+) release in heart cells. The method involves randomly varying all parameters, running a single simulation with each set of parameters, running simulations with hundreds of model variants, then statistically relating the parameters to the simulation results using regression methods. We tested this method on a stochastic model, containing 18 parameters, of the cardiac Ca(2+) spark. Results show that multivariable linear regression can successfully relate parameters to continuous model outputs such as Ca(2+) spark amplitude and duration, and multivariable logistic regression can provide insight into how parameters affect Ca(2+) spark triggering (a probabilistic process that is all-or-none in a single simulation). Benchmark studies demonstrate that this method is less computationally intensive than standard methods by a factor of 16. Importantly, predictions were tested experimentally by measuring Ca(2+) sparks in mice with knockout of the sarcoplasmic reticulum protein triadin. These mice exhibit multiple changes in Ca(2+) release unit structures, and the regression model both accurately predicts changes in Ca(2+) spark amplitude (30% decrease in model, 29% decrease in experiments) and provides an intuitive and quantitative understanding of how much each alteration contributes to the result. This approach is therefore an effective, efficient, and predictive method for analyzing stochastic mathematical models to gain biological insight.
Charbonneau, Jeremy
As the perceived quality of a product is becoming more important in the manufacturing industry, more emphasis is being placed on accurately predicting the sound quality of everyday objects. This study was undertaken to improve upon current prediction techniques with regard to the psychoacoustic descriptor of loudness and an improved binaural summation technique. The feasibility of this project was first investigated through a loudness matching experiment involving thirty-one subjects and pure tones of constant sound pressure level. A dependence of binaural summation on frequency was observed which had previously not been a subject of investigation in the reviewed literature. A follow-up investigation was carried out with forty-eight volunteers and pure tones of constant sensation level. Contrary to existing theories in literature the resulting loudness matches revealed an amplitude versus frequency relationship which confirmed the perceived increase in loudness when a signal was presented to both ears simultaneously as opposed to one ear alone. The resulting trend strongly indicated that the higher the frequency of the presented signal, the greater the increase in observed binaural summation. The results from each investigation were summarized into a single binaural summation algorithm and inserted into an improved time-varying loudness model. Using experimental techniques, it was demonstrated that the updated binaural summation algorithm was a considerable improvement over the state of the art approach for predicting the perceived binaural loudness. The improved function retained the ease of use from the original model while additionally providing accurate estimates of diotic listening conditions from monaural WAV files. It was clearly demonstrated using a validation jury test that the revised time-varying loudness model was a significant improvement over the previously standardized approach.
Sridhar, A.; Pullin, D. I.; Cheng, W.
2016-11-01
An empirical model is presented, after Rotta (1962), that describes the development of a fully-developed turbulent boundary layer in the presence of surface roughness with nominal roughness length-scale ks that varies with stream-wise distance x. For Rex =Ue (x) x / ν large, use is made of the log-wake model of the local turbulent mean-velocity profile that contains the Hama roughness correction ΔU+ (ks+) for the asymptotic, fully rough regime. It is shown that the skin friction coefficient Cf is constant in x only for ks = αx , where α is a dimensionless number. For Ue (x) = Axm , this then gives a two-parameter (α , m) family of solutions for boundary-layer flows that are self similar in the variable z / (α x) where z is the wall-normal co-ordinate. Trends observed in this model are supported by wall-modeled LES of the zero-pressure-gradient turbulent boundary layer (m = 0) at very large Rex . It is argued that the present results suggest that, in the sense that Cf is spatially constant and independent of Rex , this class of flows can be interpreted as providing the asymptotically-rough equivalent of Moody-like diagrams for boundary layers in the presence of small-scale roughness. Supported partially by KAUST OCRF Award No. URF/1/1394-01 and partially by NSF award CBET 1235605.
A comprehensive parameter study of an active magnetic regenerator using a 2D numerical model
DEFF Research Database (Denmark)
Nielsen, Kaspar Kirstein; Bahl, Christian Robert Haffenden; Smith, Anders
2010-01-01
A two-dimensional numerical heat transfer model is used to investigate an active magnetic regenerator (AMR) based on parallel plates of magnetocaloric material. A large range of parameter variations are performed to study the optimal AMR. The parameters varied are the plate and channel thicknesses......, cycle frequency and fluid movement. These are cast into the non-dimensional units utilization, porosity and number of transfer units (NTU). The cooling capacity vs. temperature span is mapped as a function of these parameters and each configuration is evaluated through the maximum temperature span...
Using the power balance model to simulate cross-country skiing on varying terrain.
Moxnes, John F; Sandbakk, Oyvind; Hausken, Kjell
2014-01-01
The current study adapts the power balance model to simulate cross-country skiing on varying terrain. We assumed that the skier's locomotive power at a self-chosen pace is a function of speed, which is impacted by friction, incline, air drag, and mass. An elite male skier's position along the track during ski skating was simulated and compared with his experimental data. As input values in the model, air drag and friction were estimated from the literature based on the skier's mass, snow conditions, and speed. We regard the fit as good, since the difference in racing time between simulations and measurements was 2 seconds of the 815 seconds racing time, with acceptable fit both in uphill and downhill terrain. Using this model, we estimated the influence of changes in various factors such as air drag, friction, and body mass on performance. In conclusion, the power balance model with locomotive power as a function of speed was found to be a valid tool for analyzing performance in cross-country skiing.
Using the power balance model to simulate cross-country skiing on varying terrain
Directory of Open Access Journals (Sweden)
Moxnes JF
2014-05-01
Full Text Available John F Moxnes,1 Øyvind Sandbakk,2 Kjell Hausken31Department for Protection, Norwegian Defence Research Establishment, Kjeller, Norway; 2Center for Elite Sports Research, Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway; 3Faculty of Social Sciences, University of Stavanger, Stavanger, NorwayAbstract: The current study adapts the power balance model to simulate cross-country skiing on varying terrain. We assumed that the skier’s locomotive power at a self-chosen pace is a function of speed, which is impacted by friction, incline, air drag, and mass. An elite male skier’s position along the track during ski skating was simulated and compared with his experimental data. As input values in the model, air drag and friction were estimated from the literature based on the skier's mass, snow conditions, and speed. We regard the fit as good, since the difference in racing time between simulations and measurements was 2 seconds of the 815 seconds racing time, with acceptable fit both in uphill and downhill terrain. Using this model, we estimated the influence of changes in various factors such as air drag, friction, and body mass on performance. In conclusion, the power balance model with locomotive power as a function of speed was found to be a valid tool for analyzing performance in cross-country skiing.Keywords: air drag, efficiency, friction coefficient, speed, locomotive power
Anisotropic Bianchi Type-Ⅰ Model with a Varying A Term
Institute of Scientific and Technical Information of China (English)
R.K.Tiwari; Divya Singh
2012-01-01
Einstein field equations with variable gravitational and cosmological constants are considered in the presence of a perfect fluid for a Bianchi type-I universe by assuming that the cosmological term is proportional to the Hubble parameter..The variation law for vacuum density was recently proposed by Schützhold on the basis of quantum field estimation in a curved expanding background.The cosmological term tends asymptotically to a genuine cosmological constant and the model tends to a de Sitter universe.We obtain that the present universe is accelerating with a large fraction of cosmological density in the form of a cosmological term.
Towards predictive food process models: A protocol for parameter estimation.
Vilas, Carlos; Arias-Méndez, Ana; Garcia, Miriam R; Alonso, Antonio A; Balsa-Canto, E
2016-05-31
Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.
Estimation of the input parameters in the Feller neuronal model
Ditlevsen, Susanne; Lansky, Petr
2006-06-01
The stochastic Feller neuronal model is studied, and estimators of the model input parameters, depending on the firing regime of the process, are derived. Closed expressions for the first two moments of functionals of the first-passage time (FTP) through a constant boundary in the suprathreshold regime are derived, which are used to calculate moment estimators. In the subthreshold regime, the exponentiality of the FTP is utilized to characterize the input parameters. The methods are illustrated on simulated data. Finally, approximations of the first-passage-time moments are suggested, and biological interpretations and comparisons of the parameters in the Feller and the Ornstein-Uhlenbeck models are discussed.
Mass Varying Neutrinos in Supernovae
Rossi-Torres, F; de Holanda, P C; Peres, O L G
2010-01-01
We study limits for the mass varying neutrino model, using constrains from supernova neutrinos placed by the r-process condition, $Y_e<0.5$. Also, we use this model in a supernova environment to study the regions of survival probability in the oscillation space parameter ($\\tan^2\\theta$ and $\\Delta m^2_0$), considering the channel $\
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-05-01
Full Text Available Physical parameterizations in General Circulation Models (GCMs, having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
Optimal parameters for the FFA-Beddoes dynamic stall model
Energy Technology Data Exchange (ETDEWEB)
Bjoerck, A.; Mert, M. [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H.A. [Risoe National Lab., Roskilde (Denmark)
1999-03-01
Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)
Marton, F. C.
2001-12-01
The thermal, mineralogical, and buoyancy structures of thermal-kinetic models of subducting slabs are highly dependent upon a number of parameters, especially if the metastable persistence of olivine in the transition zone is investigated. The choice of starting thermal model for the lithosphere, whether a cooling halfspace (HS) or plate model, can have a significant effect, resulting in metastable wedges of olivine that differ in size by up to two to three times for high values of the thermal parameter (ǎrphi). Moreover, as ǎrphi is the product of the age of the lithosphere at the trench, convergence rate, and dip angle, slabs with similar ǎrphis can show great variations in structures as these constituents change. This is especially true for old lithosphere, as the lithosphere continually cools and thickens with age for HS models, but plate models, with parameters from Parson and Sclater [1977] (PS) or Stein and Stein [1992] (GDH1), achieve a thermal steady-state and constant thickness in about 70 My. In addition, the latent heats (q) of the phase transformations of the Mg2SiO4 polymorphs can also have significant effects in the slabs. Including q feedback in models raises the temperature and reduces the extent of metastable olivine, causing the sizes of the metastable wedges to vary by factors of up to two times. The effects of the choice of thermal model, inclusion and non-inclusion of q feedback, and variations in the constituents of ǎrphi are investigated for several model slabs.
Szalai, Emily B.; Fleischer, Guy W.; Bence, James R.
2003-01-01
A concurrent increase in lakewide abundance and decrease in size-at-age of bloater (Coregonus hoyi) in Lake Michigan have suggested density-dependent growth regulation. We investigated these temporal patterns by fitting a dynamic von Bertalanffy model and lengthweight relationship with time-varying parameters to mean length- and weight-at-ages (ages 17) from annual surveys (1965-1999). We modeled yearling length, asymptotic size (L‰), and the parameters of a power relationship between mean weight and mean length (α and β) as changing slowly over time using a random walk model. The Brody growth coefficient (k) was modeled as a linear function of L‰ with year-specific random deviations. Our results support a positive relationship between L‰ and k, indicating that under conditions supporting larger asymptotic lengths, individuals approach the asymptote more rapidly. We explored the relationship between year-specific growth parameters and indices of lakewide bloater abundance and found evidence of density-dependent growth. However, in the most recent years, L‰ and yearling length have remained low in Lake Michigan despite low bloater abundances, suggesting the occurrence of a fundamental shift in the food web.
Jomaa, Seifeddine; Jiang, Sanyuan; Yang, Xiaoqiang; Rode, Michael
2016-04-01
Eutrophication is a serious environmental problem. Despite numerous experimental and modelling efforts, understanding of the effect of land use and agriculture practices on in-stream nitrogen fluxes is still not fully achieved. This study combined intensive field monitoring and numerical modelling using 30 years of surface water quality data of a drinking water reservoir catchment in central Germany. The Weida catchment (99.5 km2) is part of the Elbe river basin and has a share of 67% of agricultural land use with significant changes in agricultural practices within the investigation period. The geology of the Weida catchment is characterized by clay schists and eruptive rocks, where rocks have low permeability. The semi-distributed hydrological water quality HYPE (Hydrological Predictions for the Environment) model was used to reproduce the measured data. First, the model was calibrated for discharge and nitrate-N concentrations (NO3-N) during the period 1997-2000. Then, the HYPE model was validated successfully for three different periods 1983-1987, 1989-1996 and 2000-2003, which are charaterized by different fertilizer application rates (with lowest discharge prediction performance of NSE = 0.78 and PBIAS = 3.74%, considering calibration and validation periods). Results showed that the measured as well as simulated in-stream nitrate-N concentration respond quickly to fertilizer application changes (increase/decrease). This rapid response can be explained with short residence times of interflow and baseflow runoff components due to the hardrock geological properties of the catchment. Results revealed that the surface runoff and interflow are the most dominant runoff components. HYPE model could reproduce reasonably well the NO3-N daily loads for varying fertilizer application, when detailed input data in terms of crop management (field-specific survey) are considered.
Thanh, Mai Duc
We consider an elliptic-hyperbolic model of phase transitions and we show that any Lax shock can be approximated by a traveling wave with a suitable choice of viscosity and capillarity. By varying viscosity and capillarity coefficients, we can cover any Lax shock which either remains in the same phase, or admits a phase transition. The argument used in this paper extends the one in our earlier works. The method relies on LaSalle's invariance principle and on estimating attraction region of the asymptotically stable of the associated autonomous system of differential equations. We will show that the saddle point of this system of differential equations lies on the boundary of the attraction region and that there is a trajectory leaving the saddle point and entering the attraction region. This gives us a traveling wave connecting the two states of the Lax shock. We also present numerical illustrations of traveling waves.
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation of struct......This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation...... response during excitation and the geometrical damping related to free vibrations of a hexagonal footing. The optimal order of a lumped-parameter model is determined for each degree of freedom, i.e. horizontal and vertical translation as well as torsion and rocking. In particular, the necessity of coupling...... between horizontal sliding and rocking is discussed....
A New Approach for Parameter Optimization in Land Surface Model
Institute of Scientific and Technical Information of China (English)
LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin
2011-01-01
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
. Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied...
Estimating winter wheat phenological parameters: Implications for crop modeling
Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...
Bates, P. D.; Neal, J. C.; Fewtrell, T. J.
2012-12-01
In this we paper we consider two related questions. First, we address the issue of how much physical complexity is necessary in a model in order to simulate floodplain inundation to within validation data error. This is achieved through development of a single code/multiple physics hydraulic model (LISFLOOD-FP) where different degrees of complexity can be switched on or off. Different configurations of this code are applied to four benchmark test cases, and compared to the results of a number of industry standard models. Second we address the issue of how parameter sensitivity and transferability change with increasing complexity using numerical experiments with models of different physical and geometric intricacy. Hydraulic models are a good example system with which to address such generic modelling questions as: (1) they have a strong physical basis; (2) there is only one set of equations to solve; (3) they require only topography and boundary conditions as input data; and (4) they typically require only a single free parameter, namely boundary friction. In terms of complexity required we show that for the problem of sub-critical floodplain inundation a number of codes of different dimensionality and resolution can be found to fit uncertain model validation data equally well, and that in this situation Occam's razor emerges as a useful logic to guide model selection. We find also find that model skill usually improves more rapidly with increases in model spatial resolution than increases in physical complexity, and that standard approaches to testing hydraulic models against laboratory data or analytical solutions may fail to identify this important fact. Lastly, we find that in benchmark testing studies significant differences can exist between codes with identical numerical solution techniques as a result of auxiliary choices regarding the specifics of model implementation that are frequently unreported by code developers. As a consequence, making sound
Turbulent flow structure response to a varying wall-roughness arrangement: a modelling study
Jakirlic, Suad; Krumbein, Benjamin; Fooroghi, Pourya; Magagnato, Franco; Frohnapfel, Bettina; Darmstadt Collaboration; Karlsruhe Collaboration
2016-11-01
Presently adopted approach to the modelling of rough surfaces relies on introducing an additional drag term in the appropriately 'filtered' Navier-Stokes equations, accounting for the form drag and blockage effects, the roughness elements exert on the flow. A non-dimensional drag function D(y) accounting for the shape of roughness elements is introduced. It is evaluated by applying a reference DNS of an open channel flow over a wall characterized by varying arrangement (aligned/staggered) of differently-shaped/sized roughness elements at a bulk Reynolds number Re =6500 by Fooroghi et al.. The prime objective of the present work is to assess the roughness model capability to predict mean velocities and turbulent intensities in conjunction with a recently formulated hybrid LES/RANS (Reynolds-Averaged Navier-Stokes) model, based on the Very Large Eddy Simulation (VLES) concept of Speziale. A seamless transition from RANS to LES is enabled depending on the ratio of the turbulent viscosities associated with the unresolved scales corresponding to the LES cut-off and those related to the turbulent properties of the VLES residual motion.
A First-Order Electroweak Phase Transition in the Standard Model from Varying Yukawas
Baldes, Iason; Servant, Geraldine
2016-01-01
We show that the dynamics responsible for the variation of the Yukawa couplings of the Standard Model fermions generically leads to a very strong first-order electroweak phase transition, assuming that the Yukawa couplings are large and of order 1 before the electroweak phase transition and reach their present value afterwards. There are good motivations to consider that the flavour structure could emerge during electroweak symmetry breaking, for example if the Froggatt-Nielsen field dynamics were linked to the Higgs field. In this paper, we do not need to assume any particular theory of flavour and show in a model-independent way how the nature of the electroweak phase transition is completely changed when the Standard Model Yukawas vary at the same time as the Higgs is acquiring its vacuum expectation value. The thermal contribution of the fermions creates a barrier between the symmetric and broken phase minima of the effective potential, leading to a first-order phase transition. This offers new routes for...
Institute of Scientific and Technical Information of China (English)
Farzin Adabi; Kayoomars Karami; Fereshte Felegary; Zohre Azarmi
2012-01-01
We study the entropy-corrected version of the holographic dark energy (HDE) model in the framework of modified Friedmann-Robertson-Walker cosmology.We consider a non-flat universe filled with an interacting viscous entropy-corrected HDE (ECHDE) with dark matter.Also included in our model is the case of the variable gravitational constant G.We obtain the equation of state and the deceleration parameters of the interacting viscous ECHDE.Moreover,we reconstruct the potential and the dynamics of the quintessence,tachyon,K-essence and dilaton scalar field models according to the evolutionary behavior of the interacting viscous ECHDE model with time-varying G.
Retrospective forecast of ETAS model with daily parameters estimate
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Parameter Estimates in Differential Equation Models for Population Growth
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
Dynamic Modeling and Parameter Identification of Power Systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
@@ The generator, the excitation system, the steam turbine and speed governor, and the load are the so called four key models of power systems. Mathematical modeling and parameter identification for the four key models are of great importance as the basis for designing, operating, and analyzing power systems.
Wheeler, Lorien; Mathias, Donovan; NASA Engineering Risk Assessment Team, NASA Asteroid Threat Assessment Project
2016-10-01
As an asteroid descends toward Earth, it deposits energy in the atmosphere through aerodynamic drag and ablation. Asteroid impact risk assessments rely on energy deposition estimates to predict blast overpressures and ground damage that may result from an airburst, such as the one that occurred over Chelyabinsk, Russia in 2013. The rates and altitudes at which energy is deposited along the entry trajectory depend upon how the bolide fragments, which in turn depends upon its internal structure and composition. In this work, an analytic asteroid fragmentation model has been developed to model the atmospheric breakup and resulting energy deposition of asteroids with a range of internal structures. The modeling approach combines successive fragmentation of larger independent pieces with aggregate debris clouds released with each fragmentation event. The model can vary the number and masses of fragments produced, the amount of mass released as debris clouds, and the size-strength scaling used to increase the robustness of smaller fragments. The initial asteroid body can be seeded with a distribution of independent fragment sizes amid a remaining debris mass to represent loose rubble pile conglomerations, or can be defined as a monolith with an outer regolith layer. This approach enables the model to represent a range of breakup behaviors and reproduce detailed energy deposition features such as multiple flares due to successive burst events, high-altitude regolith blow-off, or initial disruption of rubble piles followed by more energetic breakup of the constituent boulders. These capabilities provide a means to investigate sensitivities of ground damage to potential variations in asteroid structure.
Dynamic Load Model using PSO-Based Parameter Estimation
Taoka, Hisao; Matsuki, Junya; Tomoda, Michiya; Hayashi, Yasuhiro; Yamagishi, Yoshio; Kanao, Norikazu
This paper presents a new method for estimating unknown parameters of dynamic load model as a parallel composite of a constant impedance load and an induction motor behind a series constant reactance. An adequate dynamic load model is essential for evaluating power system stability, and this model can represent the behavior of actual load by using appropriate parameters. However, the problem of this model is that a lot of parameters are necessary and it is not easy to estimate a lot of unknown parameters. We propose an estimating method based on Particle Swarm Optimization (PSO) which is a non-linear optimization method by using the data of voltage, active power and reactive power measured at voltage sag.
Parameter Estimation for the Thurstone Case III Model.
Mackay, David B.; Chaiy, Seoil
1982-01-01
The ability of three estimation criteria to recover parameters of the Thurstone Case V and Case III models from comparative judgment data was investigated via Monte Carlo techniques. Significant differences in recovery are shown to exist. (Author/JKS)
Institute of Scientific and Technical Information of China (English)
Youlong XIA; Zong-Liang YANG; Paul L. STOFFA; Mrinal K. SEN
2005-01-01
Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.
State and Parameter Estimation for a Coupled Ocean--Atmosphere Model
Ghil, M.; Kondrashov, D.; Sun, C.
2006-12-01
The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.
Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.
Liu, Fei; Heiner, Monika; Yang, Ming
2016-01-01
Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.
Hoonhout, Bas M.; Vries, Sierd de
2016-08-01
Aeolian sediment transport is influenced by a variety of bed surface properties, like moisture, shells, vegetation, and nonerodible elements. The bed surface properties influence aeolian sediment transport by changing the sediment transport capacity and/or the sediment availability. The effect of bed surface properties on the transport capacity and sediment availability is typically incorporated through the velocity threshold. This approach appears to be a critical limitation in existing aeolian sediment transport models for simulation of real-world cases with spatiotemporal variations in bed surface properties. This paper presents a new model approach for multifraction aeolian sediment transport in which sediment availability is simulated rather than parameterized through the velocity threshold. The model can cope with arbitrary spatiotemporal configurations of bed surface properties that either limit or enhance the sediment availability or sediment transport capacity. The performance of the model is illustrated using four prototype cases, the simulation of two wind tunnel experiments from literature and a sensitivity analysis of newly introduced parameters.
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan D.; Stieglitz, Marc
2015-06-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal aspects of catchment hydrological variability.
Model parameter uncertainty analysis for an annual field-scale P loss model
Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie
2016-08-01
Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.
Demissie, Yonas; Valocchi, Albert; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Parameter estimation for groundwater models under uncertain irrigation data
Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
the parameters, including the noise terms. The parameter estimation method is a maximum likelihood method (ML) where the likelihood function is evaluated using a Kalman filter technique. The ML method estimates the parameters in a prediction error settings, i.e. the sum of squared prediction error is minimized....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
Transformations among CE–CVM model parameters for multicomponent systems
Indian Academy of Sciences (India)
B Nageswara Sarma; Shrikant Lele
2005-06-01
In the development of thermodynamic databases for multicomponent systems using the cluster expansion–cluster variation methods, we need to have a consistent procedure for expressing the model parameters (CECs) of a higher order system in terms of those of the lower order subsystems and to an independent set of parameters which exclusively represent interactions of the higher order systems. Such a procedure is presented in detail in this communication. Furthermore, the details of transformations required to express the model parameters in one basis from those defined in another basis for the same system are also presented.
Directory of Open Access Journals (Sweden)
S. Sedigh Marvasti
2015-07-01
Full Text Available We examine interannual variability of phytoplankton blooms in northwestern Arabian Sea and Gulf of Oman. Satellite data (SeaWIFS ocean color shows two climatological blooms in this region, a wintertime bloom peaking in February and a summertime bloom peaking in September. A pronounced anti-correlation between the AVISO sea surface height anomaly (SSHA and chlorophyll is found during the wintertime bloom. On a regional scale, interannual variability of the wintertime bloom is thus dominated by cyclonic eddies which vary in location from one year to another. These results were compared against the outputs from three different 3-D Earth System models. We show that two coarse (1° models with the relatively complex biogeochemistry (TOPAZ capture the annual cycle but neither eddies nor the interannual variability. An eddy-resolving model (GFDL CM2.6 with a simpler biogeochemistry (miniBLING displays larger interannual variability, but overestimates the wintertime bloom and captures eddy-bloom coupling in the south but not in the north. The southern part of the domain is a region with a much sharper thermocline and nutricline relatively close to the surface, in which eddies modulate diffusive nutrient supply to the surface (a mechanism not previously emphasized in the literature. We suggest that for the model to simulate the observed wintertime blooms within cyclones, it will be necessary to represent this relatively unusual nutrient structure as well as the cyclonic eddies. This is a challenge in the Northern Arabian Sea as it requires capturing the details of the outflow from the Persian Gulf.
A Loudness Model for Time-Varying Sounds Incorporating Binaural Inhibition
Directory of Open Access Journals (Sweden)
Brian C. J. Moore
2016-12-01
Full Text Available This article describes a model of loudness for time-varying sounds that incorporates the concept of binaural inhibition, namely, that the signal applied to one ear can reduce the internal response to a signal at the other ear. For each ear, the model includes the following: a filter to allow for the effects of transfer of sound through the outer and middle ear; a short-term spectral analysis with greater frequency resolution at low than at high frequencies; calculation of an excitation pattern, representing the magnitudes of the outputs of the auditory filters as a function of center frequency; application of a compressive nonlinearity to the output of each auditory filter; and smoothing over time of the resulting instantaneous specific loudness pattern using an averaging process resembling an automatic gain control. The resulting short-term specific loudness patterns are used to calculate broadly tuned binaural inhibition functions, the amount of inhibition depending on the relative short-term specific loudness at the two ears. The inhibited specific loudness patterns are summed across frequency to give an estimate of the short-term loudness for each ear. The overall short-term loudness is calculated as the sum of the short-term loudness values for the two ears. The long-term loudness for each ear is calculated by smoothing the short-term loudness for that ear, again by a process resembling automatic gain control, and the overall loudness impression is obtained by summing the long-term loudness across ears. The predictions of the model are more accurate than those of an earlier model that did not incorporate binaural inhibition.
A Loudness Model for Time-Varying Sounds Incorporating Binaural Inhibition.
Moore, Brian C J; Glasberg, Brian R; Varathanathan, Ajanth; Schlittenlacher, Josef
2016-01-01
This article describes a model of loudness for time-varying sounds that incorporates the concept of binaural inhibition, namely, that the signal applied to one ear can reduce the internal response to a signal at the other ear. For each ear, the model includes the following: a filter to allow for the effects of transfer of sound through the outer and middle ear; a short-term spectral analysis with greater frequency resolution at low than at high frequencies; calculation of an excitation pattern, representing the magnitudes of the outputs of the auditory filters as a function of center frequency; application of a compressive nonlinearity to the output of each auditory filter; and smoothing over time of the resulting instantaneous specific loudness pattern using an averaging process resembling an automatic gain control. The resulting short-term specific loudness patterns are used to calculate broadly tuned binaural inhibition functions, the amount of inhibition depending on the relative short-term specific loudness at the two ears. The inhibited specific loudness patterns are summed across frequency to give an estimate of the short-term loudness for each ear. The overall short-term loudness is calculated as the sum of the short-term loudness values for the two ears. The long-term loudness for each ear is calculated by smoothing the short-term loudness for that ear, again by a process resembling automatic gain control, and the overall loudness impression is obtained by summing the long-term loudness across ears. The predictions of the model are more accurate than those of an earlier model that did not incorporate binaural inhibition.
SPOTting Model Parameters Using a Ready-Made Python Package.
Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz
2015-01-01
The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
SPOTting Model Parameters Using a Ready-Made Python Package.
Directory of Open Access Journals (Sweden)
Tobias Houska
Full Text Available The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool, an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI. We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
Numerical modeling of piezoelectric transducers using physical parameters.
Cappon, Hans; Keesman, Karel J
2012-05-01
Design of ultrasonic equipment is frequently facilitated with numerical models. These numerical models, however, need a calibration step, because usually not all characteristics of the materials used are known. Characterization of material properties combined with numerical simulations and experimental data can be used to acquire valid estimates of the material parameters. In our design application, a finite element (FE) model of an ultrasonic particle separator, driven by an ultrasonic transducer in thickness mode, is required. A limited set of material parameters for the piezoelectric transducer were obtained from the manufacturer, thus preserving prior physical knowledge to a large extent. The remaining unknown parameters were estimated from impedance analysis with a simple experimental setup combined with a numerical optimization routine using 2-D and 3-D FE models. Thus, a full set of physically interpretable material parameters was obtained for our specific purpose. The approach provides adequate accuracy of the estimates of the material parameters, near 1%. These parameter estimates will subsequently be applied in future design simulations, without the need to go through an entire series of characterization experiments. Finally, a sensitivity study showed that small variations of 1% in the main parameters caused changes near 1% in the eigenfrequency, but changes up to 7% in the admittance peak, thus influencing the efficiency of the system. Temperature will already cause these small variations in response; thus, a frequency control unit is required when actually manufacturing an efficient ultrasonic separation system.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Modeling of the Feed-Motor Transient Current in End Milling by Using Varying-Coefficient Model
Directory of Open Access Journals (Sweden)
Mi Xiao
2015-01-01
Full Text Available In order to ensure the stability of the machining process, it is vital to control the machining condition during the milling process. While the feed-motor current is related to many physical variables, such as the cutting force and tool wear, we can indicate it as the key variables to monitoring the conditions of the milling process. A predictive model of the feed-motor current amplitude is established in this paper. The change regulation of the transient current amplitude during the milling process is investigated, and the effect of the spindle speed on the transient current amplitude is studied as well. Since the transient current amplitude is time-varying, the predictive model is a typical panel data type. In this case, the varying-coefficient model (VCM, a potential soft computing method, is applied to solve this predictive model. Then several experiments are conducted to evaluate the performance of VCM method. Results show that the predicted values match the experimental value well, and the correctness of the predictive model for transient current amplitude is also validated.
An Effective Parameter Screening Strategy for High Dimensional Watershed Models
Khare, Y. P.; Martinez, C. J.; Munoz-Carpena, R.
2014-12-01
Watershed simulation models can assess the impacts of natural and anthropogenic disturbances on natural systems. These models have become important tools for tackling a range of water resources problems through their implementation in the formulation and evaluation of Best Management Practices, Total Maximum Daily Loads, and Basin Management Action Plans. For accurate applications of watershed models they need to be thoroughly evaluated through global uncertainty and sensitivity analyses (UA/SA). However, due to the high dimensionality of these models such evaluation becomes extremely time- and resource-consuming. Parameter screening, the qualitative separation of important parameters, has been suggested as an essential step before applying rigorous evaluation techniques such as the Sobol' and Fourier Amplitude Sensitivity Test (FAST) methods in the UA/SA framework. The method of elementary effects (EE) (Morris, 1991) is one of the most widely used screening methodologies. Some of the common parameter sampling strategies for EE, e.g. Optimized Trajectories [OT] (Campolongo et al., 2007) and Modified Optimized Trajectories [MOT] (Ruano et al., 2012), suffer from inconsistencies in the generated parameter distributions, infeasible sample generation time, etc. In this work, we have formulated a new parameter sampling strategy - Sampling for Uniformity (SU) - for parameter screening which is based on the principles of the uniformity of the generated parameter distributions and the spread of the parameter sample. A rigorous multi-criteria evaluation (time, distribution, spread and screening efficiency) of OT, MOT, and SU indicated that SU is superior to other sampling strategies. Comparison of the EE-based parameter importance rankings with those of Sobol' helped to quantify the qualitativeness of the EE parameter screening approach, reinforcing the fact that one should use EE only to reduce the resource burden required by FAST/Sobol' analyses but not to replace it.
Characterizing urban vulnerability to heat stress using a spatially varying coefficient model.
Heaton, Matthew J; Sain, Stephan R; Greasby, Tamara A; Uejio, Christopher K; Hayden, Mary H; Monaghan, Andrew J; Boehnert, Jennifer; Sampson, Kevin; Banerjee, Deborah; Nepal, Vishnu; Wilhelmi, Olga V
2014-04-01
Identifying and characterizing urban vulnerability to heat is a key step in designing intervention strategies to combat negative consequences of extreme heat on human health. This study combines excess non-accidental mortality counts, numerical weather simulations, US Census and parcel data into an assessment of vulnerability to heat in Houston, Texas. Specifically, a hierarchical model with spatially varying coefficients is used to account for differences in vulnerability among census block groups. Socio-economic and demographic variables from census and parcel data are selected via a forward selection algorithm where at each step the remaining variables are orthogonalized with respect to the chosen variables to account for collinearity. Daily minimum temperatures and composite heat indices (e.g. discomfort index) provide a better model fit than other ambient temperature measurements (e.g. maximum temperature, relative humidity). Positive interactions between elderly populations and heat exposure were found suggesting these populations are more responsive to increases in heat. Copyright © 2014 Elsevier Ltd. All rights reserved.
Hertog, Maarten L. A. T. M.; Scheerlinck, Nico; Nicolaï, Bart M.
2009-01-01
When modelling the behaviour of horticultural products, demonstrating large sources of biological variation, we often run into the issue of non-Gaussian distributed model parameters. This work presents an algorithm to reproduce such correlated non-Gaussian model parameters for use with Monte Carlo simulations. The algorithm works around the problem of non-Gaussian distributions by transforming the observed non-Gaussian probability distributions using a proposed SKN-distribution function before applying the covariance decomposition algorithm to generate Gaussian random co-varying parameter sets. The proposed SKN-distribution function is based on the standard Gaussian distribution function and can exhibit different degrees of both skewness and kurtosis. This technique is demonstrated using a case study on modelling the ripening of tomato fruit evaluating the propagation of biological variation with time.
Energy Technology Data Exchange (ETDEWEB)
Hamimid, M., E-mail: Hamimid_mourad@hotmail.com [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Mimoune, S.M., E-mail: s.m.mimoune@mselab.org [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Feliachi, M., E-mail: mouloud.feliachi@univ-nantes.fr [IREENA-IUT, CRTT, 37 Boulevard de l' Universite, BP 406, 44602 Saint Nazaire Cedex (France)
2012-07-01
In this present work, the minor hysteresis loops model based on parameters scaling of the modified Jiles-Atherton model is evaluated by using judicious expressions. These expressions give the minor hysteresis loops parameters as a function of the major hysteresis loop ones. They have exponential form and are obtained by parameters identification using the stochastic optimization method 'simulated annealing'. The main parameters influencing the data fitting are three parameters, the pinning parameter k, the mean filed parameter {alpha} and the parameter which characterizes the shape of anhysteretic magnetization curve a. To validate this model, calculated minor hysteresis loops are compared with measured ones and good agreements are obtained.
Effects of model schematisation, geometry and parameter values on urban flood modelling.
Vojinovic, Z; Seyoum, S D; Mwalwaka, J M; Price, R K
2011-01-01
One-dimensional (1D) hydrodynamic models have been used as a standard industry practice for urban flood modelling work for many years. More recently, however, model formulations have included a 1D representation of the main channels and a 2D representation of the floodplains. Since the physical process of describing exchanges of flows with the floodplains can be represented in different ways, the predictive capability of different modelling approaches can also vary. The present paper explores effects of some of the issues that concern urban flood modelling work. Impacts from applying different model schematisation, geometry and parameter values were investigated. The study has mainly focussed on exploring how different Digital Terrain Model (DTM) resolution, presence of different features on DTM such as roads and building structures and different friction coefficients affect the simulation results. Practical implications of these issues are analysed and illustrated in a case study from St Maarten, N.A. The results from this study aim to provide users of numerical models with information that can be used in the analyses of flooding processes in urban areas.
Modeling hyporheic exchange and in-stream transport with time-varying transit time distributions
Ball, A.; Harman, C. J.; Ward, A. S.
2014-12-01
Transit time distributions (TTD) are used to understand in-stream transport and exchange with the hyporheic zone by quantifying the probability of water (and of dissolved material) taking time T to traverse the stream reach control volume. However, many studies using this method assume a TTD that is time-invariant, despite the time-variability of the streamflow. Others assume that storage is 'randomly sampled' or 'well-mixed' with a fixed volume or fixed exchange rate. Here we present a formulation for a time-variable TTD that relaxes both the time-invariant and 'randomly sampled' assumptions and only requires a few parameters. The framework is applied to transient storage, representing some combination of in-stream and hyporheic storage, along a stream reach. This approach does not assume that hyporheic and dead-zone storage is fixed or temporally-invariant, and allows for these stores to be sampled in more physically representative ways determined by the system itself. Instead of using probability distributions of age, probability distributions of storage (ranked by age) called Ω functions are used to describe how the off-stream storage is sampled in the outflow. Here the Ω function approach is used to describe hyporheic exchange during diurnal fluctuations in streamflow in a gaining reach of the H.J. Andrews Experimental Forest. The breakthrough curves of salt slugs injected four hours apart over a 28-hour period show a systematic variation in transit time distribution. This new approach allows us to relate these salt slug TTDs to a corresponding time-variation in the Ω function, which can then be related to changes in in-stream storage and hyporheic zone mobilization under varying flow conditions. Thus, we can gain insights into how channel storage and hyporheic exchange are changing through time without having to specify difficult to measure or unmeasurable quantities of our system, such as total storage.
Directory of Open Access Journals (Sweden)
PASĂRE Minodora Maria
2012-05-01
Full Text Available Results obtained from Vickers hardness tests were used for analytical modeling models Buckle, Jönsson, Hogmark. Ni-P electrodeposition were obtained by varying the elaboration time. The analytic models obtained by theoretical means, by applying the corresponding formulas to each model have been compared to the experimental results obtained at hardness tests.
Schiavazzi, Daniele E; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L
2017-03-01
Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. Copyright © 2016 John Wiley & Sons, Ltd.
MODELING OF FUEL SPRAY CHARACTERISTICS AND DIESEL COMBUSTION CHAMBER PARAMETERS
Directory of Open Access Journals (Sweden)
G. M. Kukharonak
2011-01-01
Full Text Available The computer model for coordination of fuel spray characteristics with diesel combustion chamber parameters has been created in the paper. The model allows to observe fuel sprays develоpment in diesel cylinder at any moment of injection, to calculate characteristics of fuel sprays with due account of a shape and dimensions of a combustion chamber, timely to change fuel injection characteristics and supercharging parameters, shape and dimensions of a combustion chamber. Moreover the computer model permits to determine parameters of holes in an injector nozzle that provides the required fuel sprays characteristics at the stage of designing a diesel engine. Combustion chamber parameters for 4ЧН11/12.5 diesel engine have been determined in the paper.
Mathematically Modeling Parameters Influencing Surface Roughness in CNC Milling
Directory of Open Access Journals (Sweden)
Engin Nas
2012-01-01
Full Text Available In this study, steel AISI 1050 is subjected to process of face milling in CNC milling machine and such parameters as cutting speed, feed rate, cutting tip, depth of cut influencing the surface roughness are investigated experimentally. Four different experiments are conducted by creating different combinations for parameters. In conducted experiments, cutting tools, which are coated by PVD method used in forcing steel and spheroidal graphite cast iron are used. Surface roughness values, which are obtained by using specified parameters with cutting tools, are measured and correlation between measured surface roughness values and parameters is modeled mathematically by using curve fitting algorithm. Mathematical models are evaluated according to coefficients of determination (R2 and the most ideal one is suggested for theoretical works. Mathematical models, which are proposed for each experiment, are estipulated.
Regionalization parameters of conceptual rainfall-runoff model
Osuch, M.
2003-04-01
Main goal of this study was to develop techniques for the a priori estimation parameters of hydrological model. Conceptual hydrological model CLIRUN was applied to around 50 catchment in Poland. The size of catchments range from 1 000 to 100 000 km2. The model was calibrated for a number of gauged catchments with different catchment characteristics. The parameters of model were related to different climatic and physical catchment characteristics (topography, land use, vegetation and soil type). The relationships were tested by comparing observed and simulated runoff series from the gauged catchment that were not used in the calibration. The model performance using regional parameters was promising for most of the calibration and validation catchments.
Extraction of Spatial Parameters from Classified LIDAR Data and Aerial Photograph for Sound Modeling
Biswas, S.; Lohani, B.
2012-07-01
Prediction of outdoor sound levels in 3D space is important for noise management, soundscaping etc. Sound levels at outdoor can be predicted using sound propagation models which need terrain parameters. The existing practices of incorporating terrain parameters into models are often limited due to inadequate data or inability to determine accurate sound transmission paths through a terrain. This leads to poor accuracy in modelling. LIDAR data and Aerial Photograph (or Satellite Images) provide opportunity to incorporate high resolution data into sound models. To realize this, identification of building and other objects and their use for extraction of terrain parameters are fundamental. However, development of a suitable technique, to incorporate terrain parameters from classified LIDAR data and Aerial Photograph, for sound modelling is a challenge. Determination of terrain parameters along various transmission paths of sound from sound source to a receiver becomes very complex in an urban environment due to the presence of varied and complex urban features. This paper presents a technique to identify the principal paths through which sound transmits from source to receiver. Further, the identified principal paths are incorporated inside the sound model for sound prediction. Techniques based on plane cutting and line tracing are developed for determining principal paths and terrain parameters, which use various information, e.g., building corner and edges, triangulated ground, tree points and locations of source and receiver. The techniques developed are validated through a field experiment. Finally efficacy of the proposed technique is demonstrated by developing a noise map for a test site.
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... distribution. Methods from structural reliability analysis are used to model the uncertainties and to assess the reliability for fatigue failure. Maximum Likelihood and Least Square estimation techniques are used to estimate fatigue life distribution parameters....
Environmental Transport Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. Wasiolek
2004-09-10
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis
An approach to measure parameter sensitivity in watershed hydrological modelling
Hydrologic responses vary spatially and temporally according to watershed characteristics. In this study, the hydrologic models that we developed earlier for the Little Miami River (LMR) and Las Vegas Wash (LVW) watersheds were used for detail sensitivity analyses. To compare the...
Inhalation Exposure Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
K. Rautenstrauch
2004-09-10
This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.
Environmental Transport Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. A. Wasiolek
2003-06-27
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699
Construction of constant-Q viscoelastic model with three parameters
Institute of Scientific and Technical Information of China (English)
SUN Cheng-yu; YIN Xing-yao
2007-01-01
The popularly used viscoelastic models have some shortcomings in describing relationship between quality factor (Q) and frequency, which is not consistent with the observation data. Based on the theory of viscoelasticity, a new approach to construct constant-Q viscoelastic model in given frequency band with three parameters is developed. The designed model describes the frequency-independence feature of quality factor very well, and the effect of viscoelasticity on seismic wave field can be studied relatively accurate in theory with this model. Furthermore, the number of required parameters in this model has been reduced fewer than that of other constant-Q models, this can simplify the solution of the viscoelastic problems to some extent. At last, the accuracy and application range have been analyzed through numerical tests. The effect of viscoelasticity on wave propagation has been briefly illustrated through the change of frequency spectra and waveform in several different viscoelastic models.
Modular use of human body models of varying levels of complexity: Validation of head kinematics.
Decker, William; Koya, Bharath; Davis, Matthew L; Gayzik, F Scott
2017-05-29
. Scores at lower g levels yielded similar results, 0.667 (M50-O), 0.675 (M50-OS), and 0.710 (M50-OS+B) for the 8g frontal impact. The 7g lateral simulations also compared fairly with an average ISO score of 0.565 for the M50-O, 0.634 for the M50-OS, and 0.606 for the M50-OS+B. The three HBMs experienced similar head and neck motion in the frontal simulations, but the M50-O predicted significantly greater head rotation in the lateral simulation. The greatest departure from the detailed occupant models were noted in lateral flexion, potentially indicating the need for further study. Precise modeling of the belt system however was limited by available data. A sensitivity study of these parameters in the frontal condition showed that belt slack and muscle activation have a modest effect on the ISO score. The reduction in computation time of the M50-OS+B reduces the burden of high computational requirements when handling detailed HBMs. Future work will focus on harmonizing the lateral head response of the models and studying localized injury criteria within the brain from the M50-O and M50-OS+B.
Global-scale regionalization of hydrologic model parameters
Beck, Hylke E.; van Dijk, Albert I. J. M.; de Roo, Ad; Miralles, Diego G.; McVicar, Tim R.; Schellekens, Jaap; Bruijnzeel, L. Adrian
2016-05-01
Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10,000 km2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Köppen-Geiger climate types and even for evaluation catchments > 5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via www.gloh2o.org.
Bayesian parameter estimation for nonlinear modelling of biological pathways
Directory of Open Access Journals (Sweden)
Ghasemi Omid
2011-12-01
Full Text Available Abstract Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC method. We applied this approach to the biological pathways involved in the left ventricle (LV response to myocardial infarction (MI and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly
Mirror symmetry for two-parameter models, 1
Candelas, Philip; Font, A; Katz, S; Morrison, Douglas Robert Ogston; Candelas, Philip; Ossa, Xenia de la; Font, Anamaria; Katz, Sheldon; Morrison, David R.
1994-01-01
We study, by means of mirror symmetry, the quantum geometry of the K\\"ahler-class parameters of a number of Calabi-Yau manifolds that have $b_{11}=2$. Our main interest lies in the structure of the moduli space and in the loci corresponding to singular models. This structure is considerably richer when there are two parameters than in the various one-parameter models that have been studied hitherto. We describe the intrinsic structure of the point in the (compactification of the) moduli space that corresponds to the large complex structure or classical limit. The instanton expansions are of interest owing to the fact that some of the instantons belong to families with continuous parameters. We compute the Yukawa couplings and their expansions in terms of instantons of genus zero. By making use of recent results of Bershadsky et al. we compute also the instanton numbers for instantons of genus one. For particular values of the parameters the models become birational to certain models with one parameter. The co...