Browsing by Subject "Uncertainty analysis"

Now showing 1 - 14 of 14

Open Access
A capacitated hub location problem under hose demand uncertainty
(Elsevier, 2017) Meraklı, M.; Yaman, H.
In this study, we consider a capacitated multiple allocation hub location problem with hose demand uncertainty. Since the routing cost is a function of demand and capacity constraints are imposed on hubs, demand uncertainty has an impact on both the total cost and the feasibility of the solutions. We present a mathematical formulation of the problem and devise two different Benders decomposition algorithms. We develop an algorithm to solve the dual subproblem using complementary slackness. In our computational experiments, we test the efficiency of our approaches and we analyze the effects of uncertainty. The results show that we obtain robust solutions with significant cost savings by incorporating uncertainty into our problem.
Open Access
A gPC-based approach to uncertain transonic aerodynamics
(2010) Simon F.; Guillen P.; Sagaut P.; Lucor, D.
The present paper focus on the stochastic response of a two-dimensional transonic airfoil to parametric uncertainties. Both the freestream Mach number and the angle of attack are considered as random parameters and the generalized Polynomial Chaos (gPC) theory is coupled with standard deterministic numerical simulations through a spectral collocation projection methodology. The results allow for a better understanding of the flow sensitivity to such uncertainties and underline the coupling process between the stochastic parameters. Two kinds of non-linearities are critical with respect to the skin-friction uncertainties: on one hand, the leeward shock movement characteristic of the supercritical profile and on the other hand, the boundary-layer separation on the aft part of the airfoil downstream the shock. The sensitivity analysis, thanks to the Sobol' decomposition, shows that a strong non-linear coupling exists between the uncertain parameters. Comparisons with the one-dimensional cases demonstrate that the multi-dimensional parametric study is required to get the correct shape and magnitude of the standard deviation distributions of the flow quantities such as pressure and skin-friction. © 2009 Elsevier B.V.
Open Access
Inflation uncertainty and interest rates: Is the Fisher relation universal?
(Routledge, 2007) Berument, Hakan; Ceylan, N. B.; Olgun, H.
This paper tests the validity of the Fisher hypothesis, which establishes a positive relation between interest rates and expected inflation, for the G7 countries and 45 developing economies. For this purpose, we estimate a version of the GARCH specification of the hypothesis for all countries included in the sample. We also test the augmented Fisher relation by including the inflation uncertainty in the equation. The simple Fisher relation holds in all G7 countries but in only 23 developing countries. There is a positive and statistically significant relationship between interest rates and inflation uncertainty for six of the G7 and 18 of the developing countries and this relationship is negative for seven developing countries.
Open Access
Is Roger Federer more loss averse than Serena Williams?
(Routledge, 2017) Anbarci, N.; Arin, K. P.; Okten, C.; Zenker, C.
Using data from the high-stakes 2013 Dubai professional tennis tournament, we find that, compared with a tied score, (i) male players have a higher serve speed and thus exhibit more effort when behind in score, and their serve speeds get less sensitive to losses or gains when score difference gets too large, and (ii) female players do not change their serve speed when behind, while serving slower when ahead. Thus, male players comply more with Prospect Theory exhibiting more loss aversion and reflection effect. Our results are robust to controlling for player fixed effects and characteristics with player random effects. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Open Access
Multi input dynamical modeling of heat flow with uncertain diffusivity parameter
(Taylor & Francis, 2003) Efe, M. Ö.; Özbay, Hitay
This paper focuses on the multi-input dynamical modeling of one-dimensional heat conduction process with uncertainty on thermal diffusivity parameter. Singular value decomposition is used to extract the most significant modes. The results of the spatiotemporal decomposition have been used in cooperation with Galerkin projection to obtain the set of ordinary differential equations, the solution of which synthesizes the temporal variables. The spatial properties have been generalized through a series of test cases and a low order model has been obtained. Since the value of the thermal diffusivity parameter is not known perfectly, the obtained model contains uncertainty. The paper describes how the uncertainty is modeled and how the boundary conditions are separated from the remaining terms of the dynamical equations. The results have been compared with those obtained through analytic solution. © Taylor and Francis Ltd.
Open Access
Reactive footstep planning for a planar spring mass hopper
(IEEE, 2009-10) Arslan, Ömür; Saranlı, Uluç; Morgül, Ömer
The main driving force behind research on legged robots has always been their potential for high performance locomotion on rough terrain and the outdoors. Nevertheless, most existing control algorithms for such robots either make rigid assumptions about their environments (e.g flat ground), or rely on kinematic planning at low speeds. Moreover, the traditional separation of planning from control often has negative impact on the robustness of the system against model uncertainty and environment noise. In this paper, we introduce a new method for dynamic, fully reactive footstep planning for a simplified planar spring-mass hopper, a frequently used model for running behaviors. Our approach is based on a careful characterization of the model dynamics and an associated deadbeat controller, used within a sequential composition framework. This yields a purely reactive controller with a very large, nearly global domain of attraction that requires no explicit replanning during execution. Finally, we use a simplified hopper in simulation to illustrate the performance of the planner under different rough terrain scenarios and show that it is extremely robust to both model uncertainty and measurement noise. © 2009 IEEE.
Open Access
Reactive planning and control of planar spring-mass running on rough terrain
(Institute of Electrical and Electronics Engineers, 2012) Arslan, Ö.; Saranlı, U.
An important motivation for work on legged robots has always been their potential for high-performance locomotion on rough terrain. Nevertheless, most existing control algorithms for such robots either make rigid assumptions about their environments or rely on kinematic planning at low speeds. Moreover, the traditional separation of planning from control often has negative impact on the robustness of the system. In this paper, we introduce a new method for dynamic, fully reactive footstep planning for a planar spring-mass hopper, based on a careful characterization of the model dynamics and the design of an associated deadbeat controller, used within a sequential composition framework. This yields a purely reactive controller with a large domain of attraction that requires no explicit replanning during execution. We show in simulation that plans constructed for a simplified dynamic model can successfully control locomotion of a more complete model across rough terrain. We also characterize the performance of the planner over rough terrain and show that it is robust against both model uncertainty and measurement noise without replanning. © 2012 IEEE.
Open Access
Robust antiwindup compensation for high-precision tracking of a piezoelectric nanostage
(Institute of Electrical and Electronics Engineers Inc., 2016) Liu, P.; Yan, P.; Zhang Z.; Özbay, Hitay
Ultrahigh-precision tracking in nanomanipulations poses major challenges for mechanical design as well as servo control, due to the general confliction between the precision requirement and large stroke tracking. The situation is further complicated by input saturation, which is almost inevitable for microactuators. This paper presents a novel control architecture combining a parallel internal-model-based tracking design and a robust antiwindup control structure, such that asymptotic tracking can be achieved for nanoservo systems in the presence of saturation nonlinearity and model uncertainties. For the augmented system with internal-model dynamics, an I/O-based equivalent representation from control (free of saturation) to system output is derived by incorporating the dead-zone nonlinearity, saturation compensation blocks, as well internal-model units. The robustness condition on the saturation compensator is also derived based on the sector bound criterion and an H∞-optimal design is developed accordingly. The proposed robust antiwindup tracking control architecture is deployed on a customize-designed nanostage driven by a piezoelectric (PZT) actuator, where numerical simulations and real-time experiments demonstrate excellent tracking performance and saturation compensation capability, achieving tracking precision error less than 0.23%.
Open Access
A simulation-based support tool for data-driven decision making: operational testing for dependence modeling
(IEEE, 2014) Biller, B.; Akçay, Alp; Çorlu, C.; Tayur, S.
Dependencies occur naturally between input processes of many manufacturing and service applications. When the dependence parameters are known with certainty, the failure to factor the dependencies into decisions is well known to waste significant resources in system management. Our focus is on the case of unknown dependence parameters that must be estimated from finite amounts of historical input data. In this case, the estimates of the unknown dependence parameters are random variables and simulations are designed to account for the dependence parameter uncertainty to better support the data-driven decision making. The premise of our paper is that there are certain cases in which the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in the simulation. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence, despite the uncertainty around its estimate, in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context by considering an intermittent demand process with correlated demand size and number of interdemand periods. We propose two new finite-sample hypothesis tests to serve as the decision support tools determining when to ignore the correlation and when to account for the correlation together with the uncertainty around its estimate. We show that a statistical test accounting for the expected cost of correlation parameter uncertainty tends to reject the independence assumption less frequently than a statistical test which only considers the sampling distribution of the correlation-parameter estimator. The use of these tests is illustrated with examples and insights are provided into operational testing for dependence modeling.
Open Access
Structured least squares problems and robust estimators
(IEEE, 2010-10-22) Pilanci, M.; Arıkan, Orhan; Pinar, M. C.
A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new analytic formulation is developed in terms of the gradient flow of the residual norm to analyze and provide estimates to the regression. The presented analysis enables us to establish theoretical performance guarantees to compare with existing methods and also offers a criterion to choose the regularization parameter autonomously. Theoretical results and simulations in applications such as blind identification, multiple frequency estimation and deconvolution show that the proposed technique outperforms alternative methods in mean-squared error for a significant range of signal-to-noise ratio values.
Open Access
The time-varying effect of inflation uncertainty on inflation for Turkey
(Routledge, 2017) Varlik, S.; Ulke, V.; Berument, Hakan
We investigate the effect of inflation uncertainty on inflation from January 1982 through March 2016 for Turkey by using the Stochastic Volatility in Mean model with time-varying parameters. Our empirical evidence from consumer price index (CPI) inflation suggests that the observed positive relationship between inflation and inflation uncertainty is not robust. This positive relationship diminishes after 2002. This finding is valid for all five subcomponents of CPI inflation; however, for Health Services, Transportation Services, and Recreational and Cultural Services, an inflation-positive association is reported after 2010. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Open Access
Uncertainty analysis of cutting force coefficients during micromilling of titanium alloy
(2017-09) Gözü, Erman
Force modeling based on process input parameters is usually considered as the first step in process modeling. Predicting process forces in micromilling is dif- ficult due to complex interaction between the cutting edge and the work material, size effect, and process dynamics. This study describes the application of Bayesian inference to identify force coefficients in the micromilling process. The Metropolis-Hastings (MH) algorithm Markov chain Monte Carlo (MCMC) approach has been used to identify probability distributions of cutting, edge, and ploughing force coefficients based on experimental measurements and a mechanistic model of micromilling. The Bayesian inference scheme allows for predicting the upper and lower limits of micromilling forces, providing useful information about stability boundary calculations and robust process optimization. In the first part, experiments are performed to investigate the in uence of micromilling process parameters on machining forces, tool edge condition, and surface texture. Built-up edge formation is observed to have a significant in uence on the process outputs in micromilling of titanium alloy Ti6Al4V. In the second part, Bayesian inference is applied to model micromilling forces. The effectiveness of employing Bayesian inference in micromilling force modeling considering special machining cases is discussed. In the third part, finite element simulation of machining processes is employed and process outputs are used to update our knowledge about force coefficients. As a result of uncertainty analysis, the mean and standard deviations of the micromilling forces can be estimated. Bayesian inference can be useful since previous evidence or expertise is insufficient, or when obtaining the related information requires costly and time-consuming machining experiments.
Open Access
Uncertainty analysis of force coefficients during micromilling of titanium alloy
(Springer, 2017) Gözü, E.; Karpat, Y.
Predicting process forces in micromilling is difficult due to complex interaction between the cutting edge and the work material, size effect, and process dynamics. This study describes the application of Bayesian inference to identify force coefficients in the micromilling process. The Metropolis-Hastings (MH) algorithm Markov chain Monte Carlo (MCMC) approach has been used to identify probability distributions of cutting, edge, and ploughing force coefficients based on experimental measurements and a mechanistic model of micromilling. The Bayesian inference scheme allows for predicting the upper and lower limits of micromilling forces, providing useful information about stability boundary calculations and robust process optimization. In the first part of the paper, micromilling experiments are performed to investigate the influence of micromilling process parameters on machining forces, tool edge condition, and surface texture. Under the experimental conditions used in this study, built-up edge formation is observed to have a significant influence on the process outputs in micromilling of titanium alloy Ti6Al4V. In the second part, Bayesian inference was explained in detail and applied to model micromilling force prediction. The force predictions are validated with the experimental measurements. The paper concludes with a discussion of the effectiveness of employing Bayesian inference in micromilling force modeling considering special machining cases.
Open Access
Variable structure control in active queue management for TCP with ECN
(IEEE, 2003) Yan, P.; Gao, Y.; Özbay, Hitay
It has been shown that the TCP connections through the congested routers can be modeled as a feedback dynamic system. In this paper, we design a variable structure (VS) based control scheme in active queue management (AQM) supporting explicit congestion notification (ECN). By analyzing the robustness and performance of the control scheme for nonlinear TCP/AQM model, we show that the proposed design has good performance and robustness with respect to the uncertainties of the round-trip time (RTT) and the number of active TCP sessions, which are central to the notion of AQM. Implementation issues are discussed and ns simulations are provided to validate the design and compare its performance to other peer schemes' in different scenarios. The results show that the proposed design significantly outperforms the peer AQM schemes in terms of packet loss ratio, throughput and buffer fluctuation.