Approximate Dynamic Model Research Articles

Characterization of adhesive joints under high-speed normal impact: Part II – Numerical studies

This two-part article presents an experimental method with numerical verification of the characterization of adhesive bonds under normal impact loading. Part I presented the experimentally measured performance of static and impact behavior of two adhesives (a methacrylate and an epoxy). Part II presents the numerical modeling of the static and dynamic tests performed in Part I. The finite element simulations are used to determine approximate dynamic material modeling parameters. The simulations are also useful in estimating certain critical performance aspects of the adhesive for the proposed impact test such as the estimation of contact force time histories, the dynamic stress time history profiles within the adhesive, and to the various energy components in the adhesives and adherends.

Government Bond Market Integration in ASEAN Countries

The development and the integration of the bond market is becoming an important policy issue in ASEAN countries. We investigate government bond market integration in four ASEAN countries. We first decompose yields in ASEAN countries and the United States into global and regional factors using the approximate dynamic factor model. Next, we employ the dynamic conditional correlation method to find that regional markets have been integrated in the sense that their yields are highly and positively correlated with the common regional factor. We also find that the correlation between the global factor and the yield has different signs in different countries. Therefore, we use the pooled mean estimation method to investigate the determinants that make the correlation positive in some countries and negative in others. We find that public interest payments is an important determinant and discover a threshold that depends on public interest payments. The global factor has a significantly negative effect on the yield spread when public interest payments are above the threshold value. From above results, we can conclude that market discipline has been operating in the four ASEAN government bond markets in the sense that investors discriminate between the creditworthiness of the governments’ bonds by focusing on the public interest payments.

Understanding Consumer Dynamic Decision Making Under Competing Loyalty Programs

We develop an incentive-aligned experimental paradigm to study how consumer purchase dynamics are affected by the interplay between the loyalty programs and the pricing and promotional strategies of competing firms. In our experiment, subjects made sequential choices between two competing airlines in a stylized frequent traveler task for which an optimal dynamic decision policy can be numerically computed. We find that, on average, subjects are able to partially realize the long-term benefits from loyalty programs, though they are most sensitive to prices. We also find that the preferences and the levels of bounded rationality of our subjects depend on the nature of the competitive environment, the particular state of each decision scenario, and the type of optimal action. Accordingly, we use an approximate dynamic programming model to incorporate boundedly rational decision making. The model classifies subjects into five segments that exhibit variation in their performance and decision strategies. Importantly, we find that the overall market outcome and the performance of each firm are influenced by both the competitive environment and the assumption on the extent of consumer optimality.

An LP based approximate dynamic programming model to address airline overbooking under cancellation, refund and no-show

In this paper we simultaneously address four constraints relevant to airline revenue management problem: flight cancellation, customer no-shows, overbooking, and refunding. We develop a linear program closely related to the dynamic program formulation of the problem, which we later use to approximate the optimal decision rule for rejecting or accepting customers. First, we give a novel proof that the optimal objective function of this linear program is always an upper bound for the dynamic program. Secondly, we construct a decision rule based on this linear program and prove that it is asymptotically optimal under certain circumstances. Finally, using Monte Carlo simulation, we demonstrate that, numerically, the result of the linear programming policy presented in this paper has a short distance to the upper bound of the optimal answer, which makes it a fairly good approximate answer to the intractable dynamic program.

Multi-objective Hierarchical Optimal Control for Quadruped Rescue Robot

Timely and efficient rescue missions in disasters such as nuclear accidents, fires, and earthquakes require operating robots to work under high payload. This paper treats the problem of optimal planning and control of a walking-trot gait for a hydraulically actuated quadruped robot that has been developed in a collaborative project of the Departments of Mechanical Engineering and Automation at Shanghai Jiao Tong University. To mitigate the challenges of working with high-fidelity models of this highly complex mechanical system, a kinematics and approximate dynamics model is proposed that is shown to provide an accurate description of the walking motions we wish to study while at the same time being simple enough to support analysis. The proposed model is used to design limb movements in a walking trot gait that balance the trade off between energy consumption and the speed of execution of a desired mission. Bezier curves of different orders are used to design trajectories of the robot’s limbs. We verify the accuracy of the proposed model through experiments with the actual robot. We apply hierarchical optimization method to minimize the energy consumption, and we discuss a Pareto optimal solution that trades off mission duration and energy consumption. In the end perform several experiments to verify the effectiveness and superiority of the optimal algorithm we proposed.

Undamped vibration of fibre-reinforced polymer overwrapped pipes under fluid-hammer conditions

AN APPROXIMATE DYNAMIC MODEL describing the undamped radial vibration of thin-walled steel pipes with an overwrap of laminated fibre-reinforced polymer (FRP), due to fluid-hammer conditions, is derived. The derived model is an uncoupled model, in which the elastic properties of the pipe and fluid are used to estimate the properties of the fluid-hammer-induced pressure wave. This pressure wave is used as the exciting load in a dynamic analysis of the pipe wall. The derived governing equation is solved analytically by utilizing the properties of Fourier series. The model is implemented on a representative example. It is observed that increasing the number of FRP laminae may lead to larger radial deflections, because the natural frequency of the pipe is significantly altered.

Accounting for Macrofinancial Fluctuations and Turbulence

This paper investigates the sources of macrofinancial fluctuations and turbulence within the framework of an approximate linear dynamic stochastic general equilibrium model of the world economy, augmented with structural shocks exhibiting potentially asymmetric generalized autoregressive conditional heteroskedasticity. Very strong evidence of asymmetric autoregressive conditional heteroskedasticity is found, providing a basis for jointly decomposing the levels and volatilities of key macrofinancial variables into time varying contributions from sets of shocks. Risk premia shocks are estimated to contribute disproportionately to cyclical output fluctuations and turbulence during swings in financial conditions, across the fifteen largest national economies in the world.

Accounting for Macrofinancial Fluctuations and Turbulence

This paper investigates the sources of macrofinancial fluctuations and turbulence within the framework of an approximate linear dynamic stochastic general equilibrium model of the world economy, augmented with structural shocks exhibiting potentially asymmetric generalized autoregressive conditional heteroskedasticity. Very strong evidence of asymmetric autoregressive conditional heteroskedasticity is found, providing a basis for jointly decomposing the levels and volatilities of key macrofinancial variables into time varying contributions from sets of shocks. Risk premia shocks are estimated to contribute disproportionately to cyclical output fluctuations and turbulence during swings in financial conditions, across the fifteen largest national economies in the world.

Dynamic phasor based frequency scanning for grid-connected power electronic systems

Frequency scanning is a method of obtaining the frequency response of a system by injecting a small-amplitude wide-band signal as an input in a time domain simulation of the system. This is an alternative to analytical derivation of small-signal models, especially for complex grid-connected power electronic systems (PESs). These models are required for the study of adverse interaction of PES with lightly damped oscillatory modes in a power system. The use of the frequency scans for conventional small-signal stability analysis is predicated upon the time-invariance of the underlying model. Since PES are generally time-periodic, time-invariance may be achieved in some transformed variables. Although the DQ transformation is suitable in many situations, it is not so for systems with low-order harmonics, individual-phase schemes, unbalanced or single-phase systems, and PES with negative-sequence controllers. This paper proposes the use of dynamic phasor variables in such situations since the underlying model in these variables is time-invariant. The procedure for dynamic phasor based scanning is, however, intricate because wide-band signal injection results in the simultaneous presence of harmonic dynamic phasor components. The paper outlines this procedure and presents illustrative case studies of Thyristor Controlled Series Compensator (TCSC) and STATCOM. For the TCSC, a comparison of the frequency response obtained from the scanning method and the one obtained from an approximate analytical dynamic phasor model is also presented .

First-principles quantum dynamical theory for the dissociative chemisorption of H2O on rigid Cu(111).

Despite significant progress made in the past decades, it remains extremely challenging to investigate the dissociative chemisorption dynamics of molecular species on surfaces at a full-dimensional quantum mechanical level, in particular for polyatomic-surface reactions. Here we report, to the best of our knowledge, the first full-dimensional quantum dynamics study for the dissociative chemisorption of H2O on rigid Cu(111) with all the nine molecular degrees of freedom fully coupled, based on an accurate full-dimensional potential energy surface. The full-dimensional quantum mechanical reactivity provides the dynamics features with the highest accuracy, revealing that the excitations in vibrational modes of H2O are more efficacious than increasing the translational energy in promoting the reaction. The enhancement of the excitation in asymmetric stretch is the largest, but that of symmetric stretch becomes comparable at very low energies. The full-dimensional characterization also allows the investigation of the validity of previous reduced-dimensional and approximate dynamical models.

Delayed Purchase Options in Single-Leg Revenue Management

Many airline reservation systems offer the commitment option to their potential passengers. This option allows passengers to reserve a seat for a fixed duration before making a final purchase decision. In this study, we develop single-leg revenue management models that consider such contingent commitment decisions. We start with a dynamic programming model of this problem. This model is computationally intractable as it requires storing a multidimensional state space because of bookkeeping of the committed seats. To alleviate this difficulty, we propose an alternate dynamic programming formulation that uses an approximate model of how the contingent commitments behave and we show how to extract a capacity allocation policy from the approximate dynamic programming formulation. In addition, we present a deterministic linear programming model that gives an upper bound on the optimal expected revenue from the intractable dynamic programming model. As the problem size becomes large in terms of flight capacity and the expected number of arrivals, we demonstrate an asymptotic lower bound for the deterministic linear programming model. Our extensive numerical study indicates that offering commitment options can noticeably increase potential revenue even though offering a contingent commitment option may not always be in the best interest of the airline. Also, our results show that the proposed approximate dynamic programming model coordinates capacity allocation and commitment decisions quite well.

Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models

We introduce an approximate dynamic factor model for modeling and forecasting large panels of realized volatilities. Since the model is estimated by means of principal components and low‐dimensional maximum likelihood, it does not suffer from the curse of dimensionality. We apply the model to a panel of 90 daily realized volatilities pertaining to S&P 100 from January 2001 to December 2008. Results show that our model is able to capture the stylized facts of panels of volatilities (comovements, clustering, long memory, dynamic volatility, skewness and heavy tails), and that it performs fairly well in forecasting, in particular in periods of turmoil, in which it outperforms standard univariate benchmarks. Copyright © 2015 John Wiley & Sons, Ltd.

Approximate dynamic programming modeling for a typical blood platelet bank

This paper introduces a workable model for the establishment of an inventory bank holding perishable blood platelets with a short shelf life. The model considers a blood platelet bank with eight blood types, stochastic demand, stochastic supply, and deterministic lead time. The model is formulated using approximate dynamic programming. The model is evaluated in terms of four measures of effectiveness: blood platelet shortage, outdating, inventory level, and reward gained. Moreover, several alternative inventory control policies are analyzed. The order quantity decision is taken using a news-vendor model. In addition, the variation of the O− percentage is studied. This study confirms that the blood platelet bank reward can be maximized by operating at the optimal inventory level, thereby minimizing the number of outdated units as well as shortages. In addition, the suitable O− percentage within the blood platelet bank inventory was studied. As the O− blood type inventory levels increase to 40%, shortages drop from 3.9% to 1.5%. Outdated units drop from 4.6% to 1.8%. Furthermore, when the order quantity is received twice a day, shortages drop to 1.8% and outdated units drop to 2.1%.

A Unifying Approximate Dynamic Programming Model for the Economic Lot Scheduling Problem

We formulate the well-known economic lot scheduling problem (ELSP) with sequence-dependent setup times and costs as a semi-Markov decision process. Using an affine approximation of the bias function, we obtain a semi-infinite linear program determining a lower bound for the minimum average cost rate. Under a very mild condition, we can reduce this problem to a relatively small convex quadratically constrained linear problem by exploiting the structure of the objective function and the state space. This problem is equivalent to the lower bound problem derived by Dobson [Dobson G (1992) The cyclic lot scheduling problem with sequence-dependent setups. Oper. Res. 40:736–749] and reduces to the well-known lower bound problem introduced in Bomberger [Bomberger EE (1966) A dynamic programming approach to a lot size scheduling problem. Management Sci. 12:778–784] for sequence-dependent setups. We thus provide a framework that unifies previous work, and opens new paths for future research on tighter lower bounds and dynamic heuristics.

Dynamics and robust adaptive control of a deployable boom for a space probe

Various spacecraft missions have driven the need for lighter, stronger deployable structures, which help to hold instruments, such as a magnetometer, away from the spacecraft to avoid the disturbance caused by remanence of the spacecraft body. In this paper, we will present a type of deployable boom for small spacecraft, which is characterised by a small stowed volume, light weight and a large magnification ratio. Because the actual parameters of the deployable boom are all nonlinear, modelling of the boom becomes a key point. Considering the uncertainties in the model parameters, an approximate dynamic model with uncertain parameters is formulated by classifying the uncertainties into different types, including constant parametric, variable parametric and nonparametric uncertainties. Then, a robust adaptive control strategy is proposed to compensate for or reject these uncertainties separately; a feed-forward and feedback controller is designed to reduce the errors between the desired and the real trajectories, an adaptive controller aims at compensating for constant parametric uncertainties and a robust controller is used to reject the variable parametric and nonparametric model uncertainties. Thus, a robust adaptive control strategy does not rely on the exact dynamic model and can completely compensate for or reject the effect of model uncertainties. Finally, the simulation results show that the proposed control law is perfectly adequate for the deployable boom.

Forecasting with approximate dynamic factor models: The role of non-pervasive shocks

This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.

A dynamic hurdle model for zeroinflated panel count data

This article proposes an approximate conditional dynamic finite mixture hurdle model for panel count data with excess of zeros and endogenous initial conditions. We provide parameter estimates by using the Expectation-Maximization (EM) algorithm in a Nonparametric Maximum Likelihood (NPML) framework. An application to a unique data set on traffic violation counts of a subpopulation of Italian drivers is given.

Consistent factor estimation in dynamic factor models with structural instability

This paper considers the estimation of approximate dynamic factor models when there is temporal instability in the factor loadings. We characterize the type and magnitude of instabilities under which the principal components estimator of the factors is consistent and find that these instabilities can be larger than earlier theoretical calculations suggest. We also discuss implications of our results for the robustness of regressions based on the estimated factors and of estimates of the number of factors in the presence of parameter instability. Simulations calibrated to an empirical application indicate that instability in the factor loadings has a limited impact on estimation of the factor space and diffusion index forecasting, whereas estimation of the number of factors is more substantially affected.

Policy Analysis and Forecasting in the World Economy: A Panel Dynamic Stochastic General Equilibrium Approach

This paper develops a structural macroeconometric model of the world economy, disaggregated into thirty five national economies. This panel unobserved components model encompasses an approximate linear panel dynamic stochastic general equilibrium model featuring a monetary transmission mechanism, a fiscal transmission mechanism, and extensive macrofinancial linkages, both within and across economies. A variety of monetary policy analysis, fiscal policy analysis, spillover analysis, and forecasting applications of the estimated model are demonstrated, based on a Bayesian framework for conditioning on judgment.

Policy Analysis and Forecasting in the World Economy: A Panel Dynamic Stochastic General Equilibrium Approach

This paper develops a structural macroeconometric model of the world economy, disaggregated into thirty five national economies. This panel unobserved components model encompasses an approximate linear panel dynamic stochastic general equilibrium model featuring a monetary transmission mechanism, a fiscal transmission mechanism, and extensive macrofinancial linkages, both within and across economies. A variety of monetary policy analysis, fiscal policy analysis, spillover analysis, and forecasting applications of the estimated model are demonstrated, based on a Bayesian framework for conditioning on judgment.