Stochastic dynamic programs suffer from the so called curse of dimensionality whereby the number of evaluations grows exponentially as the number of stages increases. This curse is further magnified when the stochasti...
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Stochastic dynamic programs suffer from the so called curse of dimensionality whereby the number of evaluations grows exponentially as the number of stages increases. This curse is further magnified when the stochastic variable is discretized as this adds another dimension to the number of evaluations required. As a result, it is computationally infeasible (in some cases outright impractical) to try solving these problems using an exhaustive search over all the parameters. computational algorithms that combine decomposition techniques and simulation with reasonable assumptions on the stochastic variables can render very efficient solutions with a high degree of accuracy. This paper develops and tests such an algorithm to solve a large scale stochastic dynamic program. The algorithm's performance is tested through a simulation study of a system with the prescribed parameters determined by the algorithm. The results analyzed show that the algorithm generates very good solutions as validated by the performance indicators of the system. The solutions provide the managers of the system with insights into the complex relationships that characterize the system. (C) 2010 Published by Elsevier Ltd.
In this paper, we present a practically useful and intuitive grasp quality measure that takes into account the shapes of object geometries and the torque limits of finger actuators. The proposed grasp quality measure ...
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ISBN:
(纸本)9781424466757
In this paper, we present a practically useful and intuitive grasp quality measure that takes into account the shapes of object geometries and the torque limits of finger actuators. The proposed grasp quality measure is defined by the distance between the convex hulls of the absolute grasp wrench space (a-GWS) and the object wrench space (OWS), where a-GWS and OWS are, respectively, created by the active wrenches from the robot fingers with limited torque bounds and the uniform distribution of unitary normal disturbances on the surface of a polyhedral object. The computational algorithm for the grasp quality measure also yields the information of which spots of the object are fatal under the disturbance, which makes the algorithm practically useful. We demonstrate the validity of the proposed measure through numerical examples.
Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. However, h...
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Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. However, how to identify functional SNPs from a pool containing both functional and neutral SNPs is challenging. In this study, we analyzed the genetic variations that can alter the expression and function of a group of cytokine proteins using computational tools. As a result, we extracted 4552 SNPs from 45 cytokine proteins from SNPper database. Of particular interest, 828 SNPs were in the 5'UTR region, 961 SNPs were in the 3' UTR region, and 85 SNPs were non-synonymous SNPs (nsSNPs), which cause amino acid change. Evolutionary conservation analysis using the SIFT tool suggested that 8 nsSNPs may disrupt the protein function. Protein structure analysis using the PolyPhen tool suggested that 5 nsSNPs might alter protein structure. Binding motif analysis using the UTResource tool suggested that 27 SNPs in 5' or 3'UTR might change protein expression levels. Our study demonstrates the presence of naturally occurring genetic variations in the cytokine proteins that may affect their expressions and functions with possible roles in complex human disease, such as immune diseases. (c) 2006 Elsevier Ltd. All rights reserved.
Stochastic dynamic programs suffer from the so called curse of dimensionality whereby the number of evaluations grows exponentially as the number of stages increases. This curse is further magnified when the stochasti...
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Stochastic dynamic programs suffer from the so called curse of dimensionality whereby the number of evaluations grows exponentially as the number of stages increases. This curse is further magnified when the stochastic variable is discretized as this adds another dimension to the number of evaluations required. As a result, it is computationally infeasible (in some cases outright impractical) to try solving these problems using an exhaustive search over all the parameters. computational algorithms that combine decomposition techniques and simulation with reasonable assumptions on the stochastic variables can render very efficient solutions with a high degree of accuracy. This paper develops and tests such an algorithm to solve a large scale stochastic dynamic program. The algorithm’s performance is tested through a simulation study of a system with the prescribed parameters determined by the algorithm. The results analyzed show that the algorithm generates very good solutions as validated by the performance indicators of the system. The solutions provide the managers of the system with insights into the complex relationships that characterize the system.
Simple and efficient computational algorithms for nonparametric wavelet-based identification of nonlinearities in Hammerstein systems driven by random signals are proposed. They exploit binary grid interpolations of c...
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Simple and efficient computational algorithms for nonparametric wavelet-based identification of nonlinearities in Hammerstein systems driven by random signals are proposed. They exploit binary grid interpolations of compactly supported wavelet functions. The main contribution consists in showing how to use the wavelet values from the binary grid together with the fast wavelet algorithms to obtain the practical counterparts of the wavelet-based estimates for irregularly and randomly spaced data, without any loss of the asymptotic accuracy. The convergence and the rates of convergence are examined for the new algorithms and, in particular, conditions for the optimal convergence speed are presented. Efficiency of the algorithms for a finite number of data is also illustrated by means of the computer simulations.
Buckling of a von Karman plate unilaterally connected with an elastic, Winkler-type, tensionless foundation on both sides is considered in this article. The plate is simply supported and subjected to compressive and t...
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Buckling of a von Karman plate unilaterally connected with an elastic, Winkler-type, tensionless foundation on both sides is considered in this article. The plate is simply supported and subjected to compressive and tensile loads along its edges. A variational principle is formulated for the mechanical model. The variational problem is numerically solved by means of the spectral method and computational algorithm. The algorithm is based on Newton's scheme and a numerical continuation procedure which considers the contact conditions. The bifurcation phenomenon has been analyzed for different values of compressive and tensile loading parameters. The proposed algorithm is demonstrated with the help of numerical results. Finally, an application of the unilateral plate-bending model to layered tissues in biomechanics is considered.
In this paper, we discuss infinite-horizon soft-constrained stochastic Nash games involving state-dependent noise in weakly coupled large-scale systems. First, we formulate linear quadratic differential games in which...
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In this paper, we discuss infinite-horizon soft-constrained stochastic Nash games involving state-dependent noise in weakly coupled large-scale systems. First, we formulate linear quadratic differential games in which robustness is attained against model uncertainty. It is noteworthy that this is the first time conditions for the existence of robust equilibria have been derived based on the solutions of sets of cross-coupled stochastic algebraic Riccati equations (CSAREs). After establishing an asymptotic structure with positive definiteness for CSAREs solutions, we derive a recursive algorithm by means of Newton's method so that it can be used to obtain solutions for CSAREs. As another important feature, we propose a high-order approximate Nash strategy based on iterative solutions. Finally, we provide a numerical example to verify the efficiency of the proposed algorithms. (C) 2009 Elsevier Ltd. All rights reserved.
An approach to discovering rules in nonstationary k-valued Multidimensional time series is proposed. It allows one to discover rules that are subject to "smooth" structural changes with the course of time. A...
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An approach to discovering rules in nonstationary k-valued Multidimensional time series is proposed. It allows one to discover rules that are subject to "smooth" structural changes with the course of time. A measure of rule similarity is proposed to describe such changes, and its application in the form of weight in the graph of rules is discussed. The discovered rules can be used to predict the next elements in the multidimensional time series, to analyze the phenomenon described by this multidimensional time series, and to model it. This allows one to use the proposed algorithm for predicting time series and for examining and describing the processes that can be represented by a multidimensional time series. Means for the direct practical application of the proposed methods of the analysis and prediction of time series are described, and the use of those methods for the short-range prediction of a real-life multidimensional time series consisting of the stock prices of companies operating in similar fields is discussed.
This paper presents new geometrical flow equations for the theoretical modeling of biomolecular surfaces in the context of multiscale implicit solvent models. To account for the local variations near the biomolecular ...
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This paper presents new geometrical flow equations for the theoretical modeling of biomolecular surfaces in the context of multiscale implicit solvent models. To account for the local variations near the biomolecular surfaces due to interactions between solvent molecules, and between solvent and solute molecules, we propose potential driven geometric flows, which balance the intrinsic geometric forces that would occur for a surface separating two homogeneous materials with the potential forces induced by the atomic interactions. Stochastic geometric flows are introduced to account for the random fluctuation and dissipation in density and pressure near the solvent-solute interface. Physical properties, such as free energy minimization (area decreasing) and incompressibility (volume preserving), are realized by some of our geometric flow equations. The proposed approach for geometric and potential forces driving the formation and evolution of biological surfaces is illustrated by extensive numerical experiments and compared with established minimal molecular surfaces and molecular surfaces. Local modification of biomolecular surfaces is demonstrated with potential driven geometric flows. High order geometric flows are also considered and tested in the present work for surface generation. Biomolecular surfaces generated by these approaches are typically free of geometric singularities. As the speed of surface generation is crucial to implicit solvent model based molecular dynamics, four numerical algorithms, a semi-implicit scheme, a Crank-Nicolson scheme, and two alternating direction implicit (ADI) schemes, are constructed and tested. Being either stable or conditionally stable but admitting a large critical time step size, these schemes overcome the stability constraint of the earlier forward Euler scheme. Aided with the Thomas algorithm, one of the ADI schemes is found to be very efficient as it balances the speed and accuracy.
The values of linear operators of a given class are estimated in the case of measurements including piecewise continuous noise of deterministic structure with unknown parameters. A computational scheme producing unbia...
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The values of linear operators of a given class are estimated in the case of measurements including piecewise continuous noise of deterministic structure with unknown parameters. A computational scheme producing unbiased linear estimates that are invariant under the noise is developed. An illustrative example is presented.
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