An over-sampling technique called V-synth is proposed and compared to borderline SMOTE (bSMOTE), a common methodology used to balance an imbalanced dataset for classification purposes. V-synth is a machine learning me...
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An over-sampling technique called V-synth is proposed and compared to borderline SMOTE (bSMOTE), a common methodology used to balance an imbalanced dataset for classification purposes. V-synth is a machine learning methodology that allows synthetic minority points to be generated based on the properties of a Voronoi diagram. A Voronoi diagram is a collection of geometric regions that encapsulate classifying points in such a way that any point within the region is closest to the encapsulated classifier than any other adjacent classifiers based on their distance from one another. Because of properties inherent to Voronoi diagrams, V-synth identifies exclusive regions of feature space where it is ideal to create synthetic minority samples. To test the generalization and application of V-synth, six databases from various problem domains were selected from the University of California Irvine's Machine Learning Repository. Though not always guaranteed due to the random nature of synthetic over-sampling, significant evidence is presented that supports the hypothesis that V-synth more consistently leads to the creation of more accurate and better-balanced classification models than bSMOTE when the classification complexity of a dataset is high.
This paper presents an obstacle avoidance method for spacecraft relative motion control. In this approach, a connectivity graph is constructed for a set of relative frame points, which form a virtual net centered arou...
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This paper presents an obstacle avoidance method for spacecraft relative motion control. In this approach, a connectivity graph is constructed for a set of relative frame points, which form a virtual net centered around a nominal orbital position. The connectivity between points in the virtual net is determined based on the use of safe positively invariant sets for guaranteed collision free maneuvering. A graph search algorithm is then applied to find a maneuver that avoids specified obstacles and adheres to specified thrust limits. As compared to conventional open-loop trajectory optimization, this approach enables the handling of bounded disturbances, which can represent the effects of perturbing forces and model uncertainty, while rigorously guaranteeing that nonconvex and possibly time-varying obstacle avoidance constraints are satisfied. Details for handling a single stationary obstacle, multiple stationary obstacles, moving obstacles, and bounded disturbances are reported and illustrated with simulation case studies.
Designed microstructures, such as the ones of high strength and creep resistant modern steels and Al-alloys, are based in many cases on the understanding of the role of precipitates. The kinetics of precipitation can ...
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Designed microstructures, such as the ones of high strength and creep resistant modern steels and Al-alloys, are based in many cases on the understanding of the role of precipitates. The kinetics of precipitation can be significantly influenced by the adaption of their volumetric misfit relative to the matrix where plastification and creep of the matrix are typical accommodation processes in addition to vacancy diffusion-controlled mechanisms. A solution for creep coupled with elasticity as such an accommodation mechanism is presented. The set of governing equations including the according assumptions is provided. First, an efficient numerical algorithm is presented to solve the system of two such governing differential equations. Second, necessary steps to obtain an analytical form of the rate equation of the contact pressure are outlined. This rate equation for the contact pressure can be later implemented into models for treatment of precipitation kinetics in complex systems. (C) 2014 Elsevier Ltd. All rights reserved.
The present-day rapid growth of computer power, in particular, parallel computing systems of ultrahigh performance requires a new approach to the creation of models and solution algorithms for major problems. An algor...
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The present-day rapid growth of computer power, in particular, parallel computing systems of ultrahigh performance requires a new approach to the creation of models and solution algorithms for major problems. An algorithm for solving parabolic and elliptic equations is proposed. The capabilities of the method are demonstrated by solving astrophysical problems on high-performance computer systems with massive parallelism.
Nonlinear Model Predictive Control (NMPC) requires the online solution of an Optimal Control Problem (OCP) at every sampling instant. In the context of multiple shooting, a numerical integration is needed to discretiz...
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We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four-dimensional (4D-Var) data assimilation problems. This decomposition is based on exploiting an adapt...
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We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four-dimensional (4D-Var) data assimilation problems. This decomposition is based on exploiting an adaptive hierarchy of the observations. Starting with a low-cardinality set and the solution of its corresponding optimization problem, observations are successively added based on a posteriori error estimates. The particular structure of the sequence of associated linear systems allows the use of a variant of the conjugate gradient algorithm which effectively exploits the fact that the number of observations is smaller than the size of the vector state in the 4D-Var model. The new algorithm is tested on a one-dimensional-wave equation and on the Lorenz96 system, the latter one being of special interest because of its similarity with numerical weather prediction systems.
This paper presents a systematic numerical algorithm to design optimal Hoo continuous-time controllers to robustly stabilize periodic orbits for hybrid dynamical systems in the presence of discrete-time uncertainties....
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In our poster we show the two dimensional steady-state temperature distribution on rectangular shape surfaces under different boundary conditions. We compare the results of the analytical (Fourier series solution) and...
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This paper introduces hybrid LU-QR algorithms for solving dense linear systems of the form Ax = b. Throughout a matrix factorization, these algorithms dynamically alternate LU with local pivoting and QR elimination st...
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This paper introduces hybrid LU-QR algorithms for solving dense linear systems of the form Ax = b. Throughout a matrix factorization, these algorithms dynamically alternate LU with local pivoting and QR elimination steps based upon some robustness criterion. LU elimination steps can be very efficiently parallelized, and are twice as cheap in terms of floating-point operations, as QR steps. However, LU steps are not necessarily stable, while QR steps are always stable. The hybrid algorithms execute a QR step when a robustness criterion detects some risk for instability, and they execute an LU step otherwise. The choice between LU and QR steps must have a small computational overhead and must provide a satisfactory level of stability with as few QR steps as possible. In this paper, we introduce several robustness criteria and we establish upper bounds on the growth factor of the norm of the updated matrix incurred by each of these criteria. In addition, we describe the implementation of the hybrid algorithms through an extension of the PaRSEC software to allow for dynamic choices during execution. Finally, we analyze both stability and performance results compared to state-of-the-art linear solvers on parallel distributed multicore platforms. A comprehensive set of experiments shows that hybrid LU-QR algorithms provide a continuous range of trade-offs between stability and performances. (C) 2015 Published by Elsevier Inc.
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