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2,709
The Vector Field Histogram  Fast Obstacle Avoidance For Mobile Robots
 IEEE JOURNAL OF ROBOTICS AND AUTOMATION
, 1991
"... A new realtime obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a ..."
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Cited by 484 (24 self)
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a twodimensional Cartesian histogram gridas a world model. This world model is updated continuously with range data sampled by onboard range sensors. The VFH method subsequently employs a twostage datareduction process in order to compute the desired control commands for the vehicle
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
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Cited by 451 (0 self)
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A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural
Direct Reduction and Differential Constraints
, 1994
"... . Direct reductions of partial differential equations to systems of ordinary differential equations are in onetoone correspondence with compatible differential constraints. The differential constraint method is applied to prove that a parabolic evolution equation admits infinitely many characteris ..."
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Cited by 37 (2 self)
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characteristic second order reductions, but admits a noncharacteristic second order reduction if and only if it is linearizable. 1. Introduction. One of the most useful methods for determining particular explicit solutions to partial differential equations is to reduce them to ordinary differential equations
Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
 SIAM JOURNAL ON SCIENTIFIC COMPUTING
, 2004
"... Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized ..."
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Cited by 261 (15 self)
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to the underlying manifold by way of a partial eigendecomposition of the neighborhood connection matrix. We present a careful error analysis of our algorithm and show that the reconstruction errors are of secondorder accuracy. We illustrate our algorithm using curves and surfaces both in 2D/3D and higher
Krylov Projection Methods For Model Reduction
, 1997
"... This dissertation focuses on efficiently forming reducedorder models for large, linear dynamic systems. Projections onto unions of Krylov subspaces lead to a class of reducedorder models known as rational interpolants. The cornerstone of this dissertation is a collection of theory relating Krylov p ..."
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Cited by 213 (3 self)
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projection to rational interpolation. Based on this theoretical framework, three algorithms for model reduction are proposed. The first algorithm, dual rational Arnoldi, is a numerically reliable approach involving orthogonal projection matrices. The second, rational Lanczos, is an efficient generalization
Which Problems Have Strongly Exponential Complexity?
 Journal of Computer and System Sciences
, 1998
"... For several NPcomplete problems, there have been a progression of better but still exponential algorithms. In this paper, we address the relative likelihood of subexponential algorithms for these problems. We introduce a generalized reduction which we call SubExponential Reduction Family (SERF) t ..."
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Cited by 242 (11 self)
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) that preserves subexponential complexity. We show that CircuitSAT is SERFcomplete for all NPsearch problems, and that for any fixed k, kSAT, kColorability, kSet Cover, Independent Set, Clique, Vertex Cover, are SERFcomplete for the class SNP of search problems expressible by second order existential
Global and regional climate changes due to black carbon,
 Nat. Geosci.,
, 2008
"... Figure 1: Global distribution of BC sources and radiative forcing. a, BC emission strength in tons per year from a study by Bond et al. Full size image (42 KB) Review Nature Geoscience 1, 221 227 (2008 Black carbon in soot is the dominant absorber of visible solar radiation in the atmosphere. Ant ..."
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Cited by 228 (5 self)
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clouds, with vertical extents of 3 to 5 km. Because of the combination of high absorption, a regional distribution roughly aligned with solar irradiance, and the capacity to form widespread atmospheric brown clouds in a mixture with other aerosols, emissions of black carbon are the second strongest
Order Reduction of Second Order Systems with
 University of Düsseldorf
"... A common result of modeling (for instance by finite element methods) in some fields like Electrical Circuits and MicroElectroMechanical Systems (MEMS) is a large number of second order differential equations. The most practical solution to handle such large scale models is offered by order reducti ..."
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Cited by 2 (2 self)
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reduction. In reduced order modeling of such systems, not only the behavior of the original system should be approximated in the frequency range of interest, but also it is advisable to preserve the structure of the original model. We consider the second order system of the form, M¨z(t) + D˙z(t) + Kz
Order reduction of second order systems
 In Proc. 4th Mathmod
, 2003
"... Abstract. In this article a projection method for order reduction of large systems of linear equations is presented. Such systems arise for instance through the semidiscretization of partial differential equations from the electrical, thermal or mechanical domain. The proposed algorithm extends the ..."
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Cited by 1 (0 self)
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the efficient nodal order reduction method (ENOR) that was developed for second order systems. Our method ensures an exact solution for the static case and provides good approximations in the frequency and the time domain. The reduced systems can be used for behavioral models for system simulation. Examples
The polyadic πcalculus: a tutorial
 LOGIC AND ALGEBRA OF SPECIFICATION
, 1991
"... The πcalculus is a model of concurrent computation based upon the notion of naming. It is first presented in its simplest and original form, with the help of several illustrative applications. Then it is generalized from monadic to polyadic form. Semantics is done in terms of both a reduction syste ..."
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Cited by 187 (1 self)
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and strengthens the original result of this kind given by Bent Thomsen for secondorder processes.
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