6,348 research outputs found
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely based on data observed on an otherwise unknown system. The system's
state is extended appropriately to frame the PID policy as a static state
feedback policy. This renders PID tuning possible as the solution of a finite
horizon optimal control problem without further a priori knowledge. The
framework is applied to the task of balancing an inverted pendulum on a seven
degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International
Conference on Robotics and Automation (ICRA
Compilation techniques for irregular problems on parallel machines
Massively parallel computers have ushered in the era of teraflop computing. Even though large and powerful machines are being built, they are used by only a fraction of the computing community. The fundamental reason for this situation is that parallel machines are difficult to program. Development of compilers that automatically parallelize programs will greatly increase the use of these machines.;A large class of scientific problems can be categorized as irregular computations. In this class of computation, the data access patterns are known only at runtime, creating significant difficulties for a parallelizing compiler to generate efficient parallel codes. Some compilers with very limited abilities to parallelize simple irregular computations exist, but the methods used by these compilers fail for any non-trivial applications code.;This research presents development of compiler transformation techniques that can be used to effectively parallelize an important class of irregular programs. A central aim of these transformation techniques is to generate codes that aggressively prefetch data. Program slicing methods are used as a part of the code generation process. In this approach, a program written in a data-parallel language, such as HPF, is transformed so that it can be executed on a distributed memory machine. An efficient compiler runtime support system has been developed that performs data movement and software caching
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
Solar Force-free Magnetic Fields
The structure and dynamics of the solar corona is dominated by the magnetic
field. In most areas in the corona magnetic forces are so dominant that all
non-magnetic forces like plasma pressure gradient and gravity can be neglected
in the lowest order. This model assumption is called the force-free field
assumption, as the Lorentz force vanishes. This can be obtained by either
vanishing electric currents (leading to potential fields) or the currents are
co-aligned with the magnetic field lines. First we discuss a mathematically
simpler approach that the magnetic field and currents are proportional with one
global constant, the so-called linear force-free field approximation. In the
generic case, however, the relation between magnetic fields and electric
currents is nonlinear and analytic solutions have been only found for special
cases, like 1D or 2D configurations. For constructing realistic nonlinear
force-free coronal magnetic field models in 3D, sophisticated numerical
computations are required and boundary conditions must be obtained from
measurements of the magnetic field vector in the solar photosphere. This
approach is currently of large interests, as accurate measurements of the
photospheric field become available from ground-based (for example SOLIS) and
space-born (for example Hinode and SDO) instruments. If we can obtain accurate
force-free coronal magnetic field models we can calculate the free magnetic
energy in the corona, a quantity which is important for the prediction of
flares and coronal mass ejections. Knowledge of the 3D structure of magnetic
field lines also help us to interpret other coronal observations, e.g.,
EUV-images of the radiating coronal plasma.Comment: 49 pages, 11 figures, Living Reviews in Solar Physics, accepte
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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