2,688 research outputs found
Cascaded Iterative Learning Control for Improved Task Execution of Optimal Control
Iterative learning control (ILC) is used as a means for task execution of a time sub-optimal trajectory. The use of cascaded ILC procedures in a robot application is experimentally shown to drastically enlarge the region of convergence and efficiently compensate for unmodeled dynamics in the motion system. Objectives considered are the duration of motion, maximum liquid slosh during the motion, and the residual slosh after the motion
Random Neural Networks and Optimisation
In this thesis we introduce new models and learning algorithms for the Random
Neural Network (RNN), and we develop RNN-based and other approaches for the
solution of emergency management optimisation problems.
With respect to RNN developments, two novel supervised learning algorithms are
proposed. The first, is a gradient descent algorithm for an RNN extension model
that we have introduced, the RNN with synchronised interactions (RNNSI), which
was inspired from the synchronised firing activity observed in brain neural circuits.
The second algorithm is based on modelling the signal-flow equations in RNN as a
nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory
quasi-Newton algorithm specifically designed for the RNN case.
Regarding the investigation of emergency management optimisation problems,
we examine combinatorial assignment problems that require fast, distributed and
close to optimal solution, under information uncertainty. We consider three different
problems with the above characteristics associated with the assignment of
emergency units to incidents with injured civilians (AEUI), the assignment of assets
to tasks under execution uncertainty (ATAU), and the deployment of a robotic
network to establish communication with trapped civilians (DRNCTC).
AEUI is solved by training an RNN tool with instances of the optimisation problem
and then using the trained RNN for decision making; training is achieved using
the developed learning algorithms. For the solution of ATAU problem, we introduce
two different approaches. The first is based on mapping parameters of the
optimisation problem to RNN parameters, and the second on solving a sequence of
minimum cost flow problems on appropriately constructed networks with estimated
arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer
linear programming formulation, which is based on network flows. Finally, we design
and implement distributed heuristic algorithms for the deployment of robots
when the civilian locations are known or uncertain
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications
Auditory models are commonly used as feature extractors for automatic
speech-recognition systems or as front-ends for robotics, machine-hearing and
hearing-aid applications. Although auditory models can capture the biophysical
and nonlinear properties of human hearing in great detail, these biophysical
models are computationally expensive and cannot be used in real-time
applications. We present a hybrid approach where convolutional neural networks
are combined with computational neuroscience to yield a real-time end-to-end
model for human cochlear mechanics, including level-dependent filter tuning
(CoNNear). The CoNNear model was trained on acoustic speech material and its
performance and applicability were evaluated using (unseen) sound stimuli
commonly employed in cochlear mechanics research. The CoNNear model accurately
simulates human cochlear frequency selectivity and its dependence on sound
intensity, an essential quality for robust speech intelligibility at negative
speech-to-background-noise ratios. The CoNNear architecture is based on
parallel and differentiable computations and has the power to achieve real-time
human performance. These unique CoNNear features will enable the next
generation of human-like machine-hearing applications
CAD enabled trajectory optimization and accurate motion control for repetitive tasks
As machine users generally only define the start
and end point of the movement, a large trajectory optimization potential rises for single axis mechanisms performing repetitive tasks. However, a descriptive mathematical model of the mecha- nism needs to be defined in order to apply existing optimization techniques. This is usually done with complex methods like virtual work or Lagrange equations. In this paper, a generic technique is presented to optimize the design of point-to-point trajectories by extracting position dependent properties with CAD motion simulations. The optimization problem is solved by a genetic algorithm. Nevertheless, the potential savings will only be achieved if the machine is capable of accurately following the optimized trajectory. Therefore, a feedforward motion controller is derived from the generic model allowing to use the controller for various settings and position profiles. Moreover, the theoretical savings are compared with experimental data from a physical set-up. The results quantitatively show that the savings potential is effectively achieved thanks to advanced torque feedforward with a reduction of the maximum torque by 12.6% compared with a standard 1/3-profil
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
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