1,009 research outputs found
Multi-facet classification of e-mails in a helpdesk scenario
Helpdesks have to manage a huge amount of
support requests which are usually submitted
via e-mail. In order to be assigned to experts
e ciently, incoming e-mails have to be classi-
ed w. r. t. several facets, in particular topic,
support type and priority. It is desirable to
perform these classi cations automatically.
We report on experiments using Support Vector
Machines and k-Nearest-Neighbours, respectively,
for the given multi-facet classi -
cation task. The challenge is to de ne suitable
features for each facet. Our results suggest
that improvements can be gained for all
facets, and they also reveal which features are
promising for a particular facet
Data-driven Bayesian Control of Port-Hamiltonian Systems
Port-Hamiltonian theory is an established way to describe nonlinear physical
systems widely used in various fields such as robotics, energy management, and
mechanical engineering. This has led to considerable research interest in the
control of Port-Hamiltonian systems, resulting in numerous model-based control
techniques. However, the performance and stability of the closed-loop typically
depend on the quality of the PH model, which is often difficult to obtain using
first principles. We propose a Gaussian Processes (GP) based control approach
for Port-Hamiltonian systems (GPC-PHS) by leveraging gathered data. The
Bayesian characteristics of GPs enable the creation of a distribution
encompassing all potential Hamiltonians instead of providing a singular point
estimate. Using this uncertainty quantification, the proposed approach takes
advantage of passivity-based robust control with interconnection and damping
assignment to establish probabilistic stability guarantees
Stable Gaussian Process based Tracking Control of Lagrangian Systems
High performance tracking control can only be achieved if a good model of the
dynamics is available. However, such a model is often difficult to obtain from
first order physics only. In this paper, we develop a data-driven control law
that ensures closed loop stability of Lagrangian systems. For this purpose, we
use Gaussian Process regression for the feed-forward compensation of the
unknown dynamics of the system. The gains of the feedback part are adapted
based on the uncertainty of the learned model. Thus, the feedback gains are
kept low as long as the learned model describes the true system sufficiently
precisely. We show how to select a suitable gain adaption law that incorporates
the uncertainty of the model to guarantee a globally bounded tracking error. A
simulation with a robot manipulator demonstrates the efficacy of the proposed
control law.Comment: Please cite the conference paper. arXiv admin note: text overlap with
arXiv:1806.0719
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