133,387 research outputs found
On multi-objective optimization of planetary exploration rovers applied to ExoMars-type rovers
ExoMars is the first robotic mission of the Aurora program of the European Space Agency (EAS). Surface mobility (as provided by ExoMarks rover) is one of the enabling technologies necessary for future exploration missions. This work uses previouly developed mathematical models to represent an ExoMars rover operation in soft/rocky terrain. The models are used in an optimization loop to evaluate multiple objective functions affected by the change in geometrical design parameters. Several objective funktions can be used in our optimization environment powered by MOPS (Multi-Objective Parameter Synthesis). Two environments are used to simulate the rover in stability sensitive conditions and power and sinkage sensitive conditions. Finally, an ExoMars-like configuration is proposed and consistent improvemnt directions are pointed out
A Geometric Approach to Pairwise Bayesian Alignment of Functional Data Using Importance Sampling
We present a Bayesian model for pairwise nonlinear registration of functional
data. We use the Riemannian geometry of the space of warping functions to
define appropriate prior distributions and sample from the posterior using
importance sampling. A simple square-root transformation is used to simplify
the geometry of the space of warping functions, which allows for computation of
sample statistics, such as the mean and median, and a fast implementation of a
-means clustering algorithm. These tools allow for efficient posterior
inference, where multiple modes of the posterior distribution corresponding to
multiple plausible alignments of the given functions are found. We also show
pointwise credible intervals to assess the uncertainty of the alignment
in different clusters. We validate this model using simulations and present
multiple examples on real data from different application domains including
biometrics and medicine
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
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