2,898 research outputs found
Adaptive stochastic Galerkin FEM for lognormal coefficients in hierarchical tensor representations
Stochastic Galerkin methods for non-affine coefficient representations are
known to cause major difficulties from theoretical and numerical points of
view. In this work, an adaptive Galerkin FE method for linear parametric PDEs
with lognormal coefficients discretized in Hermite chaos polynomials is
derived. It employs problem-adapted function spaces to ensure solvability of
the variational formulation. The inherently high computational complexity of
the parametric operator is made tractable by using hierarchical tensor
representations. For this, a new tensor train format of the lognormal
coefficient is derived and verified numerically. The central novelty is the
derivation of a reliable residual-based a posteriori error estimator. This can
be regarded as a unique feature of stochastic Galerkin methods. It allows for
an adaptive algorithm to steer the refinements of the physical mesh and the
anisotropic Wiener chaos polynomial degrees. For the evaluation of the error
estimator to become feasible, a numerically efficient tensor format
discretization is developed. Benchmark examples with unbounded lognormal
coefficient fields illustrate the performance of the proposed Galerkin
discretization and the fully adaptive algorithm
Position control of an industrial robot using an optical measurement system for machining purposes
A series of mechanical properties and disturbances limit the accuracy achievable in robotic applications. External control of the end effector position is commonly known as being an appropriate mean to increase accuracy. This paper presents an approach for position control of industrial robots using the pass-through between an industrial CNC and servomotors. A CNC-controlled robot is used together with an external optical measurement system to close the feedback loop of robot end effector and robot controller in order to improve robot accuracy. For short cycle times and implementation reasons a PLC is used for signal processing and control implementation. The relevance of the approach is outlined in experiments. The robot behaviour in free space motion and in machining application is analysed with the optical measurement system and a CMM
A new method to determine multi-angular reflectance factor from lightweight multispectral cameras with sky sensor in a target-less workflow applicable to UAV
A new physically based method to estimate hemispheric-directional reflectance
factor (HDRF) from lightweight multispectral cameras that have a downwelling
irradiance sensor is presented. It combines radiometry with photogrammetric
computer vision to derive geometrically and radiometrically accurate data
purely from the images, without requiring reflectance targets or any other
additional information apart from the imagery. The sky sensor orientation is
initially computed using photogrammetric computer vision and revised with a
non-linear regression comprising radiometric and photogrammetry-derived
information. It works for both clear sky and overcast conditions. A
ground-based test acquisition of a Spectralon target observed from different
viewing directions and with different sun positions using a typical
multispectral sensor configuration for clear sky and overcast showed that both
the overall value and the directionality of the reflectance factor as reported
in the literature were well retrieved. An RMSE of 3% for clear sky and up to 5%
for overcast sky was observed
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