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The ClearPET® scanners developed by the Crystal Clear Collaboration use multichannel PMTs as photodetectors with scintillator pixels coupled individually to each channel. In order to localize an event each channel anode is connected to a comparator that triggers when the anode signal exceeds a common predefined threshold. Two major difficulties here are crosstalk of light and the gain nonuniformity of the PMT channels. Crosstalk can generate false triggering in channels adjacent to the actual event. On the one hand this can be suppressed by sufficiently increasing the threshold, but on the other hand a threshold too high can already prevent valid events on the lower gain channels from being detected. Finally, both effects restrict the dynamic range of pulse heights that can be processed. The requirements to the dynamic range are not low as the ClearPET® scanners detect the depth of interaction by phoswich pixels consisting of LSO and Lu0.7Y0.3AP, two scintillators with different light yields. We will present a model to estimate the achievable dynamic range and show solutions to increase it.
Studieren lehren
(2005)
Flow visualization by means of PIV of an artificial aortic heart valve fixed into a mock aorta
(2005)
Das Drallrohr
(2005)
Multiple Near-Earth Asteroid Rendezvous and Sample Return Using First Generation Solar Sailcraft
(2005)
After a short introduction of a new nonconforming linear finite element on quadrilaterals recently developed by Park, we derive a dual weighted residual-based a posteriori error estimator (in the sense of Becker and Rannacher) for this finite element. By computing a corresponding dual solution we estimate the error with respect to a given target error functional. The reliability and efficiency of this estimator is analyzed in several numerical experiments.
Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).
Solar Sails for Near- and Medium-Term Scientific Deep Space Missions / W. Sebolt ; B. Dachwald
(2005)