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Low-thrust space propulsion systems enable flexible high-energy deep space missions, but the design and optimization of the interplanetary transfer trajectory is usually difficult. It involves much experience and expert knowledge because the convergence behavior of traditional local trajectory optimization methods depends strongly on an adequate initial guess. Within this extended abstract, evolutionary neurocontrol, a method that fuses artificial neural networks and evolutionary algorithms, is proposed as a smart global method for low-thrust trajectory optimization. It does not require an initial guess. The implementation of evolutionary neurocontrol is detailed and its performance is shown for an exemplary mission.
Flow visualization by means of PIV of an artificial aortic heart valve fixed into a mock aorta
(2005)
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.