Fachbereich 1 - Institut Energiesysteme und Energiewirtschaft
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In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.
While more and more nuclear installations facing the end of their lifetime, decommissioning financing issues gain importance in political discussions.
The financing needs are huge along the Uranium value chain. Following the polluter pays principle the operator of a nuclear installation is expected to accumulate all the necessary decommissioning funds during the operating life of its facility. However, since decommissioning experience is still limited,
since the decommissioning process can take several decades and since the time
period between the shutdown of a nuclear installation and the final disposal of radioactive waste can be very long, there are substantial risks that costs will be underestimated and that the liable party and the funds accumulated might
not be available anymore when decommissioning activities have to be paid.
Nevertheless, these financing risks can be reduced by the implementation of transparent, restricted, well-governed decommissioning financing schemes, with a system of checks and balances that aims at avoiding negative effects
stemming from conflicts of interests.