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Die Coronakrise hat weltweit das Wirtschafts- und Gesellschaftsleben in bisher ungekannter Weise verändert. Die ohnehin bereits komplexen Herausforderungen in Zeiten des Klimawandels sind damit noch gestiegen. Genossenschaftliche Innovationsökosysteme können Lösungsansätze für die gravierenden Veränderungen im unternehmerischen, kommunalen und gesellschaftlichen Umfeld schaffen.
Jedes Projektteam braucht engagierte Teammitglieder, der NQSZ 147-04 AA zum Projektmanagement auch
(2020)
… nicht nur in der Energiewirtschaft, sondern auch im positiven und wahrsten Sinne des Wortes in der Fachgruppe PM Windenergie. In über 40 Jahren GPM wurde immer wieder deutlich, dass Projekte und Programme in Wirtschaft und Gesellschaft nicht nur begleitet, sondern auch aus der GPM heraus mitgestaltet werden. Die Entwicklung und die Ergebnisse der Fachgruppe Windenergie machen dies besonders deutlich.
Additive manufacturing (AM) and in particular the application of 3D multi material printing offers completely new production technologies thanks to the degree of freedom in design and the simultaneous processing of several materials in one component. Today's CAD systems for product development are volume-based and therefore cannot adequately implement the multi-material approach. Voxel-based CAD systems offer the advantage that a component can be divided into many voxels and different materials and functions can be assigned to these voxels. In this contribution two voxel-based CAD systems will be analyzed in order to simplify the AM on voxel level with different materials. Thus, a number of suitable criteria for evaluating voxel-based CAD systems are being developed and applied. The results of a technical-economic comparison show the differences between the voxel-based systems and disclose their disadvantages compared to conventional CAD systems. In order to overcome these disadvantages, a new method is therefore presented as an approach that enables the voxelization of a component in a simple way based on a conventional CAD model. The process chain of this new method is demonstrated using a typical component from product design. The results of this implementation of the new method are illustrated and analyzed.
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an autoencoder to generate suitable latent representation. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features could be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
We introduce an open source python framework named PHS-Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efficient method to propose the next query set of parameters.