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This paper describes the effectiveness and efficiency of Virtual Reality training during a commissioning process. Therefore, 500 picking orders with more than 2000 part-picking operations with 30 test persons have been conducted and analyzed in the Modellfabrik Bodensee. The study points out the advantages and disadvantages of virtual training in comparison to a real execution of a picking process with and without any training.
Simulationsmethoden bei der Entwicklung von Extremleichtbaukomponenten in Faserverbundbauweise
(2018)
Research on Shadow IT is facing a conceptual dilemma in cases where previously "covert" systems developed by business entities (individual users, business workgroups, or business units) are integrated in the organizational IT management. These systems become visible, are therefore not "in the shadows" anymore, and subsequently do not fit to existing definitions of Shadow IT. Practice shows that some information systems share characteristics of Shadow IT, but are created openly in alignment with the IT department. This paper therefore proposes the term "Business-managed IT" to describe "overt" information systems developed or managed by business entities. We distinguish Business-managed IT from Shadow IT by illustrating case vignettes. Accordingly, our contribution is to suggest a concept and its delineation against other concepts. In this way, IS researchers interested in IT originated from or maintained by business entities can construct theories with a wider scope of application that are at the same time more specific to practical problems. In addition, value-laden terminology is complemented by a vocabulary that values potentially innovative developments by business entities more adequately. From a practical point of view, the distinction can be used to discuss the distribution of task responsibilities for information systems.
Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition
(2018)
Deep learning is a field of machine learning that has been the focus of active research and successful applications in recent years. Offline handwriting recognition is one of the research fields and applications were deep neural networks have shown high accuracy. Deep learning models and their training pipeline show a large amount of hyper-parameters in their data selection, transformation, network topology and training process that are sometimes interdependent. This increases the overall difficulty and time necessary for building and training a model for a specific data set and task at hand. This work proposes a novel visualization-assisted workflow that guides the model developer through the hyper-parameter search in order to identify relevant parameters and modify them in a meaningful way. This decreases the overall time necessary for building and training a model. The contributions of this work are a workflow for hyper-parameter search in offline handwriting recognition and a heat map based visualization technique for deep neural networks in multi-line offline handwriting recognition. This work applies to offline handwriting recognition, but the general workflow can possibly be adapted to other tasks as well.