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Monsberger Michael, Koppelhuber Daniela, Sabol Vedran, Gursch Heimo, Spataru Adrian, Prentner Oliver

An Innovative User Feedback System for Sustainable Buildings

Sustainable Built Environment D-A-CH Conference 2019 (SBE19), IOP Publishing Ltd, Bristol, UK, 2019

A lot of research is currently focused on studying user behavior indirectly by analyzing sensor data. However, only little attention has been given to the systematic acquisition of immediate user feedback to study user behavior in buildings. In this paper, we present a novel user feedback system which allows building users to provide feedback on the perceived sense of personal comfort in a room. To this end, a dedicated easy-to-use mobile app has been developed; it is complemented by a supporting infrastructure, including a web page for an at-a-glance overview. The obtained user feedback is compared with sensor data to assess whether building services (e.g., heating, ventilation and air-conditioning systems) are operated in accordance with user requirements. This serves as a basis to develop algorithms capable of optimizing building operation by providing recommendations to facility management staff or by automatic adjustment of operating points of building services. In this paper, we present the basic concept of the novel feedback system for building users and first results from an initial test phase. The results show that building users utilize the developed app to provide both, positive and negative feedback on room conditions. They also show that it is possible to identify rooms with non-ideal operating conditions and that reasonable measures to improve building operation can be derived from the gathered information. The results highlight the potential of the proposed system.

Gursch Heimo, Cemernek David, Wuttei Andreas, Kern Roman

Cyber-Physical Systems as Enablers in Manufacturing Communication and Worker Support

Mensch und Computer 2019, Frank Steinicke und Katrin Wolf, Gesellschaft für Informatik e.V., Bonn, Germany, 2019

The increasing potential of Information and Communications Technology (ICT) drives higher degrees of digitisation in the manufacturing industry. Such catchphrases as “Industry 4.0” and “smart manufacturing” reflect this tendency. The implementation of these paradigms is not merely an end to itself, but a new way of collaboration across existing department and process boundaries. Converting the process input, internal and output data into digital twins offers the possibility to test and validate the parameter changes via simulations, whose results can be used to update guidelines for shop-floor workers. The result is a Cyber-Physical System (CPS) that brings together the physical shop-floor, the digital data created in the manufacturing process, the simulations, and the human workers. The CPS offers new ways of collaboration on a shared data basis: the workers can annotate manufacturing problems directly in the data, obtain updated process guidelines, and use knowledge from other experts to address issues. Although the CPS cannot replace manufacturing management since it is formalised through various approaches, e. g., Six-Sigma or Advanced Process Control (APC), it is a new tool for validating decisions in simulation before they are implemented, allowing to continuously improve the guidelines.

Kowald Dominik, Traub Matthias, Theiler Dieter, Gursch Heimo, Lacic Emanuel, Lindstaedt Stefanie , Kern Roman, Lex Elisabeth

Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

REVEAL Workshop co-located with RecSys'2019, ACM, 2019


Thalmann Stefan, Gursch Heimo, Suschnigg Josef, Gashi Milot, Ennsbrunner Helmut, Fuchs Anna Katharina, Schreck Tobias, Mutlu Belgin, Mangler Jürgen, Huemer Christian, Lindstaedt Stefanie

Cognitive Decision Support for Industrial Product Life Cycles: A Position Paper

Proceedings of the Eleventh International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE 2019), Marta Franova, Charlotte Sennersten, Jayfus T. Doswell, IARIA, Venice, Italy, 2019

Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising approach. With two high-tech companies (world market leader in their domains) from Austria, we are approaching this challenge and jointly develop cognitive decision support systems for three real world industrial use cases. Within this position paper, we introduce our understanding of cognitive decision support and we introduce three industrial use cases, focusing on the requirements for cognitive decision support. Finally, we describe our preliminary solution approach for each use case and our next steps.
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