@misc{GebauerAdemiBraun, author = {Gebauer, Marc and Ademi, Bejtush and Braun, Julia}, title = {Sustainable Business Models Beyond the Carbon Footprint - A Review of Perceived Sustainability Dimensions in Practical BMI Based on Case Studies}, series = {Digital Economy. Emerging Technologies and Business Innovation : 8th International Conference, ICDEc 2023, Braga, Portugal, May 2-4, 2023, Proceedings}, journal = {Digital Economy. Emerging Technologies and Business Innovation : 8th International Conference, ICDEc 2023, Braga, Portugal, May 2-4, 2023, Proceedings}, isbn = {978-3-031-42787-9}, doi = {10.1007/978-3-031-42788-6_4}, pages = {50 -- 63}, abstract = {Sustainability has become an important goal of business model innovation for managers and researchers alike. Nonetheless, we do recognize a strong focus on the carbon footprint. Since sustainability consists at least of the three dimensions economy, ecology and social, we provide a structured literature review on the dimension researchers and managers do work with. In a first step we develop a framework of sustainability dimensions researchers have provided the literature with. In the second step we search the literature for cases of sustainable business model innovation and analyze the results with the framework of sustainability dimensions. A main finding is, that companies from our sample of cases generally long for more than one SDG. This article provides a collection of very recent case studies of SBMI.}, language = {en} } @misc{SchneidereitMansouriYarahmadiSchneidereitetal., author = {Schneidereit, Slavomira and Mansouri Yarahmadi, Ashkan and Schneidereit, Toni and Breuß, Michael and Gebauer, Marc}, title = {YOLO- based Object detection in industry 4.0 Fischertechnik Model Environment}, series = {Computer Science > Computer Vision and Pattern Recognition, Intelligent Systems Conference 2023 (IntelliSys 2023)}, journal = {Computer Science > Computer Vision and Pattern Recognition, Intelligent Systems Conference 2023 (IntelliSys 2023)}, doi = {10.48550/arXiv.2301.12827}, pages = {20}, abstract = {In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.}, language = {en} } @misc{SchneidereitYarahmadiSchneidereitetal., author = {Schneidereit, Slavomira and Yarahmadi, Ashkan Mansouri and Schneidereit, Toni and Breuß, Michael and Gebauer, Marc}, title = {YOLO-Based Object Detection in Industry 4.0 Fischertechnik Model Environment}, series = {Lecture Notes in Networks and Systems}, volume = {823}, journal = {Lecture Notes in Networks and Systems}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031477232}, issn = {2367-3370}, doi = {10.1007/978-3-031-47724-9_1}, pages = {1 -- 20}, abstract = {In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik Industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.}, language = {en} }