Automated 3D-Objectdocumentation on the Base of an Image Set

Digital stereo-photogrammetry allows users an automatic evaluation of the spatial dimension and the surface texture of objects. The integration of image analysis techniques simplifies the automation of evaluation of large image sets and offers a high accuracy [1]. Due to the substantial similarities of stereoscopic image pairs, correlation techniques provide measurements of subpixel precision for corresponding image points. With the help of an automated point search algorithm in image sets identical points are used to associate pairs of images to stereo models and group them. The found identical points in all images are basis for calculation of the relative orientation of each stereo model as well as defining the relation of neighboured stereo models. By using proper filter strategies incorrect points are removed and the relative orientation of the stereo model can be made automatically. With the help of 3D-reference points or distances at the object or a defined distance of camera basis the stereo model is orientated absolute. An adapted expansion- and matching algorithm offers the possibility to scan the object surface automatically. The result is a three dimensional point cloud; the scan resolution depends on image quality. With the integration of the iterative closest point- algorithm (ICP) these partial point clouds are fitted to a total point cloud. In this way, 3D-reference points are not necessary. With the help of the implemented triangulation algorithm a digital surface models (DSM) can be created. The texturing can be made automatically by the usage of the images that were used for scanning the object surface. It is possible to texture the surface model directly or to generate orthophotos automatically. By using of calibrated digital SLR cameras with full frame sensor a high accuracy can be reached. A big advantage is the possibility to control the accuracy and quality of the 3d-objectdocumentation with the resolution of the images. The procedure described here is implemented in software Metigo 3D

Dauernachtarbeit: Eine Sichtung des vorhandenen Wissenstandes mit Thesen, Empfehlungen & Forschungsfragen

Im Falle der Dauernachtarbeit, also einer dauerhaften Schichtarbeit bei Nacht, ste-hen begründete Befürchtungen erheblicher Nachteile anderen Sichtweisen gegen-über. Dieser Artikel beruht auf einer umfassenden Literatursuche und einer breiten Diskussion in der Arbeitszeitgesellschaft. Es werden von Expertinnen und Experten konsensual abgestimmte Thesen zu der Wirkung von Dauernachtarbeit sowie zum Forschungsbedarf vorgestellt. Das Kernergebnis der Diskussion ist, dass Dauer-nachtarbeit in den meisten Fällen problematisch ist, aber auch Konstellationen exis-tieren, bei denen die Dauernachtarbeit wenig problematisch oder vielleicht sogar po-sitiv für manche Personen wirkt. Es besteht ein erheblicher Forschungsbedarf, um hier ein klares Bild zu gewinnen

Editorial: Vielfalt der Arbeitszeiten

Editorial zum Schwerpunktheft "Vielfalt der Arbeitszeiten

What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core

Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations

Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for an instance of a task or to delegate it to a human by considering both parties\u27 capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams -- compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when they are aware that an AI model delegated task instances to them. In an experimental study with 196 participants, we show that task performance and task satisfaction improve through AI delegation, regardless of whether humans are aware of the delegation. Additionally, we identify humans\u27 increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces

Arbeitszeitentwicklungen in Deutschland, Österreich und der Schweiz

Editorial des Schwerpunktheftes "Arbeitszeitentwicklungen in Deutschland, Österreich und der Schwei

Charring Rates for Different Cross Sections of Laminated Veneer Lumber (LVL)

Current research at the University of Canterbury is investigating the performance of a new type of timber floor system made of a timber-concrete composite. This newly proposed timber floor system uses double LVL members connected together with screwed connections to form one larger LVL member. Recent large scale fire tests showed that the joint between these two screwed LVL members opened up during fire exposure. This opening phenomenon causes concerns as the overall charring rate of the joint LVL members is subsequently increased. The main focus of this research, therefore, was to examine the charring rate for different cross sections of single and double LVL members, with different connection types for the double members. The single LVL member examined was 63mm width whereas the double LVL members examined were 90mm and 126mm width. Three connection types were investigated which were nails, screws and glue. Their corresponding charring rates and burning characteristics were examined both in the small furnace provided by the University of Canterbury and in the pilot furnace at the Building Research Association of New Zealand (BRANZ) in Wellington. The overall finding from the small furnace testing shows that the overall average side charring rate for a 30 minute fire exposure was 0.76mm/min; whereas the overall average side charring rate for a 60 minute fire exposure was 0.66mm/min. Moreover for a 30 minute fire exposure, the average bottom charring rates for nail, screw and glue connected double LVL members were 1.00mm/min, 0.83mm/min and 0.83mm/min, respectively. For a 60 minute fire exposure, the average bottom charring rates for screw and glue connected double LVL members were 0.97mm/min and 0.57mm/min, respectively. The nail connected double LVL members experienced the highest bottom charring rate as it suffered the largest bottom separation which allowed the heat to travel into the mid-span resulting in a higher bottom charring rate. Out of these three connection types, the glued connection was the best connection type. Experimental findings were compared with the simulated results generated by the SAFIR finite element program. Experimental findings were also used to modify the spreadsheet design tool which predicts the fire resistance rating of a timber-concrete composite floor under user defined load conditions and floor geometries