Deep focus states, like Immersion and Flow are important parameters when it comes to an enjoyable experience during learning activities. Exploring the Physiology of deep focus, in the course of prior studies, physiolo...
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Serious Games are an important part of technology aided learning. Since the learning outcome of intrinsically motivated learners is generally higher, a learning system should support the learner in achieving this stat...
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A holistic approach to determine the surface orientation and film thickness for nonplanar isotropic three-phase systems (ambient-film-substrate) by retroreflex ellipsometry is presented. After scanning the surface of ...
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This work presents a method that is able to predict the geolocation of a street-view photo taken in the wild within a state-sized search region by matching against a database of aerial reference imagery. We partition ...
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Spatial augmented reality is playing an increasingly important role in the design of digital assistance systems for industrial applications. Most of these systems use integrated computer vision technologies for suppor...
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ISBN:
(数字)9798350374490
ISBN:
(纸本)9798350374506
Spatial augmented reality is playing an increasingly important role in the design of digital assistance systems for industrial applications. Most of these systems use integrated computer vision technologies for supporting direct interactions in the physical workspace of the user. While these systems work well under lab conditions, field tests in real production environments revealed shortcomings of the underlying computer vision approaches with regard to their reliability. In this paper, we present a concept combing machine learning with the theories of Dempster-Schafer and fuzzy set for improved classification and reliability. For the classification, the Mediapipe-Hand machine learning framework and a segmentation-based method were combined. The fusion with the indices and the classification results is done with fuzzy inference. The subsequent results are merged with Dempster's combination rule. The results proved that the presented concept increases the reliability of the detection up to 97.8 percent.
The FungiCLEF 2024 challenge aims to foster research in the field of application-oriented fine-grained open-set classification. Particularly, it sets the challenge to optimize fungi species classification while recogn...
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Accurate prediction of the vertical distribution of optical turbulence strength (see manuscript PDF for symbol) is essential for several applications, such as ground-to-satellite optical communications and astronomica...
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The safety validation of AI and ML-based systems is challenging, as (i) analytical validation needs to include the interaction with a complex and stochastic physical environment and (ii) empirical validation needs to ...
The safety validation of AI and ML-based systems is challenging, as (i) analytical validation needs to include the interaction with a complex and stochastic physical environment and (ii) empirical validation needs to observe very long time-horizons to get enough “statistical signal” for the typically very low safety-related incident rate. This paper proposes an approach that amplifies the empirical evidence by introducing a handicap that reduces the system performance—making safety-related failures empirically more visible in a controlled environment—and gradually removing the handicap so that the convergence to the final incident rate can be estimated. Two numerical case studies are used to support and exemplify the approach.
We present a new simple graph-Theoretic formulation of the exploratory blockmodeling problem on undirected and unweighted one-mode networks. Our formulation takes as input the network G and the maximum number t of blo...
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The height dependency of the outer scale of turbulence in the atmospheric surface layer is investigated using Ultrasonic anemometer measurements. The focus of the analysis was on different atmospheric stability condit...
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