5 Papers
Dominik Ernst is an academic researcher from University of Natural Resources and Life Sciences, Vienna. The author has contributed to research in topics: Ranging & Basis (linear algebra). The author has an hindex of 3, co-authored 3 publications.
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Papers
How to Avoid Random Market Segmentation Solutions
Dominik Ernst,Sara Dolnicar +1 more
TL;DR: The present study explains the problem, assesses how high the risk is of random solutions occurring in tourism market segmentation studies, and recommends an approach that can be used to avoid random solutions.
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What makes foster carers think about quitting? Recommendations for improved retention of foster carers
TL;DR: In this paper, a posteriori segmentation analysis identifies groups of carers dissatisfied with the same aspects of their role, and one group is particularly dissatisfied with factors that are within the control of foster care agencies and also reports high levels of discontinuation ideation.
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LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset
Jeldrik Axmann,Rozhin Moftizadeh,Jing-wen Su,Benjamin Tennstedt,Qianqian Zou,Yunshuang Yuan,Dominik Ernst,Hamza Alkhatib,C. Bronner,Steffen Schön +9 more
- 04 Jun 2023
TL;DR: The novel LUCOOP dataset is introduced, which provides time-synchronized multi-modal data collected by three interacting measurement vehicles, and includes a precise, dense 3D map point cloud, acquired simultaneously by a mobile mapping system, as well as an LOD2 city model of the measurement area.
8
Analysis of Multiple Positions for the Intrinsic and Extrinsic Calibration of a Multi-Beam LiDAR
Dominik Ernst,Hamza Alkhatib,Ingo Neumann,Sören Vogel +3 more
- 04 Jul 2022
TL;DR: In this paper , the authors combined the determination of extrinsic and intrinsic parameters with the approximation of a stochastic model for a multi-beam LiDAR, which is demonstrated on a real-data set of a Velodyne VLP-16, for which the transformation parameters between sensor frame and body frame are determined.
2
Generalization, Combination and Extension of Functional Clustering Algorithms: The R Package funcy
TL;DR: This paper aims to show the common elements between existing models in highly cited articles, first on a theoretical basis and later their implementation is analyzed and it is illustrated how they could be improved and extended to a more general level.