Essays about: "Hierarchical data models"
Showing result 1 - 5 of 64 essays containing the words Hierarchical data models.
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1. Unsupervised Online Anomaly Detection in Multivariate Time-Series
University essay from Uppsala universitet/DatorteknikAbstract : This research aims to identify a method for unsupervised online anomaly detection in multivariate time series in dynamic systems in general and on the case study of Devwards IoT-system in particular. A requirement of the solution is its explainability, online learning and low computational expense. READ MORE
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2. Evaluating the user experience of different representations of organizational structures
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Information overload is a widely recognized problem in the workplace, with an overwhelmingamount of information being presented from several sources constantly. By being able tovisually represent organizational structures in an efficient way, it can significantly aid in makingthe data easier to consume, utilize and incorporate in future decision making. READ MORE
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3. Fault Detection in PV System using Machine Learning Technique
University essay from Högskolan Dalarna/MikrodataanalysAbstract : With the steady and rapid reliance on solar power as a viable alternative to traditional fuel-based energy, maintenance of solar panels is becoming an unavoidable issue for both producers and consumers. Machine learning techniques are useful in detecting solar panel faults and their life span. READ MORE
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4. Space Weather Simulation Model Integration
University essay from Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska fakultetenAbstract : Space weather is the field within the space sciences that studies how the Earths magnetosphere is influenced by the Sun. The Sun is constantly emitting dangerous radiation and plasma which in some cases can affect or damage the systems on Earth. READ MORE
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5. Through the Blur with Deep Learning : A Comparative Study Assessing Robustness in Visual Odometry Techniques
University essay from Uppsala universitet/Avdelningen för systemteknikAbstract : In this thesis, the robustness of deep learning techniques in the field of visual odometry is investigated, with a specific focus on the impact of motion blur. A comparative study is conducted, evaluating the performance of state-of-the-art deep convolutional neural network methods, namely DF-VO and DytanVO, against ORB-SLAM3, a well-established non-deep-learning technique for visual simultaneous localization and mapping. READ MORE