G
Göran Falkman
Researcher at University of Skövde
Publications - 103
Citations - 2012
Göran Falkman is an academic researcher from University of Skövde. The author has contributed to research in topics: Anomaly detection & Situation awareness. The author has an hindex of 20, co-authored 102 publications receiving 1712 citations. Previous affiliations of Göran Falkman include Chalmers University of Technology.
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Proceedings ArticleDOI
Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving
TL;DR: Results show that the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead.
Proceedings Article
Anomaly detection in sea traffic - A comparison of the Gaussian Mixture Model and the Kernel Density Estimator
TL;DR: This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian Mixture Model and the adaptive Kernel Density Estimator, and indicates that KDE more accurately captures finer details of normal data.
Journal ArticleDOI
Online Learning and Sequential Anomaly Detection in Trajectories
Rikard Laxhammar,Göran Falkman +1 more
TL;DR: This article proposes and investigates the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) and the discords algorithm, a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold.
Journal ArticleDOI
Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design.
TL;DR: A fragment-based reinforcement learning approach based on an actor-critic model for the generation of novel molecules with optimal properties for medicinal chemistry programs, demonstrating that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives.
Proceedings Article
Improving maritime anomaly detection and situation awareness through interactive visualization
TL;DR: A combined methodology of data visualization, interaction and mining techniques that allows filtering out anomalous vessels, by building a model over normal behavior from which the user can detect deviations.