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Anomaly Detection for Automotive Diagnostic Applications Based on N-Grams.

, , , , and . COMPSAC, page 1423-1429. IEEE, (2020)

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Isolation Forest for Anomaly Detection in Raw Vehicle Sensor Data., and . VEHITS, page 411-416. SciTePress, (2018)Digitalization in automotive and industrial systems., , , , and . DATE, page 1203-1204. IEEE, (2018)A multi-objective optimization approach for analysing and architecting system of systems., , , and . SysCon, page 1-8. IEEE, (2018)Model-based validation and testing of industry 4.0 plants., , , , and . SysCon, page 1-8. IEEE, (2018)Estimating the Uniqueness of Test Scenarios derived from Recorded Real-World-Driving-Data using Autoencoders., , , , , and . Intelligent Vehicles Symposium, page 1860-1866. IEEE, (2018)Classification of Automotive Electric/Electronic Features and the Consequent Hierarchization of the Logical System Architecture - From Functional Chains to Functional Networks., , and . SMARTGREENS/VEHITS (Selected Papers), volume 921 of Communications in Computer and Information Science, page 229-255. Springer, (2017)Continuous Safety Assessment of Updated Supervised Learning Models in Shadow Mode., , and . ICSA-C, page 301-308. IEEE, (2023)Seamless Testing of Embedded Control Systems., , and . LATW, page 151-153. IEEE, (2002)Optimizing test-set diversity: Trajectory clustering for scenario-based testing of automated driving systems., , and . ITSC, page 1371-1378. IEEE, (2021)Semantic Comparison of Driving Sequences by Adaptation of Word Embeddings., , , and . ITSC, page 1-7. IEEE, (2020)