Department of Automatic Control
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- 2023
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Mark
A Deep Learning Approach for Real-Time Application-Level Anomaly Detection in IoT Data Streaming
2023)(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
A Deep Learning Approach for Root Cause Analysis of Application Anomalies for Real-Time IIoT
2023)(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Efficient Security Protocols for Constrained Devices
2023) In EIT series of licentiate and doctoral theses(
- Thesis › Doctoral thesis (compilation)
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Mark
Transient Analysis and Control for Scalable Network Systems
2023) In Research Reports TFRT-3280(
- Thesis › Licentiate thesis
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Mark
Learning pharmacometric covariate model structures with symbolic regression networks
2023) In Journal of Pharmacokinetics and Pharmacodynamics(
- Contribution to journal › Article
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Mark
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
(
- Contribution to journal › Article
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Mark
Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
2023) IEEE Intelligent Vehicles Symposium (IV 2023)(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Distributionally Robust Covariance Steering with Optimal Risk Allocation
2023)(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Path Planning Using Wassertein Distributionally Robust Deep Q-learning
2023)(
- Chapter in Book/Report/Conference proceeding › Book chapter
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Mark
Distributionally Robust RRT with Risk Allocation
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding