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ten Voorde, Maarten (author)
The use of machine learning (ML), especially neural networks, in modeling control systems has shown promise, particularly for systems with complex physics. However, applying these models in safety-critical areas requires reliable verification and control synthesis methods due to their inherent complexity. Formal methods, using stochastic finite...
master thesis 2024
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Gao, Yuxing (author)
The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety. These systems are of paramount importance due to their direct impact on the lives of passengers and pedestrians....
master thesis 2023
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Chen, Shuxin (author)
Noise's impact on biochemical systems has long been a focal point of investigation, given its potential to compromise signal accuracy and disrupt system functionality. This paper conducts a comprehensive exploration into the noise characteristics within a set of signal differentiators recognized for their high precision. Noteworthy for their...
master thesis 2023
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Tan, Martin (author)
In the field of Systems and Control, optimal control problem-solving for complex systems is a core task. The development of accurate mathematical models to represent these systems’ dynamics is often difficult. This complexity comes from potential uncertainties, complex non-linearities, or unknown factors that might affect the system. Because of...
master thesis 2023
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Gracia Merino, Ibón (author)
There is growing interest to control cyber-physical systems under complex specifications while retaining formal performance guarantees. In this thesis we present a framework for formal control of uncertain systems under complex specifications. We consider dynamical systems with random disturbances, whose probability distribution is unknown. When...
master thesis 2022
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