Disbond Detection through Thermal Testing of Materials under Noisy Observations

Inverse Reinforcement Learning Under Noisy Observations

We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, a noisy continuoustime observation of the trajectory is provided to the learner. This problem exhibits wide-ranging applications and the specific application we consider here is the scenario in which the learner seeks to penetrate a perimeter ...

Performance and testing of thermal interface materials

The increasing power and reduced die size of CPUs used in computers increases a need for significantly improved thermal interface materials (TIM). The TIM is used to reduce contact resistance at the CPU-heat sink interface. This work provides a state-of-the-art assessment on “thermal interface materials,” including fundamentals, materials used, their performance, and how interface resistance is...

Inverse Reinforcement Learning Under Noisy Observations (Extended Abstract)

We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, noisy observations of the trajectory are available. We generalize the previous method of expectation-maximization for inverse reinforcement learning, which allows the trajectory of the expert to be partially hidden from the learner, to incorpo...

Behavior of Materials under Conditions of Thermal Stress

Disbond detection in adhesively bonded composite structures using vibration signatures

This paper presents a structural health monitoring technique based on analysis of the dynamic signature of the structure, which changes as damage occurs, due to alterations in structural properties such as stiffness and damping. Experiments performed on plate specimens and T-joint specimens which had various degrees of damage in the form of delaminations showed that it is possible to quantify t...