ROM-FOM Interface Optimization for Efficient Thermomechanical Simulations of Electronic Components

Model order reduction techniques are developed and utilized to make numerical simulations more efficient. The use of Reduce Order Models (ROM) also enables data exchange with external parties without disclosing the sensitive information present in a Full-Order Model (FOM). It is crucial to optimize for both the efficiency and accuracy of a ROM...

Secure Logistic Regression for Vertical Federated Learning

Data island effectively blocks the practical application of machine learning. To meet this challenge, a new framework known as federated learning was created. It allows model training on a large amount of scattered data owned by different data providers. This article presents a parallel solution for computing logistic regression based on...

MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like...

Prognostics and health monitoring of electronic system: A review

Miniaturization of electronics, reduction of time to market and new functionalities in the current context of autonomous driving, electrification and connectivity, are bringing new reliability challenges. Prognostics and Health Management (PHM) can be used effectively to address some of the key challenges, in particular new challenges...