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Caffe: a fast open framework for deep learning.

Home Page: http://caffe.berkeleyvision.org/

License: Other

CMake 2.83% Shell 0.47% C++ 80.06% Python 9.10% MATLAB 0.88% Cuda 5.92% Makefile 0.66% Dockerfile 0.08%

caffe's Introduction

Welcome to my page👋

I received my PhD from the University of Antwerp, Belgium. My research is concerned with dependability analysis, robustness checking, validation, and verification technique for Cyber-Physical Systems (CPS) using model-based fault-injection. My main focus is on the optimization of model-based fault-injection using a combination of machine learning and model-based techniques.

I am currently working on an Reinforcement Learning (RL)-based method to optimize fault-injcention. I am currently learning different machine learning approches that can be applied on black-box (FMI standard based model) and white-box (Simulink-based model) CPSs for dependability analysis.

Research interest

  • Dependability Analysis:
    • Safety Assessment
    • Fault-Injection
    • Hazard Analysis
    • Validation and Verification
  • Model-Based System Engineering
  • Machine Learning
  • Embedded System Design and Analysis
  • Electronic Design Automation (ASIC/FPGA)
  • Image Processing

caffe's People

Contributors

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