|
|
| (2 intermediate revisions by one user not shown) |
| Line 1: |
Line 1: |
| | {{StudentProjectTemplate | | {{StudentProjectTemplate |
| − | |Summary=The topic focuses on generative models (GAN) for CAN-bus data and investigating the representation learning capabilities of such techniques | + | |Summary=The topic focuses on generative models (VAE) for CAN-bus data and investigating the representation learning capabilities of such techniques |
| − | |Keywords=GAN, CAN data, MAR | + | |Keywords=VAE, Time-series data, Streaming data, MAR |
| − | |TimeFrame=2020 Fall - 2021 Summer | + | |TimeFrame=2021 Fall - 2022 Summer |
| | |References=https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf | | |References=https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf |
| | | | |
| − | https://arxiv.org/abs/1706.02633 | + | https://openreview.net/pdf?id=Sy2fzU9gl |
| | | | |
| − | https://openreview.net/pdf?id=rJedV3R5tm
| + | https://www.sciencedirect.com/science/article/pii/S092658051930367X |
| − | | + | |Supervisor=Kunru Chen, Abdallah Alabdallah, Thorsteinn Rögnvaldsson |
| − | https://www.aaai.org/Conferences/AAAI/2017/PreliminaryPapers/12-Yu-L-14344.pdf | + | |
| − | | + | |
| − | https://arxiv.org/pdf/1511.06434.pdf
| + | |
| − | |Prerequisites=Excellent Programming Skills
| + | |
| − | Excellent knowledge in Machine Learning and Neural Networks
| + | |
| − | |Supervisor=Kunru Chen, Tiago Cortinhal, Thorsteinn Rögnvaldsson, | + | |
| | |Level=Master | | |Level=Master |
| | |Status=Open | | |Status=Open |
| | }} | | }} |
| − | Control Area Network (CAN) is a protocol that is used to manipulate vehicles. It is multidimensional and consists of control and sensor signals to and from different parts of the equipment. Since this data comes internally from the machine itself, it is stable and cheap to collect it. Previous work has shown that CAN data can be used to build representations for machine activity recognition (MAR) for forklift trucks. However, those representations are limited to only describing the existing data in both realism and diversity. Creating representation by training a vanilla autoencoder has disadvantages when trying to explore the entire space of CAN signals.
| |
| − |
| |
| − | Generative approaches have been used mostly in traditional types of data, like images, and have shown to have great capabilities to learn the underlying distribution as well as allowing us to sample new unseen data points. This has shown great results as we can see in https://thispersondoesnotexist.com, or even in pictures to picture translations and style transfers. This generative capability also allows us to perform arithmetic operations on the vector and see the underlying structure of each different “class” of outputs.
| |
| − |
| |
| − | Nevertheless, the work done in other data modalities is still sparse but nevertheless growing in interest. In this thesis, the main interest is focused on a very specific type of data that might bring all kinds of hardships and obstacles to overcome. Some of those hardships might come from the type of data we are trying to generate. This needs to be investigated and solutions to overcome these types of situations are a key aspect we will be looking for.
| |
| − | The students need to develop a GAN-based network to generate CAN data, to evaluate the quality of the generated data, and to use that data in a MAR task.
| |
| − |
| |
| − | Research Questions:
| |
| − | Can GANs generate realistic CAN data?
| |
| − | Can GANs generate/predict the (near) future CAN signals?
| |
| − | Is the latent space an informative representation about the CAN signals?
| |
| − |
| |
| − |
| |
| − | If you want more information about this topic you can contact us at kunru.chen@hh.se and tiago.cortinhal@hh.se or pass by our office at E522!
| |
