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2017 GTC San Jose

S7514 - Deep Representation and Reinforcement Learning for Anomaly Detection and Control in Multi-Modal Aerospace Applications

Session Speakers
Session Description

We'll discuss how deep auto-encoder (DAE) and deep reinforcement learning (DRL) can be formulated to address multimodal anomaly detection and additive manufacturing control problems in aerospace domain. DAE-based representation learning is constructed by multi-layered neural-net architecture to model complex data non-linearity. We use DAE via NVIDIA GPU implementation for: (1) unsupervised fault disambiguation from big multimodal data, and (2) structural health monitoring (crack detection) from experiment video frames on aerospace material. At the second half of the talk, we show how guided policy search (GPS) based DRL framework can be implemented for optimally planning and generalizing trajectory nozzle dynamics in a wide range of cold spray type of additive manufacturing application.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Intelligent Machines and IoT
Aerospace
50 minutes
Session Schedule