Recent advances in machine learning techniques such as deep learning (DL) has rejuvenated data-driven analysis in aerospace and integrated building systems. DL algorithms have been successful due to the presence of large volumes of data and its ability to learn the features during the learning process. The performance improvement is significant from the features learnt from DL techniques as compared to the hand crafted features. This talk demonstrates using deep belief networks (DBN), deep auto encoders (DAE), deep reinforcement learning (DRL) and generative adversarial networks (GAN) in five different aerospace and building systems applications: (i) estimation of fuel flow rate in jet engines, (ii) fault detection in elevator cab doors using smart phone, (iii) prediction of chiller power consumption in heating, ventilation, and air conditioning (HVAC) systems, (iv) material and structural characterization of aerospace parts, and (v) end-to-end control of high-precision additive manufacturing process. Do you like this material? See a lot of videos related to this topic for FREE at our AI Learning Accelerator – #DeepLearning #Aerospace #ODSC
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