Abstract: 

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.

Bio: 

Kishore K. Reddy is a Staff Research Scientist at the United Technologies Research Center (UTRC) working in the area of computer vision, human machine interaction (HMI) and machine learning. He is currently leading the Digital Initiative at UTRC primarily focusing on Deep Learning applications in aerospace and building systems to perform outliers and anomalies detection, multi-modal sensor fusion and data compression. Kishore earned his Ph.D. in 2012 from University of Central Florida, where he developed advanced video and image analysis algorithms, primarily segmentation and classification approaches, for multiple contracts funded by DARPA, IARPA, and NIH.