Feasibility Study of Transfer Learning on LSTM Recurrent Neural Networks for Fiber Manufacturing Commercialization
This thesis explores business pathways to commercialize Device Realization Lab’s technology that uses deep reinforcement learning for optical fiber manufacturing control systems. A viable business solution is proposed based on feedback from venture capital investors. The solution comprises developin...
Main Author: | Sawant, Nilay |
---|---|
Other Authors: | Anthony, Brian W. |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2023
|
Online Access: | https://hdl.handle.net/1721.1/147394 |
Similar Items
-
Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning
by: Longbin Zhang, et al.
Published: (2022-01-01) -
Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context
by: Mahamadou Klanan Diarra, et al.
Published: (2023-12-01) -
Modelling Hysteresis in Shape Memory Alloys Using LSTM Recurrent Neural Network
by: Mohammad Reza Zakerzadeh, et al.
Published: (2024-01-01) -
Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network
by: Chuanhui Li, et al.
Published: (2020-12-01) -
Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network
by: Anna Manowska, et al.
Published: (2022-07-01)