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Found 6 papers, 3 papers with code
Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time.
Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
Deep reinforcement learning has been the pioneer for solving this problem without the need for relying on the physical model of complex systems by just interacting with it.
Ranked #1 on
Decision Making
on NASA C-MAPSS
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Deep reinforcement learning has the potential to address these problems by learning optimal control policies through exploration in an environment.
Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain.
Siamese Network Training Using Artificial Triplets By Sampling and Image Transformation
The device used in this work detects the objects over the surface of the water using two thermal cameras which aid the users to detect and avoid the objects in scenarios where the human eyes cannot (night, fog, etc.).
Experimental Analysis of Trajectory Control Using Computer Vision and Artificial Intelligence for Autonomous Vehicles
Perception of the lane boundaries is crucial for the tasks related to autonomous trajectory control.
