Benjamin Y. Choo
University of Virginia
10 Papers
61 Citations
Benjamin Y. Choo is an academic researcher from University of Virginia. The author has contributed to research in topics: Prognostics & Markov decision process. The author has an hindex of 6, co-authored 10 publications. Previous affiliations of Benjamin Y. Choo include Rolls-Royce Motor Cars.
Chat about Author
Papers
Statistical analysis-based error models for the Microsoft Kinect(TM) depth sensor.
TL;DR: Measurement and statistics-based models are presented for the stochastic error in each axis direction, which are based on the location and depth value of empirical data measured for each pixel across the entire field of view.
45
The WEAR methodology for prognostics and health management implementation in manufacturing
TL;DR: In this article, the authors present a new methodology for (i) targeting areas in a manufacturing setting that could benefit from a diagnostics and health management (PHM) system, and (ii) testing and comparing PHM strategies for implementation on the targeted areas.
21
Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems.
Benjamin Y. Choo,Stephen Adams,Brian A. Weiss,Jeremy A. Marvel,Peter A. Beling +4 more
- 13 Nov 2020
TL;DR: The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy.
Health-aware hierarchical control for smart manufacturing using reinforcement learning
Benjamin Y. Choo,Stephen Adams,Peter A. Beling +2 more
- 19 Jun 2017
TL;DR: A novel model of a control system that makes uses of health information throughout the manufacturing hierarchy is proposed and a reinforcement learning based approach to solving the MDPs for optimal or near-optimal policies is defined.
12
Adaptive Multi-scale PHM for Robotic Assembly Processes.
Benjamin Y. Choo,Peter A. Beling,Amy LaViers,Jeremy A. Marvel,Brian A. Weiss +4 more
- 01 Jan 2015
TL;DR: Adaptive multiscale prognostics and health management (AM-PHM) leverages and integrates component-level PHM information with hierarchical relationships across the component, machine, work cell, and production line levels in a manufacturing system.