H
Hyunbo Cho
Researcher at Pohang University of Science and Technology
Publications - 114
Citations - 1950
Hyunbo Cho is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Supply chain & Interoperability. The author has an hindex of 24, co-authored 110 publications receiving 1810 citations. Previous affiliations of Hyunbo Cho include Texas A&M University.
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Enabling technologies of agile manufacturing and its related activities in Korea
TL;DR: The concept of agile manufacturing is defined as the capability of surviving and prospering in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services as mentioned in this paper.
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A structured approach to deadlock detection, avoidance and resolution in flexible manufacturing systems
TL;DR: In this paper, a structured model for deadlock detection, avoidance and resolution caused by part flow in manufacturing systems is presented, where a system status graph can be constructed for the parts currently in the system.
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Intelligent workstation controller for computer-integrated manufacturing: Problems and models
Hyunbo Cho,Richard A. Wysk +1 more
TL;DR: In this article, the authors present the problems and models necessary to develop an intelligent workstation controller (IWC) at the middle level of a shop floor control system (SFCS), which is responsible for selecting a specific process routing, allocating resources, scheduling and coordinating activities across the equipment, monitoring the progress of activities, detecting and recovering from errors, and preparing reports.
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A robust adaptive scheduler for an intelligent workstation controller
Hyunbo Cho,Richard A. Wysk +1 more
TL;DR: In this article, a shop floor control system (SFCS) consisting of three hierarchical control levels (shop, workstation, and equipment) is described, where each controller plans, schedules, and executes the activities necessary to process an order.
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Improving memory-based collaborative filtering via similarity updating and prediction modulation
TL;DR: A similarity update method that uses an iterative message passing procedure and a novel modulation method and an accuracy metric are presented in order to minimize the predictive accuracy error and to evenly distribute predicted ratings over true rating scales.