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Jaeyoung Cho

Researcher at Lamar University

Publications -  22
Citations -  539

Jaeyoung Cho is an academic researcher from Lamar University. The author has contributed to research in topics: Liquefied natural gas & Chemistry. The author has an hindex of 8, co-authored 17 publications receiving 344 citations. Previous affiliations of Jaeyoung Cho include University of Houston & Prairie View A&M University.

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Drone-Aided Healthcare Services for Patients with Chronic Diseases in Rural Areas

TL;DR: This paper addresses the drone-aided delivery and pickup planning of medication and test kits for patients with chronic diseases who are required to visit clinics for routine health examinations and/or refill medicine in rural areas.
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Multi-UAV Pre-Positioning and Routing for Power Network Damage Assessment

TL;DR: A two-stage stochastic integer programming optimization model is presented for damage assessment in which the first stage determines the optimal UAV locations anticipating an arrival of an extreme weather event, and the second stage is to adjust the Uav locations when the arrival time of the predicted extreme weather becomes closer with updated information.
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Drone flight scheduling under uncertainty on battery duration and air temperature

TL;DR: A robust optimization approach to find the optimal flight schedule in the flight network considering uncertain battery duration is proposed, and three flight duration uncertainty sets are explored based on the regression model.
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Models and computational algorithms for maritime risk analysis: a review

TL;DR: A detailed literature review of over 180 papers about different threats, their consequences pertinent to the maritime industry, and a discussion on various risk assessment models and computational algorithms are provided.
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A quantitative approach for assessment and improvement of network resilience

TL;DR: A conceptual framework featuring the ability of the network system to adopt alternative plans when a component is disrupted is introduced and an optimization model is further introduced to maximize the network resilience under budget constraint through reinforcing the weakest components in the network.