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Hien T. Tran

Researcher at North Carolina State University

Publications -  143
Citations -  3292

Hien T. Tran is an academic researcher from North Carolina State University. The author has contributed to research in topics: Nonlinear system & Kalman filter. The author has an hindex of 26, co-authored 143 publications receiving 3002 citations.

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Modeling and control of physical processes using proper orthogonal decomposition

TL;DR: The aim of the paper is to show that, using the obtained ensemble of data, POD can be used to model accurately the natural convection and this approach is very efficient in the sense that it uses the smallest possible number of parameters, and thus, is suited for process control.
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Blood pressure and blood flow variation during postural change from sitting to standing: model development and validation

TL;DR: A mathematical model is presented that can predict dynamic changes in beat-to-beat arterial blood pressure and middle cerebral artery blood flow velocity during postural change from sitting to standing and is in agreement with physiological data from a young subject during postures change from Sitting to standing.
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Dynamic multidrug therapies for hiv: optimal and sti control approaches.

TL;DR: A dynamic mathematical model is formulated that describes the interaction of the immune system with the human immunodeficiency virus and that permits drug "cocktail" therapies and supports a scenario in which STI therapies can lead to long-term control of HIV by the immune response system after discontinuation of therapy.
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Nonlinear feedback controllers and compensators: a state-dependent Riccati equation approach

TL;DR: This paper addresses the effectiveness and potential of the SDRE technique for the design of nonlinear compensator-based feedback controllers and the asymptotic convergence of the estimator and the compensated system.
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HIV dynamics: Modeling, data analysis, and optimal treatment protocols

TL;DR: In this article, the authors present an overview of some concepts and methodologies useful in modeling HIV pathogenesis, including parameter estimation, data reduction and representation, and optimal control relative to STI.