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Intelligent Environments Laboratory

The University of Texas at Austin
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    • Prof. Zoltan Nagy, PhD
    • June Young Park
    • José Ramón Vázquez-Canteli
    • Megan K. McHugh, MSE
    • Ayşegül Demir Dilsiz
    • Hagen Fritz
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November 23, 2018, Filed Under: Past Projects II

Intraocular Microrobots and Control Using Field Gradients

This project aimed at developing magnetic microrobotics for ophtalmic surgery.

I was involved in developing the magnetic model for assembled-MEMS microrobots. The model is validated through FEM simulations and experiments, and captures the characteristics of complex 3-D structures. It allows us, for the first time, to consider full 6-DOF control of untethered devices.

 

Publications

Z. Nagy, O. Ergeneman, J. J. Abbott, M. Hutter, A. M. Hirt, and B. J. Nelson, Modeling Assembled-MEMS Microrobots for Wireless Magnetic Control, in Proc. IEEE International Conference on Robotics and Automation (ICRA), 2008, Pasadena, CA, USA
doi: 10.1109/ROBOT.2008.4543315

Z. Nagy and B. J. Nelson, Lagrangian Modeling of the Magnetization and the Magnetic Torque on Assembled Soft-Magnetic MEMS Devices for Fast Computation and Analysis, IEEE Transactions on Robotics, Vol.28, No.4, pp.787-797, August 2012
doi: 10.1109/TRO.2012.2193230

Research Highlight

Whole Communities—Whole Health

CHANGING THE WAY SCIENCE HELPS SOCIETY THRIVE IS OUR GRAND CHALLENGE Traditional research studies take “snapshots” of people’s lives at different Read more 

About Us

The Intelligent Environments Laboratory (IEL), led by Prof. Zoltán Nagy, is an interdisciplinary research group within the Building Energy & Environments (BEE) and Sustainable Systems (SuS) Programs of the Department of Civil, Architectural and Environmental Engineering (CAEE) in the Cockrell School of Engineering of the University of Texas at Austin.

The aim of our research is to rethink the built environment and define Smart Buildings and Cities as spaces that adapt to their occupants and reduce their energy consumption.

We combine data science with building science and apply machine learning to the building and urban scale

Take a look at our projects !

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air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
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Research

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UT Energy App – Privacy Policy

Fault detection and diagnostics of air handling units using machine learning and expert rule-sets

Reinforcement Learning in the Built Environment

Reinforcement learning for urban energy systems & demand response

Multi-Agent Reinforcement Learning for demand response & building coordination

IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings

People

  • Prof. Zoltan Nagy, PhD
  • June Young Park
  • José Ramón Vázquez-Canteli
  • Megan K. McHugh, MSE

Tags

air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
ITS

301 E Dean Keeton St
Austin, TX 78712
512-555-5555
nagy@utexas.edu

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