Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Nghiem, Truong X."'
Autor:
Nguyen, Binh, Nguyen, Linh, Nghiem, Truong X., La, Hung, Baca, Jose, Rangel, Pablo, Montoya, Miguel Cid, Nguyen, Thang
This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to buil
Externí odkaz:
http://arxiv.org/abs/2403.16489
Autor:
Nghiem, Truong X., Drgoňa, Ján, Jones, Colin, Nagy, Zoltan, Schwan, Roland, Dey, Biswadip, Chakrabarty, Ankush, Di Cairano, Stefano, Paulson, Joel A., Carron, Andrea, Zeilinger, Melanie N., Cortez, Wenceslao Shaw, Vrabie, Draguna L.
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As oppos
Externí odkaz:
http://arxiv.org/abs/2306.13867
Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is difficult to apply offline learning-based methods in these time-varying
Externí odkaz:
http://arxiv.org/abs/2303.13694
Publikováno v:
In e-Prime - Advances in Electrical Engineering, Electronics and Energy September 2024 9
Publikováno v:
In Franklin Open March 2024 6
Autor:
Le, Viet-Anh, Nghiem, Truong X.
This paper focuses on distributed learning-based control of decentralized multi-agent systems where the agents' dynamics are modeled by Gaussian Processes (GPs). Two fundamental problems are considered: the optimal design of experiment for concurrent
Externí odkaz:
http://arxiv.org/abs/2103.14156
Autor:
Le, Viet-Anh, Nghiem, Truong X.
This paper proposes a receding horizon active learning and control problem for dynamical systems in which Gaussian Processes (GPs) are utilized to model the system dynamics. The active learning objective in the optimization problem is presented by th
Externí odkaz:
http://arxiv.org/abs/2101.10351
This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positi
Externí odkaz:
http://arxiv.org/abs/2101.10500
Publikováno v:
In EURO Journal on Computational Optimization 2024 12
Autor:
Nghiem, Truong X.
Data-driven Model Predictive Control (MPC), where the system model is learned from data with machine learning, has recently gained increasing interests in the control community. Gaussian Processes (GP), as a type of statistical models, are particular
Externí odkaz:
http://arxiv.org/abs/1812.10579