Results 41 to 50 of about 2,570,203 (288)

Advanced Data Collection and Analysis in Data-Driven Manufacturing Process

Chinese Journal of Mechanical Engineering, 2020
The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control, rather than using simplified physical models and human expertise.
Ke Xu, Yingguang Li, Changqing Liu, Xu Liu, Xiaozhong Hao, James Gao, Paul G. Maropoulos   +6 more
doaj   +1 more source

Data-driven adaptive model-based predictive control with application in wastewater systems [PDF]

, 2011
This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based ...
Balderud, J., Katebi, M.R., Rahmat, M.F., Wahab, N.A.   +3 more
core   +1 more source

Neural Nonlinear Autoregressive Model with Exogenous Input (NARX) for Turboshaft Aeroengine Fuel Control Unit Model

Aerospace, 2021
One of the most important parts of a turboshaft engine, which has a direct impact on the performance of the engine and, as a result, on the performance of the propulsion system, is the engine fuel control system.
Maria Grazia De Giorgi, Luciano Strafella, Antonio Ficarella   +2 more
doaj   +1 more source

Data‐driven predictive direct load control of refrigeration systems

IET Control Theory & Applications, 2015
A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control is proposed to fulfil two important objectives: to secure
Shafiei, Seyed Ehsan, Knudsen, Torben, Wisniewski, Rafal, Andersen, Palle   +3 more
openaire   +1 more source

The neuron as a direct data-driven controller

Proceedings of the National Academy of Sciences
In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit
Jason J. Moore, Alexander Genkin, Magnus Tournoy, Joshua L. Pughe-Sanford, Rob R. de Ruyter van Steveninck, Dmitri B. Chklovskii   +5 more
openaire   +3 more sources

Energy Theft in Smart Grids: A Survey on Data-Driven Attack Strategies and Detection Methods

IEEE Access, 2021
The convergence of legacy power system components with advanced networking and communication facilities have led towards the development of smart grids.
Ahlam Althobaiti, Anish Jindal, Angelos K. Marnerides, Utz Roedig   +3 more
doaj   +1 more source

Asymptotically exact direct data-driven multivariable controller tuning

IFAC-PapersOnLine, 2015
In this paper, a data-driven controller design method for multivariable systems is introduced and analyzed. The proposed technique is direct, as it is entirely based on experimental data and does not rely on a physical description of the system, and non-iterative, as it does not require controller adjustments based on additional experiments.
FORMENTIN, SIMONE, Bisoffi, Andrea, Oomen, Tom   +2 more
openaire   +3 more sources

On input design for direct data-driven controller tuning [PDF]

IFAC Proceedings Volumes, 2012
In recent years, noniterative Correlation-based Tuning (CbT) and Virtual Reference Feedback Tuning (VRFT) have been proposed as an alternative to the standard model-based approach for model-reference control design. In this work, the problem of input design for direct data-driven controller tuning methods is investigated.
FORMENTIN, SIMONE, A. Karimi, SAVARESI, SERGIO MATTEO   +2 more
openaire   +2 more sources

A new perspective towards the development of robust data-driven intrusion detection for industrial control systems

Nuclear Engineering and Technology, 2020
Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection.
Abiodun Ayodeji, Yong-kuo Liu, Nan Chao, Li-qun Yang   +3 more
doaj   +1 more source

Rapid predictive simulations with complex musculoskeletal models suggest that diverse healthy and pathological human gaits can emerge from similar control strategies [PDF]

, 2019
Physics-based predictive simulations of human movement have the potential to support personalized medicine, but large computational costs and difficulties to model control strategies have limited their use.
De Groote, Friedl, Dembia, Christopher, Falisse, Antoine dit Motte, Gillis, Joris, Jonkers, Ilse, Serrancolí, Gil   +5 more
core   +2 more sources