Results 241 to 250 of about 18,502,554 (380)

Design of GHz Mechanical Nanoresonator with High Q-Factor Based on Optomechanical System. [PDF]

Micromachines (Basel), 2022
Jin J, Hu N, Zhan L, Wang X, Zhang Z, Hu H.   +5 more
europepmc   +1 more source

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

Microfluidic QCM enables ultrahigh Q-factor: a new paradigm for in-liquid gravimetric sensing. [PDF]

Microsyst Nanoeng
Zhao Y, Parlak Z, Yu W, French D, Aquino W, Zauscher S.   +5 more
europepmc   +1 more source

Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization. [PDF]

Front Comput Neurosci, 2021
Li S, Lyu X, Zhao L, Chen Z, Gong A, Fu Y.   +5 more
europepmc   +1 more source

Resonant Frequency and Q-Factor of a Dielectric-Loaded-Cavity Resonator with a Conductive Layer on Its Metal Wall

, 2003
Hiroyuki Tanaka, Akihiro Tsutsumi
openalex   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Effects of Small and Normalized Q-Factor Changes and Knee Alignment on Knee Biomechanics during Stationary Cycling. [PDF]

Bioengineering (Basel)
Wilbert J, Brown S, Weinhandl JT, Tatarski R, Zhang S.   +4 more
europepmc   +1 more source

High Q-Factor Hybrid Metamaterial Waveguide Multi-Fano Resonance Sensor in the Visible Wavelength Range. [PDF]

Nanomaterials (Basel), 2021
Yang H, Chen Y, Liu M, Xiao G, Luo Y, Liu H, Li J, Yuan L.   +7 more
europepmc   +1 more source

Publisher's Note: Proton elastic form factor ratios toQ2=3.5GeV2by polarization transfer [Phys. Rev. C 71, 055202 (2005)] [PDF]

, 2005
V. Punjabi, C. F. Perdrisat, K. Aniol, F. T. Baker, J. Berthot, P.Y. Bertin, W. Bertozzi, Anne Besson, L. Bimbot, W. Boeglin, E. J. Brash, D. Brown, J. R. Calarco, L. S. Cardman, Z. Chai, C. C. Chang, J.-P. Chen, E. Chudakov, S. Churchwell, E. Cisbani, D. Dale, R. De Leo, A. Deur, B. Diederich, J. Domingo, Michael Epstein, L. A. Ewell, K. Fissum, Andre Fleck, H. Fonvieille, S. Frullani, Jiazi Gao, F. Garibaldi, A. Gasparian, G. M. Gerstner, S. Gilad, R. Gilman, A. Glamazdin, C. Glashausser, J. Gómez, V. Gorbenko, A. Green, O. Hansen, C. R. Howell, G. M. Huber, M. Iodice, C. W. de Jager, S. Jaminion, Xiang Jiang, M. K. Jones, W. Kahl, James J. Kelly, M. Khayat, L. H. Kramer, G. Kumbartzki, M. Kuss, E. Lakuriki, G. Laveissière, J. J. LeRose, M. Liang, R. Lindgren, N. Liyanage, G. J. Lolos, R. A. Macri, R. Madey, S. Malov, D. J. Margaziotis, P. Markowitz, K. McCormick, Justin I. McIntyre, R. L. J. van der Meer, R. Michaels, B. D. Milbrath, J. Mougey, S. K. Nanda, E. A. J. M. Offermann, Z. Papandreou, L. Pentchev, G. G. Petratos, Н. М. Пискунов, R. Pomatsalyuk, D. L. Prout, G. Qúeḿener, R. D. Ransome, B. A. Raue, Y. Roblin, R. Roché, G. Rutledge, P. M. Rutt, A. Saha, M. Saito, A. Sarty, T. Smith, P. Sorokin, S. Strauch, R. Suleiman, Koichi Takahashi, J. A. Templon, L. Todor, P. E. Ulmer   +99 more
openalex   +1 more source

Neural Network Adaptive Control With Long Short‐Term Memory

International Journal of Adaptive Control and Signal Processing, EarlyView.
ABSTRACT In this study, we propose a novel adaptive control architecture that provides dramatically better transient response performance compared to conventional adaptive control methods. This is accomplished by the synergistic employment of a traditional adaptive neural network (ANN) controller and a long short‐term memory (LSTM) network.
Emirhan Inanc, Abdullah Habboush, Yigit Gurses, Yildiray Yildiz, Anuradha M. Annaswamy   +4 more
wiley   +1 more source