Results 41 to 50 of about 3,141,474 (179)
A Stochastic Framework for Subspace Identification of a Strongly Nonlinear Aerospace Structure [PDF]
, 2014 The present study exploits the maximum likelihood identification framework for deriving statistically-optimal models of nonlinear mechanical systems. The identification problem is formulated in the frequency domain, and model parameters are calculated by minimising a weighted least-squares cost function.
Noël, Jean-Philippe +2 more
openaire +2 more sources
New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
Advanced Engineering Materials, EarlyView.This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
wiley +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Subspace-based System Identification for Helicopter Dynamic Modelling.
, 2007 This paper investigates the problem of helicopter dynamic modelling using time-domain system identification techniques. The paper begins with a brief introduction to the state-space form of the perturbation model for helicopters, based on which, system ...
Ian Postlethwaite (36312) +2 more
core +6 more sources
Journal of Modern Power Systems and Clean Energy, 2017 In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate
Deyou YANG, Guowei CAI, Kevin CHAN
doaj +1 more source
Subspace System Identification of the Kalman Filter [PDF]
Modeling, Identification and Control, 2003 Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati ...
David Di Ruscio
doaj +1 more source
Applied Sciences, 2021 The present paper is a study of output-only modal estimation based on the stochastic subspace identification technique (SSI) to avoid the restrictions of well-controlled laboratory conditions when performing experimental modal analysis and aims to ...
Chang-Sheng Lin, Yi-Xiu Wu
doaj +1 more source
Estimation of hysteretic damping of structures by stochastic subspace identification [PDF]
Mechanical Systems and Signal Processing, 2018 Abstract Output-only system identification techniques can estimate modal parameters of structures represented by linear time-invariant systems. However, the extension of the techniques to structures exhibiting non-linear behavior has not received much attention.
Bajric, Anela +1 more
openaire +2 more sources
Dipolar Coupling Errors in Nuclear Spin Qubits
Advanced Science, EarlyView.Multi‐nuclear spin registers of phosphorus‐atom qubits in silicon have demonstrated 99.99% single and >$>$99.9% two qubit gate fidelities. Despite nanometer separation, read‐out fidelities between individual nuclear spins within the register are not identical.
I. D. Thorvaldson +15 more
wiley +1 more source
Extension of Subspace Identification to LPTV Systems: Application to Helicopters [PDF]
, 2012 In this paper, we focus on extending the subspace identification to the class of linear periodically time-varying (LPTV) systems. The Lyapunov-Floquet transformation is first applied to the system’s state-space model in order to get the monodromy matrix (
Jhinaoui, Ahmed +5 more
core +1 more source