Results 31 to 40 of about 264 (161)
Automated Harmonic Signal Removal Technique Using Stochastic Subspace-Based Image Feature Extraction
Journal of Imaging, 2020 This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a ...
Muhammad Danial Bin Abu Hasan +3 more
doaj +1 more source
Model-Free Predictive Anti-Slug Control of a Well-Pipeline-Riser [PDF]
Modeling, Identification and Control, 2016 Simplified linearized discrete time dynamic state space models are developed for a 3-phase well-pipeline-riser and tested together with a high fidelity dynamic model built in K-Spice and LedaFlow. In addition the Meglio pipeline-riser model is used as an
Christer Dalen, David Di Ruscio
doaj +1 more source
Experimental Modal Analysis of Angle Signals Based on the Stochastic Subspace Identification Method
NUST Journal of Engineering Sciences, 2022 This paper aims to verify the extraction of modal parameters from angle signals using the stochastic subspace identification (SSI) method. The use of angle signal-based mode shapes can reduce the loss of node information and enhance the robustness in ...
In-Ho Kim
doaj +1 more source
Fast Multi-Order Stochastic Subspace Identification⋆
IFAC Proceedings Volumes, 2011 Abstract Stochastic subspace identification methods are an efficient tool for system identification of mechanical systems in Operational Modal Analysis (OMA), where modal parameters are estimated from measured vibrational data of a structure. System identification is usually done for many successive model orders, as the true system order is unknown ...
Michael Duhler, Laurent Mevel
openaire +1 more source
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
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
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
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