Results 71 to 80 of about 13,850 (305)

Subspace identification of local 1D homogeneous systems

, 2015
This paper studies the local subspace identification of 1D homogeneous networked systems. The main challenge lies at the unmeasurable interconnection signals between neighboring subsystems.
Yu, C. (author), Hansson, A (author), Verhaegen, M.H.G. (author)   +2 more
core   +1 more source

A least squares approach to the subspace identification problem [PDF]

2008 47th IEEE Conference on Decision and Control, 2008
In this paper, we propose a new method for the identification of linear Multiple Inputs-Multiple Outputs (MIMO) systems. By introducing a particular user-defined matrix that does not change the rank of the extended observability matrix when multiplying this latter matrix on the left, the subspace identification problem is recasted into a simple least ...
Bako, Laurent, Mercère, Guillaume, Lecoeuche, Stéphane   +2 more
openaire   +2 more sources

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Research on Model Identification of Permanent Magnet DC Brushless Motor Based on Auxiliary Variable Subspace Identification Algorithm

World Electric Vehicle Journal
This paper proposes a model identification method based on the auxiliary variable closed-loop subspace identification algorithm to address the problem of modeling difficulties caused by various complex factors affecting permanent magnet brushless DC ...
Jing Zhang, Yuhui Liu, Te Chen, Guowei Dou   +3 more
doaj   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Convergence analysis of instrumental variable recursive subspace identification algorithms

, 2006
The convergence properties of recently developed recursive subspace identification methods are investigated in this paper. The algorithms operate on the basis of instrumental variable (IV) versions of the propagator method for signal subspace estimation.
G. Mercere, LOVERA, MARCO, Mercère, Guillaume   +2 more
core   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Bilinear Subspace Identification of Induction Motor Dynamic Characteristics

, 2004
The inherent properties of the induction motor of multivariate, strong coupling and nonlinearity result that the mechanism model cannot describe motor characteristics accurately.
Yu HB(于海斌), Yuan DC(袁德成), Fan LP(樊立萍)   +2 more
core  

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Real-Time Identification of Hyperspectral Subspaces

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
The identification of signal subspace is a crucial operation in hyperspectral imagery, enabling a correct dimensionality reduction that often yields gains in algorithm performance and efficiency. This paper presents new parallel implementations of a widely used hyperspectral subspace identification with minimum error (HySime) algorithm on different ...
Emanuele Torti, Marco Acquistapace, Giovanni Danese, Francesco Leporati, Antonio Plaza   +4 more
openaire   +2 more sources