Results 91 to 100 of about 4,486 (172)

Diffusion-driven self-assembly of emerin nanodomains at the nuclear envelope

Physical Review Research
Emerin, a nuclear membrane protein with important biological roles in mechanotransduction and nuclear shape adaptation, self-assembles into nanometer-size domains at the inner nuclear membrane.
Carlos D. Alas, Liying Wu, Fabien Pinaud, Christoph A. Haselwandter   +3 more
doaj   +1 more source

Kernel EDMD for data-driven nonlinear Koopman MPC with stability guarantees

IFAC-PapersOnLine
Extended dynamic mode decomposition (EDMD) is a popular data-driven method to predict the action of the Koopman operator, i.e., the evolution of an observable function along the flow of a dynamical system. In this paper, we leverage a recently-introduced kernel EDMD method for control systems for data-driven model predictive control.
Bold, Lea, Schaller, Manuel, Schimperna, Irene, Worthmann, Karl   +3 more
openaire   +2 more sources

EDMD-Based Robust Observer Synthesis for Nonlinear Systems

This paper presents a data driven Koopman operator based framework for designing robust state observers for nonlinear systems. Based on a finite dimensional surrogate of the Koopman generator, identified via an extended dynamic mode decomposition procedure, a tractable formulation of the observer design is enabled on the data driven model with conic ...
Ye, Xiuzhen, Tang, Wentao
openaire   +2 more sources

K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles

IEEE Access
This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space.
Jin Sung Kim, Ying Shuai Quan, Chung Choo Chung, Woo Young Choi   +3 more
doaj   +1 more source

Genetic investigation of an Iraqi family with Emery-Dreifuss muscular dystrophy

Journal of Rare Diseases
Background Emery-Dreifuss muscular dystrophy (EDMD) is a rare genetic disorder characterized by a distinctive combination of symptoms that affect both the skeletal muscles and the heart.
Mostafa Neissi, Ayoob Radhi Al-Zaalan, Adnan Issa Al-Badran, Javad Mohammadi-Asl   +3 more
doaj   +1 more source

Risk stratification in laminopathies and Emery Dreifuss muscular dystrophy

Neurology International, 2018
Laminopathies are genetic disorders due to gene mutation encoding for proteins of the nuclear envelope. Patients are at risk of conduction defect, arrhythmia, sudden death and heart failure.
Abdallah Fayssoil
doaj   +1 more source

Expression and localization of nuclear proteins in autosomal-dominant Emery-Dreifuss muscular dystrophy with LMNA R377H mutation

BMC Cell Biology, 2004
Background The autosomal dominant form of Emery-Dreifuss muscular dystrophy (AD-EDMD) is caused by mutations in the gene encoding for the lamins A and C (LMNA).
Ewald Andrea, Motsch Isabell, Krüger Eleonora, Dreger Christine, Klafke Ruth, Reichart Beate, Schäfer Jochen, Reichmann Heinz, Müller Clemens R, Dabauvalle Marie-Christine   +9 more
doaj  

Adaptive Koopman Operator Learning via Iterative Projections: Time-Series Data Prediction Using Extended Dynamic Mode Decomposition

IEEE Access
This paper presents a novel framework for adaptive learning of Koopman operator to predict the behavior of nonlinear time-varying dynamical systems based on the celebrated extended dynamic mode decomposition (EDMD).
Reiya Asuke, Masahiro Yukawa
doaj   +1 more source

Koopman-Based Methods for EV Climate Dynamics: Comparing eDMD Approaches

2025 IEEE Conference on Control Technology and Applications (CCTA)
6 pages ...
Meda, Luca, Stockar, Stephanie
openaire   +2 more sources

Error Analysis of Kernel EDMD for Prediction and Control in the Koopman Framework

Journal of Nonlinear Science
Abstract Extended dynamic mode decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is linear and encompasses full information on the (expected stochastic) dynamics. In this paper, we analyze kernel EDMD (kEDMD), where the dictionary consists
Philipp, F., Schaller, M., Worthmann, K., Peitz, S., Nüske, F. ; https://orcid.org/0000-0003-2444-7889   +4 more
openaire   +3 more sources