The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system mo...The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.展开更多
Phospholipids are the major building blocks of the biological membranes.Additionally,phospholipids modulate membrane trafficking and metabolites derived from their degradation are important intracellular signaling mol...Phospholipids are the major building blocks of the biological membranes.Additionally,phospholipids modulate membrane trafficking and metabolites derived from their degradation are important intracellular signaling molecules involved in processes such as proliferation and apoptosis.The improvement of mass spectrometry techniques for phospholipid analysis has facilitated simultaneous analysis of hundreds of phospholipid species.展开更多
Traditional parametric system identification methods usually rely on apriori knowledge of the targeted system,which may not always be available,especially for complex systems.Although neural networks(NNs)have been inc...Traditional parametric system identification methods usually rely on apriori knowledge of the targeted system,which may not always be available,especially for complex systems.Although neural networks(NNs)have been increasingly adopted in system iden-tification,most studies have failed to derive interpretable parametric models for further analysis.In this paper,we propose a novel end-to-end autoregressive with exogenous input(ARX)model identification framework using NNs.An order-wise neural network structure is introduced and trained using a multitask learning approach to simultaneously identify both the model terms and coefficients of the ARX model.Through testing with various neural network backbones and training data sizes in different scenarios,we empirically demonstrate that the proposed framework can effectively identify an arbitrary stable ARX model with finite simulation training data.This study opens up a new research opportunity for parametric system identification by harnessing the power of deep learning.展开更多
基金Supported by the China Scholarship Council,National Natural Science Foundation of China(Grant No.11402022)the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office(DYSCO)+1 种基金the Fund for Scientific Research–Flanders(FWO)the Research Fund KU Leuven
文摘The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.
文摘Phospholipids are the major building blocks of the biological membranes.Additionally,phospholipids modulate membrane trafficking and metabolites derived from their degradation are important intracellular signaling molecules involved in processes such as proliferation and apoptosis.The improvement of mass spectrometry techniques for phospholipid analysis has facilitated simultaneous analysis of hundreds of phospholipid species.
文摘Traditional parametric system identification methods usually rely on apriori knowledge of the targeted system,which may not always be available,especially for complex systems.Although neural networks(NNs)have been increasingly adopted in system iden-tification,most studies have failed to derive interpretable parametric models for further analysis.In this paper,we propose a novel end-to-end autoregressive with exogenous input(ARX)model identification framework using NNs.An order-wise neural network structure is introduced and trained using a multitask learning approach to simultaneously identify both the model terms and coefficients of the ARX model.Through testing with various neural network backbones and training data sizes in different scenarios,we empirically demonstrate that the proposed framework can effectively identify an arbitrary stable ARX model with finite simulation training data.This study opens up a new research opportunity for parametric system identification by harnessing the power of deep learning.
