The virtual displacement principle of elasto-plastic damage mechanics is presented. A linear complementary method for elasto-plastic damage problem is proposed by using FEM technique. This method is applicable to solv...The virtual displacement principle of elasto-plastic damage mechanics is presented. A linear complementary method for elasto-plastic damage problem is proposed by using FEM technique. This method is applicable to solving the damage structure analysis of hardened and softened nonlinear material.展开更多
The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics.The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic...The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics.The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram(EEG)signals.We proposed a complementary set of methods considering envelope power,focal sharpness changes,and nonlinear patterns of EEG signals of 79 neonates with seizures.Features derived from EEG signals were used as input to the machine learning classifier.All three characteristics were significantly elevated during seizure events,as agreed upon by all viewers(P<0.0001).Envelope power was elevated in the entire seizure period,and the degree of nonlinearity rose at the termination of a seizure event.Epileptic sharpness effectively characterizes an entire seizure event,complementing the role of envelope power in identifying its onset.However,the degree of nonlinearity showed superior discriminability for the termination of a seizure event.The proposed computational methods for intrinsic sharp or nonlinear EEG patterns evolving during neonatal seizure could share some features with envelope power.Current findings may be helpful in developing strategies to improve neonatal seizure monitoring.展开更多
In this paper, a type of accurate a posteriori error estimator is proposed for the Steklov eigenvalue problem based on the complementary approach, which provides an asymptotic exact estimate for the approximate eigenp...In this paper, a type of accurate a posteriori error estimator is proposed for the Steklov eigenvalue problem based on the complementary approach, which provides an asymptotic exact estimate for the approximate eigenpair. Besides, we design a type of cascadic adaptive finite element method for the Steklov eigenvalue problem based on the proposed a posteriori error estimator. In this new cascadic adaptive scheme, instead of solving the Steklov eigenvalue problem in each adaptive space directly, we only need to do some smoothing steps for linearized boundary value problems on a series of adaptive spaces and solve some Steklov eigenvalue problems on a low dimensional space. Furthermore, the proposed a posteriori error estimator provides the way to refine meshes and control the number of smoothing steps for the cascadic adaptive method. Some numerical examples are presented to validate the efficiency of the algorithm in this paper.展开更多
文摘The virtual displacement principle of elasto-plastic damage mechanics is presented. A linear complementary method for elasto-plastic damage problem is proposed by using FEM technique. This method is applicable to solving the damage structure analysis of hardened and softened nonlinear material.
基金partially supported by the National Natural Science Foundation of China(Nos.62171028 and 62001026)the Beijing Natural Science Foundation(No.L232139)+2 种基金the Open Project of Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province(Nos.MEDH202204 and MEDC202303)the High-Level Fellow Research Fund Program(No.3050012222022)the BIT Research and Innovation Promoting Project(No.2023YCXZ009).
文摘The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics.The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram(EEG)signals.We proposed a complementary set of methods considering envelope power,focal sharpness changes,and nonlinear patterns of EEG signals of 79 neonates with seizures.Features derived from EEG signals were used as input to the machine learning classifier.All three characteristics were significantly elevated during seizure events,as agreed upon by all viewers(P<0.0001).Envelope power was elevated in the entire seizure period,and the degree of nonlinearity rose at the termination of a seizure event.Epileptic sharpness effectively characterizes an entire seizure event,complementing the role of envelope power in identifying its onset.However,the degree of nonlinearity showed superior discriminability for the termination of a seizure event.The proposed computational methods for intrinsic sharp or nonlinear EEG patterns evolving during neonatal seizure could share some features with envelope power.Current findings may be helpful in developing strategies to improve neonatal seizure monitoring.
基金supported by National Natural Science Foundation of China(Grant Nos.11801021 and 11571027)Foundation for Fundamental Research of Beijing University of Technology(Grant No.006000546318504)International Research Cooperation Seed Fund of Beijing University of Technology(Grant No.2018B32)。
文摘In this paper, a type of accurate a posteriori error estimator is proposed for the Steklov eigenvalue problem based on the complementary approach, which provides an asymptotic exact estimate for the approximate eigenpair. Besides, we design a type of cascadic adaptive finite element method for the Steklov eigenvalue problem based on the proposed a posteriori error estimator. In this new cascadic adaptive scheme, instead of solving the Steklov eigenvalue problem in each adaptive space directly, we only need to do some smoothing steps for linearized boundary value problems on a series of adaptive spaces and solve some Steklov eigenvalue problems on a low dimensional space. Furthermore, the proposed a posteriori error estimator provides the way to refine meshes and control the number of smoothing steps for the cascadic adaptive method. Some numerical examples are presented to validate the efficiency of the algorithm in this paper.
