单面柱局域共振声子晶体低频带隙特性分析及结构改进研究

基于有限元法对单面柱局域共振声子晶体进行带隙特性分析,研究了结构参数对该类型声子晶体的影响。结果表明:随着散射体高度的增加,单面柱声子晶体的第一完全带隙的起始频率逐渐降低,带宽逐渐增大;随着基板厚度的增大,单面柱声子晶体的起始频率逐渐升高,截止频率先增大后减小。并且在经典单面柱声子晶体的基础上,组合了两种新型的三组元单面柱声子晶体结构:嵌入式单面柱声子晶体(以下简称结构Ⅰ)和粘接式单面柱声子晶体(以下简称结构Ⅱ)。通过对其带隙特性的分析得出:这两种新结构与经典的单面柱声子晶体相比,都具有更低频的带隙,这对于低频减振降噪是非常有利的。本文的结果将对实际的工程应用提供一定的理论指导。     

Coexistence of double-parameter nonlinear dynamics and metastable chaos for bistable asymmetric composite laminated square panel under combined external and parametric excitations

Nonlinear Dynamics - This paper studies the double-parameter multi-pulse jumping chaotic vibrations and metastable chaos of the bistable asymmetric composite laminated square panel under combined...     

吸收波长和吸收效率可控的超材料吸收器

为了使超材料完美吸收器(metamaterial perfect absorber,MPA)能够同时实现吸收效率和吸收波长的控制,本文提出利用二氧化钒(VO2)和石墨烯作为MPA的材料,通过对MPA的结构设计,在红外波段实现了高吸收,吸收效率最高可达99%.研究发现通过改变VO2的温度和石墨烯的化学势,可同时实现MPA吸收效率和吸收波长的控制,吸收效率调制深度和吸收波长调谐范围分别可达97.08%和3.2μm.通过对MPA在吸收波长处的磁场分布分析可以得出,MPA能够产生高吸收是由于其形成了法布里-帕罗(Fabry-Pérot,FP)干涉腔共振,研究发现MPA的结构参数对FP腔的共振波长具有显著的影响.     

Unified convergence analysis of a second-order method of multipliers for nonlinear conic programming

Science China Mathematics - In this paper, we accomplish the unified convergence analysis of a second-order method of multipliers (i.e., a second-order augmented Lagrangian method) for solving the...     

改进型单晶炉提拉系统偏心平衡研究

随着光伏行业的快速发展, 对硅单晶的品质和长晶装备的稳定性的要求也不断提高。直拉法是生产硅单晶的主要方法,通过提高单晶炉副室的高度以扩大单晶硅的生产规模。由于副室高度的大幅增加,且单晶炉提拉头质心相对于旋转轴心有一定距离,对单晶炉整体稳定性有较大影响,从而降低了单晶硅的生产质量。针对此问题,对单晶炉建立可靠的力学分析模型,采用数值仿真方法,对单晶炉整体进行动力学响应分析,计算得到副室高度增加后的单晶炉工作时中钨丝绳下端晶棒的运动规律以及最大摆动幅度,为改进设计提供依据。数值仿真分析表明提高单晶炉副室高度后,提拉头较大的质心偏心是单晶炉提拉系统发生摆动的主要原因。在此基础上提出在提拉头上添加质心调节装置,通过控制系统调节可保证提拉头质心位置在旋转轴线上以降低提拉系统的摆动。     

Grubbs Catalysts Immobilized on Merrifield Resin for Metathesis of Leaf Alcohols by using a Convenient Recycling Approach

Three new types of heterogeneous catalysts were prepared using a facile approach by the immobilization of Grubbs catalysts on PEGylated Merrifield resin. One of the immobilized catalysts was more efficient than the free catalyst for the metathesis of leaf alcohols in conversion and selectivity and was reused repeatedly (up to 5 cycles) with only a slight loss of activity (10.5 %). The long-chain PEGylated linker provided an appropriate distance between the resin and the catalytic center so that the ruthenium catalysts acted as the free catalyst.     

Global asymptotic stability for a periodic delay hematopoiesis model with impulses

In this paper, sufficient conditions for the global asymptotic stability of a broad family of periodic impulsive scalar delay differential equations are obtained. These conditions are applied to a periodic hematopoiesis model with multiple time-dependent delays and linear impulses, in order to establish criteria for the global asymptotic stability of a positive periodic solution. The present results are discussed within the context of recent literature. In conclusion, when compared with previous works, not only sharper stability criteria are obtained here, even for models without impulses, but also the usual constraints imposed on the linear impulses are relaxed.     

Global dynamics of a flexible asymmetrical rotor

Nonlinear Dynamics - Global dynamics of a flexible asymmetrical rotor resting on vibrating supports is investigated. Hamilton’s principle is used to derive the partial differential governing...     

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.     

Point and interval forecasting for carbon price based on an improved analysis-forecast system

Effective analysis and forecasting of carbon prices, which is an essential endeavor for the carbon trading market, is still considered a difficult task because of the nonlinearity and nonstationarity inherent in carbon prices. Previous studies have failed at the analysis and interval prediction of carbon prices and are limited to point forecasts. Therefore, an improved carbon price analysis and forecasting system that consists of an analysis module and a forecasting module is established in this study; more importantly, the forecasting module includes point forecasting and interval forecasting as well. Aimed at investigating the characteristics of the carbon price series, a chaotic analysis based on the maximum Lyapunov exponent is performed, the determination of appropriate distribution functions based on our newly proposed hybrid optimization algorithm is conducted, and different distribution functions are effectively designed in the analysis module. Furthermore, in the point forecasting model, the phase space reconstruction technique is applied to reconstruct the sequences decomposed by variational mode decomposition due to the chaotic characteristics of the carbon price series, and the reconstructed sequences are considered as the optimal input–output variables of the forecasting model. Then, an adaptive neuro-fuzzy inference system model is trained by the newly proposed hybrid optimization algorithm, which is developed for the first time in the domain of carbon price point forecasting. Moreover, based on the results of point forecasting and the distribution function of the carbon price series determined by the analysis module, the interval forecasting results can be obtained and implemented to provide more reliable information for decision making. Empirical results based on the carbon price data of the European Union Emissions Trading System and Shenzhen of China demonstrate that the proposed system achieves better results than other benchmark models in point forecasting as well as interval forecasting.