正交匹配追踪算法的迭代残差重建方法

正交匹配追踪(Orthogonal Matching Pursuit, OMP)算法是一种重要的压缩感知重构算法. OMP算法在每次迭代中选择与当前残差最相关的原子. 针对每次迭代需要重新计算残差的问题, 本文考虑偶数次迭代下残差未知的情况. 首先, 研究了奇数次迭代的残差与下一次迭代的残差之间的关系, 得到了一种偶数次迭代时选择原子的标准. 然后, 引入一种回溯机制来处理前面所得的迭代结果, 这种机制通过剔除其中多余的原子来实现精确重建. 据此, 提出了可减少计算残差的改进型正交匹配追踪算法.     

硫化丁苯橡胶纳观界面耐磨机理及参数影响分析

硫化橡胶因其良好的力学和物理化学性能而被广泛作为摩擦副的基础材料. 本文提出了一种硫化交联算法, 实现了C—C键的硫化互交联和自交联, 构建了硫化丁苯橡胶的分子动力学磨损模型, 从微观摩擦学的角度阐明了硫化交联结构对改善丁苯橡胶磨损性能的机理, 研究了不同界面参数对硫化橡胶微观磨损性能的影响. 结果发现 硫化使丁苯橡胶分子链的界面黏附能力和活动能力更弱, 拉伸和解缠能力更低, 磨损过程中界面累积能量更低, 更不容易脱离橡胶基体, 因此可以表现出更好的摩擦学性能, 更强的抗磨损性能; 随着速度的增大, 硫化橡胶的磨损率降低, 与宏观实验结果一致, 原因是硫化橡胶的原子分布函数和相互作用能随着速度增大而降低, 说明橡胶分子链的黏附能力和活动能力随着速度增加趋弱, 温升更低, 导致较低的磨损率; 压入深度对磨损率的影响规律则呈现相反的结果和趋势.     

基于MRS-SJC-Copula模型对A股与港股的动态联动性研究

由于经济增长周期变化,导致不同股票市场存在高低状态转换的现象,在研究不同股票市场之间联动性的研究时,需要考虑股票波动均值和方差两种结构变化。基于具有马尔可夫状态转换的动态SJC-Copula,结合修正ICSS算法对我国内地股票市场和香港股票市场之间的联动性进行方差结构突变点的检验。实证结果表明:国内和香港股票市场之间存在非线性非对称的时变相依性,并持续存在高低两种不同状态的概率转换。股票指数由于动态联动受到负面消息的下跌幅度大于正面消息的变化幅度,且上下尾部均受上期信息的持续影响。“沪港通”、“深港通”、中美贸易战等因素使得其上下尾部发生结构突变,内地与香港股票市场的联动性增大和市场波动幅度趋强。     

Investigation on the effect of density ratio on the convergence behavior of partitioned method for fluid–structure interaction simulation

In order to investigate the effect of density ratio of fluid and solid on the convergence behavior of partitioned FSI algorithm, three strong-coupling partitioned algorithms (fixed-point method with a constant under-relaxation parameter, Aitken’s method and Quasi-Newton inverse least squares (QN-ILS) method) have been considered in the context of finite element method. We have employed the incompressible Navier–Stokes equations for a Newtonian fluid domain and the total Lagrangian formulation for a non-linear motion of solid domain. Linear-elastic (hyper-elastic) model has been employed for solid material with small (large) deformation. A pulsatile inlet-flow interacting with a 2D circular channel of linear-elastic material and a pressure wave propagation in a 3D flexible vessel have been simulated. Both linear-elastic and hyper-elastic (Mooney–Rivlin) models have been adopted for the 3D flexible vessel. From the present numerical experiments, we have found that QN-ILS outperforms the others leading to a robust convergence regardless of the density ratio for both linear-elastic and hyper-elastic models. On the other hand, the performances of the fixed-point method with a constant under-relaxation parameter and the Aitken’s method depend strongly on the density ratio, relaxation parameter selected for coupling iteration, and degree of deformation. Although the QN-ILS of this work is still slower than a monolithic method for serial computation, it has an advantage of easier parallelization due to the modularity of the partitioned FSI algorithm.     

A volatolomic approach using cerumen as biofluid to diagnose bovine intoxication by Stryphnodendron rotundifolium

An innovative volatolomic approach employs the detection of biomarkers present in cerumen (earwax) to identify cattle intoxication by Stryphnodendron rotundifolium Mart., Fabaceae (popularly known as barbatimão). S. rotundifolium is a poisonous plant with the toxic compound undefined and widely distributed throughout the Brazilian territory. Cerumen samples from cattle of two local Brazilian breeds (‘Curraleiro Pé-Duro’ and ‘Pantaneiro’) were collected during an experimental intoxication protocol and analyzed using headspace (HS)/GC–MS followed by multivariate analysis (genetic algorithm for a partial least squares, cluster analysis, and classification and regression trees). A total of 106 volatile organic metabolites were identified in the cerumen samples of bovines. The intoxication by S. rotundifolium influenced the cerumen volatolomic profile of the bovines throughout the intoxication protocol. In this way, it was possible to detect biomarkers for cattle intoxication. Among the biomarkers, 2-octyldecanol and 9-tetradecen-1-ol were able to discriminate all samples between intoxicated and nonintoxicated bovines. The cattle intoxication diagnosis by S. rotundifolium was accomplished by applying the cerumen analysis using HS/GC–MS, in an easy, accurate, and noninvasive way. Thus, the proposed bioanalytical chromatography protocol is a useful tool in veterinary applications to determine this kind of intoxication.     

A survey on HHL algorithm: From theory to application in quantum machine learning

The Harrow-Hassidim-Lloyd (HHL) algorithm is a method to solve the quantum linear system of equations that may be found at the core of various scientific applications and quantum machine learning models including the linear regression, support vector machines and recommender systems etc. After reviewing the necessary background on elementary quantum algorithms, we provide detailed account of how HHL is exploited in different quantum machine learning (QML) models, and how it provides the desired quantum speedup in all these models. At the end, we briefly discuss some of the remaining challenges ahead for HHL-based QML models and related methods.     

Experimental and theoretical assessment of Fe-doped indium-oxide-based dilute magnetic semiconductors

Specimens of iron-doped indium oxide (In_1-xFe_x)₂O₃ with x?=?0.015, 0.03, 0.045 and 0.06, amalgamated through a traditional solid-state reaction method followed by H₂/air sintering, were characterised using an X-ray diffractometer (XRD), a vibrating sample magnetometer (VSM), and a scanning electron microscope (SEM) to investigate their structural, magnetic and morphological properties respectively. According to XRD plots, all the specimens exhibit cubic bixbyite structures along with ancillary phases. Magnetic assessment showed that In₂O₃ has a negative susceptibility, exhibiting diamagnetic behaviour at room temperature. The doping of Fe ions induces ferromagnetic (FM) ordering, which is enhanced with increasing doping content. The strength of the magnetisation increases when the specimens are exposed to H₂ but is reduced on further air sintering. A bound magnetic polaron (BMP) model is successfully fitted to the observed FM data involving localised carriers and magnetic cations. A multivariate assessment viz. a hierarchical cluster analysis (HCA) was used to corroborate and strengthen the experimental determined magnetic properties. A homogeneous particle distribution was observed in all SEM micrographs and is validated through MATLAB-based simulation by applying a watershed segmentation algorithm. Surface plots also confirm the change in magnetic properties with increase in doping concentration.     

基于LS -DYNA公路桥车桥耦合的车辆模型研究

研究公路桥梁在移动车辆荷载作用下的动力响应,建立合理的车辆模型非常重要。为更真实地体现桥梁在车载作用下的动力响应,基于LS-DYNA程序,结合常用重型车辆的结构特性及参数,对车辆的橡胶轮胎、轮胎内气体压力、车轮转动和车辆悬架系统进行模拟,使车辆模型更接近实际车辆。通过车辆轴重和动力特性初步验证车辆有限元模型的有效性;同时,以一座混凝土简支空心板梁桥为算例,验证车轮转动和车桥相互接触力,并将LS-DYNA计算结果与桥梁实测结果进行对比,进一步验证车辆有限元模型的有效性。研究结果表明,基于LS-DYNA建立的三维车辆有限元模型是可行的,可以用于研究车桥相互作用。     

语音通信降噪研究

语音通信系统中，语音通过信道传输将不可避免地引入码间串扰和信号畸变，同时受到噪声污染。本文在分析自适应盲均衡算法CMA(constant modulus algorithm）和改进盲均衡算法的基础上，考虑到自适应盲均衡技术在语音噪声控制方面能力有限，将自适应盲均衡技术与小波包掩蔽阈值降噪算法联合使用，形成一种基带语音增强新方法。仿真试验结果显示自适应盲均衡技术可以使星座图变得清晰而紧凑，有效减小误码率。研究证实该方法在语音信号ISI和畸变严重情况下，在白噪及有色噪声不同的噪声环境中都具有稳定的降噪能力，消噪同时可获得汉语普通话良好的听觉效果。