Blocks adjustment—reduction of bias and variance of detrended fluctuation analysis using Monte Carlo simulation

The length of minimal and maximal blocks equally distant on log-log scale versus fluctuation function considerably influences bias and variance of DFA. Through a number of extensive Monte Carlo simulations and different fractional Brownian motion/fractional Gaussian noise generators, we found the pair of minimal and maximal blocks that minimizes the sum of mean-squared error of estimated Hurst exponents for the series of length . Sensitivity of DFA to sort-range correlations was examined using ARFIMA(p,d,q) generator. Due to the bias of the estimator for anti-persistent processes, we narrowed down the range of Hurst exponent to      

Generalized Complex Spectral Decomposition for a Quantum Decay Process

By extending the notion of mixed states to functionals acting on the space of observables with diagonal singularity we obtain a well-defined complex spectral decomposition of the time evolution for a quantum decaying system. In this formalism, generalized Gamow states are obtained with well-defined physical properties.     

不同模板剂合成SAPO-34分子筛的表征与热分解过程研究

采用XRD、SEM、FT IR、TG DTA等表征手段 ,对分别以正磷酸、拟薄水铝石和硅溶胶为磷源、铝源和硅源 ,用五种模板剂合成的SAPO 34分子筛进行了表征 .不同模板剂合成的SAPO 34分子筛在晶粒粒度分布上有相当大的差异 .通过TEAOH Et3 N或者TEAOH Morpholine复合模板剂法可以有效地调节晶粒粒度 .不同模板剂合成的SAPO 34分子筛的红外骨架振动相似 ;而模板剂分子与分子筛骨架的作用方式有区别 .模板剂种类对SAPO 34分子筛骨架热稳定性没有影响 ,但对其热分解的行为有影响     

热等离子体裂解天然气制备C2烃

采用氮气热等离子体来裂解天然气制备乙炔乙烯,着重研究了天然气转化率和乙炔、乙烯收率随氮气流量和天然气流量的变化.结果表明,天然气流量与氮气流量之比为11时,可得到较好的结果.当等离子体功率为15kW、天然气流量为3Nm3*h-1、氮气流量为3Nm3*h-1时得到最好的结果.这时天然气转化率为57%,乙炔、乙烯的收率分别为34%和9%;乙炔在反应气中的体积浓度为7.5%,与部分氧化法相似;扣除不参加反应的氮气,乙炔在气相产品中的体积浓度为13.2%,与热力学平衡计算结果基本符合.     

Fe40—45Cr30—35Co20—25Mo0—4Zr0—2合金微观结构对矫顽力的影响

研究了高矫顽力型FeCrCo合金的磁性和微观结构.实验结果表明，适当的合金成分及热处理 条件可以明显提高FeCrCo合金的矫顽力.通过透射电镜、x射线衍射等手段得出结论：主体元 素含量的提高和微量元素的掺杂可以有效地控制相的组成，磁场热处理导致强磁性相和弱磁 性相的分离，回火后形成起磁硬化作用的调幅结构.该调幅结构中较大的两相成分差有利于 提高FeCrCo合金的矫顽力. 关键词： 矫顽力 调幅分解 磁场热处理 两相结构 成分差     

改进的K均值聚类红外目标检测方法

利用图像方差能很好地反映目标边缘信息的特点,提出一种基于方差的K均值聚类红外目标检测算法。利用形态学方法对红外图像进行预处理,运用相应的模板计算得到红外图像的方差图像,利用K均值聚类算法对方差图像进行聚类,从而分离出目标类别和背景类别。实验表明,该算法提取的红外图像中目标信息的兰德指数最高,说明该算法能有效地提取红外图像中目标信息,从而达到目标检测的目的。     

Discovering Higher-Order Interactions Through Neural Information Decomposition

If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.     

Spectral Properties of Effective Dynamics from Conditional Expectations

The reduction of high-dimensional systems to effective models on a smaller set of variables is an essential task in many areas of science. For stochastic dynamics governed by diffusion processes, a general procedure to find effective equations is the conditioning approach. In this paper, we are interested in the spectrum of the generator of the resulting effective dynamics, and how it compares to the spectrum of the full generator. We prove a new relative error bound in terms of the eigenfunction approximation error for reversible systems. We also present numerical examples indicating that, if Kramers–Moyal (KM) type approximations are used to compute the spectrum of the reduced generator, it seems largely insensitive to the time window used for the KM estimators. We analyze the implications of these observations for systems driven by underdamped Langevin dynamics, and show how meaningful effective dynamics can be defined in this setting.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Gauss Decomposition of Dynamical Elliptic Algebra Aq,p,π(gln)

We propose a dynamical elliptic algebra which is based on the relations RLL = LLR^*, where R and R^* are the dynamical R-matrices of A_n-1⁽¹⁾-type face model with the elliptic module shifted by the center of algebra. nom the high-rank Gauss decomposition,h we obtain the Drinfeld currents of dynarnical elliptic algebra .