Application of a Bayesian algorithm for the Statistical Energy model updating of a railway coach

The classical statistical energy analysis (SEA) theory is a common approach for vibroacoustic analysis of coupled complex structures, being efficient to predict high-frequency noise and vibration of engineering systems. There are however some limitations in applying the conventional SEA. The presence of possible strong coupling between subsystems and the lack of diffuseness result in a significant uncertainty. This is the main motivation for the present study, where a procedure to update SEA models is proposed. The proposed procedure is the combination of the classical SEA method and a Bayesian technique. Due to reasons such as finding a limited number of important parameters, using a limited search range, avoiding matrix inversion and taking the effect of noise into account, the proposed strategy can be considered as a proper alternative to the experimental SEA approach. To investigate the performance of the proposed strategy, the SEA model updating of a railway passenger coach is carried out. First, a sensitivity analysis is carried out to select the most sensitive parameters of the SEA model. For the selected parameters of the model, prior probability density functions are then taken into account based on published data on comparison between experimental and theoretical results, so that the variance of the theory is estimated. The Monte Carlo Metropolis Hastings algorithm is employed to estimate the modified values of the parameters. It is shown that the algorithm can be efficiently used to update the SEA models with a high number of unknown parameters.     

Modeling of Terahertz Radiation from InSb and InAs

In this paper the THz radiation dynamics of InSb and InAs, two typical narrow band semiconductors, was investigated using the ensemble Monte Carlo method. Our simulations indicated that the single mechanism of current surge only can result in small difference of THz emission efficiency for InSb and InAs. The great advantage of InAs over InSb in THz emission efficiency that was found in a published work is possibly due to the mechanism of optical rectification. In addition, under low excitation level, we found the emission efficiency of InSb is advantage over that of InAs, but under high excitation level, the result is reversed. On the other hand, through the Fourier transforms of temporal THz waveforms we found that the main frequency of THz pulses from InAs is always higher than that of InSb.     

Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets

Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.     

A Bayesian Approach to the Estimation of Parameters and Their Interdependencies in Environmental Modeling

We present a case study for Bayesian analysis and proper representation of distributions and dependence among parameters when calibrating process-oriented environmental models. A simple water quality model for the Elbe River (Germany) is referred to as an example, but the approach is applicable to a wide range of environmental models with time-series output. Model parameters are estimated by Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling. While the best-fit solution matches usual least-squares model calibration (with a penalty term for excessive parameter values), the Bayesian approach has the advantage of yielding a joint probability distribution for parameters. This posterior distribution encompasses all possible parameter combinations that produce a simulation output that fits observed data within measurement and modeling uncertainty. Bayesian inference further permits the introduction of prior knowledge, e.g., positivity of certain parameters. The estimated distribution shows to which extent model parameters are controlled by observations through the process of inference, highlighting issues that cannot be settled unless more information becomes available. An interactive interface enables tracking for how ranges of parameter values that are consistent with observations change during the process of a step-by-step assignment of fixed parameter values. Based on an initial analysis of the posterior via an undirected Gaussian graphical model, a directed Bayesian network (BN) is constructed. The BN transparently conveys information on the interdependence of parameters after calibration. Finally, a strategy to reduce the number of expensive model runs in MCMC sampling for the presented purpose is introduced based on a newly developed variant of delayed acceptance sampling with a Gaussian process surrogate and linear dimensionality reduction to support function-valued outputs.     

时序建模方法在滑油光谱分析中的应用

本文讨论了时序建模方法在机械设备滑油光谱分析中的应用。通过运用ＡＲ模型对采集的航空发动机滑油光谱数据进行时序建模和预测分析,获得了满意的效果。这一成果,为机械设备的状态监控和故障预报提供了一种实用方法。     

基于时域有限差分法的势垒透射过程的蒙特卡罗模拟

采用时域有限差分法,计算势垒透射过程中电子波的波函数和概率密度.根据蒙特卡罗随机方法,以随机点的密集程度代表概率密度的大小,模拟势垒透射的动态过程.     

基于SARIMA-BP神经网络组合方法的MODIS叶面积指数时间序列建模与预测

植被叶面积指数(LAI)时间序列的建模及预测是陆面过程模型和遥感数据同化方法的重要组成部分。MODIS数据产品MOD15A2是目前应用最为广泛的LAI数据源之一,然而MODIS LAI时间序列产品包含了一些低质量的数据,例如由于云层、气溶胶等的影响,该产品在时间和空间上缺乏连续性。MODIS LAI时间序列包含线性部分和外在干扰产生的非线性部分,单一的线性方法或非线性方法都不能对其精确建模和预测。首先利用Savitzky-Golay(SG)滤波和线性插值平滑受到干扰的LAI时间序列,然后采用季节自回归积分滑动平均(SARIMA)方法、BP神经网络方法及二者的组合方法(SARIMA-BP)对MODIS LAI时间序列进行建模及预测。在SARIMA-BP神经网络组合方法中,各自在线性与非线性建模的优势得以充分发挥,其中SARIMA方法用于建模及预测LAI时间序列中的线性部分,BP神经网络方法用于对非线性残差部分进行建模及预测。实验结果显示：SG滤波和线性插值后的LAI时间序列比原LAI时间序列更平滑;SARIMA-BP神经网络组合方法的决定系数为0.981,比SARIMA和BP神经网络的0.941和0.884更接近于1;SARIMA-BP神经网络组合方法的预测值同观测值之间的相关系数为0.991,高于SARIMA(0.971)和BP神经网络(0.942)的相关系数。由此得出结论：SARIMA-BP神经网络组合方法对MODIS LAI时间序列具有更好的适应性,其建模和预测准确性高于SARIMA方法或BP神经网络方法。     

基于电热模型的SNSPD响应过程模拟与时间抖动分析

超导单光子探测器(SNSPD)具有高探测效率,低时间抖动、低暗计数率、高计数率等优点.文章通过对纳米线电热过程的建模,分析了纳米线的电热响应过程,探讨了负载电阻、偏置电流和动态电感对SNSPD响应时间的影响,结合蒙特卡洛方法来模拟研究了纳米线空间不均匀性对于SNSPD时间抖动的影响,结果表明随着纳米线空间不均匀性的增加,纳米线的时间抖动不断增加,并求出了典型的时间抖动约为7.73 ps.     

Critical dynamics of cluster algorithms in the dilute Ising model

Autocorrelation times for thermodynamic quantities atT _C are calculated from Monte Carlo simulations of the site-diluted simple cubic Ising model, using the Swendsen-Wang and Wolff cluster algorithms. Our results show that for these algorithms the autocorrelation timesdecrease when reducing the concentration of magnetic sites from 100% down to 40%. This is of crucial importance when estimating static properties of the model, since the variances of these estimators increase with autocorrelation time. The dynamical critical exponents are calculated for both algorithms, observing pronounced finite-size effects in the energy autocorrelation data for the algorithm of Wolff. We conclude that, when applied to the dilute Ising model, cluster algorithms become even more effective than local algorithms, for whichincreasing autocorrelation times are expected.     

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.     

Sorption and permeation of gaseous molecules in amorphous and crystalline PPX C membranes: molecular dynamics and grand canonical Monte Carlo simulation studies

Amorphous and crystalline poly (chloro-p-xylylene) (PPX C) membranes are constructed by using a novel computational technique, that is, a combined method of NVT+NPT-molecular dynamics (MD) and gradually reducing the size (GRS) methods. The related free volumes are defined as homology clusters. Then the sorption and the permeation of gases in PPX C polymers are studied using grand canonical Monte Carlo (GCMC) and NVT-MD methods. The results show that the crystalline PPX C membranes provide smaller free volumes for absorbing or transferring gases relative to the amorphous PPX C area. The gas sorption in PPX C membranes mainly belongs to the physical one, and H bonds can appear obviously in the amorphous area. By cluster analyzing on the mean square displacement of gases, we find that gases walk along the x axis in the crystalline area and walk randomly in the amorphous area. The calculated permeability coefficients are close to the experimental data.     

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.     

Classical and Bayesian Inference of an Exponentiated Half-Logistic Distribution under Adaptive Type II Progressive Censoring

The point and interval estimations for the unknown parameters of an exponentiated half-logistic distribution based on adaptive type II progressive censoring are obtained in this article. At the beginning, the maximum likelihood estimators are derived. Afterward, the observed and expected Fisher’s information matrix are obtained to construct the asymptotic confidence intervals. Meanwhile, the percentile bootstrap method and the bootstrap-t method are put forward for the establishment of confidence intervals. With respect to Bayesian estimation, the Lindley method is used under three different loss functions. The importance sampling method is also applied to calculate Bayesian estimates and construct corresponding highest posterior density (HPD) credible intervals. Finally, numerous simulation studies are conducted on the basis of Markov Chain Monte Carlo (MCMC) samples to contrast the performance of the estimations, and an authentic data set is analyzed for exemplifying intention.     

超高速摄影仪转镜运转可靠性数值与实验分析

为探讨分析转镜运转可靠性的数值方法,运用Monte Carlo理论并结合ANSYS参量化设计语言,根据小样本原理和可靠性理论建立了转镜运转可靠性分析数学模型.对转镜运转可靠性进行了数值分析,并对数值分析结果进行了实验验证.数值分析结果表明:转镜的功能函数值远大于零,最大应力与转镜转速的线性相关系数达0.9,镜体密度的相关系数值为0.15,最大应力、应变、位移的偏斜度和峭度值均为正值,统计结果服从正态分布呈右偏态,在95%的置信度水平下,转镜可靠度为0.999.这说明在理想状态下转镜的运转可靠性满足要求,转镜的最大应力值取决于转镜转速,并且镜体的密度对转镜最大应力有较大影响.转镜运转可靠性试验中没有转镜出现破坏,表明转镜运转可靠性数值分析方法是正确的,为转镜的运转可靠性分析提供一种可行、高效的数值分析手段.     

Chaoticity analysis of the current through pure, hydrogenated and hydrophobically modified PEG-Si thin films under varying relative humidity

Polyethylene Glycol has an irregular current characteristic under constant voltage and slowly varying relative humidity. The current through a thin film of Gamma-isocyanatopropyltriethoxysilane added Polyethylene glycol (PEG-Si), its hydrogenated and hydrophobically modified forms, as a function of increasing relative humidity at equal time steps is analyzed for chaoticity. We suggest that the irregular behavior of current through PEG-Si thin films as a function of increasing relative humidity could best be analyzed for chaoticity using both time series analysis and detrended uctuation analysis; the relative humidity is kept as a slowly varying parameter. The presence of more then one regime is suggested by the calculation of the maximal Lyapunov exponents. Furthermore, the maximal Lyapunov exponent in each of the regimes was positive, thus confirming the presence of low dimensional chaos. DFA also confirms the presence of at least two different regimes, in agreement with the behavior of the maximal Lyapunov exponent in the time series analysis. We also suggest that the irregular behavior of the current through PEG-Si can be reduced by hydrogenating and hydrophobically modifying PEG-Si and the improvement in stability can be confirmed by our study.      

超高速摄影仪转镜运转可靠性数值与实验分析

为探讨分析转镜运转可靠性的数值方法,运用Monte Carlo理论并结合ANSYS参量化设计语言,根据小样本原理和可靠性理论建立了转镜运转可靠性分析数学模型.对转镜运转可靠性进行了数值分析,并对数值分析结果进行了实验验证.数值分析结果表明:转镜的功能函数值远大于零,最大应力与转镜转速的线性相关系数达0.9,镜体密度的相...     

一种废束站束窗结构设计与优化

废束站束窗是废束站的重要部件。利用束流的束斑尺寸及功率确定束窗能量的高斯分布方式;通过蒙特卡罗方法计算束窗的沉积能量。利用ANSYS稳态分析确定束窗的材料、截面形状和厚度。通过计算比较束窗在不同材料、截面形状和厚度条件下的温度、应力和变形,得出合适的束窗材料、截面形状和厚度,从而确定束窗的最终结构。最后利用瞬态方法模拟束窗调束时的温度分布情况。     

光通过纳米颗粒随机散射体透射光强的计算及分析

光通过随机散射体后透射光强的估算,对于提取散射体内部无法直接测量或者无法直接观察的信息至关重要。在分析平行光束通过纳米圆形颗粒随机散射体出射面上光强组成的基础上推导了透射光强的计算公式,并指出多重散射理论、一阶多重散射理论和朗伯-比尔定律在一定近似程度上可以相对精确地估算透射光强。这三种方法的估算结果之间会出现四种相对关系。根据这些相对关系,分析了光在散射体内部传输时散射过程的特征以及各种散射过程对出射面光强的贡献大小。     

Multiple scattering and modeling of laser in fog

When a laser is transmitted in fog, and the water droplets will scatter and absorb the laser, which affects the intensity of the laser transmission and the accuracy of radar detection. Therefore, it is of great significance to study the laser transmission in the fog. At present, the main method of calculating the scattering and attenuation characteristics of fog is based on the radiation transmission theory, which is realized by a large number of numerical calculations or physical simulation methods, which takes time and cannot meet the requirements for obtaining the fast and accurate results. Therefore, in this paper established are a new laser forward attenuation model and backward attenuation model in low visibility fog. It is found that in low visibility environments, the results calculated by the Monte Carlo method are more accurate than those from most of the existing forward attenuation models. For the cases of 0.86-μm, 1.06-μm, 1.315-μm, 10.6-μm typical lasers incident on different fogs with different visibilities, a backscatter model is established, the error between the fitting result and the calculation result is analyzed, the backward attenuation fitting parameters of the new model are tested, and a more accurate fitting result is obtained.