用于厚屏蔽小探测器的蒙特卡罗模拟减方差方法研究

反应堆屏蔽计算中经常出现厚屏蔽、小探测器问题，常规蒙特卡罗方法难以有效解决。基于自动重要抽样（AIS）方法，本文提出了小探测器自动重要抽样(SDAIS)方法，并针对小探测器问题，优化了AIS方法的虚粒子赌分裂算法。该方法在自主开发的蒙特卡罗屏蔽程序MCShield上进行了实现。使用NUREG/CR-6115 PWR基准题验证该方法的正确性和计算效率。结果表明，SDAIS方法可有效地解决厚屏蔽小探测器问题，相比AIS方法及传统的重要性方法，计算效率提升1~2个量级。     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

Mechanism of methanol decomposition on the Pd/WC(0001) surface unveiled by first-principles calculations

In this study, the decomposition of methanol into the CO and H species on the Pd/tungsten carbide (WC)(0001) surface is systematically investigated using periodic density functional theory (DFT) calculations. The possible reaction pathways and intermediates are determined. The results reveal that saturated molecules, i.e., methanol and formaldehyde, adsorb weakly on the Pd/ WC(0001) surface. Both CO and H prefer three-fold sites, with adsorption energies of −1.51 and −2.67 eV, respectively. On the other hand, CH₃O stably binds at three-fold and bridge sites, with an adsorption energy of −2.58 eV. However, most of the other intermediates tend to adsorb to the surface with the carbon and oxygen atoms in their sp³ and hydroxyl-like configurations, respectively. Hence, the C atom of CH₂OH preferentially attaches to the top sites, CHOH and CH₂O adsorb at the bridge sites, while COH and CHO occupy the three-fold sites. The DFT calculations indicate that the rupture of the initial C–H bond promotes the decomposition of CH₃OH and CH₂OH, whereas in the case of CHOH, O–H bond scission is favored over the C–H bond rupture. Thus, the most probable methanol decomposition pathway on the Pd/WC(0001) surface is CH₃OH → CH₂OH → trans-CHOH → CHO → CO. The present study demonstrates that the synergistic effect of WC (as carrier) and Pd (as catalyst) alters the CH₃OH decomposition pathway and reduces the noble metal utilization.     

提取地震信号高频信息方法的若干问题

岩性油气藏勘探已成为当代油气勘探的重要组成部分,因此提取地震资料高频信息已成为一个重要研究课题。文中对分辨率、分频处理地震信号、波阻抗反演中的一些问题进行了较深入的讨论,并得出以下认识:1要提高垂向和横向分辨率,必须设法提高野外采集地震资料的频率;2地震分频处理是偏移后提高分辨率的常用方法之一,这种方法应该限定在地震波的有效频带内,而超出有效频带,可能会带来很多假像;3低频合成地震记录中高频反射系数存在不确定性。在基于模型波阻抗反演的方法中,反演结果受初始模型的影响非常大,初始模型是依据测井资料建立的,初始模型与实际情况越接近,反演效果就越好;否则,会产生模型化的现象;4在相对波阻抗数据体(相当于地震数据体)作为约束条件下的SP测井参数协克里金插值是正演过程,消除了模型化的问题,使反演结果更接近真实情况,提高了砂层真实的分辨率。     

邻—(4—乙基苯甲酰基)苯甲酸的研究

本文阐述了通过Friedel-Crafts反应合成邻-(4-乙基苯甲酰基)苯甲酸的方法.研究了影响产物质量的因素,提出了适宜的工艺条件.     

Cif2000平台下的核磁共振测井解谱方法研究

介绍了在Cif2000多井解释平台下的核磁共振解谱方法与编程实现。解谱采用加入平滑因子后在特征矩阵的奇异值分解中截去小的非零奇异值的方法，可以在低信噪比时得到稳定的弛豫谱，在油田实际应用中证明了该方法的有效性。根据该方法在Cif2000平台上编制了完整的解谱处理程序，可以直接用于油田的生产实际。     

A stochastic realization algorithm via block LQ decomposition in Hilbert space

A stochastic realization problem of a stationary stochastic process is re-visited, and a new stochastically balanced realization algorithm is derived in a Hilbert space generated by second-order stationary processes. The present algorithm computes a stochastically balanced realization by means of the singular value decomposition of a weighted block Hankel matrix derived by a “block LQ decomposition”. Extension to a stochastic subspace identification method explains how the proposed abstract algorithm is implemented in system identification.