水平岩层软弱基座危岩体演化过程研究

以重庆市甄子岩29号危岩体为研究对象，基于现场调查和分析解剖，建立UDEC离散元数值计算模型，对其演化变形过程进行模拟分析。研究表明，在巨大的自重作用下，由于基座岩体岩质软，岩体较破碎，且基座外侧为直立的陡崖，具有完整的临空面，从而使基座岩体易发生压缩流变及剪切流变，进而使危岩体存在滑移垮塌的危险。     

综放高瓦斯矿井煤层瓦斯涌出量综合预测

为了成功预测竹林山煤矿综放高瓦斯矿井大采高工作面煤层瓦斯涌出量,以主采3号煤层为主要研究对象,针对3号煤层以往开采情况,通过布设测点测量其煤层瓦斯含量和了解相邻矿井瓦斯含量,采用分源预测法、回归法及统计法等预测方法得到了3号煤层瓦斯含量的分布规律,并绘制了3号煤层的瓦斯含量等值线图。对矿井不同生产时期的瓦斯含量进行预测,得到了生产前期、中期及后期采区的最大绝对瓦斯涌出量和最大相对瓦斯涌出量,说明了竹林山煤矿各个时期均属于高瓦斯矿井。     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

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.     

基于云概率密度分布估计的大坝监测数据分析

大坝运行监测易受自然环境和监测条件影响，存在时间和空间上的变异性，监测数据具有不确定性。以云理论的随机性和不确定性分析方法为基础，并与空间数据辐射思想相结合，建立了云滴概率密度分布估计模型，然后导出云概率密度分布函数，依据样本监测数据推求母体空间数据的分布特征，并设计了基于逆向云算法云变换的计算程序。分析陆浑水库1979~1999年测压管监测数据和位移变形数据的云概率密度分布特征和云数字特征，得出了20 a来大坝的数据分布特征和运行状态。监测数据分析结果表明，云概率密度分布估计不仅能有效合理地分析大坝的运行状态，而且能够依据云数字特征来判断监测状态和监测环境的异常变化。      

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。     

离心泵并联运行工况点的数值求解

建立了求解多台不同型号离心泵并联运行工况点的数学模型,给出了用数值计算中的“二分法”求解该模型的方法,并附有用Exce l计算的实例。     

HEGF中CO井筒流动及扩散规律研究

目前,高能气体压裂广泛的应用于低渗透油气田,可以有效清除近井地带由于钻井、射孔和各种措施造成的污染和堵塞,达到油气井增产、注水井增注的目的。但最近一些低渗透油气田却出现了频繁的CO气体中毒事件,给油田及员工带来巨大的损失。针对这一现象,运用现场数据以及物理模拟和数学模拟方法首次通过对CO气体流动、扩散等的综合研究,建立了CO井筒流动及大气扩散模型。同时,也考察了井筒压力、气油比、风速、气体泄放速率、大气稳定度等主要因素对CO气体扩散的影响。通过现场实例计算,证实模型具有较高的准确性。此项工作的完成为建立监控系统提供基础技术依据,同时对合理、有效的开发油气田以及煤层气的开发将具有重要的实际意义。     

A comparison of transformation methods for evaluating two‐dimensional weakly singular integrals

Accurate numerical evaluation of integrals arising in the boundary element method is fundamental to achieving useful results via this solution technique. In this paper, a number of techniques are considered to evaluate the weakly singular integrals which arise in the solution of Laplace's equation in three dimensions and Poisson's equation in two dimensions. Both are two‐dimensional weakly singular integrals and are evaluated using (in a product fashion) methods which have recently been used for evaluating one‐dimensional weakly singular integrals arising in the boundary element method. The methods used are based on various polynomial transformations of conventional Gaussian quadrature points where the transformation polynomial has zero Jacobian at the singular point. Methods which split the region of integration into sub‐regions are considered as well as non‐splitting methods. In particular, the newly introduced and highly accurate generalized composite subtraction of singularity and non‐linear transformation approach (GSSNT) is applied to various two‐dimensional weakly singular integrals. A study of the different methods reveals complex relationships between transformation orders, position of the singular point, integration kernel and basis function. It is concluded that the GSSNT method gives the best overall results for the two‐dimensional weakly singular integrals studied. Copyright © 2002 John Wiley & Sons, Ltd.     

储层泥浆侵入深度预测方法研究

泥浆侵入半径的确定尚缺乏严格的验证标准.以油水两相渗流理论和离子扩散方程为基础,结合储集层特点,研究了不同储层参数下泥浆滤液对地层的侵入特性.数值模拟侵入时间选取10 d和20 d.数值模拟结果表明,侵入半径在渗透率不变的情况下随孔隙度的增大而减小,在孔隙度不变的情况下随渗透率的增大而增大;当渗透率和孔隙度都发生变化时,泥浆侵入半径一般随孔隙度的增加呈幂函数增加.依据这种关系对测井资料约束处理,得到的泥浆侵入半径较客观地反映了地层的真实情况.