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.     

采用八叉树和OSG分页结点的海量点云三维可视化

针对现有海量点云可视化方法存在索引构建时间长、内存占用大等问题，研究一种八叉树索引结合OSG分页结点的快速可视化方法，可在占用较小内存的基础上快速建立点云索引并实时调度。采用八叉树索引结构对海量点云进行数据组织，建立各层级的八叉树结点并以文件映射的方式分块保存，对结点文件重组织转换为支持OSG渲染引擎的多分辨率点云数据。采用基于OSG分页结点的实时调度技术，对海量点云进行高质量可视化。与目前两款主流的点云数据处理商业软件进行实验对比分析，结果表明所提方法具有索引建立速度快、内存占用小等优点，同时可视化交互更加流畅，适用于各种配置计算机下海量点云数据的调度管理与实时可视化。     

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.     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

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。     

红外光谱法用于快速测定车用柴油酸度等性质的研究

针对车用柴油酸度等性质测定过程繁琐，采用衰减全反射样品池测定车用柴油的红外光谱，用偏最小二乘法（PLS）建立红外光谱测定车用柴油酸度、密度、闪点和凝点的4个校正模型，验证标准误差分别为0.46 mg/(100 mL),0.77kg/m3,2.60 ℃,2.77 ℃，该方法符合标准方法再现性要求。与标准方法相比，该方法具有无需预处理、操作简单、测量快速、重复性好等优点。     

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.     

XMAC纵波时差高分辨率处理方法

在充分调研阵列声波测井分波提取方法的基础上，针对阿特拉斯公司的交叉多极子阵列声波测井（XMACⅡ）提供的单极波形信息，研究纵波时差提取及高分辨率处理方法。首先利用相关-互功谱法高精度地提取纵波时差，然后针对XMACⅡ仪器的接收阵列，研究组合子阵列的方法，形成共发射子阵列和共接收子阵列，最后利用子阵列对提取的纵波时差做高分辨率处理。处理现场实际测井资料，并将高分辨率处理成果与引进的eXpress软件处理成果、实测的国产高分辨率声波（纵波）测井曲线及岩心纵波时差对比分析，表明该方法提取的纵波时差曲线的纵向分辨率有很大提高，时差计算准确可靠，提取的单极纵波时差与国产高分辨率声波时差及岩心纵波时差的平均相对误差均小于7%。该方法具有较高的实用价值和广阔的应用前景。