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.     

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.     

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。     

碳酸化分解工艺的改进与实践

本文针对烧结法生产氧化铝过程中,铝酸钠溶液的碳酸化分解工艺,由间断分解到连续分解的改进进行了论述,指出连续分解是碳酸化分解的发展方向。     

焚烧炉控制系统

化工废气、废液焚烧炉是一种常用的环保处理装置，就其工艺详细介绍焚烧炉燃烧过程的控制及其安全联锁措施。     

一个表示和运用非功能性需求的框架

本文分析了一个软件开发进程中表示和运用非功能性需求的综合框架，该框架的目的是开发一套技术以调整软件开发进程中的设计决策，从而强调了依据非功能需求，探索合理化的软件开发进程。     

小型密闭压力容器测试系统重复性的研究

小型密闭压力容器测试（MCPVT）法是安全工程领域中近几年发展起来的、评价有机过氧化物及自反应物质受热分解反应激烈程度的新方法。pmax是MCPVT方法中用来表征自反应物质受热分解反应激烈程度的物理量之一。文中通过大量实验，研究了样品容器容积V、升温速率R及样品质量M对pmax重复性误差的影响，研究了一系列典型的有机过氧化物和自反应物质在标准实验条件下pmax重复性误差。结果表明，小型密闭压力容器测试系统重复性好，测试结果可靠，从而为MCPVT法最终成为国际上评价自反应物质受热分解反应激烈程度标准方法奠定了基础。     

氧浓度对近临界水中纤维素分解的影响

研究了不同初始氧浓度对纤维素在近临界水中分解的影响，实验结果表明，氧浓度显著地影响产物总碳收支平衡，液相不同组分的收率、气体的产量以及气相产物的组成，微量氧对产物总碳收支平衡影响不大，而对液相不同组分的收率有一定的影响。从实验结果可以看出，离子型反应机理在纤维素水解的第一步中起主导作用。而单糖的进一步分解是一个以自由基反应机理为主的过程。