斜拉索耦合振动模型及其参数分析

考虑支撑构件对斜拉索参数振动的影响,从整体振动角度研究斜拉索的非线性参数振动特性,提出一个耦合索支撑结构模型.该模型既考虑了斜拉索的几何非线性,也考虑了支撑构件的运动对斜拉索振动的影响.应用多尺度法对该模型进行摄动分析,得到该模型参数振动时的激励频率及稳定区域边界,讨论模型质量比、刚度比和倾角等参数对幅频响应及稳定性的影响.研究表明,索的非线性参数振动特性并不随质量比和刚度比单调递增,而是存在某一临界值,使索的非线性参数振动特性最大化;随着倾斜角度的增加,索的非线性参数振动特性是单调递增.     

规范法土钉轴力计算对比分析

土钉墙支护结构在目前具有很强的现实意义,由于工程设计人员对土钉受力机理的不同理解,从而选择不同的设计规范,得出不同的计算结果.本文以包头某深基坑工程为实例,使用建筑工程现行的3种规范对上部土钉墙支护结构的土钉轴力计算,对比分析这3种规范法计算结果的差异性,并讨论产生差异性的原因,为以后本地区类似工程提供一些计算参考.     

Hydraulic Self Servo Swing Cylinder Structure Optimization and Dynamic Characteristics Analysis Based on Genetic Algorithm

The dynamic characteristics of hydraulic self servo swing cylinder were analyzed according to the hydraulic system natural frequency formula. Based on that, a method of the hydraulic self servo swing cylinder structure optimization based on genetic algorithm was proposed in this paper. By analyzing the four parameters that affect the dynamic characteristics, we had to optimize the structure to obtain as larger the Dm(displacement) as possible under the condition with the purpose of improving the dynamic characteristics of hydraulic self servo swing cylinder. So three state equations were established in this paper. The paper analyzed the effect of the four parameters in hydraulic self servo swing cylinder natural frequency equation and used the genetic algorithm to obtain the optimal solution of structure parameters. The model was simulated by substituting the parameters and initial value to the simulink model. Simulation results show that: using self servo hydraulic swing cylinder natural frequency equation to study its dynamic response characteristics is very effective. Compared with no optimization, the overall system dynamic response speed is significantly improved.     

A Semi-Supervised WLAN Indoor Localization Method Based on 1-Graph Algorithm

For indoor location estimation based on received signal strength (RSS) in wireless local area networks (WLAN), in order to reduce the influence of noise on the positioning accuracy, a large number of RSS should be collected in offline phase. Therefore, collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper, the traditional semi-supervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed, and the result shows that the k-NN or ε-NN graph are sensitive to data noise, which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem, it proposes a 1-graph-algorithm-based semi-supervised learning (LG-SSL) indoor localization method in which the graph is built by 1-norm algorithm. In our system, it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase, and then uses LG-SSL to estimate user’s location in online phase. Extensive experimental results show that, benefit from the robustness to noise and sparsity of 1-graph, LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.     

火驱燃烧前缘位置预测与影响因素研究

及时掌握火驱过程中火线的位置,有助于进行生产调控,通过调研现场预测火线位置的方法,得出一套针对不同特征油藏的适用方法:红外照相法适用于浅层油藏;用测温元件直接测试法适用于观察井和生产井密集的油藏;生产动态和物质平衡相结合的方法适用于均质性好且井网形式规范的油藏;示踪剂监测和生产动态相结合的方法适用于非均质性严重或井网形式不规范的油藏;压降试井法由于计算复杂,有待进一步研究。最后指出应在前期地质筛选的基础上,做到"少注气、多采油",并结合生产动态分析加强对油井的管理,引导燃烧前缘均匀稳定的推进。     

锂离子电池正极材料LiMn_2O_4紫外可见光谱研究

<正>极材料因为其成本低廉,环境友好,高能量密度而被广泛应用.本文采用高温固相法制备Li Mn2O4正极材料,运用X射线衍射仪和扫描电镜分析其结构和微观形貌,采用紫外可见光分光光度仪测试该材料的光谱特征,并计算禁带宽度.结果表明:Li Mn2O4材料有菱形结构的几何外形,粒径分布处于1 um~2 um之间,为立方尖晶石结构,Li Mn2O4溶于双氧水和HNO3样品,溶解时间为40 min,80 min,120 min时,禁带宽度分别为2.24eV,2.22eV和1.85eV.     

气井携砂临界参数与出砂量预测模型应用研究（65）

为了解决生产中气井产量临界值的相关矛盾,对砂粒直径所需的气体临界流量进行研究.结合气井实际出砂现状及规律,修正已有的携砂临界参数与出砂量模型,探讨出砂气井合理生产制度和冲砂周期的确定方法.     

大学生信访工作的特征与应对方法

当代大学生是个特殊的群体,他们正经历心理由不成熟到成熟的过渡,他们敏感、不稳定,并且迫切地要求个人价值的实现。因此,在高校工作中加强学生信访工作建设,不仅有利于当代大学生人生发展,更有利于高校建设。     

两类ω-超广义函数空间的结构表示

利用ω-超广义函数空间与某些实解析函数空间之间的拓扑同构对应关系,通过实解析函数空间考察了两类ω-超广义函数空间,给出了RN中开集Ω上由任意的权函数引出的ω-超广义函数E′*(Ω)和由非伪解析的权函数引出的ω-超广义函数D′*(Ω)的某种结构表示