基于PSO-ELM模型的潜油柱塞泵冲次优化方法

为改进油井后期泵效低下、能耗高等缺陷，提出了一种基于PSO-ELM模型的潜油柱塞泵冲次优化方法.采用粒子群（PSO）算法与极限学习机（ELM）相结合的方式来实现动液面软测量建模；根据动液面及潜油柱塞泵工作电流变化，以油井运行经济性最优为目的建立目标函数得到潜油柱塞泵冲次，解决了在油井生产时不能准确调节抽油机冲次问题；最后以目标函数关系建立模糊控制器模型，根据输入参数调整潜油柱塞泵冲次.实验结果表明，建立的软测量模型预测动液面精度高，模糊控制器能够更加合理地调整抽油机冲次，最终达到智能调整冲次大小、提高油井采油率及节能的目的.     

重力注水过程流动不稳定现象关键影响因素研究

重力驱动注水过程中由于流量较小以及蒸汽的积聚可能导致流动不稳定现象的发生，对核反应堆安全运行具有重要的影响。通过实验研究的方法，搭建了重力注水模拟实验装置，研究了不同蒸汽出口形阻、高位储水箱水位和加热棒初始温度下流动不稳定现象的变化规律。结果表明，重力驱动注水过程流动不稳定现象包含冷却水初次注入阶段、注入水逐出阶段和冷却水再注入阶段等。在一定冷却水初始温度、冷却水入口形阻以及系统压力下，蒸汽排出速度以及实验本体内筒顶部的聚集情况取决于蒸汽出口形阻，减小蒸汽出口形阻可加快蒸汽排放速度，压力峰峰值降低、振荡周期变长，有利于系统稳定；提高高位储水箱水位加快了冷却水注入速率，增加了加热棒被淹没率，降低了流动不稳定现象的发生次数和持续时间；随加热棒初始温度的升高，冷却水流量出现了波动向停滞的转变，流动不稳定现象发生的次数增加且持续时间加长。     

A Dynamic Memory Allocation Optimization Mechanism Based on Spark

Spark is a distributed data processing framework based on memory. Memory allocation is a focus question of Spark research. A good memory allocation scheme can effectively improve the efficiency of task execution and memory resource utilization of the Spark. Aiming at the memory allocation problem in the Spark2.x version, this paper optimizes the memory allocation strategy by analyzing the Spark memory model, the existing cache replacement algorithms and the memory allocation methods, which is on the basis of minimizing the storage area and allocating the execution area according to the demand. It mainly including two parts: cache replacement optimization and memory allocation optimization. Firstly, in the storage area, the cache replacement algorithm is optimized according to the characteristics of RDD Partition, which is combined with PCA dimension. In this section, the four features of RDD Partition are selected. When the RDD cache is replaced, only two most important features are selected by PCA dimension reduction method each time, thereby ensuring the generalization of the cache replacement strategy. Secondly, the memory allocation strategy of the execution area is optimized according to the memory requirement of Task and the memory space of storage area. In this paper, a series of experiments in Spark on Yarn mode are carried out to verify the effectiveness of the optimization algorithm and improve the cluster performance.     

Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-of-the-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.     

医院级CT辐射剂量及诊断剂量水平研究

探讨医院级各部位CT扫描辐射剂量及成人CT检查诊断剂量水平。采集2018年2月—2019年1月本院在GE Light Speed 16 CT设备上进行诊断性CT检查的全部成年人共15 440例的CT检查数据,按照检查部位进行分类,统计各部位的容积剂量指数(CTDI_vol)、剂量长度乘积(DLP)。使用SPSS 19统计软件分析CTDI_vol、DLP的四分位数并计算有效剂量(ED)。结果表明,各部位CTDI_vol的诊断剂量参考水平(DRL)值为:颅脑87.75 mGy、肺部8.09 mGy、上下腹15.82 mGy、上下腹盆腔15.79 mGy;各部位DLP的诊断剂量参考水平(DRL)值为:颅脑1 053.03 mGy·cm、肺部245.19 mGy·cm、上下腹269.96 mGy·cm、上下腹盆腔835.20 mGy·cm。研究中发现肺部CTDI_vol、DLP的DRL值(分别为8.09 mGy、245.19 mGy·cm)低于我国其他地区和其他国家的DRL值;60岁以下患者肺部CT检查CTDI_vol、DLP的DRL值低于8.09 mGy、245.19 mGy·cm,60岁以上患者肺部CT检查CTDI_vol、DLP的DRL值高于8.09 mGy、245.19 mGy·cm。通过研究可以确定医院级各部位CT检查辐射剂量及成人CT检查诊断剂量水平,能为优化CT检查扫描技术和监测辐射剂量提供基础。     

高效液相色谱法测定多环芳烃的出峰顺序探讨

目的探讨水﹑气﹑土壤中多环芳烃检测标准(HJ 478-2009﹑HJ 647-2013、HJ 784-2016)的正确出峰时间和顺序。方法用高效液相色谱来对苊烯、芴、苊、?、苯并(a)蒽进行定性分析,并与3个标准中的出峰顺序进行比较。结果苊烯、苊、芴、苯并(a)蒽、?的出峰时间分别为6.450、7.923、8.233、17.760、18.740min,与标准HJ478-2009﹑HJ647-2013的出峰顺序存在差异。结论在使用标准HJ478-2009﹑HJ647-2013、HJ 784-2016同时测定16种多环芳烃时,多环芳烃的出峰顺序及时间应以HJ 784-2016为准。     

基于改进飞蛾扑火优化算法的MIMO雷达相位编码信号集设计

提出一种MIMO雷达相位编码信号集设计方法，该方法对飞蛾扑火优化算法进行改进，利用改进的飞蛾扑火算法优化相位编码信号集，提高其正交性.在改进方法中，增加对火焰的更新过程，使火焰不仅能够保存最优飞蛾位置还具有搜索最优解的功能.优化过程以自相关函数旁瓣峰值和、互相关函数峰值和，以及自相关函数旁瓣能量、互相关函数能量作为算法适应度函数.仿真实验结果表明，改进方法明显提高了飞蛾扑火优化算法的收敛速度和收敛精度，且优化得到的信号集具有较低的自相关旁瓣值和互相关值.     

TLCD基础隔震框架结构的减振控制研究

为了避免和减轻由过大隔震层位移引起的损害,对基础隔震框架结构装设调频液柱阻尼器(tuned liquid column damper,简称TLCD)后混合系统的减振效果进行研究。建立了单层和多层混合控制系统在地震作用下的运动方程,采用TLCD-结构体系转化为调频质量阻尼器(tuned mass damper,简称TMD)-结构体系的等效方法,利用TMD参数优化公式,得到单个TLCD初始设计参数,并采用状态空间方程得到多个TLCD最优设计参数。通过对某8层基础隔震结构进行模拟,证明了该理论设计方法的合理性。该混合结构不仅可以减小隔震层位移和加速度,而且对上部结构位移和加速度反应都能更有效的控制。     

Preparation,characterization and investigation of molecular films coated on diamond-like carbon substrate

For the successful application of boundary lubrication, detailed investigations about the influence of preparation process on molecular films are needed. In this paper, a specially designed device was used for the film preparation. The scanning electron microscope (SEM) combined with atomic force microscope (AFM) was employed to characterize the surface morphology and nanotribological behavior of molecular films. After the liquid phase deposition, molecular films are randomly and densely distributed over Ti-doped diamond-like carbon (Ti-DLC) substrates. Through rigorous surface treatments, island-like molecular films were finally achieved on substrate surfaces. The surface friction of molecular films is obviously lower than that of Ti-DLC surfaces. Then, pin-on-disk tribotests were performed to study the macrofriction behavior of molecular films under different preparation parameters. Based on the orthogonal experiment, the effect of five preparation parameters (solution weight percent, smearing force and processing time of three smearing steps) on initial friction coefficient of molecular films was investigated. The results indicated that the order of significance levels is as follows: processing time of the second smearing step > solution weight percent > processing time of step 1 > processing time of step 3 > smearing force. For the purpose of friction reduction, the appropriate level ranges are 0.75% (Solution), 2.5 N–15 N (Force), 1 min–10 min (Step 1), 1 min–2 min (Step 2) and 1 min, 2 min, 5 min and 15 min (Step 3). The initial friction coefficient under the optimized conditions is around 0.112, and the equilibrium friction coefficient is around 0.162, which is lower than that of unlubricated Ti-DLC substrates.