基于电荷平衡原理的引信模拟自毁电路

针对传统分立电子元件模拟自毁技术的功耗高、可靠性较差,提出了一种基于电荷平衡原理的引信模拟自毁电路。该电路利用电荷平衡原理取能,辅以负电压计时模块和正电压起爆模块完成自毁功能。仿真计算和试验验证表明,该电路功耗低、计时精度高、体积小、可靠性高。     

螺旋藻多糖粗提的新方法工艺条件研究

开发了一种提取螺旋藻多糖的新方法——超声提取和冷碱液提取相结合的方法。正交实验确定的最佳提取条件为:温度为常温,超声时间1 h,提取时间2 h,溶液的pH值为9,固液比9∶1。在此条件下,提取率较传统方法提高5%左右。     

电动机绕组极性的测定与分析

1绕组极性测定的意义三相电动机具有结构简单,制造、使用、维护简便,成本低廉,运行可靠,效率高等优点,因此在工农业生产及日常生活中得以广泛应用。但由于使用频率较高,特别是在环境条件恶劣,超负荷运转的情况下,容易出现故障现象,俗称"电机烧坏"。由于电动机绕组联接可分为星形接法和三角形接法,重新缠绕的电动机绕组需确定定子绕组极性(亦称首末端)后,再按不同的接线方式进行接线。     

EMAP处理方法在MT静校正中的应用

在大地电磁勘探中,当地下近地表出现电性不均匀体时,使相邻测点的视电阻率发生较大变化,在视电阻率断面图上会出现虚假的陡立深大断裂或垂向大延深的异常体,产生静态效应。为解决大地电磁中静态效应所带来的影响,本文采用EMAP处理方法,通过对在测点距、测线长度和滤波系数不同情况时的MT正演模拟数据进行处理,获得了较好的效果,进而证明EMAP处理方法对静态效应校正的有效性。     

A novel un-doped long lasting phosphorescence phosphor:SrZrSi2O7

A novel phosphor of un-doped Sr Zr Si2O7 material with blue long lasting phosphorescence which could be recorded for about 5400 s(0.32 mcd/m2) was developed. It revealed that both the emission centers and traps levels were related to the oxygen-deficient defects which were induced in reducing atmosphere. The filling and fading experiments indicated that the traps levels in Sr Zr Si2O7 were continuous in distribution. The influence of shallow traps on deep traps was investigated and the important role of the bridge effect was revealed. The un-doped Sr Zr Si2O7 material obtained in reducing atmosphere showed potential application as a cheap blue long lasting phosphorescence phosphor.     

Performance Evaluation of Machine Learning Methods in Cultural Modeling

Cultural modeling (CM) is an emergent and promising research area in social computing. It aims to develop behavioral models of human groups and analyze the impact of culture factors on human group behavior using computational methods. Machine learning methods, in particular classification, play a critical role in such applications. Since various cultural-related data sets possess different characteristics, it is important to gain a computational understanding of performance characteristics of various machine learning methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling data set and analyze the experimental results as to group behavior forecasting.