龙胜里骆林区森林鸟类群落研究

本文对龙胜里骆林区的四种不同植被类型:灌丛—经济林、针叶林、针叶—阔叶混交林和常绿阔叶林中的鸟类群落作了研究,结果表明:阔叶林鸟类群落的鸟种多样性最高(3.14),针叶林鸟类群落的鸟种多样性最低(1.94)。造成这种状况的原因是林型间结构的差异,而不是海拔高度的变化和人类活动的干扰;单纯针叶林生境结构简单,因而栖息于其中的鸟类较少。文中还讨论了鸟类群落与森林虫害的关系。     

Immobilization of arsenic in soils by stabilized nanoscale zero-valent iron, iron sulfide (FeS), and magnetite (Fe3O4) particles

Arsenic is a widespread contaminant in soils and groundwater. While various iron-based materials have been studied for immobilizing arsenic in contaminated soils, the feasibility of stabilized iron-based nanoparticles has not been reported. This study investigates the effectiveness of using three types of starch-stabilized iron-based nanoparticles, including zero-valent iron (ZVI), iron sulfide (FeS), and magnetite (Fe₃O₄), for immobilization of arsenic in two representative As-contaminated soils (an orchard soil and a fire range soil). To test the effect of the nanoparticles on the arsenic leachability, As-contaminated soils were amended with the nanoparticles at various Fe/As molar ratios (5:1–100:1) and contact time (3 and 7 d). After three days’ treatments of a field-contaminated sandy soil, the PBET-based bioaccessibility of As decreased from an initial (71.3±3.1)% (mean±SD) to (30.9±3.2)% with ZVI, (37.6±1.2)% with FeS, and (29.8± 3.1)% with Fe₃O₄ at an Fe/As molar ratio of 100:1. The TCLP-based leachability of arsenic in a spiked fire range soil decreased from an initial (0.51±0.11)% to (0.24±0.03)%, (0.27±0.04)% and (0.17±0.04)% by ZVI, FeS, and Fe₃O₄ nanoparticles, respectively. The Fe₃O₄ nanoparticles appeared to be more effective (5% or more) than other nanoparticles for immobilizing arsenic. When the two soils were compared, the treatment is more effective on the orchard soil that has a lower iron content and higher initial leachability than on the range soil that already has a high iron content. These results suggest that these innocuous iron-based nanoparticles may serve as effective media for immobilization of As in iron-deficient soils, sediments or solid wastes.     

基于多串口卡的Ad hoc网络仿真系统的设计与实现

Ad hoc网络是一种无中心的自组织网络，其多跳性和移动性使得很难在传统的局域网或有中心的移动网络中仿真和调试其协议和算法。首先分析了在传统局域网和移动网进行Ad hoc网络仿真的难点和问题所在，接着提出了基于MOXA Smartio CP-168U／PCI多串口卡的Ad hoc网络仿真方案，然后详细介绍了仿真系统的功能和系统主要模块在VC 6．0环境下的实现，最后对系统作出分析和评价。     

UUV编队协同最优扩方应召搜索方法

针对螺旋应召搜索方法理论上可行但实际上无法实现的问题,提出了基于改进的粒子群优化（particle swarm optimization,PSO）算法的无人水下航行器（unmanned underwater vehicle,UUV）编队协同最优扩方应召搜索水下匀速直线运动目标的方法。首先,根据UUV的搜索宽度,建立了最优转向角模型,给出了采用改进的PSO算法的具体求解流程,并在此基础上继续探讨当目标速度未知情况下的搜索计划。然后,根据投放方式的不同,探讨了UUV编队的几种协同搜索方法。最后,通过仿真实验,证明了该方法的有效性,并根据仿真结果给出了对未来UUV 装备技术发展方向的建议。     

合成狭缝彩虹全息图特性研究

研究了合成狭缝彩虹全息图的衍射效率、分辨本邻和单色性。给出了其衍射效率与合成狭窄宽度之间的关系，证明了其分辨本领和单色性都低于一般彩虹全息图。     

竞争失效下多元退化建模的导弹贮存寿命评估

导弹失效是弹上多部件退化失效与突发失效相互竞争的结果。根据导弹失效模式分析,建立导弹竞争失效模型。针对贮存期内测试到的弹上多个关键部件特征电压数据,引入状态空间模型,将弹上多个关键部件的特征电压转化为整弹退化程度,采用Wiener、Gamma和Inverse-Gaussian等随机过程模型来拟合整弹退化程度数据,采用赤池信息准则来确定最优的随机过程模型。假定导弹突发失效时间服从Weibull分布,采用比例危险模型,建立了考虑性能退化影响的突发失效率函数。采用两步极大似然估计法求解出模型中未知参数。结合某批次导弹贮存案例分析,验证了本文所建模型的正确性和优势。     

机动目标拦截新型微分几何制导律设计

针对大气层内机动目标拦截问题,基于广义微分几何制导体制设计了一种新型的微分几何制导律。与控制视线转率有限时间内收敛不同,采用视线转率随着弹目相对距离减小而渐进收敛的滑模面。鉴于观测器估计目标加速度存在初始尖峰现象,且嵌入观测器的制导律难以证明稳定性,假设目标加速度是具有未知上界的干扰,提出一种双幂次的自适应律对其上界进行估计。通过添加修正项,消除了饱和函数替代符号函数对稳定性的影响,并证明了所设计的制导律是渐进稳定的。仿真结果表明,所设计的新型的微分几何制导律能够有效拦截机动目标,且过载分布均匀、能量消耗少。     

生产现场定型管理模型——系统分析方法在企业管理中的应用

运用系统分析方法，研究了生产现场定型管理系统模型的设计、优化和应用。文中对定型管理的概念、系统模型结构和系统模型设计与应用原则、系统模型的评价分析、以及系统模型的优化进行了深入的阐述.     

高分辨率谱估计算法MUSIC和Minimum-Norm及性能比较

本文从理论上详细叙述了高分辨率谱估计技术MUSIC和Minimum-Norm算法的机理,给出并比较了它们的空域谱平坦度、3dB谱宽度和谱峰值随信噪比的变化情况.文章对工程设计人员加深理解、选择算法具有一定的参考意义.     

Efficient hybrid neural network for spike sorting

Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike sorting method with low complexity and high efficiency is proposed based on the hybrid neural network with the principal component analysis network (PCAN) and normal boundary response (NBR) self-organizing mapping (SOM) network classifier. An automatic PCAN technique is used to reduce the dimension and eliminate the correlation of the spike signal. The NBR-SOM network performs the spike sorting challenge and improves the classification performance. The experimental results show that based on the hybrid neural network, the spike sorting method achieves the accuracy above 97.91% with signals containing five classes. The proposed NBR-SOM network classifier is to further improve the stability and effectiveness of the classification system.