六维腕力传感器非线性正解耦方法设计

多维力传感器利用其多个转换单元完成测量加载于其结构上未知负载的作用效果,解耦是其设计的重要组成部分。针对传统静态线性解耦方法的不足,试图将传统线性解耦方程扩展为多项式结构,受其多元高次方式通解形式的启发,构造了一种多项式非线性静态正解耦方程,该方法无需传统线性解耦方法中的曲线拟合、逆解,不依赖以系统是线性为前提,且方程可以扩展成任意结构的多项式。实验结果表明:该方法能降低输出耦合误差。     

溴虫腈土壤和甘蓝微量残留量的气相色谱法测定

采用气相色谱法定量分析溴虫腈土壤和甘蓝微量残留量。样品经丙酮提取 ,液 液分配及氧化铝柱层析法净化后 ,以GC ECD测定 ,DB 170 1毛细管柱 ,氮气为载气 ,柱温 2 6 0℃ ,汽化室温度 2 5 0℃ ,检测器温度 2 80℃ ,外标法定量。该方法快速、准确 ,在 0 0 1～ 2 0 0mg/L范围内线性相关系数r2 =0 9999,平均回收率 90 0 %～ 10 3 6 7% ,变异系数 4 4 8%～ 5 89% ,最小检测量 2× 10 12 g ,最低检出浓度 3 3× 10 4mg/kg。     

典型案例教学在《Android 开发技术》课程中的应用

随着移动互联技术的迅猛发展,很多高等学校在软件工程和计算机科学与技术专业开设《Android开发技术》课程,培养学生从事移动互联终端软件开发的能力,但在该课程教学过程中存在教学内容枯燥乏味、学生学习缺乏积极性和教学效果差等问题。分析该课程知识点的特点,提出利用“典型案例”贯穿该课程的核心知识点,教学实践显示该教学方法适合该课程的教学。     

四元数与欧拉角刚体动力学数值积分算法及其比较

为推广四元数保辛积分在工程中的应用,对欧拉角表示的状态方程数值积分与四元数的保辛积分进行比较.重陀螺的数值仿真结果表明四元数保辛积分的数值结果明显优于欧拉角状态方程积分.与欧拉角状态方程积分相比,四元数保辛积分在刚体动力学的数值仿真中更具优势.     

基于祖冲之类方法的多体动力学方程保能量保约束积分

针对一类多体动力学问题导出的微分 代数方程,提出一种保能量、保约束的算法.该算法基于祖冲之类方法和欧拉中点保辛差分,利用祖冲之类方法保证在时间格点上精确满足约束方程,避免约束违约问题;并进一步证明该算法在时间格点上可以精确保能量.数值算例进一步验证该算法的可靠性.     

CO2在全氟羧酸离子交换膜内促进传质特性的研究

本文以全氟羧酸离子交换膜为支撑体、乙二胺(EDA)为活性络合剂、制备促进传质膜。在常用的工业条件下,测定了纯CO_2以及CO_2/N_2二元混合物在该膜内的渗透通量。实测结果表明,此膜对CO_2的促进因子可达13.8,而对CO_2/N_2的分离因子高达316。本文假设促进传质过程的控制步骤为气体在膜相的扩散,据此导出了相应的机理模型,该模型与实测结果吻合良好。     

用磷石膏生产建筑材料的新进展

针对湿法磷酸副产的磷石膏在建筑材料中的应用及其国内外进展，介绍了用磷石膏制高强度砌块的中间试验流程，并指出在磷石膏中搀入其他材料，采用特殊复合工艺，可生产出新型功能性建筑材料，如石膏基导电材料和石膏基磁性材料等这不仅拓展了石膏的用途，又可获得巨大的经济效益。     

HT-730改性酚醛-丁腈胶粘剂的研制

对酚醛-丁腈胶粘剂进行改性，添加5．0％的硅烷偶联剂，改善了酚醛-丁腈胶粘剂的工艺性能，提高了粘接强度，特别是降低了酚醛-丁腈胶对环境湿度的敏感性。研制的HT-730胶在环境湿度90％时仍有很高的粘接强度，同时老化性能和适用期并没有受到影响。     

Seasonal evolution of the interannual variability of chlorophyll-a concentration and its forcing factors in the northwestern Pacific from 1998 to 2010

The northwestern Pacific Ocean is a complex region with significant biological spatial variations on a seasonal timescale. To investigate the joint variation patterns on both seasonal and interannual timescales, a season-reliant empirical orthogonal function (S-EOF) analysis was applied to seasonal mean chlorophyll-a concentration (chl-a) anomalies in the northwestern Pacific Ocean during the period 1998–2010. The first two dominant modes accounted for nearly 31% of the total interannual variance, with the second S-EOF mode (S-EOF2) lagging behind the first S-EOF mode (S-EOF1) by one year. S-EOF1 featured a strong variation pattern to the north of 30° N, with maximum chl-a in winter and minimum chl-a in summer. However, S-EOF2 indicated an opposite seasonally evolving pattern compared with S-EOF1, with chl-a increasing along the Kuroshio and extension current from boreal winter to autumn. Both these modes revealed significant relationships with climate-related indices. The two modes corresponded to the central Pacific (CP) La Niña developing episodes and the turnaround from eastern Pacific (EP) La Niña to CP El Niño, respectively. Both modes were associated with the cold phase of the Pacific Decadal Oscillation, which played an important role in prolonging the impact of the El Niño/Southern Oscillation on chl-a seasonal evolution from 1998 to 2010. In addition, we discuss the possible factors dominating chl-a seasonal variation, in terms of the subregions of the northwestern Pacific Ocean. In the subtropical northwestern Pacific Ocean (15° N – 30° N), the chl-a growth was primarily nutrient-limited, whereas in the mid-latitude northwestern Pacific Ocean (35° N – 50° N), the chl-a growth was mainly light-limited.     

Discovering small-world in association link networks for association learning

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among multimedia Web resources for effectively supporting Web intelligent application such as Web-based learning, and semantic search. This paper explores the Small-World properties of ALN to provide theoretical support for association learning (i.e., a simple idea of “learning from Web resources”). First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN, aiming to observe the Small-World properties of ALN at given network size and filtering parameter. Comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. Then, we investigate the evolution of Small-World properties over time at several incremental network sizes. The average path length of ALN scales with the network size, while clustering coefficient of ALN is independent of the network size. And we find that ALN has smaller average path length and higher clustering coefficient than WWW at the same network size and network average degree. After that, based on the Small-World characteristic of ALN, we present an Association Learning Model (ALM), which can efficiently provide association learning of Web resources in breadth or depth for learners.