基于势函数的多智能体避撞队形控制

研究了一类多智能体系统的队形控制问题,改进了现有避撞势函数的构造方法.基于势函数的方法设计了控制策略,在控制策略的作用下,多智能体系统实现了期望的队形,避免了碰撞的发生.最后,仿真表明所提方法的可行性和有效性.     

时延相关网络控制系统的输出反馈镇定

研究了一类具有时变时延网络控制系统的输出反馈镇定问题.对于传感器与控制器间产生时变时延情形,采用时延相关切换控制器,通过Lyapunov-Krasovskii泛函方法和广义模型变换,得到了使闭环系统渐近稳定的线性矩阵不等式充分条件,该控制器是可以降阶的.仿真算例验证了该方法的有效性.     

邻苯二甲酸二丁酯致小鼠卵巢毒性的实验研究

目的:探讨邻苯二甲酸二丁酯对小鼠卵巢的影响及其机制.方法:320只小鼠分别灌胃不同剂量的邻苯二甲酸二丁酯(DBP)(0,100,250,500 mg/(kg·d))30天,在5 d,10 d,20 d,30 d时用分光光度法检测小鼠卵巢超氧化物歧化酶(SOD)、过氧化氢酶(CAT)、碱性磷酸酶(AKP)和酸性磷酸酶(ACP)的活性及丙二醛(MDA)的含量,用免疫组织化学法检测卵巢Bax蛋白的表达情况.结果:DBP降低了SOD、CAT、AKP、ACP的活性,增加了MDA的含量和Bax蛋白的表达.双因素方差分析表明,剂量因素对结果的影响大于时间因素.结论:DBP促进了细胞凋亡,抑制了卵巢的抗氧化能力.     

基于贪婪算法获取稀疏表示探讨研究

本文给出一个反例,指出在一般的字典中,稀疏表示中正交匹配搜索（OMP）算法不能得到测量的最稀疏信号,从而说明匹配搜索、正交匹配搜索等贪婪算法在稀疏表示中不能保证每一个测量能得到信号的最稀疏表示.     

AR(p)扰动下最小均方误差受控过程的联合监控

针对最小均方误差(Minimum Mean Square Error,MMSE)受控过程联合监控可控输入与过程输出这一问题,在任意阶自回归AR(p)平稳扰动模型下开发了一种通用的联合控制图,并将界内点排列非随机判异规则引入其中.同时,对AR(p)平稳扰动模型下生产过程的MMSE控制器和输入与输出的平均链长(Average Run Length,ARL)进行了推导.最后,通过仿真实验验证了该联合控制图的有效性.     

基于Gabor小波和LPP的浮选过程泡沫纹理特征提取及应用

针对Gabor小波进行特征提取时易造成维数灾难和识别效率不高的问题,提出一种基于Gabor小波滤波和局部保持投影(LPP)降维算法相结合的泡沫纹理特征提取方法.首先,利用Gabor滤波器获得原始泡沫图像5个尺度和8个方向的高维特征描述向量;然后,利用LPP算法得到降维特征向量;最后,利用此降维特征向量通过反向传播(BP)神经网络进行不同工况下泡沫类别的识别,进而指导实际矿物浮选生产.实验结果表明,相对于传统的GLCM方法和Gabor小波纹理特征提取方法,该方法可有效降低泡沫纹理特征向量维数并具有更高的识别效率.     

A multi-path routing algorithm of LEO satellite networks based on an improved ant colony system

Geography rectangle is used to reduce signaling overhead of the LEO satellite networks.Moreover,a multi-path routing algorithm based on an improved ant colony system （MPRA-AC） is proposed.Matrix indicating the importance of the link between satellites is introduced into MPRA-AC in order to find the optimal path more quickly.Simulation results show that MPRA-AC reduces the number of iterations to achieve a satisfactory solution.At the same time,the packet delivery ratio of LEO satellite networks when running MPRA-AC and DSR-LSN （dynamic source routing algorithm for LEO satellite networks） is compared.The packet delivery ratio is about 7.9％ lower when running DSR-LSN.Moreover,because of the mechanism of active load balancing of MPRA-AC,simulation results show that MPRA-AC outperforms DSR-LSN in link utilization when data packets are transmitted in the networks.     

Utility-optimization dynamic subcarrier allocation algorithm for SC-FDMA systems

Two utility-optimization dynamic subcarrier allocation （DSA） algorithms are designed for single carrier frequency division multiple access system （SC-FDMA）. The two proposed algorithms aim to support diverse transmission capacity requirements in wireless networks, which consider both the channel state information （CSI） and the capacity requirements of each user by setting appropriate utility functions. Simulation results show that with considerable lower computational complexity, the first utility-optimization algorithm can meet the system capacity requirements of each user effectively. However, the rate-sum capacity performance is poor. Furthermore, the second proposed utility-opti- mization algorithm can contribute a better trade-off between system rate-sum capacity requirement and the capacity requirements of each user by introducing the signal to noise ratio （SNR） informa- tion to the utility function based on the first utility-optimization algorithm, which can improve the us- er requirements processing capability as well as achieve a better sum-rate capacity.     

拉布拉多犬与金毛猎犬毛色基因MC1R(R306ter)与TYRP1(Q331ter)SNP位点的检测

目的建立犬毛色基因MC1R R306ter(c.C916T)与TYRP1 Q331ter(c.C991T)SNP位点的检测方法。方法采用PCR/测序的方法,对19只拉布拉多犬和15只金毛猎犬的MC1R基因c.C916T SNP位点及TYRP1基因c.C991T SNP位点的多态性进行检测,根据MC1R和TYRP1的多态性对犬的毛色基因型进行分析。结果 PCR/测序法能够对TYRP1基因c.C991T SNP位点进行明确和有效的检测。但MC1R基因c.C916T SNP位点的测序结果出现了特殊峰,经克隆/测序法确认该特殊峰型为杂合性SNP位点。毛色基因型分析表明,金毛猎犬的毛色基因型皆为纯合的eeBB(15只),未见其他基因型。拉布拉多犬的毛色基因型分别为:黑色犬EeBB(6只)、EeBb(2只);黄色犬eeBB(9只)、eeBb(2只)。未发现其他如EEBB和EEBb(黑色)、eebb(黄色)的毛色基因型。另外,拉布拉多犬样本中没有巧克力色犬,也未检测出其相应的Eebb和EEbb基因型。结论本研究成功建立了犬毛色基因MC1R R306ter(c.C916T)与TYRP1 Q331ter(c.C991T)SNP位点的检测方法,为分析毛色基因型与导盲犬培训成功率之间的相关性提供了前期工作基础。     

Comparison between PCA and KPCA methods in comprehensive evaluation of robotic kinematic dexterity

Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis （PCA） and kernel principal component analysis （KPCA） based on linear dimension reduction and nonlinear dimension reduction principle could be respectively introduced into comprehensive kinematic dexterity performance evaluation of space 3R robot of different tasks. By comparing different dimension reduction effects, the KPCA method could deal more effectively with the nonlinear relationship among different single kinematic dexterity indexes, and its calculation result is more reasonable for containing more comprehensive information. KPCA＇ s calculation provides scientific basis for optimum order of robotic tasks, and furthermore a new optimization method for robotic task selection is proposed based on various performance indexes.