广义系统多传感器分布式融合降阶Kalman滤波器

对于带多传感器的广义线性离散随机系统,应用奇异值分解,将其变换为等价的两个降阶多传感器子系统,提出了基于变换后的状态融合器构造原始状态融合器的新的融合方法。应用Kalman滤波方法,在线性最小方差按矩阵加权、按对角阵加权和按标量加权融合准则下,分别提出了三种最优加权融合降阶广义Kalman滤波器。可统一处理融合滤波、平滑和预报问题。可减少计算负担和改善局部滤波精度。证明了三种融合器和局部估值器之间的精度关系。为了计算最优加权。提出了局部滤波误差协方差阵的计算公式。一个Monte Carlo仿真例子说明了其有效性。     

数控车床主轴组件的新工艺

介绍了数控车床主轴组件采用过盈连接的新结构,取代了键连接、锁紧螺母固定的传统结构,并介绍了过盈配合的选择与计算、主轴组件的热套装配工艺及过盈配合的油压拆卸工艺.     

基于多尺度光流融合特征点视觉-惯性SLAM方法

为提高视觉-惯性导航系统在弱纹理环境下的鲁棒性和精度,结合特征点法精度高和光流法速度快的特点以及惯性信息,提出一种多尺度均匀化光流融合特征点法的视觉-惯性同时定位与地图(simultaneous localization and mapping, SLAM)构建方法。首先,改进快速特征点提取和描述(oriented fast and rotated brief, ORB)特征提取过程,采用多尺度网格化的方法提取ORB特征点并利用四叉树均匀分配特征点,提高特征分布离散性。其次,在帧间采用LK(Lucas and Kanade)光流法追踪特征点进行帧间的数据关联,在关键帧对特征点进行描述子的计算和匹配从而实现关键帧间的数据关联,保证算法速度的同时提高定位精度和鲁棒性。最后,基于光流法建立的数据关联得到的初始位姿为后端优化提供初始值,整合ORB特征点重投影误差、惯性测量单元(inertial measurement unit, IMU)预积分误差以及滑动窗口先验误差构建最小化目标函数采用滑动窗口非线性优化进行求解。实验表明,所提方法相比单目视觉惯性系统具有更高的定位精度和鲁棒性,定位精度平均...     

激光作用生成的网孔硅结构

作者结合量子受限效应,提出纳硅晶与氧化硅界面态发光模型来解释激光作用生成的纳米网孔壁结构的强荧光效应。将功率为50W、波长为1064nm的YAG激光束(束斑直径0.05mm)照射在硅样品表面打出小孔,在孔内的侧壁上,有很特殊的网孔形结构,其中的网孔壁厚为纳米尺度,这里有很强的受激荧光发光效应,发光峰中心约在700nm处。我们将激光与硅样品的作用隔离于无氧化的环境里,分别比较了将硅样品浸入酒精、氢氟酸和水中的激光加工结果,其发光情况证实了该发光模型的真实性。优化激光加工的条件,我们获得了较强发光的样品。     

激光作用生成的纳晶硅与氧化硅界面态发光模型

我们结合量子受限效应,提出纳硅晶与氧化硅界面态发光模型来解释激光作用生成的纳米网孔壁结构的强荧光效应.将功率为50W、波长为1 064nm的YAG激光束(束斑直径0.05mm)照射在硅样品表面打出小孔,在孔内的侧壁上,有很特殊的网孔形结构,其中的网孔壁厚为纳米尺度,这里有很强的受激荧光发光效应,发光峰中心约在700nm处.我们将激光与硅样品的作用隔离于无氧化的环境里,分别比较了将硅样品浸入酒精、氢氟酸和水中的激光加工结果,其发光情况证实了该发光模型的真实性.优化激光加工的条件,我们获得了较强发光的样品.     

局部变权的公理体系

提出了局部变权的公理化定义,研究了与之相应的局部状态变权,得到了两类基本均衡函数.最后,作为原理的应用,给出了一个例子.     

延迟离散Hopfield-型网络广义异步收敛性分析

给出了延迟离散Hopfield -型神经网络的收敛性定理。在广义异步运行方式下 ,证明了对称连接权阵 (只要w0 对称 )条件下的收敛性定理 ,推广了已有的延迟离散Hopfield -型神经网络的收敛性结果 ,表明网络收敛滞后于能量函数收敛最多 2n 1步。最后给出了能量函数的极大值点与延迟离散Hopfield -型神经网络的稳定态的关系。     

Targeted inhibition of Livin resensitizes renal cancer cells towards apoptosis

Cancer cells are typically characterized by apoptosis deficiency. In order to investigate a possible role for the anti-apoptotic livin gene in renal cell cancer (RCC), we analyzed its expression in tumor tissue samples and in RCC-derived cell lines. In addition, we studied the contribution of livin to the apoptotic resistance of RCC cells by RNA interference (RNAi). Livin gene expression was detected in a significant portion of RCC tumor tissue specimens (13/14, 92.9%) and tumor-derived cell lines (12/15, 80.0%). Moreover, targeted inhibition of livin by RNAi markedly sensitized RCC cells towards proapoptotic stimuli, such as UV irradiation or the chemotherapeutic drugs etoposide, 5-fluorouracil, and vinblastine. These effects were specific for livin expressing tumor cells. We conclude that livin can contribute significantly to the apoptosis resistance of RCC cells. Targeted inhibition of livin could represent a novel therapeutic strategy to increase the sensitivity of renal cancers towards pro-apoptotic agents. Received 30 November 2006; received after revision 22 February 2007; accepted 20 March 2007     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.