瓦斯突变预警上限值确定方法的研究

针对现有煤矿安全监控系统采用超限原理,以瓦斯传感器测量值为判断依据,无法及时发现预定报警值以下范围内瓦斯异常变化的问题,提出了一种不超限瓦斯突变预警方法,介绍了静态预警和动态预警两种不超限瓦斯突变报警方法。结合余吾煤业N2202工作面探头数据,分别对不超限瓦斯突变预警基础数据的静态数据和动态数据进行了分层抽样及分布的验证和拟合,得出静态预警和动态预警的基础数据基本符合正态分布的结论。提出采用假设检验和区间估计等实时计算静态预警和动态预警上限值的方法,并采用N2202工作面探头数据进行了验证。     

六轴工业机器人的参数辨识方法

机器人末端位姿的精度依赖于各关节几何参数的精度。为了提高机器人的位姿精度,提出了一种简单实用的机器人参数辨识方法。利用D-H 算法建立的机器人运动学方程,得到了末端位姿与各关节参数的关系。为了解决辨识过程中的复杂计算问题,设计了一种采用拆分参数的迭代方法和相应的实验方法,运用激光跟踪仪对位姿进行精确测量,求得实际几何结构参数。实验结果验证了所提出方法的有效性。     

一种面向多核处理器I/O系统软错误容错方法

集成电路制造工艺的飞速发展,使得集成电路的特征尺寸不断减少和集成度不断提高,造成集成电路对工作环境的影响越来越敏感,发生软错误的几率不断增加,对可靠性造成重要影响。随着微处理器进入了多核时代,丰富的片上资源给软错误加固带来了很好的机遇。本文针对多核处理器中I/O系统软错误,提出了一种基于多核处理器的软件Scrub方法对软错误进行加固。测试结果表明,我们提出的软错误容错方法可以大大提高I/O系统的可靠性。     

一种新的无陀螺惯性测量组合标定方法

无陀螺惯性测量组合在角速率解算过程中不仅需要知道加速度计标定因数、零偏及实际安装方向向量,还需要准确获取加速度计的实际安装位置.针对上述特点,以加速度计输出模型为基础,提出并建立了一种广泛适用于各种无陀螺惯性测量组合的标定方法.该方法经转台位置和速率实验验证可以准确得到加速度计标度因数、零偏及安装误差,进一步提高了载体线加速度和角速率解算精度,从而为后续的姿态解算奠定基础.     

3D靶标的摄像机三步标定算法与实现

摄像机标定是计算机视觉中的一个必不可少的重要环节,其标定精度影响着三维重建结果的精度。利用摄像机对一个三维正交的棋盘格拍摄一幅有效靶标图像,提取角点,第一步通过透视变换矩阵算法线性求解各内外参数;第二步引入径向和切向畸变,将第一步求得的内外参数作为初始值,求得畸变系数的解;第三步对内参数进行线性优化,得到更为精确的内参数值;最后求解反投影后图像坐标的绝对误差。实验结果表明,该方法具有较高的精度,且简单有效。     

射频仿真多辐射中心方案设计及幅相误差分析

现有的射频仿真系统在单通道中同一时间只能模拟一个辐射中心,因此难以对多目标与分布式杂波进行仿真。通过改进原有馈电电路并调整开关矩阵与阵列天线的连接顺序,现提出了一套新型的多辐射中心仿真系统的设计方案。新方案能够在单通道单周期中模拟两个辐射中心,为在经费有限、通道数量较少的条件下,模拟多目标和分布式杂波提供了技术基础。为了验证新方案的可行性,利用计算机仿真分析了新型方案引入的幅、相误差对辐射中心位置精度的影响,得出了几种情况下位置误差的统计分布。仿真结果表明,虽然新型方案中的位置误差相对更大些,但其辐射中心的位置精度仍然达到了仿真系统的技术要求,新型方案是可行的。     

基于改进的GRNN的销量预测

根据灯具销量具有增长趋势和周期性二者合成的非线性特点,本文利用相空间重构理论确定GRNN的输入节点数,以加权误差代价函数代替传统误差代价函数为目标训练网络,采用粒子群优化算法优化平滑参数,并在此基础上将GRNN用于灯具销量的短期预测。与传统的BPNN相比,改进后的GRNN预测精度更高,抗噪声能力更强,模型更稳健。     

Piecewise affine kernel tracking for non-planar targets

Since the multiple kernel representation opened in tracking the possibility of representing several features of the target in the same model, tracking multiple features using kernel-based methods has received a great attention. In spite of these efforts, the formulation has been reduced to tracking planar targets or targets rotating inside a plane parallel to the image plane. The aim of this paper is to extend the multi-kernel tracking to cope with situations different to those. To this end, we consider the triangular mesh described by the centers of the kernels and we develop the estimation of a set of affine transforms, one at each mesh triangle, subject to the constraints that each affine transform of a triangle must be compatible with the affine transforms coming from contiguous triangles. The method is applied to sequences including face and car tracking. Results show an outperformance respect to previous kernel tracking methods, which generally work with a too restricted set of movements.     

The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process

Many empirical studies have found that software metrics can predict class error proneness and the prediction can be used to accurately group error-prone classes. Recent empirical studies have used open source systems. These studies, however, focused on the relationship between software metrics and class error proneness during the development phase of software projects. Whether software metrics can still predict class error proneness in a system’s post-release evolution is still a question to be answered. This study examined three releases of the Eclipse project and found that although some metrics can still predict class error proneness in three error-severity categories, the accuracy of the prediction decreased from release to release. Furthermore, we found that the prediction cannot be used to build a metrics model to identify error-prone classes with acceptable accuracy. These findings suggest that as a system evolves, the use of some commonly used metrics to identify which classes are more prone to errors becomes increasingly difficult and we should seek alternative methods (to the metric-prediction models) to locate error-prone classes if we want high accuracy.     

Robust Kalman filtering for uncertain discrete-time systems with probabilistic parameters bounded within a polytope

We consider the problem of finite horizon discrete-time Kalman filtering for systems with parametric uncertainties. Specifically, we consider unknown but deterministic uncertainties where the uncertain parameters are assumed to lie in a convex polyhedron with uniform probability density. The condition and a procedure for the construction of a suboptimal filter that minimizes an expected error covariance over-bound are derived.