Human tracking system based on adaptive multi-feature mean-shift for robot under the double-layer locating mechanism

Human tracking has been a challenging task for robot in the past decades. In this paper, to realize the human following in a cluttered environment, a human tracking system based on adaptive multi-feature mean-shift (AMF-MS) under the double-layer locating mechanism (DLLM) is proposed to solve the problem of distinguishing target, occlusion, and quick turning. The DLLM, considering the course location processing and fine location processing, is designed to estimate the person’s position using the fusion of heterogeneous data. As an ID tag attached on target can be detected by RF antennas, the course locating method can track the target easily and quickly. The Bayes rule is introduced to calculate the probability where the tag exists due to the instability of RF signals. In the fine locating step, the AMF-MS is proposed because it can reduce computational load and represent target by multi-feature histogram function. Meanwhile, we combine extended Kalman filter and AMF-MS to overcome MS’s inability of occlusion. To control the robot following the target person precisely, an intelligent gear shift strategy based on fuzzy control is implemented by analyzing the robot structure. Experiments demonstrate that the proposed approach is robust to handle complex tracking conditions, and show the system has an optimum performance.     

WALL AND PARTICLE-SHAPE EFFECTS ON HEAT TRANSFER IN PACKED BEDS

The effective radial thermal conductivity and apparent heat transfer coefficient for a packed bed were experimentally determined for beds of spheres, full cylinders and hollow cylinders, for flow rates giving particle Reynolds numbers in the range 100-1000, and for tube to particle diameter ratios of 5-12. Over these ranges the radial Peclet number Pe_r showed significant dependence on solid conductivity, gas flow rate and particle shape, while the wall Biot number Bi showed significant dependence on tube to particle diameter ratio, gas flow rate and particle shape. These dependencies were predicted well by equations incorporating the effects of these variables into individual gas and solid phase parameters, which were then combined to give the effective or lumped parameters     

云数据中心心跳检测与故障评估

为提高云数据中心的抗毁能力,针对软硬件日趋复杂化带来的诸多故障问题,以传统的服务器心跳检测机制为基础,结合云计算的特点,提出一种保证云数据中心有效运行的心跳检测与故障评估方案。在实验室搭建仿真平台进行实验,实验结果表明,该方案能够及时检测到节点故障,依据预先制定的故障等级评估标准,采用模糊综合法进行评估,为云数据中心的管理与运作提供参考依据与保障。     

基于自由运动一维标定物的多摄像机参数标定方法与实验

一维标定方法易于实现且标定效率高,为了克服现有一维标定方法的一些不足,本文提出一种用一维标定物标定多摄像机内外参数的方法,首先进行两两标定,在此过程中,假定主点坐标近似已知而仅考虑畸变、焦距、旋转和平移等参数,接着利用基本矩阵及一维标定物上特征点之间的几何约束,估计两摄像机的内外参数,两两标定完成后,采用Dijkstra最短路径法和捆绑调整对多摄像机系统进行全局标定(含主点坐标),仿真和真实实验表明本文的方法是切实有效的.     

多源信息融合的测井曲线分层算法研究

测井数据解释中,针对单一测井曲线无法真实反映地层属性问题,提出以多条测井曲线的滤波因子为权值,融合出一条综合特征曲线,对该特征曲线相继采用层内差异法细分层与模糊聚类校正分层,实现特征曲线的合理分层。实验结果表明:该方法避免了海量数据处理过程,剔除了噪点数据的影响,提高了分层的速度与精度,能够为应用测井资料进行岩性识别、测井相分析、储层划分等研究提供有利的技术支撑。     

基于工作流和智能手机的城市综合管理平台

为适应现代城市综合管理的需要,提高城市管理水平,提出利用GIS技术的二三维一体化联动提高管理平台可视化;基于工作流的管理使事件处理更加自动化;利用智能手机的方便使管理人员与系统进行动态交互;利用模糊数学提高任务完成质量评价的科学性。整个平台在Windows7下进行开发,基于B/S(浏览器/服务器)和C/S(客户端/服务器)结合的架构,运用JavaEE、工作流、模糊数学、无线通信、视频融合等技术,实现平台对交通灯、人员、车辆、消防、事件和任务的管理。     

基于模糊梯度卷积核的在线棒材断面中心识别

针对传动带上严重堆叠、形状尺寸不规则的棒材,提出基于模糊梯度卷积核的在线棒材断面中心识别方法.以标准圆边缘为中心环,结合棒材断面边缘点偏离中心环的程度定义一个模糊梯度卷积核;通过模糊梯度卷积核与实时棒材断面梯度图像的卷积操作,实现棒材断面中心的增强;依次执行候选点选取、局部峰值抑制、距离检测步骤确定中心位置.实验结果表明,该方法能有效抑制棒材中心增强过程中易产生的峰值扩散现象,减少误识别和漏识别;平均计算时间为8.3 ms,满足系统实时性要求.     

使用新混合模糊聚类算法的模糊系统建模方法

为了进一步提高模糊系统建立模型的精度,提出一种新的模糊系统算法ANFIS-HC-QPSO：采用一种混合型模糊聚类算法来对模糊系统的输入空间进行划分,每一个聚类通过高斯函数的拟合产生一个隶属度函数,即完成ANFIS系统的前件参数--隶属度函数参数的初始识别,通过具有量子行为的粒子群算法QPSO与最小二乘法优化前件参数,直至达到停机条件,最终得到ANFIS的前件及后件参数,从而得到满意的模糊系统模型。实验表明,AN-FIS-HC-QPSO算法与传统算法相比,能在只需较少模糊规则的前提下就使模糊系统达到更高的精度。