利用调和映射的复杂网格曲面螺旋刀轨生成算法

针对截平面法规划的复杂网格曲面刀轨的加工效率不高问题,提出一种复杂网格曲面螺旋刀轨生成算法.首先采用调和映射的方法对网格曲面进行参数化,然后根据残留高度确定参数网格中的参数环,在相邻参数环的参数点之间进行“分组匹配”,并依次计算初始和精确的对角参数螺旋线;在此基础上采用“区域划分”的方法快速生成了无干涉的网格曲面螺旋刀轨.对于复杂网格曲面的实验结果表明,文中的螺旋刀轨能够有效地提高截平面法刀轨的加工效率,且能够保证较好的加工质量.     

遥感数据时空融合技术在农作物监测中的适应性研究

受卫星回访周期及云的影响,大范围研究区同一时期的Landsat卫星数据很难获取,因而国内外学者提出了遥感影像时空融合技术。以石河子为实验区,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合生成了高时空分辨率TM影像,对不同作物类型真实反射率与融合影像反射率作相关性分析,分析了遥感数据时空融合技术在新疆农作物监测中的适用性。结果表明:利用STARFM模型模拟得到的融合影像与真实影像间的相关性较高,但当地物类型发生变化时,融合影像与真实影像间将存在明显的差异。地物类型变化作物融合影像反射率与真实影像反射率间的相关性较小。     

“三位一体”数字校园的设计与实现

介绍了集＂日常管理＂、＂水平评估＂、＂决策规划＂三位于一体的数字校园设计方案。通过规范基础数据,采用ESB整合各业务子系统,解决＂信息孤岛＂问题,并为状态数据采集提供便利。利用BI软件进行数据挖据,为管理者进行科学规划和决策提供数据支持。系统采用单点登录,个性化的门户展现满足了各类用户对资源和服务的需求。     

Evaluating Particle Deposition in the Artificial Groundwater Recharge Process by Physical and CT Imaging Experiments

Particle clogging in the artificial groundwater recharge process is one of the main factors influencing the artificial groundwater recharge efficiency, and particle deposition is the microscopic mechanism of the occurrence and development of particle clogging. Particle deposition in porous media changes the pore structure. The computed tomography (CT) scanning technique is a nondestructive testing method and determines the spatial distribution of pores in porous media. This study combines physical and CT scanning experiments to identify the change process of the pore structure in the artificial groundwater recharge process and compares the pore changes during recharge experiments between two columns containing different media. Porous media changes are observed with the CT scanning technique. The fractal theory is applied in the analysis of CT scan images and physical experiment results. The results of this study indicate that particle deposition can be examined by using CT scan images to obtain pore-related parameters, the internal pore structure of porous media determined through CT scan images can be applied in numerical simulation, and a mathematical model for particle deposition calculation in porous media is established. Compared to the physical experiment measurements, the spatial particle deposition information acquired with the CT scanning technique exhibits a higher accuracy and contains much more relevant data. Not only does this research reveal more clearly the particle clogging mechanism which is based on particle deposition, but also characterize, simulate and predict more accurately the development tendency of particle clogging during artificial groundwater recharge.

     

Study of the hinge thickness deviation for a 316L parallelogram flexure mechanism fabricated via selective laser melting

3D printing offers great potential for developing complex flexure mechanisms. Recently, thickness-correction factors (TCFs) were introduced to correct the thickness and stiffness deviations of powder-based metal 3D printed flexure hinges during design and analysis. However, the reasons for the different TCFs obtained in each study are not clear, resulting in a limited value of these TCFs for future design and fabrication. Herein, the influence of the porous layer of 3D printed flexure hinges on the hinge thickness is investigated. Samples of parallelogram flexure mechanisms (PFMs) were 3D printed using selective laser melting (SLM) and 316L stainless steel powder. A 3D manufacturing error analysis was completed for each PFM sample via 3D scanning, surface roughness measurement and morphological observation. The thickness of the porous layer of the flexure hinge was independent of the designed hinge thickness and remained close to the average powder particle diameter. The effective hinge thickness could be estimated by subtracting twice the value of the porous layer thickness from the designed value. Guidelines based on finite element analysis and stiffness experiments are proposed. The limitations of the presented method for evaluating the effective hinge thickness of flexure hinges 3D printed via SLM are also discussed.

     

基于Express Card接口的千兆以太网网卡设计

针对现最常用的10/100Mbps自适应网卡网速过慢的问题,设计了一种含Express Card接口的千兆以太网网卡;用户可以方便、快捷地将其插入笔记本电脑的Express Card接口,实现高速通信;与以往采用FPGA、DSP等芯片设计的千兆以太网网卡相比,该千兆以太网网卡采用内含以太网MAC控制器、10Mbps/100Mbps/1000Mbps自适应收发器、PCI Express总线控制器等的网卡芯片RTL8111DL作为主芯片,无需再外接一片物理层芯片和一片MAC芯片;经测试:笔记本电脑可以通过此千兆以太网网卡借助五类线与外部设备握手成功,实现约260兆的高速数据传输。     

基于DSP的程控阵列信号源设计

为了模拟复杂回波信号,设计一种程控阵列信号源;下位机采用数字信号处理芯片TMS320VC5502作为信号源的硬件平台核心,结合DDS技术采用软件编程的方法实现信号源的输出;通过设计通信接口以及定义通行协议,上位机通过操作PXI-6508模块,实现对阵列信号源的程控输出;实验表明,该阵列信号源能同步触发输出96路阵列信号,也能单独输出一路或几路信号,样点率达400kSa/s,输出信号种类有正弦波、方波、三正弦叠加波和调频波等波形。     

基于双核变频注塑机控制优化方法研究

针对传统的注塑机采用单核变频控制技术,很难处理相同时域内的多个变频控制信号,造成SPWM波形的质量不高,注塑机控制精度效果不好的问题,提出了一种基于ARM+DSP双核变频技术的注塑机节能控制方法,使得变频系统能够同时处理时域内多个变频控制信号;运用离线计算且带误差补偿的不对称规则算法,改善载波比N较小情况下SPWM波形的质量;实验结果表明,在不改变变频器主电路及结构的情况下,双核控制的硬件结构及离线计算的软件设计,显著提高了变频器功能扩展的自由度和变频器的控制性能,为注塑机节能改造提供了一种切实可行的方案。     

CBR系统中一种实例相似度算法的改进

本文对CBR系统中实例相似度的算法进行了改进,在传统实例相似性算法的基础上加入实例属性缺失度因子和实例可复用性因子。因此在实例检索中考虑实例检索结果的精确度和实例的可复用度,进而使检索的精确度提高和修改难度降低,最终提高CBR系统的精确度和实用性。     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.