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。     

完备ARRs集的生成方法及传感器优化配置应用

广泛应用于故障诊断和传感器优化、分析、证实的解析冗余关系(Analytical redundancy relations,ARRs)缺乏系统、有效的方法来产生完备ARRs集,为此,提出了一种逐次消元法。该方法以系统元关系(Primary relations,PRs)为基础,通过若干次循环消元过程,生成了完备ARRs集,同时生成了对应的假定特征矩阵(Hypothetical signature matrix,HSM);基于HSM,把传感器优化配置问题映射为一个特殊的0-1整数规划模型,并用分支定界法求解该模型。应用表明,该方法能在不降低故障检测率、隔离率的前提下减少传感器数目,降低了测试代价,对故障诊断中的传感器配置问题有借鉴意义。     

“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.

     

航天器用实时操作系统设计

针对航天器在应用实时操作系统时的特殊需要,设计了航天器用实时操作系统的总体设计方案及内核;总体设计中,将操作系统分为板卡级驱动层、系统内核层、接口层三个层次,并确定了满足航天器任务的最简需求与各层的关系;在内核设计中,设计了双向链表管理器、多任务调度管理器、时钟管理器、消息队列管理器、中断管理器,并在其中体现了最简设计的思想;最后,对航天器用实时操作系统的可靠性设计思路进行了描述;此操作系统目前已应用多个航天器中。     

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。     

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。     

物联网概述第6篇:物联网的前沿技术与国家工程建设

物联网/云计算时代是一个无人化的普适服务时代。物联网的前沿技术,有RFID的智能化与普适化、智能手机的归一化交互、专家系统的软件集成、智能电网的物联技术等等。物联网时代的国家工程建设,有标准时空参数的国家工程建设、有效证券电子化的国家工程建设、重大的民生公共工程建设与物联网医院的示范工程建设等。     

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.