古尔班通古特沙漠梭梭林地NDVI的时间效应-基于物候变化和MODIS图像的研究

遥感图像信息提取研究是遥感研究中的一个关键问题,也是遥感研究的热点和难点之一。使用2000~2010年MODIS-NDVI 16 d合成数据和物候记录,借助GIS空间分析和统计分析方法,重构了古尔班通古特沙漠梭梭林地Mean NDVI时间序列特征曲线。分析物候与Mean NDVI时间序列表明,梭梭林地内的短命植物生长期早于梭梭。研究梭梭林地Mean NDVI时间序列曲线发现,曲线中存在一个明显区别于其他地物的特征点,该点可以作为梭梭林地信息“诊断点”。根据“诊断点”特征构建了梭梭林地特征指数模型(HFFI),进而反演了古尔班通古特沙漠梭梭林地信息,并利用地面实际观测资料进行验证,结果表明分类精度达到83%。     

锡膏激光扫描三维测量系统光强自适应调节技术

根据锡膏激光扫描三维测量系统在测量PCB电路板时底板和锡膏对激光光强的散射效果不同,设计并研制了一套光强自适应调节光源。该光源以程控电源为基础,分析光条中心提取效果与激光光强的关系,应用小波分解与频带能量比相结合的光强自适应调节算法实现测量系统光强自适应调节功能。进行了相关实验,实验结果表明该光源能够很好地实现锡膏激光扫描三维测量系统的激光光强自适应调节,并能选出最适合测量的激光光强,具有很好的适用性。     

基于XML的新闻管理系统设计

简要分析基于XML的新闻管理系统的架构及主要功能模块设计,构建Web Service实现新闻发布旨在实现信息资源共享,并通过案例加以说明。     

Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification

Knowledge and Information Systems - Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn...     

A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems

Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.

     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Effects of assembly errors on axial positioning accuracy for rotating machinery with magnetoresistance-based magnetic encoders

Microsystem Technologies - In positioning systems, magnetic scales consisting of magnetoresistive sensors are widely used in the generation of position signals for machine tools. To obtain a...     

基于云计算技术的信息安全试验系统设计与实现

在云计算领域,信息安全显得尤为重要,是一个长期的研究课题。文章利用云计算开源平台,设计一套信息安全试验系统,利用该系统,将重要信息置于云端,客户端通过虚拟机实现远程调用,可以大大提高运行的安全性和健壮性,同时也能大大提升工作效率。     

基于SaaS的矿震远程预警系统关键技术初探

现有的矿震预警系统都是以独立形式运行于各矿区,相互之间没有关联,重复投资严重,且矿震数据海量繁杂,仅依靠现场分析无法满足实时预警需求。文章基于感知矿山物联网示范工程(夹河矿),对采用云计算SaaS模式构建的矿震远程预警系统关键技术进行了相关研究:首先给出了系统整体框架,然后重点探讨了该系统所涉及的三个关键技术,包括基于SaaS的矿震预警专家分析软件架构、多源SaaS数据库构建技术以及基于MapReduce的并发数据过滤接收技术。现场应用表明,所构建的系统已基本具备全国统一远程矿震预警系统的架构,能满足多矿区远程矿震预警的基本要求。     

矿用区域温度监测系统研制

针对现有的火灾探测器仅能对已发生的火灾进行探测,而在火灾发生前、环境温度较低时,却无法发出报警信号的问题,设计了一种可应用于非煤矿山的基于测温电缆的区域温度监测系统。该系统采用线状温度传感器连续监测探测区域的最高温度,在火灾未发生时即可发出预警信号,从而避免事故发生或引起火灾。试验结果验证了该系统的可行性。