Monte Carlo simulation-based traffic speed forecasting using historical big data

Because the traffic patterns on roads vary according to the roads’ specific spatio-temporal behavior, if we would like to forecast the traffic speed by day of the week, it is necessary to determine an optimal set of the highly related historical patterns to achieve high prediction accuracy. The goal of our paper is to suggest a new statistical modeling method that finds the best historical dataset according to various analyses for each link and provides a more accurate prediction of traffic flow by day of the week. First, we suggest a three-step filtering algorithm based on changepoint analysis, correlation analysis, and Monte Carlo simulation to simultaneously find and remove historical data outliers. Second, we determine the optimal historical data range by using decision factors such as the Mean Squared Error (MSE) and Akaike Information Criterion. Moreover, to verify our statistical model, we use various prediction accuracy measures such as Mean Absolute Percentage Error (MAPE), R-squared value, and Root MSE (RMSE). Finally, we construct a big data processing framework to handle the overall prediction process and calculate large amounts of traffic data. The forecasting results show that the proposed model can achieve a high prediction accuracy for each road by using three measures: less than 20% for MAPE, more than 80% for R-squared value, and less than 1 on average for RMSE.     

An on-line predictive system for steel wire straightening using genetic programming

Dimensional stability of forming processes is becoming more and more important in the modern production world. Especially when mass production is concerned, the technological system has to be reliable and accurate. Growing market demands are forcing production engineers towards process optimisation in order to achieve high machinery efficiency and reduce the production costs. An important precondition for improving the process chain is the prediction of process behaviour in advance. The paper is presenting the use of genetic programming to predict the wire geometry after forming. The results can be used as the basis for later optimisation of forming processes.     

某型导弹引信备件需求预测模型研究

准确的备件需求预测在备件库存管理中尤为重要,但是备件间歇性需求的特点以及需求的历史数据不足使得备件需求预测特别困难,在一些部门,导弹备件库存管理水平决定了设备如何使用和维修.为解决上述问题,建立一种新的预测方法,综合评价备件需求预测的自相关与解释变量的关系:通过回归分析确定由解释变量引起的非零需求,判别一个非零需求发生的原因,进而得到修正后的非零需求序列的预测结果,最后通过LTD估计,得到具体的非零需求预测值.通过理论分析以及实例结果均证明,改进方法在预测精度上优于其它方法,为导弹备件预测研究提供一定的参考与借鉴.     

流量仪表等精度传递的实验研究和准确度分析

采用实验研究的方式,讨论了用同等级的流量仪表作为标准表检定其它流量计的可能性与可行性,提出了等精度传递的概念,分析了等精度传递系统中影响传递精度的因素。试验表明,只要仪表具有足够好的重复性,用其作为传递标准组建标准表法流量标准装置,可以使装置的准确度接近上一级流量标准的准确度。     

智能时间继电器

提出了基于单片机技术的一种静态型时间继电器。它较之以往计数式静态型时间继电器具有精度高、功能多、电路简单、抗干扰能力强等特点。本文描述了该时间继电器的原理和实现方法,着重对整定数据读人和抗干扰的方法进行了详细讨论。     

WSN中基于超椭圆判决边界的异常检测的动态建模

为了实现无线传感器网络的动态数据流环境中的异常检测,本文提出了一种迭代超椭圆判决边界方法。首先建立起异常检测超椭圆模型,然后每个节点基于到当前时间为止的测量值来调整其超椭圆模型,最终收敛到覆盖正常和异常测量值的超椭圆边界;为了提高模型对监测环境中数据变化的跟踪能力,提出了一种采用遗忘因子并结合滑动窗口的基准估计和有效N跟踪的方法,从而实现对数据真实流属性的捕捉;仿真实验结果表明,本文提出的动态建模方法相比于目前先进的静态建模方法,不仅具有更高的准确性和异常检测能力,而且具有更强的数据变化的跟踪能力和检测能力。     

Deep Learning for EMG-based Human-Machine Interaction: A Review

Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.     

用于三维尺寸检测的双目视觉传感器

全面分析了双目视觉传感器中各个结构参数对测量精度的影响。在外极约束理论的基础上提出了外极斜率匹配法,不驻实现了快速立体匹配,而且传感器测量精度也得提高。实验数据表明,传感器测量精度优于±０．０５ｍｍ。     

Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests

The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and advanced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semi-deciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes.The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors.When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed.     

GIS支持下的湿地遥感信息高精度分类方法研究

遥感影像高精度自动分类方法的实现是制约遥感数据应用的瓶颈之一。以知识和地理信息系统为支撑,进行湿地遥感影像的分类,并对各项分类方法的精度进行比较评价,从而为湿地遥感的分类方法提供依据。实验结果表明经辐射增强降噪处理后湿地边界更加明晰;而对于处于生长期的湿地影像,经过光谱增强缨帽处理后,明显提高了区分湿地亚类的精度。结合以上两种分类方法的优势,利用GIS技术对二者进行空间处理,取长补短,生成了湿地遥感影像分类图。实验证明基于3S技术的分类方法精度更高,是一种较好的湿地影像自动分类方法。