基于数据融合的自来水浊度检测方法

为了提高流动电流仪(SCM)对自来水浊度检到的精度，文章提出了一种基于神经网络数据融合技术的数据处理方法。该方法可以消除到量中的原水流量、供电电源波动、絮凝剂浓度、温度等干扰量对流动电流仪的影响，提高了测量的精度。     

浊度仪可测定甜蜜素含量

食品、饮料中甜蜜素的检测方法目前有气相色谱法、比色法和薄层层析法,但使用起来各有局限。笔者在参考相关技术论文后,经多次试验,发现用测量水浊度的浊度仪可进行甜蜜素含量检测。此方法所用设备简单、操作简便、灵敏度高     

智能检测系统中的数据融合技术

近年来,数据融合技术获得了普遍的关注和广泛的应用,其理论和方法已成为智能信息处理的一个重要研究领域。本文概述了数据融合的概念、层次,介绍了融合算法。并以应用多传感器数据融合的轨道衡误差补偿为例说明了数据融合技术的具体应用情况。     

基于数据融合的轨道计算方法研究

目前测量船轨道计算使用的是基于单一测量设备的外测数据,而在实际任务中往往会有来自多种不同测量设备的外测数据。每种测量设备对目标的跟踪精度是不同的,利用数据融合方法对不同精度的测量数据进行综合利用,可以提高轨道计算结果精度,也为轨道预报及指挥决策提供更为准确的参考数据。     

BP神经网络在WSN数据融合中的应用

在无线传感器网络(WSNs)的研究与应用中,利用数据融合来提高网络中能量利用率是其中一个重要的研究方向。文章利用BP(Back Propagation)神经网络能够对曲线进行无限逼近的特性来对无线传感器的监测数据进行数据拟合,然后传输拟合好的权值与阈值,同时通过将上一次拟合的权值与阈值赋予下一次拟合来减少神经网络的训练步数。模拟实验表明利用该方案能够有效减少数据的传输量,从而达到高效利用传感器能量的目的。     

基于多源数据融合的信号交叉口延误估计研究

为了获得更准确的信号交叉口延误,为评价信号交叉口的运行效率和服务水平提供精确的评价指标,论述了利用线圈检测器和浮动车数据来估计信号交叉口延误的方法。在对BP神经网络作简要介绍的基础上提出基于改进BP算法的信号交叉口延误估计模型。把通过线圈检测器和浮动车数据估计的信号交叉口平均延误进行融合,得到更为精确的信号交叉口平均延误。最后通过实例对该模型进行了验证,分析结果表明:采用数据融合方法得到的信号交叉口延误的相对误差明显低于基于线圈检测器和浮动车数据估计的信号交叉口延误的相对误差,因此采用数据融合方法估计信号交叉口的延误可以提高延误估计的精度,论证了该模型的实用性和有效性。     

数据融合技术在交通状态评价中的应用研究

本文基于浮动车和固定检测器技术采集数据的特点,研究了两种数据源的融合问题,利用动量法改进的BP神经网络构建了基于道路交通状态指数的数据融合模型。通过仿真实验,验证了融合模型的有效性。     

基于双kinect数据融合技术的工人作业疲劳智能化检测研究

本文针对制造型企业工人的作业疲劳检测问题,首先采用双kinect数据融合技术,提出了骨骼数据融合算法模型并应用于人体姿势识别,实现对工人作业疲劳姿势的智能化检测;然后将该算法与单kinect、Lin等算法比较,验证该算法的可靠性及识别精度;最后通过企业案例进行工人作业疲劳检测,结合人因工程学对工人动作进行分析并改善,对比验证结果表明,基于姿态角的双kinect人体姿势识别算法识别时间明显缩短,识别率显著增强,精准度较高,工人姿势识别数据具有较高可靠性.     

基于双kinect数据融合技术的工人作业疲劳智能化检测研究

本文针对制造型企业工人的作业疲劳检测问题,首先采用双kinect数据融合技术,提出了骨骼数据融合算法模型并应用于人体姿势识别,实现对工人作业疲劳姿势的智能化检测;然后将该算法与单kinect、Lin等算法比较,验证该算法的可靠性及识别精度;最后通过企业案例进行工人作业疲劳检测,结合人因工程学对工人动作进行分析并改善,对比验证结果表明,基于姿态角的双kinect人体姿势识别算法识别时间明显缩短,识别率显著增强,精准度较高,工人姿势识别数据具有较高可靠性.     

基于双kinect数据融合技术的工人作业疲劳智能化检测研究

本文针对制造型企业工人的作业疲劳检测问题,首先采用双kinect数据融合技术,提出了骨骼数据融合算法模型并应用于人体姿势识别,实现对工人作业疲劳姿势的智能化检测;然后将该算法与单kinect、Lin等算法比较,验证该算法的可靠性及识别精度;最后通过企业案例进行工人作业疲劳检测,结合人因工程学对工人动作进行分析并改善,对比验证结果表明,基于姿态角的双kinect人体姿势识别算法识别时间明显缩短,识别率显著增强,精准度较高,工人姿势识别数据具有较高可靠性.     

Dispatching and routing of emergency vehicles in disaster mitigation using data fusion

The aim of this paper is to develop a robust methodology for the dispatching and routing of emergency vehicles (EVs) in a post-disaster environment with the support of data fusion. In this work, we consider an earthquake scenario with a large number of casualties needing medical attention. Given an influx of information (regarding casualties, road, traffic conditions, etc.), data are fused to provide estimates of the entities under consideration. We use this information to dispatch and route EVs to casualty pickup locations, followed by delivery to appropriate hospitals. Key factors here include patient priorities, clustering criteria, and distance. Similarly, factors affecting the dispatching of EVs from patient locations to hospitals include waiting time at hospital emergency rooms, hospital capacity, and distance. Routes must be generated for EVs by accounting for real-world road networks, existing road damage, congestion, and related issues. We develop a dispatching and routing simulation model, and utilize a case study to evaluate the performance of our proposed methodology.     

Some remarks on measurement errors and the identification of panel data models*

We demonstrate that despite the common worry about the possible correlations between the unobserved individual effects and the explanatory variables in panel data models the likelihood approach can provide a unified framework towards the study of the identification of a panel data model subject to measurement errors. In fact, it can also serve as a basis for deriving efficient estimation methods.     

Using forecast evaluation to improve the accuracy of the Greenbook forecast

Recently, Patton and Timmermann (2012) proposed a more powerful kind of forecast efficiency regression at multiple horizons, and showed that it provides evidence against the efficiency of the Fed’s Greenbook forecasts. I use their forecast efficiency evaluation to propose a method for adjusting the Greenbook forecasts. Using this method in a real-time out-of-sample forecasting exercise, I find that it provides modest improvements in the accuracies of the forecasts for the GDP deflator and CPI, but not for other variables. The improvements are statistically significant in some cases, with magnitudes of up to 18% in root mean square prediction error.     

Technology fusion: Identification and analysis of the drivers of technology convergence using patent data

The concepts of technology convergence or technology fusion describe the phenomenon of technology overlap. Despite evidence of the higher value associated to interdisciplinary research and cross-industry innovation, few studies have investigated the characteristics of technology fusion based on patent data. This study identifies new cases of convergence relying on the International Patent Classification (IPC) of patents filed at the European Patent Office between 1991 and 2007: the first occurrence of a patent incorporating a combination of IPC subclasses signals a new instance of fusion. Duration models are employed to investigate the impact of field level characteristics derived from patent bibliometrics on the likelihood of identifying a new fusion. The results show that merges are more frequent if the focal technology fields are closely related (based on a higher number of cross citations), are characterized by wide technological scope, and are the result of an inter-firm collaboration. In contrast to previous findings, the results show that the more complex the technologies involved, the less the likelihood of their convergence or fusion. The correlation between fusion likelihood and the characteristics of the merging fields could help managers and policymakers to predict the emergence of new technology areas.     

Applying efficiency measurement techniques to the measurement of productivity change

Deterministic frontier analysis (DFA), stochastic frontier analysis (SFA), and data envelopment analysis (DEA) are alternative analytical techniques designed to measure the efficiency of producers. All three techniques were originally developed within a cross-sectional context, in which the objective is to compare the efficiencies of producers. More recently all three techniques have been extended for use in a panel data context. In the latter context it is possible to measure productivity change, and to decompose measured productivity change into its sources, one of which is efficiency change. However when efficiency measurement techniques, particularly SFA, have been applied to panel data, it has infrequently been made clear what the objective of the analysis is: the measurement of efficiency, which may vary through time as well as across producers, or the measurement and decomposition of productivity change. In this paper I explore the use of each technique in a panel data context. I find DFA and DEA to have achieved a more satisfactory reorientation toward productivity measurement than SFA has.     

Knowledge integration via the fusion of the data models used in automotive production systems

ABSTRACT

In this paper a novel information model that can be used in Manufacturing Execution Systems is presented. The model is based on the fusion of ISA95, AML and OPC UA. ISA95 is used to define, unify and describe the details of a product and production technology. It also enables communication with ERP systems. The AML standard allows information about the production facilities to be presented. The OPC UA address space represents different parts of an information model while the OPC communication protocol enables it to be linked to actual production systems. The proposed concept is illustrated using an actual example of a production line for electronic devices.     

Production frontier estimation to measure efficiency: A critical evaluation in light of data envelopment analysis

The ranking and measurement of efficiency of decision-making units by two methods—data envelopment analysis and frontier production function—may not always lead to identical results. In this framework we attempt here a critical evaluation of the frontier production function theory in terms of theoretical and empirical implications. It is shown that under certain conditions the two approaches to effciency measurement may lead to identical results.     

顾客声音数据处理一致化映射关系研究

在顾客声音数据一致化处理过程中,原始客户需求信息容易丢失是企业营销决策难题。文中尝试用图形化形式描述顾客声音数据一致化转化过程,给出了一种面向群决策的三类不同顾客声音(效用值型的顾客声音、次序值型的顾客声音、语言短语型的顾客声音)的一致化映射方法。该方法是一种考虑企业知识背景等相关要素的非强制性数据转换方法,能最大限度地保证数据在转换的过程中不丢失,对支持企业营销决策具有重要理论意义及实用价值。     

Fifty years of measurement and scaling in the Dutch social sciences

It is evident from recent issues of journals like Psychometrika and Applied Psychological Measurement that the Dutch contribution to the development of the area of measurement and scaling is considerable. In the first part of this paper, we try to answer the question how this could happen. In the early development De Groot in Amsterdam and Van de Geer in Leiden created an academic climate that made it possible for many researchers to develop their skills. Some other possible reasons are sketched for the flourishing of this area in the Netherlands, but it is difficult to assess which reasons were most important. In the second part of the paper, we analyze the international impact of the Dutch contribution by a citation analysis in four fields of measurement and scaling: factor analysis, test theory including item response theory, latent class analysis, and optimal scaling and multidimensional scaling. It appears that Dutch researchers not only publish a lot, but also that the impact of their publications is at the same level as publications produced world-wide.