高炉铁水含硅量的分形结构分析

以山东莱钢1号高炉和山西临钢6号高炉在线采集的铁水含硅量(［Si］)的时间序列为样本, 利用重标级差分析(R/S)和盒维数计算方法, 得出两座高炉［Si］时间序列的Hurst指数H分别约为0.257和0.224, 盒维数D_B分别约为1.712和1.724, 完成了对［Si］值波动复杂程度的定量估计, 证明两者的时间序列均为长程负相关的分形时间序列.随后根据分形迭代函数的理论与方法,确定相关参数,迭代生成模拟的［Si］时间序列, 拟合效果较好. 关键词： 铁水含硅量 重标级差分析 Hurst指数 分形拟合     

大数据预处理中属性约简的特性保持分析

伴随着工业及社会信息化程度的增强,各个领域的自动化程度越来越高,大规模及超大规模海量数据应运而生,呈现出大数据特征。这些海量数据在提升行业发展动力的同时,也带来了巨大的挑战性问题—数据可用性。为了从海量数据中甄别出无用信息、挖掘有利于相关领域发展的有价值信息,就需要对其进行数据分析。数据预处理技术可以极大地减少数据分析时的处理量,提高数据分析处理的效率,而属性约简在数据预处理中是一个比较重要的环节。在分析大数据属性特征的基础上,较系统地分析了目前几种主流的属性约简算法,对各类算法的性能进行了剖析,并展望了今后大数据预处理的研究工作思路。     

高炉铁水含硅量序列的动力学结构分析

以邯郸钢铁公司7号高炉在线采集的2000炉铁水含硅量［Si］数据为样本,对［Si］时间序列作了基于逆序数的平稳性检验.然后,在关联积分的基础上,定义了衡量不同时间序列间动力学相似性的“距离”,通过等分采集得到的［Si］序列,计算子序列间的“距离”,发现了高炉冶炼过程中存在显著的动力学结构突变性,最后应用DVV算法分析动力学性质变动下,高炉铁水含硅量［Si］的可预测性. 关键词： 高炉冶炼 平稳性 动力学相似性 可预测性     

基于多分辨分析的高炉铁水含硅量波动多重分形辨识

以包钢6号高炉、邯钢7号高炉和莱钢1号高炉在线采集的铁水含硅量(［Si］)的时间序列为样本, 利用多分辨分析剔除样本的长期趋势,对样本保留的波动趋势进行多重分形特征辨识. 通过计算广义Hurst指数、尺度函数、多重分形谱, 全面、细致量化了序列的局部及不同层次的波动奇异性. 计算结果表明: 去除长期趋势后, 三座高炉［Si］序列的波动呈现显著多重分形特征, 这样的波动过程仅用单一的Hurst指数或box维数来描述是不够的. 关键词： 多分辨分析 铁水含硅量 波动 多重分形     

近红外光谱的数据预处理研究

进行了小麦样品近红外光谱数据的预处理研究,一般仪器记录的样品近红外光谱数据中包含有一系列噪声和干扰信号,因此适当的预处理是进行后续光谱定标、建模及模型传递的基础,对可靠地获得准确分析结果具有很重要的作用。结合小麦样品蛋白质含量近红外光谱分析工作,对由近红外光栅光谱仪和傅里叶变换近红外光谱分别记录的66种小麦样品光谱数据,采用高斯一阶、二阶导数小波变换方法进行了预处理。对比常用的一阶差分预处理,证明高斯函数导数小波变换方法是十分有效、实用的,预处理后光谱曲线非常光滑、噪声消除效果明显,富含有用光谱分析信息的区域更加清晰显示,因而非常有助于后续的光谱定标、建模和模型传递工作。     

遥测数据选择器的设计与研究

飞行控制组件测试中,多套组件需要在同一温度梯度进行串行测试;针对这一需求,设计开发了一种既可程序控制,又可手动控制的多通道BMK遥测数据选择器;该数据选择器主要包括FPGA模块、通道选通控制地址缓冲模块、BMK接收模块、BMK转发模块、指示电路模块、供电模块和时钟电路模块;该设计以FPGA为核心,功能灵活、结构精简,而且每个通道BMK遥测数据中的各路信号延时可调,提高了BMK遥测数据的传输精度;试验中对BMK遥测数据选择器的5个通道均进行上百次数据帧收发实验,接收数据正确;结果表明,该数据选择器工作稳定,设计合理,而且有效缩短了测试时间,提高了测试效率。     

航天测控网实时数据流量监控与分析技术

航天测控网实时数据是航天发射中最重要的试验信息,实时数据交换直接影响着任务的顺利实施。由于航天中心采用自定义网络协议,商业软件不能够对协议数据进行有效监控和深入分析。根据航天测控网体系结构和业务特点,设计了被动式网络数据监控方案,采用专用硬件和底层驱动程序进行网络高速数据采集,实现了基于开源软件Wireshark的航天测控实时包数据交换协议(PDXP)的具体解析。实验结果表明,提出的实时数据监控和分析技术具备强大的数据包捕获和过滤能力,同时便于扩展网络协议,可以对航天中心各类传输信息进行监控和分析,为航天测控网数据监控、性能评估、故障诊断提供有效手段。     

大数据分析技术在装备监测系统中应用研究

针对装备状态监测系统中长期积累的大量数据缺乏有效分析手段、利用率低等问题,对装备监测系统中存储的在线监测数据和积累的历史数据,建立可分析挖掘的大数据集;运用数据分析和机理分析的方法建立比较模型,采用异步信息融合的算法对数据进行处理,利用大数据的多尺度特性研究装备生命周期的分析预测、优化运行、预知维修等目标,提高了装备运用知识的获取速度,有利于构建完整的装备生命周期预测诊断体系。     

宽谱调谐二极管激光吸收光谱的数据预处理研究

二极管激光吸收光谱以温度扫描实现快速宽谱调谐,其原始数据不便于传统的处理及运算,适当的预处理是进行后续数据处理的基础。在分析了温度调谐数据特征的基础上,利用Savitzky-Golay滤波器去除了波长序列中温度检测噪声的干扰,选择单调的波长区间对信号幅值进行三次样条插值,以固定的波长点对应采样点,得到一组位置固定的幅值序列用于后期处理。此方法将两列检测数据合并为一列,相同的起始点和固定的波长位置不仅将多周期平均后的信噪比提升为原始数据的6倍,也使多组数据的比较及评价成为可能,一致的波长分辨率保证了线形检测的精确度,减小了吸收峰的频带宽度,有助于后续的处理、运算以及评价。     

基于图像处理技术的电能表外观检测系统

为适应智能电能表自动化检定的要求,设计了由图像处理技术完成外观检测功能的系统,实现了外观检查的自动化,解决了人工检测方法工作量大、效果不佳的问题。系统采用高性能CCD相机完成图像采集,确保图像信息丰富完整。利用图像平滑去噪、形态学处理等技术完成图像的预处理,大幅降低后续比对的难度。运用基于高斯金字塔的混合配准算法完成图像配准,最终实现待检图像与模板图像之间匹配程度的检测。该方法检测时间短,正确率高,已成功应用于省级计量中心电能表自动化检定线,满足生产需求。     

玉米叶片重金属铅含量的SVD-ANFIS高光谱预测模型

重金属污染农作物后可通过食物链进入人体从而严重危害身体健康.如何快速准确地监测农作物中重金属含量已成为当今生态与粮食安全等领域的重要研究内容.常规的生化监测方法存在操作繁琐、过程长、具有破坏性等缺点,而高光谱遥感具有光谱分辨率高、信息量大、生化反演能力强、方便快捷、对监测对象无损伤等优势,因此利用高光谱遥感技术监测农作...     

Logistic Regression Model for a Bivariate Binomial Distribution with Applications in Baseball Data Analysis

There has been a considerable amount of literature on binomial regression models that utilize well-known link functions, such as logistic, probit, and complementary log-log functions. The conventional binomial model is focused only on a single parameter representing one probability of success. However, we often encounter data for which two different success probabilities are of interest simultaneously. For instance, there are several offensive measures in baseball to predict the future performance of batters. Under these circumstances, it would be meaningful to consider more than one success probability. In this article, we employ a bivariate binomial distribution that possesses two success probabilities to conduct a regression analysis with random effects being incorporated under a Bayesian framework. Major League Baseball data are analyzed to demonstrate our methodologies. Extensive simulation studies are conducted to investigate model performances.     

基于Matlab分析数据和传感器控制的金属杆的线膨胀与温度关系的实验研究

以电脑控制金属杆热膨胀的传感器实验为基础,用Matlab来处理实验数据,并进行数据的拉格朗日插值拟合,从而得出金属杆的线膨胀系数与温度变化成正比关系.     

土壤重金属铅、锌高光谱反演模型可迁移能力分析研究

现有基于高光谱遥感技术的土壤重金属含量反演模型,大多是采用同一试验区且有限的样本点进行定量反演建模。但考虑到实际应用需求,该类模型在不同试验区是否具有较好的迁移推广能力是目前迫切需要回答的问题。如不可行,是否存在其他可行手段用于土壤重金属污染评估？ 为回答上述问题,选取湖南省郴州市和衡阳市两铅锌矿区作为实验研究区,并首先利用郴州地区采样点分别对Pb和Zn两种重金属进行定量回归建模和定性分类建模,然后比较两种模型在衡阳实验区的可迁移能力。实验结果表明：(1)基于偏最小二乘回归(PLSR)的定量回归模型可迁移能力较差。分别采用四种光谱预处理方式建模,发现回归模型对异地采样的预测精度很低,难以正确反演衡阳试验区重金属Pb和Zn的含量。(2)基于支持向量机(SVM)分类的定性反演模型具有一定的可迁移能力,以郴州地区采样数据训练得到的SVM分类模型能有效判定衡阳试验区Pb、Zn的污染状况,分类精度分别达到84.78%和86.96%。结果表明,在快速检测土壤重金属污染状况的问题上,定性分类是一种更加切实可行的方式。     

Hotel Performance in the UK: The Role of Information Entropy in a Novel Slack-Based Data Envelopment Analysis

Previous hotel performance studies neglected the role of information entropy in feedback processes between input and output management. This paper focuses on this gap by exploring the relationship between hotel performance at the industry level and the capability of learning by doing and adopting best practices using a sample of 153 UK hotels over a 10-year period between 2008–2017. Besides, this research also fills a literature gap by addressing the issues of measuring hotel performance in light of negative outputs. In order to achieve this, we apply a novel Modified slack-based model for the efficiency analysis and Least Absolute Shrinkage and Selection Operator to examine the influence of entropy related variable on efficiency score. The Results indicate that less can be learnt from inputs than from outputs to improve efficiency levels and resource allocation is more balanced than cash flow and liquidity. The findings suggest that market dynamics explains the cash flow generation potential and liquidity. We find that market conditions are increasingly offering the opportunities for learning and improving hotel efficiency. The results report that the distinctive characteristic of superior performance in hotel operations is the capability to match the cash flow generation potential with market opportunities.     

Modeling Overdispersed Dengue Data via Poisson Inverse Gaussian Regression Model: A Case Study in the City of Campo Grande,MS, Brazil

Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.     

An Exploratory Study on the Complexity and Machine Learning Predictability of Stock Market Data

This paper shows if and how the predictability and complexity of stock market data changed over the last half-century and what influence the M1 money supply has. We use three different machine learning algorithms, i.e., a stochastic gradient descent linear regression, a lasso regression, and an XGBoost tree regression, to test the predictability of two stock market indices, the Dow Jones Industrial Average and the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite. In addition, all data under study are discussed in the context of a variety of measures of signal complexity. The results of this complexity analysis are then linked with the machine learning results to discover trends and correlations between predictability and complexity. Our results show a decrease in predictability and an increase in complexity for more recent years. We find a correlation between approximate entropy, sample entropy, and the predictability of the employed machine learning algorithms on the data under study. This link between the predictability of machine learning algorithms and the mentioned entropy measures has not been shown before. It should be considered when analyzing and predicting complex time series data, e.g., stock market data, to e.g., identify regions of increased predictability.     

代谢组学数据分析方法及在糖尿病研究中的应用

对NMR波谱数据的统计分析是基于NMR代谢组学研究的关键问题之一. 鉴于NMR波谱信号可以近似为样品中各种成分谱信号的线性叠加,本文将非负矩阵分解(NMF)方法引入基于NMR代谢组学的数据处理中,并与代谢组学中常用的统计方法--主成分分析(PCA)进行比较. 通过NMF和PCA两种方法对健康志愿者与2型糖尿患者血液和尿液的NMR谱图的统计分析,对所获取的特征代谢物进行比较和验证,并探讨了PCA方法可能存在的不足之处及其原因;阐明了NMF方法是基于NMR的代谢组学研究中较理想的数据分析方法. 最后,讨论了基于NMR代谢组学在糖尿病研究中的前景.