造纸企业能量系统优化信息平台数据交互机制设计与开发

设计与开发了造纸企业能量系统优化信息平台（MEOP）与过程控制层（PCS层）的数据交互机制,实现了＂信息孤岛＂中能量系统数据的有效集成,在此基础上,设计开发了历史数据库与关系数据库的数据交互机制,实现了自主开发模型与美国通用电气（GE）公司MES智能平台的集成。     

基于本体的异构数据集成模型的研究

针对企业存在的＂信息孤岛＂现象,介绍了几种常用的数据集成方法,但语义异构的问题仍存在于各种数据集成方案之中。基于本体能够精确描述概念体系和领域知识,提出了一种基于本体的异构数据集成系统模型,描述了模型的各组成部分,重点分析了模型的关键技术即本体库的建立与查询的处理过程。     

注塑模表面自适应三角网格剖分策略

利用映射法对注塑模复杂表面进行网格剖分时,经常会产生一些狭长、边界节点混乱等畸变单元。为了减少畸变网格提高一次性生成网格的品质,采用STEP接口解决了多系统信息＂孤岛＂问题;采用模型边界分割数控制和顶点邻域网格尺寸修正形成单元尺寸控制策略实现了网格均匀过渡;利用区域边界布点和邻接域边界协调形成边界离散策略避免了边界节点的混乱。综合这些策略开发出的网格剖分软件完成了小家电机壳注塑模型的高品质表面三角网格生成。     

人工智能背景下制革企业信息化与自动化管理平台的设计及应用研究

以我国某制革企业为例,对该企业2017-2020年引入人工智能前后企业信息化与自动化管理水平进行对比.认为未引入人工智能平台以前,该企业的信息化与自动化管理存在系统整合成本高、效率低,信息孤岛问题严重,系统辅助企业管理作用不明显等问题.在引入人工智能平台以后,该制革企业的生产项目管理实现了精准化,打破了信息孤岛,企业的...     

智慧城市与信息化的不协调发展亟待缓解

信息孤岛是指相互之间在功能上不关联互助、信息不共享互换以及信息与业务流程和应用相互脱节的计算机应用系统。应当看到,在整个信息技术产业飞速发展过程中,企业的it应用也伴随着技术的发展而前进。但与企业的其它变革明显不同的是,it应用的变化速度更快,也就是说,企业进行的每一次局部的it应用都可能与以前的应用不配套,也可能与以后的＂更高级＂的应用不兼容。     

基于MES的农机制造业信息系统开发

某农机加工装配制造企业在生产经营过程中,为了解决车间任务管理控制调度等方面的问题,以及为了实现任务图纸文件的电子化管理,来提高企业的信息化能力,根据分析企业的生产特点,提出了基于B/S结构的体系结构。在此基础上构建一个以任务分配调度管理为核心的农机行业MES信息系统。实现了企业各个车间和部门之间的信息集成与共享,实现对车间的管控。有效地解决了实际生产中的信息孤岛现象,实现了企业的信息化建设。     

纸卷分切与库存综合优化系统软件结构的设计与开发

设计开发了纸卷分切与库存综合优化系统软件。首先建立了纸卷分切与库存优化的混合整数规划模型,然后设计了在VC++6.0中实现求解混合整数线性规划模型的求解器,并实现求解器同关系数据库的数据交互,最后用GE的智能软件平台iFIX将各功能模块集成为一个基于MES框架的优化控制系统。该系统同ERP层与PCS层进行数据交互,实现信息集成,避免了形成信息孤岛。     

物联网环境下数据交换模型研究

随着信息化技术的不断发展,在物联网环境下,产生了大量的数据,并且数据间的结构及存储方式各不相同,使得数据源之间的交互变得异常复杂,形成了"信息孤岛",而解决"信息孤岛"问题最有效的方法就是数据交换。基于此,构建一个在物联网环境下的数据交换模型,用以解决数据间的脱节问题,并将构建的数据交换模型与传统的数据交换方法进行对比实验。实验结果表明,构建的交换模型可以有效保证数据信息的安全性。     

二维码信息系统在油脂加工企业中的应用

以二维码信息系统在国内某油脂加工企业具体应用为实例,系统介绍了二维码信息系统的平台组成及各自功能,阐述了企业利用二维码信息系统实现产品从生产源头到终端消费者全过程追溯的过程。二维码信息系统的应用使得应用企业形成了企业的大数据平台,很好地解决了信息孤岛和数据碎片的问题,帮助企业从产品的采购源头到销售终端打通数据链。     

溢流染色机控制管理系统的应用研究

针对目前染色机机台之间相对孤立,不能实现信息共享,不利于一次化准确生产等问题,采用了西门子的可编程逻辑控制器PLC和Postgre SQL构成溢流染色机控制管理系统,通过PROFIBUSDP和工业以太网,实现了染整企业的原料管理、配方管理、自动称重管理与染色机的工艺管理和设备管理、染色过程的网络化控制、现场参数的监控等。系统可以直接对染色机台进行控制,与染整企业的生产信息联网,避免了自动化孤岛的问题,达到了信息共享,降低生产成本,缩短交货周期,藉此实现了一次化生产和准确化生产。     

Heritability,reliability of genetic evaluations and response to selection in proportional hazard models

The purposes of this study were 1) to investigate the heritability, reliability, and selection response for survival traits following a Weibull frailty proportional hazard model; and 2) to examine the relationship between genetic parameters from a Weibull model, a discrete proportional hazard model, and a binary data analysis using a linear model. Both analytical methods and Monte Carlo simulations were used to achieve these aims. Data were simulated using the Weibull frailty model with two different shapes of the Weibull distribution. Breeding values of 100 unrelated sires with 50 to 100 progeny (with different levels of censoring) were generated from a normal distribution and two different sire variances. For analysis of longevity data on the discrete scale, simulated data were transformed to a discrete scale using arbitrary ends of discrete intervals of 400, 800, or 1200 d. For binary data analysis, an individual's longevity was either 0 (when longevity was less than the end of interval) or 1 (when longevity was equal or greater than the end of interval). Three different statistical models were investigated in this study: a Weibull model, a discrete-time model (a proportional hazard model assuming that the survival data are measured on a discrete scale with few classes), and a linear model based upon binary data. An alternative derivation using basic expressions of reliabilities in sire models suggests a simple equation for the heritability on the original scale (effective heritability) that is not dependent on the Weibull parameters. The predictions of reliabilities using the proposed formulae in this study are in very good agreement with reliabilities observed from simulations. In general, the estimates of reliability from either the discrete model or the binary data analysis were close to estimates from the Weibull model for a given number of uncensored records in this simplified case of a balanced design. Although selection response from the binary data analysis depends on the end of interval point, there is a relatively good agreement between selection responses in the Weibull model and the binary data analysis. In general, when the underlying survival data is from a Weibull distribution, it appears that the method of analyzing data does not greatly affect the results in terms of sire ranking or response to selection, at least for the simplified context considered in this study.     

时序分析用于航空预测模型的研究

利用样本数据建立了时序模型，利用组合模型将样本序列分解成确定性成分和随机成分两部分，对随机成分用ＡＲ模型拟合，并采用Ｍａｒｐｌｅ算法进行参数估计，然后预报检验模型，取得好的效果。     

三维纬编提花织物的计算机仿真及系统实现

针对三维纬编提花织物在仿真速度及真实感模拟等方面的不足,提出从曲面模型入手建立基于Unity3D平台的织物计算机仿真系统。通过三维织物的曲面数据采集和坐标数据统一,实现三维模型、纹理数据的建立和导入,针对复杂提花织物提出一种三因素模拟算法控制织物模型凹凸效果。确定下陷深度、迭代次数和影响范围3个参数,最大限度地与实际织物真实数据进行拟合。在此基础上结合三维引擎Unity3D实现三维模型数据与二维空间数据整合和三维虚拟展示。与线圈仿真相比较,该系统以织物真实数据为基础,可较好地实现具有三维效果的纬编提花织物的预测与仿真,且效率较高。     

Dose response modelling of Escherichia coli O157 incorporating data from foodborne and environmental outbreaks

A human dose response model for Escherichia coli O157 would enable prediction of risk of infection to humans following exposure from either foodborne or environmental pathways. However, due to the severe nature of the disease, volunteer human dose response studies cannot be carried out. Surrogate models from Shigella fed to humans and E. coli O157 to rabbits have been utilised but are significantly different to one another. In addition data obtained by animal exposure may not be representative for human beings. An alternative approach to generating and validating a dose response model is to use quantitative data obtained from actual human outbreaks. This work collates outbreak data obtained from global sources and these are fitted using exponential and beta-Poisson models. The best fitting model was found to be the beta-Poisson model using a beta-binomial likelihood and the authors favour the exact version of this model. The confidence levels in this model encompass a previously published Shigella dose response model. The potential incorporation of this model into QMRAs is discussed together with applications of the model to help explain foodborne outbreaks.     

A mathematical model for colour loss in paprikas containing differing proportions of seed

In this paper a general kinetic model for predicting the colour of paprikas with differing proportions of seed as a function of the storage conditions is developed. We apply this model to study the effect of time and storage conditions on colour loss in paprikas made from the same pepper variety, Ocal, but with differing proportions of seed. The agreement between the experimental colour data for this paprika and the results predicted by the mathematical model is very good. Both the model and the experimental data show that the rate of colour loss decreases with increasing proportion of seed, and after a determined inversion time the samples with the lowest initial colour values actually show the highest values. Likewise, the experimental data show that for a given proportion of seed the colour loss decreases as the storage relative humidity increases. © 2000 Society of Chemical Industry     

Combining model results and monitoring data for water quality assessment

A Bayesian approach is used to update and improve water quality model predictions with monitoring data. The objective of this work is to facilitate adaptive management by providing a framework for sequentially updating the assessment of water quality status, to evaluate compliance with water quality standards, and to indicate if modification of management strategies is needed. Currently, most water quality or watershed models are calibrated using historical data that typically reflect conditions different from those being forecast. In part because of this, predictions are often subject to large errors. Fortunately, in many instances, postmanagement implementation monitoring data are available, although often with limited spatiotemporal coverage. These monitoring data support an alternative to the one-time prediction: pool the information from both the initial model prediction and postimplementation monitoring data. To illustrate this approach, a watershed nutrient loading model and a nitrogen-chlorophyll a model for the Neuse River Estuary were applied to develop a nitrogen total maximum daily load program for compliance with the chlorophyll a standard. Once management practices were implemented, monitoring data were collected and combined with the model forecast on an annual basis using Bayes Theorem. Ultimately, the updated posterior distribution of chlorophyll a concentration indicated that the Neuse River Estuary achieved compliance with North Carolina's standard.     

神经网络对SBR工艺处理造纸废水建模的研究

本文在实验室条件下研究了SBR工艺对造纸废水的处理效果,利用其中的20组试验数据建立了针对SBR工艺处理造纸废水的BP神经网络模型,并用另外的10组试验数据对神经网络模型进行了验证,验证结果表明神经网络模型输出与实际输出比较接近,说明BP神经网络模型准确性比较高,为进一步实现该工艺的自动控制打下了基础.     

Study on water vapor diffusing coefficient of fibrous materials by fractal method

In this paper, a fractal model for predicting water vapor diffusion coefficient of hydrophobic fibrous materials is presented. To verify the validity of the model, the predicted results from the fractal model are compared with the experimental data and calculated values from the previous models. It is found that the fractal model predication shows better agreement with the experimental data than the calculated values by previous models.     

Milk urea nitrogen target concentrations for lactating dairy cows fed according to National Research Council recommendations.

The objectives of this study were to develop and evaluate a mathematical model to predict milk urea N and to use this model to establish target concentrations. A mechanistic model to predict milk urea N was developed using raw data from 3 studies (10 diets, 40 cows, and 70 observations) and was evaluated with 18 independent studies (89 treatment means). For the independent literature data set, the model prediction error was approximately 35%; the majority of the error was due to variation among experiments. A mean of at least 25 cows was determined to be necessary for reliable model predictions. This model, which uses such data as protein intake and milk production, was used to predict milk urea N concentrations when cattle are fed according to National Research Council recommendations. Target values calculated in this manner for a typical lactation were 10 to 16 mg/dl, depending on days in milk. Target concentrations were sensitive to changes in milk production and amount of N intake and were relatively insensitive to body weight, parity, and grouping strategy. Analysis of data from the Lancaster Dairy Herd Improvement Association (n = 133,057) indicated that cows in the region were being fed diets containing approximately 17% crude protein, regardless of parity. A comparison to target milk urea N concentrations for this data indicated that cows were being fed 8 to 16% more protein than recommended by the National Research Council. Target milk urea N concentrations have been established, and dairy farmers now have a definitive way to interpret milk urea N concentrations.