基于物质流分析的工业园生态效率研究

生态工业园是工业园发展的新方向,是一种新的可持续发展模式,因此,必须建立科学的评价方法用于工业园生态化建设。本文首先进行工业园物质流分析,进而介绍了物质流分析指标,然后将该方法引入生态效率理论,建立起基于物质流分析的工业园生态效率评价指标及其计算公式,旨在能够定量化地评价工业园生态化效率,指导和优化工业园可持续发展模式。     

基于概率推理的知识图谱中实体间关联度计算

针对知识图谱中实体间的关联关系存在不确定性、实体间关联度计算复杂度高等问题,提出一种基于贝叶斯网的实体间关联度的计算方法.针对知识图谱做预处理,利用剪枝后获取的核心子图构建贝叶斯网,提出基于知识图谱的贝叶斯网构建方法;利用贝叶斯网作为知识图谱中实体之间关联关系的量化和推理框架,基于贝叶斯网的概率推理,提出知识图谱中实体间关联度的定量计算方法.建立在真实数据之上的实验结果验证了方法的有效性.     

智能学习系统学生模型的设计

为了客观的评价学生的认知能力,提高整个智能学习系统的智能性,提出了一种采用模糊综合评判和贝叶斯网结合的方法构造学生模型.以离散数学课程为背景,以学生的各个知识点成绩作为学生的行为数据,通过模糊综合评判法将其量化为学生对知识点的掌握情况,将量化结果作为贝叶斯网的输入节点,对学生的下一步行为进行了推理.实验结果表明了该算法的有效性,并得到了良好的效果.     

贝叶斯网学习算法模型及参数学习算法

1 引言近年来,贝叶斯网(又称随机信息网)作为处理人工智能中不确定性问题的建模工具受到学术界的广泛关注,并成功地应用在医学诊断、模式识别、故障诊断各个方面。作为一种有向图表示的建模方法,贝叶斯网由于其表达方式自然、紧凑,深受知识工程师喜受,已广泛地用于知识获取和表示。但是,利用专家知识构造贝叶斯网是一件烦琐的工作,特别是网络节点数很大时更是这样。因此,利用数据例子,通过学习自动生成贝叶斯网的方法日益受到重视,有一些学习     

基于贝叶斯网络的网构软件可信性评估模型

对于运行在开放、动态、难控的互联网环境的网构软件,其可信性评估是一个重要课题,但目前大量研究中可信性计算多是基于黑盒的,没有深入考虑系统结构,且评价指标过于单一.因此,提出了一个基于贝叶斯网络的网构软件可信性评估模型.该模型通过对网构软件进行结构分析,根据其结构模式,建立多层的网构软件可信性评估指标体系.基于贝叶斯网络采用自底向上逐层分析计算的方法,对网构软件的各组成实体及其系统整体的多方面可信性指标进行评估,形成统一的可信性结果,并使用客观数据对其进行修正.实验证明,该模型可以明确、客观地对网构软件的可信性进行评估,并能够对网构软件的设计、开发和部署提供帮助.     

一种用于贝叶斯分类器的文本特征选择方法

特征选择是文本分类中一种重要的文本预处理技术,它能够有效地提高分类器的精度和效率。文本分类中特征选择的关键是寻求有效的特征评价指标。一般来说,同一个特征评价指标对不同的分类器,其效果不同,由此,一个好的特征评价指标应当考虑分类器的特点。由于朴素贝叶斯分类器简单、高效而且对特征选择很敏感,因此,对用于该种分类器的特征选择方法的研究具有重要的意义。有鉴于此,提出了一种有效的用于贝叶斯分类器的多类别文本特征评价指标：CDM。利用贝叶斯分类器在两个多类别的文本数据集上进行了实验。实验结果表明提出的CDM指标具有比其它特征评价指标更好的特征选择效果。     

类基于蚁群优化的贝叶斯置信网结构学习策略及性能分析*

针对贝叶斯置信网的结构学习问题,提出一种遵循典型ACO算法框架(ACO-TSP)的贝叶斯网结构学习算法（ACO-BN）,并拓展为包括EAS-BN、ACS-BN和MMAS-BN在内的一类算法。用这类算法在若干典型贝叶斯网络结构学习问题上分别与经典贝叶斯网学习算法(K2、B)、用于贝叶斯网学习的通用优化算法（simulated annealing、Tabu searching和genetic searching）以及L. M. de Campos等人提出的基于蚁群优化的贝叶斯网络结构学习算法 Ant-K2SN     

基于模糊逻辑的发供电企业供应商评价方法

发供电企业属于资产密集型企业,供应链关系复杂,针对其特点,提出一种基于模糊逻辑的供应商评价方法.分析现代供应链条件下的供应商评价指标体系,采用三角模糊数来表示评价指标的不定性;应用层次分析得到评价指标权重;通过计算备选供应商和理想供应商之间模糊贴近度选择供应商.应用实例分析说明了该方法的实用性.所提出的供应商评价方法成功应用于某大型发供电企业的设备采购,取得了很好的应用效果.     

一种基于贝叶斯Petri网的故障诊断方法

针对Petri网在分析复杂电力系统时的容错性差且难以适应网络拓扑变化的问题,提出一种贝叶斯Petri网模型(BPN),并基于该模型提出一种电网故障诊断方法.该方法通过电力系统网络拓扑分析确定停电区域,随后按照故障蔓延方向对停电区域内的元件分别建立BPN模型,应用Petri网推理和贝叶斯概率计算确定故障元件,最后采用均值方法对各方向上的分析结果进行融合,诊断分析表明,该方法在信息不完备的情况下具有较好的容错性,并且在网络拓扑结构发生变化后仍具有较好的适应性.由于BPN推理时根据基于统计的先验概率求取元件故障的发生概率,避免了直接对计算参数进行设定的主观性.     

贝叶斯统计识别法在录井油气评价中的应用

综合录井技术广泛应用于油气勘探活动中,而油气资源评价是勘探活动中最主要的工作之一.针对原有传统油气评价方法的不足,探索用模式识别技术进行统计分析.通过对录井特征参数的选择,采用了窗函数法和近邻法求其特征分布,并以此来构造概率密度分布函数,并应用贝叶斯(Bayes)判别决策方法训练分类的有关参数,确定了基于线性窗函数的贝叶斯方法和基于独立近邻原则的贝叶斯方法.最后分别采用传统图版解释法和统计模式识别法进行了对比分析,使其油气评价的符合率得到上升,为油气评价方法提供了一种新的思路.     

A NEW METHOD FOR INFLUENCE DIAGRAM EVALUATION

As influence diagrams become a popular representational tool for decision analysis, influence diagram evaluation attracts more and more research interests. In this article, we present a new, two-phase method for influence diagram evaluation. In our method, an influence diagram is first mapped onto a decision graph and then the analysis is carried out by evaluating the decision graph. Our method is more efficient than Howard and Matheson's because, among other reasons, our method generates a much smaller decision graph for the same influence diagram. Like those most recent algorithms reported in the literature, our method also provides a clean interface between influence diagram evaluation and Bayesian net evaluation. Consequently, various well-established algorithms for Bayesian net evaluation can be used in influence diagram evaluation. Furthermore, our method has a few unique merits. First, it takes advantage of asymmetry in influence diagrams to avoid unnecessary computation. Second, by using heuristic search techniques, it provides an explicit mechanism for using heuristic information that may be available in a domain-specific form. These additional merits make our method more efficient than the current algorithms in general. Finally, by using decision graphs as an intermediate representation, the value of perfect information can be computed in a more efficient way.     

合成氨清洁生产监控系统设计与实现

清洁生产作为一套完整的可持续发展战略,已得到社会的广泛认同,但国内尚未实现清洁生产评估考核系统数据的整合。为解决这个问题,对基于InTouch的合成氨企业清洁生产监控系统实现方法进行了研究和开发,并在此基础上设计和实现了基于Matlab与InTouch混合编程的合成氨企业清洁生产在线评估考核示范系统。模糊分层评估算法给出了母系统和不同层次子系统清洁生产水平隶属度判断,评估结果验证了该示范系统的可行性和有效性。     

磁盘垃圾文件快速清理工具设计及实现

针对磁盘清理工具速度慢、清理不彻底的情况,采用搜索与清理功能分离、程序与数据库分离的方法,加快磁盘清理的速度,并且用户可在数据库中增加垃圾文件的类型,从而实现软件升级,彻底清除磁盘垃圾文件.     

Using a Bayesian framework and global sensitivity analysis to identify strengths and weaknesses of two process-based models differing in representation of autotrophic respiration

Process-based models are powerful tools for sustainable and adaptive forest management. Bayesian statistics and global sensitivity analysis allow to reduce uncertainties in parameters and outputs, and they provide better insight of model behaviour. In this work two versions of a process-based model that differed in the autotrophic respiration modelling were analysed. The original version (3PGN) was based on a constant ratio between net and gross primary production, while in a new version (3PGN¹) the autotrophic respiration was modelled as a function of temperature and biomass. A Bayesian framework, and a global sensitivity analysis (Morris method) were used to reduce parametric uncertainty, to highlight strengths and weaknesses of the models and to evaluate their performances. The Bayesian approach allowed also to identify the weaknesses and strengths of the dataset used for the analyses. The Morris method in combination with the Bayesian framework helped to identify key parameters and gave a deeper understanding of model behaviour. Both model versions reliably predicted average stand diameter at breast height, average stand height, stand volume and stem biomass. On the contrary, the models were not able to accurately predict net ecosystem production. Bayesian model comparison showed that 3PGN¹, with the new autotrophic respiration model, has a higher conditional probability of being correct than the original 3PGN model.     

Data-driven cleaner production strategy for energy-intensive manufacturing industries: Case studies from Southern and Northern China

Cleaner production is an effective strategy for improving material utilization, reducing energy consumption, maximizing product output, and minimizing emissions during the entire manufacturing processes. An increasing number of enterprises promote the implementation of cleaner production. In the Industry 4.0 context, product-embedded information devices are widely used to capture product lifecycle data. Product lifecycle management provides an opportunity to achieve cleaner production strategy. This paper presents a data-driven cleaner production strategy by product lifecycle management for energy-intensive manufacturing industries. The data-driven strategy is proposed and explained by the lifecycle of cleaner production data. Implementation models are developed to illustrate the proposed data-driven cleaner production strategy. Furthermore, two case studies from Southern and Northern China are presented to demonstrate the strategy. Results show that the unit energy consumption and the energy cost of production in Company A are reduced by at least 3% after applying the proposed strategy. In addition, the “cradle-to-gate” lifecycle data analysis indicates that the costs of environmental pollution in Company B are diminished significantly. Finally, energy-intensive manufacturing companies in China are compared and evaluated, and the effectiveness of the proposed strategy is discussed.     

Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology

Neural Computing and Applications - This study aimed to develop a new methodology for evaluating and benchmarking a multi-agent learning neural network and Bayesian model for real-time skin...     

基于纹理粗细度测量的铝土矿浮选过程最佳精选泡沫状态分析

为实现铝土矿浮选生产工况的自动监测和智能评价,提出一种基于机器视觉的精选泡沫最佳生产状态量化分析与选择方法。首先,通过改进LBP算子实现精选泡沫图像表面纹理粗细度特征的提取;然后,进一步分析了精矿品位与精选泡沫纹理特征间的关系,以获得最佳生产工况下的精选泡沫表面纹理粗细度特征区间。该方法可以实时监测精选泡沫表面纹理的变化,并自动鉴别精选泡沫是否处于最佳生产状态,为实现铝土矿浮选过程优化控制奠定了基础。     

导航卫星载荷分系统健康评估方法设计

面向下一代导航系统结合高中低轨构建导航星座的设想,随着遥测参数数量和种类的激增,针对传统健康评估方法面临的过往专家知识难以适用、故障机理储备难以覆盖全面的问题,提出了基于局部异常因子检测-贝叶斯网络结构学习的导航卫星载荷分系统健康评估方法;通过采集某卫星系统实际故障时间点前后数据,设计实验验证了局部异常因子检测方法能够以粗粒度正确输出单机级的健康状况;分析比较了三种评分函数下,贝叶斯结构学习的效率和模型的准确度;实验结果表明,当评分函数分别选为BDeuScore、 K2Score以及BicScore时,学习到的模型对系统的健康评估准确度分别为87.4%、80.5%和85.2%;总结了局部异常因子检测-贝叶斯网络结构学习方法各自的不足,为导航卫星分系统健康评估方法提供了新方向和思路。     

Bayesian model checking for multivariate outcome data

Bayesian models are increasingly used to analyze complex multivariate outcome data. However, diagnostics for such models have not been well developed. We present a diagnostic method of evaluating the fit of Bayesian models for multivariate data based on posterior predictive model checking (PPMC), a technique in which observed data are compared to replicated data generated from model predictions. Most previous work on PPMC has focused on the use of test quantities that are scalar summaries of the data and parameters. However, scalar summaries are unlikely to capture the rich features of multivariate data. We introduce the use of dissimilarity measures for checking Bayesian models for multivariate outcome data. This method has the advantage of checking the fit of the model to the complete data vectors or vector summaries with reduced dimension, providing a comprehensive picture of model fit. An application with longitudinal binary data illustrates the methods.     

基于先验贝叶斯推断验证法的高可靠性评估

评估是保证高可信软件中高可靠性的重要手段。传统的评估方法要么不适应高可靠性的特点,要么不符合真正意义上的评估。基于现状,提出了一种基于先验贝叶斯推断验证的评估方法,该方法不仅利用了先验贝叶斯推断验证法的优势,而且结合了二分查找算法的迭代理论,二者结合起来实现了真正意义上有效的高可靠性评估。实例应用证明,这种评估方法比较客观、可行,具有较高的实用价值。