Evaluation across and within collaborative manufacturing networks: a comparison of manufacturers’ interactions and attributes

Evaluation on collaborative manufacturing network (CMN) structure characteristics has important implications for network operations such as production decision-making, product recovery, creating consensus. Several recent studies suggest that augmenting network structure with nodes’ attributes can provide a more fine-grained understanding of the network. However, there have been few studies to provide a systematic understanding of these effects in a CMN at scale. This gap is bridged using an industrial printing machinery CMN data-set collected on a web-based producing and outsourcing service platform. Novel phenomena with respect to both interaction and attribute metrics across and within the CMNs are observed. Moreover, although many studies employ either interaction or attribute data to study the relative roles of manufacturers in a CMN, relatively little is known about the relationship between these two types of data. This study explores this relationship by comparing two defined metrics (i.e. relational capability and node capability), which evaluate the manufacturers’ interactions and attributes, respectively. We examine to what extent the two metrics of manufacturers correlate with each other, and how possible dissimilarities and similarities can be explained based on the collected industrial CMN data-set. The insights thereby provide a better basis for efficient operations decision-making in CMN.     

基于心理图式的知识挖掘与创新设计——以国庆彩车设计为例

为了提高设计创新效率,本研究基于心理图式提出了国庆彩车的设计程序与方法。研究按照浅层、中层和深层的次序递进,设计心理图式划分为“造型图式”“叙事与动作图式”以及“人物和情感图式”的基本类别,并提出了综合性、时态性、差异性和集体性四类基本属性。而后结合新中国成立70周年彩车设计,对不同心理图式的应用方法和属性进行了案例分析。本文所提出的方法在设计实践中得到了实证,具有一定的实用价值。     

Multi-factor impact mechanism on combustion efficiency of a hydrogen-fueled micro-cylindrical combustor

As it is important to achieve higher combustion efficiency for applications of micro-cylindrical combustor, the multi-factor impact mechanism on the combustion efficiency of a hydrogen-fuelled micro-cylindrical combustor is investigated in this work. Firstly, six factors such as hydrogen/air equivalence ratio, inlet velocity, inlet temperature, wall thermal conductivity, wall emissivity and convective heat transfer coefficient of outer wall and five levels of each factor are determined. Orthogonal design table L₂₅(5⁶) is introduced to arrange cases. Secondly, grey relational analysis is adopted to investigate the effects of the six factors on combustion efficiency. Finally, the results of grey relational analysis are validated by analysis of variance. Based on grey relational analysis and analysis of variance, it is determined that the impact ranking from the largest to the smallest is hydrogen/air equivalence ratio, inlet velocity and inlet temperature, followed by the other three factors. The impact of wall thermal conductivity, convective heat transfer coefficient of outer wall and wall emissivity is considered to be equal due to their difference of impact on combustion efficiency is very small. This work provides us significant reference for optimizing combustion efficiency of a hydrogen-fuelled micro-cylindrical combustor.     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.     

软土地基承载力指标灰色关联分析

该文运用灰色关联分析方法探讨了影响软土地基承载力大小的各种因素。结果表明 :轻型动力触探击数 (N1 0 )对承载力大小变化最为敏感 ,施工过程中用于检测软土地基承载力既简便又较准确。其次为软土的天然密度 (ρ)、压缩模量 (Es)、粘聚力 (c )和塑性指数 (Ip) ,在室内试验中可优先利用这些参数来确定承载力大小。而天然含水量 (w)、孔隙比 (e)和液性指数 (IL)相对较不敏感。     

电测式十字板剪切试验的研究与应用

十字板剪切试验能够明显地提高饱和软粘性土力学指标的准确度,适合沿海等软土分布地区,适用于求算地基承载力、软粘土的灵敏度等重要的地基力学参数。根据电测式十字板剪切试验的原理、标定方法,结合东营沿海软土分布区地基土的建筑经验进行系统地总结和归纳,提出了该地区十字板剪切试验指标应用的相应关系式。     

建筑路径的关联化

当代城市中的建筑尺度越来越大,越来越多的城市功能被纳入建筑,建筑内的路径也越来越长,并且越显重要。然而,当今一部分建筑内的路径被处理为消极的纯交通体,忽视了对人性的关怀。本文提到的关联化是对这一现象的回应。希望通过对建筑内路径的分析研究,探讨如何在有条件的情况下将路径设计得更具人性。     

住宅建筑能耗基准确定及用能评价新方法

针对当前建筑能耗基准确定及用能评价方法仍不完善的现状,提出了一种适用于住宅建筑的新方法。通过灰色关联分析确定了不同影响因素(即特征参数)与建筑能耗的关联度,将关联度作为该因素的权值并结合聚类分析对建筑进行合理分类。在此基础上采用累积频率分布法确定了每类建筑的能耗基准值,并对建筑住户进行用能评价。为验证该方法的可行性,将其应用于日本建筑学会所建立的住宅建筑能耗数据库,为聚类后的每类建筑确定了相应的能耗基准并对住户进行了用能评价。结果表明该方法能够综合考虑不同特征参数的影响大小,科学合理地对建筑细化分类,根据分类后的基准评价能够更好地评估住户的节能潜力并提供可行的节能建议。     

基于XML模式的D-矩阵描述及诊断应用

测试—故障依赖性矩阵(D-矩阵)是大系统装备故障诊断的基础和关键。针对目前TEAMS软件在舰船武器装备综合诊断应用中存在的D-矩阵数据不开放问题,本文借鉴EXPRESS诊断推理模型描述方法,设计了易在建模和算法间共享或交换的D-矩阵XML模式;并针对TEAMS软件,分析了基于多信号流模型的D-矩阵XML文档生成方法;最后以某超短波设备为例,对基于D-矩阵XML描述文档的诊断应用过程方法进行了详细分析。该方法增强了TEAMS软件的适用性,可解决D-矩阵数据在不同应用领域的共享和可交换难题。     

基于改进灰色关联分析的GIS质量评价

提出了基于改进灰色关联分析法的气体绝缘金属封闭式开关设备(GIS)质量评价方法。该方法结合熵权法和专家打分法确定各评价指标的组合权重,克服了原始灰色关联分析计算中指标均权及专家赋权的不足。然后采用改进后的灰色关联分析法对GIS进行质量评价。将组合赋权法引入灰色关联分析方法中,充分利用各指标的全部信息,有效提高了评价精度,使得评价结果更加科学、合理。