基于乘法偏好关系的群一致性偏差熵多属性群决策方法研究

在多属性群决策中, 针对每一个属性下决策者都有一个关于决策方案的乘法偏好关系的决策问题, 提出一种基于乘法偏好关系的群一致性偏差熵多属性群决策方法. 此方法考虑到不同属性下的决策者具有不同的权重, 并通过迭代运算可以达到群一致性水平, 从而得出最终的不同属性下决策者的权重; 同时, 可以利用偏差熵模型来求解属性权重, 利用这两个权重最终获得一个综合各方意见的群一致性乘法偏好关系. 最后通过算例分析验证了所提出方法的有效性.     

基于一致性的小型四旋翼机群自主编队分布式运动规划

设计一种小型四旋翼无人机群起飞后自主形成正多边形编队的分布式运动规划方法.在四旋翼无人机的串级控制系统框架下,分布式编队控制器以简化agent模型为基础,同时采用平均一致性算法和有领导一致性算法,共同产生各无人机位置与偏航角的期望轨迹.讨论了达成最终协调目标队形的拓扑条件,并给出一种基于有向Hamilton环的通信拓扑设计方案.最后通过数值仿真验证了所提出算法的有效性.     

硫化机网络群控系统

对硫化机群采用分散控制-集中管理式中央监控系统.系统分底层网络和上层网络两部分.底层网络分二级控制,采集现场硫化机温度、压力等参数.上层网络用于对生产数据进行处理显示、打印报表等.各层控制系统实现相对独立,该系统运行多年,未发生异常现象.     

A群脑膜炎球菌多糖菌苗的接种反应及血清学效果

本文对A群脑膜炎球菌多糖菌苗不同剂量、针次和不同年龄儿童接种后反应及血清学效果进行了研究。根据上海市区和山东青州市儿童接种一般反应以及青州市10万余名儿童接种后异常反应观察表明,接种50μg和30μg菌苗后的反应都是很轻微的,但3岁以下婴幼儿的接种反应高于7岁儿童。3岁以下婴幼儿中强体温反应,50μg高于30μg;两种剂量菌苗接种后的免疫应答相似,接种2针后的杀菌抗体水平高于1针,13～24月龄幼儿接种后的杀菌抗体水平明显高于6～12月龄婴儿,但均低于7岁儿童。     

基于多子群的社会群体优化算法

社会群体优化算法(social group optimization,SGO)是一种基于社会群体学习而提出的一种新型优化算法。针对社会群体优化算法易于陷入局部最优问题,提出了一种多群社会群体学习算法（MPSGO）。本算法采用多子群学习方法,对算法两个阶段的个体学习方法进行改进,在维持群体收敛性能的前提下提高群体多样性,同时对部分个体中引入量子学习,使个体学习的有用信息得以增强;此外,每隔一定代数对子群进行随机重组,既能保证各子群个体充分进化,又维持了子群多样性。在设计算法的基础上,分析了其收敛性和多样性;通过与其他四种算法进行对比实验,验证了改进后算法性能更优。     

直纺长丝工艺中聚酯熔体端羧基值控制的工业研究

本文从工业生产的实践出发对杜邦聚酯技术直接纺工艺中端羧基控制进行了研究。通过对熔体端羧基值对无油长丝端羧基、粘度及纺丝加工影响的分析 ,阐明了聚酯端羧基控制范围 ;通过摸索三个反应釜工艺条件对端羧基值的影响 ,找出了端羧基工业控制的有效途径 ,为同类技术装置生产提供了依据     

基于1H NMR测定和基团贡献法预测石油馏分的热化学性质

测定了两种原油 2 0个石油馏分的¹H NMR谱和氢、碳元素含量等结构参数 ,以及馏分的燃烧焓、蒸发焓等热化学性质 .假设每个石油馏分仅由CH₃ 、CH₂ 、CH、ACH和C这 5种简单基团构成 ,基于¹H NMR测定和元素分析结果解出石油馏分平均分子结构中的基团数目 ,再用纯有机物的基团贡献法预测石油馏分的燃烧焓、蒸发焓 ,预测值与实验数据吻合良好     

丙烯酸型亲水涂料乳胶粒表面羧基分布的研究

研究了以苯乙烯为核,丙烯酸酯类为壳的核壳乳液,采用丙烯酸为功能性单体,成膜后羧基为极性基团分布在涂膜表面达到亲水目的。讨论了功能单体丙烯酸(AA)在乳胶粒表面的分布情况及其加量对涂膜亲水性的影响,通过电导滴定发现当AA的加入量为3%时,水相中游离的丙烯酸含量为5.57%,聚合物表面的羧基含量为36.11%,涂膜的亲水性达到最佳,水接触角为51.1°。本文还讨论了核壳法和核壳翻转法对乳胶粒表面羧基分布及涂膜亲水性能的影响,两者的电导滴定曲线形状一致,由两个当量点可以计算出水相中游离的丙烯酸含量为1.47%,而乳胶粒表面羧基分布的含量为43.59%,可见,用核壳翻转法合成的乳液,乳胶粒表面羧基分布含量比普通聚合方法合成的乳胶粒表面羧基分布含量高,涂膜亲水性进一步提高,水接触角降低为44.5°。     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

Group‐in‐a‐Box Meta‐Layouts for Topological Clusters and Attribute‐Based Groups: Space‐Efficient Visualizations of Network Communities and Their Ties

An important part of network analysis is understanding community structures like topological clusters and attribute‐based groups. Standard approaches for showing communities using colour, shape, rectangular bounding boxes, convex hulls or force‐directed layout algorithms remain valuable, however our Group‐in‐a‐Box meta‐layouts add a fresh strategy for presenting community membership, internal structure and inter‐cluster relationships. This paper extends the basic Group‐in‐a‐Box meta‐layout, which uses a Treemap substrate of rectangular regions whose size is proportional to community size. When there are numerous inter‐community relationships, the proposed extensions help users view them more clearly: (1) the Croissant–Doughnut meta‐layout applies empirically determined rules for box arrangement to improve space utilization while still showing inter‐community relationships, and (2) the Force‐Directed layout arranges community boxes based on their aggregate ties at the cost of additional space. Our free and open source reference implementation in NodeXL includes heuristics to choose what we have found to be the preferable Group‐in‐a‐Box meta‐layout to show networks with varying numbers or sizes of communities. Case study examples, a pilot comparative user preference study (nine participants), and a readability measure‐based evaluation of 309 Twitter networks demonstrate the utility of the proposed meta‐layouts.