向量多项式优化问题的数值方法

向量多项式优化问题中的目标函数和约束条件都是由多项式描述的.先将多目标多项式函数分别通过主要目标法、线性加权和法和理想点法等转化为单目标多项式函数,再利用Lasserre松弛方法求解该多项式优化问题,从而得到原向量多项式优化问题的弱有效解或有效解.数值实验结果表明该数值方法是有效的.     

变拓扑机构可变运动副设计目录研究

可变运动副作为变拓扑机构改变拓扑结构的关键所在,其结构设计是变拓扑机构构型综合的难点.基于可变运动副拓扑变化方式的研究,构建可变运动副设计目录,用于可变运动副的结构设计.分别针对可变运动副的改变轴线方位、改变数目和改变类型等三类拓扑变化过程,分析可变运动副的约束变化特征,并采用运动副约束函数的算术运算进行描述.在归纳设计可变运动副结构形式的基础上,对可变运动副拓扑变化方式、约束变化特征和对应结构等设计信息进行分类和汇编,构建可变运动副设计目录.应用实例表明,采用可变运动副设计目录,能快速有效地设计可变运动副结构,为含可变运动副的变拓扑机构构型综合提供了方便.     

利用伪速度方法压制浅海鬼波

采用基于用传统方法获取的海上单分量地震资料, 估算出同一位置速度的垂直分量(伪速度), 再与单分量压力数据联合, 达到压制鬼波的目的。将稀疏约束的反卷积方法作为鬼波压制的关键环节, 使得在浅海环境下, 由海水表面引起的检波器端上、下行波干涉产生的陷波问题得到改善, 同时低频能量得到增强, 提高了数据的分辨率。该方法可用于海上单分量压力数据的再处理, 拓宽数据的频带范围。     

Debunking material induction

In this paper, we present an explanatory objection to Norton's material theory of induction, as applied to predictive inferences. According to the objection we present, there is an explanatory disconnect between our beliefs about the future and the relevant future facts. We argue that if we recognize such a disconnect, we are no longer rationally entitled to our future beliefs.     

舰载机出动离场调度优化算法

出动离场调度是舰载机起降作业中关键一环, 可抽象为NP(non-deterministic pdynoial)难问题的混合车间调度问题。首先，在传统数学规划模型基础上, 引入逻辑约束及间隔变量, 建立了约束规划模型。然后，通过调度分解技术构建多机调度转化为单机调度的启发式规则, 并提出了单机约束引导启发式搜索与约束规划二分法迭代算法, 给出了问题的求解流程。算例仿真表明, 约束规划可有效解决不同规模下的离场调度, 并快速收敛到阈值内; 在中小规模出动时, 所提算法效率比传统智能方法提升约2个数量级, 具有较强实时规划能力, 但随着实验规模增大算法收敛时间呈线性变化趋势, 而在本文研究范围内仍优于传统智能算法, 具有良好实用价值。最后，用起飞位数量对出动效率进行灵敏度分析, 发现C2起飞位对出动效能贡献最大。     

一种由三角模所诱导的粗糙集间的相似性度量方法

给出两粗糙集间一种新的相似度计算方法,并将其应用到模糊模式识别问题中,通过算例说明这种方法的可行性和有效性.     

一种由三角模诱导的粗糙集间的相似性度量方法

首先介绍了粗糙集、模糊集与三角模的概念,接着指出利用三角模可以诱导出相应的包含度,进而可得到一个相似度,提出了一种利用三角模对粗糙集间的相似性进行度量的新方法.最后借助算例验证了该方法的可行性.     

基于正交试验设计的区间数立体决策方法

为了减少决策信息损失,针对区间数决策问题,提出了一种立体决策方法。将m维区间数决策向量看作具有2m个顶点的超矩形。利用Ls(2m)正交表在超矩形上均匀、分散地选取s个顶点代表该超矩形,再根据不同超矩形对应顶点之间的相对熵度量超矩形之间的差异,最后利用各决策方案同理想解的贴近度进行决策方案排序。实例验证表明该文方法有效、可行。由于选取了多个多维实数点代表区间数决策向量,在丰富了决策信息的同时也增加了计算量。     

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).     

Study of the Influence of Parent-brand Similarity,Association,and Complimentary Fit on Co-brand Attributes

In this paper,it investigates the factors affecting successive brand alliance in which two brands from different product categories are featured together to introduce a co-brand.Based on the brand alliance theories,the degree of association,similarity,and complimentary fit between parents brands are important factors in determining successive brand alliance.By using the 5P's,which are premium price,perceived quality,product features,performance,and perceived image,as the measuring scale in brand attributes ...