Structural Synthesis of Compliant Metamorphic Mechanisms Based on Adjacency Matrix Operations

A compliant metamorphic mechanism attributes to a new type of metamorphic mechanisms evolved from rigid metamorphic mechanisms. The structural characteristics and representations of a compliant metamorphic mechanism are different from its rigid counterparts, so does the structural synthesis method. In order to carry out its structural synthesis, a constraint graph representation for topological structure of compliant metamorphic mechanisms is introduced, which can not only represent the structure of a compliant metamorphic mechanism, but also describe the characteristics of its links and kinematic pairs. An adjacency matrix representation of the link relationships in a compliant metamorphic mechanism is presented according to the constraint graph. Then, a method for structural synthesis of compliant metamorphic mechanisms is proposed based on the adjacency matrix operations. The operation rules and the operation procedures of adjacency matrices are described through synthesis of the initial configurations composed of s＋1 links from an s-link mechanism （the final configuration）. The method is demonstrated by synthesizing all the possible four-link compliant metamorphic mechanisms that can transform into a three-link mechanism through combining two of its links. Sixty-five adjacency matrices are obtained in the synthesis, each of which corresponds to a compliant metamorphic mechanism having four links. Therefore, the effectiveness of the method is validated by a specific compliant metamorphic mechanism corresponding to one of the sixty-five adjacency matrices. The structural synthesis method is put into practice as a fully compliant metamorphic hand is presented based on the synthesis results. The synthesis method has the advantages of simple operation rules, clear geometric meanings, ease of programming with matrix operation, and provides an effective method for structural synthesis of compliant metamorphic mechanisms and can be used in the design of new compliant metamorphic mechanisms.

Configuration Analysis of Metamorphic Mechanisms Based on Extended Adjacency Matrix Operations

The adjacency matrix operations,which connect with configuration transformation correspondingly,can be used for analysis of configuration transformation of metamorphic mechanisms and the corresponding algorithm can easily be simulated by computer.But the adjacency matrix based on monochrome topological graph is not suitable for the topological representation of mechanisms with multiple joints.The method of adjacency matrix operations has its own limitations for analysis of configuration transformation of metamorphic mechanisms because it can only be used in the topological representation of mechanisms with single joints.In order to overcome the drawback of the adjacency matrix,a kind of new matrix named as extended adjacency matrix is proposed to express topological structures of all mechanisms.The extended adjacency matrix is not only suitable for the topological representation of mechanisms with single joints,but also can be used in that of mechanisms with multiple joints.On this basis,a method of matrix operations based on the extended adjacency matrix is proposed to analyze the configuration transformation of metamorphic mechanisms.The method is not only suitable for configuration analysis of metamorphic mechanisms with single joints as well as metamorphic mechanisms with multiple joints.The method is evaluated by calculating two examples representing metamorphic mechanisms with single joint and multiple joints respectively.It can be concluded that the method is effective and correct for analysis of configuration transformation of all metamorphic mechanisms.The proposed method is simple and easy to be achieved by computer programming.It provides a basis for structural synthesis of all metamorphic mechanisms.

Drawing Weighted Directed Graph from It's Adjacency Matrix

This paper proposes an algorithm for building weighted directed graph, defmes the weighted directed relationship matrix of the graph, and describes algorithm implementation using this matrix. Based on this algorithm, an effective way for building and drawing weighted directed graphs is presented, forming a foundation for visual implementation of the algorithm in the graph theory.

Probability Distribution of Edge in Adjacent Matrix of Aviation Network of China and Algorithm of Searching Non-overlap Community Structure Based on Complex Network

In order to discover the probability distribution feature of edge in aviation network adjacent matrix of China and on the basis of this feature to establish an algorithm of searching non-overlap community structure in network to reveal the inner principle of complex network with the feature of small world in aspect of adjacent matrix and community structure,aviation network adjacent matrix of China was transformed according to the node rank and the matrix was arranged on the basis of ascending node rank with the center point as original point.Adjacent probability from the original point to extension around in approximate area was calculated.Through fitting probability distribution curve,power function of probability distribution of edge in adjacent matrix arranged by ascending node rank was found.According to the feature of adjacent probability distribution,deleting step by step with node rank ascending algorithm was set up to search non-overlap community structure in network and the flow chart of algorithm was given.A non-overlap community structure with 10 different scale communities in aviation network of China was found by the computer program written on the basis of this algorithm.

广义θ-图和广义梅花图φ的奇异性

利用奇异图的邻接矩阵行列式等于零的方法讨论广义θ-图和广义梅花图φ的奇异性,分别给出广义θ-图θ(a_(1),a_(2),…,a_(k))和广义梅花图φ(a_(1),a_(2),…,a_(k))是奇异图的充分必要条件,并计算这两类图中奇异图发生的概率值.

基于会话的独立邻域矩阵偏好交互推荐

通过合并输入和输出邻域矩阵可以使一些工作生成全局和局部偏好,并直接对这2个偏好建模来构建会话表示,从而实现改进。然而,一个会话的输入矩阵和输出矩阵并没有很强的相关性,它们的连接可能会为构建2个偏好引入噪声。其次,全局偏好和局部偏好可以相互促进,且邻域会话的协同信息可能有助于提高推荐性能。因此,一种基于会话的偏好交互推荐被提出,它来自独立的输入邻域矩阵和输出邻域矩阵框架,包括2个并行模块:输入会话表示编码器(ISE)和输出会话表示编码器(OSE)。ISE通过GNN和并行协同注意力机制对具有输入信息的会话表示进行建模。OSE通过GNN和并行协同注意力机制对具有输出信息的会话表示进行建模。最后,引入一种融合门控机制来平衡ISE和OSE产生的会话表示的重要性。结果表明,在Yoochoose和Diginetica数据集上,提出的模型明显优于其他先进的方法。

高斯混合模型与文本图卷积网络结合的虚假评论识别算法

针对文本图卷积网络(Text GCN)窗口边权阈值策略不足的问题,为了更精准地挖掘相关的词关联结构、提高预测精度,提出一种高斯混合模型(GMM)与Text GCN结合的虚假评论识别算法F-Text GCN。首先,利用GMM分离噪声边权分布的特性,提高虚假评论在训练数据上相对正常评论数不足的边信号强度;然后,考虑到信源的多样性,综合文档、词汇和评论以及非文本特征构造邻接矩阵;最后,通过Text GCN的谱分解提取邻接矩阵的虚假评论关联结构实施预测。根据国内某大型电商平台采集的126086条实际中文评论数据开展实证研究,实验结果表明,F-Text GCN识别虚假评论的F1值达到82.92%,与预训练表征模型BERT和文本卷积神经网络相比分别提升了10.46%和11.60%,相较于只使用评论文本信源的Text GCN模型F1值提升了2.94%;研究了高仿虚假评论的预测错误率,在支持向量机(SVM)作用后难识别的评论样本上尝试二次识别,F-Text GCN整体预测准确率可达94.71%,相较于Text GCN和SVM,在识别准确率上分别提升了2.91%和14.54%。研究发现,虚假评论的二阶图邻居结构显示出较强的干预消费者决策的词汇,这表明所提算法特别适用于提取用于虚假评论检测的长程词语搭配结构和全局句子特征模式变化的场景。

基于随机矩阵的变电站多源异常声音信号的定向方法

变电站的安全稳定运行至关重要,通过分析变电站中电力设备的可听声特征变化可以判断设备是否处于不正常运行状态,对异常声源进行主动定位可以快速确定故障区段,进而提高异常声音的检测效率。在实际应用中,由于准确性和实时性的要求常采用大阵列小快拍系统,造成当多个目标声源角度相近时,传统的声音定位方法不能对其有效分辨。根据随机矩阵理论提出基于均匀平面矩阵的声源定位方法,利用Stieltjes变换校正样本协方差矩阵的特征值和特征向量,然后通过空间谱的谱峰搜索对异常声源定位,可以有效提高分辨相邻声源的成功率。研究结果表明,所提方法可以使用更少的快拍数分辨相邻声源,且分辨成功率得到有效提升。

融合改进图卷积的跨模态检索

针对现有跨模态检索在公共子空间度量时难以充分挖掘模态内局部一致性的问题,提出了一种融合改进图卷积的跨模态检索方法。为了提升各模态内的局部一致性,以单个完整样本为节点构建模态图,充分挖掘特征间的交互信息;为了解决图卷积网络只能做浅层学习的问题,采用在每一层图卷积添加初始残差链接和权重恒等映射的方法来缓解此现象;为了通过高低阶邻居信息共同更新中心节点特征,提出减少邻居节点、增加图卷积网络层数的改进;为了学习高度局部一致且语义一致的公共表征,共享公共表征学习层权重,并联合优化公共子空间中模态内的语义约束和模态间的模态不变约束。实验结果表明,在Wikipedia和Pascal sentence这两个跨模态数据集上,不同检索任务的平均mAP值比11种现有方法分别提升了2.2%~42.1%和3.0%~54.0%。

多项正则化约束的伪标签传播优化脑电信号聚类

作为一种非侵入式分析载体,脑电信号目前被广泛应用于脑-机接口、医疗辅助诊断及康复领域,但这些应用通常依赖需要完整标签的有监督分析技术,如分类.随着无标签脑电信号的与日俱增,现有的有监督方法不能有效解决无标签脑电信号分析问题,也在一定程度上限制了无标签脑电信号这类新型数据的应用拓展.为了解决无标签脑电信号的无监督分析问题,提出了一种基于多项正则化约束的伪标签传播优化聚类模型.该模型通过同时优化学习伪标签传播矩阵、脑电信号相似度邻接矩阵、标签分类器的方式实现聚类.将提出的脑电信号聚类模型转化为一个多目标优化问题,并提出了一种基于梯度下降策略的聚类算法EEGapc(electroencephalogram clustering with pseudo label propagation).该算法不仅充分考虑了脑电信号之间的相关性及脑电信号间的信息传递,还能快速收敛到局部最优.在14个真实脑电信号数据集上的实验结果表明,提出的EEGapc脑电信号聚类算法比现有的8种聚类算法性能更好,且在平均NMI(normalized mutual information),ARI(adjusted rand index),F-score,kappa这4个指标上,EEGapc与现有的8种聚类算法相比,分别至少提升了86.88%,58.01%,6.29%,61.17%.

Revealing connectivity in residential Architecture: An algorithmic approach to extracting adjacency matrices from floor plans

In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural plan is designed in different dimensions. While designing all these modes takes considerable time, there are numerous potential applications for artificial intelligence (AI) in this domain. This study aims to compute and use an adjacency matrix to generate architectural residential plans. Additionally, it develops a plan generation algorithm in Rhinoceros software, utilizing the Grasshopper plugin to create a dataset of architectural plans. In the following step, the data was entered into a neural network to identify the architectural plan's type, furniture, icons, and use of spaces, which was achieved using YOLOv4, EfficientDet, YOLOv5, DetectoRS, and RetinaNet. The algorithm's execution, testing, and training were conducted using Darknet and PyTorch. The research dataset comprises 12,000 plans, with 70% employed in the training phase and 30% in the testing phase. The network was appropriately trained practically and precisely in relation to an average precision (AP) resulting of 91.50%. After detecting the types of space use, the main research algorithm has been designed and coded, which includes determining the adjacency matrix of architectural plan spaces in seven stages. All research processes were conducted in Python, including dataset preparation, network object detection, and adjacency matrix algorithm design. Finally, the adjacency matrix is given to the input of the proposed plan generator network, which consequently, based on the resulting adjacency, obtains different placement modes for spaces and furniture.

基于邻井灰色信息的钻井风险评价方法

为解决油气开发过程中未知地层钻井风险难以预测的问题,基于邻井灰色信息提出钻井风险评价方法。首先采用三参数区间灰数描述地层信息;其次采用深度平差和加权差值方法,根据邻井灰色信息构建目标井灰色井筒信息矩阵,并在此基础上建立三参数灰色安全钻井液密度窗口;然后依据广义强度—干涉理论,建立包括溢流、井漏、坍塌及吸附卡等风险的评价模型;最后对西部某油田的5口井进行实例分析。结果表明:引入的井筒信息矩阵概念,描述和计算深度平差和加权插值优化参数,构建目标井井筒信息剖面;利用三参数区间灰数和井筒信息矩阵构建的灰色安全钻井液密度窗口,大幅降低钻井风险评估中的不确定性;基于广义强度—干涉理论的钻井风险评价模型得出的评估结果与实际工况一致。

一类树的邻接矩阵的Moore-Penrose广义逆

根据矩阵结构性质,利用分块矩阵技巧给出任意点数、任意长直径的毛毛虫树邻接矩阵的Moore-Penrose广义逆的具体形式,为进一步研究毛毛虫树的代数性质提供理论支撑.

给定点连通度的图的补图的谱半径

在给定点连通度的直径不小于3的连通图的所有补图中,确定了谱半径达到最小时的极图,并证明它是唯一的.

复杂无向图的同构判定方法

针对一般复杂无向图的同构判定问题,给出了基于邻接矩阵之和的特征多项式判定条件;针对复杂无向连通图的同构判定问题,给出了基于距离矩阵特征多项式和邻接矩阵特征多项式的同构判定条件,将该条件用于复杂无向不连通图的各个连通子图,就可解决复杂无向不连通图的同构判定问题.上述两个判定条件均是充要条件且当复杂无向图退化为简单无向图时仍然适用.

基于融合邻接矩阵的多时态局域电网拓扑分析技术

针对传统邻接矩阵在进行多个节点的电网拓扑分析时存在矩阵计算量大、效率偏低且难以满足实时性要求等问题,文中提出了一种基于融合邻接矩阵的多时态局域电网拓扑分析技术。该技术在将电网模型抽象为映射图的基础上,建立与映射图对应的邻接矩阵,通过该矩阵计算相应的全连通矩阵。同时为了提高计算效率,还在计算全连通矩阵的过程中引入了准平方的思想,并在每计算出一个新的连通矩阵元素后,使用该元素更新老元素及其对称位置元素的值,以得到全连通矩阵,从而完成电网拓扑中的母线与电气岛分析。测试结果表明,在相同的实验条件下,所提方法的运算次数相较于其他算法减少了60%以上,且运算时间大幅缩短。

基于回溯搜索的电力时序数据库集群拓展方法

现有的电力时序数据库集群拓展方法拓展响应时间较低,导致集群效率较差,因此基于回溯搜索研究了一种新的电力时序数据库集群拓展方法。通过数据库集群搜索拓扑结构的邻接矩阵,计算电力数据库中关联性较高的数据拓扑结构,直接明确数据搜索的目标,缩小数据搜索可确定的范围。针对搜索拓扑内的电力数据,采用回溯搜索算法中的分层回溯原则,为数据库集群内数据搜索行为增加约束条件。根据开关矩阵判断数据节点之间的关系,实现电力时序数据库的集群拓展。实验结果表明,基于回溯搜索的电力时序数据库集群拓展方法的拓展响应时间低于0.5 s,集群效率在98%以上。

求解电动汽车车辆路径问题的双种群协同进化算法

绿色物流领域新兴的电动汽车车辆路径问题,由于需要对车辆路径和充电决策同时优化,搜索空间急剧增大,且需要同时满足容量和电量双重约束,现有方法难以快速找到质量较优的可行解。为此,提出一种基于双种群的协同进化算法,通过忽略电量约束构造简单带容量约束的车辆路径问题,辅助原始复杂问题的快速求解。为实现其间信息交互,设计一种基于改进距离邻接矩阵的解序列特征表示方法,旨在同时获取客户访问顺序和车辆指派信息;利用降噪自编码器构建2个问题解之间转换关系,以实现问题域间知识迁移。将该算法与目前常用的3种启发式算法和2种进化算法在不同规模测试集上进行对比,试验结果表明所提算法具有更快收敛速度且所获解集具有更好收敛性。

基于改进Fi-GNN模型的点击率预测方法

为解决基线模型(Fi-GNN)特征交互模块设计不合理的问题,提出一种基于改进Fi-GNN模型的点击率预测方法(Fi-GNN-V2)。针对特征交互模块的邻接矩阵没有考虑到异构节点间的多元关系,在计算异构节点间相互作用的权重时增加边类型的嵌入向量,得到更合理的邻接矩阵;通过多头聚合多个子空间的邻居信息学习不同方式的特征交互;融合二阶以及三阶特征组合解决特征交互模块造成特征域的语义信息丢失问题,设计注意力模块抑制无用特征组合对模型学习的干扰;为进一步提升模型的性能,结合深度神经网络隐式捕捉高阶非线性的特征组合进行联合预测。实验结果表明,该方法优于其它主流点击率预测模型。