Channel Assignment Method Using Parallel Tabu Search Based on Graph Theory in Wireless Sensor Networks

Wireless sensor networks are suffering from serious frequency interference.In this paper,we propose a channel assignment algorithm based on graph theory in wireless sensor networks.We first model the conflict infection graph for channel assignment with the goal of global optimization minimizing the total interferences in wireless sensor networks.The channel assignment problem is equivalent to the generalized graph-coloring problem which is a NP-complete problem.We further present a meta-heuristic Wireless Sensor Network Parallel Tabu Search(WSN-PTS) algorithm,which can optimize global networks with small numbers of iterations.The results from a simulation experiment reveal that the novel algorithm can effectively solve the channel assignment problem.

Memoryless cooperative graph search based on the simulated annealing algorithm

We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter--radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment.

Search algorithm on strongly regular graphs based on scattering quantum walks

Janmark, Meyer, and Wong showed that continuous-time quantum walk search on known families of strongly regular graphs（SRGs） with parameters（N, k, λ, μ） achieves full quantum speedup. The problem is reconsidered in terms of scattering quantum walk, a type of discrete-time quantum walks. Here, the search space is confined to a low-dimensional subspace corresponding to the collapsed graph of SRGs. To quantify the algorithm＇s performance, we leverage the fundamental pairing theorem, a general theory developed by Cottrell for quantum search of structural anomalies in star graphs.The search algorithm on the SRGs with k scales as N satisfies the theorem, and results can be immediately obtained, while search on the SRGs with k scales as√N does not satisfy the theorem, and matrix perturbation theory is used to provide an analysis. Both these cases can be solved in O（√N） time steps with a success probability close to 1. The analytical conclusions are verified by simulation results on two SRGs. These examples show that the formalism on star graphs can be applied more generally.

基于Graph Cut的快速纹理合成算法

提出一种带边界匹配的基于Graph Cut的快速纹理合成算法.通过将纹理样本以不同的位移贴到输出图中完成合成,重叠区域的像素取值由Graph Cut确定.引入边界图辅助位移搜索,以增强合成结果的边界连续性;在预处理过程中计算2个相同样本在所有相对位移下的匹配误差,选取一部分误差最小的位移组成"优选位移"集合,合成过程中的块间相对位移仅从此集合中选取,大大地提高了合成速度.实验结果表明,该算法合成质量高、适用范围广、合成速度快.

基于词频差异特征选取的Context Graph算法改进

为了解决传统主题爬虫效率偏低的问题,在分析了启发式网络爬虫搜索算法Context Graph的基础上,提出了一种改进的Context Graph爬虫搜索策略。该策略利用基于词频差异的特征选取方法和改进后的TF-IDF公式对原算法进行了改进,综合考虑了网页不同部分的文本信息对特征选取的影响,及特征词的类间权重和类中权重,以提高特征选取和评价的质量。实验结果表明,与既定传统方法进行实验对照,改进后的策略效率更高。

基于深度优先搜索分支定界法的Graph-SLAM后端优化算法改进

针对未知环境中机器人定位的问题,提出了一种深度优先搜索分支定界法的优化改进算法。graph-slam是一种离线slam方法 ,通过采用该优化改进算法可以使graph-slam的后端优化所需耗时减少,使整个系统的效率提高,使其能基本达到一个实时的效果。实验结果表明,该优化改进算法能够使系统运行效率提高近50%,同时能保证系统的稳定性和精确度的要求。

基于ElasticSearch和语义相似度匹配的教学资源搜索策略

【目的】整合多种教学资源,并在此场景下设计和实现一种高效准确的搜索策略,帮助用户获取丰富的教学内容。【应用背景】教学资源类型众多,数量庞大,用户对于准确检索的需求日益增长,仅基于ElasticSearch进行搜索的效果不尽人意。【方法】在对用户输入的Query进行预处理和分词后,通过ER-BERT语义相似度模型在Query库中匹配出n条近似结果,将其输入到ElasticSearch并构建相关度计算公式,最后按照综合评估的最终得分将匹配结果进行排序。【结果】利用知识图谱技术整合复杂的教学资源,并在此基础上实现了一种基于ElasticSearch和语义相似度匹配的教学资源搜索策略,在保证检索速度的同时可以根据用户检索Query的语义信息进行检索。【结论】实验结果表明使用该教学资源搜索策略增加了检索结果的数量,并在保证检索速度的同时提升了结果的准确性,显著改善了用户的搜索体验。

一种基于Context Graph主题爬行算法的改进

为了解决传统的基于上下文图形爬行模型爬取效率低、特征词提取不精确的问题,本文采用了在现有的技术支持上,融入机器学习算法的思想,设计并实现了一种改进的基于主题的爬虫算法,结果表明该算法是有效的.

基于RedisGraph的电网CIM/XML数据模型构建与应用

电网拓扑分析在电力系统运行规划中应用广泛。现有研究中,电网拓扑信息多以关系型数据库或图数据库的方式存储于磁盘,在查询速度上受限于数据库本身的特性。为了提高电网拓扑信息的查询速度,文章提出了一种基于内存型图数据库RedisGraph的电网拓扑模型构建与查询方法。首先概述了CIM/XML文件的数据结构和RedisGraph存储数据的方式,然后据此设计了将CIM/XML数据模型转换为电网拓扑图数据模型的方案,并实现了基于RedisGraph的拓扑搜索,最后基于构建的图数据模型进行拓扑搜索测试。结果表明,提出的基于RedisGraph的电网拓扑构建与查询方案能更好地满足大规模复杂电网的拓扑分析业务的性能需求。

Markov Models for the Tipsy Cop and Robber Game on Graph

In this paper we analyze and model three open problems posed by Harris, Insko, Prieto-Langarica, Stoisavljevic, and Sullivan in 2020 concerning the tipsy cop and robber game on graphs. The three different scenarios we model account for different biological scenarios. The first scenario is when the cop and robber have a consistent tipsiness level through the duration of the game;the second is when the cop and robber sober up as a function of time;the third is when the cop and robber sober up as a function of the distance between them. Using Markov chains to model each scenario we calculate the probability of a game persisting through M rounds of the game and the expected game length given different starting positions and tipsiness levels for the cop and robber.

面向代码搜索的函数功能多重图嵌入

如何提高异构的自然语言查询输入和高度结构化程序语言源代码的匹配准确度,是代码搜索的一个基本问题.代码特征的准确提取是提高匹配准确度的关键之一.代码语句表达的语义不仅与其本身有关,还与其所处的上下文相关.代码的结构模型为理解代码功能提供了丰富的上下文信息.提出一个基于函数功能多重图嵌入的代码搜索方法.在所提方法中,使用早期融合的策略,将代码语句的数据依赖关系融合到控制流图中,构建函数功能多重图来表示代码.该多重图通过数据依赖关系显式表达控制流图中缺乏的非直接前驱后继节点的依赖关系,增强语句节点的上下文信息.同时,针对多重图的边的异质性,采用关系图卷积网络方法从函数多重图中提取代码的特征.在公开数据集的实验表明,相比现有基于代码文本和结构模型的方法,所提方法的MRR提高5%以上.通过消融实验也表明控制流图较数据依赖图在搜索准确度上贡献较大.

Search Processes in the Exploration of Complex Data under Different Display Conditions

The study investigated user experience, display complexity, display type (tables versus graphs), and task difficulty as variables affecting the user’s ability to navigate through complex visual data. A total of 64 participants, 39 undergraduate students (novice users) and 25 graduate students (intermediate-level users) participated in the study. The experimental design was 2 × 2 × 2 × 3 mixed design using two between-subject variables (display complexity, user experience) and two within-subject variables (display format, question difficulty). The results indicated that response time was superior for graphs (relative to tables), especially when the questions were difficult. The intermediate users seemed to adopt more extensive search strategies than novices, as revealed by an analysis of the number of changes they made to the display prior to answering questions. It was concluded that designers of data displays should consider the (a) type of display, (b) difficulty of the task, and (c) expertise level of the user to obtain optimal levels of performance.

基于端到端深度神经网络和图搜索的OCT图像视网膜层边界分割方法

视网膜层边界的形态变化是眼部视网膜疾病出现的重要标志,光学相干断层扫描(optical coherence tomography,OCT)图像可以捕捉其细微变化,基于OCT图像的视网膜层边界分割能够辅助相关疾病的临床判断.在OCT图像中,由于视网膜层边界的形态变化多样,其中与边界相关的关键信息如上下文信息和显著性边界信息等对层边界的判断和分割至关重要.然而已有分割方法缺乏对以上信息的考虑,导致边界不完整和不连续.针对以上问题,提出一种“由粗到细”的基于端到端深度神经网络和图搜索(graph search,GS)的OCT图像视网膜层边界分割方法,避免了非端到端方法中普遍存在的“断层”现象.在粗分割阶段,提出一种端到端的深度神经网络—注意力全局残差网络(attention global residual network,AGR-Net),以更充分和有效的方式提取上述关键信息.具体地,首先设计一个全局特征模块(global feature module,GFM),通过从图像的4个方向扫描以捕获OCT图像的全局上下文信息;其次,进一步将通道注意力模块(channel attention module,CAM)与全局特征模块串行组合并嵌入到主干网络中,以实现视网膜层及其边界的上下文信息的显著性建模,有效解决OCT图像中由于视网膜层形变和信息提取不充分所导致的误分割问题.在细分割阶段,采用图搜索算法去除AGR-Net粗分割结果中的孤立区域或和孔洞等,保持边界的固定拓扑结构和连续平滑,以实现整体分割结果的进一步优化,为医学临床的诊断提供更完整的参考.最后,在两个公开数据集上从不同的角度对所提出的方法进行性能评估,并与最新方法进行比较.对比实验结果也表明所提方法在分割精度和稳定性方面均优于现有方法.

基于属性图的社区搜索模式及其分类体系

当前在属性图中的社区搜索方法较多、类型繁杂,没有系统的分类方式,约束了社区搜索的应用。为明确属性图社区搜索的类别,对属性图社区搜索分类方法进行研究。首先,首次提出属性图社区搜索模式的概念,深入分析属性图社区搜索模式之间存在的联系,提出属性图社区搜索模式的等价、从属、交叉、全异4种关系;其次,以搜索模式的输入图属性、输出图拓扑结构和各属性图社区搜索模式的实际意义为基础,构建两层分类体系,第1层是由输入属性图相同的模式集合构成的集族,这里的输入属性图包括时序、空间、关键字、权值、空属性图,第2层是由输出图拓扑结构及实际意义定位到的每一个具体的属性图社区搜索模式;然后,针对第2层中每一种模式,给出对应社区搜索算法的对比分析结果;最后,对所有属性图社区搜索模式的特性集中分析。总体而言,属性图社区搜索模式不仅为理解和分析复杂网络结构提供有力工具,也为解决实际问题提供新的视角和方法。

Intermediate Representation Using Graph Visualization Software

In this paper, a method to initiate, develop and visualize an abstract syntax tree (AST) in C++ source code is presented. The approach is in chronological order starting with collection of program codes as a string and split into individual characters using regular expression. This will be followed by separating the token grammar using best first search (BFS) algorithm to determine node having lowest value, lastly followed by graph presentation of intermediate representation achieved with the help of graph visualization software (GraphViz) while former is implemented using python programming language version 3. The efficacy of our approach is used in analyzing C++ code and yielded a satisfactory result.

一种基于图神经网络的改进邻域搜索算法

近年来图神经网络与深度强化学习的发展为组合优化问题的求解提供了新的方法。当前此类方法大多未考虑到算法参数学习问题,为解决该问题,基于图注意力网络设计了一种智能优化模型。该模型对大量问题数据进行学习,自动构建邻域搜索算子与序列破坏终止符,并使用强化学习训练模型参数。在标准算例集上测试模型并进行三组不同实验。实验结果表明,该模型学习出的邻域搜索算子具备较强的寻优能力和收敛性,同时显著降低了训练占用显存。该模型能够在较短时间内求解包含数百节点的CVRP问题,并具有一定的扩展潜力。

Network Decomposition and Maximum Independent Set Part Ⅱ: Application Research

According to the researches on theoretic basis in part Ⅰ of the paper, the spanning tree algorithms solving the maximum independent set both in even network and in odd network have been developed in this part, part Ⅱ of the paper. The algorithms transform first the general network into the pair sets network, and then decompose the pair sets network into a series of pair subsets by use of the characteristic of maximum flow passing through the pair sets network. As for the even network, the algorithm requires only one time of transformation and decomposition, the maximum independent set can be gained without any iteration processes, and the time complexity of the algorithm is within the bound of O(V3). However, as for the odd network, the algorithm consists of two stages. In the first stage, the general odd network is transformed and decomposed into the pseudo-negative envelope graphs and generalized reverse pseudo-negative envelope graphs alternately distributed at first; then the algorithm turns to the second stage, searching for the negative envelope graphs within the pseudo-negative envelope graphs only. Each time as a negative envelope graph has been found, renew the pair sets network by iteration at once, and then turn back to the first stage. So both stages form a circulation process up to the optimum. Two available methods, the adjusting search and the picking-off search are specially developed to deal with the problems resulted from the odd network. Both of them link up with each other harmoniously and are embedded together in the algorithm. Analysis and study indicate that the time complexity of this algorithm is within the bound of O(V5).

异质信息网络中基于解耦图神经网络的社区搜索

在异质信息网络(HINs)中搜索包含给定查询节点的社区具有广泛的应用价值,如好友推荐、疫情监控等。现有HINs社区搜索方法大多基于预定义的子图模式对社区的拓扑结构施加一个严格的要求,忽略了节点间的属性相似性,导致结构关系弱而属性相似性高的社区难以定位,并且采用的全局搜索模式难以有效处理大规模的网络数据。为解决这些问题,首先设计解耦图神经网络和基于元路径的局部模块度,分别用于度量节点间的属性相似性和结构内聚性,并利用0/1背包问题优化属性和结构两种凝聚性度量指标,定义了最有价值的c大小社区搜索问题,进而提出了一种基于解耦图神经网络的价值最大化社区搜索模型,执行3个阶段的搜索过程。第一阶段,依据查询信息与元路径,构造候选子图,将搜索范围控制在查询节点的局部范围内,保证整个模型的搜索效率;第二阶段,利用解耦图神经网络,融合异质图信息和用户标签信息,计算节点间的属性相似度;第三阶段,根据社区定义以及凝聚性度量指标,设计贪心算法查找属性相似度高且结构凝聚的c大小社区。最后,在真实的同质和异质网络数据集上测试了搜索模型的性能,大量实验结果验证了模型的有效性和高效性。

基于可微池化的层级图相似性学习

目前,大多数关于图相似性学习的工作仅考虑图级匹配或节点级匹配,忽略了多层级的粗图级匹配,为解决该问题提出一种可微池化层级图匹配网络(PHMN)模型。逐层将图的节点表示进行软聚类进而将源图转化为尺寸缩小的粗图;在图对上使用多角度多层级的跨图匹配层,获取匹配矩阵;由注意力机制将图对匹配矩阵转化为匹配向量后,传入LSTM模型和多层感知机进行相似度预测。该模型在图回归任务和图分类任务的对比实验中,分别取得8项最优表现和6项最优表现。

基于图搜索的陆空两栖平台3D路径规划算法

为了解决陆空两栖平台路径规划能效与寻径效率问题,提出了一种基于图搜索算法的全局路径规划算法。考虑了空中飞行能耗与地面行驶能耗模型的不同,结合起飞阈值、地面移动成本系数、空中移动成本系数等参数决定模态切换机制,在遇到难以越过的障碍时使用逃脱算法脱困,当允许地面通过时优先采用地面行驶的方式到达目标点以提升寻径效率与能效。为验证该算法,分别建立了针对陆空2种模式下的能耗模型,在现有交通规则约束下完成城市场景和虚拟迷宫测试。结果表明:本算法的搜索效率相较于对照组算法提升了30%以上,有效提升搜寻效率的同时降低了能量消耗。