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.

A Fuzzy Directed Graph-Based QoS Model for Service Composition

Web service composition lets developers create applications on top of service-oriented computing and its native description, discovery, and communication capabilities. This paper mainly focuses on the QoS when the concrete composition structure is unknown. A QoS model of service composition is presented based on the fuzzy directed graph theory. According to the model, a recursive algorithm is also described for calculating such kind of QoS. And, the feasibility of this QoS model and the recursive algorithm is verified by a case study. The proposed approach enables customers to get a possible value of the QoS before they achieve the service.

Optimization Ensemble Weights Model for Wind Forecasting System

Effective technology for wind direction forecasting can be realized using the recent advances in machine learning.Consequently,the stability and safety of power systems are expected to be significantly improved.However,the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem.This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models.This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization(PSO-Guided WOA).The proposed optimized weighted ensemble predicts the wind direction given a set of input features.The conducted experiments employed the wind power forecasting dataset,freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight hours.The recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind direction.In addition,a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its superiority.Moreover,statistical analysis using one-way analysis of variance(ANOVA)and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.

基于Modelica语言的复杂机械系统统一建模平台研究

研究了基于Modelica语言的复杂机械系统统一建模平台MWorks的关键技术。在分析Modelica模型描述和Modelica标准库(MSL)结构的基础上,设计了基于Modelica语言的可视化建模系统框架,提出了基于有向无环图的MSL内存存储模式,提高了可视化建模速度;提出了基于实体句子匹配的快速图文切换算法;设计了基于句子的线性二叉树结构,实现了文本和图形编辑环境相统一的恢复操作。最后给出了MWorks系统的建模实例。

SDG-based Model Validation in Chemical Process Simulation

Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software environment that can automate the validation activity. This paper is concentrated on the pretreatment of the model validation. We use the validation scenarios and standard sequences generated by well-established SDG model to validate the trends fitted from the simulation model. The results are helpful to find potential problems, assess possible bugs in the simulation model and solve the problem effectively. A case study on a simulation model of boiler is presented to demonstrate the effectiveness of this method.

Surrogate model-assisted interactive genetic algorithms with individual’s fuzzy and stochastic fitness

We propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract a user’s cognition on evaluated individuals in order to alleviate user fatigue in interactive genetic algorithms with an individual’s fuzzy and stochastic fitness. We firstly present an approach to construct a directed fuzzy graph of an evolutionary population according to individuals’ dominance relations, cut-set levels and interval dominance probabilities, and then calculate an individual’s crisp fitness based on the out-degree and in-degree of the fuzzy graph. The approach to obtain training data is achieved using the fuzzy entropy of the evolutionary system to guarantee the credibilities of the samples which are used to train the surrogate model. We adopt a support vector regression machine as the surrogate model and train it using the sampled individuals and their crisp fitness. Then the surrogate model is optimized using the traditional genetic algorithm for some generations, and some good individuals are submitted to the user for the subsequent evolutions so as to guide and accelerate the evolution. Finally, we quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to find the satisfactory individuals, and also apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.

A version management model of PDM system and its realization

Based on the key function of version management in PDM system, this paper discusses the function and the realization of version management and the transitions of version states with a workflow. A directed aeyclic graph is used to describe a version model. Three storage modes of the directed acyelic graph version model in the database, the bumping block and the PDM working memory are presented and the conversion principle of these three modes is given. The study indicates that building a dynamic product structure configuration model based on versions is the key to resolve the problem. Thus a version model of single product object is built. Then the version management model in product structure configuration is built and the application of version management of PDM syster is presented as a case.

Modeling and Decoupling of Coupling Tasks in Collaborative Development Process of Complicated Electronic Products

It is important to improve the development efficiency of decoupling a coupling task package according to the information relevancy relation between development tasks in the collaborative development process of complicated electronic products.In order to define the task coupling model in the development process,the weighted directed graph based on the information relevancy is established,and the correspondence between weighted directed graph model and numerical design structure matrix model of coupling tasks is introduced.The task coupling model is quantized,thereby the interactivity matrix of task package is built.A multi-goal task decoupling method based on improved genetic algorithm is proposed to decouple the task coupling model,which transforms the decoupling of task package into a multi-goal optimization issue.Then the improved genetic algorithm is used to solve the interactivity matrix of coupling tasks.Finally,the effectiveness of this decomposition method is proved by using the example of task package decoupling of collaborative development of a radar’s phased array antenna.

An Improved Iterative Reconstruction Algorithm for Traveltime Tomography

Traveltime tomography is a technique to reconstruct acoustic, seismic, or electromagnetic wave-speed distributions from first arrival traveltime data. The ray paths that should be used for tomographic techniques strongly depend on the wave-speed distribution. In this paper, a new method is proposed for finding out the ray paths from Fermat's principle, that means the traveltime of the ray path should be a minimum value. The problem of finding out the ray path is actually an optimum problem. Our new method uses the idea to find out the shortest path in a weighted directed graph to solve the problem. The ray paths found out by this method are used in the iterative reconstruction algorithm. Computer simulation result produced by this reconstruction algorithm is better than that by the conventional ones. It also shows that the new algorithm is effective with good convergency and stability.

Confounding of Three Binary-Variable Counterfactual Model with DAG

Confounding of three binary-variable counterfactual model with directed acyclic graph (DAG) is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three causal counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses and ignorability, the sufficient conditions to determine whether the covariate variable is an irrelevant factor or whether there is no confounding in each counterfactual model are obtained.

Research on Qualitative Model Decomposition

Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incomprehensible behavioral descriptions and the simulation for complex systems frequently is intractable. Two model de- composition methods are proposed in this paper to eliminate or decrease the insujficiency of this algorithm. Using a directed graph to represent the qualitative model, the strongly connected graph based theory and genetic algorithm based model decomposition are proposed to decompose the model. A new simple system model is reconstructed by subgraphs and causal relations when the system directed graph is decomposed completely. Each sub-graph is viewed as a separate system and will be simulated separately, and the simulation result of causally upstream subsystem is used to constrain the behavior of downstream subsystems. The model decomposition algorithm provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.

车联网中基于有向无环图区块链的个性化联邦互蒸馏学习方法

联邦学习(FL)作为一种分布式训练方法,在车联网(IoV)中得到了广泛应用。区别于传统机器学习,FL允许智能网联车辆(CAVs)通过共享模型而非原始数据来协同训练全局模型,从而保护CAV隐私和数据安全。为了提升联邦学习模型精度,降低通信开销,该文首先提出一种基于有向无环图(DAG)区块链和CAVs的IoV架构,分别负责全局模型共享和本地模型训练。其次,设计了一种基于DAG区块链的异步联邦互蒸馏学习(DAFML)算法在本地同时训练教师和学生模型,教师模型的专业级网络结构可取得更高精度,学生模型的轻量级网络结构可降低通信开销,并采用互蒸馏学习使教师模型和学生模型从互相转移的软标签中学习知识以更新模型。此外,为了进一步提高模型精度,基于全局训练轮次和模型精度设定个性化权值来调节互蒸馏占比。仿真结果表明,DAFML算法在模型精度和蒸馏比率方面优于其他比较算法。

有向图上的影响力社区搜索

现有社区搜索方法用于从无向图中挖掘满足内聚性和影响力要求的社区,没有考虑有向图中边的方向对社区的影响,导致有向图上社区挖掘的结果出现影响力和内聚性不足的问题。基于此,提出有向图上的影响力社区搜索问题,并设计相应的在线搜索算法;为进一步提升社区挖掘的效率,提出有向图上基于索引的影响力搜索方法及其优化策略。此外,提出一种基于并行思想的索引构建方法,加速索引的构建过程。最后,基于8个真实数据集进行验证,实验结果验证了所提算法的有效性和高效性。

基于知识图谱与加权贝叶斯机制的刑侦推断模型

随着信息化和数字化的高速发展,各类人工智能方法在现代公共安全领域得到充分应用,其中知识图谱以其强大的知识表征能力和数学建模能力,大大提高了智能决策的可解释性,成为领域内研究和应用的热点.一直以来,基于贝叶斯机制的推断模型是公安情报与刑侦分析的重要手段.本文将公安知识图谱引入传统贝叶斯模型,在充分考虑参数耦合性的基础上,对后验概率进行加权更新,有效提高了推断的准确性.本文回避敏感的真实数据,以《红楼梦》中“绣春囊”一案为例完成实证分析,科学地揭秘了经典悬案的真相.本文提出的基于知识图谱的自适应加权方案有效提高了贝叶斯模型的效率,是人工智能助力智慧公安的一种有益尝试.

ElasticDAG:弹性图式区块链

图式区块链采用有向无环图(directed acyclic graph,DAG)的并行拓扑结构,相较于基于串行拓扑结构的传统链式区块链,能够显著提升系统性能,已受到业界广泛关注.然而,现有图式区块链的共识协议与存储模型高度耦合,缺乏灵活性,难以适应多元化应用需求.同时,大部分图式区块链在共识协议层面上缺乏灵活性,局限于概率性共识协议,难以兼顾确认延迟和安全性,尤其对于延迟敏感型应用很不友好.为此,提出弹性图式区块链系统ElasticDAG,其核心思想是将存储模型和共识协议进行解耦,让两者并行、独立地运行,从而灵活适配多元化应用.针对提升系统吞吐量和活性的需求,为存储模型设计自适应区块确认策略和基于划分的确认区块排序算法;针对降低交易确认延迟的需求,设计低延迟DAG区块链混合共识协议.实验结果表明,ElasticDAG原型系统在广域网下的吞吐量高达11 Mb/s,并具有10秒级确认性能.与OHIE相比,Elastic DAG在实现同等吞吐量的情况下,可将确认延迟降低17倍;与Haootia相比,Elastic DAG在实现同等共识延迟的情况下,可将安全性从91.04%提升到99.999914%.

结合力导向图分布算法的特征加权深度嵌入聚类

聚类分析作为无监督学习领域的一个重要研究方向,是许多数据驱动应用的核心.但是高维数据特有的高维距离趋同特性,使得高维空间样本近邻结构遭到破坏,从而使得大量基于距离(基于近邻)的聚类算法性能急剧下降.目前,大量研究者认为,高维数据往往包含大量与任务不相关特征及相互关联的特征,其真实特征维度往往要比原始特征维度低很多.在学习样本低维等价表示上,基于深度自编码器的深度嵌入学习尽可能地保留重构信息.然而,现有此类方法往往需要聚类损失引导聚类,这虽然提高了聚类性能,但聚类损失与重构损失间的内在矛盾,限制了聚类性能的进一步提高.基于力导向图分布算法的降维算法则是尽可能保留近邻结构信息的基础上学习样本低维表示,但是高维距离趋同的特性使得此类算法较难准确获取样本高维近邻结构信息.本文在深度自编码器与力导向图分布算法的基础上引入特征加权思想,使模型在具有强大的低维等价表示能力及根据数据近邻结构凸显簇结构能力的同时考虑特征对聚类任务的适合程度.5个数据集上与最新高维聚类算法的对比实验充分证明了本文算法的合理性与优越性.

Cloud-based parallel power flow calculation using resilient distributed datasets and directed acyclic graph

With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more complex.They require faster computing speed and better scalability for power flow calculations to support unit dispatch.Based on the analysis of a variety of parallelization methods, this paper deploys the large-scale power flow calculation task on a cloud computing platform using resilient distributed datasets(RDDs).It optimizes a directed acyclic graph that is stored in the RDDs to solve the low performance problem of the MapReduce model.This paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test data.Experiments are conducted on Spark cluster which is deployed as a cloud computing platform.They show that the advantages of this method are not obvious at small scale, but the performance is superior to the stand-alone model and the MapReduce model for large-scale calculations.In addition, running time will be reduced when adding cluster nodes.Although not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.

考虑综合成本的常规公交客流分配方法

为改善常规公交客流数据传统调查方法效率低、准确性差,以及常规公交客流分配时对出行成本考虑不全面、个体间出行成本存在较大差距的缺点,开展了考虑综合成本的常规公交客流分配方法研究。以数据即服务为基础开发的手机信令数据平台作为常规公交客流分配数据来源。通过经纬度坐标匹配,得到用户与交通小区之间的空间关系。利用数据仓库工具筛取数据字典索引,界定时间、速度、起终点类型等数据参数,通过时间匹配、路径匹配进行交通方式识别,将用户比例外推扩样至全国人口,得到常驻居民早高峰常规公交通勤起讫点(origin-destination,OD)量。分析常规公交客流个体的出行时间成本、拥挤成本、票价成本,建立以个体利益最大为原则、考虑综合成本的常规公交客流分配模型。将交通小区间常规公交客流分配问题转换为有向赋权图路径选择问题,并采用深度优先搜索与连续平均法混合算法求解,进行常规公交出行方案筛选以及客流分配。选取哈尔滨市典型交通小区为案例,开展常规公交客流分配,并与传统Logit路径选择概率模型分配结果、人工调查结果对比分析。结果表明:模型分配结果与人工调查结果的平均绝对百分比误差为4%,Logit模型为17.5%。模型分配客流后个体出行成本极差、方差、总和分别为0.03,0.0001,1108.35,Logit模型分别为3.28,1.58,1127.02。验证了模型分配客流的准确性以及考虑综合成本的必要性,分配客流后个体出行成本差距更小,更符合利益最大原则。

基于观测器的有向通信下多母线直流微电网分布式自触发二次调节

基于动态一致性的均值观测是多母线微电网分布式二次调节的常用方法,然而,当前该方法通信和计算频率高,而现有针对静态一致性的自触发方法难以直接应用于动态一致性上,且并未考虑有向通信网络平衡性可能引起的观测误差和二次调节误差等问题。因此,该文提出有向通信下基于观测器的分布式自触发二次调节策略。首先,为直流微电网设计基于动态一致性的分布式平均状态观测器,定量证明在权重平衡有向通信网络下的收敛特性。基于观测器设计分散式二次调节器,并证明该二次调节结构可等效为虚拟全连接通信拓扑,具有良好的动态性能。然后,设计基于分布式自触发的通信和更新机制,实现微源-微源间、微源-远端母线电压采集器间的间歇式通信以及触发式电流和平均母线电压二次调节,并证明李雅普诺夫稳定性。最后,设计触发时间修正方法,提高自触发响应速度。MATLAB/Simulink仿真表明,相比无触发二次调节,准稳态时,可降低二次调节系统通信频率95%、计算频率75%;相比现有自触发方法,可降低平均触发频率54.6%,进而更加有效地降低系统通信和网络负载,提高二次调节系统的实用性和可靠性。

基于改进FPPN的飞控系统故障传播路径分析方法

针对飞控系统多冗余、多闭环的结构特性,结合有向图模型和模糊Petri网(FPN)模型,构建飞控系统故障传播模糊概率Petri网(FPPN)模型,以解决飞控系统特定结构下的故障传播路径问题。改进的FPPN模型包含飞控系统有向图模型、故障传播特性量化计算模型和故障传播FPPN模型3个部分。采用面向对象技术分析飞控系统的功能行为和物理结构,综合复杂网络理论,构建系统故障传播有向图模型;引入Floyd算法,开展系统耦合关联分析,基于节点的度和边介数定义系统故障传播特性;在有向图模型的基础上,提出相应的结构映射规则,构建飞控系统故障传播FPPN模型,融合改进后的参数量化方法,设定2种推理算法对系统多冗余、闭环结构特性下的故障传播路径进行有效分析。通过数值分析与实例验证,获取飞控系统典型故障传播路径及路径上相关节点的状态值,验证所提方法的有效性。