A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes.However,transaction performance and scalability has become the main challenges hindering the widespread adoption of blockchain.Due to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many scenarios.To improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling technology.Nevertheless,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected aim.This paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction distribution.Bymitigating crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the blockchain.Therefore,the scheme can achieve a high-frequency transaction as well as a better blockchain scalability.Experiments results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

Hybrid Strategy of Partitioned and Monolithic Methods for Solving Strongly Coupled Analysis of Inverse and Direct Piezoelectric and Circuit Coupling

The inverse and direct piezoelectric and circuit coupling are widely observed in advanced electro-mechanical systems such as piezoelectric energy harvesters.Existing strongly coupled analysis methods based on direct numerical modeling for this phenomenon can be classified into partitioned or monolithic formulations.Each formulation has its advantages and disadvantages,and the choice depends on the characteristics of each coupled problem.This study proposes a new option:a coupled analysis strategy that combines the best features of the existing formulations,namely,the hybrid partitioned-monolithic method.The analysis of inverse piezoelectricity and the monolithic analysis of direct piezoelectric and circuit interaction are strongly coupled using a partitioned iterative hierarchical algorithm.In a typical benchmark problem of a piezoelectric energy harvester,this research compares the results from the proposed method to those from the conventional strongly coupled partitioned iterative method,discussing the accuracy,stability,and computational cost.The proposed hybrid concept is effective for coupled multi-physics problems,including various coupling conditions.

Restraint effect of partition wall on the tunnel floor heave in layered rock mass

The presence of horizontal layered rocks in tunnel engineering significantly impacts the stability and strength of the surrounding rock mass,leading to floor heave in the tunnel.This study focused on preparing layered specimens of rock-like material with varying thickness to investigate the failure behaviors of tunnel floors.The results indicate that thin-layered rock mass exhibits weak interlayer bonding,causing rock layers near the surface to buckle and break upwards when subjected to horizontal squeezing.With an increase in the layer thickness,a transition in failure mode occurs from upward buckling to shear failure along the plane,leading to a noticeable reduction in floor heave deformation.The primary cause of significant deformation in floor heave is upward buckling failure.To address this issue,the study proposes the installation of a partition wall in the middle of the floor to mitigate heave deformation of the rock layers.The results demonstrate that the partition wall has a considerable stabilizing effect on the floor,reducing the zone of buckling failure and minimizing floor heave deformation.It is crucial for the partition wall to be sufficiently high to prevent buckling failure and ensure stability.Through simulation calculations on an engineering example,it is confirmed that implementing a partition wall can effectively reduce floor heave and enhance the stability of tunnel floor.

Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.

Partitioning Calculation Method of Short-Circuit Current for High Proportion DG Access to Distribution Network

Aiming at the problemthat the traditional short-circuit current calculationmethod is not applicable to Distributed Generation(DG)accessing the distribution network,the paper proposes a short-circuit current partitioning calculation method considering the degree of voltage drop at the grid-connected point of DG.Firstly,the output characteristics of DG in the process of low voltage ride through are analyzed,and the equivalent output model of DG in the fault state is obtained.Secondly,by studying the network voltage distribution law after fault in distribution networks under different DG penetration rates,the degree of voltage drop at the grid-connected point of DG is used as a partition index to partition the distribution network.Then,iterative computation is performed within each partition,and data are transferred between partitions through split nodes to realize the fast partition calculation of short-circuit current for high proportion DG access to distribution network,which solves the problems of long iteration time and large calculation error of traditional short-circuit current.Finally,a 62-node real distribution network model containing a high proportion of DG access is constructed onMATLAB/Simulink,and the simulation verifies the effectiveness of the short-circuit current partitioning calculation method proposed in the paper,and its calculation speed is improved by 48.35%compared with the global iteration method.

Airgap-harmonic-oriented Partitioned Design Method of PMV Motor with Improved Torque Performances

Here,we introduce a partitioned design method that is oriented toward airgap harmonic for permanent magnet vernier(PMV)motors.The method proposes the utilization of airgap flux harmonics as an effective bridge between the torque design region and the torque performances.To illustrate the efficacy of this method,a partitioned design PMV motor is presented and compared with the initial design.Firstly,the torque design region of the rotor is artfully divided into the torque enhancement region and ripple reduction region.Meanwhile,the main harmonics that generate output torque are chosen and enhanced,optimization.Moreover,the harmonics that generate torque ripple are selected and reduced based on torque harmonics optimization.Finally,the functions of the partitioned PMV motor torque are assessed based on the finite element method.By the purposeful design of these two regions,the output torque is strengthened while torque ripple is inhibited effectively,verifying the effectiveness and reasonability of the proposed design method.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

光伏波动平抑下改进K-means的电池储能动态分组控制策略

针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。

加入跳跃连接的深度嵌入K-means聚类

现有的深度聚类算法大多采用对称的自编码器来提取高维数据的低维特征,但随着自编码器训练次数的不断增加,数据的低维特征空间在一定程度上发生了扭曲,这样得到的数据低维特征空间无法反映原始数据空间中潜在的聚类结构信息.为了解决上述问题,本文提出了一种新的深度嵌入K-means算法(SDEKC).首先,在低维特征提取阶段,在对称的卷积自编码器中相对应的编码器与解码器之间以一定的权重加入两个跳跃连接,以减弱解码器对编码器的编码要求同时突出卷积自编码器的编码能力,这样可以更好地保留原始数据空间中蕴含的聚类结构信息;其次,在聚类阶段,通过一个标准正交变换矩阵将低维数据空间转换为一个新的揭示聚类结构信息的空间;最后,本文以端到端的方式采用贪婪算法迭代优化数据的低维表示及其聚类,在6个真实数据集上验证了本文提出新算法的有效性.

基于K-Means聚类与熵权TOPSIS法的岩石可爆性评价研究

露天矿山的爆破块度分布,直接影响到后续的采装、运输和破碎工作。为了控制石墨矿山不同区域爆破块度分布,基于K-means无监督聚类学习法与熵权TOPSIS评价法建立了一种新的岩石可爆性评价模型,选取岩石密度、动力能量耗散率、动态抗压强度、平均应变率、脆性指数作为评价指标,通过熵权计算,发现岩石破碎程度受脆性指数影响最大,受平均应变率影响最小。将此模型应用于实际石墨矿山,可爆性分为10个等级,统计不同分级下的岩石平均破碎粒径,发现可爆性分级等级越高平均粒径越大,有明显的分级特征,验证了模型的有效性。从爆破石墨矿石岩体类型看,岩石可爆性从易到难排序为:片岩、片麻岩、变粒岩、混合岩。结合石墨矿石微观观测结果分析可知:岩性从片岩向混合岩转变,岩石内部石墨晶质呈下降趋势,石墨矿石可爆性等级也随之越来越高。岩石密度、能量耗散率、动态抗压强度之间呈线性正相关,岩石可爆性与平均应变率、脆性指数存在负相关性。研究成果为矿山矿岩可爆性评价提供了一条新思路,对露天矿山爆破块度优化具有一定的理论和实践指导意义。

基于K-means聚类和特征空间增强的噪声标签深度学习算法

深度学习中神经网络的性能依赖于高质量的样本,然而噪声标签会降低网络的分类准确率。为降低噪声标签对网络性能的影响,噪声标签学习算法被提出。该算法首先将训练样本集划分成干净样本集和噪声样本集,然后使用半监督学习算法对噪声样本集赋予伪标签。然而,错误的伪标签以及训练样本数量不足的问题仍然限制着噪声标签学习算法性能的提升。为解决上述问题,提出基于K-means聚类和特征空间增强的噪声标签深度学习算法。首先,该算法利用K-means聚类算法对干净样本集进行标签聚类,并根据噪声样本集与聚类中心的距离大小筛选出难以分类的噪声样本,以提高训练样本的质量;其次,使用mixup算法扩充干净样本集和噪声样本集,以增加训练样本的数量;最后,采用特征空间增强算法抑制mixup算法新生成的噪声样本,从而提高网络的分类准确率。并在CIFAR10、CIFAR100、MNIST和ANIMAL-10共4个数据集上试验验证了该算法的有效性。

基于K-means算法的建筑群震害分析模型缩减方法

基于建筑群模型和弹塑性时程分析的精细化城市震害模拟技术能够为防震减灾及应急救援决策提供必要的依据和参考。为了减小城市建筑群震害模拟的计算量和计算时间,本文提出一种基于聚类算法的建筑群模型缩减方法。该方法采用K-means聚类算法,首先基于建筑结构属性向量对建筑群进行聚类,将相似的建筑结构聚为一组;然后从每组选取一个代表建筑组成建筑群缩减模型,通过减少需要分析的建筑结构数量来减少建筑群震害模拟的计算量。本文对传统的K-means算法进行改进,通过设定组内建筑结构的差异上限自动调整聚类分组数量;提出将具体地震动作用下结构地震损伤指数作为结构属性向量进行聚类,并通过算例对比分别采用两种缩减模型,即基于损伤指数聚类的缩减模型与基于结构力学模型参数聚类的缩减模型,计算结构损伤状态准确程度。对比结果表明:在聚类分组数量相同的情况下,基于损伤指数的分组明显优于基于模型参数的分组,采用模型缩减方法能够在保证足够计算精度前提下显著减少建筑群震害模拟计算量和计算时间。

基于改进K-means算法的物流配送中心选址研究

针对传统K-means算法需要主观设定K值及无法处理类别型数据问题,文章运用肘部法及轮廓系数法确定合理K值,对类别型数据采取独热编码(One-Hot Encoding)转换为可以处理的连续型数据,并将其运用到在物流配送中心选址中;并综合考虑多种类别的影响因素,构建了相应的影响因素指标体系,提出的模型能够识别输入数据的数值型及类别型数据,实现样本的有效聚类。相关的案例分析结果表明,相比传统K-means聚类,文章的改进K-means算法选址结果可使物流总成本降低8.76%,运营成本降低14.85%,固定成本降低8.09%,效果显著。

基于主题词向量中心点的K-means文本聚类算法

K-means由于其时间复杂度低运行速度快一直是最为流行的聚类算法之一,但是该算法在进行聚类时需要预先给出聚类个数和初始类中心点,其选取得合适与否会直接影响最终聚类效果。该文对初始类中心和迭代类中心的选取进行大量研究,根据决策图进行初始类中心的选择,利用每个类簇的主题词向量替代均值作为迭代类中心。实验表明,该文的初始点选取方法能够准确地选取初始点,且利用主题词向量作为迭代类中心能够很好地避免噪声点和噪声特征的影响,很大程度上地提高了K-means算法的性能。

基于蚁群算法的三支k-means聚类算法

在聚类分析中,三支k-means聚类算法较具有较强的处理边界不确定数据的能力,但仍然存在对初始聚类中心敏感的问题.通过将蚁群算法和三支k-means聚类算法相结合,给出了一种基于蚁群算法的三支k-means聚类算法来解决这一问题.利用蚁群算法中随机概率选择策略和信息素的正负反馈机制,动态调整权重的方法,对三支k-means聚类算法进行优化.在UCI数据集上实验证明,该方法对聚类结果的性能指标有所提高.

基于改进K-means数据聚类算法的网络入侵检测

随着入侵手段的不断更新和升级,传统入侵检测方法准确率下降、检测时间延长,无法满足网络防御要求。为此,提出一种经过改进K均值(K-means)数据聚类算法,以应对不断升级的网络入侵行为。先以防火墙日志为基础转换数值,然后基于粒子群算法求取最优初始聚类中心,实现K-means数据聚类算法的改进;最后以计算得出的特征值为输入项,实现对网络入侵行为的精准检测。结果表明:K-means算法改进后较改进前的戴维森堡丁指数更小,均低于0.6,达到了改进目的。改进K-means算法各样本的准确率均高于90%,相对更高,检测时间均低于10 s,相对更少,说明该方法能够以高效率完成更准确的网络入侵检测。

改进k-means的多域光纤通信非线性失真补偿方法

为了解决光纤通信信号在传输过程中受到非线性影响而产生的失真问题,提高光纤通信系统的稳定性,提出了改进k-means的多域光纤通信非线性失真补偿方法。构建多域光纤通信传输模型,在传输端利用波长转换器将输入信号传输到光纤,结合干扰原理线性化脉冲恢复光信号。以信噪比描述光纤通信的色散特性,明确信号交互出现非线性失真变化。通过Dijkstra方法改进k-means方法,解调失真星座,避免聚类陷入局部最优,使全部簇信号尽可能接近原始调制中心,实现失真补偿。实验结果表明:利用所提方法对光纤通信非线性失真进行补偿后,聚类效果较佳,信息误码率可降至10^(-7),有效减少了网络传输消耗,提高光纤通信信号质量。

管制扇区运行稳度K-means聚类与分析

为更好地分析管制扇区运行存在的稳定亚安全状态和不稳定亚安全状态,使用K-means算法划分超容比(ECR)、滞留度和飞行姿态混合比3个管制扇区运行稳度评价指标聚类,确定管制扇区运行稳度最佳等级划分;聚类分析单一指标,获得各等级对应的指标阈值,结合熵权法计算的指标权重,遵循隶属度最大原则,获取各时间段的管制扇区运行稳度等级,构建管制扇区运行稳度综合评价模型;选取厦门01号扇区的实际飞行数据,从稳度和趋度2个角度更加全面地分析管制扇区运行态势。结果表明:管制扇区运行稳度等级划分为3类时效果最好;稳度受空中交通流和管制状况的影响会随时间而变化,尤其7:30—9:15和20:00—21:00这2个时间段管制扇区运行稳度的变化最为明显,需引起管制员高度重视,提高空域运行安全。

基于特征分箱和K-Means算法的用户行为分析方法

针对网购用户所产生的购物行为进行分析,首先通过数据处理构建客户关系管理模型(RFM模型),在此模型的基础上采用特征分箱法和K-Means聚类两种方法对用户进行细分,并对2种模型结果进行比较分析,讨论二者的差异性和具体的应用范围和意义.其中,基于特征分箱法的RFM模型将变量转化到相似的尺度上并将变量离散化,使得用户分类标签更加清晰,也可依据各类标签分类出不同类型的用户.K-Means算法通过轮廓系数评估聚类算法质量以至于选取最优K值.本文实验分析结果可为运营商提供更加可靠直观的数据,使得运营商可以根据不同用户的不同行为进行市场细分,进而进行精准营销和服务设置.

基于改进K-means聚类和遗传算法的混合算法求解异构车辆路径问题

由于目前单一车型配送存在资源浪费和效率低下等问题,选取确定数量的不同车型对各客户点进行配送服务往往可以得到更优的配送路径方案。针对这一点,描述了一种异构车辆路径问题,并建立了具有固定车辆数且考虑固定成本、可变成本以及时间窗惩罚成本的混合整数规划模型。同时,提出了一种基于改进K-means聚类和遗传算法的混合算法对模型进行求解。实验仿真先求解不考虑时间窗的问题初步证明混合算法的有效性,再在带时间窗的问题中求解不同规模算例的单一及异构车型结果,以证明异构车型配送更优。最后,对该混合算法的求解结果与其他混合算法的求解结果进行对比分析,证明了混合算法的优越性。研究结果表明:该混合算法求解的异构车型结果优于单一车型,并且比其他混合算法求解的异构车型结果更优,异构车辆配送使用的配送车辆数更少,总成本也更低,该混合算法具有更好的效率和性能。