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.

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.

Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method)–Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm

Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems.Three methods are commonly used for assessing the weights of criteria:objective,subjective,and integrated methods.In this study,an objective approach is proposed to assess the weights of criteria,called SPCmethod(Symmetry Point of Criterion).This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making.The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example.To validate the developed method,we used our numerical example and calculated the weights of criteria by CRITIC,Entropy,Standard Deviation and MEREC methods.Comparative analysis between these methods and the SPC method reveals that the developedmethod is a very reliable objective way to determine the weights of criteria.Additionally,in this study,we proposed the application of SPCmethod to evaluate the efficiency of themulti-criteria partitioning algorithm.The main idea of the evaluation is based on the following fact:the greater the uniformity of the weights of criteria,the higher the efficiency of the partitioning algorithm.The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.

Fingerprint Directional Image Partitioning Based on Genetic Algorithm

In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.

Adaptive Nonlinear PD Controller of Two-Wheeled Self-Balancing Robot with External Force

This paper proposes an adaptive nonlinear proportional-derivative(ANPD)controller for a two-wheeled self-balancing robot(TWSB)modeled by the Lagrange equation with external forces.The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative(NPD)controller and a genetic algorithm,in which the proportional-derivative(PD)parameters are updated online based on the tracking error and the preset error threshold.In addition,the genetic algorithm is employed to adaptively select initial controller parameters,contributing to system stability and improved control accuracy.The proposed controller is basic in design yet simple to implement.The ANPD controller has the advantage of being computationally lightweight and providing high robustness against external forces.The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov theory,providing theoretical assurance of its robustness.Simulations and experimental results show that the TWSB robot with the proposed ANPD controller achieves quick balance and tracks target values with very small errors,demonstrating the effectiveness and performance of the proposed controller.The proposed ANPD controller demonstrates significant improvements in balancing and tracking performance for two-wheeled self-balancing robots,which has great applicability in the field of robot control systems.This represents a promising solution for applications requiring precise and stable motion control under varying external conditions.

Multi-objective Evolutionary Algorithm Based on Target Space Partitioning Method

Considering the defects of conventional optimization methods, a novel optimization algorithm is introduced in this paper. Target space partitioning method is used in this algorithm to solve multi-objective optimization problem, thus achieve the coherent solution which can meet the requirements of all target functions, and improve the population's overall evolution level. The algorithm which guarantees diversity preservation and fast convergence to the Pareto set is applied to structural optimization problems. The empirical analysis supports the algorithm and gives an example with program.

Hardware/software partitioning based on dynamic combination of maximum entropy and chaos optimization algorithm

This paper presents an algorithm that combines the chaos optimization algorithm with the maximum entropy ( COA-ME) by using entropy model based on chaos algorithm,in which the maximum entropy is used as the second method of searching the excellent solution. The search direction is improved by chaos optimization algorithm and realizes the selective acceptance of wrong solution. The experimental result shows that the presented algorithm can be used in the partitioning of hardware/software of reconfigurable system. It effectively reduces the local extremum problem,and search speed as well as performance of partitioning is improved.

Mathematical Models for a Social Partitioning Problem

In this paper we develop modeling techniques for a social partitioning problem. Different social interaction regulations are imposed during pandemics to prevent the spread of diseases. We suggest partitioning a set of company employees as an effective way to curb the spread, and use integer programming techniques to model it. The goal of the model is to maximize the number of direct interactions between employees who are essential for company’s work subject to the constraint that all employees should be partitioned into components of no more than a certain size implied by the regulations. Then we further develop the basic model to take into account different restrictions and provisions. We also give heuristics for solving the problem. Our computational results include sensitivity analysis on some of the models and analysis of the heuristic performance.

Graph Partitioning Method to Determine Servers Placement in CDN

To determine CDN cache servers'placement reasonably,an idea that using graph partitioning to solve the problem was put forward through theoretical analysis and the specific algorithm of partitioning was researched. The concept of graph partitioning for CDN was defined. The conditions of graph partitioning for CDN were demonstrated: the sum of the weights of the nodes in each subarea is as close as possible; edge cut between the subareas is as large as possible; internal nodes in each subarea are connected as far as possible. By reference to light vertex matching algorithm of graph partitioning for network simulation,a multilevel k-way algorithm of graph partitioning for CDN was proposed. The maximized edge cut k-way KL refinement algorithm was discussed. Graph partitioning is a feasible way to solve the problem of CDN servers'placement. Multilevel k-way algorithm is a feasible algorithm for CDN graph partitioning.

Betweenness-based algorithm for a partition scale-free graph

Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.

Parallel finite element algorithm based on full domain partition for stationary Stokes equations

Based on the full domain partition, a parallel finite element algorithm for the stationary Stokes equations is proposed and analyzed. In this algorithm, each subproblem is defined in the entire domain. Majority of the degrees of freedom are associated with the relevant subdomain. Therefore, it can be solved in parallel with other subproblems using an existing sequential solver without extensive recoding. This allows the algorithm to be implemented easily with low communication costs. Numerical results are given showing the high efficiency of the parallel algorithm.

Henry’s Equilibrium Partitioning between Ground Water and Soil Air: Predictions versus Observations

Humans spend 64% - 94% of their time indoors;therefore, indoor air quality is very important for potential exposure to volatile organic compounds (VOC). The source of VOC in the subsurface may come from accidental or intentional releases, leaking landfills or leaking underground and above-ground storage tanks. Once these contaminants are present near or beneath buildings, they may move as a vapour through soil gas and enter the building. A large number of vapour intrusion (VI) algorithms have been published in peer-reviewed publications that link indoor VOC concentrations to the contamination of soils. These models typically include phase partitioning calculations of VOC based on Henry’s law to estimate the concentration of a particular contaminant in soil gas. This paper presents the results from a series of laboratory experiments concerning the use of the Henry’s Law constant for the calculation of toluene concentrations in equilibrium between ground water and soil air. A series of column experiments were conducted with various toluene concentrations in artificial (ground) water to contrast the predicted and observed (soil) air concentrations. The experiments which exclude soil material show a toluene fugacity behaviour roughly in line with Henry’s law whereas the experiments which include soil material result in equilibrium soil concentrations which were around one order-of-magnitude lower than was expected from a Henry Law-based estimation. It is concluded that for toluene inclusion of Henry’s Law in VI algorithms does not provide an adequate description of volatilisation in soils and may lead to an overestimation of health risk. Instead, a model based on a simple description of the relevant intermolecular interactions could be explored.

Impact of joint resource partitioning and routing on performance of relay enhanced cellular networks

Radio resource assignment schemes and routing strategies in relay enhanced cellular networks are proposed in this paper. Under the reuse partitioning-based frequency planning framework, the intra-cell resource partitioning between the base station and relay nodes was addressed firstly by introducing a metric of effective reuse factor. Then, coverage-oriented and capacity-oriented rantings, as well as two link bandwidth assignment schemes＂ equal-bandwidth per link＂ and ＂equal-bandwidth per mobile station＂ were developed. These key issues and their impacts on the system performance were analyzed comprehensively and supported by simulations. Results show that the cell capacity and edge user throughput of the proposed network are superior to the traditional non-relay network when an appropriate effective reuse factor is adopted.

Evaluation of an Evolutionary Algorithm to Dynamically Alter Partition Sizes in Web Caching Systems

There has been an explosion in the volume of data that is being accessed from the Internet. As a result, the risk of a Web server being inundated with requests is ever-present. One approach to reducing the performance degradation that potentially comes from Web server overloading is to employ Web caching where data content is replicated in multiple locations. In this paper, we investigate the use of evolutionary algorithms to dynamically alter partition size in Web caches. We use established modeling techniques to compare the performance of our evolutionary algorithm to that found in statically-partitioned systems. Our results indicate that utilizing an evolutionary algorithm to dynamically alter partition sizes can lead to performance improvements especially in environments where the relative size of large to small pages is high.

基于遗传算法的划分序乘积空间问题求解层选择

划分序乘积空间作为一种新的粒计算模型,可以从多个视角和多个层次对问题进行描述和求解.其解空间是由多个问题求解层组成的格结构,其中每个问题求解层由多个单层次视角构成.如何在划分序乘积空间中选择问题求解层是一个NP难问题.为此,提出一种两阶段自适应遗传算法TSAGA(two stage adaptive genetic algorithm)来寻找问题求解层.首先,采用实数编码对问题求解层进行编码,然后根据问题求解层的分类精度和粒度定义适应度函数.算法第1阶段基于经典遗传算法,预选出一些优秀问题求解层作为第2阶段初始种群的一部分,从而优化解空间.算法第2阶段,提出随当前种群进化迭代次数动态变化的自适应选择算子、自适应交叉算子以及自适应大变异算子,从而在优化的解空间中进一步选择问题求解层.实验结果证明了所提方法的有效性.

基于CatBoost算法的配电网分区拓扑辨识

含多分布式电源配电网的拓扑结构具有多样性与多变性,影响拓扑辨识的实时性和准确性。提出一种基于CatBoost算法的配电网分区拓扑辨识方法。构建结合拓扑分区的配电网拓扑辨识框架,采用区域开关状态矩阵描述拓扑结构,以进行物理上的辨识降维;提出基于CatBoost算法的特征选择与拓扑辨识方法,通过分区并行离线训练得到历史拓扑和未知拓扑的区域拓扑辨识CatBoost模型,通过在线应用得到实时的区域开关状态矩阵标签,形成配电网开关状态矩阵,实现系统拓扑辨识。配电网算例系统测试结果验证了所提方法的有效性。

面向区域自治的配电网动态区域划分方法

具有间隙性及波动性特征的大量分布式光伏接入配电网,为配电网的源荷功率平衡及节点电压调节等带来了新的技术挑战。为充分利用配电网中多类型调节资源的调节能力,提高新能源高比例接入下配电网的自治能力,提出了一种面向区域自治的配电网动态区域划分方法。首先,结合分布式光伏和负荷预测数据,衡量配电网中多类型可调资源的调节能力,建立考虑模块度、电压调节能力和功率持续调节能力的综合指标体系。其次,采用遗传算法求解各区域最优综合指标,结合运行状态及调控需求设计分区结构更新触发机制,更新分区结果。最后,结合实际配电网典型日运行数据的仿真算例及对比分析验证了所提区域动态划分方法在提高区域电压调节能力和功率持续调节能力方面的优势,并分析了不同指标权重,不同触发阈值以及不同时间尺度对分区结果的影响。

基于D-分割法的直流变换器遗传自抗扰控制器设计

针对光伏发电系统中DC-DC变换器由于负载和工作环境条件变化等扰动引起的输出波动问题,提出一种基于D-分割法的直流变换器遗传自抗扰控制器(ADRC)设计方法。该方法适用于设计光伏发电领域中的双向DC-DC变换器,采用D-分割法获得满足闭环系统鲁棒稳定的ADRC控制器参数范围;利用具有全局寻优能力的遗传算法,按综合性能指标在该范围内进行参数寻优。实验结果表明,所提基于D-分割法的直流变换器遗传自抗扰控制器设计方法能有效抑制微网母线侧的电压波动和负载突变,提高控制器的鲁棒性,增强光伏发电系统的动态响应性能和抗干扰能力。

量子近似优化算法在数字分区问题中的应用

量子近似优化算法(QAOA)是一种近似求解组合优化问题的方法,它通过反复调整电路参数,以获取问题哈密顿量的最大期望值为目的来解决问题,在组合优化问题领域具有广阔的应用前景。将QAOA应用于数字分区问题(二分区问题),通过将问题函数转换成对应的哈密顿量,构造了量子线路,采用线性近似约束优化(COBYLA)方法对电路参数进行了优化,并使用IBMQ模拟平台进行了模拟实验。研究发现量子近似优化算法在数字分区问题中有着良好的性能表现,可在多项式时间内给出问题的解,并且降低了问题的时间复杂度。

云存储环境下基于模格算法的数据存储完整度检测

在云存储环境下进行数据保存后,需要依托于数据持有性证明检测数据存储完整度,每次验证的计算较为庞大,导致完整度检测的时间较长.对此,提出存储环境下基于模格算法的数据存储完整度检测方法 .基于热点循迹的数据分块技术,得到待检测的存储数据子块.根据数据块数量搭建包含众多叶子节点的多分支路径树,并通过哈希值匹配实现验证数据块动态更新.考虑数据存储完整度检测的3种参与实体,建立包含6种算法的验证机制,指导完整度检测的具体流程.最后,利用模格算法实现数据存储完整度检测.实验结果表明:在数据量为1 024 MB时,采用所提方法进行数据存储完整度检测的时间开销仅为3.5 s,满足了云存储数据检测要求.