t/k-fault diagnosis algorithm of n-dimensional hypercube network based on the MM*model

Compared with accurate diagnosis, the system’s selfdiagnosing capability can be greatly increased through the t/kdiagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model, where k is typically a small number. Based on the Preparata, Metze, and Chien(PMC)model, the n-dimensional hypercube network is proved to be t/kdiagnosable. In this paper, based on the Maeng and Malek(MM)*model, a novel t/k-fault diagnosis(1≤k≤4) algorithm of ndimensional hypercube, called t/k-MM*-DIAG, is proposed to isolate all faulty processors within the set of nodes, among which the number of fault-free nodes identified wrongly as faulty is at most k. The time complexity in our algorithm is only O(2~n n~2).

Diagnosabilities of exchanged hypercube networks under the pessimistic one-step diagnosis strategy

The exchanged hypercube EH（s, t） （where s ≥ 1 and t ≥ 1） is obtained by systematically reducing links from a regular hypercube Q,＋t＋l. One-step diagnosis of exchanged hypercubes which involves only one testing phase during which processors test each other is discussed. The diagnosabilities of exchanged hypercubes are studied by using the pessimistic one-step diagno- sis strategy under two kinds of diagnosis models： the PMC model and the MM model. The main results presented here are the two proofs that the degree of diagnosability of the EH（s, t） under pessimistic one-step tl/tl fault diagnosis strategy is 2s where I ≤ s ≤ t （respectively, 2t, where 1 ≤ t ≤ s） based on the PMC model and that it is also 2s where 1 ≤ s ≤ t （respectively, 2t, where 1 ≤ t ≤ s） based on the MM＊ model.

Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

Improving the efficiency of ship optimization is crucial for modern ship design. Compared with traditional methods, multidisciplinary design optimization (MDO) is a more promising approach. For this reason, Collaborative Optimization (CO) is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.

On Certain Connected Resolving Parameters of Hypercube Networks

Given a graph , a set is a resolving set if for each pair of distinct vertices there is a vertex such that . A resolving set containing a minimum number of vertices is called a minimum resolving set or a basis for . The cardinality of a minimum resolving set is called the resolving number or dimension of and is denoted by . A resolving set is said to be a star resolving set if it induces a star, and a path resolving set if it induces a path. The minimum cardinality of these sets, denoted respectively by and are called the star resolving number and path resolving number. In this paper we investigate these re-solving parameters for the hypercube networks.

光RP(k)网络上Hypercube通信模式的波长指派算法

波长指派是光网络设计的基本问题,设计波长指派算法是洞察光网络通信能力的基本方法.基于光RP(k)网络,讨论了其波长指派问题. 含有N=2n个节点的Hypercube通信模式,构造了节点间的一种排列次序Xn,并设计了RP(k)网络上的波长指派算法.在构造该算法的过程中,得到了在环网络上实现n维Hypercube通信模式的波长指派算法.这两个算法具有较高的嵌入效率.在RP(k)网络上,实现Hypercube通信模式需要max{2,52n-5/3}个波长.而在环网络上,实现该通信模式需要复用N/3+N/12个波长,比已有算法需要复用N/3+N/4个波长有较大的改进.这两个算法对于光网络的设计具有较大的指导价值.

Hypercube中LIP长度的上下界估计

给出了超立方体网络中LIP容错模型的上下界估计及一个非常有意义的猜想,并且结合已有结果对上下界及猜想进行了验证。验证结果表明,对LIP的上下界估计,当n较小时还是比较好的;此外,猜想当n=2,3,4,5,6,7时均严格成立,具有非常好的理论价值和实际意义,有待进一步证明。

A Proof for g-Good-Neighbor Diagnosability of Exchanged Hypercubes

The diagnosability of a multiprocessor system or an interconnection network is an important research topic. The system and an interconnection network have an underlying topology, which is usually presented by a graph. In this paper, we show proof for the g-good-neighbor diagnosability of the exchanged hypercube EH (s,t) under the PMC model and MM* model.

CONNECTIVITY OF CARTESIAN PRODUCT DIGRAPHS AND FAULT-TOLERANT ROUTINGS OF GENERALIZED HYPERCUBE

Abstract In this paper, the problem of fault tolerant routings in fault tolerant networks is considered. A routing in a network assigns to each ordered pair of nodes a fixed path. All communication among nodes must go on this routing. When either a node or a link in a fault tolerant network fails, the communication from one node to another using this faulty element must be sent via one or more intermediate nodes along a sequence of paths determined by this routing. An important and practical problem is how to choose a routing in the network such that intermediate nodes to ensure communication are small for any fault set. Let C d be a directed cycle of order d . In this paper. The author first discusses connectivity of Cartesian product digraphs, then proves that the Cartesian product digraph C d 1 ×C d 2 ×...×C d n (d i≥2,1≤i≤n) has a routing such that at most one intermediate node is needed to ensure transmission of messages among all non faulty nodes so long as the number of faults is less than n . This is a generalization of Dolev et al's result for the n dimensional cube.

多孔质静压轴承静态特性预测研究

在多孔质静压轴承设计中,轴承设计参数是影响其静动态特性的关键因素之一,通常情况下,要得到合适的轴承设计参数,需要多次重复建模和仿真,且由于轴承结构复杂,建模难度大,仿真时间长,严重影响了轴承的设计效率。本文构建了一种基于遗传算法(Genetic algorithm,GA)优化反向传播(Back propagation, BP)神经网络的轴承静态特性预测模型,采用拉丁超立方抽样方法在轴承参数设计空间内采样,并进行Fluent流体仿真,将仿真数据用于GA-BP神经网络模型的训练与测试,实现了对设计空间内任意设计参数下的多孔质静压轴承静态特性的预测。研究结果表明,训练出的GA-BP神经网络模型能够准确预测多孔质静压轴承的静态特性,预测精度在95%以上,对多孔质静压轴承的快速设计和参数优化具有重要意义。

铝合金薄板冲压成形过程中的侧壁起皱及有限元模拟

针对铝合金薄板的侧壁起皱问题,本文通过有限元软件分析工艺参数对成形质量的影响,提出了一种基于数值模拟与智能算法相结合的优化方法。首先,利用最优拉丁超立方抽样进行实验设计,并依据数值模拟获取实验值;其次,基于BP神经网络拟合工艺参数与成形质量之间的关系,预测结果的平均相对误差为2.69%,建立了准确的预测模型;最后,用遗传算法极值寻优获取了一组最优的工艺参数组合,起皱幅值的预测值和仿真值相对误差仅为4.03%,实验结果与仿真分析结果相近,验证了该优化方法的合理性和有效性。研究表明:以料厚、摩擦系数和压边力作为优化变量,以最大起皱幅值最小化为优化目标,建立几何模型,并利用有限元软件Autoform进行仿真分析;依据起皱轮廓线径向位移的实验和数值模拟对比,验证了有限元模型的正确性,表明利用神经网络和遗传算法极值寻优可以有效解决铝合金侧壁起皱缺陷。

基于多目标粒子群优化算法的动力电池仿生冷板结构优化设计

为了提高锂离子电池的冷却效果,提出一种高度对称的仿生网状流道冷板。首先,利用单因子分析法分析了冷板结构参数对其性能的影响,然后,以冷板的平均温度、温度标准差和冷却液压力损失为性能指标,采用多目标粒子群优化(MOPSO)算法对冷板的结构参数进行了优化,得到性能最优时的流道宽度、流道深度和冷板壁厚分别为9.0 mm、1.5 mm和1.4 mm,对应的平均温度、温度标准差和压力损失分别为33.20℃、1.33℃和65.63 Pa,相比于初始结构参数,优化后的平均温度和温度标准差分别下降1.92℃和0.02℃,但压力损失增大27.10 Pa。最后,在电池模组层面验证了优化结果。

The Non-inclusion Diagnosability of Hypercubes Under the PMC Model

Diagnosability of a multiprocessor system is an important measure of the reliability of interconnection networks.System-level diagnosis is a primary strategy to identify the faulty processors in a multiprocessor system.Based on a sound assumption proposed by Zhu et al.recently,we proposed a new diagnosability named non-inclusion diagnosability and showed that the non-inclusion diagnosability tN(Q_(n))of the hypercube under the PMC model is 2n-2.That is,assume that if two vertex sets Fi and F2 are both consistent with a syndrome and F_(1)C F_(2),then F2 is not the faulty set which we are looking for;the faulty set F is 1-step diagnosable if|F|≤2n-2 in Qn under the PMC model.

基于改进蜣螂优化算法优化BP的短期风电功率预测

为了提高短期风电功率预测精度,提出一种改进蜣螂优化算法优化BP神经网络的短期风电功率预测模型。针对蜣螂算法中存在易陷入局部最优解的问题,采用最优拉丁超立方方法初始化蜣螂种群,使蜣螂种群分布更为均匀,以提高算法跳出局部最优解的可能性。利用改进后的蜣螂优化算法对BP神经网络的权值和阈值进行优化,并以内蒙古某电厂的风电功率数据为研究对象,进行模型搭建和仿真测试。经仿真验证,预测模型能有效提高风电功率的预测精度。

基于GA-PSO算法计及风电时序特性的主动配电网双层规划

合理规划配电网是提高风电消纳能力的重要一环,但风电出力与负荷时序特性场景应用较多,会增加模型求解难度,对优化配置结果有不利影响。引入拉丁超立方抽样结合K-means聚类技术削减样本数量得到典型风电与负荷多场景模型以提高模型计算效率。考虑风电运营商与国网公司利益,建立计及风电时序特性的主动配电网双层规划模型,即上层以风电运营商收益最大为目标确定风电规划方案,下层以配电网络损耗最小优化系统运行状态。基于IEEE 33节点系统进行算例分析,验证规划模型的有效性。研究结果表明:采用GA-PSO联合优化算法规划后配电系统损耗成本为26.04万元,比单一GA算法和PSO算法规划分别降低了5.03%和0.77%,场景成本减少了4.0万元。因此,验证了所提规划模型的有效性。

考虑不确定性的配电网损耗分析方法研究

针对配电网损耗分析时分布式电源(DG)功率输出存在的不确定性问题,提出了一种基于拉丁超立方体采样(LHS)的考虑不确定性的配电网损耗分析方法。首先,建立了配电网损耗模型,分析了负荷配电损耗和DG损耗计算方法。其次,针对DG功率输出的不确定性问题,应用LHS评估配电网随机损耗。在试验阶段,以IEEE 36馈线系统为例验证所提方法。与随机采样方法相比,所提方法电压平均值误差更低,且误差曲线更加光滑。该方法可以更精确地计算配电节点的损耗。

基于深度学习的水风光短期随机优化调度研究

我国致力于可再生能源发展,提出水-风-光多能互补系统,因风光能源的不确定性,需实时电网调度调整。文章运用深度学习(DQN)优化系统的短期调度,最大化发电效益。采用拉丁超立方抽样和考虑Kantorovich距离的场景削减技术,反映可再生能源不确定性分布,结合深度强化学习建立多能互补系统短期优化调度模型。模拟实际数据,显示该方法有效解决高维等问题,较于传统方法有显著优势。

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Reliability and sensitivity analysis of wedge stability in the abutments of an arch dam using artificial neural network

In this study,the seismic stability of arch dam abutments is investigated within the framework of the probabilistic method.A large concrete arch dam is considered with six wedges for each abutment.The seismic safety of the dam abutments is studied with quasi-static analysis for different hazard levels.The Londe limit equilibrium method is utilized to calculate the stability of the wedges in the abutments.Since the finite element method is time-consuming,the neural network is used as an alternative for calculating the wedge safety factor.For training the neural network,1000 random samples are generated and the dam response is calculated.The direction of applied acceleration is changed within 5-degree intervals to reveal the critical direction corresponding to the minimum safety factor.The Latin hypercube sampling(LHS)is employed for sample generation,and the safety level is determined with reliability analysis.Three sample numbers of 1000,2000 and 4000 are used to examine the average and standard deviation of the results.The global sensitivity analysis is used to identify the effects of random variables on the abutment stability.It is shown that friction,cohesion and uplift pressure have the most significant effects on the wedge stability variance.

基于RBF神经网络模型的数控车床主轴箱优化设计

针对复杂机床结构优化中,通常难以获得设计变量与性能目标之间显式函数关系式的问题,提出了一种基于RBF神经网络模型和组合优化策略的结构优化设计方法。以某型精密数控车床主轴箱为研究对象,通过有限元软件ANSYS Workbench和多学科优化软件Isight联合仿真技术对主轴箱设计尺寸进行最优拉丁超立方实验设计和灵敏度分析,根据实验样本点构建RBF神经网络模型代替主轴箱有限元模型。采用多岛遗传算法(MIGA)和序列二次规划法(NLPQL)相结合的组合优化策略,对RBF神经网络模型进行优化设计。优化结果表明,在保证主轴箱静动态性能的前提下,质量减轻12.89%,达到了预期的效果。

关于网络的控制数的几点注记

(d,w)-控制数是一个度量共享网络资源的可靠性的重要参数。(1,1)-控制数就是图论中的经典控制数,(d,w)-控制数是(1,1)-控制数的一个直接推广。本文给出了计算(1,w)-控制数的一个下界方法和一个上界方法。应用这两个方法,求出了超立方体的(1,n-1)-控制数和(1,n)-控制数,求出了4基n立方体的(1,2n-1)-控制数和(1,2n)-控制数,求出了n维折叠超立方体的(1,n)-控制数。