Sampling from binomial and poisson distributions: A method with bounded computation times

An accurate acceptance-rejection algorithm is devised and tested. The procedure requires an average of less than 3 uniform deviates whenever the standard deviation of the distribution is at least 4, and this number decreases monotonically to 2.63 as . Variable parameters are permitted, and no subroutines for sampling from other statistical distributions are needed.This research was supported by the Austrian Research Council (Fonds zur Förderung der wissenschaftlichen Forschung).     

操作参数对玻纤毡增强复合材料流动成型的影响

研究了玻纤毡增强热塑性复合材料(GMT)流动成型过程中模腔压力和材料流动方向对制品力学性能和纤维取向的影响。研究表明：随着压力提高，弯曲强度和模量同步增加；但压力超过18MPa后，性能变化不明显。随布料面积分数减小，单轴拉伸流动成型将导致沿流动方向和垂直流动方向强度及模量差异变大，纤维取向严重；但双轴拉伸流动成型得到的制品各方向性能较均匀，基本呈现各向同性。     

Distribution of ground stress on Puhe Coal Mine

Puhe Coal Mine is a typical Tertiary coal in Shenbei mining area. With an increase in mining depth, tectonic stress field becomes more complex, leading to increased deformation and failure of the soft rock roadway. Stress becomes an important factor of mine safety and stability. This paper analyzes the distribution of the regional tectonic field, and determines the distribution of situ stress measurement through measuring the ground stress field in the main mining area level of Puhe Coal Mine using stress relief method. The acquired in situ stress data at different locations and depths provide a reference for the rational arrangement of the stop and mine roadway supporting design, which are of great significance for the efficient safety production of the mine.     

A genetic algorithm for solving the fixed-charge transportation model: Two-stage problem

Transportation of goods in a supply chain from plants to customers through distribution centers (DCs) is modeled as a two-stage distribution problem in the literature. In this paper we propose genetic algorithms to solve a two-stage transportation problem with two different scenarios. The first scenario considers the per-unit transportation cost and the fixed cost associated with a route, coupled with unlimited capacity at every DC. The second scenario considers the opening cost of a distribution center, per-unit transportation cost from a given plant to a given DC and the per-unit transportation cost from the DC to a customer. Subsequently, an attempt is made to represent the two-stage fixed-charge transportation problem (Scenario-1) as a single-stage fixed-charge transportation problem and solve the resulting problem using our genetic algorithm. Many benchmark problem instances are solved using the proposed genetic algorithms and performances of these algorithms are compared with the respective best existing algorithms for the two scenarios. The results from computational experiments show that the proposed algorithms yield better solutions than the respective best existing algorithms for the two scenarios under consideration.     

基于蚁群算法的配电网重构

本文提出了一种基于蚁群系统的配电网重构算法（ACSA）[12],该算法用于减少在正常运行情况下的电能损耗的问题。结合相应的数学模型和算法,对一个典型的配电网重构的问题进行了验证。基本蚁群算法的缺点是收敛速度慢和进化停滞,本文提出了一些新的措施来克服这些缺点。     

一种α稳定分布下的变步长最小ρ-范数算法

本文提出一种新的基于α稳定分布噪声环境下的自适应滤波算法,这种算法针对变步长自适应滤波算法收敛速度和稳态误差相矛盾的不足,建立了步长μ（n）与误差信号e（n）之间的新的非线性函数关系。该函数能够削弱输入端不相关α稳定分布噪声对步长调整的影响,更好地解决稳态误差与收敛时间之间的矛盾。通过系统辨识仿真结果表明,新的算法α对稳定分布下的尖峰脉冲噪声有较强的韧性,比传统的NLMP算法有更快的参数辨识速度和更小的稳态误差,同时还具有很好地跟踪多时变系统的能力。     

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.     

分布式多维标度定位算法的研究

针对经典MDS-MAP定位算法在定位精度和算法复杂度方面的不足,提出一种分布式多维标度定位算法。改进后的算法加入了分簇的思想,将大规模网络分成多个具有簇首的局部网络。局部定位时,引入Hop-Euclidean算法,计算簇内节点间距离,再用局部网络融合算法将局部相对坐标图合并成全局相对坐标图。仿真分析表明,提出的算法在各向同性和各向异性网络中都有很好的定位精度,而且在定位精度提高的情况下可用于不规则网络,有利于网络的扩展,更适用于大规模密集型网络。     

一种基于进化过程学习的粒子群优化算法

随机优化的PSO只利用了进化过程中的上一时刻t的速度v(t)和位置x(t)信息,以及个体最优值Pi和群体最优值Pg,缺乏对待优化目标函数特征的充分认识,导致了后期进化过程的长期停滞现象。PSO在长期进化过程中,尤其是在经历了大量函数评估次数的进化后期,待优化的目标函数的性态特征可以从进化迭代过程信息中得到了解。通过采集学习PSO进化过程中的目标函数的解分布特征信息,使PSO可以利用这些特征信息来控制部分粒子的重新初始化过程和交叉选择过程,以及在参数选择中平衡探索模式和开采模式。实验结果表明,利用了进化过程信息的PSO可以增加种群的多样性,从而获得更高的优化精度和更少的期望迭代次数,虽然其轻微地增加了进化过程特征采集的时间和空间复杂性。