LT码的BPML译码算法

采用置信度传播算法(BP)对LT码进行译码时,停止集是影响译码效率的重要因素。对LT码停止集的大小进行了理论分析和仿真,提出了置信度传播-最大似然联合译码算法(BPML)。该算法首先采用BP算法译码,当遇到停止集时再采用最大似然译码算法(ML)对停止集进行处理,能够有效消除停止集的影响,提高LT码的译码效率。仿真结果表明,BPML算法结合了BP算法复杂度低和ML算法译码效率高的优点。研究结果对提高计算机网络中数据分发应用的分发效率具有重要的实用价值。     

基于样本选择的启发式属性约简方法研究

属性约简是粗糙集理论的核心研究内容之一。借鉴于贪心策略的启发式算法是求解约简的一种有效技术手段。传统的启发式算法使用了决策系统中的所有样本,但实际上每个样本对约简的贡献程度是不同的,这在一定程度上增加了启发式算法的时间消耗。为解决这一问题,提出了一种基于样本选择的启发式算法,该算法主要分为3步:首先从样本集中挑选出重要的样本;然后利用选取出的样本构建新的决策系统;最后利用启发式算法求解约简。实验结果表明,新算法能够有效地减少约简的求解时间。     

大数据集快速均值漂移谱聚类算法及其在图像分割中的应用

均值漂移谱聚类(MSSC)算法为模式识别聚类任务提供了一种较新的方案.然而由于其内嵌均值漂移过程的时问复杂度与样本容量呈平方关系,其在大数据集环境的实用性受到大大削弱.利用快速压缩集密度估计器(FRSDE)替代Parren窗密度估计式(PW)并融合基于图的松弛聚类(GRC)方法,提出了快速均值漂移谱聚类(FMSSC)算法.相比原MSSC,该算法的总体渐进时间复杂度与样本容量呈线性关系,并具有自适应性和便捷性.     

A multi-objective intelligent water drop algorithm to minimise cost Of goods sold and time to market in logistics networks

The Intelligent Water Drop (IWD) algorithm is inspired by the movement of natural water drops (WD) in a river. A stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a sea. In the process of reaching such destination, the WD and the environment interact with each other as the WD moves through the river bed. Similarly, the supply chain problem can be modelled as a flow of stages that must be completed and optimised to obtain a finished product that is delivered to the end user. Every stage may have one or more options to be satisfied such as suppliers, manufacturing or delivery options. Each option is characterised by its time and cost. Within this context, multi–objective optimisation approaches are particularly well suited to provide optimal solutions. This problem has been classified as NP hard; thus, this paper proposes an approach aiming to solve the logistics network problem using a modified multi–objective extension of the IWD which returns a Pareto set.Artificial WD, flowing through the supply chain, will simultaneously minimise the cost of goods sold and the lead time of every product involved by using the concept of Pareto optimality. The proposed approach has been tested over instances widely used in literature yielding promising results which are supported by the performance measurements taken by comparison to the ant colony meta-heuristic as well as the true fronts obtained by exhaustive enumeration. The Pareto set returned by IWD is computed in 4 s and the generational distance, spacing, and hyper–area metrics are very close to those computed by exhaustive enumeration. Therefore, our main contribution is the design of a new algorithm that overcomes the algorithm proposed by Moncayo-Martínez and Zhang (2011).This paper contributes to enhance the current body of knowledge of expert and intelligent systems by providing a new, effective and efficient IWD-based optimisation method for the design and configuration of supply chain and logistics networks taking into account multiple objectives simultaneously.     

扩展job shop模型的启发式批量流水线调度算法

提出了改进的job shop模型,定义了工作中心以及关键节点的概念。将流水线与批量调度策略引入到新的模型中,并在此基础上提出了一个关于扩展job shop模型的启发式批量流水线调度算法。在关键节点进行选择时,通过回溯计算其余产品的开始时间,使用贪心算法选择优先级最高的产品进行排序。该优先级法则由三个优先级变量组合而成,分别囊括了价值高低、时间紧要程度以及剩余加工时间这三个影响因素。进行加工时,设定最小加工批量,在一个工序内实现多套设备的并行加工,同时在两个相邻工序之间实现流水线加工,从而缩短加工时间,提高了生产效率。通过仿真表明该策略能取得较好的结果。     

基于统计学习框架的中文新词检测方法

近邻(Nearest Neighbor,NN)算法是一种简单实用的监督分类算法。但NN算法在分类未知类标的样例时,需要存储整个训练集,还要计算该样例到训练集中每一个样例之间的距离,所以NN算法的计算复杂度非常高。为了克服这一缺点,P.Hart提出了压缩近邻(Condensed Nearest Neighbor,CNN)规则算法,即从整个训练集中找原样例集的一致子集(一致子集是能正确分类训练集中其他样例的子集)。其计算复杂度依然比较高,特别是对于大型数据库,寻找其一致子集是非常耗费时间的。针对这一问题,提出了基于粗糙集技术的压缩近邻规则算法。该算法分为3步,首先利用粗糙集方法求属性约简(特征选择),以将冗余的属性去掉。然后选取靠近边界域的样例,以将冗余的样例去掉。最后从选出的样例中计算一致子集。该算法能同时沿垂直方向和水平方法进行数据约简。实验结果显示,所提出的方法是行之有效的。     

Application of interval predictor model into robust model predictive control

Here we apply interval prediction model into robust model predictive control (MPC) strategy. After introducing the family of models and some basic information, we present the computational results for the construction of interval predictor model, whose regression parameters are included in a sphere parameter set. Given a size measure to scale the average amplitude of the predictor interval, one optimal model that minimises a size measure is efficiently computed by solving a linear programming problem. We apply the active set approach to solve the linear programming problem and based on these optimisation variables, the predictor interval of the considered model with sphere parameter set can be constructed. As for a fixed non-negative number from the size measure, we propose a better choice by using the optimality conditions. In order to apply interval prediction model into robust MPC, two strategies are proposed to analyse a min-max optimisation problem. After input and output variables are regarded as decision variables, a standard quadratic optimisation is obtained and its dual form is derived, then Gauss–Seidel algorithm is proposed to solve the dual problem and convergence of Gauss–Seidel algorithm is provided too. Finally two simulation examples confirm the theoretical results.     

Robust validity index for a modified subtractive clustering algorithm

A novel robust validity index is proposed for subtractive clustering (SC) algorithm. Although the SC algorithm is a simple and fast data clustering method with robust properties against outliers and noise; it has two limitations. First, the cluster number generated by the SC algorithm is influenced by a given threshold. Second, the cluster centers obtained by SC are based on data that have the highest potential values but may not be the actual cluster centers. The validity index is a function as a measure of the fitness of a partition for a given data set. To solve the first problem, this study proposes a novel robust validity index that evaluates the fitness of a partition generated by SC algorithm in terms of three properties: compactness, separation and partition index. To solve the second problem, a modified algorithm based on distance relations between data and cluster centers is designed to ascertain the actual centers generated by the SC algorithm. Experiments confirm that the preferences of the proposed index outperform all others.     

On the computation of invariant sets for constrained nonlinear systems: An interval arithmetic approach

This paper deals with the computation of control invariant sets for constrained nonlinear systems. The proposed approach is based on the computation of an inner approximation of the one step set, that is, the set of states that can be steered to a given target set by an admissible control action. Based on this procedure, control invariant sets can be computed by recursion.We present a method for the computation of the one-step set using interval arithmetic. The proposed specialized branch and bound algorithm provides an inner approximation with a given bound of the error; this makes it possible to achieve a trade off between accuracy of the computed set and computational burden. Furthermore an algorithm to approximate the one step set by an inner bounded polyhedron is also presented; this allows us to relax the complexity of the obtained set, and to make easier the recursion and storage of the sets.     

基于Sector/Sphere的气相色谱-质谱联用多样本并行对齐算法

针对气相色谱-质谱联用(GC-MS)数据处理过程复杂且计算量大、处理时间过长而严重拖延实验进度的问题,以多样本保留时间对齐为例,设计了基于分布式平台Sector/Sphere的GC-MS数据处理并行框架,实现了多样本并行对齐算法。首先分布式计算所有样本的相似度矩阵;然后依据层次聚类原理将原样本集划分为小样本集,分布式对齐各小样本集内部的样本;最后以各小样本集的平均样本作为对齐依据合并各样本集的对齐结果。实验结果表明：多样本并行对齐算法的错误率为2.9%,由4台PC组成的集群处理大量样本时,最高加速比达到3.29;能够在保证较高正确率的前提下提升计算速度,解决处理时间过长的问题。