Polynomial time approximation algorithms for proportionate open‐shop scheduling

This paper deals with the presentation of polynomial time (approximation) algorithms for a variant of open‐shop scheduling, where the processing times are only machine‐dependent. This variant of scheduling is called proportionate scheduling and its applications are used in many real‐world environments. This paper develops three polynomial time algorithms for the problem. First, we present a polynomial time algorithm that solves the problem optimally if , where n and m denote the numbers of jobs and machines, respectively. If, on the other hand, , we develop a polynomial time approximation algorithm with a worst‐case performance ratio of that improves the bound existing for general open‐shops. Next, in the case of , we take into account the problem under consideration as a master problem and convert it into a simpler secondary approximation problem. Furthermore, we formulate both the master and secondary problems, and compare their complexity sizes. We finally present another polynomial time algorithm that provides optimal solution for a special case of the problem where .     

正态分布属性数据集上的Skyline查询

Skyline查询适用于多目标决策、数据挖掘等领域。以往的研究工作主要关注确定型数据集,少数针对不确定型数据集的研究处理的也多是离散型不确定数据。为此,定义属性呈连续型分布的不确定数据集,并给出一种针对该种数据集的Skyline查询算法,利用索引和分治思想解决其Skyline查询问题。实验结果表明,该算法可以在牺牲少量精度的情况下提高查询效率。     

视觉传感网中基于二次规划的图像压缩感知

为降低视觉传感网络中图像压缩感知算法的计算复杂度,提出一种基于二次规划的网络图像恢复算法。该算法将压缩感知重构中的欠定线性方程组求解问题,转化为有界约束二次规划问题,在此基础上结合阿米霍步长准则,设计一种压缩感知图像恢复算法,通过求解二次规划问题对网络图像数据进行恢复。理论分析和仿真结果表明,与传统图像压缩感知算法相比,该算法可减少约1/3的图像数据恢复运算时间,且图像重构质量提高3 dB~6 dB,有效提高了视觉传感器网络图像恢复算法的实时性。     

Evaluation of optimisation techniques and control variable formulations for a batch cooling crystallization process

The mathematical optimisation of a batch cooling crystallization process is considered in this work. The objective is to minimize the standard deviation of the final crystal size distribution (CSD), which is an important feature in many industrial processes. The results with the problem written as a nonlinear programming and solved with the successive quadratic programming (SQP) coupled with the discretization of the control variable are compared with those obtained when SQP coupled with the parameterisation of the control variable is applied. Also it is proposed the implementation of the genetic algorithm (GA) coupled with parameterisation of the control variable. Extensive evaluations show that the SQP method is sensitive both to the parameterisation formulation and to the initial estimate. The solution with GA provided the control variable profile that leads to the minimum standard deviation of the final CSD. Nevertheless, it is a very time-consuming technique, which hampers its utilization in real time applications. However, its feature of global searching suggests its suitability in solving offline problems, in order to provide initial setup profiles. Bearing this in mind, it is proposed an algorithm which allows for the implementation of GA solution in a real time fashion, taking advantage of its robustness to find out the optimal solution.     

受限波兹曼机联合稀疏近似的脑功能检测模型

人脑功能连通性检测是神经科学研究的重要技术．使用受限制波兹曼机（RestrictedBoltzmannMachine,RBM）对大量多被试功能磁共振（functionalMagneticResonanceImaging,fMRI）数据进行建模可以检测人脑功能连接,但是不能有效检测单被试数据的功能连接．本文研究一种新颖的融合了稀疏近似与RBM技术的脑功能连通性检测模型,该模型充分利用fMRI数据的稀疏性,采用稀疏近似理论对fMRI数据进行空间域稀疏近似压缩,然后使用RBM建立模型,以检测脑功能连通性．实验结果表明,该融合模型可以有效地提取单被试数据的脑功能时间域混合模型及其相应的脑功能图谱,解决了RBM在单被试数据分析上的瓶颈．     

基于多代表点学习的RSKNN分类算法

RSKNN 算法是一种基于变精度粗糙集理论的 k-近邻改进算法,该算法能够保证在一定分类精度的前提下,有效地降低分类的计算量,提高分类效率。但由于 RSKNN 算法只是简单地将每个类中的样本划分成一个核心和边界区域,并没有根据数据集本身的特点进行划分,因而存在极大的局限性。针对存在的问题,提出一种多代表点学习算法,运用结构风险最小化理论对影响分类模型期望风险的因素进行分析,并使用无监督的局部聚类算法学习优化代表点集合。在UCI公共数据集上的实验表明,该算法比RSKNN算法具有更高的分类精度。     

贝叶斯网络分类器近似学习算法

贝叶斯网络在很多领域应用广泛,作为分类器更是一种有效的常用分类方法,它有着很高复杂度,这使得贝叶斯网络分类器在应用中受到诸多限制。通过对贝叶斯网络分类器算法的近似处理,可以有效减少计算量,并且得到令人满意的分类准确率。通过分析一种将判别式算法变为产生式算法的近似方法,介绍了这种算法的近似过程,并将其应用在了贝叶斯网分类算法中。接着对该算法进行分析,利用该算法的稳定性特点,提出Bagging-aCLL 集成分类算法,它进一步提高了该近似算法的分类精度。最后通过实验确定了该算法在分类准确率上确有不错的表现。     

基于时变时滞动态拓扑的二阶Leader-Following多智能体一致性分析

针对具有时变时滞的二阶Leader-Following多智能体系统,研究了其一致性问题。假设其通信拓扑是时刻切换的,并且每个子时间段内系统拓扑不完全连通,采用Lyapunov-Krasovskii泛函和矛盾分析法,对系统进行了解耦分析,得出只需所有时间段内的拓扑并集连通,且系统的特征根满足一定条件,系统能达到一致,并进行了理论证明。以4个following智能体与1个leader智能体做成的网络为例进行具体说明,所得仿真结果验证了理论的有效性。     

An approach for minimizing a quadratically constrained fractional quadratic problem with application to the communications over wireless channels

Studies for the cognitive model are relatively new in the literature; however there is a growing interest in the communication field nowadays. This paper considers the cognitive model in the communication field as the problem of minimizing a fractional quadratic problem, subject to two or more quadratic constraints in complex field. Although both denominator and numerator in the fractional problem are convex, this problem is not so simple since the quotient of convex functions is not convex in most cases. We first change the fractional problem into a non-fractional one. Second, we consider the semi-definite programming (SDP) method. For the problem with m (m≤2) constraints, we use the SDP relaxation and obtain the exact optimal solution. However, for the problem with m (m>2) constraints, we choose the randomization method to gain an approximation solution in the complex case. At last, we apply this method to practical communications over wireless channels with good results.     

"胶乳法"氯化天然橡胶薄层干燥影响因素的研究

采用二次回归正交设计方法,对“胶乳法“氯化天然橡胶(CNR)进行薄层干燥试验.分析了热风温度、风速和物料的粒径等因素对干燥速率、干燥后物料性能的影响规律,确定了影响物料薄层干燥速率各因素的主次,提出了较佳的干燥工艺和合理的性能参数.