Computation of medial axis and offset curves of curved boundaries in planar domain

In this paper, we begin our research from the generating theory of the medial axis. The normal equidistant mapping relationships between two boundaries and its medial axis have been proposed based on the moving Frenet frames and Cesaro’s approach of the differential geometry. Two pairs of adjoint curves have been formed and the geometrical model of the medial axis transform of the planar domains with curved boundaries has been established. The relations of position mapping, scale transform and differential invariants between the curved boundaries and the medial axis have been investigated. Based on this model, a tracing algorithm for the computation of the medial axis has been generated. In order to get the accurate medial axis and branch points, a Two_Tangent_Points_Circle algorithm and a Three_Tangent_Points_Circle algorithm have been generated, which use the results of the tracing algorithm as the initial values to make the iterative process effective. These algorithms can be used for the computation of the medial axis effectively and accurately. Based on the medial axis transform and the envelope theory, the trimmed offset curves of curved boundaries have been investigated. Several numerical examples are given at the end of the paper.     

时变需求下生产与库存系统的最优控制决策方法

针对供应链中库存随着需求的变化可能导致的积压和对生产(或采购)产生的不利影响,为了更好地协调生产(或采购)并减少产品库存,本文研究了以生产为中心的一类基于库存约束和动态线性时变需求下的多品种、多周期、多循环的生产与库存的最优控制模型,结合应用最优控制理论,给出了一种采用切比雪夫多项式逼近和高斯.切比雪夫数值积分对最优控制问题进行数值求解的方法.最后,对某一时变需求情况下的模型应用MATLAB软件进行了求解,得到了生产(采购)与库存的最优控制策略,有效地保证了供应链系统的持续稳定的循环.     

Web检索查询意图分类技术综述

查询分类是近年来信息检索领域的研究热点,并且在很多领域得到了广泛地关注。主要讨论根据查询的意图进行分类的研究工作,从查询分类的诞生背景、关键技术、所使用的分类方法和评价方法方面进行综述评论,提出了查询意图分类面临的问题和挑战。认为缺乏权威的评测标准、在大规模数据集上的未经全面测试的性能、如何准确地获取查询的特征以及如何证明分类体系的完备性和独立性是目前查询意图分类研究的关键问题。     

网络控制系统鲁棒性能μ综合控制

针对网络控制系统的延时问题,分析了具有乘性输入不确定性的被控对象中嵌入加权函数的μ鲁棒性能,并利用鲁棒控制中μ分析的D-K迭代法设计出网络控制器。仿真结果表明μ控制器能够保证系统在一定延时范围内的稳定性和鲁棒性。     

矩形件带排样的一种遗传算法

采用遗传算法解决矩形件带排样问题,用带符号的有序整数串作为初始种群个体,改善了初始个体解的质量．提出基于最低水平线的择优插入算法,在解码过程中动态地调整个体中的零件顺序,选取最适合的零件进行填充,使零件排放紧凑,提高了材料的利用率．对20多道基准排样例题的实验计算结果表明,文中算法速度快,所得排样方案的材料利用率高．最后提出利用该算法解决VLSI模块布局问题的方法框架．     

用于文本校对的分词与词性标注一体化算法

分词和词性标注是中文处理中的一项基本步骤,其性能的好坏很大程度上影响了中文处理的效果。传统上人们使用基于词典的机械分词法,但是,在文本校对处理中的文本错误会恶化这种方法的结果,使之后的查错和纠错就建立在一个不正确的基础上。文中试探着寻找一种适用于文本校对处理的分词和词性标注算法。提出了全切分和一体化标注的思想。试验证明,该算法除了具有较高的正确率和召回率之外,还能够很好地抑制文本错误给分词和词性标注带来的影响。     

Horizontal Voting Ensemble Based Predictive Modeling System for ColonCancer

Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ~8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.     

Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties

For networked sensor systems (NSSs) with hard and soft sensors including five uncertainties, two universal approaches of solving the robust fusion estimation problems are presented. It includes an integrated sequential covariance intersection (SCI) fusion minimax robust Kalman filtering approach with cross-covariance information and a generalized Lyapunov equation approach with four pairs of Lyapunov equations. Applying them, the robust local and SCI fused time-varying and steady-state Kalman filters are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. The equivalent batch SCI fusers are also presented. Their robustness and accuracy relations are proved, and the sensitivity of the SCI fuser with respect to the fused orders of sensors is analyzed. Applying the dynamic error system analysis method and the dynamic variance error system analysis method, a new convergence and absolute asymptotic stability theory of robust fusion Kalman filtering is presented. The classical Kalman filtering convergence and stability theory is developed. Compared with the original covariance intersection fuser, they significantly reduced the computational complexity and burden. Compared with the optimal and conservative SCI fusers, they significantly improved the robust accuracies. They are suitable to deal with asynchronous or random delayed data and are suitable for real-time applications. A simulation applied to the two-mass spring damper mechanical system shows their effectiveness.     

Unsupervised learning of multi-task deep variational model

We propose a general deep variational model (reduced version, full version as well as the extension) via a comprehensive fusion approach in this paper. It is able to realize various image tasks in a completely unsupervised way without learning from samples. Technically, it can properly incorporate the CNN based deep image prior (DIP) architecture into the classic variational image processing models. The minimization problem solving strategy is transformed from iteratively minimizing the sub-problem for each variable to automatically minimizing the loss function by learning the generator network parameters. The proposed deep variational (DV) model contributes to the high order image edition and applications such as image restoration, inpainting, decomposition and texture segmentation. Experiments conducted have demonstrated significant advantages of the proposed deep variational model in comparison with several powerful techniques including variational methods and deep learning approaches.