Approximation algorithm for coloring of dotted interval graphs

Dotted interval graphs were introduced by Aumann et al. [Y. Aumann, M. Lewenstein, O. Melamud, R. Pinter, Z. Yakhini, Dotted interval graphs and high throughput genotyping, in: ACM-SIAM Symposium on Discrete Algorithms, SODA 2005, pp. 339-348] as a generalization of interval graphs. The problem of coloring these graphs found application in high-throughput genotyping. Jiang [M. Jiang, Approximating minimum coloring and maximum independent set in dotted interval graphs, Information Processing Letters 98 (2006) 29-33] improves the approximation ratio of Aumann et al. [Y. Aumann, M. Lewenstein, O. Melamud, R. Pinter, Z. Yakhini, Dotted interval graphs and high throughput genotyping, in: ACM-SIAM Symposium on Discrete Algorithms, SODA 2005, pp. 339-348]. In this work we improve the approximation ratio of Jiang [M. Jiang, Approximating minimum coloring and maximum independent set in dotted interval graphs, Information Processing Letters 98 (2006) 29-33] and Aumann et al. [Y. Aumann, M. Lewenstein, O. Melamud, R. Pinter, Z. Yakhini, Dotted interval graphs and high throughput genotyping, in: ACM-SIAM Symposium on Discrete Algorithms, SODA 2005, pp. 339-348]. In the exposition we develop a generalization of the problem of finding the maximum number of non-attacking queens on a triangle.     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Evolving BlenX programs to simulate the evolution of biological networks

We present a formal approach to study the evolution of biological networks. We use the Beta Workbench and its BlenX language to model and simulate networks in connection with evolutionary algorithms. Mutations are done on the structure of BlenX programs and networks are selected at any generation by using a fitness function. The feasibility of the approach is illustrated with a simple example.     

Parallel Lagrange interpolation on <Emphasis Type="Italic">k</Emphasis>-ary <Emphasis Type="Italic">n</Emphasis>-cubes with maximum channel utilization

This paper proposes an efficient parallel algorithm for computing Lagrange interpolation on k-ary n-cube networks. This is done using the fact that a k-ary n-cube can be decomposed into n link-disjoint Hamiltonian cycles. Using these n link-disjoint cycles, we interpolate Lagrange polynomial using full bandwidth of the employed network. Communication in the main phase of the algorithm is based on an all-to-all broadcast algorithm on the n link-disjoint Hamiltonian cycles exploiting all network channels, and thus, resulting in high-efficiency in using network resources. A performance evaluation of the proposed algorithm reveals an optimum speedup for a typical range of system parameters used in current state-of-the-art implementations.

多模型自适应控制的空分系统整体能耗优化

运用多模型自适应控制技术,针对空分系统的整体能耗优化,就空分系统模型集的建立、控制器的设计、切换算法等方面进行了分析。空分系统作为一个实时变化的有机整体,在整体运行过程中存在的操作环境改变对系统的影响,当系统发生变化及波动时,根据相应切换算法切换到可以满足生产变化的模型,采用适合的控制器,以达到实时监控、安全生产的效果,最大限度地优化了系统运行。     

两级分销系统的订货策略研究

研究的两级分销库存系统由一个分销中心和N个零售商组成。在资源和能力有限的情况下,给定用户满足率,确定一个分销中心和多个零售商的最佳订货量,使得库存总费用最小。对库存建立费用模型,用改进的自适应遗传算法对其进行求解。     

基于决策树及遗传算法的人工免疫入侵检测算法

针对传统的否定选择算法中生成大量无效抗体,并且抗体之间缺乏多样性的问题,设计了基于决策树及遗传算法的人工免疫入侵检测算法。将决策树和遗传算法引入传统的否定选择算法中,利用决策树计算抗原和抗体之间的亲和力,提出了新fitness的计算公式,并利用抗体浓度衡量抗体集的多样性,将低浓度抗体代替高浓度抗体,实现了抗体的多样性,确保了当抗体集的数量一定时尽可能覆盖最大的非自体集空间,以提高抗体集的性能。     

全自动酶免工作站计算机控制系统设计

介绍了酶免工作站软件控制部分的分析和设计。按软件工程的设计思想,归纳出系统的对象,给出系统实现单文档多视和控制类的详细设计。并将一种改进的自适应遗传算法应用于系统执行效率的优化。此开发系统已投入实际试验,从实际使用的效果看,试验执行效率提高了69.9%,血样检测的准确率也达到了99.6%以上,因此,每台酶免仪每天可以多检测120个左右的血样,如果按照现在医院血液检测的指导价,每台酶免仪每年可为医院多创造150万人民币左右的利润。     

The anatomy of vampire

We present an implementation technique for a class of bottom-up logic procedures. The technique is based oncode trees. It is intended to speed up most important and costly operations, such as subsumption and resolution. As an example, we consider the forward subsumption problem which is the bottleneck of many systems implementing first-order logic.To efficiently implement subsumption, we specialize subsumption algorithms at run time, using theabstract subsumption machine. The abstract subsumption machine makes subsumption-check using sequences of instructions that are similar to the WAM instructions. It gives an efficient implementation of the clause at a time subsumption problem. To implement subsumption on the set at a time basis, we combine sequences of instructions incode trees.We show that this technique yields a new way of indexing clauses. Some experimental results are given.The code trees technique may be used in various procedures, including binary resolution, hyper-resolution, UR-resolution, the inverse method, paramodulation and rewriting, OLDT-resolution, SLD-AL-resolution, bottom-up evaluation of logic programs, and disjunctive logic programs.Supported by Swedish TFR grant No. 93–409     

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating the pathways using multiple criteria and analytics. We present a Group Adaptive Hybrid Routing Algorithm (GAHRA) for gathering portability, which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations. Based on this aspect, the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach, with the objectives of enhancing the output of MANET routing computation in each hub. Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.