改进游程编码在天气雷达数据压缩中的应用

为了有效地传输和存储海量遥感数据,必须研究符合遥感应用的压缩技术。通过研究传统的熵编码方法,结合雷达体扫数据的相关性,提出了一种基于改进游程编码的综合无失真压缩算法,应用于天气雷达原始数据压缩。实验结果表明,所给算法优于传统算法及目前流行的压缩软件。     

On diagonal actions of branch groups and the corresponding characters

We introduce notions of absolutely non-free and perfectly non-free group actions and use them to study the associated unitary representations. We show that every weakly branch group acting on a regular rooted tree acts absolutely non-freely on the boundary of the tree. Using this result and the symmetrized diagonal actions we construct for every countable branch group infinitely many different ergodic perfectly non-free actions, infinitely many II₁-factor representations, and infinitely many continuous ergodic invariant random subgroups.     

New existence and nonexistence results for strong external difference families

In this paper, we use character-theoretic techniques to give new nonexistence results for $(n,m,k,\lambda )$-strong external difference families (SEDFs). We also use cyclotomic classes to give two new classes of SEDFs with $m=2$.     

具有遗传性疾病和性状的遗传位点分析

随着信息技术的进步和发展,现代生物学越来越多地将这些技术用于大规模生物数据的收集、分析、挖掘等过程.大量计算机技术,特别是统计方法被用来进行复杂疾病的分析.大量研究表明,人体的许多表型性状差异以及对药物和疾病的易感性等都可能与某些位点相关联,或和包含有多个位点的基因相关联.因此,定位与性状或疾病相关联的位点在染色体或基因中的位置,能帮助研究人员了解性状和一些疾病的遗传机理,也能使人们对致病位点加以干预,防止一些遗传病的发生.利用随机森林方法、Bootstrap重抽样、logistic回归等大数据分析方法,意在解决优化生物学位点关联性分析中单一致病位点识别、多位点相互作用和多性状位点关联性分析等子问题.     

Solvable <Emphasis Type="Italic">D</Emphasis><Subscript>2</Subscript>-groups

Let G be a finite group, Irr¹(G) be the set of nonlinear irreducible characters of G and cd¹(G) the set of degrees of the characters in Irr¹(G). A group G is said to be a D₂-group if|cd¹(G)|=|Irr¹(G)|-2. In this paper, we give a complete classification of solvable D₂-groups.     

改进的布谷鸟算法求解考虑运输时间的分布式柔性流水车间调度问题

针对分布式制造环境下多车间调度问题特点,结合企业实际生产情况,考虑相邻工序间的运输时间,建立以最小化最大完工时间为优化目标的分布式柔性流水车间调度模型,提出一种改进布谷鸟算法用于求解该模型。算法改进包括设计了一种基于工序、车间和机器的三层编码方案;根据问题特点设计了混合种群初始化策略以提高种群质量;改进了布谷鸟搜索操作使其适用于求解该模型;设计了一种种群进化策略以提高算法收敛速度及解的质量。最后通过仿真实验,与多种算法对比,验证所提算法的有效性和优越性。     

Nonbinary convolutional codes derived from group character codes

New families of unit memory as well as multi-memory nonbinary convolutional codes are constructed algebraically in this paper. These convolutional codes are derived from the class of group character codes. The proposed codes have basic generator matrices, consequently, they are noncatastrophic. Additionally, the new code parameters are better than the ones available in the literature.