树结构法及其在医学研究中的应用

目的：介绍树结构法的基本原理与方法，并用分类树法对医学研究中的实例进行分析。方法：采用单变量分裂，以Ｇｉｎｉ相异性指标为分裂原则，１０层互相证实法及１－ＳＥ准则估计错分率，用最小复杂性指标的向上删剪法产生最终树。结果：分类树共引入５个费用影响因素，并最终分为６个终节点。结论：该分类树结果简单、直观，易于理解与解释，与实际的疾病费用分布不矛盾。

改进哈里斯鹰优化算法在匹配地面点云孔洞修补中的应用

针对无人机匹配点云经地面点滤波后存在较多孔洞的问题,本文提出了利用改进哈里斯鹰算法优化最小二乘支持向量机修补地面点云孔洞。首先,利用八叉树结构法对滤波后的点云数据进行地面特征点提取;然后,采用非线性收敛因子和自适应逃脱概率策略对哈里斯鹰算法进行改进,并将其用于最小二乘支持向量机孔洞修补模型的参数优化。试验结果表明,与常规最小二乘支持向量机相比,组合模型的孔洞修补精度提高了34.3%,其稳定性也得到增强,具备一定的时效性和现实性。

Listless zerotree image compression algorithm

In this paper,an improved zerotree structure and a new coding procedure are adopted,which improve the reconstructed image qualities. Moreover, the lists in SPIHT are replaced by flag maps, and lifting scheme is adopted to realize wavelet transform, which lowers the memory requirements and speeds up the ceding process. Experimental results show that the algorithm is more effective and efficient compared with SPIHT.

Optimization of Synthesis of Spherical Lignosulphonate Resin and Its Structure Characterization

Calcium lignosulphonate was used to synthesize a spherical lignosulphonate resin in a cheap and non-toxic disperse medium by reversed phase suspension polymerization. The process conditions were optimized by orthogonal experiments. Under .the optxmal conditxons （T=95 ℃, CHCl= 3 mol·L^-1, mHCHO： mCLS=7%, WCLS=50%）, globulation took about 20 min and the product was featured with excellent spherical shape, narrow particle size range, 61.20% of water retention capacity, 0.83 mmol·ml^- 1 of total volume exchange capacity and 3.46 mmol·g^- 1 of total exchange capacity. The results of Scanning Electron Micrograph and Scanning Probe Micrograph indicate that spherical lignosulphonate resin has a rugged surface with porous microstructure in the gel skeleton. The average pore size of dry samples was determined to be 10.46 nm. by the BET method.

Hierarchical Semantic-Category-Tree Model for Chinese-English Machine Translation

We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.

Visualizing Data Tree-Table Based on the Database

The data tree table is a type of data structure consisting of data tree and table, which has a wide field of applications. The visual and dynamic growing algorithm of data tree table and its software method are presented based on the theory of the data structure and visual technology of software. The method of the expression and management of data tree table with relational mode are explored.

参数效应的仿真与反馈环及延迟三组合分析法——以场供给侧改革为例

规划落实、政策执行、措施实施皆是通过参数调控系统相关发展速度实现,为此进行参数调控流率效应方法研究.调控流率效应研究必须建立参数调控的可靠性高的模型,必须进行仿真、反馈环、延迟3组合分析,才可能具体清楚回答运行中的问题,实际应用需要,查文献还未见此种三组合分析法,为此,进行“参数调控流率入树建模和仿真、反馈环、延迟三组合分析法”研究.顶天立地,通过南昌大学系统工程科研教学基地明鑫农场供给侧改革,生产雷竹笋、有机鱼、有机生猪特色农产品系统实例,阐述此“三组合分析法”.首先基于逐树设撤关联数结构行为检验法,建立场的8参数调控的8棵流率基本入树可靠性高的模型.接着,依据实际分析与场规划,分别确定8参数的调控区间,设参数的调控区间最小值组合为最不满意方案,区间最大值组合为最满意方案,对两方案进行参数调控效应仿真分析.随后,建立8阶参数枝向量行列式计算反馈环,获12条参数调控反馈环.接着,分别围绕雷竹笋产业、有机鱼产业和开发青饲料发展有机猪促进圈养猪转型3个子系统的6条反馈环分别进行反馈环结构图、反馈环正负极性、延迟阶数确定研究后,进行仿真、反馈环、延迟3结合具体分析结果,然后,依据3结合的具体分析和反馈环、延迟原理,提出了供给侧改革的操作性强的管理思路与技术.通过研究,建立了实际背景强的“参数调控入树流率建模和仿真、反馈环、延迟三组合分析”新方法.

Hierarchical topic modeling with nested hierarchical Dirichlet process

This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonpara-metric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic rela-tionships compared to the hierarchical latent Dirichlet allocation model.