级进模结构设计中的装配约束表达及求解

针对级进模结构特点，提出了能完整描述级进模组成部件功能及约束的 约束树模型，并采用基于变量表达式的约束求解方法实现了级进模装配模型的快速更新。该方法为实现基于约束的自顶向下的级进模结构设计奠定了基础。     

机床故障诊断系统知识表示的研究

针对所研究的机床故障诊断专家系统，以故障树分析法为基础，采用面向对象的事件知识表示方法。并以面向对象编程技术给出了知识表示的示例。分析了这种知识表示方法的特点。实践证明，这种知识表示方法在故障诊断专家系统中是确实有效的，而且这种表示方法融合了多种传统知识表示方法的长处，是一种值得研究与应用的方法。     

铝液充型过程计算机可视化技术研究

采用透明的耐高温石英玻璃观察窗,观察和记录铝液充型过程的图像信息,分析铝液的充型过程;以图像处理技术和计算机技术为基础,通过充型过程铝液自由表面的边缘检测及提取技术,获取液体充型的自由表面曲线,定量分析铝液充型过程的自由表面变化;定量分析的试验结果表明,铝液实际充型过程自由表面的变化对浇注系统设计的合理性具有一定的指导意义.     

Monoterpene concentrations in fresh,senescent, and decaying foliage of singleleaf pinyon (Pinus monophylla Torr. & Frem.: Pinaceae) from the western Great Basin

Senescent foliage from pines is potentially a large contributor to the total monoterpene content of the litter layer, and the availability of these compounds as phytotoxins may result from release of these compounds into the vapor phase. In order to determine the fate of several monoterpene hydrocarbons in the natural environment, we examined their concentrations in fresh, senescent, and decaying needles from 32 single-leaf pinyon pine (Pinus monophylla Torr. & Frem.: Pinaceae) trees growing at two different locations. Total monoterpene content was highest in the fresh needles (mean=5.6 ± 2.2 mg/g extracted air dry weight), but also remained relatively high in senescent needles (mean=3.6 ±1.8 mg/g extracted air dry weight), either still attached to the tree or forming the freshest layer of understory litter. Decaying needles within a dark decomposing layer of litter material 5–20 cm from the surface were found to contain much lower amounts of total monoterpenes (average: =0.12 ±0.06 mg/g extracted air dry weight). Further investigation of the fate of these compounds in the pinyon understory is required to determine if these hydrocarbons are indeed exerting phytotoxic characteristics.     

A language-independent approach to the extraction of dependencies between source code entities

ContextSoftware networks are directed graphs of static dependencies between source code entities (functions, classes, modules, etc.). These structures can be used to investigate the complexity and evolution of large-scale software systems and to compute metrics associated with software design. The extraction of software networks is also the first step in reverse engineering activities.ObjectiveThe aim of this paper is to present SNEIPL, a novel approach to the extraction of software networks that is based on a language-independent, enriched concrete syntax tree representation of the source code.MethodThe applicability of the approach is demonstrated by the extraction of software networks representing real-world, medium to large software systems written in different languages which belong to different programming paradigms. To investigate the completeness and correctness of the approach, class collaboration networks (CCNs) extracted from real-world Java software systems are compared to CCNs obtained by other tools. Namely, we used Dependency Finder which extracts entity-level dependencies from Java bytecode, and Doxygen which realizes language-independent fuzzy parsing approach to dependency extraction. We also compared SNEIPL to fact extractors present in language-independent reverse engineering tools.ResultsOur approach to dependency extraction is validated on six real-world medium to large-scale software systems written in Java, Modula-2, and Delphi. The results of the comparative analysis involving ten Java software systems show that the networks formed by SNEIPL are highly similar to those formed by Dependency Finder and more precise than the comparable networks formed with the help of Doxygen. Regarding the comparison with language-independent reverse engineering tools, SNEIPL provides both language-independent extraction and representation of fact bases.ConclusionSNEIPL is a language-independent extractor of software networks and consequently enables language-independent network-based analysis of software systems, computation of design software metrics, and extraction of fact bases for reverse engineering activities.     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

低空复杂环境下基于采样空间约减的无人机在线航迹规划算法

针对低空复杂环境下障碍物密集且类型多样、带有多通道并存在不确定信息的无人机在线航迹规划问题,为了减少碰撞检测次数,提高航迹搜索速度,降低航迹代价,提出一种基于采样空间约减的无人机在线航迹规划算法. 算法通过引入代价模型,提出约减域逐步构造方法,引导规划树快速有效扩展,改善了基于动态域的快速拓展随机树（Dynamic domain rapidly-exploring random tree,DDRRT） 算法中存在的采样空间过度约减问题. 算法通过密度划分索引的方法逐步构建多棵Kd 树（K-dimensional tree）并采用多近邻节点搜索方法,加快了近邻树节点搜索速度. 仿真实验结果表明,与DDRRT方法相比,该方法在保证对采样空间约减合理性的同时,提高了航迹规划效率和通道内的寻路能力.     

FA composition and regiospecific analysis of Acer saccharum (sugar maple tree) and Acer saccharinum (silver maple tree) seed oils

GC analysis was performed to determine regiospecific distribution and FA composition in seed oils of the Aceraceae species, Acer saccharum and A. saccharinum. The oil content in the seeds was low at 5.0% in A. saccharum and 5.8% in A. saccharinum, and the main FA were linoleic (30.8 and 29.4%), oleic (21.3 and 27.6%), palmitic (10.1 and 10.5%), and cis-vaccenic (9.4 and 7.9%) acids, respectively. In addition, both oils contained long-chain monoenes of the n−9 and n−7 groups, including 11-eicosenoic, 13-docosenoic, 15-tetracosenoic, 13-eicosenoic, and 15-docosenoic acids, whereas γ-linolenic acid accounted for 0.8% of total FA in A. saccharum, and 0.5% in A. saccharinum. Regiospecific analysis, performed using the methodology of dibutyroyl derivatives of MAG, indicated that linoleic, oleic, and linolenic acids were mainly esterified at the internal position of TAG in both seed oils, whereas long-chain monoenes of the n−7 group were almost exclusively esterified on the external positions.     

一种应用层组播可靠性技术

组播是一种高效的多点通信方式,IP组播效率高,但需专门的组播路由器,代价大,至今未能大规模部署.应用层组播数据的复制转发功能由终端主机完成,不涉及现有网络基础设施的更新,在Internet上容易实现,但终端主机的稳定性和安全性不如组播路由器,容易导致单点失效,且数据恢复困难.为解决应用层组播存在的可靠性问题,提出了一种树环结构的应用层组播方案,通过环结构来增加数据传输的可靠性,并采用主动随机转发技术来解决树环结构的多点失效问题.该方案既有效解决了应用层组播可靠传输问题,又降低了恢复时延和系统开销.     

前向树状紧急疏散网络的自适应性控制策略分析

针对前向树状的紧急疏散网络,提出了一种不需要进行需求预测且具有适应性和强鲁棒性的分散式控制疏散策略.通过分析如何确定典型匝道与快速路交汇点处的最优疏散路线,明确了构建可控疏散网络的基本原则.定义了前向树状紧急疏散网络,并对所构建网络的适用范围和具体风险水平标定处理作了明确说明.针对前向树状网络的典型子结构,给出了可以始终充分利用下游通行能力,且充分考虑上游来流具有较高风险水平特点的具体疏散路线选择及放流策略.在上述典型子结构分析基础上,给出了包括如何逆流向确定路段限制性通行能力、顺流向确定路段最大可行流量以及实时动态调整疏散策略等基本步骤的网络疏散策略.最后,通过一个数值算例,比较了新策略与纯粹上游优先策略、纯粹下游优先策略的运行效率,证明了新的适应控制策略的可行性与有效性.