调速控制的轴流式通风机最小能耗运行条件的研究

针对轴流式通风机调速节能的问题 ,从通风机全压方程入手 ,利用最优控制原理 ,推导出了调速控制的轴流式通风机最小能耗运行条件 ,为此类通风机调速节能运行提供了理论依据     

机床液压系统故障树分析

应用故障树分析法对某厂一台机床液压系统的典型故障进行了故障树分析，取得了满意的结果。实践证明，将故障树分析法应用于机床液压系统故障诊断中是非常有效的。     

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.     

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.     

一种改进的超宽条带噪声消除算法

针对高光谱遥感图像中的超宽条带噪声干扰现象,在深入研究高光谱图像特点和条带噪声产生机理的基础上,提出了一种新的基于最小序列值、小波变换和矩匹配相结合的滤波算法（OWM算法）。该算法主要包括灰度对比度处理、最小序列值处理、小波变换系数归零处理和矩匹配处理等四个步骤。用实际的高光谱图像进行了一系列的验证比较实验,获得了好的实验效果。实验结果表明OWM算法不仅能够有效滤除高光谱图像中的超宽条带噪声,而且还具有较好的普适性。     

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.