基于组播源移动的组播树修改过程研究

特定源组播(SSM)是现在应用最广泛的组播模型。随着无线网络的快速发展,移动用户不断增加,移动环境为越来越多的用户提供有限的、共享的带宽,这样的现实更加重了网络对组播服务的需要。然而,特定源组播(SSM)对组播源移动的支持仍然是一个有待解决的问题。本文将针对组播树修改协议(TM)进行分析和改进,进一步完善组播树修改协议过程。     

机车110V充电装置实时故障预警系统研究

110V充电装置作为机车的一个重要部分,其性能的好坏直接影响到机车的运行。本文以专家系统及故障树理论为基础,进行机车充电装置实时故障预警系统的研究。基于专家系统的故障诊断及预警方法进行分析研究后,将其应用到充电装置的针对预警中,建立了充电装置的故障树、知识获取、知识表示、故障预警推理机方法。然后,建立实时验证系统验证该理论分析及方法的可行性、有效性。     

一种面向目标的情感极性分析方法

面向目标的情感分析是细粒度情感分析的重要任务之一,旨在预测句子中给定目标实体的情感极性.当前大多数研究方法忽略了句法结构信息,在情感判别时往往会关注无关词汇,从而使分类性能下降.为此,设计了一种新的引入句法结构的模型,该模型利用双向预训练编码器和作用于依存句法树的图卷积网络分别捕获文本的上下文信息和句法结构信息,并使用多头注意力机制进行信息聚合得到目标的情感分类表征.此外,还将该模型与现有的领域自适应方法相结合,同时向模型中引入领域知识和句法结构知识,进一步提升了模型效果.在几个常用的标准数据集上的实验结果表明了上述模型的有效性.     

哈夫曼树的异构部分重复码构造

针对分布式存储系统中数据被访问频率的不同,提出一种基于哈夫曼树的可变重复度的异构部分重复(HVFR)码,将不同访问频率的数据块作为哈夫曼树带有确定权值的叶子节点,构造哈夫曼树并确定数据块的重复度,利用成对平衡设计构造异构部分的重复码,能够提高热数据的并行访问速度和系统存储效率. 性能分析和实验结果表明,与里所码以及简单再生码相比,HVFR码可以显著减少故障节点的修复时间及修复局部性,提高热数据的并行访问速度,达到负载均衡,且计算复杂度低.     

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.