基于局部密度的最小生成树聚类算法及其在电力大数据的应用

电力大数据主要来源于电力生产和电能使用的发电、输电、变电、配电、用电和调度各个环节,,如何运用这些数据提高电力管理工作的智能化水平已经成为相关电力环节十分重要的研究课题之一。但现有电力大数据中用到的聚类方法却不能发现任意形状的数据集聚类类别(即类簇),这影响了电力大数据在应用中的计算精度与计算时长。因此提出了一种新的算法,即使用局部密度峰值和基于共享邻点的距离,更好地结合了密度与距离的关系,表示出数据之间的差异。使用局部密度峰值并用基于共享邻点的距离来构造最小生成树,然后重复切割最长的边,直到找到给定数量的簇。在电力大数据应用上的实验结果表明,该算法在具有良好的效果。     

基于国外现货市场与四川实际的现货市场建设分析

现货市场是电力市场建设的关键环节,同时对推进国家电力体制改革、打破电力行业垄断、实现电力资源市场化的合理配置具有重大意义。本文基于分析国外电力现货市场建设经验,结合四川水电清洁能源大省、电网结构复杂的特点,从市场模式、市场主体、交易品种、电价机制、结算机制以及弃水量大、阻塞断面较多、弃水期与非弃水期市场差异化较大等问题出发,对四川电力现货市场建设进行了思考与分析,为建设具有四川特色的电力现货市场方案提供建议。     

基于多光谱和LiDAR数据融合技术的树种分布识别及树障分级预警系统研究

为快速、可靠实现“线树”距离判断,通过无人机搭载三维LiDAR设备,利用多光谱和LiDAR数据融合技术,以精确判断输电线路通道的“线树”距离和实现树种分布识别。通过采集输电线路通道的各类环境实测信息数据,构建输电线路通道的典型树障生长周期模型,根据树木的生长速度,分析预测树木可能形成隐患的时间节点。建立树障分级预警系统,对预测的隐患点及时处理,不仅可以提高输电线路运维巡视效率,而且大大降低了输电线路的维护成本。     

基于随机经济模型预测控制的电热综合能源系统运行优化

北方供暖地区存在大量热电联产机组,其"以热定电"的运行特性限制了机组调峰能力.同时,随着我国能源替代的提出以及风电的快速发展,建立包含风电的电热综合能源系统是降低电热耦合以及系统运行成本的重要手段.因此,建立电热综合能源系统模型,在满足用户电热负荷需求的基础上,综合考虑风电不确定性以及系统动态过程中的经济性,提出一种基于情景树结构的随机经济模型预测控制策略.为了降低风电功率随机性干扰和风电功率变化难以描述的问题,提出采用一种改进的非线性变换方法建立风电功率非高斯分布模型,在此基础上建立基于情景树结构的风电功率变化显式表达结构,在保证鲁棒性的同时降低了优化过程中风电随机性的干扰.最后,通过累计节约成本的复合单步增长率指标验证所提策略的有效性.     

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。     

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.