C105管桩混凝土设计参数取值的探讨

通过试验得出了C105混凝土100 mm×100 mm×100 mm立方体非标准试件抗压强度换算系数以及C105混凝土的标准方差和强度变异系数。采用理论计算,得到了棱柱体抗压强度与立方体抗压强度的比值α_c1,并与试验实测平均值进行了比较,验证了理论计算方法的可行性。此外,通过对比分析,探讨了直接外延法和引用公式法两种C105混凝土设计参数计算方法的正确性,研究结果可为C105管桩混凝土的设计和应用提供参考。     

“方变圆”变径筒状结构滑模施工技术

为解决塔式光热的核心构筑物吸热塔"方变圆"变径超高筒体结构施工难题,选用一套可变形的滑模平台系统,通过操作系统上可调千斤顶支座、门架横向液压千斤顶装置、多处可调节模板角度和收缩模板的丝杆等,使得滑模根据施工需求可在一定的范围内调节模板倾角、间距,并逐步完成收缩变形,其理论滑模允许偏角20°,最终高效完成异形结构施工。该滑模系统刚度大、承载及堆料能力强、空间大、机械化程度高,施工操作简单,安全性高,适应沙漠极端环境。全过程仅需使用一套滑模体系,就能完成方形段、方变圆的变形段及圆形段施工,大大提高了施工效率,为类似环境下相似结构的施工提供了一定的参考。     

Coverage optimization algorithm based on VF-CS in mobile sensor network

A random placement of large-scale sensor network in the outdoor environment often causes low coverage.An area coverage optimization algorithm of mobile sensor network (MSN) based on virtual force perturbation and Cuckoo search (VF-CS) was proposed.Firstly,the virtual force of the sensor nodes within the Thiessen polygon was analyzed based on the partitioning of Voronoi diagram of the monitoring area.Secondly,the force of polygon vertices and neighbor nodes was taken as the perturbation factor for updating the node’s location of the Cuckoo search (CS).Finally,the VF-CS guided the node to move so as to achieve the optimal coverage.The simulation results demonstrate that the proposed algorithm has higher coverage and shorter average moving distance of nodes than the Voronoi diagram based algorithms in literatures.     

车用有机朗肯循环系统中计量泵的性能研究

在模拟车用有机朗肯循环(organic Rankine cycle,ORC)系统的工作环境下,设计并搭建以R123作为工质的液压隔膜计量泵性能测试平台。在额定转速(2900 r/min)下,通过调节液压隔膜计量泵的冲程(25%、50%、75%、100%)和出口压力,得到变工况下液压隔膜计量泵的特性曲线,并分析液压隔膜计量泵的关键参数对车用ORC系统性能的影响情况。结果表明:液压隔膜计量泵的流量受冲程影响,与出口压力基本无关。其输入功率和实际运行效率均随着冲程的增加而升高,最高分别可达523.91 W、88.27%。应用该液压隔膜计量泵的模拟车用ORC系统吸热量变化范围较广,系统热效率最高可达12.81%。液压隔膜计量泵可应用于车用ORC系统,通过调节液压隔膜计量泵的冲程和出口压力,可实现提高车用ORC系统热效率、增加系统净输出功率的目的。     

直流配网型混合动力系统在车客渡船上的应用

针对车客渡船动力负荷切换频繁,传统柴油机推进油耗高、排放和噪声大的问题,提出一种基于变速发电机组和超级电容储能装置的直流配网型混合动力系统。目前系统已成功应用于“江苏路渡3011”轮,实船运行数据显示:该混合动力系统不仅能达到较好的节油效果、降低排放,更在操控性、舒适性上优于传统的柴油机推进模式。     

Relevance- and interface-driven clustering for visual information retrieval

Search results of spatio-temporal data are often displayed on a map, but when the number of matching search results is large, it can be time-consuming to individually examine all results, even when using methods such as filtered search to narrow the content focus. This suggests the need to aggregate results via a clustering method. However, standard unsupervised clustering algorithms like $K$-means (i) ignore relevance scores that can help with the extraction of highly relevant clusters, and (ii) do not necessarily optimize search results for purposes of visual presentation. In this article, we address both deficiencies by framing the clustering problem for search-driven user interfaces in a novel optimization framework that (i) aims to maximize the relevance of aggregated content according to cluster-based extensions of standard information retrieval metrics and (ii) defines clusters via constraints that naturally reflect interface-driven desiderata of spatial, temporal, and keyword coherence that do not require complex ad-hoc distance metric specifications as in $K$-means. After comparatively benchmarking algorithmic variants of our proposed approach – RadiCAL – in offline experiments, we undertake a user study with 24 subjects to evaluate whether RadiCAL improves human performance on visual search tasks in comparison to $K$-means clustering and a filtered search baseline. Our results show that (a) our binary partitioning search (BPS) variant of RadiCAL is fast, near-optimal, and extracts higher-relevance clusters than $K$-means, and (b) clusters optimized via RadiCAL result in faster search task completion with higher accuracy while requiring a minimum workload leading to high effectiveness, efficiency, and user satisfaction among alternatives.     

An up to date review of continuously variable speed wind turbines with mechatronic variable transmissions

As a promising and potential alternative to conventional fixed or variable speed wind turbines, continuously variable speed wind turbines (CVSWTs) with variable transmissions offer improved power efficiency and enhanced power control capabilities. The CVSWTs can be generally achieved by adapting mechatronic variable transmissions in the turbine drive train for continuously variable speed operations for wind turbines. Therefore, this paper serves to provide an up to date and exhaustive review of the CVSWTs with mechatronic variable transmissions such as mechanical variable transmission, electrical variable transmission, and power splitting transmission. In this paper, the analysis of CVSWTs with different mechatronic transmission topologies is performed regarding basic configurations, dynamic characteristics, control principles, and experimental or simulation results. Review results indicate the feasibility of applying CVSWTs with such mechatronic transmissions and highlight superiorities of the CVSWTs with power splitting transmission. The CVSWT with power splitting transmission will be particularly suitable for megawatt‐scale turbine systems and will hence increase the economic competitiveness of these turbines due to its large power capacity and high reliability. The directions or challenges for future investigations of CVSWTs with such mechatronic transmissions are also presented to foster in‐depth understanding of such CVSWTs and their control strategies.     

“电网一张图”时空信息管理系统

文章首先总结了电力企业信息管理系统的现状,提出了多源信息管理系统融合集成的邦联、子集和完全融合3种方式,分析了3种方式的优缺点,以建设企业"互联网搜索"型数字引擎为目标,提出了融合基于本体的图数据库建模,多维关联与路径因果分析,和人机共生互动可视化决策于一体的企业信息管理系统技术发展趋势;在系统分析目前正在快速发展的图数据库技术与传统关系数据库之间的本质区别基础上,总结原创的电力图计算平台技术的特点;在此基础上讨论"电网一张图"在建设能源互联网时空信息管理平台数据关联索引图中的核心作用,提出了支持能源互联网建设的"电网一张图"时空信息管理系统的技术架构与特点,最后总结"电网一张图"时空信息管理系统在提高电网安全运行水平、提升客户服务水平、增强清洁能源消纳水平、改善经营绩效水平、以及为综合能源服务和建设能源生态等提供数据服务方面的广阔应用场景。     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

温拌LDPE改性沥青混合料性能研究

选择Sasobit和Evotherm 3G两种温拌剂对温拌LDPE沥青混合料制备工艺和性能进行研究。首先,比较变温等体积法和等黏温度法两种方法,并确定两种温拌混合料的拌和及击实温度。在此基础上,分别采用车辙试验、低温弯曲试验、中点加载弯曲试验以及冻融劈裂试验评价70#、LDPE、Sasobit-LDPE以及3G-LDPE四种沥青混合料的高温性能、低温性能、疲劳性能和水稳定性。结果表明,两种温拌技术均是可行的,Sasobit和3G温拌技术可使施工温度降低20℃和15℃。与热拌LDPE沥青混合料相比,Sasobit温拌沥青混合料具有更好的高温性能和水稳定性,在高应力比条件下抗疲劳性能更好,低温性能虽变差,但是优于70#沥青混合料;3G温拌沥青混合料的高温性能,低温性能以及水稳定性则与热拌LDPE沥青混合料各项性能较为接近,抗疲劳性能变差,但是优于70#沥青混合料。