基于连锁环网与改进离散粒子群算法的多目标配电网重构

配电网重构本质上是一个复杂的高维数非线性组合优化问题。为避免其不可行解的影响,同时实现快速寻优,提出了一种通过连锁环网矩阵快速判断粒子是否满足配电网拓扑约束的方法。采用基于Pareto准则的离散二进制粒子群算法(Binary Particle Swarm Optimization,BPSO)以求解配电网重构多目标优化问题。从三方面对BPSO算法进行改进:改进粒子更新策略以提升新代粒子的可行概率;改进sigmoid函数同时提出邻域搜索机制以强化算法后期的收敛能力;提出基于次优解保留策略的小生境共享机制以改进群体最优粒子更新方式,进而强化算法的全局搜索能力。对IEEE33系统算例进行仿真,结果表明改进BPSO算法在求解含分布式电源(Distributed Generation,DG)的配电网重构多目标优化问题时,能够更加精确高效地收敛至Pareto最优前沿。     

反求工程中散乱点云的曲线构造

对反求工程中的点云数据处理,给出一种基于特征的区域分割法来划分点云,为后续曲面拟合提供有利条件。     

燃煤电厂脱硝和省煤器自动控制优化

介绍华能临沂电厂5号机组脱硝SCR及低低温省煤器改造基本工艺,并对脱硝改造中氨气流量的自动控制策略和低低温省煤器改造中混水温度、出口烟温的自动控制策略进行论述。根据实际情况,提出以带前馈作用的单回路控制的优化控制策略,解决了脱硝及低低温省煤器中控制对象存在大惯性、大扰动难以控制的问题。     

Study on process and mechanism of laser drilling in water and air

This paper describes a particle expert system (PES) that was developed to improve particle classification for burrs, casts, and chips in the automotive production process. Image-processing techniques were deployed to extract particle information and remove noise from industrial filters and lighting systems. An advanced model of a particle classification algorithm (PCA) was built with 12 particle classes and a particle-expert-system (PES) algorithm was developed to train the PCA parameters to achieve the target success rate. The particle expert system will help the user locate the source of particles with high reliability and a stable success rate immediately after image processing and training the PCA parameters.     

Toughening modification of (Acrylonitrile‐styrene‐acrylic copolymer)/(α‐Methylstyrene‐acrylonitrile copolymer) binary blend via using different impact modifiers

In this work, different impact modifiers such as acrylic resin impact modifier, chlorinated polyethylene (CPE), nitrile rubber, powdered nitrile rubber, and hydrogenated nitrile rubber, were chosen to improve the toughness of (acrylonitrile‐styrene‐acrylic copolymer)/(α‐methylstyrene‐acrylonitrile copolymer) (ASA/α‐MSAN) binary blend. The blend ratios of the ASA/(α‐MSAN)/(impact modifier) ternary system were 30/70/20 and 70/30/20 by mass, respectively. The results showed that the impact strength significantly increased, nearly 30 times (22.59 kJ·m^?2, 22.26 kJ·m^?2, and 25.24 kJ·m^?2) compared with that of control samples (0.80 kJ·m^?2) when nitrile rubber, powdered nitrile rubber, or hydrogenated nitrile rubber was added to the ASA/(α‐MSAN) (30/70) matrix, respectively. Moreover, the impact strength of ASA/(α‐MSAN) (70/30) was dramatically enhanced to 46 kJ·m^?2 with the addition of 20 parts by weight per hundred parts of resin of chlorinated polyethylene. The toughness of ASA/(α‐MSAN) with or without impact modifiers was also characterized via fracture energy calculated from stress‐strain curves. The results were perfectly consistent with that of impact strength. The results of dynamic mechanical analysis demonstrated the existence of α‐MSAN (glass transition temperature at approximately 140°C). The heat distortion temperature was barely changed, indicating the addition of impact modifiers barely affects the heat resistance. J. VINYL ADDIT. TECHNOL., 22:326–335, 2016. © 2014 Society of Plastics Engineers     

基于CPTu数据的黏性土不排水抗剪强度#br# 神经网络预测

利用人工神经网络模型,建立基于孔压静力触探（CPTu）现场测试数据的黏性土不排水抗剪强度的预测方法。为建立和验证人工神经网络模型,在3个场地开展CPTu和十字板剪切现场测试,共取得33个测孔的CPTu试验数据和相对应的不排水抗剪强度实测值。通过对比分析不同输入向量、不同网络隐层数、不同神经元数及不同改进算法对人工神经网络模型性能的影响,确定人工神经网络模型的具体形式。通过对训练组数据开展机器学习,所建立的人工神经网络模型能够有效地基于CPTu获得的端阻力和孔隙水压力现场测试数据对黏土不排水抗剪强度进行预测,预测结果与十字板剪切试验实测结果非常接近。与传统用于估算不排水强度的经验关系相比,采用人工神经网络模型预测结果与实测结果相关性显著提高、误差明显降低。     

浅谈BaO-Ln2O3-TiO2体系微波介质陶瓷的热稳定性

本文介绍了目前提高BaO-Ln2O3-TiO2体系微波介质陶瓷热稳定性的几种方法,指出了有待解决的主要问题;而加强对陶瓷结构的理论剖析、寻找合适的添加剂、改进制备工艺等是今后研究工作的重点。     

火力发电厂电气节能设计方法

火力发电厂作为能源消耗的大户,应该更多地承担节能降耗的责任,从设计的角度,对火力发电厂电气节能技术进行分析和介绍.