多年生宿根花卉芙蓉葵耐涝性及栽植技术研究

为雨季易涝的滨海淤泥质盐碱地园林绿化及生态植被修复应用提供科学依据,以一年生越冬根为试材,以具有防雨功能、可人为控制排灌水的水泥池为试验设施,设长期保持2 cm水层和干湿交替水分管理(CK)2个处理,对芙蓉葵的耐涝性进行了研究。结果表明:株高、基径粗和叶片叶绿素含量,长期淹水处理与对照为差异显著,说明芙蓉葵具有很强的耐涝性。以一年生越冬根为试材,以栽植时间、栽植深度、栽植密度和起苗放置时间为参试因素,采用4因素3水平正交设计,对芙蓉葵栽植技术进行了研究,结果表明最佳移栽时间为4月中旬,栽植深度15 cm,栽植密度8穴/m2,起苗放置时间不超过1 d。

Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence” and “support” is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support” is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.