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| 1 | WCM-WTrA:A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning显示文摘Cross-project defect prediction is a hot topic in the field of defect prediction.How to reduce the difference between projects and make the model have better accuracy is the core problem.This paper starts from two perspectives:feature selection and distance-weight instance transfer.We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA.We have tested on AEEEM and ReLink datasets,and the results show that our method has an average improvement of 23%compared with TCA+algorithm on AEEEM datasets,and an average improvement of 5%on ReLink datasets. | LEI Tianwei XUE Jingfeng WANG Yong NIU Zequn SHI Zhiwei ZHANG Yu | 2022 | Chinese Journal of Electronics2022，31，2： | 0 |
| 2 | 基于神经网络数据分析方法的电网高损耗元件识别技术显示文摘针对大部分电网损耗研究不全面且损耗类型识别不准确等问题,提出了一种基于神经网络数据分析方法的电网高损耗元件识别技术。该技术在全面分析线路及变压器损耗的基础上,将台区损耗类型划分成六类,并将台区电表数据输入全连接神经网络模型完成学习分析,从而实现高损耗元件的识别。基于某台区的109块电表数据样本进行实验分析,结果表明所提技术对于无异常损耗电表的识别准确率高达98.5%,且整体识别准确率可达93.4%,同时识别时间仅为0.35 s,优于其他对比技术。 | 吴悦 梁琛 张光儒 马振祺 张家午 | 2023 | 电子设计工程2023，31，10： | 0 |
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