To improve the accuracy of short text matching,a short text matching method with knowledge and structure enhancement for BERT(KS-BERT)was proposed in this study.This method first introduced external knowledge to the i...To improve the accuracy of short text matching,a short text matching method with knowledge and structure enhancement for BERT(KS-BERT)was proposed in this study.This method first introduced external knowledge to the input text,and then sent the expanded text to both the context encoder BERT and the structure encoder GAT to capture the contextual relationship features and structural features of the input text.Finally,the match was determined based on the fusion result of the two features.Experiment results based on the public datasets BQ_corpus and LCQMC showed that KS-BERT outperforms advanced models such as ERNIE 2.0.This Study showed that knowledge enhancement and structure enhancement are two effective ways to improve BERT in short text matching.In BQ_corpus,ACC was improved by 0.2%and 0.3%,respectively,while in LCQMC,ACC was improved by 0.4%and 0.9%,respectively.展开更多
面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提...面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提出了一种基于改进层次分析法(improved analytic hierarchy process,IAHP)-改进反熵权法(improved anti-entropy method,IAM)-博弈组合赋权法-改进逼近理想解法(improved technique for order preference by similarity to ideal solution,improved TOPSIS)的评价方法,先根据改进的层次分析法进行主观赋权,同时考虑到指标间的相关性和波动性,采用所提改进反熵权法确定各指标的客观权重,再基于博弈论思想获取综合权重,以确保权重的合理性,然后,为提高各方案的整体区分度,采用所提改进逼近理想解法对屋顶光伏接入方案进行排序。最后,以IEEE 33节点系统为例,在MATLAB平台验证了所提指标体系和排序方法的有效性。展开更多
特殊环境下道路目标的三维感知对汽车的全天时、全气候自动驾驶具有重要意义,红外双目视觉模仿人眼实现微光/无光等特殊环境下目标的立体感知,目标检测与匹配是双目视觉立体感知的关键技术。针对当前分步实现目标检测与目标匹配的过程...特殊环境下道路目标的三维感知对汽车的全天时、全气候自动驾驶具有重要意义,红外双目视觉模仿人眼实现微光/无光等特殊环境下目标的立体感知,目标检测与匹配是双目视觉立体感知的关键技术。针对当前分步实现目标检测与目标匹配的过程冗杂问题,提出了一个可以同步检测与匹配红外目标的深度学习网络——SODMNet(Synchronous Object Detection and Matching Network)。SODMNet融合了目标检测网络和目标匹配模块,以目标检测网络为主要架构,取其分类与回归分支深层特征为目标匹配模块的输入,与特征图相对位置编码拼接后通过卷积网络输出左右图像特征描述子,根据特征描述子之间的欧氏距离得到目标匹配结果,实现双目视觉目标检测与匹配。与此同时,采集并制作了一个包含人、车辆等标注目标的夜间红外双目数据集。实验结果表明,SODMNet在该红外双目数据集上的目标检测精度mAP(Mean Average Precision)提升84.9%以上,同时目标匹配精度AP(Average Precision)达到0.5777。结果证明,SODMNet能够高精度地同步实现红外双目目标检测与匹配。展开更多
文摘To improve the accuracy of short text matching,a short text matching method with knowledge and structure enhancement for BERT(KS-BERT)was proposed in this study.This method first introduced external knowledge to the input text,and then sent the expanded text to both the context encoder BERT and the structure encoder GAT to capture the contextual relationship features and structural features of the input text.Finally,the match was determined based on the fusion result of the two features.Experiment results based on the public datasets BQ_corpus and LCQMC showed that KS-BERT outperforms advanced models such as ERNIE 2.0.This Study showed that knowledge enhancement and structure enhancement are two effective ways to improve BERT in short text matching.In BQ_corpus,ACC was improved by 0.2%and 0.3%,respectively,while in LCQMC,ACC was improved by 0.4%and 0.9%,respectively.
文摘面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提出了一种基于改进层次分析法(improved analytic hierarchy process,IAHP)-改进反熵权法(improved anti-entropy method,IAM)-博弈组合赋权法-改进逼近理想解法(improved technique for order preference by similarity to ideal solution,improved TOPSIS)的评价方法,先根据改进的层次分析法进行主观赋权,同时考虑到指标间的相关性和波动性,采用所提改进反熵权法确定各指标的客观权重,再基于博弈论思想获取综合权重,以确保权重的合理性,然后,为提高各方案的整体区分度,采用所提改进逼近理想解法对屋顶光伏接入方案进行排序。最后,以IEEE 33节点系统为例,在MATLAB平台验证了所提指标体系和排序方法的有效性。
文摘特殊环境下道路目标的三维感知对汽车的全天时、全气候自动驾驶具有重要意义,红外双目视觉模仿人眼实现微光/无光等特殊环境下目标的立体感知,目标检测与匹配是双目视觉立体感知的关键技术。针对当前分步实现目标检测与目标匹配的过程冗杂问题,提出了一个可以同步检测与匹配红外目标的深度学习网络——SODMNet(Synchronous Object Detection and Matching Network)。SODMNet融合了目标检测网络和目标匹配模块,以目标检测网络为主要架构,取其分类与回归分支深层特征为目标匹配模块的输入,与特征图相对位置编码拼接后通过卷积网络输出左右图像特征描述子,根据特征描述子之间的欧氏距离得到目标匹配结果,实现双目视觉目标检测与匹配。与此同时,采集并制作了一个包含人、车辆等标注目标的夜间红外双目数据集。实验结果表明,SODMNet在该红外双目数据集上的目标检测精度mAP(Mean Average Precision)提升84.9%以上,同时目标匹配精度AP(Average Precision)达到0.5777。结果证明,SODMNet能够高精度地同步实现红外双目目标检测与匹配。
