天然河川随水流输砂能力、底床条件及来水来砂条件等变化而演变,以相应调整河床型态与水砂关系,促使河川趋向近似冲淤平衡稳定状态。在一定流域来水来砂的条件下,河流将调整它的比降、型态、河床物质组成及河型进而达到稳定状态。河川...天然河川随水流输砂能力、底床条件及来水来砂条件等变化而演变,以相应调整河床型态与水砂关系,促使河川趋向近似冲淤平衡稳定状态。在一定流域来水来砂的条件下,河流将调整它的比降、型态、河床物质组成及河型进而达到稳定状态。河川型态分类将有助于了解河床稳定特性、来水来砂条件及河床地质条件相适应之均衡型态。本研究以台湾重要河川荖浓溪流域为研究范围,利用Leopold and Wolman(1957)[1]、Ferguson(1984)[2]、许炯心(2004)[3]及陈树群五层分类法(2001)[4]等方式进行荖浓溪河川型态分类,透过水文地文数据分析、河川型态分类与水力几何型态判别等计算,以进行河川型态特性分类及河床稳定等课题之分析探讨,研究成果将有助于了解流域河川特性及河床演变之定性定量评估,并提供作为河川复育与流域经营管理之参考。展开更多
The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data.Due to the strong rule associations inherent in alarm log data,existing data augmentation algorithms cannot obtain good r...The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data.Due to the strong rule associations inherent in alarm log data,existing data augmentation algorithms cannot obtain good results for alarm log data.To address this problem,this paper introduces a new algorithm for augmenting alarm log data,termed APRGAN,which combines a generative adversarial network(GAN)with the Apriori algorithm.APRGAN generates alarm log data under the guidance of rules mined by the rule miner.Moreover,we propose a new dynamic updating mechanism to alleviate the mode collapse problem of the GAN.In addition to updating the real reference dataset used to train the discriminator in the GAN,we dynamically update the parameters and the rule set of the Apriori algorithm according to the data generated in each epoch.Through extensive experimentation on two public datasets,it is demonstrated that APRGAN surpasses other data augmentation algorithms in the domain with respect to alarm log data augmentation,as evidenced by its superior performance on metrics such as BLEU,ROUGE,and METEOR.展开更多
文摘天然河川随水流输砂能力、底床条件及来水来砂条件等变化而演变,以相应调整河床型态与水砂关系,促使河川趋向近似冲淤平衡稳定状态。在一定流域来水来砂的条件下,河流将调整它的比降、型态、河床物质组成及河型进而达到稳定状态。河川型态分类将有助于了解河床稳定特性、来水来砂条件及河床地质条件相适应之均衡型态。本研究以台湾重要河川荖浓溪流域为研究范围,利用Leopold and Wolman(1957)[1]、Ferguson(1984)[2]、许炯心(2004)[3]及陈树群五层分类法(2001)[4]等方式进行荖浓溪河川型态分类,透过水文地文数据分析、河川型态分类与水力几何型态判别等计算,以进行河川型态特性分类及河床稳定等课题之分析探讨,研究成果将有助于了解流域河川特性及河床演变之定性定量评估,并提供作为河川复育与流域经营管理之参考。
基金supported by the National Key Research and Development Program of China under Grant No.2019YFB-2103202.
文摘The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data.Due to the strong rule associations inherent in alarm log data,existing data augmentation algorithms cannot obtain good results for alarm log data.To address this problem,this paper introduces a new algorithm for augmenting alarm log data,termed APRGAN,which combines a generative adversarial network(GAN)with the Apriori algorithm.APRGAN generates alarm log data under the guidance of rules mined by the rule miner.Moreover,we propose a new dynamic updating mechanism to alleviate the mode collapse problem of the GAN.In addition to updating the real reference dataset used to train the discriminator in the GAN,we dynamically update the parameters and the rule set of the Apriori algorithm according to the data generated in each epoch.Through extensive experimentation on two public datasets,it is demonstrated that APRGAN surpasses other data augmentation algorithms in the domain with respect to alarm log data augmentation,as evidenced by its superior performance on metrics such as BLEU,ROUGE,and METEOR.
