The traditional grey incidence degree is mainly based on the distance analysis methods, which is measured by the displacement difference between corresponding points between sequences. When some data of sequences are ...The traditional grey incidence degree is mainly based on the distance analysis methods, which is measured by the displacement difference between corresponding points between sequences. When some data of sequences are missing (inconsistency in the length of the sequences), the only way is to delete the longer sequences or to fill the shorter sequences. Therefore, some uncertainty is introduced. To solve this problem, by introducing three-dimensional grey incidence degree (3D-GID), a novel GID based on the multidimensional dynamic time warping distance (MDDTW distance-GID) is proposed. On the basis of it, the corresponding grey incidence clustering (MDDTW distance-GIC) method is constructed. It not only has the simpler computation process, but also can be applied to the incidence comparison between uncertain multidimensional sequences directly. The experiment shows that MDDTW distance-GIC is more accurate when dealing with the uncertain sequences. Compared with the traditional GIC method, the precision of the MDDTW distance-GIC method has increased nearly 30%.展开更多
Ground-based interferometric synthetic aperture radar(GB-InSAR)can take deformation measurement with a high accuracy.Partition of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring...Ground-based interferometric synthetic aperture radar(GB-InSAR)can take deformation measurement with a high accuracy.Partition of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring scene better.Existing partition methods rely on labelled datasets or single deformation feature,and they cannot be effectively utilized in GBInSAR applications.This paper proposes an improved partition method of the GB-InSAR deformation map based on dynamic time warping(DTW)and k-means.The DTW similarities between a reference point and all the measurement points are calculated based on their time-series deformations.Then the DTW similarity and cumulative deformation are taken as two partition features.With the k-means algorithm and the score based on multi evaluation indexes,a deformation map can be partitioned into an appropriate number of classes.Experimental datasets of West Copper Mine are processed to validate the effectiveness of the proposed method,whose measurement points are divided into seven classes with a score of 0.3151.展开更多
反应谱是抗震设计的重要依据,传统数值方法的本构模型不能满足复杂场地和具有强不确定性土体动力过程的模拟,导致计算结果与实测反应谱之间差距较大。以一维场地反应分析为背景,从日本KiK-net强震台网搜集了2428组来自水平场地台站的基...反应谱是抗震设计的重要依据,传统数值方法的本构模型不能满足复杂场地和具有强不确定性土体动力过程的模拟,导致计算结果与实测反应谱之间差距较大。以一维场地反应分析为背景,从日本KiK-net强震台网搜集了2428组来自水平场地台站的基岩和地表地震动记录,将土层信息和基岩输入作为主要特征,使用场地类别引导的分层抽样训练策略,建立了BO-XGBoost-SS地表加速度反应谱预测模型。结果表明,构建的模型具有良好的预测性能,对地表加速度反应谱的决定系数R^(2)评价指标为0.87,各周期点的R^(2)均大于0.8,应用动态时间规整(dynamic time warping,DTW)的距离分析单条反应谱的预测匹配性,模型在各类场地表现稳定,克服了数值方法高频低估和长周期异常放大的不足。使用最新地震动记录作为外部数据集,进一步验证了模型的泛化能力。通过(shapley additive explanations,SHAP)解释分析特征对模型预测的贡献,揭示了影响反应谱预测的关键特征,各特征影响规律和现有研究成果一致。研究结果为场地反应预测模型的开发提供训练策略和评估指导,为机器学习在地震区划和工程结构抗震设计中的应用提供了新思路。展开更多
传统动态时间规整算法(Dynamic Time Warping,DTW)及其变种算法被广泛应用于多维时间序列的相似性分析,但它们通常只关注单个时间点的信息而忽略了上下文信息,从而很可能匹配两个形状完全不同的点。因此提出一种结合形状特征及其上下文...传统动态时间规整算法(Dynamic Time Warping,DTW)及其变种算法被广泛应用于多维时间序列的相似性分析,但它们通常只关注单个时间点的信息而忽略了上下文信息,从而很可能匹配两个形状完全不同的点。因此提出一种结合形状特征及其上下文的多维DTW算法(Multi-Dimensional Contextual Dynamic Time Warping,MDCDTW)。该算法首先计算多维时间序列的一阶梯度,然后对其进行采样处理,并以多维梯度矩阵表示当前时间点的形状信息及其上下文信息,最后利用DTW求解多维时间序列间的最短匹配路径。为检测算法设计的合理性,对算法进行了定性分析和定量分析,实验结果表明MDC-DTW算法设计是合理的;为检测MDC-DTW的性能,选用5个多维时间序列数据集,并与4个优异的多维DTW算法进行对比实验,实验结果表明MDC-DTW具有较高的准确率和运行效率。展开更多
基金supported by the National Natural Science Foundation of China(6153302061309014)the Natural Science Foundation Project of CQ CSTC(cstc2017jcyj AX0408)
文摘The traditional grey incidence degree is mainly based on the distance analysis methods, which is measured by the displacement difference between corresponding points between sequences. When some data of sequences are missing (inconsistency in the length of the sequences), the only way is to delete the longer sequences or to fill the shorter sequences. Therefore, some uncertainty is introduced. To solve this problem, by introducing three-dimensional grey incidence degree (3D-GID), a novel GID based on the multidimensional dynamic time warping distance (MDDTW distance-GID) is proposed. On the basis of it, the corresponding grey incidence clustering (MDDTW distance-GIC) method is constructed. It not only has the simpler computation process, but also can be applied to the incidence comparison between uncertain multidimensional sequences directly. The experiment shows that MDDTW distance-GIC is more accurate when dealing with the uncertain sequences. Compared with the traditional GIC method, the precision of the MDDTW distance-GIC method has increased nearly 30%.
基金supported by the National Natural Science Foundation of China(61971037,61960206009,61601031)the Natural Science Foundation of Chongqing,China(cstc2020jcyj-msxm X0608,cstc2020jcyj-jq X0008)。
文摘Ground-based interferometric synthetic aperture radar(GB-InSAR)can take deformation measurement with a high accuracy.Partition of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring scene better.Existing partition methods rely on labelled datasets or single deformation feature,and they cannot be effectively utilized in GBInSAR applications.This paper proposes an improved partition method of the GB-InSAR deformation map based on dynamic time warping(DTW)and k-means.The DTW similarities between a reference point and all the measurement points are calculated based on their time-series deformations.Then the DTW similarity and cumulative deformation are taken as two partition features.With the k-means algorithm and the score based on multi evaluation indexes,a deformation map can be partitioned into an appropriate number of classes.Experimental datasets of West Copper Mine are processed to validate the effectiveness of the proposed method,whose measurement points are divided into seven classes with a score of 0.3151.
文摘反应谱是抗震设计的重要依据,传统数值方法的本构模型不能满足复杂场地和具有强不确定性土体动力过程的模拟,导致计算结果与实测反应谱之间差距较大。以一维场地反应分析为背景,从日本KiK-net强震台网搜集了2428组来自水平场地台站的基岩和地表地震动记录,将土层信息和基岩输入作为主要特征,使用场地类别引导的分层抽样训练策略,建立了BO-XGBoost-SS地表加速度反应谱预测模型。结果表明,构建的模型具有良好的预测性能,对地表加速度反应谱的决定系数R^(2)评价指标为0.87,各周期点的R^(2)均大于0.8,应用动态时间规整(dynamic time warping,DTW)的距离分析单条反应谱的预测匹配性,模型在各类场地表现稳定,克服了数值方法高频低估和长周期异常放大的不足。使用最新地震动记录作为外部数据集,进一步验证了模型的泛化能力。通过(shapley additive explanations,SHAP)解释分析特征对模型预测的贡献,揭示了影响反应谱预测的关键特征,各特征影响规律和现有研究成果一致。研究结果为场地反应预测模型的开发提供训练策略和评估指导,为机器学习在地震区划和工程结构抗震设计中的应用提供了新思路。
文摘传统动态时间规整算法(Dynamic Time Warping,DTW)及其变种算法被广泛应用于多维时间序列的相似性分析,但它们通常只关注单个时间点的信息而忽略了上下文信息,从而很可能匹配两个形状完全不同的点。因此提出一种结合形状特征及其上下文的多维DTW算法(Multi-Dimensional Contextual Dynamic Time Warping,MDCDTW)。该算法首先计算多维时间序列的一阶梯度,然后对其进行采样处理,并以多维梯度矩阵表示当前时间点的形状信息及其上下文信息,最后利用DTW求解多维时间序列间的最短匹配路径。为检测算法设计的合理性,对算法进行了定性分析和定量分析,实验结果表明MDC-DTW算法设计是合理的;为检测MDC-DTW的性能,选用5个多维时间序列数据集,并与4个优异的多维DTW算法进行对比实验,实验结果表明MDC-DTW具有较高的准确率和运行效率。
