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
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
Aiming at the diversity of hand gesture traces by different people,the article presents novel method called cluster dynamic time warping( CDTW),which is based on the main axis classification and sample clustering of i...Aiming at the diversity of hand gesture traces by different people,the article presents novel method called cluster dynamic time warping( CDTW),which is based on the main axis classification and sample clustering of individuals. This method shows good performance on reducing the complexity of recognition and strong robustness of individuals. Data acquisition is implemented on a triaxial accelerometer with 100 Hz sampling frequency. A database of 2400 traces was created by ten subjects for the system testing and evaluation. The overall accuracy was found to be 98. 84% for user independent gesture recognition and 96. 7% for user dependent gesture recognition,higher than dynamic time warping( DTW),derivative DTW( DDTW) and piecewise DTW( PDTW) methods.Computation cost of CDTW in this project has been reduced 11 520 times compared with DTW.展开更多
针对体育教学过程中,单纯依靠教师人工观察识别学生的体育动作容易出现反馈不及时、主观评价等问题,提出一种基于融合姿态特征与动态时间规整算法(dynamic time warping,DTW)的体育动作识别方法.基于OpenPose的骨骼点特征输出,融合学生...针对体育教学过程中,单纯依靠教师人工观察识别学生的体育动作容易出现反馈不及时、主观评价等问题,提出一种基于融合姿态特征与动态时间规整算法(dynamic time warping,DTW)的体育动作识别方法.基于OpenPose的骨骼点特征输出,融合学生在体育动作中的重心、肢体角度、朝向等特征,并采用DTW进行规整和评价.实验结果显示,在训练数据集的400份动作样本中,OpenPose+DTW模型正确识别样本数为372,总识别率为93%.高于其他模型.同时广播体操教学的50个动作实验中,OpenPose+DTW模型的误判样本为5个,识别精度为90%.结果表明,基于融合姿态特征以及DTW的体育动作识别模型具备优秀的识别性能,能够满足在线体育教学的应用场景.展开更多
Multi-source track-to-track association(TTTA),which identifies trajectories from multiple sensors or data sources of the same dynamic vehicle,is an important data fusion technique widely applied to vehicle detection i...Multi-source track-to-track association(TTTA),which identifies trajectories from multiple sensors or data sources of the same dynamic vehicle,is an important data fusion technique widely applied to vehicle detection in the fields of road,marine,and aviation transporta-tion.However,issues such as time asynchrony,heterogeneous sampling intervals,and ran-dom sensing errors have posed considerable challenges to the accuracy and robustness of TTTA.Aiming to address these issues in an integrated manner,this paper proposes a TTTA algorithm that comprehensively calculates the similarity between trajectories using mul-tiple trajectory features through dynamic time warping(DTW)and Cauchy distribution degree of membership function.Multiple experimental datasets were generated by ran-domly sampling real AIS trajectory data into two trajectory data sources and adding ran-dom errors.The average association accuracy of all scenarios and error levels of the proposed method reached 97.33%,far higher than other benchmark methods.Experimental results demonstrated the advantage of the proposed algorithm in various TTTA scenarios,especially its robustness in intricate trajectory situations.The results also indicated that more features can maintain the stability of associations in the presence of larger random errors,and DTW can improve association accuracy in intricate scenarios.This study provides a practical solution for the problem of time asynchrony,heterogeneous sampling intervals,and random errors in multi-source trajectory data fusion,showcasing promising applications across diverse domains.展开更多
针对传统拓扑识别方法不适用于电压数据质量差、三相不平衡度低的台区的问题,提出了基于电流平衡和电压曲线动态时间规整(Dynamic Time Warping,DTW)距离的户变户相组合优化识别方法。基于二次优化的思想,利用电流平衡原理为组合优化提...针对传统拓扑识别方法不适用于电压数据质量差、三相不平衡度低的台区的问题,提出了基于电流平衡和电压曲线动态时间规整(Dynamic Time Warping,DTW)距离的户变户相组合优化识别方法。基于二次优化的思想,利用电流平衡原理为组合优化提供了一个次优的户变户相初始解集,利用DTW距离度量用户与台区变压器电压时间序列的相似性,构建了电流平衡和电压DTW距离的组合优化模型,采用自适应变异概率遗传算法求解,改善了寻优过程中易陷入局部最优、早熟的问题,提高了求解效率。仿真结果表明,与传统方法相比,所提方法可以有效提升三相不平衡度低和数据缺失台区的拓扑识别准确率。展开更多
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.
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National Key R&D Program of China(No.2016YFB1001401)
文摘Aiming at the diversity of hand gesture traces by different people,the article presents novel method called cluster dynamic time warping( CDTW),which is based on the main axis classification and sample clustering of individuals. This method shows good performance on reducing the complexity of recognition and strong robustness of individuals. Data acquisition is implemented on a triaxial accelerometer with 100 Hz sampling frequency. A database of 2400 traces was created by ten subjects for the system testing and evaluation. The overall accuracy was found to be 98. 84% for user independent gesture recognition and 96. 7% for user dependent gesture recognition,higher than dynamic time warping( DTW),derivative DTW( DDTW) and piecewise DTW( PDTW) methods.Computation cost of CDTW in this project has been reduced 11 520 times compared with DTW.
文摘针对体育教学过程中,单纯依靠教师人工观察识别学生的体育动作容易出现反馈不及时、主观评价等问题,提出一种基于融合姿态特征与动态时间规整算法(dynamic time warping,DTW)的体育动作识别方法.基于OpenPose的骨骼点特征输出,融合学生在体育动作中的重心、肢体角度、朝向等特征,并采用DTW进行规整和评价.实验结果显示,在训练数据集的400份动作样本中,OpenPose+DTW模型正确识别样本数为372,总识别率为93%.高于其他模型.同时广播体操教学的50个动作实验中,OpenPose+DTW模型的误判样本为5个,识别精度为90%.结果表明,基于融合姿态特征以及DTW的体育动作识别模型具备优秀的识别性能,能够满足在线体育教学的应用场景.
基金supported by the National Natural Science Foundation of China(Nos.72071163 and 72111540273)the Natural Science Foundation of Sichuan Province through(No.2022NSFSC0474).
文摘Multi-source track-to-track association(TTTA),which identifies trajectories from multiple sensors or data sources of the same dynamic vehicle,is an important data fusion technique widely applied to vehicle detection in the fields of road,marine,and aviation transporta-tion.However,issues such as time asynchrony,heterogeneous sampling intervals,and ran-dom sensing errors have posed considerable challenges to the accuracy and robustness of TTTA.Aiming to address these issues in an integrated manner,this paper proposes a TTTA algorithm that comprehensively calculates the similarity between trajectories using mul-tiple trajectory features through dynamic time warping(DTW)and Cauchy distribution degree of membership function.Multiple experimental datasets were generated by ran-domly sampling real AIS trajectory data into two trajectory data sources and adding ran-dom errors.The average association accuracy of all scenarios and error levels of the proposed method reached 97.33%,far higher than other benchmark methods.Experimental results demonstrated the advantage of the proposed algorithm in various TTTA scenarios,especially its robustness in intricate trajectory situations.The results also indicated that more features can maintain the stability of associations in the presence of larger random errors,and DTW can improve association accuracy in intricate scenarios.This study provides a practical solution for the problem of time asynchrony,heterogeneous sampling intervals,and random errors in multi-source trajectory data fusion,showcasing promising applications across diverse domains.
文摘针对传统拓扑识别方法不适用于电压数据质量差、三相不平衡度低的台区的问题,提出了基于电流平衡和电压曲线动态时间规整(Dynamic Time Warping,DTW)距离的户变户相组合优化识别方法。基于二次优化的思想,利用电流平衡原理为组合优化提供了一个次优的户变户相初始解集,利用DTW距离度量用户与台区变压器电压时间序列的相似性,构建了电流平衡和电压DTW距离的组合优化模型,采用自适应变异概率遗传算法求解,改善了寻优过程中易陷入局部最优、早熟的问题,提高了求解效率。仿真结果表明,与传统方法相比,所提方法可以有效提升三相不平衡度低和数据缺失台区的拓扑识别准确率。
