With the rapid development of mobile communication all over the world,the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities.Mobile ph...With the rapid development of mobile communication all over the world,the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities.Mobile phone communication data can be regarded as a type of time series and dynamic time warping(DTW)and derivative dynamic time warping(DDTW)are usually used to analyze the similarity of these data.However,many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series.In this paper,a novel hybrid method based on the combination of dynamic time warping and derivative dynamic time warping is proposed.The new method considers not only the distance between time series,but also the shape characteristics of time series.We demonstrated that our method can outperform DTW and DDTW through extensive experiments with respect to cophenetic correlation.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
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
Dynamic time warping(DTW)spends most of the time in generating the correlation table,and it establishes the global path constraints to reduce the time complexity.However,the global constraints restrain just in terms o...Dynamic time warping(DTW)spends most of the time in generating the correlation table,and it establishes the global path constraints to reduce the time complexity.However,the global constraints restrain just in terms of the time axis.In this paper,we therefore propose another version of DTW,to be called branch-and-bound DTW(BnB-DTW),which adaptively controb its global path constraints by reflecting the contents of input patterns. Experimental results show that the suggested BnB-DTW algorithm performs more efficiently than other conventional DTW approaches while not increasing the optimal warping cost.展开更多
对装备中具体故障进行可诊断性评估,可以量化地表明故障被诊断的难易程度。针对故障可诊断性评价方法中存在的不客观及度量方法选择不恰当的问题,提出一种基于动态时间规整(Dynamic Time Warping,DTW)的时序距离度量方法。定义可诊断性...对装备中具体故障进行可诊断性评估,可以量化地表明故障被诊断的难易程度。针对故障可诊断性评价方法中存在的不客观及度量方法选择不恰当的问题,提出一种基于动态时间规整(Dynamic Time Warping,DTW)的时序距离度量方法。定义可诊断性评估模型,从实际系统中抽取系统的结构、信号、测试、故障模式4种要素,为故障可诊断性评估工作的开展打下基础。摒弃传统的从信号中提取特征进行相似性度量的做法,将信号看做时序序列,基于DTW方法衡量不同状态下各信号之间的相似程度,作为故障可诊断性的评估依据。使用动量轮系统对新方法进行仿真实验,仿真结果表明新方法在进行可诊断性评估方面具有客观性和有效性。展开更多
大部分系统使用深度学习技术完成语音识别任务,并取得良好的效果。但是,基于深度学习的语音识别技术对计算机硬件算力的要求较高,同时需要大量的语音样本对语音识别模型进行训练。针对这些问题,基于改进后的动态时间归整(Dynamic Time W...大部分系统使用深度学习技术完成语音识别任务,并取得良好的效果。但是,基于深度学习的语音识别技术对计算机硬件算力的要求较高,同时需要大量的语音样本对语音识别模型进行训练。针对这些问题,基于改进后的动态时间归整(Dynamic Time Warping,DTW)算法设计并实现了一个语音识别系统。展开更多
锂离子电池工作温度及老化程度是影响其荷电状态(State of Charge,SOC)估计算法准确性的关键因素.在二阶RC等效电路模型的基础上,提出了一种由动态时间规整算法(Dynamic Time Warping,DTW)与KiBaM模型(Kinetic Battery Model,KiBaM)相...锂离子电池工作温度及老化程度是影响其荷电状态(State of Charge,SOC)估计算法准确性的关键因素.在二阶RC等效电路模型的基础上,提出了一种由动态时间规整算法(Dynamic Time Warping,DTW)与KiBaM模型(Kinetic Battery Model,KiBaM)相结合的混合模型.利用DTW算法基于充电电压数据确定电池老化状态,通过KiBaM模型计算电池由于电流效应导致的不可用容量,结合二阶RC等效电路模型推导新的SOC计算矩阵,采用无迹卡尔曼滤波算法(Unscented Kalman Filter,UKF)实现SOC估计.基于城市道路循环工况(Urban Dynamometer Driving Schedule,UDDS)验证了混合模型的准确性,实验结果表明,在10℃低温环境或经过200次循环老化后工作的锂离子电池,模型估计SOC的最大误差小于2%.展开更多
针对舰载机着舰训练,提出了一种基于微分思想和动态时间弯曲(dynamic time warping, DTW)的飞行员驾驶技能评估方法。首先,将着舰过程分解为多元时间序列,通过对着舰过程的分析,选取飞行高度、下沉速度和对中偏差等5个特征参数作为指标...针对舰载机着舰训练,提出了一种基于微分思想和动态时间弯曲(dynamic time warping, DTW)的飞行员驾驶技能评估方法。首先,将着舰过程分解为多元时间序列,通过对着舰过程的分析,选取飞行高度、下沉速度和对中偏差等5个特征参数作为指标,并且拟合出各指标的标准曲线;然后,对选取的特征参数进行微分分段处理,利用特征参数间的关系进行降维表示;最后,利用DTW对评估模型进行计算,实现着舰技能的评估。算例分析表明,基于微分思想和DTW方法对飞行员着舰技能评估具有很好的适用性,有利于促进飞行员着舰技能的提高。展开更多
基金This work is supported in part by the National Natural Science Foundation of China and Civil Aviation Administration of China under grant No.U1533133the National Natural Science Foundation of China under grant No.61002016 and No.61711530653+2 种基金the Humanities and Social Sciences Research Project of Ministry of Education of China under grant No.15YJCZH095the China Scholarship Council under grant No.201708330439the 521 Talents Project of Zhejiang Sci-Tech University and the First Class Discipline B in Zhejiang Province:The Software Engineering Subject of Zhejiang Sci-Tech University.
文摘With the rapid development of mobile communication all over the world,the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities.Mobile phone communication data can be regarded as a type of time series and dynamic time warping(DTW)and derivative dynamic time warping(DDTW)are usually used to analyze the similarity of these data.However,many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series.In this paper,a novel hybrid method based on the combination of dynamic time warping and derivative dynamic time warping is proposed.The new method considers not only the distance between time series,but also the shape characteristics of time series.We demonstrated that our method can outperform DTW and DDTW through extensive experiments with respect to cophenetic correlation.
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Dynamic time warping(DTW)spends most of the time in generating the correlation table,and it establishes the global path constraints to reduce the time complexity.However,the global constraints restrain just in terms of the time axis.In this paper,we therefore propose another version of DTW,to be called branch-and-bound DTW(BnB-DTW),which adaptively controb its global path constraints by reflecting the contents of input patterns. Experimental results show that the suggested BnB-DTW algorithm performs more efficiently than other conventional DTW approaches while not increasing the optimal warping cost.
文摘对装备中具体故障进行可诊断性评估,可以量化地表明故障被诊断的难易程度。针对故障可诊断性评价方法中存在的不客观及度量方法选择不恰当的问题,提出一种基于动态时间规整(Dynamic Time Warping,DTW)的时序距离度量方法。定义可诊断性评估模型,从实际系统中抽取系统的结构、信号、测试、故障模式4种要素,为故障可诊断性评估工作的开展打下基础。摒弃传统的从信号中提取特征进行相似性度量的做法,将信号看做时序序列,基于DTW方法衡量不同状态下各信号之间的相似程度,作为故障可诊断性的评估依据。使用动量轮系统对新方法进行仿真实验,仿真结果表明新方法在进行可诊断性评估方面具有客观性和有效性。
文摘针对舰载机着舰训练,提出了一种基于微分思想和动态时间弯曲(dynamic time warping, DTW)的飞行员驾驶技能评估方法。首先,将着舰过程分解为多元时间序列,通过对着舰过程的分析,选取飞行高度、下沉速度和对中偏差等5个特征参数作为指标,并且拟合出各指标的标准曲线;然后,对选取的特征参数进行微分分段处理,利用特征参数间的关系进行降维表示;最后,利用DTW对评估模型进行计算,实现着舰技能的评估。算例分析表明,基于微分思想和DTW方法对飞行员着舰技能评估具有很好的适用性,有利于促进飞行员着舰技能的提高。
