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Quaternion Dynamic Time Warping
IEEE Transactions on Signal Processing, 2012Dynamic time warping (DTW) is used for the comparison and processing of nonlinear signals and constitutes a widely researched field of study. The method has been initially designed for, and applied to, signals representing audio data. Afterwords it has been successfully modified and applied to many other fields of study.
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2017
Dynamic time warping (DTW) has been applied to a wide range of machine learning problems involving the comparison of time series. An important feature of such time series is that they can sometimes be sparse in the sense that the data takes zero value at many epochs.
Youngha Hwang, Saul B. Gelfand
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Dynamic time warping (DTW) has been applied to a wide range of machine learning problems involving the comparison of time series. An important feature of such time series is that they can sometimes be sparse in the sense that the data takes zero value at many epochs.
Youngha Hwang, Saul B. Gelfand
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2009
This chapter contains sections titled: Introduction Dynamic Programming Dynamic Time Warping Applied to IWR DTW Applied to CSR Training Issues in DTW Algorithms Conclusions Problems ]]>
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This chapter contains sections titled: Introduction Dynamic Programming Dynamic Time Warping Applied to IWR DTW Applied to CSR Training Issues in DTW Algorithms Conclusions Problems ]]>
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Feature Based Dynamic Time Warping
2009Dynamic time warping (DTW) has been widely used in various pattern recognition and time series data mining applications. However, as examples will illustrate, both the classic DTW and its later alternative, derivative DTW, may fail to align a pair of sequences on their common trends or patterns.
Ying Xie, Li Fangping
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SSDTW: Shape segment dynamic time warping
Expert Systems with Applications, 2020Abstract In order to increase the yield of a process, it is essential to establish a process control based on manufacturing data. Process management systems mainly consist of statistical process control (SPC), fault detection and classification (FDC), and advanced process control (APC), and are modeled using time series data.
Jae Yeol Hong +2 more
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Dynamic time warping of spectroscopic BATCH data
Analytica Chimica Acta, 2003This paper discusses a method for warping spectral batch data. This method is a modification of a procedure proposed by Kassidas et al. [AIChE Journal 44 (1998) 864; Journal of Process Control 8 (1998) 381]. This iterative procedure is based on the dynamic time warping (DTW) algorithm. The symmetric DTW algorithm is discussed in this paper.
Ramaker, H.J. +3 more
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Drift time estimation by dynamic time warping
SEG Technical Program Expanded Abstracts 2015, 2015Summary The drift time is the traveltime difference at the seismic frequency and the sonic logging frequency in anelastic media. In this abstract, we use dynamic time warping to estimate the drift time. Stationary and nonstationary seismograms are constructed based on well log data and the constant-Q theory.
Tianci Cui*, Gary Margrave
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Least-squares reverse time migration with dynamic time warping
SEG Technical Program Expanded Abstracts 2018, 2018Least-squares reverse-time migration (LSRTM) has been shown to improve image quality over conventional RTM by enhancing the resolution, balancing illumination, and suppressing migration artefacts. However, it is also known to be sensitive to velocity errors.
W. Dai, X. Cheng, K. Jiao, D. Vigh
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Gait recognition using dynamic time warping
IEEE 6th Workshop on Multimedia Signal Processing, 2004., 2005We propose a methodology for gait recognition based on dynamic time warping. The gait sequences are initially partitioned into gait cycles and then the test cycles are compared to reference cycles using dynamic time warping. The final distance between a test and a reference sequence is determined using a nonlinear rule.
Boulgouris, N V +2 more
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Mammogram classification using dynamic time warping
Multimedia Tools and Applications, 2017This paper presents a new approach for breast cancer classification using time series analysis. In particular, the region of interest (ROI) in mammogram images is classified as normal or abnormal using dynamic time warping (DTW) as a similarity measure. According to the analogous case in time series analysis, the DTW subsumes Euclidean distance (ED) as
Syed Jamal Safdar Gardezi +4 more
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