Salient time steps selection from large scale time-varying data sets with dynamic time warping

Traditional gun archiving methods are mostly carried out through bullets’ physics or photography, which are inefficient and difficult to trace, and cannot meet the needs of large-scale archiving. Aiming at such problems, a rapid archival technology of bullets based on graph convolutional neural network has been studied and developed. First, the spot laser is used to take the circle points of the bullet rifling traces. The obtained data is filtered and noise-reduced to make the corresponding line graph, and then the dynamic time warping (DTW) algorithm convolutional neural network model is used to perform the processing on the processed data. Not only is similarity matched, the rapid matching of the rifling of the bullet is also accomplished. Comparison of experimental results shows that this technology has the advantages of rapid archiving and high accuracy. Furthermore, it can be carried out in large numbers at the same time, and is more suitable for practical promotion and application.

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A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/614 ◽

2020 ◽

Author(s):

Lu Bai ◽

Lixin Cui ◽

Yue Wang ◽

Yuhang Jiao ◽

Edwin R. Hancock

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Similarity Measure ◽

Dynamic Time Warping ◽

Financial Time Series ◽

Time Varying ◽

Time Warping ◽

Classical Dynamic ◽

Financial Time ◽

Dynamic Time

Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.

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IMU Consensus Exception Detection with Dynamic Time Warping—A Comparative Approach

Sensors ◽

10.3390/s19102237 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2237 ◽

Cited By ~ 1

Author(s):

Chan-Yun Yang ◽

Pei-Yu Chen ◽

Te-Jen Wen ◽

Gene Eu Jan

Keyword(s):

Template Matching ◽

Dynamic Time Warping ◽

Performance Comparison ◽

Measurement Unit ◽

Comparative Approach ◽

Time Varying ◽

Abnormality Detection ◽

Time Warping ◽

Motion Signal ◽

Dynamic Time

A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult. To this end, the DTW algorithm, as a kind of nonlinear pattern-matching approach, is able to optimally align motion signal sequences tending towards time-varying or speed-varying expressions, which is especially suitable to capturing exceptional motions. Thus, this paper evaluated this kind of abnormality detection using the proposed DTW algorithm on the basis of its theoretical fundamentals to significantly enhance the viability of the methodology. To validate the methodological viability, an artificial neural network (ANN) framework was intentionally introduced for performance comparison. By incorporating two types of designated preprocessors, i.e., a DFT interpolation preprocessor and a convolutional preprocessor, to equalize the unequal lengths of the matching sequences, two kinds of ANN frameworks were enumerated to compare the potential applicability. The comparison eventually confirmed that the direct template-matching DTW is excellent in practical application for the detection of time-varying or speed-varying abnormality, and reliably captures the consensus exceptions.

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Modeling and Analyzing Neural Signals with Phase Variability using the Fisher-Rao Registration

10.1101/2020.04.30.069120 ◽

2020 ◽

Author(s):

Weilong Zhao ◽

Zishen Xu ◽

Wen Li ◽

Wei Wu

Keyword(s):

Dynamic Time Warping ◽

Superior Performance ◽

Data Sets ◽

Time Warping ◽

Neural Signals ◽

Data Set ◽

Mathematical Properties ◽

Eeg Data ◽

Phase Variability ◽

Dynamic Time

AbstractThe Dynamic Time Warping (DTW) has recently been introduced to analyze neural signals such as EEG and fMRI where phase variability plays an important role in the data. In this study, we propose to adopt a more powerful method, referred to as the Fisher-Rao Registration (FRR), to study the phase variability. We systematically compare the FRR with the DTW in three aspects: 1) basic framework, 2) mathematical properties, and 3) computational efficiency. We show that the FRR has superior performance in all these aspects and the advantages are well illustrated with simulation examples. We then apply the FRR method to two real experimental recordings – one fMRI and one EEG data set. It is found the FRR method properly removes the phase variability in each set. Finally, we use the FRR framework to examine brain networks in these two data sets and the result demonstrates the effectiveness of the new method.

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A New Approximate Matching Algorithm and its Application in Internet Music Search by Humming

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.3662 ◽

2012 ◽

Vol 433-440 ◽

pp. 3662-3668

Author(s):

Yun Feng Dong ◽

Bei Qi

Keyword(s):

Dynamic Time Warping ◽

Large Scale ◽

Time Warping ◽

Similarity Matching ◽

Matching Algorithm ◽

Approximate Matching ◽

Query By Humming ◽

Dynamic Time

This paper has proposed a new approximate matching algorithm—similarity matching, and use the characteristics of algorithm to establish a system of internet music search by humming. The author compared the similarity matching algorithm and dynamic time warping (DTW) algorithm, which is most commonly used to query by humming, by the system of internet music search by humming. On the two standard of the query hit ratio and query speed, we got the result that similarity matching algorithm's comprehensive efficiency is superior, is one of QBH (query by humming) algorithm, which is applicable to the large-scale music library such as internet music search.

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COHERENCE and BACKSCATTER BASED CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 with DYNAMIC TIME WARPING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b5-2021-37-2021 ◽

2021 ◽

Vol XLIII-B5-2021 ◽

pp. 37-41

Author(s):

O. G. Narin ◽

S. Abdikan ◽

C. Bayik ◽

A. Sekertekin ◽

A. Delen ◽

...

Keyword(s):

Time Series ◽

Dynamic Time Warping ◽

Weather Conditions ◽

Data Sets ◽

Atmospheric Conditions ◽

Time Warping ◽

Multi Temporal ◽

Crop Mapping ◽

Dynamic Time ◽

Cropland Mapping

Abstract. Cropland mapping is an important inventory for food security and decision making operated by governments. Crop mapping is used to identify the croplands and their spatial distribution. For a reliable analysis and forecast for projection, multi-temporal data play a key role. Even current open and frequent optical satellite data such as Sentinel-2 and Landsat support monitoring, they are not always operational due to atmospheric conditions (rain, cloud cover, haze, etc.). On the other hand, Synthetic Aperture Radar (SAR) satellites provide alternative data sets compared to optical satellites since they can acquire images under all weather conditions. In this study, an annual cropland monitoring study is conducted using Sentinel-1 SAR. For the investigation, Tokat Province an agricultural region of Turkey, where the main source of income is agriculture, was selected. There are 4 different vegetation species (wheat, sunflower, sugar beet, corn) in the study area. Sentinel-1 data was used to generate time-series of each class and phenological structures of the crops. In this context, backscatter images of both vertical-vertical (VV) and vertical-horizontal (VH) polarized data, and coherence of both VV and VH were produced from Sentinel-1 data. Time-Weighted Dynamic Time-Warping (TWDTW) classification approach was used over cropland. The produced time-series are classified under different scenarios. The results showed that only coherence has provided higher accuracies about 81% compared to using only backscatter images as 49%.

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