Time-Lapse Full-Waveform Inversion Using Cross-Correlation-Based Dynamic Time Warping

Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensive OCCUS project. A potential high-resolution method for the aforementioned purpose lies in the full-waveform inversion(FWI) of time-lapse seismic data. However, practical applications of FWI are severely restricted by the well-known cycle-skipping problem. A new time-lapse FWI method using cross-correlation-based dynamic time warping(CDTW) is proposed to detect changes in the subsurface property due to carbon dioxide(CO_(2)) injection and address the aforementioned issue. The proposed method, namely CDTW, which combines the advantages of cross-correlation and dynamic time warping, is employed in the automatic estimation of the discrepancy between the seismic signals simulated using the baseline/initial model and those acquired. The proposed FWI method can then back-project the estimated discrepancy to the subsurface space domain, thereby facilitating retrieval of the induced subsurface property change by taking the difference between the inverted baseline and monitor models. Numerical results on pairs of signals prove that CDTW can obtain reliable shifts under amplitude modulation and noise contamination conditions. The performance of CDTW substantially outperforms that of the conventional dynamic time warping method. The proposed time-lapse fullwaveform inversion(FWI) method is applied to the Frio-2 CO_(2) storage model. The baseline and monitor models are inverted from the corresponding time-lapse seismic data. The changes in velocity due to CO_(2) injection are reconstructed by the difference between the baseline and the monitor models.

Method of Time Series Similarity Measurement Based on Dynamic Time Warping

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.

Partition of GB-InSAR deformation map based on dynamic time warping and k-means

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.

Grey incidence clustering method based on multidimensional dynamic time warping distance

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%.

Hand Gesture Recognition by Accelerometer-Based Cluster Dynamic Time Warping

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.

Efficient Dynamic Time Warping by Adaptively Controlling the Valid Warping Range

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.

Merge-Weighted Dynamic Time Warping for Speech Recognition

Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.

An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle flter

The use of dead reckoning and fngerprint matching for navigation is a widespread technical method.However,fngerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems.This work presents an improved dynamic time warping and a chicken particle flter to handle these two challenges.To generate the Horizontal and Vertical(HV)fngerprint,the pitch and roll are employed instead of the original fngerprint intensity to extract the horizontal and vertical components of the magnetic feld fngerprint.Derivative dynamic time warping employs the HV fngerprint in its derivative form,which receives higher-level features because of the consideration of fngerprint shape information.Chicken Swarm Optimization(CSO)is used to enhance particle weights,which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system.The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy signifcantly.

An intelligent automatic correlation method of oilbearing strata based on pattern constraints:An example of accretionary stratigraphy of Shishen 100 block in Shinan Oilfield of Bohai Bay Basin,East China

Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.

Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

High-resolution Load Profile Clustering Approach Based on Dynamic Largest Triangle Three Buckets and Multiscale Dynamic Warping Path Under Limited Warping Path Length

With the popularity of smart meters and the growing availability of high-resolution load data, the research on the dynamics of electricity consumption at finely resolved timescales has become increasingly popular. Many existing algorithms underperform when clustering load profiles contain a large number of feature points. In addition, it is difficult to accurately describe the similarity of profile shapes when load sequences have large fluctuations, leading to inaccurate clustering results. To this end, this paper proposes a high-resolution load profile clustering approach based on dynamic largest triangle three buckets(LTTBs) and multiscale dynamic time warping under limited warping path length(LDTW). Dynamic LTTB is a novel dimensionality reduction algorithm based on LTTB. New sequences are constructed by dynamically dividing the intervals of significant feature points. The extraction of fluctuation characteristics is optimized. New curves with more concentrated features will be applied to the subsequent clustering. The proposed multiscale LDTW is used to generate a similarity matrix for spectral clustering, providing a more comprehensive and flexible matching method to characterize the similarity of load profiles. Thus, the clustering effect of a high-resolution load profile is improved. The proposed approach has been applied to multiple datasets. Experiment results demonstrate that the proposed approach significantly improves the Davies-Bouldin indicator(DBI) and validity index(VI). Therefore, better similarity and accuracy can be achieved using high-resolution load profile clustering.

Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data

In this study,a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea(OSA)from the analysis of single-channel ECG recordings.Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study.In the feature extraction stage,dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm.In the classification phase,OSA patients and normal ECG recordings were classified using Random Forest(RF)and Long Short-Term Memory(LSTM)classifier algorithms.The performance of the classifiers was evaluated as 90% training and 10%testing.According to this evaluation,the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%.Considering the results obtained,it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods.The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead.

基于距离特征的雷达辐射源信号识别方法

针对传统的雷达辐射源信号识别方法在低信噪比环境下的正确率较低,且通常只适用几种特定的雷达信号的问题,提出一种基于距离特征的辐射源信号识别方法。使用k-means算法提取若干个聚类中心,分别计算雷达信号脉冲与聚类中心之间的DTW (Dynamic Time Warping)度量值,联合这些度量值作为k邻近算法的输入进行识别。仿真结果表明,在信噪比为3d B时,所提方法对6类雷达信号的识别率达到91%。与基于小波脊频级联特征的方法相比,所提方法也表现出更好的识别效果。

Cross-Band Spectrum Prediction Based on Deep Transfer Learning

Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.

Faulty Sensor Detection and Reconstruction for a PVC Making Process

Based on principal component analysis, this paper presents an application of faulty sensor detection and reconstruction in a batch process, polyvinylchloride (PVC) making process. To deal with inconsistency in process data, it is proposed to use the dynamic time warping technique to make the historical data synchronized first,then build a consistent multi-way principal component analysis model. Fault detection is carried out based on squared prediction error statistical control plot. By defining principal component subspace, residual subspace and sensor validity index, faulty sensor can be reconstructed and identified along the fault direction. Finally, application results are illustrated in detail by use of the real data of an industrial PVC making process.

An improved morphological weighted dynamic similarity measurement algorithm for time series data

Purpose–The similarity measurement of time series is an important research in time series detection,which is a basic work of time series clustering,anomaly discovery,prediction and many other data mining problems.The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm.The subsequence morphological information is taken into account by the proposed algorithm,and time series is represented by a pattern,so the similarity measurement algorithm is more accurate.Design/methodology/approach–Following some previous researches on similarity measurement,an improved method is presented.This new method combines morphological representation and dynamic time warping(DTW)technique to measure the similarities of time series.After the segmentation of time series data into segments,three parameter values of median,point number and slope are introduced into the improved distance measurement formula.The effectiveness of the morphological weighted DTW algorithm(MW-DTW)is demonstrated by the example of momentum wheel data of an aircraft attitude control system.Findings–The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data.Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement.Practical implications–This improved method has been used to solve the problem of similarity measurement in time series,which is widely emerged in different fields of science and engineering,such as the field of control,measurement,monitoring,process signal processing and economic analysis.Originality/value–In the similarity measurement of time series,the distance between sequences is often used as the only detection index.The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence,so it is necessary to incorporate themorphological changes of the sequence into similarity measurement.The MW-DTW is more suitable for the actual situation.At the same time,the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Multivariate error assessment of response time histories method for dynamic systems

In this paper, an integrated validation method and process are developed for multivariate dynamic systems. The principal component analysis approach is used to address multivariate correlation and dimensionality reduction, the dynamic time warping and correlation coefficient are used for error assessment, and the subject matter experts (SMEs)’ opinions and principal component analysis coefficients are incorporated to provide the overall rating of the dynamic system. The proposed method and process are successfully demonstrated through a vehicle dynamic system problem.

Research on Dance Evaluation Technology Based on Human Posture Recognition

In view of the increase in the number of people participating in dance rating assessments,this paper proposes a dance assessment technology based on human body posture recognition.This technique adopts the human target detection of the dance video,extracts bone key points,and then uses the video data set col-lected by professional dancers to conduct PoseC3D model training,enabling the model to classify the basic movements of the dance;then,the dynamic time nor-malization algorithm is used to evaluate the classified movements.The experimen-tal results show that this technology can accurately identify the basic movements of various dances and accurately give the evaluation score of the corresponding movements,thus reducing the work intensity of the assessment staff.

Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering

Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.