Stratigraphic correlations are essential for the fine-scale characterization of reservoirs.However,conventional data-driven methods that rely solely on log data struggle to construct isochronous stratigraphic framewor...Stratigraphic correlations are essential for the fine-scale characterization of reservoirs.However,conventional data-driven methods that rely solely on log data struggle to construct isochronous stratigraphic frameworks for complex sedimentary environments and multi-source geological settings.In response,this study proposed an intelligent,automatic,log-seismic integrated stratigraphic correlation method that incorporates wavelet frequency-division transform(WFT)and dynamic time warping(DTW)(also referred to as the WFT-DTW method).This approach integrates seismic data as constraints into stratigraphic correlations,enabling accurate tracking of the seismic marker horizons through WFT.Under the constraints of framework construction,a DTW algorithm was introduced to correlate sublayer boundaries automatically.The effectiveness of the proposed method was verified through a stratigraphic correlation experiment on the SA0 Formation of the Xingshugang block in the Lasaxing oilfield,the Songliao Basin,China.In this block,the target layer exhibits sublayer thicknesses ranging from 5 m to 8 m,an average sandstone thickness of 2.1 m,and pronounced heterogeneity.The verification using 1760 layers in 160 post-test wells indicates that the WFT-DTW method intelligently compared sublayers in zones with underdeveloped faults and distinct marker horizons.As a result,the posterior correlation of 1682 layers was performed,with a coincidence rate of up to 95.6%.The proposed method can complement manual correlation efforts while also providing valuable technical support for the lithologic and sand body characterization of reservoirs.展开更多
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
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 comprehensi...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.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
Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involve...Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.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
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...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.展开更多
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 pres...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.展开更多
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 reduc...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.展开更多
The similarity search is one of the fundamental components in time series data mining,e.g.clustering,classification,association rules mining.Many methods have been proposed to measure the similarity between time serie...The similarity search is one of the fundamental components in time series data mining,e.g.clustering,classification,association rules mining.Many methods have been proposed to measure the similarity between time series,including Euclidean distance,Manhattan distance,and dynamic time warping(DTW).In contrast,DTW has been suggested to allow more robust similarity measure and be able to find the optimal alignment in time series.However,due to its quadratic time and space complexity,DTW is not suitable for large time series datasets.Many improving algorithms have been proposed for DTW search in large databases,such as approximate search or exact indexed search.Unlike the previous modified algorithm,this paper presents a novel parallel scheme for fast similarity search based on DTW,which is called MRDTW(MapRedcuebased DTW).The experimental results show that our approach not only retained the original accuracy as DTW,but also greatly improved the efficiency of similarity measure in large time series.展开更多
Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining s...Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.展开更多
As oil and gas exploration continues to progress into deeper and unconventional reservoirs,the likelihood of kick risk increases,making kick warning a critical factor in ensuring drilling safety and efficiency.Due to ...As oil and gas exploration continues to progress into deeper and unconventional reservoirs,the likelihood of kick risk increases,making kick warning a critical factor in ensuring drilling safety and efficiency.Due to the scarcity of kick samples,traditional supervised models perform poorly,and significant fluctuations in field data lead to high false alarm rates.This study proposes an unsupervised graph autoencoder(GAE)-based kick warning method,which effectively reduces false alarms by eliminating the influence of field engineer operations and incorporating real-time model updates.The method utilizes the GAE model to process time-series data during drilling,accurately identifying kick risk while overcoming challenges related to small sample sizes and missing features.To further reduce false alarms,the weighted dynamic time warping(WDTW)algorithm is introduced to identify fluctuations in logging data caused by field engineer operations during drilling,with real-time updates applied to prevent normal conditions from being misclassified as kick risk.Experimental results show that the GAE-based kick warning method achieves an accuracy of 92.7%and significantly reduces the false alarm rate.The GAE model continues to operate effectively even under conditions of missing features and issues kick warnings 4 min earlier than field engineers,demonstrating its high sensitivity and robustness.After integrating the WDTW algorithm and real-time updates,the false alarm rate is reduced from 17.3%to 5.6%,further improving the accuracy of kick warnings.The proposed method provides an efficient and reliable approach for kick warning in drilling operations,offering strong practical value and technical support for the intelligent management of future drilling operations.展开更多
Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used pho...Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.展开更多
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 ...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.展开更多
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...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.展开更多
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...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.展开更多
基金funded by the Major Science and Technology Project of China National Petroleum Corporation(No.2023ZZ22YJ01).
文摘Stratigraphic correlations are essential for the fine-scale characterization of reservoirs.However,conventional data-driven methods that rely solely on log data struggle to construct isochronous stratigraphic frameworks for complex sedimentary environments and multi-source geological settings.In response,this study proposed an intelligent,automatic,log-seismic integrated stratigraphic correlation method that incorporates wavelet frequency-division transform(WFT)and dynamic time warping(DTW)(also referred to as the WFT-DTW method).This approach integrates seismic data as constraints into stratigraphic correlations,enabling accurate tracking of the seismic marker horizons through WFT.Under the constraints of framework construction,a DTW algorithm was introduced to correlate sublayer boundaries automatically.The effectiveness of the proposed method was verified through a stratigraphic correlation experiment on the SA0 Formation of the Xingshugang block in the Lasaxing oilfield,the Songliao Basin,China.In this block,the target layer exhibits sublayer thicknesses ranging from 5 m to 8 m,an average sandstone thickness of 2.1 m,and pronounced heterogeneity.The verification using 1760 layers in 160 post-test wells indicates that the WFT-DTW method intelligently compared sublayers in zones with underdeveloped faults and distinct marker horizons.As a result,the posterior correlation of 1682 layers was performed,with a coincidence rate of up to 95.6%.The proposed method can complement manual correlation efforts while also providing valuable technical support for the lithologic and sand body characterization of reservoirs.
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.
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.
基金the National Natural Science Foundation of China(42374133)the Beijing Nova Program(2022056)for their funding of this research。
文摘Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.
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.
基金supported by the Research Plan Project of National University of Defense Technology under Grant No.JC13-06-01the OCRit Project made possible by the Global Leadership Round in Genomics&Life Sciences Grant(GL2)
文摘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.
基金supported by Grant EGD21QD15,the Research project of Shanghai Polytechnic University。
文摘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.
基金supported by the National Natural Science Fundation of China (No. 51075262)the New Century Excellent Talents in University Program (No. NCET-08-0361)the Fund for the Doctoral Program of Higher Education (No. 200802480036), China
文摘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.
基金supported in part by National High-tech R&D Program of China under Grants No.2012AA012600,2011AA010702,2012AA01A401,2012AA01A402National Natural Science Foundation of China under Grant No.60933005+1 种基金National Science and Technology Ministry of China under Grant No.2012BAH38B04National 242 Information Security of China under Grant No.2011A010
文摘The similarity search is one of the fundamental components in time series data mining,e.g.clustering,classification,association rules mining.Many methods have been proposed to measure the similarity between time series,including Euclidean distance,Manhattan distance,and dynamic time warping(DTW).In contrast,DTW has been suggested to allow more robust similarity measure and be able to find the optimal alignment in time series.However,due to its quadratic time and space complexity,DTW is not suitable for large time series datasets.Many improving algorithms have been proposed for DTW search in large databases,such as approximate search or exact indexed search.Unlike the previous modified algorithm,this paper presents a novel parallel scheme for fast similarity search based on DTW,which is called MRDTW(MapRedcuebased DTW).The experimental results show that our approach not only retained the original accuracy as DTW,but also greatly improved the efficiency of similarity measure in large time series.
基金the Key National Natural Science Foundation of China(No.U1864211)the National Natural Science Foundation of China(No.11772191)the Natural Science Foundation of Shanghai(No.21ZR1431500)。
文摘Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.
基金Youth Foundation of National Natural Science Foundation of China (No. 52204020)Distinguished Young Foundation of National Natural Science Foundation of China (No. 52125401).
文摘As oil and gas exploration continues to progress into deeper and unconventional reservoirs,the likelihood of kick risk increases,making kick warning a critical factor in ensuring drilling safety and efficiency.Due to the scarcity of kick samples,traditional supervised models perform poorly,and significant fluctuations in field data lead to high false alarm rates.This study proposes an unsupervised graph autoencoder(GAE)-based kick warning method,which effectively reduces false alarms by eliminating the influence of field engineer operations and incorporating real-time model updates.The method utilizes the GAE model to process time-series data during drilling,accurately identifying kick risk while overcoming challenges related to small sample sizes and missing features.To further reduce false alarms,the weighted dynamic time warping(WDTW)algorithm is introduced to identify fluctuations in logging data caused by field engineer operations during drilling,with real-time updates applied to prevent normal conditions from being misclassified as kick risk.Experimental results show that the GAE-based kick warning method achieves an accuracy of 92.7%and significantly reduces the false alarm rate.The GAE model continues to operate effectively even under conditions of missing features and issues kick warnings 4 min earlier than field engineers,demonstrating its high sensitivity and robustness.After integrating the WDTW algorithm and real-time updates,the false alarm rate is reduced from 17.3%to 5.6%,further improving the accuracy of kick warnings.The proposed method provides an efficient and reliable approach for kick warning in drilling operations,offering strong practical value and technical support for the intelligent management of future drilling operations.
基金Supported by the National Natural Science Foundation of China(No.52005442)the Technology Project of Zhejiang Huayun Information Technology Co.,Ltd.(No.HYJT/JS-2020-004).
文摘Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.
文摘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。
基金supported by the National Key R&D Program of China under Grant 2018AAA0102303 and Grant 2018YFB1801103the National Natural Science Foundation of China (No. 61871398 and No. 61931011)+1 种基金the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030)the Equipment Advanced Research Field Foundation (No. 61403120304)
文摘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.
基金Supported by the National Natural Science Foundation of China (No. 60025307, No. 60234010, No. 60028001), partially sup- ported by the National 863 Project (No. 2002AA412420),Rrsearch Fund for the Doctoral Program of Higer Education (No. 20020003063) and
文摘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.
文摘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.
